{"collections":[{"type":"Collection","title":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Historical Simulation - ICON - Generation-1 - Realization-1","id":"EO.ECMWF.DAT.D1.DT_CLIMATE.G1.CMIP6_HIST_ICON.R1","description":"Note: This Generation 1 Collection has been superseded by Generation 2 Simulation-level Collections\n\nThe DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities by providing innovative climate information on multi-decadal timescales, globally, at scales at which many impacts of climate change are observed. It combines cutting-edge global Earth-system models, impact-sector applications and observations into a unified framework to provide global climate projections and impact-sector information on multi-decadal timescales (1990 to ~2050), at very high spatial resolutions (5 to 10 km).\n\nThe Climate DT represents the first ever attempt to operationalise the production of global multi-decadal climate projections, leveraging the world-leading supercomputing facilities of the EuroHPC Joint Undertaking along with some of the leading European climate models. A concise overview of what the Climate DT aims to achieve, and of the different concepts essential for an understanding of the Digital Twin’s characteristics, is included in the [Climate DT factsheet](https://destine.ecmwf.int/wp-content/uploads/2024/06/2024.06.07_Climate-DT-Fact-Sheet_V7-2.pdf)\n\n## Historical Simulation \n\n Starting in 1990, the forcing follows observed changes in greenhouse gases, aerosols etc. until 2020. The simulations are using standardised CMIP6 forcing. Historical simulations are essential for model evaluation and quality control as they allow a comparison to observations.\n\n## Models\n\nThe Climate DT exploits and further evolves a new generation of global storm-resolving and eddy-rich models built through a cooperative model development approach. For more information on models please click [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#models)\n\n## Simulations\n\nThe Climate DT team carries out several types of digital twin simulations on the EuroHPC supercomputers.  Multi-decadal simulations are produced to cover the recent past (from 1990) and possible future evolutions of the climate up to 2050. See [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#simulations) for more information on Simulations\n\n## Parameters\n\nBelow we see the list of parameters extracted from the 'DestinE Climate DT data portfolio', for more information please refer to the page [Climate DT Phase 1 CLTE Parameters](https://confluence.ecmwf.int/display/DDCZ/Climate+DT+Phase+1+data+catalogue)","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G1.CMIP6_HIST_ICON.R1/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G1.CMIP6_HIST_ICON.R1/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G1.CMIP6_HIST_ICON.R1/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G1.CMIP6_HIST_ICON.R1","title":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Historical Simulation - ICON - Generation-1 - Realization-1"},{"rel":"example","type":"application/x-ipynb+json","href":"https://raw.githubusercontent.com/destination-earth/DestinE-DataLake-Lab/refs/heads/main/HDA/DestinE%20Digital%20Twins/DEDL-HDA-EO.ECMWF.DAT.DT_CLIMATE.ipynb","title":"Destination Earth - Climate DT Parameter - Data Access using DEDL HDA","application:type":"jupyter-notebook","application:embedded":true,"application:language":"Python"},{"rel":"example","type":"application/x-ipynb+json","href":"https://raw.githubusercontent.com/destination-earth/DestinE-DataLake-Lab/refs/heads/main/HDA/DestinE%20Digital%20Twins/ClimateDT-ParameterPlotter.ipynb","title":"Destination Earth - HDA Climate DT Parameter Plotter Tutorial","application:type":"jupyter-notebook","application:embedded":true,"application:language":"Python"},{"rel":"example","type":"application/x-ipynb+json","href":"https://raw.githubusercontent.com/destination-earth/DestinE-DataLake-Lab/refs/heads/main/HDA/DestinE%20Digital%20Twins/DEDL-HDA-EO.ECMWF.DAT.DT_CLIMATE-Series.ipynb","title":"Destination Earth - Climate DT Parameter Series Plot- Data Access using DEDL HDA","application:type":"jupyter-notebook","application:embedded":true,"application:language":"Python"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Parameters","title":"DestinE ClimateDT Parameters"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/Climate+DT+Phase+1+data+catalogue","title":"Climate DT Phase 1 data catalogue"},{"rel":"cite-as","type":"text/html","href":"https://dl.acm.org/doi/abs/10.1109/MCSE.2023.3260519","title":"Destination Earth: High-Performance Computing for Weather and Climate"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.21957/d3f982672e","title":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"assets":{"thumbnail":{"href":"https://s3.central.data.destination-earth.eu/swift/v1/dedl-public/collections/climate-dt-min.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1993-01-01T00:00:00Z","2019-12-31T23:59:59Z"]]}},"license":"CC-BY-4.0","keywords":["Digital Twins","Climate Change","Atmosphere","Ocean","Land","Sea Ice","Snow","Soil","Earth","High Performance Computing","Decision Making","Europe","Meteorology","class:d1","dataset:climate-dt","generation:1","expver:0001","stream:clte","type:fc","activity:CMIP6","experiment:hist","realization:1","model:ICON","resolution:high","levtype:o2d,o3d,pl,sfc"],"summaries":{"federation:backends":["dedt_lumi"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/datacube/v2.1.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json","https://stac-extensions.github.io/application/v0.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["producer","processor","licensor"],"url":"https://www.ecmwf.int/"}],"created":"2024-04-11T22:31:55Z","updated":"2026-04-27T14:04:20Z","published":"2024-04-11T22:31:55Z","cube:dimensions":{"lat":{"axis":"y","description":"latitude","extent":[-90,90],"type":"spatial"},"lon":{"axis":"x","description":"longitude","extent":[-180,180],"type":"spatial"},"time":{"extent":["1993-01-01T00:00:00Z","2019-12-31T23:59:59Z"],"step":"P0Y0M0DT1H0M0S","type":"temporal"}},"cube:variables":{"10_metre_U_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"10 metre U wind component","parameter_ID":"165","product_type":"forecast","shortName":"10u","standard_name":"10_metre_U_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/165"},"description":"This parameter is the eastward component of the 10m wind. It is the horizontal speed of air moving towards the east, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the V component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_V_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"10 metre V wind component","parameter_ID":"166","product_type":"forecast","shortName":"10v","standard_name":"10_metre_V_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/166"},"description":"This parameter is the northward component of the 10m wind. It is the horizontal speed of air moving towards the north, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the U component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"2_metre_dewpoint_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"2 metre dewpoint temperature","parameter_ID":"168","product_type":"forecast","shortName":"2d","standard_name":"2_metre_dewpoint_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/168"},"description":"This parameter is the temperature to which the air, at 2 metres above the surface of the Earth, would have to be cooled for saturation to occur.It is a measure of the humidity of the air. Combined with temperature and pressure, it can be used to calculate the relative humidity.2m dew point temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"2_metre_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"2 metre temperature","parameter_ID":"167","product_type":"forecast","shortName":"2t","standard_name":"2_metre_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/167"},"description":"This parameter is the temperature of air at 2m above the surface of land, sea or in-land waters.2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.Please note that the encodings listed here for s2s \u0026 uerra (which includes encodings for carra/cerra) include entries for Mean 2 metre temperature. The specific encoding for Mean 2 metre temperature can be found in 228004.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Geopotential(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Geopotential","parameter_ID":"129","product_type":"forecast","shortName":"z","standard_name":"Geopotential","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/129"},"description":"This parameter is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level.The geopotential height can be calculated by dividing the geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2). The geopotential height plays an important role in synoptic meteorology (analysis of weather patterns). Charts of geopotential height plotted at constant pressure levels (e.g., 300, 500 or 850 hPa) can be used to identify weather systems such as cyclones, anticyclones, troughs and ridges.At the surface of the Earth, this parameter shows the variations in geopotential (height) of the surface, and is often referred to as the orography.","dimensions":["lat","lon","time"],"type":"data","unit":"m2s-2"},"Mean_sea_level_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Mean sea level pressure","parameter_ID":"151","product_type":"forecast","shortName":"msl","standard_name":"Mean_sea_level_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/151"},"description":"This parameter is the pressure (force per unit area) of the atmosphere adjusted to the height of mean sea level.It is a measure of the weight that all the air in a column vertically above the area of Earth's surface would have at that point, if the point were located at the mean sea level. It is calculated over all surfaces - land, sea and in-land water.Maps of mean sea level pressure are used to identify the locations of low and high pressure systems, often referred to as cyclones and anticyclones. Contours of mean sea level pressure also indicate the strength of the wind. Tightly packed contours show stronger winds.The units of this parameter are pascals (Pa).  Mean sea level pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb = 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Relative_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Relative humidity","parameter_ID":"157","product_type":"forecast","shortName":"r","standard_name":"Relative_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/157"},"description":"This parameter is the water vapour pressure as a percentage of the value at which the air becomes saturated (the point at which water vapour begins to condense into liquid water or deposition into ice).For temperatures over 0°C (273.15 K) it is calculated for saturation over water. At temperatures below -23°C it is calculated for saturation over ice.  Between -23°C and 0°C this parameter is calculated by interpolating between the ice and water values using a quadratic function.See more information about the model's relative humidity calculation.","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Skin_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Skin temperature","parameter_ID":"235","product_type":"forecast","shortName":"skt","standard_name":"Skin_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235"},"description":"This parameter is the temperature of the surface of the Earth.The skin temperature is the theoretical temperature that is required to satisfy the surface energy balance. It represents the temperature of the uppermost surface layer, which has no heat capacity and so can respond instantaneously to changes in surface fluxes. Skin temperature is calculated differently over land and sea.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.See further information about the skin temperatureover landandover sea.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean skin temperature. The specific encoding for Mean skin temperature can be found in 235079.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Specific_cloud_liquid_water_content(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific cloud liquid water content","parameter_ID":"246","product_type":"forecast","shortName":"clwc","standard_name":"Specific_cloud_liquid_water_content","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/246"},"description":"This parameter is the mass of cloud liquid water droplets per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for agrid box.Water within clouds can be liquid or ice, or a combination of the two.See further information about the cloud formulation.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Specific_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific humidity","parameter_ID":"133","product_type":"forecast","shortName":"q","standard_name":"Specific_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/133"},"description":"This parameter is the mass of water vapour per kilogram of moist air.The total mass of moist air is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Surface_long-wave_(thermal)_radiation_downwards(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Surface long-wave (thermal) radiation downwards","parameter_ID":"175","product_type":"forecast","shortName":"strd","standard_name":"Surface_long-wave_(thermal)_radiation_downwards","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/175"},"description":"This parameter is the amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere and clouds that reaches a horizontal plane at the surface of the Earth.The surface of the Earth emits thermal radiation, some of which is absorbed by the atmosphere and clouds. The atmosphere and clouds likewise emit thermal radiation in all directions, some of which reaches the surface (represented by this parameter).See further documentation.This parameter isaccumulated over a particular time period which depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Surface_net_long-wave_(thermal)_radiation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Surface net long-wave (thermal) radiation","parameter_ID":"177","product_type":"forecast","shortName":"str","standard_name":"Surface_net_long-wave_(thermal)_radiation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/177"},"description":"Thermal radiation (also known as longwave or terrestrial radiation) refers to radiation emitted by the atmosphere, clouds and the surface of the Earth. This parameter is the difference between downward and upward thermal radiation at the surface of the Earth. It the amount passing through a horizontal plane.The atmosphere and clouds emit thermal radiation in all directions, some of which reaches the surface as downward thermal radiation. The upward thermal radiation at the surface consists of thermal radiation emitted by the surface plus the fraction of downwards thermal radiation reflected upward by the surface.See further documentation.This parameter isaccumulated over a particular time periodwhich depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds.The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Surface_net_short-wave_(solar)_radiation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Surface net short-wave (solar) radiation","parameter_ID":"176","product_type":"forecast","shortName":"ssr","standard_name":"Surface_net_short-wave_(solar)_radiation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/176"},"description":"This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo).Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The remainder is incident on the Earth's surface, where some of it is reflected.See further documentation.This parameter isaccumulated over a particular time period which depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Surface_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Surface pressure","parameter_ID":"134","product_type":"forecast","shortName":"sp","standard_name":"Surface_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/134"},"description":"This parameter is the pressure (force per unit area) of the atmosphere on the surface of land, sea and in-land water.It is a measure of the weight of all the air in a column vertically above the area of the Earth's surface represented at a fixed point.Surface pressure is often used in combination with temperature to calculate air density.The strong variation of pressure with altitude makes it difficult to see the low and high pressure systems over mountainous areas, so mean sea level pressure, rather than surface pressure, is normally used for this purpose.The units of this parameter are Pascals (Pa). Surface pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb= 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Surface_short-wave_(solar)_radiation_downwards(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Surface short-wave (solar) radiation downwards","parameter_ID":"169","product_type":"forecast","shortName":"ssrd","standard_name":"Surface_short-wave_(solar)_radiation_downwards","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/169"},"description":"This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth. This parameter comprises both direct and diffuse solar radiation.Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface (represented by this parameter).See further documentation.To a reasonably good approximation, this parameter is the model equivalent of what would be measured by a pyranometer (an instrument used for measuring solar radiation) at the surface. However, care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over amodel grid box.This parameter isaccumulated over a particular time period which depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Temperature(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Temperature","parameter_ID":"130","product_type":"forecast","shortName":"t","standard_name":"Temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/130"},"description":"This parameter is the temperature in the atmosphere.It has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.This parameter is available on multiple levels through the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Temperature","parameter_ID":"130","product_type":"forecast","shortName":"t","standard_name":"Temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/130"},"description":"This parameter is the temperature in the atmosphere.It has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.This parameter is available on multiple levels through the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-integrated_eastward_turbulent_surface_stress_due_to_surface_roughness(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Time-integrated eastward turbulent surface stress due to surface roughness","parameter_ID":"260654","product_type":"forecast","shortName":"etsssr","standard_name":"Time-integrated_eastward_turbulent_surface_stress_due_to_surface_roughness","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/260654"},"dimensions":["lat","lon","time"],"type":"data","unit":"N m-2s"},"Time-integrated_northward_turbulent_surface_stress_due_to_surface_roughness(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Time-integrated northward turbulent surface stress due to surface roughness","parameter_ID":"260655","product_type":"forecast","shortName":"ntsssr","standard_name":"Time-integrated_northward_turbulent_surface_stress_due_to_surface_roughness","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/260655"},"dimensions":["lat","lon","time"],"type":"data","unit":"N m-2s"},"Time-integrated_surface_latent_heat_net_flux(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Time-integrated surface latent heat net flux","parameter_ID":"147","product_type":"forecast","shortName":"slhf","standard_name":"Time-integrated_surface_latent_heat_net_flux","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/147"},"description":"This parameter is the transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earth's surface and the atmosphere through the effects of turbulent air motion. Evaporation from the Earth's surface represents a transfer of energy from the surface to the atmosphere.See further documentationThis parameter is accumulated over aparticular time period which depends on the data extracted.The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds.The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-integrated_surface_sensible_heat_net_flux(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Time-integrated surface sensible heat net flux","parameter_ID":"146","product_type":"forecast","shortName":"sshf","standard_name":"Time-integrated_surface_sensible_heat_net_flux","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/146"},"description":"This parameter is the transfer of heat between the Earth's surface and the atmosphere through the effects of turbulent air motion (but excluding any heat transfer resulting from condensation or evaporation).The magnitude of the sensible heat flux is governed by the difference in temperature between the surface and the overlying atmosphere, wind speed and the surface roughness. For example, cold air overlying a warm surface would produce a sensible heat flux from the land (or ocean) into the atmosphere.See further documentationThis is a single level parameter and it is accumulated over aparticular time period which depends on the data extracted.The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_eastward_sea_ice_velocity(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean eastward sea ice velocity","parameter_ID":"263003","product_type":"forecast","shortName":"avg_siue","standard_name":"Time-mean_eastward_sea_ice_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263003"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean eastward sea water velocity","parameter_ID":"263506","product_type":"forecast","shortName":"avg_uoe","standard_name":"Time-mean_eastward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263506"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_sea_ice_velocity(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean northward sea ice velocity","parameter_ID":"263004","product_type":"forecast","shortName":"avg_sivn","standard_name":"Time-mean_northward_sea_ice_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263004"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean northward sea water velocity","parameter_ID":"263505","product_type":"forecast","shortName":"avg_von","standard_name":"Time-mean_northward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263505"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_ocean_mixed_layer_depth_defined_by_sigma_theta_0.03_kg_m-3(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean ocean mixed layer depth defined by sigma theta 0.03 kg m-3","parameter_ID":"263114","product_type":"forecast","shortName":"avg_mlotst030","standard_name":"Time-mean_ocean_mixed_layer_depth_defined_by_sigma_theta_0.03_kg_m-3","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263114"},"dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Time-mean_sea_ice_area_fraction(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea ice area fraction","parameter_ID":"263001","product_type":"forecast","shortName":"avg_siconc","standard_name":"Time-mean_sea_ice_area_fraction","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263001"},"dimensions":["lat","lon","time"],"type":"data","unit":"Fraction"},"Time-mean_sea_ice_thickness(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea ice thickness","parameter_ID":"263000","product_type":"forecast","shortName":"avg_sithick","standard_name":"Time-mean_sea_ice_thickness","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263000"},"dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Time-mean_sea_surface_height(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea surface height","parameter_ID":"263124","product_type":"forecast","shortName":"avg_zos","standard_name":"Time-mean_sea_surface_height","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263124"},"dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Time-mean_sea_water_potential_temperature(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean sea water potential temperature","parameter_ID":"263501","product_type":"forecast","shortName":"avg_thetao","standard_name":"Time-mean_sea_water_potential_temperature","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263501"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_sea_water_practical_salinity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean sea water practical salinity","parameter_ID":"263500","product_type":"forecast","shortName":"avg_so","standard_name":"Time-mean_sea_water_practical_salinity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263500"},"dimensions":["lat","lon","time"],"type":"data","unit":"g kg-1"},"Time-mean_snow_volume_over_sea_ice_per_unit_area(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean snow volume over sea ice per unit area","parameter_ID":"263009","product_type":"forecast","shortName":"avg_snvol","standard_name":"Time-mean_snow_volume_over_sea_ice_per_unit_area","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263009"},"dimensions":["lat","lon","time"],"type":"data","unit":"m3m-2"},"Time-mean_upward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean upward sea water velocity","parameter_ID":"263507","product_type":"forecast","shortName":"avg_wo","standard_name":"Time-mean_upward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263507"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Top_net_long-wave_(thermal)_radiation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Top net long-wave (thermal) radiation","parameter_ID":"179","product_type":"forecast","shortName":"ttr","standard_name":"Top_net_long-wave_(thermal)_radiation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/179"},"description":"The thermal (also known as terrestrial or longwave) radiation emitted to space at the top of the atmosphere is commonly known as the Outgoing Longwave Radiation (OLR). The top net thermal radiation (this parameter) is equal to the negative of OLR.See further documentation.This parameter isaccumulated over a particular time periodwhich depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds.The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Top_net_short-wave_(solar)_radiation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Top net short-wave (solar) radiation","parameter_ID":"178","product_type":"forecast","shortName":"tsr","standard_name":"Top_net_short-wave_(solar)_radiation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/178"},"description":"This parameter is the incoming solar radiation (also known as shortwave radiation) minus the outgoing solar radiation at the top of the atmosphere. It is the amount of radiation passing through a horizontal plane. The incoming solar radiation is the amount received from the Sun. The outgoing solar radiation is the amount reflected and scattered by the Earth's atmosphere and surface.See further documentation.This parameter isaccumulated over a particular time periodwhich depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds.The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Total_Cloud_Cover(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total Cloud Cover","parameter_ID":"228164","product_type":"forecast","shortName":"tcc","standard_name":"Total_Cloud_Cover","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228164"},"description":"[NOTE: See 164 for the equivalent parameter in \"(0-1)\"]","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Total_column_cloud_ice_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud ice water","parameter_ID":"79","product_type":"forecast","shortName":"tciw","standard_name":"Total_column_cloud_ice_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/79"},"description":"This parameter is the amount of ice contained within clouds in a column extending from the surface of the Earth to the top of the atmosphere. Snow (aggregated ice crystals) is not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The  ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_cloud_liquid_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud liquid water","parameter_ID":"78","product_type":"forecast","shortName":"tclw","standard_name":"Total_column_cloud_liquid_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/78"},"description":"This parameter is the amount of liquid water contained within cloud droplets in a column extending from the surface of the Earth to the top of the atmosphere. Rain water droplets, which are much larger in size (and mass), are not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_vertically-integrated_water_vapour(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column vertically-integrated water vapour","parameter_ID":"137","product_type":"forecast","shortName":"tcwv","standard_name":"Total_column_vertically-integrated_water_vapour","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/137"},"description":"This parameter is the total amount of water vapour in a column extending from the surface of the Earth to the top of the atmosphere.This parameter represents the area averaged value for agrid box.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_precipitation_rate(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total precipitation rate","parameter_ID":"260048","product_type":"forecast","shortName":"tprate","standard_name":"Total_precipitation_rate","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/260048"},"description":"This parameter is the rate of total precipitation,at the specified time.In the ECMWF Integrated Forecasting System (IFS), total precipitation is rain and snow that falls to the Earth's surface.  It is the sum of large-scale precipitation and convective precipitation. Large-scale precipitation is generated by the cloud scheme in the IFS. The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of agrid boxor larger. Convective precipitation is generated by the convection scheme in the IFS. The convection scheme represents convection at spatial scales smaller than the grid box.Seefurther information. Precipitation parameters do not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth.1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm per second.Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box andmodel time step.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"U_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"U component of wind","parameter_ID":"131","product_type":"forecast","shortName":"u","standard_name":"U_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/131"},"description":"This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east, in metres per second. A negative sign thus indicates air movement towards the west.This parameter can be combined with the V component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"V_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"V component of wind","parameter_ID":"132","product_type":"forecast","shortName":"v","standard_name":"V_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/132"},"description":"This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north, in metres per second. A negative sign thus indicates air movement towards the south.This parameter can be combined with the U component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Vertical_velocity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Vertical velocity","parameter_ID":"135","product_type":"forecast","shortName":"w","standard_name":"Vertical_velocity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/135"},"description":"This parameter is the speed of air motion in the upward or downward direction. The ECMWF Integrated Forecasting System (IFS) uses a pressure based vertical co-ordinate system and pressure decreases with height, therefore negative values of vertical velocity indicate upward motion.Vertical velocity can be useful to understand the large-scale dynamics of the atmosphere, including areas of upward motion/ascent (negative values) and downward motion/subsidence (positive values).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa s-1"}},"sci:publications":[{"doi":"10.1109/MCSE.2023.3260519","citation":"N. Wedi et al., Destination Earth: High-Performance Computing for Weather and Climate, in Computing in Science \u0026 Engineering, vol. 24, no. 6, pp. 29-37, Nov.-Dec. 2022,"},{"doi":"10.21957/d3f982672e","citation":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"dedl:short_description":"The Climate Change Adaptation Digital Twin provides global climate projections and sector-specific information over multiple decades at high resolution via a unified framework combining advanced Earth system models, impact assessments, and observations. This Generation-1, Realization-1 Collection gives access to 'Historical Simulation' data based on the 'ICON' model."},{"type":"Collection","title":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Historical Simulation - IFS-NEMO - Generation-1 - Realization-1","id":"EO.ECMWF.DAT.D1.DT_CLIMATE.G1.CMIP6_HIST_IFS-NEMO.R1","description":"Note: This Generation 1 Collection has been superseded by Generation 2 Simulation-level Collections\n\nThe DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities by providing innovative climate information on multi-decadal timescales, globally, at scales at which many impacts of climate change are observed. It combines cutting-edge global Earth-system models, impact-sector applications and observations into a unified framework to provide global climate projections and impact-sector information on multi-decadal timescales (1990 to ~2050), at very high spatial resolutions (5 to 10 km).\n\nThe Climate DT represents the first ever attempt to operationalise the production of global multi-decadal climate projections, leveraging the world-leading supercomputing facilities of the EuroHPC Joint Undertaking along with some of the leading European climate models. A concise overview of what the Climate DT aims to achieve, and of the different concepts essential for an understanding of the Digital Twin’s characteristics, is included in the [Climate DT factsheet](https://destine.ecmwf.int/wp-content/uploads/2024/06/2024.06.07_Climate-DT-Fact-Sheet_V7-2.pdf)\n\n## Historical Simulation \n\n Starting in 1990, the forcing follows observed changes in greenhouse gases, aerosols etc. until 2020. The simulations are using standardised CMIP6 forcing. Historical simulations are essential for model evaluation and quality control as they allow a comparison to observations.\n\n## Models\n\nThe Climate DT exploits and further evolves a new generation of global storm-resolving and eddy-rich models built through a cooperative model development approach. For more information on models please click [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#models)\n\n## Simulations\n\nThe Climate DT team carries out several types of digital twin simulations on the EuroHPC supercomputers.  Multi-decadal simulations are produced to cover the recent past (from 1990) and possible future evolutions of the climate up to 2050. See [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#simulations) for more information on Simulations\n\n## Parameters\n\nBelow we see the list of parameters extracted from the 'DestinE Climate DT data portfolio', for more information please refer to the page [Climate DT Phase 1 CLTE Parameters](https://confluence.ecmwf.int/display/DDCZ/Climate+DT+Phase+1+data+catalogue)","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G1.CMIP6_HIST_IFS-NEMO.R1/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G1.CMIP6_HIST_IFS-NEMO.R1/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G1.CMIP6_HIST_IFS-NEMO.R1/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G1.CMIP6_HIST_IFS-NEMO.R1","title":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Historical Simulation - IFS-NEMO - Generation-1 - Realization-1"},{"rel":"example","type":"application/x-ipynb+json","href":"https://raw.githubusercontent.com/destination-earth/DestinE-DataLake-Lab/refs/heads/main/HDA/DestinE%20Digital%20Twins/DEDL-HDA-EO.ECMWF.DAT.DT_CLIMATE.ipynb","title":"Destination Earth - Climate DT Parameter - Data Access using DEDL HDA","application:type":"jupyter-notebook","application:embedded":true,"application:language":"Python"},{"rel":"example","type":"application/x-ipynb+json","href":"https://raw.githubusercontent.com/destination-earth/DestinE-DataLake-Lab/refs/heads/main/HDA/DestinE%20Digital%20Twins/ClimateDT-ParameterPlotter.ipynb","title":"Destination Earth - HDA Climate DT Parameter Plotter Tutorial","application:type":"jupyter-notebook","application:embedded":true,"application:language":"Python"},{"rel":"example","type":"application/x-ipynb+json","href":"https://raw.githubusercontent.com/destination-earth/DestinE-DataLake-Lab/refs/heads/main/HDA/DestinE%20Digital%20Twins/DEDL-HDA-EO.ECMWF.DAT.DT_CLIMATE-Series.ipynb","title":"Destination Earth - Climate DT Parameter Series Plot- Data Access using DEDL HDA","application:type":"jupyter-notebook","application:embedded":true,"application:language":"Python"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Parameters","title":"DestinE ClimateDT Parameters"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/Climate+DT+Phase+1+data+catalogue","title":"Climate DT Phase 1 data catalogue"},{"rel":"cite-as","type":"text/html","href":"https://dl.acm.org/doi/abs/10.1109/MCSE.2023.3260519","title":"Destination Earth: High-Performance Computing for Weather and Climate"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.21957/d3f982672e","title":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"assets":{"thumbnail":{"href":"https://s3.central.data.destination-earth.eu/swift/v1/dedl-public/collections/climate-dt-min.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1990-01-01T00:00:00Z","2002-02-28T23:59:59Z"]]}},"license":"CC-BY-4.0","keywords":["Digital Twins","Climate Change","Atmosphere","Ocean","Land","Sea Ice","Snow","Soil","Earth","High Performance Computing","Decision Making","Europe","Meteorology","class:d1","dataset:climate-dt","generation:1","expver:0001","stream:clte","type:fc","activity:CMIP6","experiment:hist","realization:1","model:IFS-NEMO","resolution:standard,high","levtype:hl,o2d,o3d,pl,sfc,sol"],"summaries":{"federation:backends":["dedt_lumi"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/datacube/v2.1.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json","https://stac-extensions.github.io/application/v0.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["producer","processor","licensor"],"url":"https://www.ecmwf.int/"}],"created":"2024-04-11T22:31:55Z","updated":"2026-04-27T14:04:20Z","published":"2024-04-11T22:31:55Z","cube:dimensions":{"lat":{"axis":"y","description":"latitude","extent":[-90,90],"type":"spatial"},"lon":{"axis":"x","description":"longitude","extent":[-180,180],"type":"spatial"},"time":{"extent":["1990-01-01T00:00:00Z","2002-02-28T23:59:59Z"],"step":"P0Y0M0DT1H0M0S","type":"temporal"}},"cube:variables":{"100_metre_U_wind_component(clte_hl)":{"attrs":{"encoding":"instantaneous","levelist":"100","levtype":"hl","long_name":"100 metre U wind component","parameter_ID":"228246","product_type":"forecast","shortName":"100u","standard_name":"100_metre_U_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228246"},"description":"This parameter is the eastward component of the 100 m wind. It is the horizontal speed of air moving towards the east, at a height of 100 metres above the surface of the Earth, in metres per second.Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over amodel grid box and model time step.This parameter can be combined with the northward component to give the speed and direction of the horizontal 100 m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"100_metre_V_wind_component(clte_hl)":{"attrs":{"encoding":"instantaneous","levelist":"100","levtype":"hl","long_name":"100 metre V wind component","parameter_ID":"228247","product_type":"forecast","shortName":"100v","standard_name":"100_metre_V_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228247"},"description":"This parameter is the northward component of the 100 m wind. It is the horizontal speed of air moving towards the north, at a height of 100 metres above the surface of the Earth, in metres per second.Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over amodel grid box and model time step.This parameter can be combined with the eastward component to give the speed and direction of the horizontal 100 m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_U_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"10 metre U wind component","parameter_ID":"165","product_type":"forecast","shortName":"10u","standard_name":"10_metre_U_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/165"},"description":"This parameter is the eastward component of the 10m wind. It is the horizontal speed of air moving towards the east, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the V component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_V_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"10 metre V wind component","parameter_ID":"166","product_type":"forecast","shortName":"10v","standard_name":"10_metre_V_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/166"},"description":"This parameter is the northward component of the 10m wind. It is the horizontal speed of air moving towards the north, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the U component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"2_metre_dewpoint_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"2 metre dewpoint temperature","parameter_ID":"168","product_type":"forecast","shortName":"2d","standard_name":"2_metre_dewpoint_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/168"},"description":"This parameter is the temperature to which the air, at 2 metres above the surface of the Earth, would have to be cooled for saturation to occur.It is a measure of the humidity of the air. Combined with temperature and pressure, it can be used to calculate the relative humidity.2m dew point temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"2_metre_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"2 metre temperature","parameter_ID":"167","product_type":"forecast","shortName":"2t","standard_name":"2_metre_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/167"},"description":"This parameter is the temperature of air at 2m above the surface of land, sea or in-land waters.2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.Please note that the encodings listed here for s2s \u0026 uerra (which includes encodings for carra/cerra) include entries for Mean 2 metre temperature. The specific encoding for Mean 2 metre temperature can be found in 228004.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Boundary_layer_height(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Boundary layer height","parameter_ID":"159","product_type":"forecast","shortName":"blh","standard_name":"Boundary_layer_height","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/159"},"description":"This parameter is the depth of air next to the Earth's surface which is most affected by the resistance to the transfer of momentum, heat or moisture across the surface.The boundary layer height can be as low as a few tens of metres, such as in cooling air at night, or as high as several kilometres over the desert in the middle of a hot sunny day. When the boundary layer height is low, higher concentrations of pollutants (emitted from the Earth's surface) can develop.The boundary layer height calculation is based on the bulk Richardson number (a measure of the atmospheric conditions) following the conclusions of a 2012 review.See further information.","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Charnock(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Charnock","parameter_ID":"148","product_type":"forecast","shortName":"chnk","standard_name":"Charnock","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/148"},"description":"This parameter accounts for increased aerodynamic roughness as wave heights grow due to increasing surface stress. It depends on the wind speed, wave age and other aspects of the sea state and is used to calculate how much the waves slow down the wind.When the atmospheric model is run without the ocean model, this parameter has a constant value of 0.018. When the atmospheric model is coupled to the ocean model, this parameter is calculated by theECMWF Wave Model.","dimensions":["lat","lon","time"],"type":"data","unit":"Numeric"},"Evaporation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Evaporation","parameter_ID":"182","product_type":"forecast","shortName":"e","standard_name":"Evaporation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/182"},"description":"This parameter is the accumulated amount of water that has evaporated from the Earth's surface, including a simplified representation of transpiration (from vegetation), into vapour in the air above.This parameter is accumulated over aparticular time period which depends on the data extracted.The ECMWF Integrated Forecasting System convention is that downward fluxes are positive. Therefore, negative values indicate evaporation and positive values indicate condensation.[NOTE: See 260259 for the equivalent parameter in \"kg m-2\"]","dimensions":["lat","lon","time"],"type":"data","unit":"m of water equivalent"},"Geopotential(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Geopotential","parameter_ID":"129","product_type":"forecast","shortName":"z","standard_name":"Geopotential","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/129"},"description":"This parameter is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level.The geopotential height can be calculated by dividing the geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2). The geopotential height plays an important role in synoptic meteorology (analysis of weather patterns). Charts of geopotential height plotted at constant pressure levels (e.g., 300, 500 or 850 hPa) can be used to identify weather systems such as cyclones, anticyclones, troughs and ridges.At the surface of the Earth, this parameter shows the variations in geopotential (height) of the surface, and is often referred to as the orography.","dimensions":["lat","lon","time"],"type":"data","unit":"m2s-2"},"Geopotential(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Geopotential","parameter_ID":"129","product_type":"forecast","shortName":"z","standard_name":"Geopotential","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/129"},"description":"This parameter is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level.The geopotential height can be calculated by dividing the geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2). The geopotential height plays an important role in synoptic meteorology (analysis of weather patterns). Charts of geopotential height plotted at constant pressure levels (e.g., 300, 500 or 850 hPa) can be used to identify weather systems such as cyclones, anticyclones, troughs and ridges.At the surface of the Earth, this parameter shows the variations in geopotential (height) of the surface, and is often referred to as the orography.","dimensions":["lat","lon","time"],"type":"data","unit":"m2s-2"},"High_cloud_cover(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"High cloud cover","parameter_ID":"188","product_type":"forecast","shortName":"hcc","standard_name":"High_cloud_cover","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/188"},"description":"The proportion of agrid boxcovered by cloud occurring in the high levels of the troposphere. High cloud is a single level field calculated from cloud occurring on model levels with a pressure less than 0.45 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), high cloud would be calculated using levels with a pressure of less than 450 hPa (approximately 6km and above (assuming a `standard atmosphere`)).The high cloud cover parameter is calculated from cloud for the appropriate model levels as described above. Assumptions are made about the degree of overlap/randomness between clouds in different model levels.Cloud fractions vary from 0 to 1.[NOTE: See 3075 for the equivalent parameter in \"%\"]","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Land-sea_mask(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Land-sea mask","parameter_ID":"172","product_type":"forecast","shortName":"lsm","standard_name":"Land-sea_mask","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/172"},"description":"This parameter is the proportion of land, as opposed to ocean or inland waters (lakes, reservoirs, rivers and coastal waters), in agrid box.This parameter has values ranging between zero and one and is dimensionless.In cycles of the ECMWF Integrated Forecasting System (IFS) from CY41R1 (introduced in May 2015) onwards, grid boxes where this parameter has a value above 0.5 can be comprised of a mixture of land and inland water but not ocean. Grid boxes with a value of 0.5 and below can only be comprised of a water surface. In the latter case, the lake cover is used to determine how much of the water surface is ocean or inland water.In cycles of the IFS before CY41R1, grid boxes where this parameter has a value above 0.5 can only be comprised of land and those grid boxes with a value of 0.5 and below can only be comprised of ocean. In these older model cycles, there is no differentiation between ocean and inland water.","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Low_cloud_cover(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Low cloud cover","parameter_ID":"186","product_type":"forecast","shortName":"lcc","standard_name":"Low_cloud_cover","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/186"},"description":"This parameter is the proportion of agrid boxcovered by cloud occurring in the lower levels of the troposphere. Low cloud is a single level field calculated from cloud occurring on model levels with a pressure greater than 0.8 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), low cloud would be calculated using levels with a pressure greater than 800 hPa (below approximately 2km (assuming a 'standard atmosphere')).The low cloud cover parameter is calculated from cloud cover for the appropriate model levels as described above. Assumptions are made about the degree of overlap/randomness between clouds in different model levels.Cloud fractions vary from 0 to 1.[NOTE: See 3073 for the equivalent parameter in \"%\"]","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Mean_sea_level_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Mean sea level pressure","parameter_ID":"151","product_type":"forecast","shortName":"msl","standard_name":"Mean_sea_level_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/151"},"description":"This parameter is the pressure (force per unit area) of the atmosphere adjusted to the height of mean sea level.It is a measure of the weight that all the air in a column vertically above the area of Earth's surface would have at that point, if the point were located at the mean sea level. It is calculated over all surfaces - land, sea and in-land water.Maps of mean sea level pressure are used to identify the locations of low and high pressure systems, often referred to as cyclones and anticyclones. Contours of mean sea level pressure also indicate the strength of the wind. Tightly packed contours show stronger winds.The units of this parameter are pascals (Pa).  Mean sea level pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb = 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Medium_cloud_cover(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Medium cloud cover","parameter_ID":"187","product_type":"forecast","shortName":"mcc","standard_name":"Medium_cloud_cover","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/187"},"description":"This parameter is the proportion of agrid boxcovered by cloud occurring in the middle levels of the troposphere. Medium cloud is a single level field calculated from cloud occurring on model levels with a pressure between 0.45 and 0.8 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), medium cloud would be calculated using levels with a pressure of less than or equal to 800 hPa and greater than or equal to 450 hPa (between approximately 2km and 6km (assuming a 'standard atmosphere')).The medium cloud parameter is calculated from cloud cover for the appropriate model levels as described above. Assumptions are made about the degree of overlap/randomness between clouds in different model levels.Cloud fractions vary from 0 to 1.[NOTE: See 3074 for the equivalent parameter in \"%\"]","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Potential_vorticity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Potential vorticity","parameter_ID":"60","product_type":"forecast","shortName":"pv","standard_name":"Potential_vorticity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/60"},"description":"Potential vorticity is a measure of the capacity for air to rotate in the atmosphere. If we ignore the effects of heating and friction, potential vorticity is conserved following an air parcel. It is used to look for places where large wind storms are likely to originate and develop.  Potential vorticity increases strongly above the tropopause and therefore, it can also be used in studies related to the stratosphere and stratosphere-troposphere exchanges.Large wind storms develop when a column of air in the atmosphere starts to rotate. Potential vorticity is calculated from the wind, temperature and pressure across a column of air in the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K m2kg-1s-1"},"Relative_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Relative humidity","parameter_ID":"157","product_type":"forecast","shortName":"r","standard_name":"Relative_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/157"},"description":"This parameter is the water vapour pressure as a percentage of the value at which the air becomes saturated (the point at which water vapour begins to condense into liquid water or deposition into ice).For temperatures over 0°C (273.15 K) it is calculated for saturation over water. At temperatures below -23°C it is calculated for saturation over ice.  Between -23°C and 0°C this parameter is calculated by interpolating between the ice and water values using a quadratic function.See more information about the model's relative humidity calculation.","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Skin_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Skin temperature","parameter_ID":"235","product_type":"forecast","shortName":"skt","standard_name":"Skin_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235"},"description":"This parameter is the temperature of the surface of the Earth.The skin temperature is the theoretical temperature that is required to satisfy the surface energy balance. It represents the temperature of the uppermost surface layer, which has no heat capacity and so can respond instantaneously to changes in surface fluxes. Skin temperature is calculated differently over land and sea.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.See further information about the skin temperatureover landandover sea.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean skin temperature. The specific encoding for Mean skin temperature can be found in 235079.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Snow_depth(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Snow depth","parameter_ID":"141","product_type":"forecast","shortName":"sd","standard_name":"Snow_depth","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/141"},"description":"This parameter is the depth of snow from the snow-covered area of agrid box.Its units are metres of water equivalent, so it is the depth the water would have if the snow melted and was spread evenly over the whole grid box. The ECMWF Integrated Forecast System represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box.See further information.\n\n[NOTE: See 228141 for the equivalent parameter in \"kg m-2\"]","dimensions":["lat","lon","time"],"type":"data","unit":"m of water equivalent"},"Snow_depth_water_equivalent(clte_sol)":{"attrs":{"encoding":"instantaneous","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Snow depth water equivalent","parameter_ID":"228141","product_type":"forecast","shortName":"sd","standard_name":"Snow_depth_water_equivalent","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228141"},"description":"Snow depth water equivalent in kg m**-2 (mm) water equivalent.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean snow depth water equivalent. The specific encoding for Time-mean snow depth water equivalent can be found in 235078.[NOTE: See 141 for the equivalent parameter in \"m of water equivalent\"]","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Snowfall(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Snowfall","parameter_ID":"144","product_type":"forecast","shortName":"sf","standard_name":"Snowfall","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/144"},"description":"This parameter is the accumulated snow that falls to the Earth's surface. It is the sum of large-scale snowfall and convective snowfall. Large-scale snowfall is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of thegrid boxor larger. Convective snowfall is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box.See further information.This parameter is the total amount of wateraccumulated over a particular time period which depends on the data extracted. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box.Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box.[NOTE: See 228144 for the equivalent parameter in \"kg m-2\"]","dimensions":["lat","lon","time"],"type":"data","unit":"m of water equivalent"},"Specific_cloud_liquid_water_content(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific cloud liquid water content","parameter_ID":"246","product_type":"forecast","shortName":"clwc","standard_name":"Specific_cloud_liquid_water_content","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/246"},"description":"This parameter is the mass of cloud liquid water droplets per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for agrid box.Water within clouds can be liquid or ice, or a combination of the two.See further information about the cloud formulation.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Specific_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific humidity","parameter_ID":"133","product_type":"forecast","shortName":"q","standard_name":"Specific_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/133"},"description":"This parameter is the mass of water vapour per kilogram of moist air.The total mass of moist air is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Sub-surface_runoff(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Sub-surface runoff","parameter_ID":"9","product_type":"forecast","shortName":"ssro","standard_name":"Sub-surface_runoff","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/9"},"description":"Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is simply called 'runoff'. This parameter is the total amount of water accumulated over aparticular time period which depends on the data extracted.The units of runoff are depth in metres.  This is the depth the water would have if it were spread evenly over thegrid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid square area.  Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here.Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood.  More information about how runoff is calculated is given in theIFS Physical Processes documentation.\n\n[NOTE: See 231012 for the equivalent parameter in \"kg m-2\"]","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Surface_long-wave_(thermal)_radiation_downwards(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Surface long-wave (thermal) radiation downwards","parameter_ID":"175","product_type":"forecast","shortName":"strd","standard_name":"Surface_long-wave_(thermal)_radiation_downwards","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/175"},"description":"This parameter is the amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere and clouds that reaches a horizontal plane at the surface of the Earth.The surface of the Earth emits thermal radiation, some of which is absorbed by the atmosphere and clouds. The atmosphere and clouds likewise emit thermal radiation in all directions, some of which reaches the surface (represented by this parameter).See further documentation.This parameter isaccumulated over a particular time period which depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Surface_net_long-wave_(thermal)_radiation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Surface net long-wave (thermal) radiation","parameter_ID":"177","product_type":"forecast","shortName":"str","standard_name":"Surface_net_long-wave_(thermal)_radiation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/177"},"description":"Thermal radiation (also known as longwave or terrestrial radiation) refers to radiation emitted by the atmosphere, clouds and the surface of the Earth. This parameter is the difference between downward and upward thermal radiation at the surface of the Earth. It the amount passing through a horizontal plane.The atmosphere and clouds emit thermal radiation in all directions, some of which reaches the surface as downward thermal radiation. The upward thermal radiation at the surface consists of thermal radiation emitted by the surface plus the fraction of downwards thermal radiation reflected upward by the surface.See further documentation.This parameter isaccumulated over a particular time periodwhich depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds.The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Surface_net_short-wave_(solar)_radiation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Surface net short-wave (solar) radiation","parameter_ID":"176","product_type":"forecast","shortName":"ssr","standard_name":"Surface_net_short-wave_(solar)_radiation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/176"},"description":"This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo).Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The remainder is incident on the Earth's surface, where some of it is reflected.See further documentation.This parameter isaccumulated over a particular time period which depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Surface_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Surface pressure","parameter_ID":"134","product_type":"forecast","shortName":"sp","standard_name":"Surface_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/134"},"description":"This parameter is the pressure (force per unit area) of the atmosphere on the surface of land, sea and in-land water.It is a measure of the weight of all the air in a column vertically above the area of the Earth's surface represented at a fixed point.Surface pressure is often used in combination with temperature to calculate air density.The strong variation of pressure with altitude makes it difficult to see the low and high pressure systems over mountainous areas, so mean sea level pressure, rather than surface pressure, is normally used for this purpose.The units of this parameter are Pascals (Pa). Surface pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb= 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Surface_runoff(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Surface runoff","parameter_ID":"8","product_type":"forecast","shortName":"sro","standard_name":"Surface_runoff","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/8"},"description":"Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is simply called 'runoff'. This parameter is the total amount of water accumulated over aparticular time period which depends on the data extracted.The units of runoff are depth in metres.  This is the depth the water would have if it were spread evenly over thegrid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid square area.  Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here.Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood.  More information about how runoff is calculated is given in theIFS Physical Processes documentation.\n\n[NOTE: See 231010 for the equivalent parameter in \"kg m-2\"]","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Surface_short-wave_(solar)_radiation_downwards(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Surface short-wave (solar) radiation downwards","parameter_ID":"169","product_type":"forecast","shortName":"ssrd","standard_name":"Surface_short-wave_(solar)_radiation_downwards","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/169"},"description":"This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth. This parameter comprises both direct and diffuse solar radiation.Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface (represented by this parameter).See further documentation.To a reasonably good approximation, this parameter is the model equivalent of what would be measured by a pyranometer (an instrument used for measuring solar radiation) at the surface. However, care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over amodel grid box.This parameter isaccumulated over a particular time period which depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"TOA_incident_short-wave_(solar)_radiation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"TOA incident short-wave (solar) radiation","parameter_ID":"212","product_type":"forecast","shortName":"tisr","standard_name":"TOA_incident_short-wave_(solar)_radiation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/212"},"description":"Accumulated field","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Temperature(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Temperature","parameter_ID":"130","product_type":"forecast","shortName":"t","standard_name":"Temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/130"},"description":"This parameter is the temperature in the atmosphere.It has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.This parameter is available on multiple levels through the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-integrated_eastward_turbulent_surface_stress(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Time-integrated eastward turbulent surface stress","parameter_ID":"180","product_type":"forecast","shortName":"ewss","standard_name":"Time-integrated_eastward_turbulent_surface_stress","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/180"},"description":"Air flowing over a surface exerts a stress that transfers momentum to the surface and slows the wind. This parameter is the accumulated stress on the Earth's surface in the eastward direction due to both the turbulent interactions between the atmosphere and the surface, and to turbulent orographic form drag. The turbulent interactions between the atmosphere and the surface are due to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km being derived from land surface data at about 1 km resolution.See further information.Positive (negative) values denote stress in the eastward (westward) direction.This parameter isaccumulated over a particular time periodwhich depends on the data extracted.","dimensions":["lat","lon","time"],"type":"data","unit":"N m-2s"},"Time-integrated_northward_turbulent_surface_stress(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Time-integrated northward turbulent surface stress","parameter_ID":"181","product_type":"forecast","shortName":"nsss","standard_name":"Time-integrated_northward_turbulent_surface_stress","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/181"},"description":"Air flowing over a surface exerts a stress that transfers momentum to the surface and slows the wind. This parameter is the accumulated stress on the Earth's surface in the northward direction due to both the turbulent interactions between the atmosphere and the surface, and to turbulent orographic form drag.The turbulent interactions between the atmosphere and the surface are due to the roughness of the surface.The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km being derived from land surface data at about 1 km resolution.See further information.Positive (negative) values denote stress in the northward (southward) direction.This parameter isaccumulated over a particular time periodwhich depends on the data extracted.","dimensions":["lat","lon","time"],"type":"data","unit":"N m-2s"},"Time-integrated_surface_latent_heat_net_flux(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Time-integrated surface latent heat net flux","parameter_ID":"147","product_type":"forecast","shortName":"slhf","standard_name":"Time-integrated_surface_latent_heat_net_flux","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/147"},"description":"This parameter is the transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earth's surface and the atmosphere through the effects of turbulent air motion. Evaporation from the Earth's surface represents a transfer of energy from the surface to the atmosphere.See further documentationThis parameter is accumulated over aparticular time period which depends on the data extracted.The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds.The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-integrated_surface_sensible_heat_net_flux(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Time-integrated surface sensible heat net flux","parameter_ID":"146","product_type":"forecast","shortName":"sshf","standard_name":"Time-integrated_surface_sensible_heat_net_flux","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/146"},"description":"This parameter is the transfer of heat between the Earth's surface and the atmosphere through the effects of turbulent air motion (but excluding any heat transfer resulting from condensation or evaporation).The magnitude of the sensible heat flux is governed by the difference in temperature between the surface and the overlying atmosphere, wind speed and the surface roughness. For example, cold air overlying a warm surface would produce a sensible heat flux from the land (or ocean) into the atmosphere.See further documentationThis is a single level parameter and it is accumulated over aparticular time period which depends on the data extracted.The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_X-component_of_sea_ice_velocity(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean X-component of sea ice velocity","parameter_ID":"263021","product_type":"forecast","shortName":"avg_six","standard_name":"Time-mean_X-component_of_sea_ice_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263021"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_Y-component_of_sea_ice_velocity(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean Y-component of sea ice velocity","parameter_ID":"263022","product_type":"forecast","shortName":"avg_siy","standard_name":"Time-mean_Y-component_of_sea_ice_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263022"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_ice_velocity(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean eastward sea ice velocity","parameter_ID":"263003","product_type":"forecast","shortName":"avg_siue","standard_name":"Time-mean_eastward_sea_ice_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263003"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75","levtype":"o3d","long_name":"Time-mean eastward sea water velocity","parameter_ID":"263506","product_type":"forecast","shortName":"avg_uoe","standard_name":"Time-mean_eastward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263506"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_sea_ice_velocity(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean northward sea ice velocity","parameter_ID":"263004","product_type":"forecast","shortName":"avg_sivn","standard_name":"Time-mean_northward_sea_ice_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263004"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75","levtype":"o3d","long_name":"Time-mean northward sea water velocity","parameter_ID":"263505","product_type":"forecast","shortName":"avg_von","standard_name":"Time-mean_northward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263505"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_sea_ice_area_fraction(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea ice area fraction","parameter_ID":"263001","product_type":"forecast","shortName":"avg_siconc","standard_name":"Time-mean_sea_ice_area_fraction","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263001"},"dimensions":["lat","lon","time"],"type":"data","unit":"Fraction"},"Time-mean_sea_ice_thickness(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea ice thickness","parameter_ID":"263000","product_type":"forecast","shortName":"avg_sithick","standard_name":"Time-mean_sea_ice_thickness","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263000"},"dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Time-mean_sea_ice_volume_per_unit_area(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea ice volume per unit area","parameter_ID":"263008","product_type":"forecast","shortName":"avg_sivol","standard_name":"Time-mean_sea_ice_volume_per_unit_area","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263008"},"dimensions":["lat","lon","time"],"type":"data","unit":"m3m-2"},"Time-mean_sea_surface_height(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea surface height","parameter_ID":"263124","product_type":"forecast","shortName":"avg_zos","standard_name":"Time-mean_sea_surface_height","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263124"},"dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Time-mean_sea_surface_practical_salinity(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea surface practical salinity","parameter_ID":"263100","product_type":"forecast","shortName":"avg_sos","standard_name":"Time-mean_sea_surface_practical_salinity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263100"},"dimensions":["lat","lon","time"],"type":"data","unit":"g kg-1"},"Time-mean_sea_surface_temperature(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea surface temperature","parameter_ID":"263101","product_type":"forecast","shortName":"avg_tos","standard_name":"Time-mean_sea_surface_temperature","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263101"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_sea_water_potential_temperature(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75","levtype":"o3d","long_name":"Time-mean sea water potential temperature","parameter_ID":"263501","product_type":"forecast","shortName":"avg_thetao","standard_name":"Time-mean_sea_water_potential_temperature","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263501"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_sea_water_practical_salinity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75","levtype":"o3d","long_name":"Time-mean sea water practical salinity","parameter_ID":"263500","product_type":"forecast","shortName":"avg_so","standard_name":"Time-mean_sea_water_practical_salinity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263500"},"dimensions":["lat","lon","time"],"type":"data","unit":"g kg-1"},"Time-mean_snow_volume_over_sea_ice_per_unit_area(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean snow volume over sea ice per unit area","parameter_ID":"263009","product_type":"forecast","shortName":"avg_snvol","standard_name":"Time-mean_snow_volume_over_sea_ice_per_unit_area","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263009"},"dimensions":["lat","lon","time"],"type":"data","unit":"m3m-2"},"Time-mean_upward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75","levtype":"o3d","long_name":"Time-mean upward sea water velocity","parameter_ID":"263507","product_type":"forecast","shortName":"avg_wo","standard_name":"Time-mean_upward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263507"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_vertically-integrated_heat_content_in_the_upper_300_m(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 300 m","parameter_ID":"263121","product_type":"forecast","shortName":"avg_hc300m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_300_m","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263121"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 700 m","parameter_ID":"263122","product_type":"forecast","shortName":"avg_hc700m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263122"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Top_net_long-wave_(thermal)_radiation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Top net long-wave (thermal) radiation","parameter_ID":"179","product_type":"forecast","shortName":"ttr","standard_name":"Top_net_long-wave_(thermal)_radiation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/179"},"description":"The thermal (also known as terrestrial or longwave) radiation emitted to space at the top of the atmosphere is commonly known as the Outgoing Longwave Radiation (OLR). The top net thermal radiation (this parameter) is equal to the negative of OLR.See further documentation.This parameter isaccumulated over a particular time periodwhich depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds.The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Top_net_short-wave_(solar)_radiation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Top net short-wave (solar) radiation","parameter_ID":"178","product_type":"forecast","shortName":"tsr","standard_name":"Top_net_short-wave_(solar)_radiation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/178"},"description":"This parameter is the incoming solar radiation (also known as shortwave radiation) minus the outgoing solar radiation at the top of the atmosphere. It is the amount of radiation passing through a horizontal plane. The incoming solar radiation is the amount received from the Sun. The outgoing solar radiation is the amount reflected and scattered by the Earth's atmosphere and surface.See further documentation.This parameter isaccumulated over a particular time periodwhich depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds.The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Total_cloud_cover(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total cloud cover","parameter_ID":"164","product_type":"forecast","shortName":"tcc","standard_name":"Total_cloud_cover","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/164"},"description":"This parameter is the proportion of agrid boxcovered by cloud. Total cloud cover is a single level field calculated from the cloud occurring at different model levels through the atmosphere. Assumptions are made about the degree of overlap/randomness between clouds at different heights.Cloud fractions vary from 0 to 1.[NOTE: See 228164 for the equivalent parameter in \"%\"]","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Total_column_cloud_ice_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud ice water","parameter_ID":"79","product_type":"forecast","shortName":"tciw","standard_name":"Total_column_cloud_ice_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/79"},"description":"This parameter is the amount of ice contained within clouds in a column extending from the surface of the Earth to the top of the atmosphere. Snow (aggregated ice crystals) is not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The  ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_cloud_liquid_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud liquid water","parameter_ID":"78","product_type":"forecast","shortName":"tclw","standard_name":"Total_column_cloud_liquid_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/78"},"description":"This parameter is the amount of liquid water contained within cloud droplets in a column extending from the surface of the Earth to the top of the atmosphere. Rain water droplets, which are much larger in size (and mass), are not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_vertically-integrated_water_vapour(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column vertically-integrated water vapour","parameter_ID":"137","product_type":"forecast","shortName":"tcwv","standard_name":"Total_column_vertically-integrated_water_vapour","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/137"},"description":"This parameter is the total amount of water vapour in a column extending from the surface of the Earth to the top of the atmosphere.This parameter represents the area averaged value for agrid box.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_precipitation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Total precipitation","parameter_ID":"228","product_type":"forecast","shortName":"tp","standard_name":"Total_precipitation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228"},"description":"This parameter is the accumulated liquid and frozen water, comprising rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation and convective precipitation. Large-scale precipitation is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of thegrid boxor larger. Convective precipitation is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box.See further information.This parameter does not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth.This parameter is the total amount of wateraccumulated over a particular time period which depends on the data extracted. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box.Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box.\n\n[NOTE: See 228228 for the equivalent parameter in \"kg m-2\"]","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Total_precipitation_rate(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total precipitation rate","parameter_ID":"260048","product_type":"forecast","shortName":"tprate","standard_name":"Total_precipitation_rate","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/260048"},"description":"This parameter is the rate of total precipitation,at the specified time.In the ECMWF Integrated Forecasting System (IFS), total precipitation is rain and snow that falls to the Earth's surface.  It is the sum of large-scale precipitation and convective precipitation. Large-scale precipitation is generated by the cloud scheme in the IFS. The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of agrid boxor larger. Convective precipitation is generated by the convection scheme in the IFS. The convection scheme represents convection at spatial scales smaller than the grid box.Seefurther information. Precipitation parameters do not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth.1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm per second.Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box andmodel time step.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"U_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"U component of wind","parameter_ID":"131","product_type":"forecast","shortName":"u","standard_name":"U_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/131"},"description":"This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east, in metres per second. A negative sign thus indicates air movement towards the west.This parameter can be combined with the V component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"V_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"V component of wind","parameter_ID":"132","product_type":"forecast","shortName":"v","standard_name":"V_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/132"},"description":"This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north, in metres per second. A negative sign thus indicates air movement towards the south.This parameter can be combined with the U component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Vertical_velocity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Vertical velocity","parameter_ID":"135","product_type":"forecast","shortName":"w","standard_name":"Vertical_velocity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/135"},"description":"This parameter is the speed of air motion in the upward or downward direction. The ECMWF Integrated Forecasting System (IFS) uses a pressure based vertical co-ordinate system and pressure decreases with height, therefore negative values of vertical velocity indicate upward motion.Vertical velocity can be useful to understand the large-scale dynamics of the atmosphere, including areas of upward motion/ascent (negative values) and downward motion/subsidence (positive values).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa s-1"}},"sci:publications":[{"doi":"10.1109/MCSE.2023.3260519","citation":"N. Wedi et al., Destination Earth: High-Performance Computing for Weather and Climate, in Computing in Science \u0026 Engineering, vol. 24, no. 6, pp. 29-37, Nov.-Dec. 2022,"},{"doi":"10.21957/d3f982672e","citation":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"dedl:short_description":"The Climate Change Adaptation Digital Twin provides global climate projections and sector-specific information over multiple decades at high resolution via a unified framework combining advanced Earth system models, impact assessments, and observations. This Generation-1, Realization-1 Collection gives access to 'Historical Simulation' data based on the 'IFS-NEMO' model."},{"type":"Collection","title":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Control Simulation - IFS-FESOM - Generation-1 - Realization-1","id":"EO.ECMWF.DAT.D1.DT_CLIMATE.G1.HIGHRESMIP_CONT_IFS-FESOM.R1","description":"Note: This Generation 1 Collection has been superseded by Generation 2 Simulation-level Collections\n\nThe DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities by providing innovative climate information on multi-decadal timescales, globally, at scales at which many impacts of climate change are observed. It combines cutting-edge global Earth-system models, impact-sector applications and observations into a unified framework to provide global climate projections and impact-sector information on multi-decadal timescales (1990 to ~2050), at very high spatial resolutions (5 to 10 km).\n\nThe Climate DT represents the first ever attempt to operationalise the production of global multi-decadal climate projections, leveraging the world-leading supercomputing facilities of the EuroHPC Joint Undertaking along with some of the leading European climate models. A concise overview of what the Climate DT aims to achieve, and of the different concepts essential for an understanding of the Digital Twin’s characteristics, is included in the [Climate DT factsheet](https://destine.ecmwf.int/wp-content/uploads/2024/06/2024.06.07_Climate-DT-Fact-Sheet_V7-2.pdf)\n\n## Control Simulation \n\n Repetitive 1990 forcing with no change in forcing over time. These simulations allow to quantify model drift and simulated inter-annual variability and provides relevant context for interpreting historical and scenario simulations.\n\n## Models\n\nThe Climate DT exploits and further evolves a new generation of global storm-resolving and eddy-rich models built through a cooperative model development approach. For more information on models please click [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#models)\n\n## Simulations\n\nThe Climate DT team carries out several types of digital twin simulations on the EuroHPC supercomputers.  Multi-decadal simulations are produced to cover the recent past (from 1990) and possible future evolutions of the climate up to 2050. See [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#simulations) for more information on Simulations\n\n## Parameters\n\nBelow we see the list of parameters extracted from the 'DestinE Climate DT data portfolio', for more information please refer to the page [Climate DT Phase 1 CLTE Parameters](https://confluence.ecmwf.int/display/DDCZ/Climate+DT+Phase+1+data+catalogue)","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G1.HIGHRESMIP_CONT_IFS-FESOM.R1/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G1.HIGHRESMIP_CONT_IFS-FESOM.R1/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G1.HIGHRESMIP_CONT_IFS-FESOM.R1/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G1.HIGHRESMIP_CONT_IFS-FESOM.R1","title":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Control Simulation - IFS-FESOM - Generation-1 - Realization-1"},{"rel":"example","type":"application/x-ipynb+json","href":"https://raw.githubusercontent.com/destination-earth/DestinE-DataLake-Lab/refs/heads/main/HDA/DestinE%20Digital%20Twins/DEDL-HDA-EO.ECMWF.DAT.DT_CLIMATE.ipynb","title":"Destination Earth - Climate DT Parameter - Data Access using DEDL HDA","application:type":"jupyter-notebook","application:embedded":true,"application:language":"Python"},{"rel":"example","type":"application/x-ipynb+json","href":"https://raw.githubusercontent.com/destination-earth/DestinE-DataLake-Lab/refs/heads/main/HDA/DestinE%20Digital%20Twins/ClimateDT-ParameterPlotter.ipynb","title":"Destination Earth - HDA Climate DT Parameter Plotter Tutorial","application:type":"jupyter-notebook","application:embedded":true,"application:language":"Python"},{"rel":"example","type":"application/x-ipynb+json","href":"https://raw.githubusercontent.com/destination-earth/DestinE-DataLake-Lab/refs/heads/main/HDA/DestinE%20Digital%20Twins/DEDL-HDA-EO.ECMWF.DAT.DT_CLIMATE-Series.ipynb","title":"Destination Earth - Climate DT Parameter Series Plot- Data Access using DEDL HDA","application:type":"jupyter-notebook","application:embedded":true,"application:language":"Python"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Parameters","title":"DestinE ClimateDT Parameters"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/Climate+DT+Phase+1+data+catalogue","title":"Climate DT Phase 1 data catalogue"},{"rel":"cite-as","type":"text/html","href":"https://dl.acm.org/doi/abs/10.1109/MCSE.2023.3260519","title":"Destination Earth: High-Performance Computing for Weather and Climate"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.21957/d3f982672e","title":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"assets":{"thumbnail":{"href":"https://s3.central.data.destination-earth.eu/swift/v1/dedl-public/collections/climate-dt-min.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1990-01-01T00:00:00Z","2004-12-31T23:59:59Z"]]}},"license":"CC-BY-4.0","keywords":["Digital Twins","Climate Change","Atmosphere","Ocean","Land","Sea Ice","Snow","Soil","Earth","High Performance Computing","Decision Making","Europe","Meteorology","class:d1","dataset:climate-dt","generation:1","expver:0001","stream:clte","type:fc","activity:HighResMIP","experiment:cont","realization:1","model:IFS-FESOM","resolution:standard,high","levtype:hl,o2d,o3d,pl,sfc,sol"],"summaries":{"federation:backends":["dedt_leonardo"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/datacube/v2.1.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json","https://stac-extensions.github.io/application/v0.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["producer","processor","licensor"],"url":"https://www.ecmwf.int/"}],"created":"2024-04-11T22:31:55Z","updated":"2026-04-27T14:04:20Z","published":"2024-04-11T22:31:55Z","cube:dimensions":{"lat":{"axis":"y","description":"latitude","extent":[-90,90],"type":"spatial"},"lon":{"axis":"x","description":"longitude","extent":[-180,180],"type":"spatial"},"time":{"extent":["1990-01-01T00:00:00Z","2004-12-31T23:59:59Z"],"step":"P0Y0M0DT1H0M0S","type":"temporal"}},"cube:variables":{"100_metre_U_wind_component(clte_hl)":{"attrs":{"encoding":"instantaneous","levelist":"100","levtype":"hl","long_name":"100 metre U wind component","parameter_ID":"228246","product_type":"forecast","shortName":"100u","standard_name":"100_metre_U_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228246"},"description":"This parameter is the eastward component of the 100 m wind. It is the horizontal speed of air moving towards the east, at a height of 100 metres above the surface of the Earth, in metres per second.Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over amodel grid box and model time step.This parameter can be combined with the northward component to give the speed and direction of the horizontal 100 m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"100_metre_V_wind_component(clte_hl)":{"attrs":{"encoding":"instantaneous","levelist":"100","levtype":"hl","long_name":"100 metre V wind component","parameter_ID":"228247","product_type":"forecast","shortName":"100v","standard_name":"100_metre_V_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228247"},"description":"This parameter is the northward component of the 100 m wind. It is the horizontal speed of air moving towards the north, at a height of 100 metres above the surface of the Earth, in metres per second.Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over amodel grid box and model time step.This parameter can be combined with the eastward component to give the speed and direction of the horizontal 100 m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_U_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"10 metre U wind component","parameter_ID":"165","product_type":"forecast","shortName":"10u","standard_name":"10_metre_U_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/165"},"description":"This parameter is the eastward component of the 10m wind. It is the horizontal speed of air moving towards the east, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the V component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_V_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"10 metre V wind component","parameter_ID":"166","product_type":"forecast","shortName":"10v","standard_name":"10_metre_V_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/166"},"description":"This parameter is the northward component of the 10m wind. It is the horizontal speed of air moving towards the north, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the U component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"2_metre_dewpoint_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"2 metre dewpoint temperature","parameter_ID":"168","product_type":"forecast","shortName":"2d","standard_name":"2_metre_dewpoint_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/168"},"description":"This parameter is the temperature to which the air, at 2 metres above the surface of the Earth, would have to be cooled for saturation to occur.It is a measure of the humidity of the air. Combined with temperature and pressure, it can be used to calculate the relative humidity.2m dew point temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"2_metre_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"2 metre temperature","parameter_ID":"167","product_type":"forecast","shortName":"2t","standard_name":"2_metre_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/167"},"description":"This parameter is the temperature of air at 2m above the surface of land, sea or in-land waters.2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.Please note that the encodings listed here for s2s \u0026 uerra (which includes encodings for carra/cerra) include entries for Mean 2 metre temperature. The specific encoding for Mean 2 metre temperature can be found in 228004.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Boundary_layer_height(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Boundary layer height","parameter_ID":"159","product_type":"forecast","shortName":"blh","standard_name":"Boundary_layer_height","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/159"},"description":"This parameter is the depth of air next to the Earth's surface which is most affected by the resistance to the transfer of momentum, heat or moisture across the surface.The boundary layer height can be as low as a few tens of metres, such as in cooling air at night, or as high as several kilometres over the desert in the middle of a hot sunny day. When the boundary layer height is low, higher concentrations of pollutants (emitted from the Earth's surface) can develop.The boundary layer height calculation is based on the bulk Richardson number (a measure of the atmospheric conditions) following the conclusions of a 2012 review.See further information.","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Charnock(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Charnock","parameter_ID":"148","product_type":"forecast","shortName":"chnk","standard_name":"Charnock","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/148"},"description":"This parameter accounts for increased aerodynamic roughness as wave heights grow due to increasing surface stress. It depends on the wind speed, wave age and other aspects of the sea state and is used to calculate how much the waves slow down the wind.When the atmospheric model is run without the ocean model, this parameter has a constant value of 0.018. When the atmospheric model is coupled to the ocean model, this parameter is calculated by theECMWF Wave Model.","dimensions":["lat","lon","time"],"type":"data","unit":"Numeric"},"Evaporation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Evaporation","parameter_ID":"182","product_type":"forecast","shortName":"e","standard_name":"Evaporation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/182"},"description":"This parameter is the accumulated amount of water that has evaporated from the Earth's surface, including a simplified representation of transpiration (from vegetation), into vapour in the air above.This parameter is accumulated over aparticular time period which depends on the data extracted.The ECMWF Integrated Forecasting System convention is that downward fluxes are positive. Therefore, negative values indicate evaporation and positive values indicate condensation.[NOTE: See 260259 for the equivalent parameter in \"kg m-2\"]","dimensions":["lat","lon","time"],"type":"data","unit":"m of water equivalent"},"Geopotential(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Geopotential","parameter_ID":"129","product_type":"forecast","shortName":"z","standard_name":"Geopotential","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/129"},"description":"This parameter is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level.The geopotential height can be calculated by dividing the geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2). The geopotential height plays an important role in synoptic meteorology (analysis of weather patterns). Charts of geopotential height plotted at constant pressure levels (e.g., 300, 500 or 850 hPa) can be used to identify weather systems such as cyclones, anticyclones, troughs and ridges.At the surface of the Earth, this parameter shows the variations in geopotential (height) of the surface, and is often referred to as the orography.","dimensions":["lat","lon","time"],"type":"data","unit":"m2s-2"},"Geopotential(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Geopotential","parameter_ID":"129","product_type":"forecast","shortName":"z","standard_name":"Geopotential","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/129"},"description":"This parameter is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level.The geopotential height can be calculated by dividing the geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2). The geopotential height plays an important role in synoptic meteorology (analysis of weather patterns). Charts of geopotential height plotted at constant pressure levels (e.g., 300, 500 or 850 hPa) can be used to identify weather systems such as cyclones, anticyclones, troughs and ridges.At the surface of the Earth, this parameter shows the variations in geopotential (height) of the surface, and is often referred to as the orography.","dimensions":["lat","lon","time"],"type":"data","unit":"m2s-2"},"High_cloud_cover(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"High cloud cover","parameter_ID":"188","product_type":"forecast","shortName":"hcc","standard_name":"High_cloud_cover","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/188"},"description":"The proportion of agrid boxcovered by cloud occurring in the high levels of the troposphere. High cloud is a single level field calculated from cloud occurring on model levels with a pressure less than 0.45 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), high cloud would be calculated using levels with a pressure of less than 450 hPa (approximately 6km and above (assuming a `standard atmosphere`)).The high cloud cover parameter is calculated from cloud for the appropriate model levels as described above. Assumptions are made about the degree of overlap/randomness between clouds in different model levels.Cloud fractions vary from 0 to 1.[NOTE: See 3075 for the equivalent parameter in \"%\"]","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Land-sea_mask(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Land-sea mask","parameter_ID":"172","product_type":"forecast","shortName":"lsm","standard_name":"Land-sea_mask","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/172"},"description":"This parameter is the proportion of land, as opposed to ocean or inland waters (lakes, reservoirs, rivers and coastal waters), in agrid box.This parameter has values ranging between zero and one and is dimensionless.In cycles of the ECMWF Integrated Forecasting System (IFS) from CY41R1 (introduced in May 2015) onwards, grid boxes where this parameter has a value above 0.5 can be comprised of a mixture of land and inland water but not ocean. Grid boxes with a value of 0.5 and below can only be comprised of a water surface. In the latter case, the lake cover is used to determine how much of the water surface is ocean or inland water.In cycles of the IFS before CY41R1, grid boxes where this parameter has a value above 0.5 can only be comprised of land and those grid boxes with a value of 0.5 and below can only be comprised of ocean. In these older model cycles, there is no differentiation between ocean and inland water.","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Low_cloud_cover(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Low cloud cover","parameter_ID":"186","product_type":"forecast","shortName":"lcc","standard_name":"Low_cloud_cover","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/186"},"description":"This parameter is the proportion of agrid boxcovered by cloud occurring in the lower levels of the troposphere. Low cloud is a single level field calculated from cloud occurring on model levels with a pressure greater than 0.8 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), low cloud would be calculated using levels with a pressure greater than 800 hPa (below approximately 2km (assuming a 'standard atmosphere')).The low cloud cover parameter is calculated from cloud cover for the appropriate model levels as described above. Assumptions are made about the degree of overlap/randomness between clouds in different model levels.Cloud fractions vary from 0 to 1.[NOTE: See 3073 for the equivalent parameter in \"%\"]","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Mean_sea_level_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Mean sea level pressure","parameter_ID":"151","product_type":"forecast","shortName":"msl","standard_name":"Mean_sea_level_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/151"},"description":"This parameter is the pressure (force per unit area) of the atmosphere adjusted to the height of mean sea level.It is a measure of the weight that all the air in a column vertically above the area of Earth's surface would have at that point, if the point were located at the mean sea level. It is calculated over all surfaces - land, sea and in-land water.Maps of mean sea level pressure are used to identify the locations of low and high pressure systems, often referred to as cyclones and anticyclones. Contours of mean sea level pressure also indicate the strength of the wind. Tightly packed contours show stronger winds.The units of this parameter are pascals (Pa).  Mean sea level pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb = 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Medium_cloud_cover(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Medium cloud cover","parameter_ID":"187","product_type":"forecast","shortName":"mcc","standard_name":"Medium_cloud_cover","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/187"},"description":"This parameter is the proportion of agrid boxcovered by cloud occurring in the middle levels of the troposphere. Medium cloud is a single level field calculated from cloud occurring on model levels with a pressure between 0.45 and 0.8 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), medium cloud would be calculated using levels with a pressure of less than or equal to 800 hPa and greater than or equal to 450 hPa (between approximately 2km and 6km (assuming a 'standard atmosphere')).The medium cloud parameter is calculated from cloud cover for the appropriate model levels as described above. Assumptions are made about the degree of overlap/randomness between clouds in different model levels.Cloud fractions vary from 0 to 1.[NOTE: See 3074 for the equivalent parameter in \"%\"]","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Potential_vorticity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Potential vorticity","parameter_ID":"60","product_type":"forecast","shortName":"pv","standard_name":"Potential_vorticity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/60"},"description":"Potential vorticity is a measure of the capacity for air to rotate in the atmosphere. If we ignore the effects of heating and friction, potential vorticity is conserved following an air parcel. It is used to look for places where large wind storms are likely to originate and develop.  Potential vorticity increases strongly above the tropopause and therefore, it can also be used in studies related to the stratosphere and stratosphere-troposphere exchanges.Large wind storms develop when a column of air in the atmosphere starts to rotate. Potential vorticity is calculated from the wind, temperature and pressure across a column of air in the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K m2kg-1s-1"},"Relative_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Relative humidity","parameter_ID":"157","product_type":"forecast","shortName":"r","standard_name":"Relative_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/157"},"description":"This parameter is the water vapour pressure as a percentage of the value at which the air becomes saturated (the point at which water vapour begins to condense into liquid water or deposition into ice).For temperatures over 0°C (273.15 K) it is calculated for saturation over water. At temperatures below -23°C it is calculated for saturation over ice.  Between -23°C and 0°C this parameter is calculated by interpolating between the ice and water values using a quadratic function.See more information about the model's relative humidity calculation.","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Skin_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Skin temperature","parameter_ID":"235","product_type":"forecast","shortName":"skt","standard_name":"Skin_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235"},"description":"This parameter is the temperature of the surface of the Earth.The skin temperature is the theoretical temperature that is required to satisfy the surface energy balance. It represents the temperature of the uppermost surface layer, which has no heat capacity and so can respond instantaneously to changes in surface fluxes. Skin temperature is calculated differently over land and sea.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.See further information about the skin temperatureover landandover sea.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean skin temperature. The specific encoding for Mean skin temperature can be found in 235079.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Snow_depth(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Snow depth","parameter_ID":"141","product_type":"forecast","shortName":"sd","standard_name":"Snow_depth","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/141"},"description":"This parameter is the depth of snow from the snow-covered area of agrid box.Its units are metres of water equivalent, so it is the depth the water would have if the snow melted and was spread evenly over the whole grid box. The ECMWF Integrated Forecast System represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box.See further information.\n\n[NOTE: See 228141 for the equivalent parameter in \"kg m-2\"]","dimensions":["lat","lon","time"],"type":"data","unit":"m of water equivalent"},"Snow_depth_water_equivalent(clte_sol)":{"attrs":{"encoding":"instantaneous","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Snow depth water equivalent","parameter_ID":"228141","product_type":"forecast","shortName":"sd","standard_name":"Snow_depth_water_equivalent","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228141"},"description":"Snow depth water equivalent in kg m**-2 (mm) water equivalent.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean snow depth water equivalent. The specific encoding for Time-mean snow depth water equivalent can be found in 235078.[NOTE: See 141 for the equivalent parameter in \"m of water equivalent\"]","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Snowfall(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Snowfall","parameter_ID":"144","product_type":"forecast","shortName":"sf","standard_name":"Snowfall","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/144"},"description":"This parameter is the accumulated snow that falls to the Earth's surface. It is the sum of large-scale snowfall and convective snowfall. Large-scale snowfall is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of thegrid boxor larger. Convective snowfall is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box.See further information.This parameter is the total amount of wateraccumulated over a particular time period which depends on the data extracted. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box.Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box.[NOTE: See 228144 for the equivalent parameter in \"kg m-2\"]","dimensions":["lat","lon","time"],"type":"data","unit":"m of water equivalent"},"Specific_cloud_liquid_water_content(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific cloud liquid water content","parameter_ID":"246","product_type":"forecast","shortName":"clwc","standard_name":"Specific_cloud_liquid_water_content","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/246"},"description":"This parameter is the mass of cloud liquid water droplets per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for agrid box.Water within clouds can be liquid or ice, or a combination of the two.See further information about the cloud formulation.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Specific_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific humidity","parameter_ID":"133","product_type":"forecast","shortName":"q","standard_name":"Specific_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/133"},"description":"This parameter is the mass of water vapour per kilogram of moist air.The total mass of moist air is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Sub-surface_runoff(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Sub-surface runoff","parameter_ID":"9","product_type":"forecast","shortName":"ssro","standard_name":"Sub-surface_runoff","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/9"},"description":"Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is simply called 'runoff'. This parameter is the total amount of water accumulated over aparticular time period which depends on the data extracted.The units of runoff are depth in metres.  This is the depth the water would have if it were spread evenly over thegrid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid square area.  Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here.Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood.  More information about how runoff is calculated is given in theIFS Physical Processes documentation.\n\n[NOTE: See 231012 for the equivalent parameter in \"kg m-2\"]","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Surface_long-wave_(thermal)_radiation_downwards(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Surface long-wave (thermal) radiation downwards","parameter_ID":"175","product_type":"forecast","shortName":"strd","standard_name":"Surface_long-wave_(thermal)_radiation_downwards","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/175"},"description":"This parameter is the amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere and clouds that reaches a horizontal plane at the surface of the Earth.The surface of the Earth emits thermal radiation, some of which is absorbed by the atmosphere and clouds. The atmosphere and clouds likewise emit thermal radiation in all directions, some of which reaches the surface (represented by this parameter).See further documentation.This parameter isaccumulated over a particular time period which depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Surface_net_long-wave_(thermal)_radiation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Surface net long-wave (thermal) radiation","parameter_ID":"177","product_type":"forecast","shortName":"str","standard_name":"Surface_net_long-wave_(thermal)_radiation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/177"},"description":"Thermal radiation (also known as longwave or terrestrial radiation) refers to radiation emitted by the atmosphere, clouds and the surface of the Earth. This parameter is the difference between downward and upward thermal radiation at the surface of the Earth. It the amount passing through a horizontal plane.The atmosphere and clouds emit thermal radiation in all directions, some of which reaches the surface as downward thermal radiation. The upward thermal radiation at the surface consists of thermal radiation emitted by the surface plus the fraction of downwards thermal radiation reflected upward by the surface.See further documentation.This parameter isaccumulated over a particular time periodwhich depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds.The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Surface_net_short-wave_(solar)_radiation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Surface net short-wave (solar) radiation","parameter_ID":"176","product_type":"forecast","shortName":"ssr","standard_name":"Surface_net_short-wave_(solar)_radiation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/176"},"description":"This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo).Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The remainder is incident on the Earth's surface, where some of it is reflected.See further documentation.This parameter isaccumulated over a particular time period which depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Surface_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Surface pressure","parameter_ID":"134","product_type":"forecast","shortName":"sp","standard_name":"Surface_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/134"},"description":"This parameter is the pressure (force per unit area) of the atmosphere on the surface of land, sea and in-land water.It is a measure of the weight of all the air in a column vertically above the area of the Earth's surface represented at a fixed point.Surface pressure is often used in combination with temperature to calculate air density.The strong variation of pressure with altitude makes it difficult to see the low and high pressure systems over mountainous areas, so mean sea level pressure, rather than surface pressure, is normally used for this purpose.The units of this parameter are Pascals (Pa). Surface pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb= 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Surface_runoff(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Surface runoff","parameter_ID":"8","product_type":"forecast","shortName":"sro","standard_name":"Surface_runoff","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/8"},"description":"Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is simply called 'runoff'. This parameter is the total amount of water accumulated over aparticular time period which depends on the data extracted.The units of runoff are depth in metres.  This is the depth the water would have if it were spread evenly over thegrid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid square area.  Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here.Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood.  More information about how runoff is calculated is given in theIFS Physical Processes documentation.\n\n[NOTE: See 231010 for the equivalent parameter in \"kg m-2\"]","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Surface_short-wave_(solar)_radiation_downwards(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Surface short-wave (solar) radiation downwards","parameter_ID":"169","product_type":"forecast","shortName":"ssrd","standard_name":"Surface_short-wave_(solar)_radiation_downwards","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/169"},"description":"This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth. This parameter comprises both direct and diffuse solar radiation.Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface (represented by this parameter).See further documentation.To a reasonably good approximation, this parameter is the model equivalent of what would be measured by a pyranometer (an instrument used for measuring solar radiation) at the surface. However, care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over amodel grid box.This parameter isaccumulated over a particular time period which depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"TOA_incident_short-wave_(solar)_radiation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"TOA incident short-wave (solar) radiation","parameter_ID":"212","product_type":"forecast","shortName":"tisr","standard_name":"TOA_incident_short-wave_(solar)_radiation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/212"},"description":"Accumulated field","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Temperature(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Temperature","parameter_ID":"130","product_type":"forecast","shortName":"t","standard_name":"Temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/130"},"description":"This parameter is the temperature in the atmosphere.It has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.This parameter is available on multiple levels through the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-integrated_eastward_turbulent_surface_stress(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Time-integrated eastward turbulent surface stress","parameter_ID":"180","product_type":"forecast","shortName":"ewss","standard_name":"Time-integrated_eastward_turbulent_surface_stress","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/180"},"description":"Air flowing over a surface exerts a stress that transfers momentum to the surface and slows the wind. This parameter is the accumulated stress on the Earth's surface in the eastward direction due to both the turbulent interactions between the atmosphere and the surface, and to turbulent orographic form drag. The turbulent interactions between the atmosphere and the surface are due to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km being derived from land surface data at about 1 km resolution.See further information.Positive (negative) values denote stress in the eastward (westward) direction.This parameter isaccumulated over a particular time periodwhich depends on the data extracted.","dimensions":["lat","lon","time"],"type":"data","unit":"N m-2s"},"Time-integrated_northward_turbulent_surface_stress(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Time-integrated northward turbulent surface stress","parameter_ID":"181","product_type":"forecast","shortName":"nsss","standard_name":"Time-integrated_northward_turbulent_surface_stress","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/181"},"description":"Air flowing over a surface exerts a stress that transfers momentum to the surface and slows the wind. This parameter is the accumulated stress on the Earth's surface in the northward direction due to both the turbulent interactions between the atmosphere and the surface, and to turbulent orographic form drag.The turbulent interactions between the atmosphere and the surface are due to the roughness of the surface.The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km being derived from land surface data at about 1 km resolution.See further information.Positive (negative) values denote stress in the northward (southward) direction.This parameter isaccumulated over a particular time periodwhich depends on the data extracted.","dimensions":["lat","lon","time"],"type":"data","unit":"N m-2s"},"Time-integrated_surface_latent_heat_net_flux(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Time-integrated surface latent heat net flux","parameter_ID":"147","product_type":"forecast","shortName":"slhf","standard_name":"Time-integrated_surface_latent_heat_net_flux","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/147"},"description":"This parameter is the transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earth's surface and the atmosphere through the effects of turbulent air motion. Evaporation from the Earth's surface represents a transfer of energy from the surface to the atmosphere.See further documentationThis parameter is accumulated over aparticular time period which depends on the data extracted.The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds.The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-integrated_surface_sensible_heat_net_flux(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Time-integrated surface sensible heat net flux","parameter_ID":"146","product_type":"forecast","shortName":"sshf","standard_name":"Time-integrated_surface_sensible_heat_net_flux","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/146"},"description":"This parameter is the transfer of heat between the Earth's surface and the atmosphere through the effects of turbulent air motion (but excluding any heat transfer resulting from condensation or evaporation).The magnitude of the sensible heat flux is governed by the difference in temperature between the surface and the overlying atmosphere, wind speed and the surface roughness. For example, cold air overlying a warm surface would produce a sensible heat flux from the land (or ocean) into the atmosphere.See further documentationThis is a single level parameter and it is accumulated over aparticular time period which depends on the data extracted.The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_eastward_sea_ice_velocity(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean eastward sea ice velocity","parameter_ID":"263003","product_type":"forecast","shortName":"avg_siue","standard_name":"Time-mean_eastward_sea_ice_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263003"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69","levtype":"o3d","long_name":"Time-mean eastward sea water velocity","parameter_ID":"263506","product_type":"forecast","shortName":"avg_uoe","standard_name":"Time-mean_eastward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263506"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_sea_ice_velocity(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean northward sea ice velocity","parameter_ID":"263004","product_type":"forecast","shortName":"avg_sivn","standard_name":"Time-mean_northward_sea_ice_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263004"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69","levtype":"o3d","long_name":"Time-mean northward sea water velocity","parameter_ID":"263505","product_type":"forecast","shortName":"avg_von","standard_name":"Time-mean_northward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263505"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_sea_ice_area_fraction(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea ice area fraction","parameter_ID":"263001","product_type":"forecast","shortName":"avg_siconc","standard_name":"Time-mean_sea_ice_area_fraction","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263001"},"dimensions":["lat","lon","time"],"type":"data","unit":"Fraction"},"Time-mean_sea_ice_thickness(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea ice thickness","parameter_ID":"263000","product_type":"forecast","shortName":"avg_sithick","standard_name":"Time-mean_sea_ice_thickness","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263000"},"dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Time-mean_sea_surface_height(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea surface height","parameter_ID":"263124","product_type":"forecast","shortName":"avg_zos","standard_name":"Time-mean_sea_surface_height","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263124"},"dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Time-mean_sea_surface_practical_salinity(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea surface practical salinity","parameter_ID":"263100","product_type":"forecast","shortName":"avg_sos","standard_name":"Time-mean_sea_surface_practical_salinity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263100"},"dimensions":["lat","lon","time"],"type":"data","unit":"g kg-1"},"Time-mean_sea_surface_temperature(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea surface temperature","parameter_ID":"263101","product_type":"forecast","shortName":"avg_tos","standard_name":"Time-mean_sea_surface_temperature","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263101"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_sea_water_potential_temperature(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69","levtype":"o3d","long_name":"Time-mean sea water potential temperature","parameter_ID":"263501","product_type":"forecast","shortName":"avg_thetao","standard_name":"Time-mean_sea_water_potential_temperature","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263501"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_sea_water_practical_salinity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69","levtype":"o3d","long_name":"Time-mean sea water practical salinity","parameter_ID":"263500","product_type":"forecast","shortName":"avg_so","standard_name":"Time-mean_sea_water_practical_salinity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263500"},"dimensions":["lat","lon","time"],"type":"data","unit":"g kg-1"},"Time-mean_snow_thickness_over_sea_ice(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean snow thickness over sea ice","parameter_ID":"263002","product_type":"forecast","shortName":"avg_sisnthick","standard_name":"Time-mean_snow_thickness_over_sea_ice","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263002"},"dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Time-mean_upward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69","levtype":"o3d","long_name":"Time-mean upward sea water velocity","parameter_ID":"263507","product_type":"forecast","shortName":"avg_wo","standard_name":"Time-mean_upward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263507"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Top_net_long-wave_(thermal)_radiation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Top net long-wave (thermal) radiation","parameter_ID":"179","product_type":"forecast","shortName":"ttr","standard_name":"Top_net_long-wave_(thermal)_radiation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/179"},"description":"The thermal (also known as terrestrial or longwave) radiation emitted to space at the top of the atmosphere is commonly known as the Outgoing Longwave Radiation (OLR). The top net thermal radiation (this parameter) is equal to the negative of OLR.See further documentation.This parameter isaccumulated over a particular time periodwhich depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds.The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Top_net_short-wave_(solar)_radiation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Top net short-wave (solar) radiation","parameter_ID":"178","product_type":"forecast","shortName":"tsr","standard_name":"Top_net_short-wave_(solar)_radiation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/178"},"description":"This parameter is the incoming solar radiation (also known as shortwave radiation) minus the outgoing solar radiation at the top of the atmosphere. It is the amount of radiation passing through a horizontal plane. The incoming solar radiation is the amount received from the Sun. The outgoing solar radiation is the amount reflected and scattered by the Earth's atmosphere and surface.See further documentation.This parameter isaccumulated over a particular time periodwhich depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds.The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Total_cloud_cover(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total cloud cover","parameter_ID":"164","product_type":"forecast","shortName":"tcc","standard_name":"Total_cloud_cover","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/164"},"description":"This parameter is the proportion of agrid boxcovered by cloud. Total cloud cover is a single level field calculated from the cloud occurring at different model levels through the atmosphere. Assumptions are made about the degree of overlap/randomness between clouds at different heights.Cloud fractions vary from 0 to 1.[NOTE: See 228164 for the equivalent parameter in \"%\"]","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Total_column_cloud_ice_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud ice water","parameter_ID":"79","product_type":"forecast","shortName":"tciw","standard_name":"Total_column_cloud_ice_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/79"},"description":"This parameter is the amount of ice contained within clouds in a column extending from the surface of the Earth to the top of the atmosphere. Snow (aggregated ice crystals) is not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The  ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_cloud_liquid_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud liquid water","parameter_ID":"78","product_type":"forecast","shortName":"tclw","standard_name":"Total_column_cloud_liquid_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/78"},"description":"This parameter is the amount of liquid water contained within cloud droplets in a column extending from the surface of the Earth to the top of the atmosphere. Rain water droplets, which are much larger in size (and mass), are not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_vertically-integrated_water_vapour(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column vertically-integrated water vapour","parameter_ID":"137","product_type":"forecast","shortName":"tcwv","standard_name":"Total_column_vertically-integrated_water_vapour","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/137"},"description":"This parameter is the total amount of water vapour in a column extending from the surface of the Earth to the top of the atmosphere.This parameter represents the area averaged value for agrid box.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_precipitation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Total precipitation","parameter_ID":"228","product_type":"forecast","shortName":"tp","standard_name":"Total_precipitation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228"},"description":"This parameter is the accumulated liquid and frozen water, comprising rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation and convective precipitation. Large-scale precipitation is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of thegrid boxor larger. Convective precipitation is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box.See further information.This parameter does not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth.This parameter is the total amount of wateraccumulated over a particular time period which depends on the data extracted. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box.Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box.\n\n[NOTE: See 228228 for the equivalent parameter in \"kg m-2\"]","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Total_precipitation_rate(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total precipitation rate","parameter_ID":"260048","product_type":"forecast","shortName":"tprate","standard_name":"Total_precipitation_rate","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/260048"},"description":"This parameter is the rate of total precipitation,at the specified time.In the ECMWF Integrated Forecasting System (IFS), total precipitation is rain and snow that falls to the Earth's surface.  It is the sum of large-scale precipitation and convective precipitation. Large-scale precipitation is generated by the cloud scheme in the IFS. The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of agrid boxor larger. Convective precipitation is generated by the convection scheme in the IFS. The convection scheme represents convection at spatial scales smaller than the grid box.Seefurther information. Precipitation parameters do not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth.1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm per second.Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box andmodel time step.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"U_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"U component of wind","parameter_ID":"131","product_type":"forecast","shortName":"u","standard_name":"U_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/131"},"description":"This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east, in metres per second. A negative sign thus indicates air movement towards the west.This parameter can be combined with the V component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"V_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"V component of wind","parameter_ID":"132","product_type":"forecast","shortName":"v","standard_name":"V_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/132"},"description":"This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north, in metres per second. A negative sign thus indicates air movement towards the south.This parameter can be combined with the U component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Vertical_velocity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Vertical velocity","parameter_ID":"135","product_type":"forecast","shortName":"w","standard_name":"Vertical_velocity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/135"},"description":"This parameter is the speed of air motion in the upward or downward direction. The ECMWF Integrated Forecasting System (IFS) uses a pressure based vertical co-ordinate system and pressure decreases with height, therefore negative values of vertical velocity indicate upward motion.Vertical velocity can be useful to understand the large-scale dynamics of the atmosphere, including areas of upward motion/ascent (negative values) and downward motion/subsidence (positive values).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa s-1"}},"sci:publications":[{"doi":"10.1109/MCSE.2023.3260519","citation":"N. Wedi et al., Destination Earth: High-Performance Computing for Weather and Climate, in Computing in Science \u0026 Engineering, vol. 24, no. 6, pp. 29-37, Nov.-Dec. 2022,"},{"doi":"10.21957/d3f982672e","citation":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"dedl:short_description":"The Climate Change Adaptation Digital Twin provides global climate projections and sector-specific information over multiple decades at high resolution via a unified framework combining advanced Earth system models, impact assessments, and observations. This Generation-1, Realization-1 Collection gives access to 'Control Simulation' data based on the 'IFS-FESOM' model."},{"type":"Collection","title":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Control Simulation - IFS-NEMO - Generation-1 - Realization-1","id":"EO.ECMWF.DAT.D1.DT_CLIMATE.G1.HIGHRESMIP_CONT_IFS-NEMO.R1","description":"Note: This Generation 1 Collection has been superseded by Generation 2 Simulation-level Collections\n\nThe DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities by providing innovative climate information on multi-decadal timescales, globally, at scales at which many impacts of climate change are observed. It combines cutting-edge global Earth-system models, impact-sector applications and observations into a unified framework to provide global climate projections and impact-sector information on multi-decadal timescales (1990 to ~2050), at very high spatial resolutions (5 to 10 km).\n\nThe Climate DT represents the first ever attempt to operationalise the production of global multi-decadal climate projections, leveraging the world-leading supercomputing facilities of the EuroHPC Joint Undertaking along with some of the leading European climate models. A concise overview of what the Climate DT aims to achieve, and of the different concepts essential for an understanding of the Digital Twin’s characteristics, is included in the [Climate DT factsheet](https://destine.ecmwf.int/wp-content/uploads/2024/06/2024.06.07_Climate-DT-Fact-Sheet_V7-2.pdf)\n\n## Control Simulation \n\n Repetitive 1990 forcing with no change in forcing over time. These simulations allow to quantify model drift and simulated inter-annual variability and provides relevant context for interpreting historical and scenario simulations.\n\n## Models\n\nThe Climate DT exploits and further evolves a new generation of global storm-resolving and eddy-rich models built through a cooperative model development approach. For more information on models please click [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#models)\n\n## Simulations\n\nThe Climate DT team carries out several types of digital twin simulations on the EuroHPC supercomputers.  Multi-decadal simulations are produced to cover the recent past (from 1990) and possible future evolutions of the climate up to 2050. See [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#simulations) for more information on Simulations\n\n## Parameters\n\nBelow we see the list of parameters extracted from the 'DestinE Climate DT data portfolio', for more information please refer to the page [Climate DT Phase 1 CLTE Parameters](https://confluence.ecmwf.int/display/DDCZ/Climate+DT+Phase+1+data+catalogue)","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G1.HIGHRESMIP_CONT_IFS-NEMO.R1/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G1.HIGHRESMIP_CONT_IFS-NEMO.R1/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G1.HIGHRESMIP_CONT_IFS-NEMO.R1/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G1.HIGHRESMIP_CONT_IFS-NEMO.R1","title":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Control Simulation - IFS-NEMO - Generation-1 - Realization-1"},{"rel":"example","type":"application/x-ipynb+json","href":"https://raw.githubusercontent.com/destination-earth/DestinE-DataLake-Lab/refs/heads/main/HDA/DestinE%20Digital%20Twins/DEDL-HDA-EO.ECMWF.DAT.DT_CLIMATE.ipynb","title":"Destination Earth - Climate DT Parameter - Data Access using DEDL HDA","application:type":"jupyter-notebook","application:embedded":true,"application:language":"Python"},{"rel":"example","type":"application/x-ipynb+json","href":"https://raw.githubusercontent.com/destination-earth/DestinE-DataLake-Lab/refs/heads/main/HDA/DestinE%20Digital%20Twins/ClimateDT-ParameterPlotter.ipynb","title":"Destination Earth - HDA Climate DT Parameter Plotter Tutorial","application:type":"jupyter-notebook","application:embedded":true,"application:language":"Python"},{"rel":"example","type":"application/x-ipynb+json","href":"https://raw.githubusercontent.com/destination-earth/DestinE-DataLake-Lab/refs/heads/main/HDA/DestinE%20Digital%20Twins/DEDL-HDA-EO.ECMWF.DAT.DT_CLIMATE-Series.ipynb","title":"Destination Earth - Climate DT Parameter Series Plot- Data Access using DEDL HDA","application:type":"jupyter-notebook","application:embedded":true,"application:language":"Python"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Parameters","title":"DestinE ClimateDT Parameters"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/Climate+DT+Phase+1+data+catalogue","title":"Climate DT Phase 1 data catalogue"},{"rel":"cite-as","type":"text/html","href":"https://dl.acm.org/doi/abs/10.1109/MCSE.2023.3260519","title":"Destination Earth: High-Performance Computing for Weather and Climate"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.21957/d3f982672e","title":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"assets":{"thumbnail":{"href":"https://s3.central.data.destination-earth.eu/swift/v1/dedl-public/collections/climate-dt-min.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1990-01-01T00:00:00Z","2007-04-30T23:59:59Z"]]}},"license":"CC-BY-4.0","keywords":["Digital Twins","Climate Change","Atmosphere","Ocean","Land","Sea Ice","Snow","Soil","Earth","High Performance Computing","Decision Making","Europe","Meteorology","class:d1","dataset:climate-dt","generation:1","expver:0001","stream:clte","type:fc","activity:HighResMIP","experiment:cont","realization:1","model:IFS-NEMO","resolution:high","levtype:hl,o2d,o3d,pl,sfc,sol"],"summaries":{"federation:backends":["dedt_lumi"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/datacube/v2.1.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json","https://stac-extensions.github.io/application/v0.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["producer","processor","licensor"],"url":"https://www.ecmwf.int/"}],"created":"2024-04-11T22:31:55Z","updated":"2026-04-27T14:04:20Z","published":"2024-04-11T22:31:55Z","cube:dimensions":{"lat":{"axis":"y","description":"latitude","extent":[-90,90],"type":"spatial"},"lon":{"axis":"x","description":"longitude","extent":[-180,180],"type":"spatial"},"time":{"extent":["1990-01-01T00:00:00Z","2007-04-30T23:59:59Z"],"step":"P0Y0M0DT1H0M0S","type":"temporal"}},"cube:variables":{"100_metre_U_wind_component(clte_hl)":{"attrs":{"encoding":"instantaneous","levelist":"100","levtype":"hl","long_name":"100 metre U wind component","parameter_ID":"228246","product_type":"forecast","shortName":"100u","standard_name":"100_metre_U_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228246"},"description":"This parameter is the eastward component of the 100 m wind. It is the horizontal speed of air moving towards the east, at a height of 100 metres above the surface of the Earth, in metres per second.Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over amodel grid box and model time step.This parameter can be combined with the northward component to give the speed and direction of the horizontal 100 m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"100_metre_V_wind_component(clte_hl)":{"attrs":{"encoding":"instantaneous","levelist":"100","levtype":"hl","long_name":"100 metre V wind component","parameter_ID":"228247","product_type":"forecast","shortName":"100v","standard_name":"100_metre_V_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228247"},"description":"This parameter is the northward component of the 100 m wind. It is the horizontal speed of air moving towards the north, at a height of 100 metres above the surface of the Earth, in metres per second.Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over amodel grid box and model time step.This parameter can be combined with the eastward component to give the speed and direction of the horizontal 100 m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_U_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"10 metre U wind component","parameter_ID":"165","product_type":"forecast","shortName":"10u","standard_name":"10_metre_U_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/165"},"description":"This parameter is the eastward component of the 10m wind. It is the horizontal speed of air moving towards the east, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the V component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_V_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"10 metre V wind component","parameter_ID":"166","product_type":"forecast","shortName":"10v","standard_name":"10_metre_V_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/166"},"description":"This parameter is the northward component of the 10m wind. It is the horizontal speed of air moving towards the north, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the U component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"2_metre_dewpoint_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"2 metre dewpoint temperature","parameter_ID":"168","product_type":"forecast","shortName":"2d","standard_name":"2_metre_dewpoint_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/168"},"description":"This parameter is the temperature to which the air, at 2 metres above the surface of the Earth, would have to be cooled for saturation to occur.It is a measure of the humidity of the air. Combined with temperature and pressure, it can be used to calculate the relative humidity.2m dew point temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"2_metre_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"2 metre temperature","parameter_ID":"167","product_type":"forecast","shortName":"2t","standard_name":"2_metre_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/167"},"description":"This parameter is the temperature of air at 2m above the surface of land, sea or in-land waters.2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.Please note that the encodings listed here for s2s \u0026 uerra (which includes encodings for carra/cerra) include entries for Mean 2 metre temperature. The specific encoding for Mean 2 metre temperature can be found in 228004.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Boundary_layer_height(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Boundary layer height","parameter_ID":"159","product_type":"forecast","shortName":"blh","standard_name":"Boundary_layer_height","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/159"},"description":"This parameter is the depth of air next to the Earth's surface which is most affected by the resistance to the transfer of momentum, heat or moisture across the surface.The boundary layer height can be as low as a few tens of metres, such as in cooling air at night, or as high as several kilometres over the desert in the middle of a hot sunny day. When the boundary layer height is low, higher concentrations of pollutants (emitted from the Earth's surface) can develop.The boundary layer height calculation is based on the bulk Richardson number (a measure of the atmospheric conditions) following the conclusions of a 2012 review.See further information.","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Charnock(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Charnock","parameter_ID":"148","product_type":"forecast","shortName":"chnk","standard_name":"Charnock","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/148"},"description":"This parameter accounts for increased aerodynamic roughness as wave heights grow due to increasing surface stress. It depends on the wind speed, wave age and other aspects of the sea state and is used to calculate how much the waves slow down the wind.When the atmospheric model is run without the ocean model, this parameter has a constant value of 0.018. When the atmospheric model is coupled to the ocean model, this parameter is calculated by theECMWF Wave Model.","dimensions":["lat","lon","time"],"type":"data","unit":"Numeric"},"Evaporation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Evaporation","parameter_ID":"182","product_type":"forecast","shortName":"e","standard_name":"Evaporation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/182"},"description":"This parameter is the accumulated amount of water that has evaporated from the Earth's surface, including a simplified representation of transpiration (from vegetation), into vapour in the air above.This parameter is accumulated over aparticular time period which depends on the data extracted.The ECMWF Integrated Forecasting System convention is that downward fluxes are positive. Therefore, negative values indicate evaporation and positive values indicate condensation.[NOTE: See 260259 for the equivalent parameter in \"kg m-2\"]","dimensions":["lat","lon","time"],"type":"data","unit":"m of water equivalent"},"Geopotential(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Geopotential","parameter_ID":"129","product_type":"forecast","shortName":"z","standard_name":"Geopotential","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/129"},"description":"This parameter is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level.The geopotential height can be calculated by dividing the geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2). The geopotential height plays an important role in synoptic meteorology (analysis of weather patterns). Charts of geopotential height plotted at constant pressure levels (e.g., 300, 500 or 850 hPa) can be used to identify weather systems such as cyclones, anticyclones, troughs and ridges.At the surface of the Earth, this parameter shows the variations in geopotential (height) of the surface, and is often referred to as the orography.","dimensions":["lat","lon","time"],"type":"data","unit":"m2s-2"},"Geopotential(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Geopotential","parameter_ID":"129","product_type":"forecast","shortName":"z","standard_name":"Geopotential","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/129"},"description":"This parameter is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level.The geopotential height can be calculated by dividing the geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2). The geopotential height plays an important role in synoptic meteorology (analysis of weather patterns). Charts of geopotential height plotted at constant pressure levels (e.g., 300, 500 or 850 hPa) can be used to identify weather systems such as cyclones, anticyclones, troughs and ridges.At the surface of the Earth, this parameter shows the variations in geopotential (height) of the surface, and is often referred to as the orography.","dimensions":["lat","lon","time"],"type":"data","unit":"m2s-2"},"High_cloud_cover(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"High cloud cover","parameter_ID":"188","product_type":"forecast","shortName":"hcc","standard_name":"High_cloud_cover","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/188"},"description":"The proportion of agrid boxcovered by cloud occurring in the high levels of the troposphere. High cloud is a single level field calculated from cloud occurring on model levels with a pressure less than 0.45 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), high cloud would be calculated using levels with a pressure of less than 450 hPa (approximately 6km and above (assuming a `standard atmosphere`)).The high cloud cover parameter is calculated from cloud for the appropriate model levels as described above. Assumptions are made about the degree of overlap/randomness between clouds in different model levels.Cloud fractions vary from 0 to 1.[NOTE: See 3075 for the equivalent parameter in \"%\"]","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Land-sea_mask(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Land-sea mask","parameter_ID":"172","product_type":"forecast","shortName":"lsm","standard_name":"Land-sea_mask","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/172"},"description":"This parameter is the proportion of land, as opposed to ocean or inland waters (lakes, reservoirs, rivers and coastal waters), in agrid box.This parameter has values ranging between zero and one and is dimensionless.In cycles of the ECMWF Integrated Forecasting System (IFS) from CY41R1 (introduced in May 2015) onwards, grid boxes where this parameter has a value above 0.5 can be comprised of a mixture of land and inland water but not ocean. Grid boxes with a value of 0.5 and below can only be comprised of a water surface. In the latter case, the lake cover is used to determine how much of the water surface is ocean or inland water.In cycles of the IFS before CY41R1, grid boxes where this parameter has a value above 0.5 can only be comprised of land and those grid boxes with a value of 0.5 and below can only be comprised of ocean. In these older model cycles, there is no differentiation between ocean and inland water.","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Low_cloud_cover(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Low cloud cover","parameter_ID":"186","product_type":"forecast","shortName":"lcc","standard_name":"Low_cloud_cover","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/186"},"description":"This parameter is the proportion of agrid boxcovered by cloud occurring in the lower levels of the troposphere. Low cloud is a single level field calculated from cloud occurring on model levels with a pressure greater than 0.8 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), low cloud would be calculated using levels with a pressure greater than 800 hPa (below approximately 2km (assuming a 'standard atmosphere')).The low cloud cover parameter is calculated from cloud cover for the appropriate model levels as described above. Assumptions are made about the degree of overlap/randomness between clouds in different model levels.Cloud fractions vary from 0 to 1.[NOTE: See 3073 for the equivalent parameter in \"%\"]","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Mean_sea_level_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Mean sea level pressure","parameter_ID":"151","product_type":"forecast","shortName":"msl","standard_name":"Mean_sea_level_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/151"},"description":"This parameter is the pressure (force per unit area) of the atmosphere adjusted to the height of mean sea level.It is a measure of the weight that all the air in a column vertically above the area of Earth's surface would have at that point, if the point were located at the mean sea level. It is calculated over all surfaces - land, sea and in-land water.Maps of mean sea level pressure are used to identify the locations of low and high pressure systems, often referred to as cyclones and anticyclones. Contours of mean sea level pressure also indicate the strength of the wind. Tightly packed contours show stronger winds.The units of this parameter are pascals (Pa).  Mean sea level pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb = 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Medium_cloud_cover(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Medium cloud cover","parameter_ID":"187","product_type":"forecast","shortName":"mcc","standard_name":"Medium_cloud_cover","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/187"},"description":"This parameter is the proportion of agrid boxcovered by cloud occurring in the middle levels of the troposphere. Medium cloud is a single level field calculated from cloud occurring on model levels with a pressure between 0.45 and 0.8 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), medium cloud would be calculated using levels with a pressure of less than or equal to 800 hPa and greater than or equal to 450 hPa (between approximately 2km and 6km (assuming a 'standard atmosphere')).The medium cloud parameter is calculated from cloud cover for the appropriate model levels as described above. Assumptions are made about the degree of overlap/randomness between clouds in different model levels.Cloud fractions vary from 0 to 1.[NOTE: See 3074 for the equivalent parameter in \"%\"]","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Potential_vorticity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Potential vorticity","parameter_ID":"60","product_type":"forecast","shortName":"pv","standard_name":"Potential_vorticity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/60"},"description":"Potential vorticity is a measure of the capacity for air to rotate in the atmosphere. If we ignore the effects of heating and friction, potential vorticity is conserved following an air parcel. It is used to look for places where large wind storms are likely to originate and develop.  Potential vorticity increases strongly above the tropopause and therefore, it can also be used in studies related to the stratosphere and stratosphere-troposphere exchanges.Large wind storms develop when a column of air in the atmosphere starts to rotate. Potential vorticity is calculated from the wind, temperature and pressure across a column of air in the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K m2kg-1s-1"},"Relative_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Relative humidity","parameter_ID":"157","product_type":"forecast","shortName":"r","standard_name":"Relative_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/157"},"description":"This parameter is the water vapour pressure as a percentage of the value at which the air becomes saturated (the point at which water vapour begins to condense into liquid water or deposition into ice).For temperatures over 0°C (273.15 K) it is calculated for saturation over water. At temperatures below -23°C it is calculated for saturation over ice.  Between -23°C and 0°C this parameter is calculated by interpolating between the ice and water values using a quadratic function.See more information about the model's relative humidity calculation.","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Skin_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Skin temperature","parameter_ID":"235","product_type":"forecast","shortName":"skt","standard_name":"Skin_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235"},"description":"This parameter is the temperature of the surface of the Earth.The skin temperature is the theoretical temperature that is required to satisfy the surface energy balance. It represents the temperature of the uppermost surface layer, which has no heat capacity and so can respond instantaneously to changes in surface fluxes. Skin temperature is calculated differently over land and sea.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.See further information about the skin temperatureover landandover sea.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean skin temperature. The specific encoding for Mean skin temperature can be found in 235079.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Snow_depth(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Snow depth","parameter_ID":"141","product_type":"forecast","shortName":"sd","standard_name":"Snow_depth","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/141"},"description":"This parameter is the depth of snow from the snow-covered area of agrid box.Its units are metres of water equivalent, so it is the depth the water would have if the snow melted and was spread evenly over the whole grid box. The ECMWF Integrated Forecast System represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box.See further information.\n\n[NOTE: See 228141 for the equivalent parameter in \"kg m-2\"]","dimensions":["lat","lon","time"],"type":"data","unit":"m of water equivalent"},"Snow_depth_water_equivalent(clte_sol)":{"attrs":{"encoding":"instantaneous","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Snow depth water equivalent","parameter_ID":"228141","product_type":"forecast","shortName":"sd","standard_name":"Snow_depth_water_equivalent","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228141"},"description":"Snow depth water equivalent in kg m**-2 (mm) water equivalent.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean snow depth water equivalent. The specific encoding for Time-mean snow depth water equivalent can be found in 235078.[NOTE: See 141 for the equivalent parameter in \"m of water equivalent\"]","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Snowfall(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Snowfall","parameter_ID":"144","product_type":"forecast","shortName":"sf","standard_name":"Snowfall","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/144"},"description":"This parameter is the accumulated snow that falls to the Earth's surface. It is the sum of large-scale snowfall and convective snowfall. Large-scale snowfall is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of thegrid boxor larger. Convective snowfall is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box.See further information.This parameter is the total amount of wateraccumulated over a particular time period which depends on the data extracted. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box.Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box.[NOTE: See 228144 for the equivalent parameter in \"kg m-2\"]","dimensions":["lat","lon","time"],"type":"data","unit":"m of water equivalent"},"Specific_cloud_liquid_water_content(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific cloud liquid water content","parameter_ID":"246","product_type":"forecast","shortName":"clwc","standard_name":"Specific_cloud_liquid_water_content","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/246"},"description":"This parameter is the mass of cloud liquid water droplets per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for agrid box.Water within clouds can be liquid or ice, or a combination of the two.See further information about the cloud formulation.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Specific_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific humidity","parameter_ID":"133","product_type":"forecast","shortName":"q","standard_name":"Specific_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/133"},"description":"This parameter is the mass of water vapour per kilogram of moist air.The total mass of moist air is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Sub-surface_runoff(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Sub-surface runoff","parameter_ID":"9","product_type":"forecast","shortName":"ssro","standard_name":"Sub-surface_runoff","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/9"},"description":"Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is simply called 'runoff'. This parameter is the total amount of water accumulated over aparticular time period which depends on the data extracted.The units of runoff are depth in metres.  This is the depth the water would have if it were spread evenly over thegrid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid square area.  Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here.Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood.  More information about how runoff is calculated is given in theIFS Physical Processes documentation.\n\n[NOTE: See 231012 for the equivalent parameter in \"kg m-2\"]","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Surface_long-wave_(thermal)_radiation_downwards(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Surface long-wave (thermal) radiation downwards","parameter_ID":"175","product_type":"forecast","shortName":"strd","standard_name":"Surface_long-wave_(thermal)_radiation_downwards","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/175"},"description":"This parameter is the amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere and clouds that reaches a horizontal plane at the surface of the Earth.The surface of the Earth emits thermal radiation, some of which is absorbed by the atmosphere and clouds. The atmosphere and clouds likewise emit thermal radiation in all directions, some of which reaches the surface (represented by this parameter).See further documentation.This parameter isaccumulated over a particular time period which depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Surface_net_long-wave_(thermal)_radiation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Surface net long-wave (thermal) radiation","parameter_ID":"177","product_type":"forecast","shortName":"str","standard_name":"Surface_net_long-wave_(thermal)_radiation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/177"},"description":"Thermal radiation (also known as longwave or terrestrial radiation) refers to radiation emitted by the atmosphere, clouds and the surface of the Earth. This parameter is the difference between downward and upward thermal radiation at the surface of the Earth. It the amount passing through a horizontal plane.The atmosphere and clouds emit thermal radiation in all directions, some of which reaches the surface as downward thermal radiation. The upward thermal radiation at the surface consists of thermal radiation emitted by the surface plus the fraction of downwards thermal radiation reflected upward by the surface.See further documentation.This parameter isaccumulated over a particular time periodwhich depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds.The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Surface_net_short-wave_(solar)_radiation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Surface net short-wave (solar) radiation","parameter_ID":"176","product_type":"forecast","shortName":"ssr","standard_name":"Surface_net_short-wave_(solar)_radiation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/176"},"description":"This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo).Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The remainder is incident on the Earth's surface, where some of it is reflected.See further documentation.This parameter isaccumulated over a particular time period which depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Surface_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Surface pressure","parameter_ID":"134","product_type":"forecast","shortName":"sp","standard_name":"Surface_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/134"},"description":"This parameter is the pressure (force per unit area) of the atmosphere on the surface of land, sea and in-land water.It is a measure of the weight of all the air in a column vertically above the area of the Earth's surface represented at a fixed point.Surface pressure is often used in combination with temperature to calculate air density.The strong variation of pressure with altitude makes it difficult to see the low and high pressure systems over mountainous areas, so mean sea level pressure, rather than surface pressure, is normally used for this purpose.The units of this parameter are Pascals (Pa). Surface pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb= 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Surface_runoff(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Surface runoff","parameter_ID":"8","product_type":"forecast","shortName":"sro","standard_name":"Surface_runoff","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/8"},"description":"Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is simply called 'runoff'. This parameter is the total amount of water accumulated over aparticular time period which depends on the data extracted.The units of runoff are depth in metres.  This is the depth the water would have if it were spread evenly over thegrid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid square area.  Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here.Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood.  More information about how runoff is calculated is given in theIFS Physical Processes documentation.\n\n[NOTE: See 231010 for the equivalent parameter in \"kg m-2\"]","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Surface_short-wave_(solar)_radiation_downwards(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Surface short-wave (solar) radiation downwards","parameter_ID":"169","product_type":"forecast","shortName":"ssrd","standard_name":"Surface_short-wave_(solar)_radiation_downwards","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/169"},"description":"This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth. This parameter comprises both direct and diffuse solar radiation.Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface (represented by this parameter).See further documentation.To a reasonably good approximation, this parameter is the model equivalent of what would be measured by a pyranometer (an instrument used for measuring solar radiation) at the surface. However, care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over amodel grid box.This parameter isaccumulated over a particular time period which depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"TOA_incident_short-wave_(solar)_radiation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"TOA incident short-wave (solar) radiation","parameter_ID":"212","product_type":"forecast","shortName":"tisr","standard_name":"TOA_incident_short-wave_(solar)_radiation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/212"},"description":"Accumulated field","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Temperature(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Temperature","parameter_ID":"130","product_type":"forecast","shortName":"t","standard_name":"Temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/130"},"description":"This parameter is the temperature in the atmosphere.It has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.This parameter is available on multiple levels through the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-integrated_eastward_turbulent_surface_stress(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Time-integrated eastward turbulent surface stress","parameter_ID":"180","product_type":"forecast","shortName":"ewss","standard_name":"Time-integrated_eastward_turbulent_surface_stress","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/180"},"description":"Air flowing over a surface exerts a stress that transfers momentum to the surface and slows the wind. This parameter is the accumulated stress on the Earth's surface in the eastward direction due to both the turbulent interactions between the atmosphere and the surface, and to turbulent orographic form drag. The turbulent interactions between the atmosphere and the surface are due to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km being derived from land surface data at about 1 km resolution.See further information.Positive (negative) values denote stress in the eastward (westward) direction.This parameter isaccumulated over a particular time periodwhich depends on the data extracted.","dimensions":["lat","lon","time"],"type":"data","unit":"N m-2s"},"Time-integrated_northward_turbulent_surface_stress(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Time-integrated northward turbulent surface stress","parameter_ID":"181","product_type":"forecast","shortName":"nsss","standard_name":"Time-integrated_northward_turbulent_surface_stress","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/181"},"description":"Air flowing over a surface exerts a stress that transfers momentum to the surface and slows the wind. This parameter is the accumulated stress on the Earth's surface in the northward direction due to both the turbulent interactions between the atmosphere and the surface, and to turbulent orographic form drag.The turbulent interactions between the atmosphere and the surface are due to the roughness of the surface.The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km being derived from land surface data at about 1 km resolution.See further information.Positive (negative) values denote stress in the northward (southward) direction.This parameter isaccumulated over a particular time periodwhich depends on the data extracted.","dimensions":["lat","lon","time"],"type":"data","unit":"N m-2s"},"Time-integrated_surface_latent_heat_net_flux(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Time-integrated surface latent heat net flux","parameter_ID":"147","product_type":"forecast","shortName":"slhf","standard_name":"Time-integrated_surface_latent_heat_net_flux","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/147"},"description":"This parameter is the transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earth's surface and the atmosphere through the effects of turbulent air motion. Evaporation from the Earth's surface represents a transfer of energy from the surface to the atmosphere.See further documentationThis parameter is accumulated over aparticular time period which depends on the data extracted.The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds.The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-integrated_surface_sensible_heat_net_flux(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Time-integrated surface sensible heat net flux","parameter_ID":"146","product_type":"forecast","shortName":"sshf","standard_name":"Time-integrated_surface_sensible_heat_net_flux","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/146"},"description":"This parameter is the transfer of heat between the Earth's surface and the atmosphere through the effects of turbulent air motion (but excluding any heat transfer resulting from condensation or evaporation).The magnitude of the sensible heat flux is governed by the difference in temperature between the surface and the overlying atmosphere, wind speed and the surface roughness. For example, cold air overlying a warm surface would produce a sensible heat flux from the land (or ocean) into the atmosphere.See further documentationThis is a single level parameter and it is accumulated over aparticular time period which depends on the data extracted.The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_X-component_of_sea_ice_velocity(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean X-component of sea ice velocity","parameter_ID":"263021","product_type":"forecast","shortName":"avg_six","standard_name":"Time-mean_X-component_of_sea_ice_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263021"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_Y-component_of_sea_ice_velocity(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean Y-component of sea ice velocity","parameter_ID":"263022","product_type":"forecast","shortName":"avg_siy","standard_name":"Time-mean_Y-component_of_sea_ice_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263022"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_ice_velocity(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean eastward sea ice velocity","parameter_ID":"263003","product_type":"forecast","shortName":"avg_siue","standard_name":"Time-mean_eastward_sea_ice_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263003"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75","levtype":"o3d","long_name":"Time-mean eastward sea water velocity","parameter_ID":"263506","product_type":"forecast","shortName":"avg_uoe","standard_name":"Time-mean_eastward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263506"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_sea_ice_velocity(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean northward sea ice velocity","parameter_ID":"263004","product_type":"forecast","shortName":"avg_sivn","standard_name":"Time-mean_northward_sea_ice_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263004"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75","levtype":"o3d","long_name":"Time-mean northward sea water velocity","parameter_ID":"263505","product_type":"forecast","shortName":"avg_von","standard_name":"Time-mean_northward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263505"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_sea_ice_area_fraction(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea ice area fraction","parameter_ID":"263001","product_type":"forecast","shortName":"avg_siconc","standard_name":"Time-mean_sea_ice_area_fraction","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263001"},"dimensions":["lat","lon","time"],"type":"data","unit":"Fraction"},"Time-mean_sea_ice_thickness(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea ice thickness","parameter_ID":"263000","product_type":"forecast","shortName":"avg_sithick","standard_name":"Time-mean_sea_ice_thickness","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263000"},"dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Time-mean_sea_ice_volume_per_unit_area(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea ice volume per unit area","parameter_ID":"263008","product_type":"forecast","shortName":"avg_sivol","standard_name":"Time-mean_sea_ice_volume_per_unit_area","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263008"},"dimensions":["lat","lon","time"],"type":"data","unit":"m3m-2"},"Time-mean_sea_surface_height(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea surface height","parameter_ID":"263124","product_type":"forecast","shortName":"avg_zos","standard_name":"Time-mean_sea_surface_height","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263124"},"dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Time-mean_sea_surface_practical_salinity(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea surface practical salinity","parameter_ID":"263100","product_type":"forecast","shortName":"avg_sos","standard_name":"Time-mean_sea_surface_practical_salinity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263100"},"dimensions":["lat","lon","time"],"type":"data","unit":"g kg-1"},"Time-mean_sea_surface_temperature(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea surface temperature","parameter_ID":"263101","product_type":"forecast","shortName":"avg_tos","standard_name":"Time-mean_sea_surface_temperature","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263101"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_sea_water_potential_temperature(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75","levtype":"o3d","long_name":"Time-mean sea water potential temperature","parameter_ID":"263501","product_type":"forecast","shortName":"avg_thetao","standard_name":"Time-mean_sea_water_potential_temperature","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263501"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_sea_water_practical_salinity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75","levtype":"o3d","long_name":"Time-mean sea water practical salinity","parameter_ID":"263500","product_type":"forecast","shortName":"avg_so","standard_name":"Time-mean_sea_water_practical_salinity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263500"},"dimensions":["lat","lon","time"],"type":"data","unit":"g kg-1"},"Time-mean_snow_volume_over_sea_ice_per_unit_area(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean snow volume over sea ice per unit area","parameter_ID":"263009","product_type":"forecast","shortName":"avg_snvol","standard_name":"Time-mean_snow_volume_over_sea_ice_per_unit_area","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263009"},"dimensions":["lat","lon","time"],"type":"data","unit":"m3m-2"},"Time-mean_upward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75","levtype":"o3d","long_name":"Time-mean upward sea water velocity","parameter_ID":"263507","product_type":"forecast","shortName":"avg_wo","standard_name":"Time-mean_upward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263507"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_vertically-integrated_heat_content_in_the_upper_300_m(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 300 m","parameter_ID":"263121","product_type":"forecast","shortName":"avg_hc300m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_300_m","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263121"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 700 m","parameter_ID":"263122","product_type":"forecast","shortName":"avg_hc700m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263122"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Top_net_long-wave_(thermal)_radiation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Top net long-wave (thermal) radiation","parameter_ID":"179","product_type":"forecast","shortName":"ttr","standard_name":"Top_net_long-wave_(thermal)_radiation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/179"},"description":"The thermal (also known as terrestrial or longwave) radiation emitted to space at the top of the atmosphere is commonly known as the Outgoing Longwave Radiation (OLR). The top net thermal radiation (this parameter) is equal to the negative of OLR.See further documentation.This parameter isaccumulated over a particular time periodwhich depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds.The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Top_net_short-wave_(solar)_radiation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Top net short-wave (solar) radiation","parameter_ID":"178","product_type":"forecast","shortName":"tsr","standard_name":"Top_net_short-wave_(solar)_radiation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/178"},"description":"This parameter is the incoming solar radiation (also known as shortwave radiation) minus the outgoing solar radiation at the top of the atmosphere. It is the amount of radiation passing through a horizontal plane. The incoming solar radiation is the amount received from the Sun. The outgoing solar radiation is the amount reflected and scattered by the Earth's atmosphere and surface.See further documentation.This parameter isaccumulated over a particular time periodwhich depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds.The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Total_cloud_cover(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total cloud cover","parameter_ID":"164","product_type":"forecast","shortName":"tcc","standard_name":"Total_cloud_cover","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/164"},"description":"This parameter is the proportion of agrid boxcovered by cloud. Total cloud cover is a single level field calculated from the cloud occurring at different model levels through the atmosphere. Assumptions are made about the degree of overlap/randomness between clouds at different heights.Cloud fractions vary from 0 to 1.[NOTE: See 228164 for the equivalent parameter in \"%\"]","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Total_column_cloud_ice_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud ice water","parameter_ID":"79","product_type":"forecast","shortName":"tciw","standard_name":"Total_column_cloud_ice_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/79"},"description":"This parameter is the amount of ice contained within clouds in a column extending from the surface of the Earth to the top of the atmosphere. Snow (aggregated ice crystals) is not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The  ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_cloud_liquid_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud liquid water","parameter_ID":"78","product_type":"forecast","shortName":"tclw","standard_name":"Total_column_cloud_liquid_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/78"},"description":"This parameter is the amount of liquid water contained within cloud droplets in a column extending from the surface of the Earth to the top of the atmosphere. Rain water droplets, which are much larger in size (and mass), are not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_vertically-integrated_water_vapour(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column vertically-integrated water vapour","parameter_ID":"137","product_type":"forecast","shortName":"tcwv","standard_name":"Total_column_vertically-integrated_water_vapour","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/137"},"description":"This parameter is the total amount of water vapour in a column extending from the surface of the Earth to the top of the atmosphere.This parameter represents the area averaged value for agrid box.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_precipitation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Total precipitation","parameter_ID":"228","product_type":"forecast","shortName":"tp","standard_name":"Total_precipitation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228"},"description":"This parameter is the accumulated liquid and frozen water, comprising rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation and convective precipitation. Large-scale precipitation is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of thegrid boxor larger. Convective precipitation is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box.See further information.This parameter does not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth.This parameter is the total amount of wateraccumulated over a particular time period which depends on the data extracted. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box.Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box.\n\n[NOTE: See 228228 for the equivalent parameter in \"kg m-2\"]","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Total_precipitation_rate(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total precipitation rate","parameter_ID":"260048","product_type":"forecast","shortName":"tprate","standard_name":"Total_precipitation_rate","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/260048"},"description":"This parameter is the rate of total precipitation,at the specified time.In the ECMWF Integrated Forecasting System (IFS), total precipitation is rain and snow that falls to the Earth's surface.  It is the sum of large-scale precipitation and convective precipitation. Large-scale precipitation is generated by the cloud scheme in the IFS. The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of agrid boxor larger. Convective precipitation is generated by the convection scheme in the IFS. The convection scheme represents convection at spatial scales smaller than the grid box.Seefurther information. Precipitation parameters do not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth.1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm per second.Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box andmodel time step.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"U_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"U component of wind","parameter_ID":"131","product_type":"forecast","shortName":"u","standard_name":"U_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/131"},"description":"This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east, in metres per second. A negative sign thus indicates air movement towards the west.This parameter can be combined with the V component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"V_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"V component of wind","parameter_ID":"132","product_type":"forecast","shortName":"v","standard_name":"V_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/132"},"description":"This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north, in metres per second. A negative sign thus indicates air movement towards the south.This parameter can be combined with the U component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Vertical_velocity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Vertical velocity","parameter_ID":"135","product_type":"forecast","shortName":"w","standard_name":"Vertical_velocity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/135"},"description":"This parameter is the speed of air motion in the upward or downward direction. The ECMWF Integrated Forecasting System (IFS) uses a pressure based vertical co-ordinate system and pressure decreases with height, therefore negative values of vertical velocity indicate upward motion.Vertical velocity can be useful to understand the large-scale dynamics of the atmosphere, including areas of upward motion/ascent (negative values) and downward motion/subsidence (positive values).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa s-1"}},"sci:publications":[{"doi":"10.1109/MCSE.2023.3260519","citation":"N. Wedi et al., Destination Earth: High-Performance Computing for Weather and Climate, in Computing in Science \u0026 Engineering, vol. 24, no. 6, pp. 29-37, Nov.-Dec. 2022,"},{"doi":"10.21957/d3f982672e","citation":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"dedl:short_description":"The Climate Change Adaptation Digital Twin provides global climate projections and sector-specific information over multiple decades at high resolution via a unified framework combining advanced Earth system models, impact assessments, and observations. This Generation-1, Realization-1 Collection gives access to 'Control Simulation' data based on the 'IFS-NEMO' model."},{"type":"Collection","title":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Future Projection - ICON - Generation-1 - Realization-1","id":"EO.ECMWF.DAT.D1.DT_CLIMATE.G1.SCENARIOMIP_SSP3-7.0_ICON.R1","description":"Note: This Generation 1 Collection has been superseded by Generation 2 Simulation-level Collections\n\nThe DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities by providing innovative climate information on multi-decadal timescales, globally, at scales at which many impacts of climate change are observed. It combines cutting-edge global Earth-system models, impact-sector applications and observations into a unified framework to provide global climate projections and impact-sector information on multi-decadal timescales (1990 to ~2050), at very high spatial resolutions (5 to 10 km).\n\nThe Climate DT represents the first ever attempt to operationalise the production of global multi-decadal climate projections, leveraging the world-leading supercomputing facilities of the EuroHPC Joint Undertaking along with some of the leading European climate models. A concise overview of what the Climate DT aims to achieve, and of the different concepts essential for an understanding of the Digital Twin’s characteristics, is included in the [Climate DT factsheet](https://destine.ecmwf.int/wp-content/uploads/2024/06/2024.06.07_Climate-DT-Fact-Sheet_V7-2.pdf)\n\n## Future Projection \n\n To project how climate will change on a global and local scale in the future, forcing changes according to the Shared Socioeconomic Pathway (SSP) 3-7.0 scenario from ScenarioMIP. The SSP3-7.0 scenario explores a future with a continuous increase in CO2 emissions with no strong mitigation efforts. All DestinE projections carried out so far follow this scenario. In the future, alternative future scenarios will be explored. The projections carried out so far are initialised in 2020 from reanalysis followed by a 5-year ocean spin-up. For the upcoming simulations carried out in phase 2 of DestinE, scenario simulations will extend the historical simulations.\n\n## Models\n\nThe Climate DT exploits and further evolves a new generation of global storm-resolving and eddy-rich models built through a cooperative model development approach. For more information on models please click [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#models)\n\n## Simulations\n\nThe Climate DT team carries out several types of digital twin simulations on the EuroHPC supercomputers.  Multi-decadal simulations are produced to cover the recent past (from 1990) and possible future evolutions of the climate up to 2050. See [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#simulations) for more information on Simulations\n\n## Parameters\n\nBelow we see the list of parameters extracted from the 'DestinE Climate DT data portfolio', for more information please refer to the page [Climate DT Phase 1 CLTE Parameters](https://confluence.ecmwf.int/display/DDCZ/Climate+DT+Phase+1+data+catalogue)","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G1.SCENARIOMIP_SSP3-7.0_ICON.R1/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G1.SCENARIOMIP_SSP3-7.0_ICON.R1/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G1.SCENARIOMIP_SSP3-7.0_ICON.R1/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G1.SCENARIOMIP_SSP3-7.0_ICON.R1","title":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Future Projection - ICON - Generation-1 - Realization-1"},{"rel":"example","type":"application/x-ipynb+json","href":"https://raw.githubusercontent.com/destination-earth/DestinE-DataLake-Lab/refs/heads/main/HDA/DestinE%20Digital%20Twins/DEDL-HDA-EO.ECMWF.DAT.DT_CLIMATE.ipynb","title":"Destination Earth - Climate DT Parameter - Data Access using DEDL HDA","application:type":"jupyter-notebook","application:embedded":true,"application:language":"Python"},{"rel":"example","type":"application/x-ipynb+json","href":"https://raw.githubusercontent.com/destination-earth/DestinE-DataLake-Lab/refs/heads/main/HDA/DestinE%20Digital%20Twins/ClimateDT-ParameterPlotter.ipynb","title":"Destination Earth - HDA Climate DT Parameter Plotter Tutorial","application:type":"jupyter-notebook","application:embedded":true,"application:language":"Python"},{"rel":"example","type":"application/x-ipynb+json","href":"https://raw.githubusercontent.com/destination-earth/DestinE-DataLake-Lab/refs/heads/main/HDA/DestinE%20Digital%20Twins/DEDL-HDA-EO.ECMWF.DAT.DT_CLIMATE-Series.ipynb","title":"Destination Earth - Climate DT Parameter Series Plot- Data Access using DEDL HDA","application:type":"jupyter-notebook","application:embedded":true,"application:language":"Python"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Parameters","title":"DestinE ClimateDT Parameters"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/Climate+DT+Phase+1+data+catalogue","title":"Climate DT Phase 1 data catalogue"},{"rel":"cite-as","type":"text/html","href":"https://dl.acm.org/doi/abs/10.1109/MCSE.2023.3260519","title":"Destination Earth: High-Performance Computing for Weather and Climate"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.21957/d3f982672e","title":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"assets":{"thumbnail":{"href":"https://s3.central.data.destination-earth.eu/swift/v1/dedl-public/collections/climate-dt-min.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2020-09-01T00:00:00Z","2039-12-31T23:59:59Z"]]}},"license":"CC-BY-4.0","keywords":["Digital Twins","Climate Change","Atmosphere","Ocean","Land","Sea Ice","Snow","Soil","Earth","High Performance Computing","Decision Making","Europe","Meteorology","class:d1","dataset:climate-dt","generation:1","expver:0001","stream:clte","type:fc","activity:ScenarioMIP","experiment:SSP3-7.0","realization:1","model:ICON","resolution:high","levtype:o2d,o3d,pl,sfc"],"summaries":{"federation:backends":["dedt_lumi"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/datacube/v2.1.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json","https://stac-extensions.github.io/application/v0.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["producer","processor","licensor"],"url":"https://www.ecmwf.int/"}],"created":"2024-04-11T22:31:55Z","updated":"2026-04-27T14:04:20Z","published":"2024-04-11T22:31:55Z","cube:dimensions":{"lat":{"axis":"y","description":"latitude","extent":[-90,90],"type":"spatial"},"lon":{"axis":"x","description":"longitude","extent":[-180,180],"type":"spatial"},"time":{"extent":["2020-09-01T00:00:00Z","2039-12-31T23:59:59Z"],"step":"P0Y0M0DT1H0M0S","type":"temporal"}},"cube:variables":{"10_metre_U_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"10 metre U wind component","parameter_ID":"165","product_type":"forecast","shortName":"10u","standard_name":"10_metre_U_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/165"},"description":"This parameter is the eastward component of the 10m wind. It is the horizontal speed of air moving towards the east, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the V component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_V_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"10 metre V wind component","parameter_ID":"166","product_type":"forecast","shortName":"10v","standard_name":"10_metre_V_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/166"},"description":"This parameter is the northward component of the 10m wind. It is the horizontal speed of air moving towards the north, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the U component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"2_metre_dewpoint_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"2 metre dewpoint temperature","parameter_ID":"168","product_type":"forecast","shortName":"2d","standard_name":"2_metre_dewpoint_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/168"},"description":"This parameter is the temperature to which the air, at 2 metres above the surface of the Earth, would have to be cooled for saturation to occur.It is a measure of the humidity of the air. Combined with temperature and pressure, it can be used to calculate the relative humidity.2m dew point temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"2_metre_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"2 metre temperature","parameter_ID":"167","product_type":"forecast","shortName":"2t","standard_name":"2_metre_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/167"},"description":"This parameter is the temperature of air at 2m above the surface of land, sea or in-land waters.2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.Please note that the encodings listed here for s2s \u0026 uerra (which includes encodings for carra/cerra) include entries for Mean 2 metre temperature. The specific encoding for Mean 2 metre temperature can be found in 228004.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Geopotential(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Geopotential","parameter_ID":"129","product_type":"forecast","shortName":"z","standard_name":"Geopotential","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/129"},"description":"This parameter is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level.The geopotential height can be calculated by dividing the geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2). The geopotential height plays an important role in synoptic meteorology (analysis of weather patterns). Charts of geopotential height plotted at constant pressure levels (e.g., 300, 500 or 850 hPa) can be used to identify weather systems such as cyclones, anticyclones, troughs and ridges.At the surface of the Earth, this parameter shows the variations in geopotential (height) of the surface, and is often referred to as the orography.","dimensions":["lat","lon","time"],"type":"data","unit":"m2s-2"},"Mean_sea_level_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Mean sea level pressure","parameter_ID":"151","product_type":"forecast","shortName":"msl","standard_name":"Mean_sea_level_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/151"},"description":"This parameter is the pressure (force per unit area) of the atmosphere adjusted to the height of mean sea level.It is a measure of the weight that all the air in a column vertically above the area of Earth's surface would have at that point, if the point were located at the mean sea level. It is calculated over all surfaces - land, sea and in-land water.Maps of mean sea level pressure are used to identify the locations of low and high pressure systems, often referred to as cyclones and anticyclones. Contours of mean sea level pressure also indicate the strength of the wind. Tightly packed contours show stronger winds.The units of this parameter are pascals (Pa).  Mean sea level pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb = 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Relative_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Relative humidity","parameter_ID":"157","product_type":"forecast","shortName":"r","standard_name":"Relative_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/157"},"description":"This parameter is the water vapour pressure as a percentage of the value at which the air becomes saturated (the point at which water vapour begins to condense into liquid water or deposition into ice).For temperatures over 0°C (273.15 K) it is calculated for saturation over water. At temperatures below -23°C it is calculated for saturation over ice.  Between -23°C and 0°C this parameter is calculated by interpolating between the ice and water values using a quadratic function.See more information about the model's relative humidity calculation.","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Skin_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Skin temperature","parameter_ID":"235","product_type":"forecast","shortName":"skt","standard_name":"Skin_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235"},"description":"This parameter is the temperature of the surface of the Earth.The skin temperature is the theoretical temperature that is required to satisfy the surface energy balance. It represents the temperature of the uppermost surface layer, which has no heat capacity and so can respond instantaneously to changes in surface fluxes. Skin temperature is calculated differently over land and sea.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.See further information about the skin temperatureover landandover sea.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean skin temperature. The specific encoding for Mean skin temperature can be found in 235079.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Specific_cloud_liquid_water_content(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific cloud liquid water content","parameter_ID":"246","product_type":"forecast","shortName":"clwc","standard_name":"Specific_cloud_liquid_water_content","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/246"},"description":"This parameter is the mass of cloud liquid water droplets per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for agrid box.Water within clouds can be liquid or ice, or a combination of the two.See further information about the cloud formulation.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Specific_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific humidity","parameter_ID":"133","product_type":"forecast","shortName":"q","standard_name":"Specific_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/133"},"description":"This parameter is the mass of water vapour per kilogram of moist air.The total mass of moist air is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Surface_long-wave_(thermal)_radiation_downwards(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Surface long-wave (thermal) radiation downwards","parameter_ID":"175","product_type":"forecast","shortName":"strd","standard_name":"Surface_long-wave_(thermal)_radiation_downwards","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/175"},"description":"This parameter is the amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere and clouds that reaches a horizontal plane at the surface of the Earth.The surface of the Earth emits thermal radiation, some of which is absorbed by the atmosphere and clouds. The atmosphere and clouds likewise emit thermal radiation in all directions, some of which reaches the surface (represented by this parameter).See further documentation.This parameter isaccumulated over a particular time period which depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Surface_net_long-wave_(thermal)_radiation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Surface net long-wave (thermal) radiation","parameter_ID":"177","product_type":"forecast","shortName":"str","standard_name":"Surface_net_long-wave_(thermal)_radiation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/177"},"description":"Thermal radiation (also known as longwave or terrestrial radiation) refers to radiation emitted by the atmosphere, clouds and the surface of the Earth. This parameter is the difference between downward and upward thermal radiation at the surface of the Earth. It the amount passing through a horizontal plane.The atmosphere and clouds emit thermal radiation in all directions, some of which reaches the surface as downward thermal radiation. The upward thermal radiation at the surface consists of thermal radiation emitted by the surface plus the fraction of downwards thermal radiation reflected upward by the surface.See further documentation.This parameter isaccumulated over a particular time periodwhich depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds.The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Surface_net_short-wave_(solar)_radiation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Surface net short-wave (solar) radiation","parameter_ID":"176","product_type":"forecast","shortName":"ssr","standard_name":"Surface_net_short-wave_(solar)_radiation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/176"},"description":"This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo).Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The remainder is incident on the Earth's surface, where some of it is reflected.See further documentation.This parameter isaccumulated over a particular time period which depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Surface_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Surface pressure","parameter_ID":"134","product_type":"forecast","shortName":"sp","standard_name":"Surface_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/134"},"description":"This parameter is the pressure (force per unit area) of the atmosphere on the surface of land, sea and in-land water.It is a measure of the weight of all the air in a column vertically above the area of the Earth's surface represented at a fixed point.Surface pressure is often used in combination with temperature to calculate air density.The strong variation of pressure with altitude makes it difficult to see the low and high pressure systems over mountainous areas, so mean sea level pressure, rather than surface pressure, is normally used for this purpose.The units of this parameter are Pascals (Pa). Surface pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb= 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Surface_short-wave_(solar)_radiation_downwards(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Surface short-wave (solar) radiation downwards","parameter_ID":"169","product_type":"forecast","shortName":"ssrd","standard_name":"Surface_short-wave_(solar)_radiation_downwards","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/169"},"description":"This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth. This parameter comprises both direct and diffuse solar radiation.Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface (represented by this parameter).See further documentation.To a reasonably good approximation, this parameter is the model equivalent of what would be measured by a pyranometer (an instrument used for measuring solar radiation) at the surface. However, care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over amodel grid box.This parameter isaccumulated over a particular time period which depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Temperature(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Temperature","parameter_ID":"130","product_type":"forecast","shortName":"t","standard_name":"Temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/130"},"description":"This parameter is the temperature in the atmosphere.It has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.This parameter is available on multiple levels through the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Temperature","parameter_ID":"130","product_type":"forecast","shortName":"t","standard_name":"Temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/130"},"description":"This parameter is the temperature in the atmosphere.It has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.This parameter is available on multiple levels through the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-integrated_eastward_turbulent_surface_stress_due_to_surface_roughness(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Time-integrated eastward turbulent surface stress due to surface roughness","parameter_ID":"260654","product_type":"forecast","shortName":"etsssr","standard_name":"Time-integrated_eastward_turbulent_surface_stress_due_to_surface_roughness","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/260654"},"dimensions":["lat","lon","time"],"type":"data","unit":"N m-2s"},"Time-integrated_northward_turbulent_surface_stress_due_to_surface_roughness(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Time-integrated northward turbulent surface stress due to surface roughness","parameter_ID":"260655","product_type":"forecast","shortName":"ntsssr","standard_name":"Time-integrated_northward_turbulent_surface_stress_due_to_surface_roughness","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/260655"},"dimensions":["lat","lon","time"],"type":"data","unit":"N m-2s"},"Time-integrated_surface_latent_heat_net_flux(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Time-integrated surface latent heat net flux","parameter_ID":"147","product_type":"forecast","shortName":"slhf","standard_name":"Time-integrated_surface_latent_heat_net_flux","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/147"},"description":"This parameter is the transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earth's surface and the atmosphere through the effects of turbulent air motion. Evaporation from the Earth's surface represents a transfer of energy from the surface to the atmosphere.See further documentationThis parameter is accumulated over aparticular time period which depends on the data extracted.The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds.The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-integrated_surface_sensible_heat_net_flux(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Time-integrated surface sensible heat net flux","parameter_ID":"146","product_type":"forecast","shortName":"sshf","standard_name":"Time-integrated_surface_sensible_heat_net_flux","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/146"},"description":"This parameter is the transfer of heat between the Earth's surface and the atmosphere through the effects of turbulent air motion (but excluding any heat transfer resulting from condensation or evaporation).The magnitude of the sensible heat flux is governed by the difference in temperature between the surface and the overlying atmosphere, wind speed and the surface roughness. For example, cold air overlying a warm surface would produce a sensible heat flux from the land (or ocean) into the atmosphere.See further documentationThis is a single level parameter and it is accumulated over aparticular time period which depends on the data extracted.The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_eastward_sea_ice_velocity(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean eastward sea ice velocity","parameter_ID":"263003","product_type":"forecast","shortName":"avg_siue","standard_name":"Time-mean_eastward_sea_ice_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263003"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean eastward sea water velocity","parameter_ID":"263506","product_type":"forecast","shortName":"avg_uoe","standard_name":"Time-mean_eastward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263506"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_sea_ice_velocity(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean northward sea ice velocity","parameter_ID":"263004","product_type":"forecast","shortName":"avg_sivn","standard_name":"Time-mean_northward_sea_ice_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263004"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean northward sea water velocity","parameter_ID":"263505","product_type":"forecast","shortName":"avg_von","standard_name":"Time-mean_northward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263505"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_ocean_mixed_layer_depth_defined_by_sigma_theta_0.03_kg_m-3(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean ocean mixed layer depth defined by sigma theta 0.03 kg m-3","parameter_ID":"263114","product_type":"forecast","shortName":"avg_mlotst030","standard_name":"Time-mean_ocean_mixed_layer_depth_defined_by_sigma_theta_0.03_kg_m-3","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263114"},"dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Time-mean_sea_ice_area_fraction(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea ice area fraction","parameter_ID":"263001","product_type":"forecast","shortName":"avg_siconc","standard_name":"Time-mean_sea_ice_area_fraction","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263001"},"dimensions":["lat","lon","time"],"type":"data","unit":"Fraction"},"Time-mean_sea_ice_thickness(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea ice thickness","parameter_ID":"263000","product_type":"forecast","shortName":"avg_sithick","standard_name":"Time-mean_sea_ice_thickness","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263000"},"dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Time-mean_sea_surface_height(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea surface height","parameter_ID":"263124","product_type":"forecast","shortName":"avg_zos","standard_name":"Time-mean_sea_surface_height","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263124"},"dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Time-mean_sea_water_potential_temperature(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean sea water potential temperature","parameter_ID":"263501","product_type":"forecast","shortName":"avg_thetao","standard_name":"Time-mean_sea_water_potential_temperature","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263501"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_sea_water_practical_salinity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean sea water practical salinity","parameter_ID":"263500","product_type":"forecast","shortName":"avg_so","standard_name":"Time-mean_sea_water_practical_salinity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263500"},"dimensions":["lat","lon","time"],"type":"data","unit":"g kg-1"},"Time-mean_snow_volume_over_sea_ice_per_unit_area(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean snow volume over sea ice per unit area","parameter_ID":"263009","product_type":"forecast","shortName":"avg_snvol","standard_name":"Time-mean_snow_volume_over_sea_ice_per_unit_area","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263009"},"dimensions":["lat","lon","time"],"type":"data","unit":"m3m-2"},"Time-mean_upward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean upward sea water velocity","parameter_ID":"263507","product_type":"forecast","shortName":"avg_wo","standard_name":"Time-mean_upward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263507"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Top_net_long-wave_(thermal)_radiation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Top net long-wave (thermal) radiation","parameter_ID":"179","product_type":"forecast","shortName":"ttr","standard_name":"Top_net_long-wave_(thermal)_radiation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/179"},"description":"The thermal (also known as terrestrial or longwave) radiation emitted to space at the top of the atmosphere is commonly known as the Outgoing Longwave Radiation (OLR). The top net thermal radiation (this parameter) is equal to the negative of OLR.See further documentation.This parameter isaccumulated over a particular time periodwhich depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds.The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Top_net_short-wave_(solar)_radiation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Top net short-wave (solar) radiation","parameter_ID":"178","product_type":"forecast","shortName":"tsr","standard_name":"Top_net_short-wave_(solar)_radiation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/178"},"description":"This parameter is the incoming solar radiation (also known as shortwave radiation) minus the outgoing solar radiation at the top of the atmosphere. It is the amount of radiation passing through a horizontal plane. The incoming solar radiation is the amount received from the Sun. The outgoing solar radiation is the amount reflected and scattered by the Earth's atmosphere and surface.See further documentation.This parameter isaccumulated over a particular time periodwhich depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds.The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Total_Cloud_Cover(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total Cloud Cover","parameter_ID":"228164","product_type":"forecast","shortName":"tcc","standard_name":"Total_Cloud_Cover","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228164"},"description":"[NOTE: See 164 for the equivalent parameter in \"(0-1)\"]","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Total_column_cloud_ice_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud ice water","parameter_ID":"79","product_type":"forecast","shortName":"tciw","standard_name":"Total_column_cloud_ice_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/79"},"description":"This parameter is the amount of ice contained within clouds in a column extending from the surface of the Earth to the top of the atmosphere. Snow (aggregated ice crystals) is not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The  ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_cloud_liquid_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud liquid water","parameter_ID":"78","product_type":"forecast","shortName":"tclw","standard_name":"Total_column_cloud_liquid_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/78"},"description":"This parameter is the amount of liquid water contained within cloud droplets in a column extending from the surface of the Earth to the top of the atmosphere. Rain water droplets, which are much larger in size (and mass), are not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_vertically-integrated_water_vapour(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column vertically-integrated water vapour","parameter_ID":"137","product_type":"forecast","shortName":"tcwv","standard_name":"Total_column_vertically-integrated_water_vapour","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/137"},"description":"This parameter is the total amount of water vapour in a column extending from the surface of the Earth to the top of the atmosphere.This parameter represents the area averaged value for agrid box.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_precipitation_rate(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total precipitation rate","parameter_ID":"260048","product_type":"forecast","shortName":"tprate","standard_name":"Total_precipitation_rate","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/260048"},"description":"This parameter is the rate of total precipitation,at the specified time.In the ECMWF Integrated Forecasting System (IFS), total precipitation is rain and snow that falls to the Earth's surface.  It is the sum of large-scale precipitation and convective precipitation. Large-scale precipitation is generated by the cloud scheme in the IFS. The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of agrid boxor larger. Convective precipitation is generated by the convection scheme in the IFS. The convection scheme represents convection at spatial scales smaller than the grid box.Seefurther information. Precipitation parameters do not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth.1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm per second.Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box andmodel time step.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"U_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"U component of wind","parameter_ID":"131","product_type":"forecast","shortName":"u","standard_name":"U_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/131"},"description":"This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east, in metres per second. A negative sign thus indicates air movement towards the west.This parameter can be combined with the V component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"V_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"V component of wind","parameter_ID":"132","product_type":"forecast","shortName":"v","standard_name":"V_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/132"},"description":"This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north, in metres per second. A negative sign thus indicates air movement towards the south.This parameter can be combined with the U component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Vertical_velocity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Vertical velocity","parameter_ID":"135","product_type":"forecast","shortName":"w","standard_name":"Vertical_velocity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/135"},"description":"This parameter is the speed of air motion in the upward or downward direction. The ECMWF Integrated Forecasting System (IFS) uses a pressure based vertical co-ordinate system and pressure decreases with height, therefore negative values of vertical velocity indicate upward motion.Vertical velocity can be useful to understand the large-scale dynamics of the atmosphere, including areas of upward motion/ascent (negative values) and downward motion/subsidence (positive values).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa s-1"}},"sci:publications":[{"doi":"10.1109/MCSE.2023.3260519","citation":"N. Wedi et al., Destination Earth: High-Performance Computing for Weather and Climate, in Computing in Science \u0026 Engineering, vol. 24, no. 6, pp. 29-37, Nov.-Dec. 2022,"},{"doi":"10.21957/d3f982672e","citation":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"dedl:short_description":"The Climate Change Adaptation Digital Twin provides global climate projections and sector-specific information over multiple decades at high resolution via a unified framework combining advanced Earth system models, impact assessments, and observations. This Generation-1, Realization-1 Collection gives access to 'Future Projection' data based on the 'ICON' model."},{"type":"Collection","title":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Future Projection - IFS-FESOM - Generation-1 - Realization-1","id":"EO.ECMWF.DAT.D1.DT_CLIMATE.G1.SCENARIOMIP_SSP3-7.0_IFS-FESOM.R1","description":"Note: This Generation 1 Collection has been superseded by Generation 2 Simulation-level Collections\n\nThe DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities by providing innovative climate information on multi-decadal timescales, globally, at scales at which many impacts of climate change are observed. It combines cutting-edge global Earth-system models, impact-sector applications and observations into a unified framework to provide global climate projections and impact-sector information on multi-decadal timescales (1990 to ~2050), at very high spatial resolutions (5 to 10 km).\n\nThe Climate DT represents the first ever attempt to operationalise the production of global multi-decadal climate projections, leveraging the world-leading supercomputing facilities of the EuroHPC Joint Undertaking along with some of the leading European climate models. A concise overview of what the Climate DT aims to achieve, and of the different concepts essential for an understanding of the Digital Twin’s characteristics, is included in the [Climate DT factsheet](https://destine.ecmwf.int/wp-content/uploads/2024/06/2024.06.07_Climate-DT-Fact-Sheet_V7-2.pdf)\n\n## Future Projection \n\n To project how climate will change on a global and local scale in the future, forcing changes according to the Shared Socioeconomic Pathway (SSP) 3-7.0 scenario from ScenarioMIP. The SSP3-7.0 scenario explores a future with a continuous increase in CO2 emissions with no strong mitigation efforts. All DestinE projections carried out so far follow this scenario. In the future, alternative future scenarios will be explored. The projections carried out so far are initialised in 2020 from reanalysis followed by a 5-year ocean spin-up. For the upcoming simulations carried out in phase 2 of DestinE, scenario simulations will extend the historical simulations.\n\n## Models\n\nThe Climate DT exploits and further evolves a new generation of global storm-resolving and eddy-rich models built through a cooperative model development approach. For more information on models please click [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#models)\n\n## Simulations\n\nThe Climate DT team carries out several types of digital twin simulations on the EuroHPC supercomputers.  Multi-decadal simulations are produced to cover the recent past (from 1990) and possible future evolutions of the climate up to 2050. See [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#simulations) for more information on Simulations\n\n## Parameters\n\nBelow we see the list of parameters extracted from the 'DestinE Climate DT data portfolio', for more information please refer to the page [Climate DT Phase 1 CLTE Parameters](https://confluence.ecmwf.int/display/DDCZ/Climate+DT+Phase+1+data+catalogue)","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G1.SCENARIOMIP_SSP3-7.0_IFS-FESOM.R1/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G1.SCENARIOMIP_SSP3-7.0_IFS-FESOM.R1/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G1.SCENARIOMIP_SSP3-7.0_IFS-FESOM.R1/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G1.SCENARIOMIP_SSP3-7.0_IFS-FESOM.R1","title":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Future Projection - IFS-FESOM - Generation-1 - Realization-1"},{"rel":"example","type":"application/x-ipynb+json","href":"https://raw.githubusercontent.com/destination-earth/DestinE-DataLake-Lab/refs/heads/main/HDA/DestinE%20Digital%20Twins/DEDL-HDA-EO.ECMWF.DAT.DT_CLIMATE.ipynb","title":"Destination Earth - Climate DT Parameter - Data Access using DEDL HDA","application:type":"jupyter-notebook","application:embedded":true,"application:language":"Python"},{"rel":"example","type":"application/x-ipynb+json","href":"https://raw.githubusercontent.com/destination-earth/DestinE-DataLake-Lab/refs/heads/main/HDA/DestinE%20Digital%20Twins/ClimateDT-ParameterPlotter.ipynb","title":"Destination Earth - HDA Climate DT Parameter Plotter Tutorial","application:type":"jupyter-notebook","application:embedded":true,"application:language":"Python"},{"rel":"example","type":"application/x-ipynb+json","href":"https://raw.githubusercontent.com/destination-earth/DestinE-DataLake-Lab/refs/heads/main/HDA/DestinE%20Digital%20Twins/DEDL-HDA-EO.ECMWF.DAT.DT_CLIMATE-Series.ipynb","title":"Destination Earth - Climate DT Parameter Series Plot- Data Access using DEDL HDA","application:type":"jupyter-notebook","application:embedded":true,"application:language":"Python"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Parameters","title":"DestinE ClimateDT Parameters"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/Climate+DT+Phase+1+data+catalogue","title":"Climate DT Phase 1 data catalogue"},{"rel":"cite-as","type":"text/html","href":"https://dl.acm.org/doi/abs/10.1109/MCSE.2023.3260519","title":"Destination Earth: High-Performance Computing for Weather and Climate"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.21957/d3f982672e","title":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"assets":{"thumbnail":{"href":"https://s3.central.data.destination-earth.eu/swift/v1/dedl-public/collections/climate-dt-min.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2020-01-01T00:00:00Z","2039-12-31T23:59:59Z"]]}},"license":"CC-BY-4.0","keywords":["Digital Twins","Climate Change","Atmosphere","Ocean","Land","Sea Ice","Snow","Soil","Earth","High Performance Computing","Decision Making","Europe","Meteorology","class:d1","dataset:climate-dt","generation:1","expver:0001","stream:clte","type:fc","activity:ScenarioMIP","experiment:SSP3-7.0","realization:1","model:IFS-FESOM","resolution:standard,high","levtype:hl,o2d,o3d,pl,sfc,sol"],"summaries":{"federation:backends":["dedt_leonardo"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/datacube/v2.1.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json","https://stac-extensions.github.io/application/v0.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["producer","processor","licensor"],"url":"https://www.ecmwf.int/"}],"created":"2024-04-11T22:31:55Z","updated":"2026-04-27T14:04:20Z","published":"2024-04-11T22:31:55Z","cube:dimensions":{"lat":{"axis":"y","description":"latitude","extent":[-90,90],"type":"spatial"},"lon":{"axis":"x","description":"longitude","extent":[-180,180],"type":"spatial"},"time":{"extent":["2020-01-01T00:00:00Z","2039-12-31T23:59:59Z"],"step":"P0Y0M0DT1H0M0S","type":"temporal"}},"cube:variables":{"100_metre_U_wind_component(clte_hl)":{"attrs":{"encoding":"instantaneous","levelist":"100","levtype":"hl","long_name":"100 metre U wind component","parameter_ID":"228246","product_type":"forecast","shortName":"100u","standard_name":"100_metre_U_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228246"},"description":"This parameter is the eastward component of the 100 m wind. It is the horizontal speed of air moving towards the east, at a height of 100 metres above the surface of the Earth, in metres per second.Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over amodel grid box and model time step.This parameter can be combined with the northward component to give the speed and direction of the horizontal 100 m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"100_metre_V_wind_component(clte_hl)":{"attrs":{"encoding":"instantaneous","levelist":"100","levtype":"hl","long_name":"100 metre V wind component","parameter_ID":"228247","product_type":"forecast","shortName":"100v","standard_name":"100_metre_V_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228247"},"description":"This parameter is the northward component of the 100 m wind. It is the horizontal speed of air moving towards the north, at a height of 100 metres above the surface of the Earth, in metres per second.Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over amodel grid box and model time step.This parameter can be combined with the eastward component to give the speed and direction of the horizontal 100 m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_U_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"10 metre U wind component","parameter_ID":"165","product_type":"forecast","shortName":"10u","standard_name":"10_metre_U_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/165"},"description":"This parameter is the eastward component of the 10m wind. It is the horizontal speed of air moving towards the east, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the V component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_V_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"10 metre V wind component","parameter_ID":"166","product_type":"forecast","shortName":"10v","standard_name":"10_metre_V_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/166"},"description":"This parameter is the northward component of the 10m wind. It is the horizontal speed of air moving towards the north, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the U component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"2_metre_dewpoint_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"2 metre dewpoint temperature","parameter_ID":"168","product_type":"forecast","shortName":"2d","standard_name":"2_metre_dewpoint_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/168"},"description":"This parameter is the temperature to which the air, at 2 metres above the surface of the Earth, would have to be cooled for saturation to occur.It is a measure of the humidity of the air. Combined with temperature and pressure, it can be used to calculate the relative humidity.2m dew point temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"2_metre_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"2 metre temperature","parameter_ID":"167","product_type":"forecast","shortName":"2t","standard_name":"2_metre_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/167"},"description":"This parameter is the temperature of air at 2m above the surface of land, sea or in-land waters.2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.Please note that the encodings listed here for s2s \u0026 uerra (which includes encodings for carra/cerra) include entries for Mean 2 metre temperature. The specific encoding for Mean 2 metre temperature can be found in 228004.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Boundary_layer_height(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Boundary layer height","parameter_ID":"159","product_type":"forecast","shortName":"blh","standard_name":"Boundary_layer_height","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/159"},"description":"This parameter is the depth of air next to the Earth's surface which is most affected by the resistance to the transfer of momentum, heat or moisture across the surface.The boundary layer height can be as low as a few tens of metres, such as in cooling air at night, or as high as several kilometres over the desert in the middle of a hot sunny day. When the boundary layer height is low, higher concentrations of pollutants (emitted from the Earth's surface) can develop.The boundary layer height calculation is based on the bulk Richardson number (a measure of the atmospheric conditions) following the conclusions of a 2012 review.See further information.","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Charnock(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Charnock","parameter_ID":"148","product_type":"forecast","shortName":"chnk","standard_name":"Charnock","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/148"},"description":"This parameter accounts for increased aerodynamic roughness as wave heights grow due to increasing surface stress. It depends on the wind speed, wave age and other aspects of the sea state and is used to calculate how much the waves slow down the wind.When the atmospheric model is run without the ocean model, this parameter has a constant value of 0.018. When the atmospheric model is coupled to the ocean model, this parameter is calculated by theECMWF Wave Model.","dimensions":["lat","lon","time"],"type":"data","unit":"Numeric"},"Evaporation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Evaporation","parameter_ID":"182","product_type":"forecast","shortName":"e","standard_name":"Evaporation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/182"},"description":"This parameter is the accumulated amount of water that has evaporated from the Earth's surface, including a simplified representation of transpiration (from vegetation), into vapour in the air above.This parameter is accumulated over aparticular time period which depends on the data extracted.The ECMWF Integrated Forecasting System convention is that downward fluxes are positive. Therefore, negative values indicate evaporation and positive values indicate condensation.[NOTE: See 260259 for the equivalent parameter in \"kg m-2\"]","dimensions":["lat","lon","time"],"type":"data","unit":"m of water equivalent"},"Geopotential(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Geopotential","parameter_ID":"129","product_type":"forecast","shortName":"z","standard_name":"Geopotential","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/129"},"description":"This parameter is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level.The geopotential height can be calculated by dividing the geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2). The geopotential height plays an important role in synoptic meteorology (analysis of weather patterns). Charts of geopotential height plotted at constant pressure levels (e.g., 300, 500 or 850 hPa) can be used to identify weather systems such as cyclones, anticyclones, troughs and ridges.At the surface of the Earth, this parameter shows the variations in geopotential (height) of the surface, and is often referred to as the orography.","dimensions":["lat","lon","time"],"type":"data","unit":"m2s-2"},"Geopotential(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Geopotential","parameter_ID":"129","product_type":"forecast","shortName":"z","standard_name":"Geopotential","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/129"},"description":"This parameter is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level.The geopotential height can be calculated by dividing the geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2). The geopotential height plays an important role in synoptic meteorology (analysis of weather patterns). Charts of geopotential height plotted at constant pressure levels (e.g., 300, 500 or 850 hPa) can be used to identify weather systems such as cyclones, anticyclones, troughs and ridges.At the surface of the Earth, this parameter shows the variations in geopotential (height) of the surface, and is often referred to as the orography.","dimensions":["lat","lon","time"],"type":"data","unit":"m2s-2"},"High_cloud_cover(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"High cloud cover","parameter_ID":"188","product_type":"forecast","shortName":"hcc","standard_name":"High_cloud_cover","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/188"},"description":"The proportion of agrid boxcovered by cloud occurring in the high levels of the troposphere. High cloud is a single level field calculated from cloud occurring on model levels with a pressure less than 0.45 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), high cloud would be calculated using levels with a pressure of less than 450 hPa (approximately 6km and above (assuming a `standard atmosphere`)).The high cloud cover parameter is calculated from cloud for the appropriate model levels as described above. Assumptions are made about the degree of overlap/randomness between clouds in different model levels.Cloud fractions vary from 0 to 1.[NOTE: See 3075 for the equivalent parameter in \"%\"]","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Land-sea_mask(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Land-sea mask","parameter_ID":"172","product_type":"forecast","shortName":"lsm","standard_name":"Land-sea_mask","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/172"},"description":"This parameter is the proportion of land, as opposed to ocean or inland waters (lakes, reservoirs, rivers and coastal waters), in agrid box.This parameter has values ranging between zero and one and is dimensionless.In cycles of the ECMWF Integrated Forecasting System (IFS) from CY41R1 (introduced in May 2015) onwards, grid boxes where this parameter has a value above 0.5 can be comprised of a mixture of land and inland water but not ocean. Grid boxes with a value of 0.5 and below can only be comprised of a water surface. In the latter case, the lake cover is used to determine how much of the water surface is ocean or inland water.In cycles of the IFS before CY41R1, grid boxes where this parameter has a value above 0.5 can only be comprised of land and those grid boxes with a value of 0.5 and below can only be comprised of ocean. In these older model cycles, there is no differentiation between ocean and inland water.","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Low_cloud_cover(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Low cloud cover","parameter_ID":"186","product_type":"forecast","shortName":"lcc","standard_name":"Low_cloud_cover","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/186"},"description":"This parameter is the proportion of agrid boxcovered by cloud occurring in the lower levels of the troposphere. Low cloud is a single level field calculated from cloud occurring on model levels with a pressure greater than 0.8 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), low cloud would be calculated using levels with a pressure greater than 800 hPa (below approximately 2km (assuming a 'standard atmosphere')).The low cloud cover parameter is calculated from cloud cover for the appropriate model levels as described above. Assumptions are made about the degree of overlap/randomness between clouds in different model levels.Cloud fractions vary from 0 to 1.[NOTE: See 3073 for the equivalent parameter in \"%\"]","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Mean_sea_level_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Mean sea level pressure","parameter_ID":"151","product_type":"forecast","shortName":"msl","standard_name":"Mean_sea_level_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/151"},"description":"This parameter is the pressure (force per unit area) of the atmosphere adjusted to the height of mean sea level.It is a measure of the weight that all the air in a column vertically above the area of Earth's surface would have at that point, if the point were located at the mean sea level. It is calculated over all surfaces - land, sea and in-land water.Maps of mean sea level pressure are used to identify the locations of low and high pressure systems, often referred to as cyclones and anticyclones. Contours of mean sea level pressure also indicate the strength of the wind. Tightly packed contours show stronger winds.The units of this parameter are pascals (Pa).  Mean sea level pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb = 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Medium_cloud_cover(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Medium cloud cover","parameter_ID":"187","product_type":"forecast","shortName":"mcc","standard_name":"Medium_cloud_cover","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/187"},"description":"This parameter is the proportion of agrid boxcovered by cloud occurring in the middle levels of the troposphere. Medium cloud is a single level field calculated from cloud occurring on model levels with a pressure between 0.45 and 0.8 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), medium cloud would be calculated using levels with a pressure of less than or equal to 800 hPa and greater than or equal to 450 hPa (between approximately 2km and 6km (assuming a 'standard atmosphere')).The medium cloud parameter is calculated from cloud cover for the appropriate model levels as described above. Assumptions are made about the degree of overlap/randomness between clouds in different model levels.Cloud fractions vary from 0 to 1.[NOTE: See 3074 for the equivalent parameter in \"%\"]","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Potential_vorticity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Potential vorticity","parameter_ID":"60","product_type":"forecast","shortName":"pv","standard_name":"Potential_vorticity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/60"},"description":"Potential vorticity is a measure of the capacity for air to rotate in the atmosphere. If we ignore the effects of heating and friction, potential vorticity is conserved following an air parcel. It is used to look for places where large wind storms are likely to originate and develop.  Potential vorticity increases strongly above the tropopause and therefore, it can also be used in studies related to the stratosphere and stratosphere-troposphere exchanges.Large wind storms develop when a column of air in the atmosphere starts to rotate. Potential vorticity is calculated from the wind, temperature and pressure across a column of air in the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K m2kg-1s-1"},"Relative_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Relative humidity","parameter_ID":"157","product_type":"forecast","shortName":"r","standard_name":"Relative_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/157"},"description":"This parameter is the water vapour pressure as a percentage of the value at which the air becomes saturated (the point at which water vapour begins to condense into liquid water or deposition into ice).For temperatures over 0°C (273.15 K) it is calculated for saturation over water. At temperatures below -23°C it is calculated for saturation over ice.  Between -23°C and 0°C this parameter is calculated by interpolating between the ice and water values using a quadratic function.See more information about the model's relative humidity calculation.","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Skin_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Skin temperature","parameter_ID":"235","product_type":"forecast","shortName":"skt","standard_name":"Skin_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235"},"description":"This parameter is the temperature of the surface of the Earth.The skin temperature is the theoretical temperature that is required to satisfy the surface energy balance. It represents the temperature of the uppermost surface layer, which has no heat capacity and so can respond instantaneously to changes in surface fluxes. Skin temperature is calculated differently over land and sea.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.See further information about the skin temperatureover landandover sea.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean skin temperature. The specific encoding for Mean skin temperature can be found in 235079.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Snow_depth(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Snow depth","parameter_ID":"141","product_type":"forecast","shortName":"sd","standard_name":"Snow_depth","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/141"},"description":"This parameter is the depth of snow from the snow-covered area of agrid box.Its units are metres of water equivalent, so it is the depth the water would have if the snow melted and was spread evenly over the whole grid box. The ECMWF Integrated Forecast System represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box.See further information.\n\n[NOTE: See 228141 for the equivalent parameter in \"kg m-2\"]","dimensions":["lat","lon","time"],"type":"data","unit":"m of water equivalent"},"Snow_depth_water_equivalent(clte_sol)":{"attrs":{"encoding":"instantaneous","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Snow depth water equivalent","parameter_ID":"228141","product_type":"forecast","shortName":"sd","standard_name":"Snow_depth_water_equivalent","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228141"},"description":"Snow depth water equivalent in kg m**-2 (mm) water equivalent.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean snow depth water equivalent. The specific encoding for Time-mean snow depth water equivalent can be found in 235078.[NOTE: See 141 for the equivalent parameter in \"m of water equivalent\"]","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Snowfall(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Snowfall","parameter_ID":"144","product_type":"forecast","shortName":"sf","standard_name":"Snowfall","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/144"},"description":"This parameter is the accumulated snow that falls to the Earth's surface. It is the sum of large-scale snowfall and convective snowfall. Large-scale snowfall is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of thegrid boxor larger. Convective snowfall is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box.See further information.This parameter is the total amount of wateraccumulated over a particular time period which depends on the data extracted. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box.Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box.[NOTE: See 228144 for the equivalent parameter in \"kg m-2\"]","dimensions":["lat","lon","time"],"type":"data","unit":"m of water equivalent"},"Specific_cloud_liquid_water_content(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific cloud liquid water content","parameter_ID":"246","product_type":"forecast","shortName":"clwc","standard_name":"Specific_cloud_liquid_water_content","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/246"},"description":"This parameter is the mass of cloud liquid water droplets per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for agrid box.Water within clouds can be liquid or ice, or a combination of the two.See further information about the cloud formulation.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Specific_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific humidity","parameter_ID":"133","product_type":"forecast","shortName":"q","standard_name":"Specific_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/133"},"description":"This parameter is the mass of water vapour per kilogram of moist air.The total mass of moist air is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Sub-surface_runoff(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Sub-surface runoff","parameter_ID":"9","product_type":"forecast","shortName":"ssro","standard_name":"Sub-surface_runoff","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/9"},"description":"Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is simply called 'runoff'. This parameter is the total amount of water accumulated over aparticular time period which depends on the data extracted.The units of runoff are depth in metres.  This is the depth the water would have if it were spread evenly over thegrid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid square area.  Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here.Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood.  More information about how runoff is calculated is given in theIFS Physical Processes documentation.\n\n[NOTE: See 231012 for the equivalent parameter in \"kg m-2\"]","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Surface_long-wave_(thermal)_radiation_downwards(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Surface long-wave (thermal) radiation downwards","parameter_ID":"175","product_type":"forecast","shortName":"strd","standard_name":"Surface_long-wave_(thermal)_radiation_downwards","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/175"},"description":"This parameter is the amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere and clouds that reaches a horizontal plane at the surface of the Earth.The surface of the Earth emits thermal radiation, some of which is absorbed by the atmosphere and clouds. The atmosphere and clouds likewise emit thermal radiation in all directions, some of which reaches the surface (represented by this parameter).See further documentation.This parameter isaccumulated over a particular time period which depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Surface_net_long-wave_(thermal)_radiation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Surface net long-wave (thermal) radiation","parameter_ID":"177","product_type":"forecast","shortName":"str","standard_name":"Surface_net_long-wave_(thermal)_radiation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/177"},"description":"Thermal radiation (also known as longwave or terrestrial radiation) refers to radiation emitted by the atmosphere, clouds and the surface of the Earth. This parameter is the difference between downward and upward thermal radiation at the surface of the Earth. It the amount passing through a horizontal plane.The atmosphere and clouds emit thermal radiation in all directions, some of which reaches the surface as downward thermal radiation. The upward thermal radiation at the surface consists of thermal radiation emitted by the surface plus the fraction of downwards thermal radiation reflected upward by the surface.See further documentation.This parameter isaccumulated over a particular time periodwhich depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds.The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Surface_net_short-wave_(solar)_radiation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Surface net short-wave (solar) radiation","parameter_ID":"176","product_type":"forecast","shortName":"ssr","standard_name":"Surface_net_short-wave_(solar)_radiation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/176"},"description":"This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo).Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The remainder is incident on the Earth's surface, where some of it is reflected.See further documentation.This parameter isaccumulated over a particular time period which depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Surface_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Surface pressure","parameter_ID":"134","product_type":"forecast","shortName":"sp","standard_name":"Surface_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/134"},"description":"This parameter is the pressure (force per unit area) of the atmosphere on the surface of land, sea and in-land water.It is a measure of the weight of all the air in a column vertically above the area of the Earth's surface represented at a fixed point.Surface pressure is often used in combination with temperature to calculate air density.The strong variation of pressure with altitude makes it difficult to see the low and high pressure systems over mountainous areas, so mean sea level pressure, rather than surface pressure, is normally used for this purpose.The units of this parameter are Pascals (Pa). Surface pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb= 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Surface_runoff(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Surface runoff","parameter_ID":"8","product_type":"forecast","shortName":"sro","standard_name":"Surface_runoff","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/8"},"description":"Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is simply called 'runoff'. This parameter is the total amount of water accumulated over aparticular time period which depends on the data extracted.The units of runoff are depth in metres.  This is the depth the water would have if it were spread evenly over thegrid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid square area.  Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here.Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood.  More information about how runoff is calculated is given in theIFS Physical Processes documentation.\n\n[NOTE: See 231010 for the equivalent parameter in \"kg m-2\"]","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Surface_short-wave_(solar)_radiation_downwards(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Surface short-wave (solar) radiation downwards","parameter_ID":"169","product_type":"forecast","shortName":"ssrd","standard_name":"Surface_short-wave_(solar)_radiation_downwards","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/169"},"description":"This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth. This parameter comprises both direct and diffuse solar radiation.Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface (represented by this parameter).See further documentation.To a reasonably good approximation, this parameter is the model equivalent of what would be measured by a pyranometer (an instrument used for measuring solar radiation) at the surface. However, care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over amodel grid box.This parameter isaccumulated over a particular time period which depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"TOA_incident_short-wave_(solar)_radiation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"TOA incident short-wave (solar) radiation","parameter_ID":"212","product_type":"forecast","shortName":"tisr","standard_name":"TOA_incident_short-wave_(solar)_radiation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/212"},"description":"Accumulated field","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Temperature(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Temperature","parameter_ID":"130","product_type":"forecast","shortName":"t","standard_name":"Temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/130"},"description":"This parameter is the temperature in the atmosphere.It has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.This parameter is available on multiple levels through the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-integrated_eastward_turbulent_surface_stress(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Time-integrated eastward turbulent surface stress","parameter_ID":"180","product_type":"forecast","shortName":"ewss","standard_name":"Time-integrated_eastward_turbulent_surface_stress","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/180"},"description":"Air flowing over a surface exerts a stress that transfers momentum to the surface and slows the wind. This parameter is the accumulated stress on the Earth's surface in the eastward direction due to both the turbulent interactions between the atmosphere and the surface, and to turbulent orographic form drag. The turbulent interactions between the atmosphere and the surface are due to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km being derived from land surface data at about 1 km resolution.See further information.Positive (negative) values denote stress in the eastward (westward) direction.This parameter isaccumulated over a particular time periodwhich depends on the data extracted.","dimensions":["lat","lon","time"],"type":"data","unit":"N m-2s"},"Time-integrated_northward_turbulent_surface_stress(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Time-integrated northward turbulent surface stress","parameter_ID":"181","product_type":"forecast","shortName":"nsss","standard_name":"Time-integrated_northward_turbulent_surface_stress","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/181"},"description":"Air flowing over a surface exerts a stress that transfers momentum to the surface and slows the wind. This parameter is the accumulated stress on the Earth's surface in the northward direction due to both the turbulent interactions between the atmosphere and the surface, and to turbulent orographic form drag.The turbulent interactions between the atmosphere and the surface are due to the roughness of the surface.The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km being derived from land surface data at about 1 km resolution.See further information.Positive (negative) values denote stress in the northward (southward) direction.This parameter isaccumulated over a particular time periodwhich depends on the data extracted.","dimensions":["lat","lon","time"],"type":"data","unit":"N m-2s"},"Time-integrated_surface_latent_heat_net_flux(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Time-integrated surface latent heat net flux","parameter_ID":"147","product_type":"forecast","shortName":"slhf","standard_name":"Time-integrated_surface_latent_heat_net_flux","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/147"},"description":"This parameter is the transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earth's surface and the atmosphere through the effects of turbulent air motion. Evaporation from the Earth's surface represents a transfer of energy from the surface to the atmosphere.See further documentationThis parameter is accumulated over aparticular time period which depends on the data extracted.The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds.The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-integrated_surface_sensible_heat_net_flux(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Time-integrated surface sensible heat net flux","parameter_ID":"146","product_type":"forecast","shortName":"sshf","standard_name":"Time-integrated_surface_sensible_heat_net_flux","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/146"},"description":"This parameter is the transfer of heat between the Earth's surface and the atmosphere through the effects of turbulent air motion (but excluding any heat transfer resulting from condensation or evaporation).The magnitude of the sensible heat flux is governed by the difference in temperature between the surface and the overlying atmosphere, wind speed and the surface roughness. For example, cold air overlying a warm surface would produce a sensible heat flux from the land (or ocean) into the atmosphere.See further documentationThis is a single level parameter and it is accumulated over aparticular time period which depends on the data extracted.The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_eastward_sea_ice_velocity(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean eastward sea ice velocity","parameter_ID":"263003","product_type":"forecast","shortName":"avg_siue","standard_name":"Time-mean_eastward_sea_ice_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263003"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69","levtype":"o3d","long_name":"Time-mean eastward sea water velocity","parameter_ID":"263506","product_type":"forecast","shortName":"avg_uoe","standard_name":"Time-mean_eastward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263506"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_sea_ice_velocity(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean northward sea ice velocity","parameter_ID":"263004","product_type":"forecast","shortName":"avg_sivn","standard_name":"Time-mean_northward_sea_ice_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263004"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69","levtype":"o3d","long_name":"Time-mean northward sea water velocity","parameter_ID":"263505","product_type":"forecast","shortName":"avg_von","standard_name":"Time-mean_northward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263505"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_sea_ice_area_fraction(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea ice area fraction","parameter_ID":"263001","product_type":"forecast","shortName":"avg_siconc","standard_name":"Time-mean_sea_ice_area_fraction","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263001"},"dimensions":["lat","lon","time"],"type":"data","unit":"Fraction"},"Time-mean_sea_ice_thickness(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea ice thickness","parameter_ID":"263000","product_type":"forecast","shortName":"avg_sithick","standard_name":"Time-mean_sea_ice_thickness","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263000"},"dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Time-mean_sea_surface_height(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea surface height","parameter_ID":"263124","product_type":"forecast","shortName":"avg_zos","standard_name":"Time-mean_sea_surface_height","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263124"},"dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Time-mean_sea_surface_practical_salinity(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea surface practical salinity","parameter_ID":"263100","product_type":"forecast","shortName":"avg_sos","standard_name":"Time-mean_sea_surface_practical_salinity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263100"},"dimensions":["lat","lon","time"],"type":"data","unit":"g kg-1"},"Time-mean_sea_surface_temperature(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea surface temperature","parameter_ID":"263101","product_type":"forecast","shortName":"avg_tos","standard_name":"Time-mean_sea_surface_temperature","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263101"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_sea_water_potential_temperature(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69","levtype":"o3d","long_name":"Time-mean sea water potential temperature","parameter_ID":"263501","product_type":"forecast","shortName":"avg_thetao","standard_name":"Time-mean_sea_water_potential_temperature","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263501"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_sea_water_practical_salinity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69","levtype":"o3d","long_name":"Time-mean sea water practical salinity","parameter_ID":"263500","product_type":"forecast","shortName":"avg_so","standard_name":"Time-mean_sea_water_practical_salinity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263500"},"dimensions":["lat","lon","time"],"type":"data","unit":"g kg-1"},"Time-mean_snow_thickness_over_sea_ice(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean snow thickness over sea ice","parameter_ID":"263002","product_type":"forecast","shortName":"avg_sisnthick","standard_name":"Time-mean_snow_thickness_over_sea_ice","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263002"},"dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Time-mean_upward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69","levtype":"o3d","long_name":"Time-mean upward sea water velocity","parameter_ID":"263507","product_type":"forecast","shortName":"avg_wo","standard_name":"Time-mean_upward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263507"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Top_net_long-wave_(thermal)_radiation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Top net long-wave (thermal) radiation","parameter_ID":"179","product_type":"forecast","shortName":"ttr","standard_name":"Top_net_long-wave_(thermal)_radiation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/179"},"description":"The thermal (also known as terrestrial or longwave) radiation emitted to space at the top of the atmosphere is commonly known as the Outgoing Longwave Radiation (OLR). The top net thermal radiation (this parameter) is equal to the negative of OLR.See further documentation.This parameter isaccumulated over a particular time periodwhich depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds.The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Top_net_short-wave_(solar)_radiation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Top net short-wave (solar) radiation","parameter_ID":"178","product_type":"forecast","shortName":"tsr","standard_name":"Top_net_short-wave_(solar)_radiation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/178"},"description":"This parameter is the incoming solar radiation (also known as shortwave radiation) minus the outgoing solar radiation at the top of the atmosphere. It is the amount of radiation passing through a horizontal plane. The incoming solar radiation is the amount received from the Sun. The outgoing solar radiation is the amount reflected and scattered by the Earth's atmosphere and surface.See further documentation.This parameter isaccumulated over a particular time periodwhich depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds.The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Total_cloud_cover(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total cloud cover","parameter_ID":"164","product_type":"forecast","shortName":"tcc","standard_name":"Total_cloud_cover","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/164"},"description":"This parameter is the proportion of agrid boxcovered by cloud. Total cloud cover is a single level field calculated from the cloud occurring at different model levels through the atmosphere. Assumptions are made about the degree of overlap/randomness between clouds at different heights.Cloud fractions vary from 0 to 1.[NOTE: See 228164 for the equivalent parameter in \"%\"]","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Total_column_cloud_ice_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud ice water","parameter_ID":"79","product_type":"forecast","shortName":"tciw","standard_name":"Total_column_cloud_ice_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/79"},"description":"This parameter is the amount of ice contained within clouds in a column extending from the surface of the Earth to the top of the atmosphere. Snow (aggregated ice crystals) is not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The  ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_cloud_liquid_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud liquid water","parameter_ID":"78","product_type":"forecast","shortName":"tclw","standard_name":"Total_column_cloud_liquid_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/78"},"description":"This parameter is the amount of liquid water contained within cloud droplets in a column extending from the surface of the Earth to the top of the atmosphere. Rain water droplets, which are much larger in size (and mass), are not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_vertically-integrated_water_vapour(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column vertically-integrated water vapour","parameter_ID":"137","product_type":"forecast","shortName":"tcwv","standard_name":"Total_column_vertically-integrated_water_vapour","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/137"},"description":"This parameter is the total amount of water vapour in a column extending from the surface of the Earth to the top of the atmosphere.This parameter represents the area averaged value for agrid box.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_precipitation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Total precipitation","parameter_ID":"228","product_type":"forecast","shortName":"tp","standard_name":"Total_precipitation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228"},"description":"This parameter is the accumulated liquid and frozen water, comprising rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation and convective precipitation. Large-scale precipitation is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of thegrid boxor larger. Convective precipitation is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box.See further information.This parameter does not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth.This parameter is the total amount of wateraccumulated over a particular time period which depends on the data extracted. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box.Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box.\n\n[NOTE: See 228228 for the equivalent parameter in \"kg m-2\"]","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Total_precipitation_rate(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total precipitation rate","parameter_ID":"260048","product_type":"forecast","shortName":"tprate","standard_name":"Total_precipitation_rate","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/260048"},"description":"This parameter is the rate of total precipitation,at the specified time.In the ECMWF Integrated Forecasting System (IFS), total precipitation is rain and snow that falls to the Earth's surface.  It is the sum of large-scale precipitation and convective precipitation. Large-scale precipitation is generated by the cloud scheme in the IFS. The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of agrid boxor larger. Convective precipitation is generated by the convection scheme in the IFS. The convection scheme represents convection at spatial scales smaller than the grid box.Seefurther information. Precipitation parameters do not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth.1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm per second.Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box andmodel time step.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"U_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"U component of wind","parameter_ID":"131","product_type":"forecast","shortName":"u","standard_name":"U_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/131"},"description":"This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east, in metres per second. A negative sign thus indicates air movement towards the west.This parameter can be combined with the V component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"V_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"V component of wind","parameter_ID":"132","product_type":"forecast","shortName":"v","standard_name":"V_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/132"},"description":"This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north, in metres per second. A negative sign thus indicates air movement towards the south.This parameter can be combined with the U component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Vertical_velocity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Vertical velocity","parameter_ID":"135","product_type":"forecast","shortName":"w","standard_name":"Vertical_velocity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/135"},"description":"This parameter is the speed of air motion in the upward or downward direction. The ECMWF Integrated Forecasting System (IFS) uses a pressure based vertical co-ordinate system and pressure decreases with height, therefore negative values of vertical velocity indicate upward motion.Vertical velocity can be useful to understand the large-scale dynamics of the atmosphere, including areas of upward motion/ascent (negative values) and downward motion/subsidence (positive values).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa s-1"}},"sci:publications":[{"doi":"10.1109/MCSE.2023.3260519","citation":"N. Wedi et al., Destination Earth: High-Performance Computing for Weather and Climate, in Computing in Science \u0026 Engineering, vol. 24, no. 6, pp. 29-37, Nov.-Dec. 2022,"},{"doi":"10.21957/d3f982672e","citation":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"dedl:short_description":"The Climate Change Adaptation Digital Twin provides global climate projections and sector-specific information over multiple decades at high resolution via a unified framework combining advanced Earth system models, impact assessments, and observations. This Generation-1, Realization-1 Collection gives access to 'Future Projection' data based on the 'IFS-FESOM' model."},{"type":"Collection","title":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Future Projection - IFS-NEMO - Generation-1 - Realization-1","id":"EO.ECMWF.DAT.D1.DT_CLIMATE.G1.SCENARIOMIP_SSP3-7.0_IFS-NEMO.R1","description":"Note: This Generation 1 Collection has been superseded by Generation 2 Simulation-level Collections\n\nThe DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities by providing innovative climate information on multi-decadal timescales, globally, at scales at which many impacts of climate change are observed. It combines cutting-edge global Earth-system models, impact-sector applications and observations into a unified framework to provide global climate projections and impact-sector information on multi-decadal timescales (1990 to ~2050), at very high spatial resolutions (5 to 10 km).\n\nThe Climate DT represents the first ever attempt to operationalise the production of global multi-decadal climate projections, leveraging the world-leading supercomputing facilities of the EuroHPC Joint Undertaking along with some of the leading European climate models. A concise overview of what the Climate DT aims to achieve, and of the different concepts essential for an understanding of the Digital Twin’s characteristics, is included in the [Climate DT factsheet](https://destine.ecmwf.int/wp-content/uploads/2024/06/2024.06.07_Climate-DT-Fact-Sheet_V7-2.pdf)\n\n## Future Projection \n\n To project how climate will change on a global and local scale in the future, forcing changes according to the Shared Socioeconomic Pathway (SSP) 3-7.0 scenario from ScenarioMIP. The SSP3-7.0 scenario explores a future with a continuous increase in CO2 emissions with no strong mitigation efforts. All DestinE projections carried out so far follow this scenario. In the future, alternative future scenarios will be explored. The projections carried out so far are initialised in 2020 from reanalysis followed by a 5-year ocean spin-up. For the upcoming simulations carried out in phase 2 of DestinE, scenario simulations will extend the historical simulations.\n\n## Models\n\nThe Climate DT exploits and further evolves a new generation of global storm-resolving and eddy-rich models built through a cooperative model development approach. For more information on models please click [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#models)\n\n## Simulations\n\nThe Climate DT team carries out several types of digital twin simulations on the EuroHPC supercomputers.  Multi-decadal simulations are produced to cover the recent past (from 1990) and possible future evolutions of the climate up to 2050. See [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#simulations) for more information on Simulations\n\n## Parameters\n\nBelow we see the list of parameters extracted from the 'DestinE Climate DT data portfolio', for more information please refer to the page [Climate DT Phase 1 CLTE Parameters](https://confluence.ecmwf.int/display/DDCZ/Climate+DT+Phase+1+data+catalogue)","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G1.SCENARIOMIP_SSP3-7.0_IFS-NEMO.R1/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G1.SCENARIOMIP_SSP3-7.0_IFS-NEMO.R1/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G1.SCENARIOMIP_SSP3-7.0_IFS-NEMO.R1/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G1.SCENARIOMIP_SSP3-7.0_IFS-NEMO.R1","title":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Future Projection - IFS-NEMO - Generation-1 - Realization-1"},{"rel":"example","type":"application/x-ipynb+json","href":"https://raw.githubusercontent.com/destination-earth/DestinE-DataLake-Lab/refs/heads/main/HDA/DestinE%20Digital%20Twins/DEDL-HDA-EO.ECMWF.DAT.DT_CLIMATE.ipynb","title":"Destination Earth - Climate DT Parameter - Data Access using DEDL HDA","application:type":"jupyter-notebook","application:embedded":true,"application:language":"Python"},{"rel":"example","type":"application/x-ipynb+json","href":"https://raw.githubusercontent.com/destination-earth/DestinE-DataLake-Lab/refs/heads/main/HDA/DestinE%20Digital%20Twins/ClimateDT-ExtractLocationValues.ipynb","title":"DT Tutorial: Is it going to rain in the next 3 weekends?","application:type":"jupyter-notebook","application:embedded":true,"application:language":"Python"},{"rel":"example","type":"application/x-ipynb+json","href":"https://raw.githubusercontent.com/destination-earth/DestinE-DataLake-Lab/refs/heads/main/HDA/DestinE%20Digital%20Twins/ClimateDT-ParameterPlotter.ipynb","title":"Destination Earth - HDA Climate DT Parameter Plotter Tutorial","application:type":"jupyter-notebook","application:embedded":true,"application:language":"Python"},{"rel":"example","type":"application/x-ipynb+json","href":"https://raw.githubusercontent.com/destination-earth/DestinE-DataLake-Lab/refs/heads/main/HDA/DestinE%20Digital%20Twins/DEDL-HDA-EO.ECMWF.DAT.DT_CLIMATE-Series.ipynb","title":"Destination Earth - Climate DT Parameter Series Plot- Data Access using DEDL HDA","application:type":"jupyter-notebook","application:embedded":true,"application:language":"Python"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Parameters","title":"DestinE ClimateDT Parameters"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/Climate+DT+Phase+1+data+catalogue","title":"Climate DT Phase 1 data catalogue"},{"rel":"cite-as","type":"text/html","href":"https://dl.acm.org/doi/abs/10.1109/MCSE.2023.3260519","title":"Destination Earth: High-Performance Computing for Weather and Climate"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.21957/d3f982672e","title":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"assets":{"thumbnail":{"href":"https://s3.central.data.destination-earth.eu/swift/v1/dedl-public/collections/climate-dt-min.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2020-01-01T00:00:00Z","2039-12-31T23:59:59Z"]]}},"license":"CC-BY-4.0","keywords":["Digital Twins","Climate Change","Atmosphere","Ocean","Land","Sea Ice","Snow","Soil","Earth","High Performance Computing","Decision Making","Europe","Meteorology","class:d1","dataset:climate-dt","generation:1","expver:0001","stream:clte","type:fc","activity:ScenarioMIP","experiment:SSP3-7.0","realization:1","model:IFS-NEMO","resolution:standard,high","levtype:hl,o2d,o3d,pl,sfc,sol"],"summaries":{"federation:backends":["dedt_lumi"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/datacube/v2.1.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json","https://stac-extensions.github.io/application/v0.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["producer","processor","licensor"],"url":"https://www.ecmwf.int/"}],"created":"2024-04-11T22:31:55Z","updated":"2026-04-27T14:04:20Z","published":"2024-04-11T22:31:55Z","cube:dimensions":{"lat":{"axis":"y","description":"latitude","extent":[-90,90],"type":"spatial"},"lon":{"axis":"x","description":"longitude","extent":[-180,180],"type":"spatial"},"time":{"extent":["2020-01-01T00:00:00Z","2039-12-31T23:59:59Z"],"step":"P0Y0M0DT1H0M0S","type":"temporal"}},"cube:variables":{"100_metre_U_wind_component(clte_hl)":{"attrs":{"encoding":"instantaneous","levelist":"100","levtype":"hl","long_name":"100 metre U wind component","parameter_ID":"228246","product_type":"forecast","shortName":"100u","standard_name":"100_metre_U_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228246"},"description":"This parameter is the eastward component of the 100 m wind. It is the horizontal speed of air moving towards the east, at a height of 100 metres above the surface of the Earth, in metres per second.Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over amodel grid box and model time step.This parameter can be combined with the northward component to give the speed and direction of the horizontal 100 m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"100_metre_V_wind_component(clte_hl)":{"attrs":{"encoding":"instantaneous","levelist":"100","levtype":"hl","long_name":"100 metre V wind component","parameter_ID":"228247","product_type":"forecast","shortName":"100v","standard_name":"100_metre_V_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228247"},"description":"This parameter is the northward component of the 100 m wind. It is the horizontal speed of air moving towards the north, at a height of 100 metres above the surface of the Earth, in metres per second.Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over amodel grid box and model time step.This parameter can be combined with the eastward component to give the speed and direction of the horizontal 100 m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_U_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"10 metre U wind component","parameter_ID":"165","product_type":"forecast","shortName":"10u","standard_name":"10_metre_U_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/165"},"description":"This parameter is the eastward component of the 10m wind. It is the horizontal speed of air moving towards the east, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the V component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_V_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"10 metre V wind component","parameter_ID":"166","product_type":"forecast","shortName":"10v","standard_name":"10_metre_V_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/166"},"description":"This parameter is the northward component of the 10m wind. It is the horizontal speed of air moving towards the north, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the U component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"2_metre_dewpoint_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"2 metre dewpoint temperature","parameter_ID":"168","product_type":"forecast","shortName":"2d","standard_name":"2_metre_dewpoint_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/168"},"description":"This parameter is the temperature to which the air, at 2 metres above the surface of the Earth, would have to be cooled for saturation to occur.It is a measure of the humidity of the air. Combined with temperature and pressure, it can be used to calculate the relative humidity.2m dew point temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"2_metre_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"2 metre temperature","parameter_ID":"167","product_type":"forecast","shortName":"2t","standard_name":"2_metre_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/167"},"description":"This parameter is the temperature of air at 2m above the surface of land, sea or in-land waters.2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.Please note that the encodings listed here for s2s \u0026 uerra (which includes encodings for carra/cerra) include entries for Mean 2 metre temperature. The specific encoding for Mean 2 metre temperature can be found in 228004.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Boundary_layer_height(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Boundary layer height","parameter_ID":"159","product_type":"forecast","shortName":"blh","standard_name":"Boundary_layer_height","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/159"},"description":"This parameter is the depth of air next to the Earth's surface which is most affected by the resistance to the transfer of momentum, heat or moisture across the surface.The boundary layer height can be as low as a few tens of metres, such as in cooling air at night, or as high as several kilometres over the desert in the middle of a hot sunny day. When the boundary layer height is low, higher concentrations of pollutants (emitted from the Earth's surface) can develop.The boundary layer height calculation is based on the bulk Richardson number (a measure of the atmospheric conditions) following the conclusions of a 2012 review.See further information.","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Charnock(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Charnock","parameter_ID":"148","product_type":"forecast","shortName":"chnk","standard_name":"Charnock","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/148"},"description":"This parameter accounts for increased aerodynamic roughness as wave heights grow due to increasing surface stress. It depends on the wind speed, wave age and other aspects of the sea state and is used to calculate how much the waves slow down the wind.When the atmospheric model is run without the ocean model, this parameter has a constant value of 0.018. When the atmospheric model is coupled to the ocean model, this parameter is calculated by theECMWF Wave Model.","dimensions":["lat","lon","time"],"type":"data","unit":"Numeric"},"Evaporation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Evaporation","parameter_ID":"182","product_type":"forecast","shortName":"e","standard_name":"Evaporation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/182"},"description":"This parameter is the accumulated amount of water that has evaporated from the Earth's surface, including a simplified representation of transpiration (from vegetation), into vapour in the air above.This parameter is accumulated over aparticular time period which depends on the data extracted.The ECMWF Integrated Forecasting System convention is that downward fluxes are positive. Therefore, negative values indicate evaporation and positive values indicate condensation.[NOTE: See 260259 for the equivalent parameter in \"kg m-2\"]","dimensions":["lat","lon","time"],"type":"data","unit":"m of water equivalent"},"Geopotential(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Geopotential","parameter_ID":"129","product_type":"forecast","shortName":"z","standard_name":"Geopotential","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/129"},"description":"This parameter is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level.The geopotential height can be calculated by dividing the geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2). The geopotential height plays an important role in synoptic meteorology (analysis of weather patterns). Charts of geopotential height plotted at constant pressure levels (e.g., 300, 500 or 850 hPa) can be used to identify weather systems such as cyclones, anticyclones, troughs and ridges.At the surface of the Earth, this parameter shows the variations in geopotential (height) of the surface, and is often referred to as the orography.","dimensions":["lat","lon","time"],"type":"data","unit":"m2s-2"},"Geopotential(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Geopotential","parameter_ID":"129","product_type":"forecast","shortName":"z","standard_name":"Geopotential","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/129"},"description":"This parameter is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level.The geopotential height can be calculated by dividing the geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2). The geopotential height plays an important role in synoptic meteorology (analysis of weather patterns). Charts of geopotential height plotted at constant pressure levels (e.g., 300, 500 or 850 hPa) can be used to identify weather systems such as cyclones, anticyclones, troughs and ridges.At the surface of the Earth, this parameter shows the variations in geopotential (height) of the surface, and is often referred to as the orography.","dimensions":["lat","lon","time"],"type":"data","unit":"m2s-2"},"High_cloud_cover(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"High cloud cover","parameter_ID":"188","product_type":"forecast","shortName":"hcc","standard_name":"High_cloud_cover","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/188"},"description":"The proportion of agrid boxcovered by cloud occurring in the high levels of the troposphere. High cloud is a single level field calculated from cloud occurring on model levels with a pressure less than 0.45 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), high cloud would be calculated using levels with a pressure of less than 450 hPa (approximately 6km and above (assuming a `standard atmosphere`)).The high cloud cover parameter is calculated from cloud for the appropriate model levels as described above. Assumptions are made about the degree of overlap/randomness between clouds in different model levels.Cloud fractions vary from 0 to 1.[NOTE: See 3075 for the equivalent parameter in \"%\"]","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Land-sea_mask(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Land-sea mask","parameter_ID":"172","product_type":"forecast","shortName":"lsm","standard_name":"Land-sea_mask","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/172"},"description":"This parameter is the proportion of land, as opposed to ocean or inland waters (lakes, reservoirs, rivers and coastal waters), in agrid box.This parameter has values ranging between zero and one and is dimensionless.In cycles of the ECMWF Integrated Forecasting System (IFS) from CY41R1 (introduced in May 2015) onwards, grid boxes where this parameter has a value above 0.5 can be comprised of a mixture of land and inland water but not ocean. Grid boxes with a value of 0.5 and below can only be comprised of a water surface. In the latter case, the lake cover is used to determine how much of the water surface is ocean or inland water.In cycles of the IFS before CY41R1, grid boxes where this parameter has a value above 0.5 can only be comprised of land and those grid boxes with a value of 0.5 and below can only be comprised of ocean. In these older model cycles, there is no differentiation between ocean and inland water.","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Low_cloud_cover(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Low cloud cover","parameter_ID":"186","product_type":"forecast","shortName":"lcc","standard_name":"Low_cloud_cover","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/186"},"description":"This parameter is the proportion of agrid boxcovered by cloud occurring in the lower levels of the troposphere. Low cloud is a single level field calculated from cloud occurring on model levels with a pressure greater than 0.8 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), low cloud would be calculated using levels with a pressure greater than 800 hPa (below approximately 2km (assuming a 'standard atmosphere')).The low cloud cover parameter is calculated from cloud cover for the appropriate model levels as described above. Assumptions are made about the degree of overlap/randomness between clouds in different model levels.Cloud fractions vary from 0 to 1.[NOTE: See 3073 for the equivalent parameter in \"%\"]","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Mean_sea_level_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Mean sea level pressure","parameter_ID":"151","product_type":"forecast","shortName":"msl","standard_name":"Mean_sea_level_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/151"},"description":"This parameter is the pressure (force per unit area) of the atmosphere adjusted to the height of mean sea level.It is a measure of the weight that all the air in a column vertically above the area of Earth's surface would have at that point, if the point were located at the mean sea level. It is calculated over all surfaces - land, sea and in-land water.Maps of mean sea level pressure are used to identify the locations of low and high pressure systems, often referred to as cyclones and anticyclones. Contours of mean sea level pressure also indicate the strength of the wind. Tightly packed contours show stronger winds.The units of this parameter are pascals (Pa).  Mean sea level pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb = 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Medium_cloud_cover(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Medium cloud cover","parameter_ID":"187","product_type":"forecast","shortName":"mcc","standard_name":"Medium_cloud_cover","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/187"},"description":"This parameter is the proportion of agrid boxcovered by cloud occurring in the middle levels of the troposphere. Medium cloud is a single level field calculated from cloud occurring on model levels with a pressure between 0.45 and 0.8 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), medium cloud would be calculated using levels with a pressure of less than or equal to 800 hPa and greater than or equal to 450 hPa (between approximately 2km and 6km (assuming a 'standard atmosphere')).The medium cloud parameter is calculated from cloud cover for the appropriate model levels as described above. Assumptions are made about the degree of overlap/randomness between clouds in different model levels.Cloud fractions vary from 0 to 1.[NOTE: See 3074 for the equivalent parameter in \"%\"]","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Potential_vorticity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Potential vorticity","parameter_ID":"60","product_type":"forecast","shortName":"pv","standard_name":"Potential_vorticity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/60"},"description":"Potential vorticity is a measure of the capacity for air to rotate in the atmosphere. If we ignore the effects of heating and friction, potential vorticity is conserved following an air parcel. It is used to look for places where large wind storms are likely to originate and develop.  Potential vorticity increases strongly above the tropopause and therefore, it can also be used in studies related to the stratosphere and stratosphere-troposphere exchanges.Large wind storms develop when a column of air in the atmosphere starts to rotate. Potential vorticity is calculated from the wind, temperature and pressure across a column of air in the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K m2kg-1s-1"},"Relative_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Relative humidity","parameter_ID":"157","product_type":"forecast","shortName":"r","standard_name":"Relative_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/157"},"description":"This parameter is the water vapour pressure as a percentage of the value at which the air becomes saturated (the point at which water vapour begins to condense into liquid water or deposition into ice).For temperatures over 0°C (273.15 K) it is calculated for saturation over water. At temperatures below -23°C it is calculated for saturation over ice.  Between -23°C and 0°C this parameter is calculated by interpolating between the ice and water values using a quadratic function.See more information about the model's relative humidity calculation.","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Skin_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Skin temperature","parameter_ID":"235","product_type":"forecast","shortName":"skt","standard_name":"Skin_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235"},"description":"This parameter is the temperature of the surface of the Earth.The skin temperature is the theoretical temperature that is required to satisfy the surface energy balance. It represents the temperature of the uppermost surface layer, which has no heat capacity and so can respond instantaneously to changes in surface fluxes. Skin temperature is calculated differently over land and sea.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.See further information about the skin temperatureover landandover sea.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean skin temperature. The specific encoding for Mean skin temperature can be found in 235079.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Snow_depth(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Snow depth","parameter_ID":"141","product_type":"forecast","shortName":"sd","standard_name":"Snow_depth","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/141"},"description":"This parameter is the depth of snow from the snow-covered area of agrid box.Its units are metres of water equivalent, so it is the depth the water would have if the snow melted and was spread evenly over the whole grid box. The ECMWF Integrated Forecast System represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box.See further information.\n\n[NOTE: See 228141 for the equivalent parameter in \"kg m-2\"]","dimensions":["lat","lon","time"],"type":"data","unit":"m of water equivalent"},"Snow_depth_water_equivalent(clte_sol)":{"attrs":{"encoding":"instantaneous","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Snow depth water equivalent","parameter_ID":"228141","product_type":"forecast","shortName":"sd","standard_name":"Snow_depth_water_equivalent","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228141"},"description":"Snow depth water equivalent in kg m**-2 (mm) water equivalent.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean snow depth water equivalent. The specific encoding for Time-mean snow depth water equivalent can be found in 235078.[NOTE: See 141 for the equivalent parameter in \"m of water equivalent\"]","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Snowfall(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Snowfall","parameter_ID":"144","product_type":"forecast","shortName":"sf","standard_name":"Snowfall","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/144"},"description":"This parameter is the accumulated snow that falls to the Earth's surface. It is the sum of large-scale snowfall and convective snowfall. Large-scale snowfall is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of thegrid boxor larger. Convective snowfall is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box.See further information.This parameter is the total amount of wateraccumulated over a particular time period which depends on the data extracted. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box.Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box.[NOTE: See 228144 for the equivalent parameter in \"kg m-2\"]","dimensions":["lat","lon","time"],"type":"data","unit":"m of water equivalent"},"Specific_cloud_liquid_water_content(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific cloud liquid water content","parameter_ID":"246","product_type":"forecast","shortName":"clwc","standard_name":"Specific_cloud_liquid_water_content","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/246"},"description":"This parameter is the mass of cloud liquid water droplets per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for agrid box.Water within clouds can be liquid or ice, or a combination of the two.See further information about the cloud formulation.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Specific_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific humidity","parameter_ID":"133","product_type":"forecast","shortName":"q","standard_name":"Specific_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/133"},"description":"This parameter is the mass of water vapour per kilogram of moist air.The total mass of moist air is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Sub-surface_runoff(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Sub-surface runoff","parameter_ID":"9","product_type":"forecast","shortName":"ssro","standard_name":"Sub-surface_runoff","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/9"},"description":"Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is simply called 'runoff'. This parameter is the total amount of water accumulated over aparticular time period which depends on the data extracted.The units of runoff are depth in metres.  This is the depth the water would have if it were spread evenly over thegrid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid square area.  Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here.Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood.  More information about how runoff is calculated is given in theIFS Physical Processes documentation.\n\n[NOTE: See 231012 for the equivalent parameter in \"kg m-2\"]","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Surface_long-wave_(thermal)_radiation_downwards(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Surface long-wave (thermal) radiation downwards","parameter_ID":"175","product_type":"forecast","shortName":"strd","standard_name":"Surface_long-wave_(thermal)_radiation_downwards","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/175"},"description":"This parameter is the amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere and clouds that reaches a horizontal plane at the surface of the Earth.The surface of the Earth emits thermal radiation, some of which is absorbed by the atmosphere and clouds. The atmosphere and clouds likewise emit thermal radiation in all directions, some of which reaches the surface (represented by this parameter).See further documentation.This parameter isaccumulated over a particular time period which depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Surface_net_long-wave_(thermal)_radiation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Surface net long-wave (thermal) radiation","parameter_ID":"177","product_type":"forecast","shortName":"str","standard_name":"Surface_net_long-wave_(thermal)_radiation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/177"},"description":"Thermal radiation (also known as longwave or terrestrial radiation) refers to radiation emitted by the atmosphere, clouds and the surface of the Earth. This parameter is the difference between downward and upward thermal radiation at the surface of the Earth. It the amount passing through a horizontal plane.The atmosphere and clouds emit thermal radiation in all directions, some of which reaches the surface as downward thermal radiation. The upward thermal radiation at the surface consists of thermal radiation emitted by the surface plus the fraction of downwards thermal radiation reflected upward by the surface.See further documentation.This parameter isaccumulated over a particular time periodwhich depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds.The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Surface_net_short-wave_(solar)_radiation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Surface net short-wave (solar) radiation","parameter_ID":"176","product_type":"forecast","shortName":"ssr","standard_name":"Surface_net_short-wave_(solar)_radiation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/176"},"description":"This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo).Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The remainder is incident on the Earth's surface, where some of it is reflected.See further documentation.This parameter isaccumulated over a particular time period which depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Surface_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Surface pressure","parameter_ID":"134","product_type":"forecast","shortName":"sp","standard_name":"Surface_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/134"},"description":"This parameter is the pressure (force per unit area) of the atmosphere on the surface of land, sea and in-land water.It is a measure of the weight of all the air in a column vertically above the area of the Earth's surface represented at a fixed point.Surface pressure is often used in combination with temperature to calculate air density.The strong variation of pressure with altitude makes it difficult to see the low and high pressure systems over mountainous areas, so mean sea level pressure, rather than surface pressure, is normally used for this purpose.The units of this parameter are Pascals (Pa). Surface pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb= 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Surface_runoff(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Surface runoff","parameter_ID":"8","product_type":"forecast","shortName":"sro","standard_name":"Surface_runoff","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/8"},"description":"Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is simply called 'runoff'. This parameter is the total amount of water accumulated over aparticular time period which depends on the data extracted.The units of runoff are depth in metres.  This is the depth the water would have if it were spread evenly over thegrid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid square area.  Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here.Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood.  More information about how runoff is calculated is given in theIFS Physical Processes documentation.\n\n[NOTE: See 231010 for the equivalent parameter in \"kg m-2\"]","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Surface_short-wave_(solar)_radiation_downwards(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Surface short-wave (solar) radiation downwards","parameter_ID":"169","product_type":"forecast","shortName":"ssrd","standard_name":"Surface_short-wave_(solar)_radiation_downwards","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/169"},"description":"This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth. This parameter comprises both direct and diffuse solar radiation.Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface (represented by this parameter).See further documentation.To a reasonably good approximation, this parameter is the model equivalent of what would be measured by a pyranometer (an instrument used for measuring solar radiation) at the surface. However, care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over amodel grid box.This parameter isaccumulated over a particular time period which depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"TOA_incident_short-wave_(solar)_radiation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"TOA incident short-wave (solar) radiation","parameter_ID":"212","product_type":"forecast","shortName":"tisr","standard_name":"TOA_incident_short-wave_(solar)_radiation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/212"},"description":"Accumulated field","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Temperature(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Temperature","parameter_ID":"130","product_type":"forecast","shortName":"t","standard_name":"Temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/130"},"description":"This parameter is the temperature in the atmosphere.It has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.This parameter is available on multiple levels through the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-integrated_eastward_turbulent_surface_stress(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Time-integrated eastward turbulent surface stress","parameter_ID":"180","product_type":"forecast","shortName":"ewss","standard_name":"Time-integrated_eastward_turbulent_surface_stress","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/180"},"description":"Air flowing over a surface exerts a stress that transfers momentum to the surface and slows the wind. This parameter is the accumulated stress on the Earth's surface in the eastward direction due to both the turbulent interactions between the atmosphere and the surface, and to turbulent orographic form drag. The turbulent interactions between the atmosphere and the surface are due to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km being derived from land surface data at about 1 km resolution.See further information.Positive (negative) values denote stress in the eastward (westward) direction.This parameter isaccumulated over a particular time periodwhich depends on the data extracted.","dimensions":["lat","lon","time"],"type":"data","unit":"N m-2s"},"Time-integrated_northward_turbulent_surface_stress(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Time-integrated northward turbulent surface stress","parameter_ID":"181","product_type":"forecast","shortName":"nsss","standard_name":"Time-integrated_northward_turbulent_surface_stress","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/181"},"description":"Air flowing over a surface exerts a stress that transfers momentum to the surface and slows the wind. This parameter is the accumulated stress on the Earth's surface in the northward direction due to both the turbulent interactions between the atmosphere and the surface, and to turbulent orographic form drag.The turbulent interactions between the atmosphere and the surface are due to the roughness of the surface.The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km being derived from land surface data at about 1 km resolution.See further information.Positive (negative) values denote stress in the northward (southward) direction.This parameter isaccumulated over a particular time periodwhich depends on the data extracted.","dimensions":["lat","lon","time"],"type":"data","unit":"N m-2s"},"Time-integrated_surface_latent_heat_net_flux(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Time-integrated surface latent heat net flux","parameter_ID":"147","product_type":"forecast","shortName":"slhf","standard_name":"Time-integrated_surface_latent_heat_net_flux","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/147"},"description":"This parameter is the transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earth's surface and the atmosphere through the effects of turbulent air motion. Evaporation from the Earth's surface represents a transfer of energy from the surface to the atmosphere.See further documentationThis parameter is accumulated over aparticular time period which depends on the data extracted.The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds.The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-integrated_surface_sensible_heat_net_flux(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Time-integrated surface sensible heat net flux","parameter_ID":"146","product_type":"forecast","shortName":"sshf","standard_name":"Time-integrated_surface_sensible_heat_net_flux","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/146"},"description":"This parameter is the transfer of heat between the Earth's surface and the atmosphere through the effects of turbulent air motion (but excluding any heat transfer resulting from condensation or evaporation).The magnitude of the sensible heat flux is governed by the difference in temperature between the surface and the overlying atmosphere, wind speed and the surface roughness. For example, cold air overlying a warm surface would produce a sensible heat flux from the land (or ocean) into the atmosphere.See further documentationThis is a single level parameter and it is accumulated over aparticular time period which depends on the data extracted.The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_X-component_of_sea_ice_velocity(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean X-component of sea ice velocity","parameter_ID":"263021","product_type":"forecast","shortName":"avg_six","standard_name":"Time-mean_X-component_of_sea_ice_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263021"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_Y-component_of_sea_ice_velocity(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean Y-component of sea ice velocity","parameter_ID":"263022","product_type":"forecast","shortName":"avg_siy","standard_name":"Time-mean_Y-component_of_sea_ice_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263022"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_ice_velocity(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean eastward sea ice velocity","parameter_ID":"263003","product_type":"forecast","shortName":"avg_siue","standard_name":"Time-mean_eastward_sea_ice_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263003"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75","levtype":"o3d","long_name":"Time-mean eastward sea water velocity","parameter_ID":"263506","product_type":"forecast","shortName":"avg_uoe","standard_name":"Time-mean_eastward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263506"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_sea_ice_velocity(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean northward sea ice velocity","parameter_ID":"263004","product_type":"forecast","shortName":"avg_sivn","standard_name":"Time-mean_northward_sea_ice_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263004"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75","levtype":"o3d","long_name":"Time-mean northward sea water velocity","parameter_ID":"263505","product_type":"forecast","shortName":"avg_von","standard_name":"Time-mean_northward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263505"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_sea_ice_area_fraction(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea ice area fraction","parameter_ID":"263001","product_type":"forecast","shortName":"avg_siconc","standard_name":"Time-mean_sea_ice_area_fraction","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263001"},"dimensions":["lat","lon","time"],"type":"data","unit":"Fraction"},"Time-mean_sea_ice_thickness(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea ice thickness","parameter_ID":"263000","product_type":"forecast","shortName":"avg_sithick","standard_name":"Time-mean_sea_ice_thickness","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263000"},"dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Time-mean_sea_ice_volume_per_unit_area(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea ice volume per unit area","parameter_ID":"263008","product_type":"forecast","shortName":"avg_sivol","standard_name":"Time-mean_sea_ice_volume_per_unit_area","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263008"},"dimensions":["lat","lon","time"],"type":"data","unit":"m3m-2"},"Time-mean_sea_surface_height(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea surface height","parameter_ID":"263124","product_type":"forecast","shortName":"avg_zos","standard_name":"Time-mean_sea_surface_height","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263124"},"dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Time-mean_sea_surface_practical_salinity(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea surface practical salinity","parameter_ID":"263100","product_type":"forecast","shortName":"avg_sos","standard_name":"Time-mean_sea_surface_practical_salinity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263100"},"dimensions":["lat","lon","time"],"type":"data","unit":"g kg-1"},"Time-mean_sea_surface_temperature(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea surface temperature","parameter_ID":"263101","product_type":"forecast","shortName":"avg_tos","standard_name":"Time-mean_sea_surface_temperature","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263101"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_sea_water_potential_temperature(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75","levtype":"o3d","long_name":"Time-mean sea water potential temperature","parameter_ID":"263501","product_type":"forecast","shortName":"avg_thetao","standard_name":"Time-mean_sea_water_potential_temperature","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263501"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_sea_water_practical_salinity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75","levtype":"o3d","long_name":"Time-mean sea water practical salinity","parameter_ID":"263500","product_type":"forecast","shortName":"avg_so","standard_name":"Time-mean_sea_water_practical_salinity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263500"},"dimensions":["lat","lon","time"],"type":"data","unit":"g kg-1"},"Time-mean_snow_volume_over_sea_ice_per_unit_area(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean snow volume over sea ice per unit area","parameter_ID":"263009","product_type":"forecast","shortName":"avg_snvol","standard_name":"Time-mean_snow_volume_over_sea_ice_per_unit_area","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263009"},"dimensions":["lat","lon","time"],"type":"data","unit":"m3m-2"},"Time-mean_upward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75","levtype":"o3d","long_name":"Time-mean upward sea water velocity","parameter_ID":"263507","product_type":"forecast","shortName":"avg_wo","standard_name":"Time-mean_upward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263507"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_vertically-integrated_heat_content_in_the_upper_300_m(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 300 m","parameter_ID":"263121","product_type":"forecast","shortName":"avg_hc300m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_300_m","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263121"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 700 m","parameter_ID":"263122","product_type":"forecast","shortName":"avg_hc700m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263122"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Top_net_long-wave_(thermal)_radiation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Top net long-wave (thermal) radiation","parameter_ID":"179","product_type":"forecast","shortName":"ttr","standard_name":"Top_net_long-wave_(thermal)_radiation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/179"},"description":"The thermal (also known as terrestrial or longwave) radiation emitted to space at the top of the atmosphere is commonly known as the Outgoing Longwave Radiation (OLR). The top net thermal radiation (this parameter) is equal to the negative of OLR.See further documentation.This parameter isaccumulated over a particular time periodwhich depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds.The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Top_net_short-wave_(solar)_radiation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Top net short-wave (solar) radiation","parameter_ID":"178","product_type":"forecast","shortName":"tsr","standard_name":"Top_net_short-wave_(solar)_radiation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/178"},"description":"This parameter is the incoming solar radiation (also known as shortwave radiation) minus the outgoing solar radiation at the top of the atmosphere. It is the amount of radiation passing through a horizontal plane. The incoming solar radiation is the amount received from the Sun. The outgoing solar radiation is the amount reflected and scattered by the Earth's atmosphere and surface.See further documentation.This parameter isaccumulated over a particular time periodwhich depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds.The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Total_cloud_cover(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total cloud cover","parameter_ID":"164","product_type":"forecast","shortName":"tcc","standard_name":"Total_cloud_cover","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/164"},"description":"This parameter is the proportion of agrid boxcovered by cloud. Total cloud cover is a single level field calculated from the cloud occurring at different model levels through the atmosphere. Assumptions are made about the degree of overlap/randomness between clouds at different heights.Cloud fractions vary from 0 to 1.[NOTE: See 228164 for the equivalent parameter in \"%\"]","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Total_column_cloud_ice_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud ice water","parameter_ID":"79","product_type":"forecast","shortName":"tciw","standard_name":"Total_column_cloud_ice_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/79"},"description":"This parameter is the amount of ice contained within clouds in a column extending from the surface of the Earth to the top of the atmosphere. Snow (aggregated ice crystals) is not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The  ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_cloud_liquid_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud liquid water","parameter_ID":"78","product_type":"forecast","shortName":"tclw","standard_name":"Total_column_cloud_liquid_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/78"},"description":"This parameter is the amount of liquid water contained within cloud droplets in a column extending from the surface of the Earth to the top of the atmosphere. Rain water droplets, which are much larger in size (and mass), are not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_vertically-integrated_water_vapour(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column vertically-integrated water vapour","parameter_ID":"137","product_type":"forecast","shortName":"tcwv","standard_name":"Total_column_vertically-integrated_water_vapour","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/137"},"description":"This parameter is the total amount of water vapour in a column extending from the surface of the Earth to the top of the atmosphere.This parameter represents the area averaged value for agrid box.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_precipitation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Total precipitation","parameter_ID":"228","product_type":"forecast","shortName":"tp","standard_name":"Total_precipitation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228"},"description":"This parameter is the accumulated liquid and frozen water, comprising rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation and convective precipitation. Large-scale precipitation is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of thegrid boxor larger. Convective precipitation is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box.See further information.This parameter does not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth.This parameter is the total amount of wateraccumulated over a particular time period which depends on the data extracted. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box.Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box.\n\n[NOTE: See 228228 for the equivalent parameter in \"kg m-2\"]","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Total_precipitation_rate(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total precipitation rate","parameter_ID":"260048","product_type":"forecast","shortName":"tprate","standard_name":"Total_precipitation_rate","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/260048"},"description":"This parameter is the rate of total precipitation,at the specified time.In the ECMWF Integrated Forecasting System (IFS), total precipitation is rain and snow that falls to the Earth's surface.  It is the sum of large-scale precipitation and convective precipitation. Large-scale precipitation is generated by the cloud scheme in the IFS. The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of agrid boxor larger. Convective precipitation is generated by the convection scheme in the IFS. The convection scheme represents convection at spatial scales smaller than the grid box.Seefurther information. Precipitation parameters do not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth.1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm per second.Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box andmodel time step.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"U_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"U component of wind","parameter_ID":"131","product_type":"forecast","shortName":"u","standard_name":"U_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/131"},"description":"This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east, in metres per second. A negative sign thus indicates air movement towards the west.This parameter can be combined with the V component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"V_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"V component of wind","parameter_ID":"132","product_type":"forecast","shortName":"v","standard_name":"V_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/132"},"description":"This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north, in metres per second. A negative sign thus indicates air movement towards the south.This parameter can be combined with the U component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Vertical_velocity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Vertical velocity","parameter_ID":"135","product_type":"forecast","shortName":"w","standard_name":"Vertical_velocity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/135"},"description":"This parameter is the speed of air motion in the upward or downward direction. The ECMWF Integrated Forecasting System (IFS) uses a pressure based vertical co-ordinate system and pressure decreases with height, therefore negative values of vertical velocity indicate upward motion.Vertical velocity can be useful to understand the large-scale dynamics of the atmosphere, including areas of upward motion/ascent (negative values) and downward motion/subsidence (positive values).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa s-1"}},"sci:publications":[{"doi":"10.1109/MCSE.2023.3260519","citation":"N. Wedi et al., Destination Earth: High-Performance Computing for Weather and Climate, in Computing in Science \u0026 Engineering, vol. 24, no. 6, pp. 29-37, Nov.-Dec. 2022,"},{"doi":"10.21957/d3f982672e","citation":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"dedl:short_description":"The Climate Change Adaptation Digital Twin provides global climate projections and sector-specific information over multiple decades at high resolution via a unified framework combining advanced Earth system models, impact assessments, and observations. This Generation-1, Realization-1 Collection gives access to 'Future Projection' data based on the 'IFS-NEMO' model."},{"type":"Collection","title":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Storyline Simulation Present Climate - IFS-FESOM - Generation-1 - Realization-1","id":"EO.ECMWF.DAT.D1.DT_CLIMATE.G1.STORY-NUDGING_CONT_IFS-FESOM.R1","description":"Note: This Generation 1 Collection has been superseded by Generation 2 Simulation-level Collections\n\nThe DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities by providing innovative climate information on multi-decadal timescales, globally, at scales at which many impacts of climate change are observed. It combines cutting-edge global Earth-system models, impact-sector applications and observations into a unified framework to provide global climate projections and impact-sector information on multi-decadal timescales (1990 to ~2050), at very high spatial resolutions (5 to 10 km).\n\nThe Climate DT represents the first ever attempt to operationalise the production of global multi-decadal climate projections, leveraging the world-leading supercomputing facilities of the EuroHPC Joint Undertaking along with some of the leading European climate models. A concise overview of what the Climate DT aims to achieve, and of the different concepts essential for an understanding of the Digital Twin’s characteristics, is included in the [Climate DT factsheet](https://destine.ecmwf.int/wp-content/uploads/2024/06/2024.06.07_Climate-DT-Fact-Sheet_V7-2.pdf)\n\n## Storyline Simulation Present Climate \n\n Storyline simulations offer a what-if capability to explore how a weather event we experienced in the recent past would change in a warmer climate and how it would have looked in past pre-industrial conditions. To constrain simulated weather events, a nudging approach is used to keep the simulated large-scale flow close to the ERA5 reanalysis for the period 2017 – 2023. Processes on smaller scales and thermodynamic processes are free to evolve, which allows scientists to study e.g. how extreme precipitation would change in a warmer world for a weather event observed in present-day conditions. Initial conditions are taken from ocean-only spinup simulations for 1950 and 2017, and from the IFS-FESOM projection for a +2 °C world. Storyline simulations will be updated regularly to include most recent months. The next update will extend all storylines to include 2024.\n\n## Models\n\nThe Climate DT exploits and further evolves a new generation of global storm-resolving and eddy-rich models built through a cooperative model development approach. For more information on models please click [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#models)\n\n## Simulations\n\nThe Climate DT team carries out several types of digital twin simulations on the EuroHPC supercomputers.  Multi-decadal simulations are produced to cover the recent past (from 1990) and possible future evolutions of the climate up to 2050. See [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#simulations) for more information on Simulations\n\n## Parameters\n\nBelow we see the list of parameters extracted from the 'DestinE Climate DT data portfolio', for more information please refer to the page [Climate DT Phase 1 CLTE Parameters](https://confluence.ecmwf.int/display/DDCZ/Climate+DT+Phase+1+data+catalogue)","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G1.STORY-NUDGING_CONT_IFS-FESOM.R1/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G1.STORY-NUDGING_CONT_IFS-FESOM.R1/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G1.STORY-NUDGING_CONT_IFS-FESOM.R1/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G1.STORY-NUDGING_CONT_IFS-FESOM.R1","title":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Storyline Simulation Present Climate - IFS-FESOM - Generation-1 - Realization-1"},{"rel":"example","type":"application/x-ipynb+json","href":"https://raw.githubusercontent.com/destination-earth/DestinE-DataLake-Lab/refs/heads/main/HDA/DestinE%20Digital%20Twins/DEDL-HDA-EO.ECMWF.DAT.DT_CLIMATE.ipynb","title":"Destination Earth - Climate DT Parameter - Data Access using DEDL HDA","application:type":"jupyter-notebook","application:embedded":true,"application:language":"Python"},{"rel":"example","type":"application/x-ipynb+json","href":"https://raw.githubusercontent.com/destination-earth/DestinE-DataLake-Lab/refs/heads/main/HDA/DestinE%20Digital%20Twins/ClimateDT-ParameterPlotter.ipynb","title":"Destination Earth - HDA Climate DT Parameter Plotter Tutorial","application:type":"jupyter-notebook","application:embedded":true,"application:language":"Python"},{"rel":"example","type":"application/x-ipynb+json","href":"https://raw.githubusercontent.com/destination-earth/DestinE-DataLake-Lab/refs/heads/main/HDA/DestinE%20Digital%20Twins/DEDL-HDA-EO.ECMWF.DAT.DT_CLIMATE-Series.ipynb","title":"Destination Earth - Climate DT Parameter Series Plot- Data Access using DEDL HDA","application:type":"jupyter-notebook","application:embedded":true,"application:language":"Python"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Parameters","title":"DestinE ClimateDT Parameters"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/Climate+DT+Phase+1+data+catalogue","title":"Climate DT Phase 1 data catalogue"},{"rel":"cite-as","type":"text/html","href":"https://dl.acm.org/doi/abs/10.1109/MCSE.2023.3260519","title":"Destination Earth: High-Performance Computing for Weather and Climate"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.21957/d3f982672e","title":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"assets":{"thumbnail":{"href":"https://s3.central.data.destination-earth.eu/swift/v1/dedl-public/collections/climate-dt-min.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2017-01-01T00:00:00Z","2024-11-15T23:59:59Z"]]}},"license":"CC-BY-4.0","keywords":["Digital Twins","Climate Change","Atmosphere","Ocean","Land","Sea Ice","Snow","Soil","Earth","High Performance Computing","Decision Making","Europe","Meteorology","class:d1","dataset:climate-dt","generation:1","expver:0001","stream:clte","type:fc","activity:story-nudging","experiment:cont","realization:1","model:IFS-FESOM","resolution:standard,high","levtype:hl,o2d,o3d,pl,sfc,sol"],"summaries":{"federation:backends":["dedt_lumi"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/datacube/v2.1.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json","https://stac-extensions.github.io/application/v0.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["producer","processor","licensor"],"url":"https://www.ecmwf.int/"}],"created":"2024-04-11T22:31:55Z","updated":"2026-04-27T14:04:20Z","published":"2024-04-11T22:31:55Z","cube:dimensions":{"lat":{"axis":"y","description":"latitude","extent":[-90,90],"type":"spatial"},"lon":{"axis":"x","description":"longitude","extent":[-180,180],"type":"spatial"},"time":{"extent":["2017-01-01T00:00:00Z","2024-11-15T23:59:59Z"],"step":"P0Y0M0DT1H0M0S","type":"temporal"}},"cube:variables":{"100_metre_U_wind_component(clte_hl)":{"attrs":{"encoding":"instantaneous","levelist":"100","levtype":"hl","long_name":"100 metre U wind component","parameter_ID":"228246","product_type":"forecast","shortName":"100u","standard_name":"100_metre_U_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228246"},"description":"This parameter is the eastward component of the 100 m wind. It is the horizontal speed of air moving towards the east, at a height of 100 metres above the surface of the Earth, in metres per second.Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over amodel grid box and model time step.This parameter can be combined with the northward component to give the speed and direction of the horizontal 100 m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"100_metre_V_wind_component(clte_hl)":{"attrs":{"encoding":"instantaneous","levelist":"100","levtype":"hl","long_name":"100 metre V wind component","parameter_ID":"228247","product_type":"forecast","shortName":"100v","standard_name":"100_metre_V_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228247"},"description":"This parameter is the northward component of the 100 m wind. It is the horizontal speed of air moving towards the north, at a height of 100 metres above the surface of the Earth, in metres per second.Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over amodel grid box and model time step.This parameter can be combined with the eastward component to give the speed and direction of the horizontal 100 m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_U_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"10 metre U wind component","parameter_ID":"165","product_type":"forecast","shortName":"10u","standard_name":"10_metre_U_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/165"},"description":"This parameter is the eastward component of the 10m wind. It is the horizontal speed of air moving towards the east, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the V component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_V_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"10 metre V wind component","parameter_ID":"166","product_type":"forecast","shortName":"10v","standard_name":"10_metre_V_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/166"},"description":"This parameter is the northward component of the 10m wind. It is the horizontal speed of air moving towards the north, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the U component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"2_metre_dewpoint_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"2 metre dewpoint temperature","parameter_ID":"168","product_type":"forecast","shortName":"2d","standard_name":"2_metre_dewpoint_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/168"},"description":"This parameter is the temperature to which the air, at 2 metres above the surface of the Earth, would have to be cooled for saturation to occur.It is a measure of the humidity of the air. Combined with temperature and pressure, it can be used to calculate the relative humidity.2m dew point temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"2_metre_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"2 metre temperature","parameter_ID":"167","product_type":"forecast","shortName":"2t","standard_name":"2_metre_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/167"},"description":"This parameter is the temperature of air at 2m above the surface of land, sea or in-land waters.2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.Please note that the encodings listed here for s2s \u0026 uerra (which includes encodings for carra/cerra) include entries for Mean 2 metre temperature. The specific encoding for Mean 2 metre temperature can be found in 228004.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Boundary_layer_height(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Boundary layer height","parameter_ID":"159","product_type":"forecast","shortName":"blh","standard_name":"Boundary_layer_height","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/159"},"description":"This parameter is the depth of air next to the Earth's surface which is most affected by the resistance to the transfer of momentum, heat or moisture across the surface.The boundary layer height can be as low as a few tens of metres, such as in cooling air at night, or as high as several kilometres over the desert in the middle of a hot sunny day. When the boundary layer height is low, higher concentrations of pollutants (emitted from the Earth's surface) can develop.The boundary layer height calculation is based on the bulk Richardson number (a measure of the atmospheric conditions) following the conclusions of a 2012 review.See further information.","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Charnock(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Charnock","parameter_ID":"148","product_type":"forecast","shortName":"chnk","standard_name":"Charnock","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/148"},"description":"This parameter accounts for increased aerodynamic roughness as wave heights grow due to increasing surface stress. It depends on the wind speed, wave age and other aspects of the sea state and is used to calculate how much the waves slow down the wind.When the atmospheric model is run without the ocean model, this parameter has a constant value of 0.018. When the atmospheric model is coupled to the ocean model, this parameter is calculated by theECMWF Wave Model.","dimensions":["lat","lon","time"],"type":"data","unit":"Numeric"},"Evaporation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Evaporation","parameter_ID":"182","product_type":"forecast","shortName":"e","standard_name":"Evaporation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/182"},"description":"This parameter is the accumulated amount of water that has evaporated from the Earth's surface, including a simplified representation of transpiration (from vegetation), into vapour in the air above.This parameter is accumulated over aparticular time period which depends on the data extracted.The ECMWF Integrated Forecasting System convention is that downward fluxes are positive. Therefore, negative values indicate evaporation and positive values indicate condensation.[NOTE: See 260259 for the equivalent parameter in \"kg m-2\"]","dimensions":["lat","lon","time"],"type":"data","unit":"m of water equivalent"},"Geopotential(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Geopotential","parameter_ID":"129","product_type":"forecast","shortName":"z","standard_name":"Geopotential","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/129"},"description":"This parameter is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level.The geopotential height can be calculated by dividing the geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2). The geopotential height plays an important role in synoptic meteorology (analysis of weather patterns). Charts of geopotential height plotted at constant pressure levels (e.g., 300, 500 or 850 hPa) can be used to identify weather systems such as cyclones, anticyclones, troughs and ridges.At the surface of the Earth, this parameter shows the variations in geopotential (height) of the surface, and is often referred to as the orography.","dimensions":["lat","lon","time"],"type":"data","unit":"m2s-2"},"Geopotential(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Geopotential","parameter_ID":"129","product_type":"forecast","shortName":"z","standard_name":"Geopotential","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/129"},"description":"This parameter is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level.The geopotential height can be calculated by dividing the geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2). The geopotential height plays an important role in synoptic meteorology (analysis of weather patterns). Charts of geopotential height plotted at constant pressure levels (e.g., 300, 500 or 850 hPa) can be used to identify weather systems such as cyclones, anticyclones, troughs and ridges.At the surface of the Earth, this parameter shows the variations in geopotential (height) of the surface, and is often referred to as the orography.","dimensions":["lat","lon","time"],"type":"data","unit":"m2s-2"},"High_cloud_cover(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"High cloud cover","parameter_ID":"188","product_type":"forecast","shortName":"hcc","standard_name":"High_cloud_cover","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/188"},"description":"The proportion of agrid boxcovered by cloud occurring in the high levels of the troposphere. High cloud is a single level field calculated from cloud occurring on model levels with a pressure less than 0.45 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), high cloud would be calculated using levels with a pressure of less than 450 hPa (approximately 6km and above (assuming a `standard atmosphere`)).The high cloud cover parameter is calculated from cloud for the appropriate model levels as described above. Assumptions are made about the degree of overlap/randomness between clouds in different model levels.Cloud fractions vary from 0 to 1.[NOTE: See 3075 for the equivalent parameter in \"%\"]","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Land-sea_mask(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Land-sea mask","parameter_ID":"172","product_type":"forecast","shortName":"lsm","standard_name":"Land-sea_mask","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/172"},"description":"This parameter is the proportion of land, as opposed to ocean or inland waters (lakes, reservoirs, rivers and coastal waters), in agrid box.This parameter has values ranging between zero and one and is dimensionless.In cycles of the ECMWF Integrated Forecasting System (IFS) from CY41R1 (introduced in May 2015) onwards, grid boxes where this parameter has a value above 0.5 can be comprised of a mixture of land and inland water but not ocean. Grid boxes with a value of 0.5 and below can only be comprised of a water surface. In the latter case, the lake cover is used to determine how much of the water surface is ocean or inland water.In cycles of the IFS before CY41R1, grid boxes where this parameter has a value above 0.5 can only be comprised of land and those grid boxes with a value of 0.5 and below can only be comprised of ocean. In these older model cycles, there is no differentiation between ocean and inland water.","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Low_cloud_cover(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Low cloud cover","parameter_ID":"186","product_type":"forecast","shortName":"lcc","standard_name":"Low_cloud_cover","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/186"},"description":"This parameter is the proportion of agrid boxcovered by cloud occurring in the lower levels of the troposphere. Low cloud is a single level field calculated from cloud occurring on model levels with a pressure greater than 0.8 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), low cloud would be calculated using levels with a pressure greater than 800 hPa (below approximately 2km (assuming a 'standard atmosphere')).The low cloud cover parameter is calculated from cloud cover for the appropriate model levels as described above. Assumptions are made about the degree of overlap/randomness between clouds in different model levels.Cloud fractions vary from 0 to 1.[NOTE: See 3073 for the equivalent parameter in \"%\"]","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Mean_sea_level_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Mean sea level pressure","parameter_ID":"151","product_type":"forecast","shortName":"msl","standard_name":"Mean_sea_level_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/151"},"description":"This parameter is the pressure (force per unit area) of the atmosphere adjusted to the height of mean sea level.It is a measure of the weight that all the air in a column vertically above the area of Earth's surface would have at that point, if the point were located at the mean sea level. It is calculated over all surfaces - land, sea and in-land water.Maps of mean sea level pressure are used to identify the locations of low and high pressure systems, often referred to as cyclones and anticyclones. Contours of mean sea level pressure also indicate the strength of the wind. Tightly packed contours show stronger winds.The units of this parameter are pascals (Pa).  Mean sea level pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb = 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Medium_cloud_cover(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Medium cloud cover","parameter_ID":"187","product_type":"forecast","shortName":"mcc","standard_name":"Medium_cloud_cover","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/187"},"description":"This parameter is the proportion of agrid boxcovered by cloud occurring in the middle levels of the troposphere. Medium cloud is a single level field calculated from cloud occurring on model levels with a pressure between 0.45 and 0.8 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), medium cloud would be calculated using levels with a pressure of less than or equal to 800 hPa and greater than or equal to 450 hPa (between approximately 2km and 6km (assuming a 'standard atmosphere')).The medium cloud parameter is calculated from cloud cover for the appropriate model levels as described above. Assumptions are made about the degree of overlap/randomness between clouds in different model levels.Cloud fractions vary from 0 to 1.[NOTE: See 3074 for the equivalent parameter in \"%\"]","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Potential_vorticity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Potential vorticity","parameter_ID":"60","product_type":"forecast","shortName":"pv","standard_name":"Potential_vorticity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/60"},"description":"Potential vorticity is a measure of the capacity for air to rotate in the atmosphere. If we ignore the effects of heating and friction, potential vorticity is conserved following an air parcel. It is used to look for places where large wind storms are likely to originate and develop.  Potential vorticity increases strongly above the tropopause and therefore, it can also be used in studies related to the stratosphere and stratosphere-troposphere exchanges.Large wind storms develop when a column of air in the atmosphere starts to rotate. Potential vorticity is calculated from the wind, temperature and pressure across a column of air in the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K m2kg-1s-1"},"Relative_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Relative humidity","parameter_ID":"157","product_type":"forecast","shortName":"r","standard_name":"Relative_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/157"},"description":"This parameter is the water vapour pressure as a percentage of the value at which the air becomes saturated (the point at which water vapour begins to condense into liquid water or deposition into ice).For temperatures over 0°C (273.15 K) it is calculated for saturation over water. At temperatures below -23°C it is calculated for saturation over ice.  Between -23°C and 0°C this parameter is calculated by interpolating between the ice and water values using a quadratic function.See more information about the model's relative humidity calculation.","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Skin_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Skin temperature","parameter_ID":"235","product_type":"forecast","shortName":"skt","standard_name":"Skin_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235"},"description":"This parameter is the temperature of the surface of the Earth.The skin temperature is the theoretical temperature that is required to satisfy the surface energy balance. It represents the temperature of the uppermost surface layer, which has no heat capacity and so can respond instantaneously to changes in surface fluxes. Skin temperature is calculated differently over land and sea.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.See further information about the skin temperatureover landandover sea.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean skin temperature. The specific encoding for Mean skin temperature can be found in 235079.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Snow_depth(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Snow depth","parameter_ID":"141","product_type":"forecast","shortName":"sd","standard_name":"Snow_depth","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/141"},"description":"This parameter is the depth of snow from the snow-covered area of agrid box.Its units are metres of water equivalent, so it is the depth the water would have if the snow melted and was spread evenly over the whole grid box. The ECMWF Integrated Forecast System represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box.See further information.\n\n[NOTE: See 228141 for the equivalent parameter in \"kg m-2\"]","dimensions":["lat","lon","time"],"type":"data","unit":"m of water equivalent"},"Snow_depth_water_equivalent(clte_sol)":{"attrs":{"encoding":"instantaneous","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Snow depth water equivalent","parameter_ID":"228141","product_type":"forecast","shortName":"sd","standard_name":"Snow_depth_water_equivalent","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228141"},"description":"Snow depth water equivalent in kg m**-2 (mm) water equivalent.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean snow depth water equivalent. The specific encoding for Time-mean snow depth water equivalent can be found in 235078.[NOTE: See 141 for the equivalent parameter in \"m of water equivalent\"]","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Snowfall(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Snowfall","parameter_ID":"144","product_type":"forecast","shortName":"sf","standard_name":"Snowfall","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/144"},"description":"This parameter is the accumulated snow that falls to the Earth's surface. It is the sum of large-scale snowfall and convective snowfall. Large-scale snowfall is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of thegrid boxor larger. Convective snowfall is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box.See further information.This parameter is the total amount of wateraccumulated over a particular time period which depends on the data extracted. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box.Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box.[NOTE: See 228144 for the equivalent parameter in \"kg m-2\"]","dimensions":["lat","lon","time"],"type":"data","unit":"m of water equivalent"},"Specific_cloud_liquid_water_content(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific cloud liquid water content","parameter_ID":"246","product_type":"forecast","shortName":"clwc","standard_name":"Specific_cloud_liquid_water_content","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/246"},"description":"This parameter is the mass of cloud liquid water droplets per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for agrid box.Water within clouds can be liquid or ice, or a combination of the two.See further information about the cloud formulation.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Specific_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific humidity","parameter_ID":"133","product_type":"forecast","shortName":"q","standard_name":"Specific_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/133"},"description":"This parameter is the mass of water vapour per kilogram of moist air.The total mass of moist air is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Sub-surface_runoff(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Sub-surface runoff","parameter_ID":"9","product_type":"forecast","shortName":"ssro","standard_name":"Sub-surface_runoff","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/9"},"description":"Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is simply called 'runoff'. This parameter is the total amount of water accumulated over aparticular time period which depends on the data extracted.The units of runoff are depth in metres.  This is the depth the water would have if it were spread evenly over thegrid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid square area.  Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here.Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood.  More information about how runoff is calculated is given in theIFS Physical Processes documentation.\n\n[NOTE: See 231012 for the equivalent parameter in \"kg m-2\"]","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Surface_long-wave_(thermal)_radiation_downwards(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Surface long-wave (thermal) radiation downwards","parameter_ID":"175","product_type":"forecast","shortName":"strd","standard_name":"Surface_long-wave_(thermal)_radiation_downwards","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/175"},"description":"This parameter is the amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere and clouds that reaches a horizontal plane at the surface of the Earth.The surface of the Earth emits thermal radiation, some of which is absorbed by the atmosphere and clouds. The atmosphere and clouds likewise emit thermal radiation in all directions, some of which reaches the surface (represented by this parameter).See further documentation.This parameter isaccumulated over a particular time period which depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Surface_net_long-wave_(thermal)_radiation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Surface net long-wave (thermal) radiation","parameter_ID":"177","product_type":"forecast","shortName":"str","standard_name":"Surface_net_long-wave_(thermal)_radiation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/177"},"description":"Thermal radiation (also known as longwave or terrestrial radiation) refers to radiation emitted by the atmosphere, clouds and the surface of the Earth. This parameter is the difference between downward and upward thermal radiation at the surface of the Earth. It the amount passing through a horizontal plane.The atmosphere and clouds emit thermal radiation in all directions, some of which reaches the surface as downward thermal radiation. The upward thermal radiation at the surface consists of thermal radiation emitted by the surface plus the fraction of downwards thermal radiation reflected upward by the surface.See further documentation.This parameter isaccumulated over a particular time periodwhich depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds.The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Surface_net_short-wave_(solar)_radiation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Surface net short-wave (solar) radiation","parameter_ID":"176","product_type":"forecast","shortName":"ssr","standard_name":"Surface_net_short-wave_(solar)_radiation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/176"},"description":"This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo).Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The remainder is incident on the Earth's surface, where some of it is reflected.See further documentation.This parameter isaccumulated over a particular time period which depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Surface_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Surface pressure","parameter_ID":"134","product_type":"forecast","shortName":"sp","standard_name":"Surface_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/134"},"description":"This parameter is the pressure (force per unit area) of the atmosphere on the surface of land, sea and in-land water.It is a measure of the weight of all the air in a column vertically above the area of the Earth's surface represented at a fixed point.Surface pressure is often used in combination with temperature to calculate air density.The strong variation of pressure with altitude makes it difficult to see the low and high pressure systems over mountainous areas, so mean sea level pressure, rather than surface pressure, is normally used for this purpose.The units of this parameter are Pascals (Pa). Surface pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb= 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Surface_runoff(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Surface runoff","parameter_ID":"8","product_type":"forecast","shortName":"sro","standard_name":"Surface_runoff","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/8"},"description":"Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is simply called 'runoff'. This parameter is the total amount of water accumulated over aparticular time period which depends on the data extracted.The units of runoff are depth in metres.  This is the depth the water would have if it were spread evenly over thegrid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid square area.  Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here.Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood.  More information about how runoff is calculated is given in theIFS Physical Processes documentation.\n\n[NOTE: See 231010 for the equivalent parameter in \"kg m-2\"]","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Surface_short-wave_(solar)_radiation_downwards(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Surface short-wave (solar) radiation downwards","parameter_ID":"169","product_type":"forecast","shortName":"ssrd","standard_name":"Surface_short-wave_(solar)_radiation_downwards","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/169"},"description":"This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth. This parameter comprises both direct and diffuse solar radiation.Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface (represented by this parameter).See further documentation.To a reasonably good approximation, this parameter is the model equivalent of what would be measured by a pyranometer (an instrument used for measuring solar radiation) at the surface. However, care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over amodel grid box.This parameter isaccumulated over a particular time period which depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"TOA_incident_short-wave_(solar)_radiation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"TOA incident short-wave (solar) radiation","parameter_ID":"212","product_type":"forecast","shortName":"tisr","standard_name":"TOA_incident_short-wave_(solar)_radiation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/212"},"description":"Accumulated field","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Temperature(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Temperature","parameter_ID":"130","product_type":"forecast","shortName":"t","standard_name":"Temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/130"},"description":"This parameter is the temperature in the atmosphere.It has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.This parameter is available on multiple levels through the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-integrated_eastward_turbulent_surface_stress(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Time-integrated eastward turbulent surface stress","parameter_ID":"180","product_type":"forecast","shortName":"ewss","standard_name":"Time-integrated_eastward_turbulent_surface_stress","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/180"},"description":"Air flowing over a surface exerts a stress that transfers momentum to the surface and slows the wind. This parameter is the accumulated stress on the Earth's surface in the eastward direction due to both the turbulent interactions between the atmosphere and the surface, and to turbulent orographic form drag. The turbulent interactions between the atmosphere and the surface are due to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km being derived from land surface data at about 1 km resolution.See further information.Positive (negative) values denote stress in the eastward (westward) direction.This parameter isaccumulated over a particular time periodwhich depends on the data extracted.","dimensions":["lat","lon","time"],"type":"data","unit":"N m-2s"},"Time-integrated_northward_turbulent_surface_stress(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Time-integrated northward turbulent surface stress","parameter_ID":"181","product_type":"forecast","shortName":"nsss","standard_name":"Time-integrated_northward_turbulent_surface_stress","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/181"},"description":"Air flowing over a surface exerts a stress that transfers momentum to the surface and slows the wind. This parameter is the accumulated stress on the Earth's surface in the northward direction due to both the turbulent interactions between the atmosphere and the surface, and to turbulent orographic form drag.The turbulent interactions between the atmosphere and the surface are due to the roughness of the surface.The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km being derived from land surface data at about 1 km resolution.See further information.Positive (negative) values denote stress in the northward (southward) direction.This parameter isaccumulated over a particular time periodwhich depends on the data extracted.","dimensions":["lat","lon","time"],"type":"data","unit":"N m-2s"},"Time-integrated_surface_latent_heat_net_flux(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Time-integrated surface latent heat net flux","parameter_ID":"147","product_type":"forecast","shortName":"slhf","standard_name":"Time-integrated_surface_latent_heat_net_flux","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/147"},"description":"This parameter is the transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earth's surface and the atmosphere through the effects of turbulent air motion. Evaporation from the Earth's surface represents a transfer of energy from the surface to the atmosphere.See further documentationThis parameter is accumulated over aparticular time period which depends on the data extracted.The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds.The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-integrated_surface_sensible_heat_net_flux(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Time-integrated surface sensible heat net flux","parameter_ID":"146","product_type":"forecast","shortName":"sshf","standard_name":"Time-integrated_surface_sensible_heat_net_flux","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/146"},"description":"This parameter is the transfer of heat between the Earth's surface and the atmosphere through the effects of turbulent air motion (but excluding any heat transfer resulting from condensation or evaporation).The magnitude of the sensible heat flux is governed by the difference in temperature between the surface and the overlying atmosphere, wind speed and the surface roughness. For example, cold air overlying a warm surface would produce a sensible heat flux from the land (or ocean) into the atmosphere.See further documentationThis is a single level parameter and it is accumulated over aparticular time period which depends on the data extracted.The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_eastward_sea_ice_velocity(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean eastward sea ice velocity","parameter_ID":"263003","product_type":"forecast","shortName":"avg_siue","standard_name":"Time-mean_eastward_sea_ice_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263003"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69","levtype":"o3d","long_name":"Time-mean eastward sea water velocity","parameter_ID":"263506","product_type":"forecast","shortName":"avg_uoe","standard_name":"Time-mean_eastward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263506"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_sea_ice_velocity(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean northward sea ice velocity","parameter_ID":"263004","product_type":"forecast","shortName":"avg_sivn","standard_name":"Time-mean_northward_sea_ice_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263004"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69","levtype":"o3d","long_name":"Time-mean northward sea water velocity","parameter_ID":"263505","product_type":"forecast","shortName":"avg_von","standard_name":"Time-mean_northward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263505"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_sea_ice_area_fraction(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea ice area fraction","parameter_ID":"263001","product_type":"forecast","shortName":"avg_siconc","standard_name":"Time-mean_sea_ice_area_fraction","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263001"},"dimensions":["lat","lon","time"],"type":"data","unit":"Fraction"},"Time-mean_sea_ice_thickness(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea ice thickness","parameter_ID":"263000","product_type":"forecast","shortName":"avg_sithick","standard_name":"Time-mean_sea_ice_thickness","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263000"},"dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Time-mean_sea_surface_height(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea surface height","parameter_ID":"263124","product_type":"forecast","shortName":"avg_zos","standard_name":"Time-mean_sea_surface_height","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263124"},"dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Time-mean_sea_surface_practical_salinity(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea surface practical salinity","parameter_ID":"263100","product_type":"forecast","shortName":"avg_sos","standard_name":"Time-mean_sea_surface_practical_salinity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263100"},"dimensions":["lat","lon","time"],"type":"data","unit":"g kg-1"},"Time-mean_sea_surface_temperature(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea surface temperature","parameter_ID":"263101","product_type":"forecast","shortName":"avg_tos","standard_name":"Time-mean_sea_surface_temperature","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263101"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_sea_water_potential_temperature(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69","levtype":"o3d","long_name":"Time-mean sea water potential temperature","parameter_ID":"263501","product_type":"forecast","shortName":"avg_thetao","standard_name":"Time-mean_sea_water_potential_temperature","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263501"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_sea_water_practical_salinity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69","levtype":"o3d","long_name":"Time-mean sea water practical salinity","parameter_ID":"263500","product_type":"forecast","shortName":"avg_so","standard_name":"Time-mean_sea_water_practical_salinity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263500"},"dimensions":["lat","lon","time"],"type":"data","unit":"g kg-1"},"Time-mean_snow_thickness_over_sea_ice(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean snow thickness over sea ice","parameter_ID":"263002","product_type":"forecast","shortName":"avg_sisnthick","standard_name":"Time-mean_snow_thickness_over_sea_ice","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263002"},"dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Time-mean_upward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69","levtype":"o3d","long_name":"Time-mean upward sea water velocity","parameter_ID":"263507","product_type":"forecast","shortName":"avg_wo","standard_name":"Time-mean_upward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263507"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Top_net_long-wave_(thermal)_radiation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Top net long-wave (thermal) radiation","parameter_ID":"179","product_type":"forecast","shortName":"ttr","standard_name":"Top_net_long-wave_(thermal)_radiation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/179"},"description":"The thermal (also known as terrestrial or longwave) radiation emitted to space at the top of the atmosphere is commonly known as the Outgoing Longwave Radiation (OLR). The top net thermal radiation (this parameter) is equal to the negative of OLR.See further documentation.This parameter isaccumulated over a particular time periodwhich depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds.The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Top_net_short-wave_(solar)_radiation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Top net short-wave (solar) radiation","parameter_ID":"178","product_type":"forecast","shortName":"tsr","standard_name":"Top_net_short-wave_(solar)_radiation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/178"},"description":"This parameter is the incoming solar radiation (also known as shortwave radiation) minus the outgoing solar radiation at the top of the atmosphere. It is the amount of radiation passing through a horizontal plane. The incoming solar radiation is the amount received from the Sun. The outgoing solar radiation is the amount reflected and scattered by the Earth's atmosphere and surface.See further documentation.This parameter isaccumulated over a particular time periodwhich depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds.The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Total_cloud_cover(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total cloud cover","parameter_ID":"164","product_type":"forecast","shortName":"tcc","standard_name":"Total_cloud_cover","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/164"},"description":"This parameter is the proportion of agrid boxcovered by cloud. Total cloud cover is a single level field calculated from the cloud occurring at different model levels through the atmosphere. Assumptions are made about the degree of overlap/randomness between clouds at different heights.Cloud fractions vary from 0 to 1.[NOTE: See 228164 for the equivalent parameter in \"%\"]","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Total_column_cloud_ice_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud ice water","parameter_ID":"79","product_type":"forecast","shortName":"tciw","standard_name":"Total_column_cloud_ice_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/79"},"description":"This parameter is the amount of ice contained within clouds in a column extending from the surface of the Earth to the top of the atmosphere. Snow (aggregated ice crystals) is not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The  ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_cloud_liquid_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud liquid water","parameter_ID":"78","product_type":"forecast","shortName":"tclw","standard_name":"Total_column_cloud_liquid_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/78"},"description":"This parameter is the amount of liquid water contained within cloud droplets in a column extending from the surface of the Earth to the top of the atmosphere. Rain water droplets, which are much larger in size (and mass), are not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_vertically-integrated_water_vapour(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column vertically-integrated water vapour","parameter_ID":"137","product_type":"forecast","shortName":"tcwv","standard_name":"Total_column_vertically-integrated_water_vapour","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/137"},"description":"This parameter is the total amount of water vapour in a column extending from the surface of the Earth to the top of the atmosphere.This parameter represents the area averaged value for agrid box.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_precipitation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Total precipitation","parameter_ID":"228","product_type":"forecast","shortName":"tp","standard_name":"Total_precipitation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228"},"description":"This parameter is the accumulated liquid and frozen water, comprising rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation and convective precipitation. Large-scale precipitation is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of thegrid boxor larger. Convective precipitation is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box.See further information.This parameter does not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth.This parameter is the total amount of wateraccumulated over a particular time period which depends on the data extracted. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box.Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box.\n\n[NOTE: See 228228 for the equivalent parameter in \"kg m-2\"]","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Total_precipitation_rate(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total precipitation rate","parameter_ID":"260048","product_type":"forecast","shortName":"tprate","standard_name":"Total_precipitation_rate","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/260048"},"description":"This parameter is the rate of total precipitation,at the specified time.In the ECMWF Integrated Forecasting System (IFS), total precipitation is rain and snow that falls to the Earth's surface.  It is the sum of large-scale precipitation and convective precipitation. Large-scale precipitation is generated by the cloud scheme in the IFS. The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of agrid boxor larger. Convective precipitation is generated by the convection scheme in the IFS. The convection scheme represents convection at spatial scales smaller than the grid box.Seefurther information. Precipitation parameters do not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth.1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm per second.Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box andmodel time step.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"U_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"U component of wind","parameter_ID":"131","product_type":"forecast","shortName":"u","standard_name":"U_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/131"},"description":"This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east, in metres per second. A negative sign thus indicates air movement towards the west.This parameter can be combined with the V component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"V_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"V component of wind","parameter_ID":"132","product_type":"forecast","shortName":"v","standard_name":"V_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/132"},"description":"This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north, in metres per second. A negative sign thus indicates air movement towards the south.This parameter can be combined with the U component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Vertical_velocity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Vertical velocity","parameter_ID":"135","product_type":"forecast","shortName":"w","standard_name":"Vertical_velocity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/135"},"description":"This parameter is the speed of air motion in the upward or downward direction. The ECMWF Integrated Forecasting System (IFS) uses a pressure based vertical co-ordinate system and pressure decreases with height, therefore negative values of vertical velocity indicate upward motion.Vertical velocity can be useful to understand the large-scale dynamics of the atmosphere, including areas of upward motion/ascent (negative values) and downward motion/subsidence (positive values).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa s-1"}},"sci:publications":[{"doi":"10.1109/MCSE.2023.3260519","citation":"N. Wedi et al., Destination Earth: High-Performance Computing for Weather and Climate, in Computing in Science \u0026 Engineering, vol. 24, no. 6, pp. 29-37, Nov.-Dec. 2022,"},{"doi":"10.21957/d3f982672e","citation":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"dedl:short_description":"The Climate Change Adaptation Digital Twin provides global climate projections and sector-specific information over multiple decades at high resolution via a unified framework combining advanced Earth system models, impact assessments, and observations. This Generation-1, Realization-1 Collection gives access to 'Storyline Simulation Present Climate' data based on the 'IFS-FESOM' model."},{"type":"Collection","title":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Storyline Simulation Past Climate - IFS-FESOM - Generation-1 - Realization-1","id":"EO.ECMWF.DAT.D1.DT_CLIMATE.G1.STORY-NUDGING_HIST_IFS-FESOM.R1","description":"Note: This Generation 1 Collection has been superseded by Generation 2 Simulation-level Collections\n\nThe DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities by providing innovative climate information on multi-decadal timescales, globally, at scales at which many impacts of climate change are observed. It combines cutting-edge global Earth-system models, impact-sector applications and observations into a unified framework to provide global climate projections and impact-sector information on multi-decadal timescales (1990 to ~2050), at very high spatial resolutions (5 to 10 km).\n\nThe Climate DT represents the first ever attempt to operationalise the production of global multi-decadal climate projections, leveraging the world-leading supercomputing facilities of the EuroHPC Joint Undertaking along with some of the leading European climate models. A concise overview of what the Climate DT aims to achieve, and of the different concepts essential for an understanding of the Digital Twin’s characteristics, is included in the [Climate DT factsheet](https://destine.ecmwf.int/wp-content/uploads/2024/06/2024.06.07_Climate-DT-Fact-Sheet_V7-2.pdf)\n\n## Storyline Simulation Past Climate \n\n Storyline simulations offer a what-if capability to explore how a weather event we experienced in the recent past would change in a warmer climate and how it would have looked in past pre-industrial conditions. To constrain simulated weather events, a nudging approach is used to keep the simulated large-scale flow close to the ERA5 reanalysis for the period 2017 – 2023. Processes on smaller scales and thermodynamic processes are free to evolve, which allows scientists to study e.g. how extreme precipitation would change in a warmer world for a weather event observed in present-day conditions. Initial conditions are taken from ocean-only spinup simulations for 1950 and 2017, and from the IFS-FESOM projection for a +2 °C world. Storyline simulations will be updated regularly to include most recent months. The next update will extend all storylines to include 2024.\n\n## Models\n\nThe Climate DT exploits and further evolves a new generation of global storm-resolving and eddy-rich models built through a cooperative model development approach. For more information on models please click [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#models)\n\n## Simulations\n\nThe Climate DT team carries out several types of digital twin simulations on the EuroHPC supercomputers.  Multi-decadal simulations are produced to cover the recent past (from 1990) and possible future evolutions of the climate up to 2050. See [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#simulations) for more information on Simulations\n\n## Parameters\n\nBelow we see the list of parameters extracted from the 'DestinE Climate DT data portfolio', for more information please refer to the page [Climate DT Phase 1 CLTE Parameters](https://confluence.ecmwf.int/display/DDCZ/Climate+DT+Phase+1+data+catalogue)","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G1.STORY-NUDGING_HIST_IFS-FESOM.R1/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G1.STORY-NUDGING_HIST_IFS-FESOM.R1/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G1.STORY-NUDGING_HIST_IFS-FESOM.R1/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G1.STORY-NUDGING_HIST_IFS-FESOM.R1","title":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Storyline Simulation Past Climate - IFS-FESOM - Generation-1 - Realization-1"},{"rel":"example","type":"application/x-ipynb+json","href":"https://raw.githubusercontent.com/destination-earth/DestinE-DataLake-Lab/refs/heads/main/HDA/DestinE%20Digital%20Twins/DEDL-HDA-EO.ECMWF.DAT.DT_CLIMATE.ipynb","title":"Destination Earth - Climate DT Parameter - Data Access using DEDL HDA","application:type":"jupyter-notebook","application:embedded":true,"application:language":"Python"},{"rel":"example","type":"application/x-ipynb+json","href":"https://raw.githubusercontent.com/destination-earth/DestinE-DataLake-Lab/refs/heads/main/HDA/DestinE%20Digital%20Twins/ClimateDT-ParameterPlotter.ipynb","title":"Destination Earth - HDA Climate DT Parameter Plotter Tutorial","application:type":"jupyter-notebook","application:embedded":true,"application:language":"Python"},{"rel":"example","type":"application/x-ipynb+json","href":"https://raw.githubusercontent.com/destination-earth/DestinE-DataLake-Lab/refs/heads/main/HDA/DestinE%20Digital%20Twins/DEDL-HDA-EO.ECMWF.DAT.DT_CLIMATE-Series.ipynb","title":"Destination Earth - Climate DT Parameter Series Plot- Data Access using DEDL HDA","application:type":"jupyter-notebook","application:embedded":true,"application:language":"Python"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Parameters","title":"DestinE ClimateDT Parameters"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/Climate+DT+Phase+1+data+catalogue","title":"Climate DT Phase 1 data catalogue"},{"rel":"cite-as","type":"text/html","href":"https://dl.acm.org/doi/abs/10.1109/MCSE.2023.3260519","title":"Destination Earth: High-Performance Computing for Weather and Climate"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.21957/d3f982672e","title":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"assets":{"thumbnail":{"href":"https://s3.central.data.destination-earth.eu/swift/v1/dedl-public/collections/climate-dt-min.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2017-01-01T00:00:00Z","2024-11-15T23:59:59Z"]]}},"license":"CC-BY-4.0","keywords":["Digital Twins","Climate Change","Atmosphere","Ocean","Land","Sea Ice","Snow","Soil","Earth","High Performance Computing","Decision Making","Europe","Meteorology","class:d1","dataset:climate-dt","generation:1","expver:0001","stream:clte","type:fc","activity:story-nudging","experiment:hist","realization:1","model:IFS-FESOM","resolution:standard,high","levtype:hl,o2d,o3d,pl,sfc,sol"],"summaries":{"federation:backends":["dedt_lumi"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/datacube/v2.1.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json","https://stac-extensions.github.io/application/v0.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["producer","processor","licensor"],"url":"https://www.ecmwf.int/"}],"created":"2024-04-11T22:31:55Z","updated":"2026-04-27T14:04:20Z","published":"2024-04-11T22:31:55Z","cube:dimensions":{"lat":{"axis":"y","description":"latitude","extent":[-90,90],"type":"spatial"},"lon":{"axis":"x","description":"longitude","extent":[-180,180],"type":"spatial"},"time":{"extent":["2017-01-01T00:00:00Z","2024-11-15T23:59:59Z"],"step":"P0Y0M0DT1H0M0S","type":"temporal"}},"cube:variables":{"100_metre_U_wind_component(clte_hl)":{"attrs":{"encoding":"instantaneous","levelist":"100","levtype":"hl","long_name":"100 metre U wind component","parameter_ID":"228246","product_type":"forecast","shortName":"100u","standard_name":"100_metre_U_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228246"},"description":"This parameter is the eastward component of the 100 m wind. It is the horizontal speed of air moving towards the east, at a height of 100 metres above the surface of the Earth, in metres per second.Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over amodel grid box and model time step.This parameter can be combined with the northward component to give the speed and direction of the horizontal 100 m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"100_metre_V_wind_component(clte_hl)":{"attrs":{"encoding":"instantaneous","levelist":"100","levtype":"hl","long_name":"100 metre V wind component","parameter_ID":"228247","product_type":"forecast","shortName":"100v","standard_name":"100_metre_V_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228247"},"description":"This parameter is the northward component of the 100 m wind. It is the horizontal speed of air moving towards the north, at a height of 100 metres above the surface of the Earth, in metres per second.Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over amodel grid box and model time step.This parameter can be combined with the eastward component to give the speed and direction of the horizontal 100 m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_U_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"10 metre U wind component","parameter_ID":"165","product_type":"forecast","shortName":"10u","standard_name":"10_metre_U_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/165"},"description":"This parameter is the eastward component of the 10m wind. It is the horizontal speed of air moving towards the east, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the V component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_V_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"10 metre V wind component","parameter_ID":"166","product_type":"forecast","shortName":"10v","standard_name":"10_metre_V_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/166"},"description":"This parameter is the northward component of the 10m wind. It is the horizontal speed of air moving towards the north, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the U component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"2_metre_dewpoint_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"2 metre dewpoint temperature","parameter_ID":"168","product_type":"forecast","shortName":"2d","standard_name":"2_metre_dewpoint_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/168"},"description":"This parameter is the temperature to which the air, at 2 metres above the surface of the Earth, would have to be cooled for saturation to occur.It is a measure of the humidity of the air. Combined with temperature and pressure, it can be used to calculate the relative humidity.2m dew point temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"2_metre_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"2 metre temperature","parameter_ID":"167","product_type":"forecast","shortName":"2t","standard_name":"2_metre_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/167"},"description":"This parameter is the temperature of air at 2m above the surface of land, sea or in-land waters.2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.Please note that the encodings listed here for s2s \u0026 uerra (which includes encodings for carra/cerra) include entries for Mean 2 metre temperature. The specific encoding for Mean 2 metre temperature can be found in 228004.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Boundary_layer_height(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Boundary layer height","parameter_ID":"159","product_type":"forecast","shortName":"blh","standard_name":"Boundary_layer_height","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/159"},"description":"This parameter is the depth of air next to the Earth's surface which is most affected by the resistance to the transfer of momentum, heat or moisture across the surface.The boundary layer height can be as low as a few tens of metres, such as in cooling air at night, or as high as several kilometres over the desert in the middle of a hot sunny day. When the boundary layer height is low, higher concentrations of pollutants (emitted from the Earth's surface) can develop.The boundary layer height calculation is based on the bulk Richardson number (a measure of the atmospheric conditions) following the conclusions of a 2012 review.See further information.","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Charnock(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Charnock","parameter_ID":"148","product_type":"forecast","shortName":"chnk","standard_name":"Charnock","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/148"},"description":"This parameter accounts for increased aerodynamic roughness as wave heights grow due to increasing surface stress. It depends on the wind speed, wave age and other aspects of the sea state and is used to calculate how much the waves slow down the wind.When the atmospheric model is run without the ocean model, this parameter has a constant value of 0.018. When the atmospheric model is coupled to the ocean model, this parameter is calculated by theECMWF Wave Model.","dimensions":["lat","lon","time"],"type":"data","unit":"Numeric"},"Evaporation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Evaporation","parameter_ID":"182","product_type":"forecast","shortName":"e","standard_name":"Evaporation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/182"},"description":"This parameter is the accumulated amount of water that has evaporated from the Earth's surface, including a simplified representation of transpiration (from vegetation), into vapour in the air above.This parameter is accumulated over aparticular time period which depends on the data extracted.The ECMWF Integrated Forecasting System convention is that downward fluxes are positive. Therefore, negative values indicate evaporation and positive values indicate condensation.[NOTE: See 260259 for the equivalent parameter in \"kg m-2\"]","dimensions":["lat","lon","time"],"type":"data","unit":"m of water equivalent"},"Geopotential(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Geopotential","parameter_ID":"129","product_type":"forecast","shortName":"z","standard_name":"Geopotential","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/129"},"description":"This parameter is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level.The geopotential height can be calculated by dividing the geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2). The geopotential height plays an important role in synoptic meteorology (analysis of weather patterns). Charts of geopotential height plotted at constant pressure levels (e.g., 300, 500 or 850 hPa) can be used to identify weather systems such as cyclones, anticyclones, troughs and ridges.At the surface of the Earth, this parameter shows the variations in geopotential (height) of the surface, and is often referred to as the orography.","dimensions":["lat","lon","time"],"type":"data","unit":"m2s-2"},"Geopotential(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Geopotential","parameter_ID":"129","product_type":"forecast","shortName":"z","standard_name":"Geopotential","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/129"},"description":"This parameter is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level.The geopotential height can be calculated by dividing the geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2). The geopotential height plays an important role in synoptic meteorology (analysis of weather patterns). Charts of geopotential height plotted at constant pressure levels (e.g., 300, 500 or 850 hPa) can be used to identify weather systems such as cyclones, anticyclones, troughs and ridges.At the surface of the Earth, this parameter shows the variations in geopotential (height) of the surface, and is often referred to as the orography.","dimensions":["lat","lon","time"],"type":"data","unit":"m2s-2"},"High_cloud_cover(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"High cloud cover","parameter_ID":"188","product_type":"forecast","shortName":"hcc","standard_name":"High_cloud_cover","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/188"},"description":"The proportion of agrid boxcovered by cloud occurring in the high levels of the troposphere. High cloud is a single level field calculated from cloud occurring on model levels with a pressure less than 0.45 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), high cloud would be calculated using levels with a pressure of less than 450 hPa (approximately 6km and above (assuming a `standard atmosphere`)).The high cloud cover parameter is calculated from cloud for the appropriate model levels as described above. Assumptions are made about the degree of overlap/randomness between clouds in different model levels.Cloud fractions vary from 0 to 1.[NOTE: See 3075 for the equivalent parameter in \"%\"]","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Land-sea_mask(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Land-sea mask","parameter_ID":"172","product_type":"forecast","shortName":"lsm","standard_name":"Land-sea_mask","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/172"},"description":"This parameter is the proportion of land, as opposed to ocean or inland waters (lakes, reservoirs, rivers and coastal waters), in agrid box.This parameter has values ranging between zero and one and is dimensionless.In cycles of the ECMWF Integrated Forecasting System (IFS) from CY41R1 (introduced in May 2015) onwards, grid boxes where this parameter has a value above 0.5 can be comprised of a mixture of land and inland water but not ocean. Grid boxes with a value of 0.5 and below can only be comprised of a water surface. In the latter case, the lake cover is used to determine how much of the water surface is ocean or inland water.In cycles of the IFS before CY41R1, grid boxes where this parameter has a value above 0.5 can only be comprised of land and those grid boxes with a value of 0.5 and below can only be comprised of ocean. In these older model cycles, there is no differentiation between ocean and inland water.","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Low_cloud_cover(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Low cloud cover","parameter_ID":"186","product_type":"forecast","shortName":"lcc","standard_name":"Low_cloud_cover","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/186"},"description":"This parameter is the proportion of agrid boxcovered by cloud occurring in the lower levels of the troposphere. Low cloud is a single level field calculated from cloud occurring on model levels with a pressure greater than 0.8 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), low cloud would be calculated using levels with a pressure greater than 800 hPa (below approximately 2km (assuming a 'standard atmosphere')).The low cloud cover parameter is calculated from cloud cover for the appropriate model levels as described above. Assumptions are made about the degree of overlap/randomness between clouds in different model levels.Cloud fractions vary from 0 to 1.[NOTE: See 3073 for the equivalent parameter in \"%\"]","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Mean_sea_level_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Mean sea level pressure","parameter_ID":"151","product_type":"forecast","shortName":"msl","standard_name":"Mean_sea_level_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/151"},"description":"This parameter is the pressure (force per unit area) of the atmosphere adjusted to the height of mean sea level.It is a measure of the weight that all the air in a column vertically above the area of Earth's surface would have at that point, if the point were located at the mean sea level. It is calculated over all surfaces - land, sea and in-land water.Maps of mean sea level pressure are used to identify the locations of low and high pressure systems, often referred to as cyclones and anticyclones. Contours of mean sea level pressure also indicate the strength of the wind. Tightly packed contours show stronger winds.The units of this parameter are pascals (Pa).  Mean sea level pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb = 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Medium_cloud_cover(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Medium cloud cover","parameter_ID":"187","product_type":"forecast","shortName":"mcc","standard_name":"Medium_cloud_cover","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/187"},"description":"This parameter is the proportion of agrid boxcovered by cloud occurring in the middle levels of the troposphere. Medium cloud is a single level field calculated from cloud occurring on model levels with a pressure between 0.45 and 0.8 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), medium cloud would be calculated using levels with a pressure of less than or equal to 800 hPa and greater than or equal to 450 hPa (between approximately 2km and 6km (assuming a 'standard atmosphere')).The medium cloud parameter is calculated from cloud cover for the appropriate model levels as described above. Assumptions are made about the degree of overlap/randomness between clouds in different model levels.Cloud fractions vary from 0 to 1.[NOTE: See 3074 for the equivalent parameter in \"%\"]","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Potential_vorticity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Potential vorticity","parameter_ID":"60","product_type":"forecast","shortName":"pv","standard_name":"Potential_vorticity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/60"},"description":"Potential vorticity is a measure of the capacity for air to rotate in the atmosphere. If we ignore the effects of heating and friction, potential vorticity is conserved following an air parcel. It is used to look for places where large wind storms are likely to originate and develop.  Potential vorticity increases strongly above the tropopause and therefore, it can also be used in studies related to the stratosphere and stratosphere-troposphere exchanges.Large wind storms develop when a column of air in the atmosphere starts to rotate. Potential vorticity is calculated from the wind, temperature and pressure across a column of air in the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K m2kg-1s-1"},"Relative_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Relative humidity","parameter_ID":"157","product_type":"forecast","shortName":"r","standard_name":"Relative_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/157"},"description":"This parameter is the water vapour pressure as a percentage of the value at which the air becomes saturated (the point at which water vapour begins to condense into liquid water or deposition into ice).For temperatures over 0°C (273.15 K) it is calculated for saturation over water. At temperatures below -23°C it is calculated for saturation over ice.  Between -23°C and 0°C this parameter is calculated by interpolating between the ice and water values using a quadratic function.See more information about the model's relative humidity calculation.","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Skin_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Skin temperature","parameter_ID":"235","product_type":"forecast","shortName":"skt","standard_name":"Skin_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235"},"description":"This parameter is the temperature of the surface of the Earth.The skin temperature is the theoretical temperature that is required to satisfy the surface energy balance. It represents the temperature of the uppermost surface layer, which has no heat capacity and so can respond instantaneously to changes in surface fluxes. Skin temperature is calculated differently over land and sea.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.See further information about the skin temperatureover landandover sea.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean skin temperature. The specific encoding for Mean skin temperature can be found in 235079.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Snow_depth(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Snow depth","parameter_ID":"141","product_type":"forecast","shortName":"sd","standard_name":"Snow_depth","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/141"},"description":"This parameter is the depth of snow from the snow-covered area of agrid box.Its units are metres of water equivalent, so it is the depth the water would have if the snow melted and was spread evenly over the whole grid box. The ECMWF Integrated Forecast System represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box.See further information.\n\n[NOTE: See 228141 for the equivalent parameter in \"kg m-2\"]","dimensions":["lat","lon","time"],"type":"data","unit":"m of water equivalent"},"Snow_depth_water_equivalent(clte_sol)":{"attrs":{"encoding":"instantaneous","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Snow depth water equivalent","parameter_ID":"228141","product_type":"forecast","shortName":"sd","standard_name":"Snow_depth_water_equivalent","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228141"},"description":"Snow depth water equivalent in kg m**-2 (mm) water equivalent.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean snow depth water equivalent. The specific encoding for Time-mean snow depth water equivalent can be found in 235078.[NOTE: See 141 for the equivalent parameter in \"m of water equivalent\"]","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Snowfall(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Snowfall","parameter_ID":"144","product_type":"forecast","shortName":"sf","standard_name":"Snowfall","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/144"},"description":"This parameter is the accumulated snow that falls to the Earth's surface. It is the sum of large-scale snowfall and convective snowfall. Large-scale snowfall is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of thegrid boxor larger. Convective snowfall is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box.See further information.This parameter is the total amount of wateraccumulated over a particular time period which depends on the data extracted. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box.Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box.[NOTE: See 228144 for the equivalent parameter in \"kg m-2\"]","dimensions":["lat","lon","time"],"type":"data","unit":"m of water equivalent"},"Specific_cloud_liquid_water_content(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific cloud liquid water content","parameter_ID":"246","product_type":"forecast","shortName":"clwc","standard_name":"Specific_cloud_liquid_water_content","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/246"},"description":"This parameter is the mass of cloud liquid water droplets per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for agrid box.Water within clouds can be liquid or ice, or a combination of the two.See further information about the cloud formulation.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Specific_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific humidity","parameter_ID":"133","product_type":"forecast","shortName":"q","standard_name":"Specific_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/133"},"description":"This parameter is the mass of water vapour per kilogram of moist air.The total mass of moist air is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Sub-surface_runoff(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Sub-surface runoff","parameter_ID":"9","product_type":"forecast","shortName":"ssro","standard_name":"Sub-surface_runoff","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/9"},"description":"Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is simply called 'runoff'. This parameter is the total amount of water accumulated over aparticular time period which depends on the data extracted.The units of runoff are depth in metres.  This is the depth the water would have if it were spread evenly over thegrid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid square area.  Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here.Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood.  More information about how runoff is calculated is given in theIFS Physical Processes documentation.\n\n[NOTE: See 231012 for the equivalent parameter in \"kg m-2\"]","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Surface_long-wave_(thermal)_radiation_downwards(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Surface long-wave (thermal) radiation downwards","parameter_ID":"175","product_type":"forecast","shortName":"strd","standard_name":"Surface_long-wave_(thermal)_radiation_downwards","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/175"},"description":"This parameter is the amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere and clouds that reaches a horizontal plane at the surface of the Earth.The surface of the Earth emits thermal radiation, some of which is absorbed by the atmosphere and clouds. The atmosphere and clouds likewise emit thermal radiation in all directions, some of which reaches the surface (represented by this parameter).See further documentation.This parameter isaccumulated over a particular time period which depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Surface_net_long-wave_(thermal)_radiation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Surface net long-wave (thermal) radiation","parameter_ID":"177","product_type":"forecast","shortName":"str","standard_name":"Surface_net_long-wave_(thermal)_radiation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/177"},"description":"Thermal radiation (also known as longwave or terrestrial radiation) refers to radiation emitted by the atmosphere, clouds and the surface of the Earth. This parameter is the difference between downward and upward thermal radiation at the surface of the Earth. It the amount passing through a horizontal plane.The atmosphere and clouds emit thermal radiation in all directions, some of which reaches the surface as downward thermal radiation. The upward thermal radiation at the surface consists of thermal radiation emitted by the surface plus the fraction of downwards thermal radiation reflected upward by the surface.See further documentation.This parameter isaccumulated over a particular time periodwhich depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds.The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Surface_net_short-wave_(solar)_radiation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Surface net short-wave (solar) radiation","parameter_ID":"176","product_type":"forecast","shortName":"ssr","standard_name":"Surface_net_short-wave_(solar)_radiation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/176"},"description":"This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo).Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The remainder is incident on the Earth's surface, where some of it is reflected.See further documentation.This parameter isaccumulated over a particular time period which depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Surface_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Surface pressure","parameter_ID":"134","product_type":"forecast","shortName":"sp","standard_name":"Surface_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/134"},"description":"This parameter is the pressure (force per unit area) of the atmosphere on the surface of land, sea and in-land water.It is a measure of the weight of all the air in a column vertically above the area of the Earth's surface represented at a fixed point.Surface pressure is often used in combination with temperature to calculate air density.The strong variation of pressure with altitude makes it difficult to see the low and high pressure systems over mountainous areas, so mean sea level pressure, rather than surface pressure, is normally used for this purpose.The units of this parameter are Pascals (Pa). Surface pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb= 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Surface_runoff(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Surface runoff","parameter_ID":"8","product_type":"forecast","shortName":"sro","standard_name":"Surface_runoff","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/8"},"description":"Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is simply called 'runoff'. This parameter is the total amount of water accumulated over aparticular time period which depends on the data extracted.The units of runoff are depth in metres.  This is the depth the water would have if it were spread evenly over thegrid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid square area.  Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here.Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood.  More information about how runoff is calculated is given in theIFS Physical Processes documentation.\n\n[NOTE: See 231010 for the equivalent parameter in \"kg m-2\"]","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Surface_short-wave_(solar)_radiation_downwards(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Surface short-wave (solar) radiation downwards","parameter_ID":"169","product_type":"forecast","shortName":"ssrd","standard_name":"Surface_short-wave_(solar)_radiation_downwards","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/169"},"description":"This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth. This parameter comprises both direct and diffuse solar radiation.Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface (represented by this parameter).See further documentation.To a reasonably good approximation, this parameter is the model equivalent of what would be measured by a pyranometer (an instrument used for measuring solar radiation) at the surface. However, care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over amodel grid box.This parameter isaccumulated over a particular time period which depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"TOA_incident_short-wave_(solar)_radiation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"TOA incident short-wave (solar) radiation","parameter_ID":"212","product_type":"forecast","shortName":"tisr","standard_name":"TOA_incident_short-wave_(solar)_radiation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/212"},"description":"Accumulated field","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Temperature(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Temperature","parameter_ID":"130","product_type":"forecast","shortName":"t","standard_name":"Temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/130"},"description":"This parameter is the temperature in the atmosphere.It has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.This parameter is available on multiple levels through the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-integrated_eastward_turbulent_surface_stress(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Time-integrated eastward turbulent surface stress","parameter_ID":"180","product_type":"forecast","shortName":"ewss","standard_name":"Time-integrated_eastward_turbulent_surface_stress","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/180"},"description":"Air flowing over a surface exerts a stress that transfers momentum to the surface and slows the wind. This parameter is the accumulated stress on the Earth's surface in the eastward direction due to both the turbulent interactions between the atmosphere and the surface, and to turbulent orographic form drag. The turbulent interactions between the atmosphere and the surface are due to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km being derived from land surface data at about 1 km resolution.See further information.Positive (negative) values denote stress in the eastward (westward) direction.This parameter isaccumulated over a particular time periodwhich depends on the data extracted.","dimensions":["lat","lon","time"],"type":"data","unit":"N m-2s"},"Time-integrated_northward_turbulent_surface_stress(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Time-integrated northward turbulent surface stress","parameter_ID":"181","product_type":"forecast","shortName":"nsss","standard_name":"Time-integrated_northward_turbulent_surface_stress","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/181"},"description":"Air flowing over a surface exerts a stress that transfers momentum to the surface and slows the wind. This parameter is the accumulated stress on the Earth's surface in the northward direction due to both the turbulent interactions between the atmosphere and the surface, and to turbulent orographic form drag.The turbulent interactions between the atmosphere and the surface are due to the roughness of the surface.The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km being derived from land surface data at about 1 km resolution.See further information.Positive (negative) values denote stress in the northward (southward) direction.This parameter isaccumulated over a particular time periodwhich depends on the data extracted.","dimensions":["lat","lon","time"],"type":"data","unit":"N m-2s"},"Time-integrated_surface_latent_heat_net_flux(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Time-integrated surface latent heat net flux","parameter_ID":"147","product_type":"forecast","shortName":"slhf","standard_name":"Time-integrated_surface_latent_heat_net_flux","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/147"},"description":"This parameter is the transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earth's surface and the atmosphere through the effects of turbulent air motion. Evaporation from the Earth's surface represents a transfer of energy from the surface to the atmosphere.See further documentationThis parameter is accumulated over aparticular time period which depends on the data extracted.The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds.The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-integrated_surface_sensible_heat_net_flux(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Time-integrated surface sensible heat net flux","parameter_ID":"146","product_type":"forecast","shortName":"sshf","standard_name":"Time-integrated_surface_sensible_heat_net_flux","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/146"},"description":"This parameter is the transfer of heat between the Earth's surface and the atmosphere through the effects of turbulent air motion (but excluding any heat transfer resulting from condensation or evaporation).The magnitude of the sensible heat flux is governed by the difference in temperature between the surface and the overlying atmosphere, wind speed and the surface roughness. For example, cold air overlying a warm surface would produce a sensible heat flux from the land (or ocean) into the atmosphere.See further documentationThis is a single level parameter and it is accumulated over aparticular time period which depends on the data extracted.The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_eastward_sea_ice_velocity(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean eastward sea ice velocity","parameter_ID":"263003","product_type":"forecast","shortName":"avg_siue","standard_name":"Time-mean_eastward_sea_ice_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263003"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69","levtype":"o3d","long_name":"Time-mean eastward sea water velocity","parameter_ID":"263506","product_type":"forecast","shortName":"avg_uoe","standard_name":"Time-mean_eastward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263506"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_sea_ice_velocity(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean northward sea ice velocity","parameter_ID":"263004","product_type":"forecast","shortName":"avg_sivn","standard_name":"Time-mean_northward_sea_ice_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263004"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69","levtype":"o3d","long_name":"Time-mean northward sea water velocity","parameter_ID":"263505","product_type":"forecast","shortName":"avg_von","standard_name":"Time-mean_northward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263505"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_sea_ice_area_fraction(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea ice area fraction","parameter_ID":"263001","product_type":"forecast","shortName":"avg_siconc","standard_name":"Time-mean_sea_ice_area_fraction","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263001"},"dimensions":["lat","lon","time"],"type":"data","unit":"Fraction"},"Time-mean_sea_ice_thickness(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea ice thickness","parameter_ID":"263000","product_type":"forecast","shortName":"avg_sithick","standard_name":"Time-mean_sea_ice_thickness","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263000"},"dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Time-mean_sea_surface_height(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea surface height","parameter_ID":"263124","product_type":"forecast","shortName":"avg_zos","standard_name":"Time-mean_sea_surface_height","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263124"},"dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Time-mean_sea_surface_practical_salinity(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea surface practical salinity","parameter_ID":"263100","product_type":"forecast","shortName":"avg_sos","standard_name":"Time-mean_sea_surface_practical_salinity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263100"},"dimensions":["lat","lon","time"],"type":"data","unit":"g kg-1"},"Time-mean_sea_surface_temperature(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea surface temperature","parameter_ID":"263101","product_type":"forecast","shortName":"avg_tos","standard_name":"Time-mean_sea_surface_temperature","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263101"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_sea_water_potential_temperature(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69","levtype":"o3d","long_name":"Time-mean sea water potential temperature","parameter_ID":"263501","product_type":"forecast","shortName":"avg_thetao","standard_name":"Time-mean_sea_water_potential_temperature","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263501"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_sea_water_practical_salinity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69","levtype":"o3d","long_name":"Time-mean sea water practical salinity","parameter_ID":"263500","product_type":"forecast","shortName":"avg_so","standard_name":"Time-mean_sea_water_practical_salinity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263500"},"dimensions":["lat","lon","time"],"type":"data","unit":"g kg-1"},"Time-mean_snow_thickness_over_sea_ice(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean snow thickness over sea ice","parameter_ID":"263002","product_type":"forecast","shortName":"avg_sisnthick","standard_name":"Time-mean_snow_thickness_over_sea_ice","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263002"},"dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Time-mean_upward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69","levtype":"o3d","long_name":"Time-mean upward sea water velocity","parameter_ID":"263507","product_type":"forecast","shortName":"avg_wo","standard_name":"Time-mean_upward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263507"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Top_net_long-wave_(thermal)_radiation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Top net long-wave (thermal) radiation","parameter_ID":"179","product_type":"forecast","shortName":"ttr","standard_name":"Top_net_long-wave_(thermal)_radiation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/179"},"description":"The thermal (also known as terrestrial or longwave) radiation emitted to space at the top of the atmosphere is commonly known as the Outgoing Longwave Radiation (OLR). The top net thermal radiation (this parameter) is equal to the negative of OLR.See further documentation.This parameter isaccumulated over a particular time periodwhich depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds.The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Top_net_short-wave_(solar)_radiation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Top net short-wave (solar) radiation","parameter_ID":"178","product_type":"forecast","shortName":"tsr","standard_name":"Top_net_short-wave_(solar)_radiation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/178"},"description":"This parameter is the incoming solar radiation (also known as shortwave radiation) minus the outgoing solar radiation at the top of the atmosphere. It is the amount of radiation passing through a horizontal plane. The incoming solar radiation is the amount received from the Sun. The outgoing solar radiation is the amount reflected and scattered by the Earth's atmosphere and surface.See further documentation.This parameter isaccumulated over a particular time periodwhich depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds.The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Total_cloud_cover(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total cloud cover","parameter_ID":"164","product_type":"forecast","shortName":"tcc","standard_name":"Total_cloud_cover","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/164"},"description":"This parameter is the proportion of agrid boxcovered by cloud. Total cloud cover is a single level field calculated from the cloud occurring at different model levels through the atmosphere. Assumptions are made about the degree of overlap/randomness between clouds at different heights.Cloud fractions vary from 0 to 1.[NOTE: See 228164 for the equivalent parameter in \"%\"]","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Total_column_cloud_ice_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud ice water","parameter_ID":"79","product_type":"forecast","shortName":"tciw","standard_name":"Total_column_cloud_ice_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/79"},"description":"This parameter is the amount of ice contained within clouds in a column extending from the surface of the Earth to the top of the atmosphere. Snow (aggregated ice crystals) is not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The  ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_cloud_liquid_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud liquid water","parameter_ID":"78","product_type":"forecast","shortName":"tclw","standard_name":"Total_column_cloud_liquid_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/78"},"description":"This parameter is the amount of liquid water contained within cloud droplets in a column extending from the surface of the Earth to the top of the atmosphere. Rain water droplets, which are much larger in size (and mass), are not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_vertically-integrated_water_vapour(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column vertically-integrated water vapour","parameter_ID":"137","product_type":"forecast","shortName":"tcwv","standard_name":"Total_column_vertically-integrated_water_vapour","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/137"},"description":"This parameter is the total amount of water vapour in a column extending from the surface of the Earth to the top of the atmosphere.This parameter represents the area averaged value for agrid box.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_precipitation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Total precipitation","parameter_ID":"228","product_type":"forecast","shortName":"tp","standard_name":"Total_precipitation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228"},"description":"This parameter is the accumulated liquid and frozen water, comprising rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation and convective precipitation. Large-scale precipitation is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of thegrid boxor larger. Convective precipitation is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box.See further information.This parameter does not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth.This parameter is the total amount of wateraccumulated over a particular time period which depends on the data extracted. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box.Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box.\n\n[NOTE: See 228228 for the equivalent parameter in \"kg m-2\"]","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Total_precipitation_rate(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total precipitation rate","parameter_ID":"260048","product_type":"forecast","shortName":"tprate","standard_name":"Total_precipitation_rate","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/260048"},"description":"This parameter is the rate of total precipitation,at the specified time.In the ECMWF Integrated Forecasting System (IFS), total precipitation is rain and snow that falls to the Earth's surface.  It is the sum of large-scale precipitation and convective precipitation. Large-scale precipitation is generated by the cloud scheme in the IFS. The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of agrid boxor larger. Convective precipitation is generated by the convection scheme in the IFS. The convection scheme represents convection at spatial scales smaller than the grid box.Seefurther information. Precipitation parameters do not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth.1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm per second.Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box andmodel time step.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"U_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"U component of wind","parameter_ID":"131","product_type":"forecast","shortName":"u","standard_name":"U_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/131"},"description":"This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east, in metres per second. A negative sign thus indicates air movement towards the west.This parameter can be combined with the V component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"V_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"V component of wind","parameter_ID":"132","product_type":"forecast","shortName":"v","standard_name":"V_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/132"},"description":"This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north, in metres per second. A negative sign thus indicates air movement towards the south.This parameter can be combined with the U component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Vertical_velocity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Vertical velocity","parameter_ID":"135","product_type":"forecast","shortName":"w","standard_name":"Vertical_velocity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/135"},"description":"This parameter is the speed of air motion in the upward or downward direction. The ECMWF Integrated Forecasting System (IFS) uses a pressure based vertical co-ordinate system and pressure decreases with height, therefore negative values of vertical velocity indicate upward motion.Vertical velocity can be useful to understand the large-scale dynamics of the atmosphere, including areas of upward motion/ascent (negative values) and downward motion/subsidence (positive values).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa s-1"}},"sci:publications":[{"doi":"10.1109/MCSE.2023.3260519","citation":"N. Wedi et al., Destination Earth: High-Performance Computing for Weather and Climate, in Computing in Science \u0026 Engineering, vol. 24, no. 6, pp. 29-37, Nov.-Dec. 2022,"},{"doi":"10.21957/d3f982672e","citation":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"dedl:short_description":"The Climate Change Adaptation Digital Twin provides global climate projections and sector-specific information over multiple decades at high resolution via a unified framework combining advanced Earth system models, impact assessments, and observations. This Generation-1, Realization-1 Collection gives access to 'Storyline Simulation Past Climate' data based on the 'IFS-FESOM' model."},{"type":"Collection","title":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Storyline Simulation Future Climate - IFS-FESOM - Generation-1 - Realization-1","id":"EO.ECMWF.DAT.D1.DT_CLIMATE.G1.STORY-NUDGING_TPLUS2.0K_IFS-FESOM.R1","description":"Note: This Generation 1 Collection has been superseded by Generation 2 Simulation-level Collections\n\nThe DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities by providing innovative climate information on multi-decadal timescales, globally, at scales at which many impacts of climate change are observed. It combines cutting-edge global Earth-system models, impact-sector applications and observations into a unified framework to provide global climate projections and impact-sector information on multi-decadal timescales (1990 to ~2050), at very high spatial resolutions (5 to 10 km).\n\nThe Climate DT represents the first ever attempt to operationalise the production of global multi-decadal climate projections, leveraging the world-leading supercomputing facilities of the EuroHPC Joint Undertaking along with some of the leading European climate models. A concise overview of what the Climate DT aims to achieve, and of the different concepts essential for an understanding of the Digital Twin’s characteristics, is included in the [Climate DT factsheet](https://destine.ecmwf.int/wp-content/uploads/2024/06/2024.06.07_Climate-DT-Fact-Sheet_V7-2.pdf)\n\n## Storyline Simulation Future Climate \n\n Storyline simulations offer a what-if capability to explore how a weather event we experienced in the recent past would change in a warmer climate and how it would have looked in past pre-industrial conditions. To constrain simulated weather events, a nudging approach is used to keep the simulated large-scale flow close to the ERA5 reanalysis for the period 2017 – 2023. Processes on smaller scales and thermodynamic processes are free to evolve, which allows scientists to study e.g. how extreme precipitation would change in a warmer world for a weather event observed in present-day conditions. Initial conditions are taken from ocean-only spinup simulations for 1950 and 2017, and from the IFS-FESOM projection for a +2 °C world. Storyline simulations will be updated regularly to include most recent months. The next update will extend all storylines to include 2024.\n\n## Models\n\nThe Climate DT exploits and further evolves a new generation of global storm-resolving and eddy-rich models built through a cooperative model development approach. For more information on models please click [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#models)\n\n## Simulations\n\nThe Climate DT team carries out several types of digital twin simulations on the EuroHPC supercomputers.  Multi-decadal simulations are produced to cover the recent past (from 1990) and possible future evolutions of the climate up to 2050. See [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#simulations) for more information on Simulations\n\n## Parameters\n\nBelow we see the list of parameters extracted from the 'DestinE Climate DT data portfolio', for more information please refer to the page [Climate DT Phase 1 CLTE Parameters](https://confluence.ecmwf.int/display/DDCZ/Climate+DT+Phase+1+data+catalogue)","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G1.STORY-NUDGING_TPLUS2.0K_IFS-FESOM.R1/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G1.STORY-NUDGING_TPLUS2.0K_IFS-FESOM.R1/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G1.STORY-NUDGING_TPLUS2.0K_IFS-FESOM.R1/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G1.STORY-NUDGING_TPLUS2.0K_IFS-FESOM.R1","title":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Storyline Simulation Future Climate - IFS-FESOM - Generation-1 - Realization-1"},{"rel":"example","type":"application/x-ipynb+json","href":"https://raw.githubusercontent.com/destination-earth/DestinE-DataLake-Lab/refs/heads/main/HDA/DestinE%20Digital%20Twins/DEDL-HDA-EO.ECMWF.DAT.DT_CLIMATE.ipynb","title":"Destination Earth - Climate DT Parameter - Data Access using DEDL HDA","application:type":"jupyter-notebook","application:embedded":true,"application:language":"Python"},{"rel":"example","type":"application/x-ipynb+json","href":"https://raw.githubusercontent.com/destination-earth/DestinE-DataLake-Lab/refs/heads/main/HDA/DestinE%20Digital%20Twins/ClimateDT-ParameterPlotter.ipynb","title":"Destination Earth - HDA Climate DT Parameter Plotter Tutorial","application:type":"jupyter-notebook","application:embedded":true,"application:language":"Python"},{"rel":"example","type":"application/x-ipynb+json","href":"https://raw.githubusercontent.com/destination-earth/DestinE-DataLake-Lab/refs/heads/main/HDA/DestinE%20Digital%20Twins/DEDL-HDA-EO.ECMWF.DAT.DT_CLIMATE-Series.ipynb","title":"Destination Earth - Climate DT Parameter Series Plot- Data Access using DEDL HDA","application:type":"jupyter-notebook","application:embedded":true,"application:language":"Python"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Parameters","title":"DestinE ClimateDT Parameters"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/Climate+DT+Phase+1+data+catalogue","title":"Climate DT Phase 1 data catalogue"},{"rel":"cite-as","type":"text/html","href":"https://dl.acm.org/doi/abs/10.1109/MCSE.2023.3260519","title":"Destination Earth: High-Performance Computing for Weather and Climate"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.21957/d3f982672e","title":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"assets":{"thumbnail":{"href":"https://s3.central.data.destination-earth.eu/swift/v1/dedl-public/collections/climate-dt-min.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2017-01-01T00:00:00Z","2024-11-15T23:59:59Z"]]}},"license":"CC-BY-4.0","keywords":["Digital Twins","Climate Change","Atmosphere","Ocean","Land","Sea Ice","Snow","Soil","Earth","High Performance Computing","Decision Making","Europe","Meteorology","class:d1","dataset:climate-dt","generation:1","expver:0001","stream:clte","type:fc","activity:story-nudging","experiment:Tplus2.0K","realization:1","model:IFS-FESOM","resolution:standard,high","levtype:hl,o2d,o3d,pl,sfc,sol"],"summaries":{"federation:backends":["dedt_lumi"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/datacube/v2.1.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json","https://stac-extensions.github.io/application/v0.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["producer","processor","licensor"],"url":"https://www.ecmwf.int/"}],"created":"2024-04-11T22:31:55Z","updated":"2026-04-27T14:04:20Z","published":"2024-04-11T22:31:55Z","cube:dimensions":{"lat":{"axis":"y","description":"latitude","extent":[-90,90],"type":"spatial"},"lon":{"axis":"x","description":"longitude","extent":[-180,180],"type":"spatial"},"time":{"extent":["2017-01-01T00:00:00Z","2024-11-15T23:59:59Z"],"step":"P0Y0M0DT1H0M0S","type":"temporal"}},"cube:variables":{"100_metre_U_wind_component(clte_hl)":{"attrs":{"encoding":"instantaneous","levelist":"100","levtype":"hl","long_name":"100 metre U wind component","parameter_ID":"228246","product_type":"forecast","shortName":"100u","standard_name":"100_metre_U_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228246"},"description":"This parameter is the eastward component of the 100 m wind. It is the horizontal speed of air moving towards the east, at a height of 100 metres above the surface of the Earth, in metres per second.Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over amodel grid box and model time step.This parameter can be combined with the northward component to give the speed and direction of the horizontal 100 m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"100_metre_V_wind_component(clte_hl)":{"attrs":{"encoding":"instantaneous","levelist":"100","levtype":"hl","long_name":"100 metre V wind component","parameter_ID":"228247","product_type":"forecast","shortName":"100v","standard_name":"100_metre_V_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228247"},"description":"This parameter is the northward component of the 100 m wind. It is the horizontal speed of air moving towards the north, at a height of 100 metres above the surface of the Earth, in metres per second.Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over amodel grid box and model time step.This parameter can be combined with the eastward component to give the speed and direction of the horizontal 100 m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_U_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"10 metre U wind component","parameter_ID":"165","product_type":"forecast","shortName":"10u","standard_name":"10_metre_U_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/165"},"description":"This parameter is the eastward component of the 10m wind. It is the horizontal speed of air moving towards the east, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the V component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_V_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"10 metre V wind component","parameter_ID":"166","product_type":"forecast","shortName":"10v","standard_name":"10_metre_V_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/166"},"description":"This parameter is the northward component of the 10m wind. It is the horizontal speed of air moving towards the north, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the U component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"2_metre_dewpoint_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"2 metre dewpoint temperature","parameter_ID":"168","product_type":"forecast","shortName":"2d","standard_name":"2_metre_dewpoint_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/168"},"description":"This parameter is the temperature to which the air, at 2 metres above the surface of the Earth, would have to be cooled for saturation to occur.It is a measure of the humidity of the air. Combined with temperature and pressure, it can be used to calculate the relative humidity.2m dew point temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"2_metre_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"2 metre temperature","parameter_ID":"167","product_type":"forecast","shortName":"2t","standard_name":"2_metre_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/167"},"description":"This parameter is the temperature of air at 2m above the surface of land, sea or in-land waters.2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.Please note that the encodings listed here for s2s \u0026 uerra (which includes encodings for carra/cerra) include entries for Mean 2 metre temperature. The specific encoding for Mean 2 metre temperature can be found in 228004.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Boundary_layer_height(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Boundary layer height","parameter_ID":"159","product_type":"forecast","shortName":"blh","standard_name":"Boundary_layer_height","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/159"},"description":"This parameter is the depth of air next to the Earth's surface which is most affected by the resistance to the transfer of momentum, heat or moisture across the surface.The boundary layer height can be as low as a few tens of metres, such as in cooling air at night, or as high as several kilometres over the desert in the middle of a hot sunny day. When the boundary layer height is low, higher concentrations of pollutants (emitted from the Earth's surface) can develop.The boundary layer height calculation is based on the bulk Richardson number (a measure of the atmospheric conditions) following the conclusions of a 2012 review.See further information.","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Charnock(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Charnock","parameter_ID":"148","product_type":"forecast","shortName":"chnk","standard_name":"Charnock","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/148"},"description":"This parameter accounts for increased aerodynamic roughness as wave heights grow due to increasing surface stress. It depends on the wind speed, wave age and other aspects of the sea state and is used to calculate how much the waves slow down the wind.When the atmospheric model is run without the ocean model, this parameter has a constant value of 0.018. When the atmospheric model is coupled to the ocean model, this parameter is calculated by theECMWF Wave Model.","dimensions":["lat","lon","time"],"type":"data","unit":"Numeric"},"Evaporation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Evaporation","parameter_ID":"182","product_type":"forecast","shortName":"e","standard_name":"Evaporation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/182"},"description":"This parameter is the accumulated amount of water that has evaporated from the Earth's surface, including a simplified representation of transpiration (from vegetation), into vapour in the air above.This parameter is accumulated over aparticular time period which depends on the data extracted.The ECMWF Integrated Forecasting System convention is that downward fluxes are positive. Therefore, negative values indicate evaporation and positive values indicate condensation.[NOTE: See 260259 for the equivalent parameter in \"kg m-2\"]","dimensions":["lat","lon","time"],"type":"data","unit":"m of water equivalent"},"Geopotential(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Geopotential","parameter_ID":"129","product_type":"forecast","shortName":"z","standard_name":"Geopotential","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/129"},"description":"This parameter is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level.The geopotential height can be calculated by dividing the geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2). The geopotential height plays an important role in synoptic meteorology (analysis of weather patterns). Charts of geopotential height plotted at constant pressure levels (e.g., 300, 500 or 850 hPa) can be used to identify weather systems such as cyclones, anticyclones, troughs and ridges.At the surface of the Earth, this parameter shows the variations in geopotential (height) of the surface, and is often referred to as the orography.","dimensions":["lat","lon","time"],"type":"data","unit":"m2s-2"},"Geopotential(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Geopotential","parameter_ID":"129","product_type":"forecast","shortName":"z","standard_name":"Geopotential","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/129"},"description":"This parameter is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level.The geopotential height can be calculated by dividing the geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2). The geopotential height plays an important role in synoptic meteorology (analysis of weather patterns). Charts of geopotential height plotted at constant pressure levels (e.g., 300, 500 or 850 hPa) can be used to identify weather systems such as cyclones, anticyclones, troughs and ridges.At the surface of the Earth, this parameter shows the variations in geopotential (height) of the surface, and is often referred to as the orography.","dimensions":["lat","lon","time"],"type":"data","unit":"m2s-2"},"High_cloud_cover(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"High cloud cover","parameter_ID":"188","product_type":"forecast","shortName":"hcc","standard_name":"High_cloud_cover","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/188"},"description":"The proportion of agrid boxcovered by cloud occurring in the high levels of the troposphere. High cloud is a single level field calculated from cloud occurring on model levels with a pressure less than 0.45 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), high cloud would be calculated using levels with a pressure of less than 450 hPa (approximately 6km and above (assuming a `standard atmosphere`)).The high cloud cover parameter is calculated from cloud for the appropriate model levels as described above. Assumptions are made about the degree of overlap/randomness between clouds in different model levels.Cloud fractions vary from 0 to 1.[NOTE: See 3075 for the equivalent parameter in \"%\"]","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Land-sea_mask(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Land-sea mask","parameter_ID":"172","product_type":"forecast","shortName":"lsm","standard_name":"Land-sea_mask","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/172"},"description":"This parameter is the proportion of land, as opposed to ocean or inland waters (lakes, reservoirs, rivers and coastal waters), in agrid box.This parameter has values ranging between zero and one and is dimensionless.In cycles of the ECMWF Integrated Forecasting System (IFS) from CY41R1 (introduced in May 2015) onwards, grid boxes where this parameter has a value above 0.5 can be comprised of a mixture of land and inland water but not ocean. Grid boxes with a value of 0.5 and below can only be comprised of a water surface. In the latter case, the lake cover is used to determine how much of the water surface is ocean or inland water.In cycles of the IFS before CY41R1, grid boxes where this parameter has a value above 0.5 can only be comprised of land and those grid boxes with a value of 0.5 and below can only be comprised of ocean. In these older model cycles, there is no differentiation between ocean and inland water.","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Low_cloud_cover(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Low cloud cover","parameter_ID":"186","product_type":"forecast","shortName":"lcc","standard_name":"Low_cloud_cover","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/186"},"description":"This parameter is the proportion of agrid boxcovered by cloud occurring in the lower levels of the troposphere. Low cloud is a single level field calculated from cloud occurring on model levels with a pressure greater than 0.8 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), low cloud would be calculated using levels with a pressure greater than 800 hPa (below approximately 2km (assuming a 'standard atmosphere')).The low cloud cover parameter is calculated from cloud cover for the appropriate model levels as described above. Assumptions are made about the degree of overlap/randomness between clouds in different model levels.Cloud fractions vary from 0 to 1.[NOTE: See 3073 for the equivalent parameter in \"%\"]","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Mean_sea_level_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Mean sea level pressure","parameter_ID":"151","product_type":"forecast","shortName":"msl","standard_name":"Mean_sea_level_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/151"},"description":"This parameter is the pressure (force per unit area) of the atmosphere adjusted to the height of mean sea level.It is a measure of the weight that all the air in a column vertically above the area of Earth's surface would have at that point, if the point were located at the mean sea level. It is calculated over all surfaces - land, sea and in-land water.Maps of mean sea level pressure are used to identify the locations of low and high pressure systems, often referred to as cyclones and anticyclones. Contours of mean sea level pressure also indicate the strength of the wind. Tightly packed contours show stronger winds.The units of this parameter are pascals (Pa).  Mean sea level pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb = 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Medium_cloud_cover(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Medium cloud cover","parameter_ID":"187","product_type":"forecast","shortName":"mcc","standard_name":"Medium_cloud_cover","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/187"},"description":"This parameter is the proportion of agrid boxcovered by cloud occurring in the middle levels of the troposphere. Medium cloud is a single level field calculated from cloud occurring on model levels with a pressure between 0.45 and 0.8 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), medium cloud would be calculated using levels with a pressure of less than or equal to 800 hPa and greater than or equal to 450 hPa (between approximately 2km and 6km (assuming a 'standard atmosphere')).The medium cloud parameter is calculated from cloud cover for the appropriate model levels as described above. Assumptions are made about the degree of overlap/randomness between clouds in different model levels.Cloud fractions vary from 0 to 1.[NOTE: See 3074 for the equivalent parameter in \"%\"]","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Potential_vorticity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Potential vorticity","parameter_ID":"60","product_type":"forecast","shortName":"pv","standard_name":"Potential_vorticity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/60"},"description":"Potential vorticity is a measure of the capacity for air to rotate in the atmosphere. If we ignore the effects of heating and friction, potential vorticity is conserved following an air parcel. It is used to look for places where large wind storms are likely to originate and develop.  Potential vorticity increases strongly above the tropopause and therefore, it can also be used in studies related to the stratosphere and stratosphere-troposphere exchanges.Large wind storms develop when a column of air in the atmosphere starts to rotate. Potential vorticity is calculated from the wind, temperature and pressure across a column of air in the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K m2kg-1s-1"},"Relative_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Relative humidity","parameter_ID":"157","product_type":"forecast","shortName":"r","standard_name":"Relative_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/157"},"description":"This parameter is the water vapour pressure as a percentage of the value at which the air becomes saturated (the point at which water vapour begins to condense into liquid water or deposition into ice).For temperatures over 0°C (273.15 K) it is calculated for saturation over water. At temperatures below -23°C it is calculated for saturation over ice.  Between -23°C and 0°C this parameter is calculated by interpolating between the ice and water values using a quadratic function.See more information about the model's relative humidity calculation.","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Skin_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Skin temperature","parameter_ID":"235","product_type":"forecast","shortName":"skt","standard_name":"Skin_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235"},"description":"This parameter is the temperature of the surface of the Earth.The skin temperature is the theoretical temperature that is required to satisfy the surface energy balance. It represents the temperature of the uppermost surface layer, which has no heat capacity and so can respond instantaneously to changes in surface fluxes. Skin temperature is calculated differently over land and sea.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.See further information about the skin temperatureover landandover sea.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean skin temperature. The specific encoding for Mean skin temperature can be found in 235079.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Snow_depth(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Snow depth","parameter_ID":"141","product_type":"forecast","shortName":"sd","standard_name":"Snow_depth","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/141"},"description":"This parameter is the depth of snow from the snow-covered area of agrid box.Its units are metres of water equivalent, so it is the depth the water would have if the snow melted and was spread evenly over the whole grid box. The ECMWF Integrated Forecast System represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box.See further information.\n\n[NOTE: See 228141 for the equivalent parameter in \"kg m-2\"]","dimensions":["lat","lon","time"],"type":"data","unit":"m of water equivalent"},"Snow_depth_water_equivalent(clte_sol)":{"attrs":{"encoding":"instantaneous","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Snow depth water equivalent","parameter_ID":"228141","product_type":"forecast","shortName":"sd","standard_name":"Snow_depth_water_equivalent","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228141"},"description":"Snow depth water equivalent in kg m**-2 (mm) water equivalent.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean snow depth water equivalent. The specific encoding for Time-mean snow depth water equivalent can be found in 235078.[NOTE: See 141 for the equivalent parameter in \"m of water equivalent\"]","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Snowfall(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Snowfall","parameter_ID":"144","product_type":"forecast","shortName":"sf","standard_name":"Snowfall","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/144"},"description":"This parameter is the accumulated snow that falls to the Earth's surface. It is the sum of large-scale snowfall and convective snowfall. Large-scale snowfall is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of thegrid boxor larger. Convective snowfall is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box.See further information.This parameter is the total amount of wateraccumulated over a particular time period which depends on the data extracted. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box.Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box.[NOTE: See 228144 for the equivalent parameter in \"kg m-2\"]","dimensions":["lat","lon","time"],"type":"data","unit":"m of water equivalent"},"Specific_cloud_liquid_water_content(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific cloud liquid water content","parameter_ID":"246","product_type":"forecast","shortName":"clwc","standard_name":"Specific_cloud_liquid_water_content","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/246"},"description":"This parameter is the mass of cloud liquid water droplets per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for agrid box.Water within clouds can be liquid or ice, or a combination of the two.See further information about the cloud formulation.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Specific_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific humidity","parameter_ID":"133","product_type":"forecast","shortName":"q","standard_name":"Specific_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/133"},"description":"This parameter is the mass of water vapour per kilogram of moist air.The total mass of moist air is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Sub-surface_runoff(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Sub-surface runoff","parameter_ID":"9","product_type":"forecast","shortName":"ssro","standard_name":"Sub-surface_runoff","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/9"},"description":"Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is simply called 'runoff'. This parameter is the total amount of water accumulated over aparticular time period which depends on the data extracted.The units of runoff are depth in metres.  This is the depth the water would have if it were spread evenly over thegrid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid square area.  Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here.Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood.  More information about how runoff is calculated is given in theIFS Physical Processes documentation.\n\n[NOTE: See 231012 for the equivalent parameter in \"kg m-2\"]","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Surface_long-wave_(thermal)_radiation_downwards(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Surface long-wave (thermal) radiation downwards","parameter_ID":"175","product_type":"forecast","shortName":"strd","standard_name":"Surface_long-wave_(thermal)_radiation_downwards","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/175"},"description":"This parameter is the amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere and clouds that reaches a horizontal plane at the surface of the Earth.The surface of the Earth emits thermal radiation, some of which is absorbed by the atmosphere and clouds. The atmosphere and clouds likewise emit thermal radiation in all directions, some of which reaches the surface (represented by this parameter).See further documentation.This parameter isaccumulated over a particular time period which depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Surface_net_long-wave_(thermal)_radiation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Surface net long-wave (thermal) radiation","parameter_ID":"177","product_type":"forecast","shortName":"str","standard_name":"Surface_net_long-wave_(thermal)_radiation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/177"},"description":"Thermal radiation (also known as longwave or terrestrial radiation) refers to radiation emitted by the atmosphere, clouds and the surface of the Earth. This parameter is the difference between downward and upward thermal radiation at the surface of the Earth. It the amount passing through a horizontal plane.The atmosphere and clouds emit thermal radiation in all directions, some of which reaches the surface as downward thermal radiation. The upward thermal radiation at the surface consists of thermal radiation emitted by the surface plus the fraction of downwards thermal radiation reflected upward by the surface.See further documentation.This parameter isaccumulated over a particular time periodwhich depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds.The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Surface_net_short-wave_(solar)_radiation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Surface net short-wave (solar) radiation","parameter_ID":"176","product_type":"forecast","shortName":"ssr","standard_name":"Surface_net_short-wave_(solar)_radiation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/176"},"description":"This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo).Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The remainder is incident on the Earth's surface, where some of it is reflected.See further documentation.This parameter isaccumulated over a particular time period which depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Surface_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Surface pressure","parameter_ID":"134","product_type":"forecast","shortName":"sp","standard_name":"Surface_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/134"},"description":"This parameter is the pressure (force per unit area) of the atmosphere on the surface of land, sea and in-land water.It is a measure of the weight of all the air in a column vertically above the area of the Earth's surface represented at a fixed point.Surface pressure is often used in combination with temperature to calculate air density.The strong variation of pressure with altitude makes it difficult to see the low and high pressure systems over mountainous areas, so mean sea level pressure, rather than surface pressure, is normally used for this purpose.The units of this parameter are Pascals (Pa). Surface pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb= 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Surface_runoff(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Surface runoff","parameter_ID":"8","product_type":"forecast","shortName":"sro","standard_name":"Surface_runoff","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/8"},"description":"Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is simply called 'runoff'. This parameter is the total amount of water accumulated over aparticular time period which depends on the data extracted.The units of runoff are depth in metres.  This is the depth the water would have if it were spread evenly over thegrid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid square area.  Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here.Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood.  More information about how runoff is calculated is given in theIFS Physical Processes documentation.\n\n[NOTE: See 231010 for the equivalent parameter in \"kg m-2\"]","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Surface_short-wave_(solar)_radiation_downwards(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Surface short-wave (solar) radiation downwards","parameter_ID":"169","product_type":"forecast","shortName":"ssrd","standard_name":"Surface_short-wave_(solar)_radiation_downwards","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/169"},"description":"This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth. This parameter comprises both direct and diffuse solar radiation.Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface (represented by this parameter).See further documentation.To a reasonably good approximation, this parameter is the model equivalent of what would be measured by a pyranometer (an instrument used for measuring solar radiation) at the surface. However, care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over amodel grid box.This parameter isaccumulated over a particular time period which depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"TOA_incident_short-wave_(solar)_radiation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"TOA incident short-wave (solar) radiation","parameter_ID":"212","product_type":"forecast","shortName":"tisr","standard_name":"TOA_incident_short-wave_(solar)_radiation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/212"},"description":"Accumulated field","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Temperature(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Temperature","parameter_ID":"130","product_type":"forecast","shortName":"t","standard_name":"Temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/130"},"description":"This parameter is the temperature in the atmosphere.It has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.This parameter is available on multiple levels through the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-integrated_eastward_turbulent_surface_stress(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Time-integrated eastward turbulent surface stress","parameter_ID":"180","product_type":"forecast","shortName":"ewss","standard_name":"Time-integrated_eastward_turbulent_surface_stress","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/180"},"description":"Air flowing over a surface exerts a stress that transfers momentum to the surface and slows the wind. This parameter is the accumulated stress on the Earth's surface in the eastward direction due to both the turbulent interactions between the atmosphere and the surface, and to turbulent orographic form drag. The turbulent interactions between the atmosphere and the surface are due to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km being derived from land surface data at about 1 km resolution.See further information.Positive (negative) values denote stress in the eastward (westward) direction.This parameter isaccumulated over a particular time periodwhich depends on the data extracted.","dimensions":["lat","lon","time"],"type":"data","unit":"N m-2s"},"Time-integrated_northward_turbulent_surface_stress(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Time-integrated northward turbulent surface stress","parameter_ID":"181","product_type":"forecast","shortName":"nsss","standard_name":"Time-integrated_northward_turbulent_surface_stress","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/181"},"description":"Air flowing over a surface exerts a stress that transfers momentum to the surface and slows the wind. This parameter is the accumulated stress on the Earth's surface in the northward direction due to both the turbulent interactions between the atmosphere and the surface, and to turbulent orographic form drag.The turbulent interactions between the atmosphere and the surface are due to the roughness of the surface.The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km being derived from land surface data at about 1 km resolution.See further information.Positive (negative) values denote stress in the northward (southward) direction.This parameter isaccumulated over a particular time periodwhich depends on the data extracted.","dimensions":["lat","lon","time"],"type":"data","unit":"N m-2s"},"Time-integrated_surface_latent_heat_net_flux(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Time-integrated surface latent heat net flux","parameter_ID":"147","product_type":"forecast","shortName":"slhf","standard_name":"Time-integrated_surface_latent_heat_net_flux","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/147"},"description":"This parameter is the transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earth's surface and the atmosphere through the effects of turbulent air motion. Evaporation from the Earth's surface represents a transfer of energy from the surface to the atmosphere.See further documentationThis parameter is accumulated over aparticular time period which depends on the data extracted.The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds.The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-integrated_surface_sensible_heat_net_flux(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Time-integrated surface sensible heat net flux","parameter_ID":"146","product_type":"forecast","shortName":"sshf","standard_name":"Time-integrated_surface_sensible_heat_net_flux","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/146"},"description":"This parameter is the transfer of heat between the Earth's surface and the atmosphere through the effects of turbulent air motion (but excluding any heat transfer resulting from condensation or evaporation).The magnitude of the sensible heat flux is governed by the difference in temperature between the surface and the overlying atmosphere, wind speed and the surface roughness. For example, cold air overlying a warm surface would produce a sensible heat flux from the land (or ocean) into the atmosphere.See further documentationThis is a single level parameter and it is accumulated over aparticular time period which depends on the data extracted.The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_eastward_sea_ice_velocity(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean eastward sea ice velocity","parameter_ID":"263003","product_type":"forecast","shortName":"avg_siue","standard_name":"Time-mean_eastward_sea_ice_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263003"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69","levtype":"o3d","long_name":"Time-mean eastward sea water velocity","parameter_ID":"263506","product_type":"forecast","shortName":"avg_uoe","standard_name":"Time-mean_eastward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263506"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_sea_ice_velocity(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean northward sea ice velocity","parameter_ID":"263004","product_type":"forecast","shortName":"avg_sivn","standard_name":"Time-mean_northward_sea_ice_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263004"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69","levtype":"o3d","long_name":"Time-mean northward sea water velocity","parameter_ID":"263505","product_type":"forecast","shortName":"avg_von","standard_name":"Time-mean_northward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263505"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_sea_ice_area_fraction(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea ice area fraction","parameter_ID":"263001","product_type":"forecast","shortName":"avg_siconc","standard_name":"Time-mean_sea_ice_area_fraction","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263001"},"dimensions":["lat","lon","time"],"type":"data","unit":"Fraction"},"Time-mean_sea_ice_thickness(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea ice thickness","parameter_ID":"263000","product_type":"forecast","shortName":"avg_sithick","standard_name":"Time-mean_sea_ice_thickness","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263000"},"dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Time-mean_sea_surface_height(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea surface height","parameter_ID":"263124","product_type":"forecast","shortName":"avg_zos","standard_name":"Time-mean_sea_surface_height","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263124"},"dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Time-mean_sea_surface_practical_salinity(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea surface practical salinity","parameter_ID":"263100","product_type":"forecast","shortName":"avg_sos","standard_name":"Time-mean_sea_surface_practical_salinity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263100"},"dimensions":["lat","lon","time"],"type":"data","unit":"g kg-1"},"Time-mean_sea_surface_temperature(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea surface temperature","parameter_ID":"263101","product_type":"forecast","shortName":"avg_tos","standard_name":"Time-mean_sea_surface_temperature","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263101"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_sea_water_potential_temperature(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69","levtype":"o3d","long_name":"Time-mean sea water potential temperature","parameter_ID":"263501","product_type":"forecast","shortName":"avg_thetao","standard_name":"Time-mean_sea_water_potential_temperature","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263501"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_sea_water_practical_salinity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69","levtype":"o3d","long_name":"Time-mean sea water practical salinity","parameter_ID":"263500","product_type":"forecast","shortName":"avg_so","standard_name":"Time-mean_sea_water_practical_salinity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263500"},"dimensions":["lat","lon","time"],"type":"data","unit":"g kg-1"},"Time-mean_snow_thickness_over_sea_ice(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean snow thickness over sea ice","parameter_ID":"263002","product_type":"forecast","shortName":"avg_sisnthick","standard_name":"Time-mean_snow_thickness_over_sea_ice","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263002"},"dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Time-mean_upward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69","levtype":"o3d","long_name":"Time-mean upward sea water velocity","parameter_ID":"263507","product_type":"forecast","shortName":"avg_wo","standard_name":"Time-mean_upward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263507"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Top_net_long-wave_(thermal)_radiation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Top net long-wave (thermal) radiation","parameter_ID":"179","product_type":"forecast","shortName":"ttr","standard_name":"Top_net_long-wave_(thermal)_radiation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/179"},"description":"The thermal (also known as terrestrial or longwave) radiation emitted to space at the top of the atmosphere is commonly known as the Outgoing Longwave Radiation (OLR). The top net thermal radiation (this parameter) is equal to the negative of OLR.See further documentation.This parameter isaccumulated over a particular time periodwhich depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds.The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Top_net_short-wave_(solar)_radiation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Top net short-wave (solar) radiation","parameter_ID":"178","product_type":"forecast","shortName":"tsr","standard_name":"Top_net_short-wave_(solar)_radiation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/178"},"description":"This parameter is the incoming solar radiation (also known as shortwave radiation) minus the outgoing solar radiation at the top of the atmosphere. It is the amount of radiation passing through a horizontal plane. The incoming solar radiation is the amount received from the Sun. The outgoing solar radiation is the amount reflected and scattered by the Earth's atmosphere and surface.See further documentation.This parameter isaccumulated over a particular time periodwhich depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds.The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Total_cloud_cover(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total cloud cover","parameter_ID":"164","product_type":"forecast","shortName":"tcc","standard_name":"Total_cloud_cover","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/164"},"description":"This parameter is the proportion of agrid boxcovered by cloud. Total cloud cover is a single level field calculated from the cloud occurring at different model levels through the atmosphere. Assumptions are made about the degree of overlap/randomness between clouds at different heights.Cloud fractions vary from 0 to 1.[NOTE: See 228164 for the equivalent parameter in \"%\"]","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Total_column_cloud_ice_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud ice water","parameter_ID":"79","product_type":"forecast","shortName":"tciw","standard_name":"Total_column_cloud_ice_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/79"},"description":"This parameter is the amount of ice contained within clouds in a column extending from the surface of the Earth to the top of the atmosphere. Snow (aggregated ice crystals) is not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The  ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_cloud_liquid_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud liquid water","parameter_ID":"78","product_type":"forecast","shortName":"tclw","standard_name":"Total_column_cloud_liquid_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/78"},"description":"This parameter is the amount of liquid water contained within cloud droplets in a column extending from the surface of the Earth to the top of the atmosphere. Rain water droplets, which are much larger in size (and mass), are not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_vertically-integrated_water_vapour(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column vertically-integrated water vapour","parameter_ID":"137","product_type":"forecast","shortName":"tcwv","standard_name":"Total_column_vertically-integrated_water_vapour","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/137"},"description":"This parameter is the total amount of water vapour in a column extending from the surface of the Earth to the top of the atmosphere.This parameter represents the area averaged value for agrid box.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_precipitation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Total precipitation","parameter_ID":"228","product_type":"forecast","shortName":"tp","standard_name":"Total_precipitation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228"},"description":"This parameter is the accumulated liquid and frozen water, comprising rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation and convective precipitation. Large-scale precipitation is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of thegrid boxor larger. Convective precipitation is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box.See further information.This parameter does not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth.This parameter is the total amount of wateraccumulated over a particular time period which depends on the data extracted. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box.Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box.\n\n[NOTE: See 228228 for the equivalent parameter in \"kg m-2\"]","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Total_precipitation_rate(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total precipitation rate","parameter_ID":"260048","product_type":"forecast","shortName":"tprate","standard_name":"Total_precipitation_rate","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/260048"},"description":"This parameter is the rate of total precipitation,at the specified time.In the ECMWF Integrated Forecasting System (IFS), total precipitation is rain and snow that falls to the Earth's surface.  It is the sum of large-scale precipitation and convective precipitation. Large-scale precipitation is generated by the cloud scheme in the IFS. The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of agrid boxor larger. Convective precipitation is generated by the convection scheme in the IFS. The convection scheme represents convection at spatial scales smaller than the grid box.Seefurther information. Precipitation parameters do not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth.1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm per second.Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box andmodel time step.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"U_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"U component of wind","parameter_ID":"131","product_type":"forecast","shortName":"u","standard_name":"U_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/131"},"description":"This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east, in metres per second. A negative sign thus indicates air movement towards the west.This parameter can be combined with the V component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"V_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"V component of wind","parameter_ID":"132","product_type":"forecast","shortName":"v","standard_name":"V_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/132"},"description":"This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north, in metres per second. A negative sign thus indicates air movement towards the south.This parameter can be combined with the U component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Vertical_velocity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Vertical velocity","parameter_ID":"135","product_type":"forecast","shortName":"w","standard_name":"Vertical_velocity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/135"},"description":"This parameter is the speed of air motion in the upward or downward direction. The ECMWF Integrated Forecasting System (IFS) uses a pressure based vertical co-ordinate system and pressure decreases with height, therefore negative values of vertical velocity indicate upward motion.Vertical velocity can be useful to understand the large-scale dynamics of the atmosphere, including areas of upward motion/ascent (negative values) and downward motion/subsidence (positive values).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa s-1"}},"sci:publications":[{"doi":"10.1109/MCSE.2023.3260519","citation":"N. Wedi et al., Destination Earth: High-Performance Computing for Weather and Climate, in Computing in Science \u0026 Engineering, vol. 24, no. 6, pp. 29-37, Nov.-Dec. 2022,"},{"doi":"10.21957/d3f982672e","citation":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"dedl:short_description":"The Climate Change Adaptation Digital Twin provides global climate projections and sector-specific information over multiple decades at high resolution via a unified framework combining advanced Earth system models, impact assessments, and observations. This Generation-1, Realization-1 Collection gives access to 'Storyline Simulation Future Climate' data based on the 'IFS-FESOM' model."},{"type":"Collection","title":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Control Simulation - ICON - Generation-2 - Realization-1","id":"EO.ECMWF.DAT.D1.DT_CLIMATE.G2.BASELINE_CONT_ICON.R1","description":"The DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities by providing innovative climate information on multi-decadal timescales, globally, at scales at which many impacts of climate change are observed. It combines cutting-edge global Earth-system models, impact-sector applications and observations into a unified framework to provide global climate projections and impact-sector information on multi-decadal timescales (1990 to ~2050), at very high spatial resolutions (5 to 10 km).\n\nThe Climate DT represents the first ever attempt to operationalise the production of global multi-decadal climate projections, leveraging the world-leading supercomputing facilities of the EuroHPC Joint Undertaking along with some of the leading European climate models. A concise overview of what the Climate DT aims to achieve, and of the different concepts essential for an understanding of the Digital Twin’s characteristics, is included in the [Climate DT factsheet](https://destine.ecmwf.int/wp-content/uploads/2024/06/2024.06.07_Climate-DT-Fact-Sheet_V7-2.pdf)\n\n## Control Simulation \n\n Repetitive 1990 forcing with no change in forcing over time. These simulations allow to quantify model drift and simulated inter-annual variability and provides relevant context for interpreting historical and scenario simulations.\n\n## Models\n\nThe Climate DT exploits and further evolves a new generation of global storm-resolving and eddy-rich models built through a cooperative model development approach. For more information on models please click [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#models)\n\n## Simulations\n\nThe Climate DT team carries out several types of digital twin simulations on the EuroHPC supercomputers.  Multi-decadal simulations are produced to cover the recent past (from 1990) and possible future evolutions of the climate up to 2050. See [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#simulations) for more information on Simulations\n\n## Parameters\n\nBelow we see the list of parameters extracted from the 'DestinE Climate DT data portfolio', for more information please refer to the pages [Climate DT Phase2 CLTE Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters) and [Climate DT Phase2 CLMN Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters)","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.BASELINE_CONT_ICON.R1/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.BASELINE_CONT_ICON.R1/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.BASELINE_CONT_ICON.R1/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.BASELINE_CONT_ICON.R1","title":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Control Simulation - ICON - Generation-2 - Realization-1"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters","title":"DestinE ClimateDT Phase 2 CLTE Parameters"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters","title":"DestinE ClimateDT Phase 2 CLMN Parameters"},{"rel":"cite-as","type":"text/html","href":"https://dl.acm.org/doi/abs/10.1109/MCSE.2023.3260519","title":"Destination Earth: High-Performance Computing for Weather and Climate"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.21957/d3f982672e","title":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"assets":{"thumbnail":{"href":"https://s3.central.data.destination-earth.eu/swift/v1/dedl-public/collections/climate-dt-min.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1990-01-01T00:00:00Z","1999-12-31T23:59:59Z"]]}},"license":"CC-BY-4.0","keywords":["Digital Twins","Climate Change","Atmosphere","Ocean","Land","Sea Ice","Snow","Soil","Earth","High Performance Computing","Decision Making","Europe","Meteorology","class:d1","dataset:climate-dt","generation:2","expver:0001","stream:clmn,clte","type:fc","activity:baseline","experiment:cont","realization:1","model:ICON","resolution:standard,high","levtype:o2d,o3d,pl,sfc,sol"],"summaries":{"federation:backends":["dedt_lumi"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/datacube/v2.1.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["producer","processor","licensor"],"url":"https://www.ecmwf.int/"}],"created":"2024-04-11T22:31:55Z","updated":"2026-04-24T10:24:59Z","published":"2024-04-11T22:31:55Z","cube:dimensions":{"lat":{"axis":"y","description":"latitude","extent":[-90,90],"type":"spatial"},"lon":{"axis":"x","description":"longitude","extent":[-180,180],"type":"spatial"},"time":{"extent":["1990-01-01T00:00:00Z","1999-12-31T23:59:59Z"],"step":"P0Y0M0DT1H0M0S","type":"temporal"}},"cube:variables":{"10_metre_U_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre U wind component","parameter_ID":"165","product_type":"forecast","shortName":"10u","standard_name":"10_metre_U_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/165"},"description":"This parameter is the eastward component of the 10m wind. It is the horizontal speed of air moving towards the east, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the V component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_V_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre V wind component","parameter_ID":"166","product_type":"forecast","shortName":"10v","standard_name":"10_metre_V_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/166"},"description":"This parameter is the northward component of the 10m wind. It is the horizontal speed of air moving towards the north, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the U component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_wind_speed(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre wind speed","parameter_ID":"207","product_type":"forecast","shortName":"10si","standard_name":"10_metre_wind_speed","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/207"},"description":"This parameter is the horizontal speed of the wind, or movement of air, at a height of ten metres above the surface of the Earth. The units of this parameter are metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.The eastward and northward components of the horizontal wind at 10m are also available as parameters.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"2_metre_dewpoint_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"2","levtype":"sfc","long_name":"2 metre dewpoint temperature","parameter_ID":"168","product_type":"forecast","shortName":"2d","standard_name":"2_metre_dewpoint_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/168"},"description":"This parameter is the temperature to which the air, at 2 metres above the surface of the Earth, would have to be cooled for saturation to occur.It is a measure of the humidity of the air. Combined with temperature and pressure, it can be used to calculate the relative humidity.2m dew point temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"2_metre_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"2","levtype":"sfc","long_name":"2 metre temperature","parameter_ID":"167","product_type":"forecast","shortName":"2t","standard_name":"2_metre_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/167"},"description":"This parameter is the temperature of air at 2m above the surface of land, sea or in-land waters.2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.Please note that the encodings listed here for s2s \u0026 uerra (which includes encodings for carra/cerra) include entries for Mean 2 metre temperature. The specific encoding for Mean 2 metre temperature can be found in 228004.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Geopotential(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Geopotential","parameter_ID":"129","product_type":"forecast","shortName":"z","standard_name":"Geopotential","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/129"},"description":"This parameter is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level.The geopotential height can be calculated by dividing the geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2). The geopotential height plays an important role in synoptic meteorology (analysis of weather patterns). Charts of geopotential height plotted at constant pressure levels (e.g., 300, 500 or 850 hPa) can be used to identify weather systems such as cyclones, anticyclones, troughs and ridges.At the surface of the Earth, this parameter shows the variations in geopotential (height) of the surface, and is often referred to as the orography.","dimensions":["lat","lon","time"],"type":"data","unit":"m2s-2"},"Land-sea_mask(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Land-sea mask","parameter_ID":"172","product_type":"forecast","shortName":"lsm","standard_name":"Land-sea_mask","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/172"},"description":"This parameter is the proportion of land, as opposed to ocean or inland waters (lakes, reservoirs, rivers and coastal waters), in agrid box.This parameter has values ranging between zero and one and is dimensionless.In cycles of the ECMWF Integrated Forecasting System (IFS) from CY41R1 (introduced in May 2015) onwards, grid boxes where this parameter has a value above 0.5 can be comprised of a mixture of land and inland water but not ocean. Grid boxes with a value of 0.5 and below can only be comprised of a water surface. In the latter case, the lake cover is used to determine how much of the water surface is ocean or inland water.In cycles of the IFS before CY41R1, grid boxes where this parameter has a value above 0.5 can only be comprised of land and those grid boxes with a value of 0.5 and below can only be comprised of ocean. In these older model cycles, there is no differentiation between ocean and inland water.","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Mean_sea_level_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Mean sea level pressure","parameter_ID":"151","product_type":"forecast","shortName":"msl","standard_name":"Mean_sea_level_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/151"},"description":"This parameter is the pressure (force per unit area) of the atmosphere adjusted to the height of mean sea level.It is a measure of the weight that all the air in a column vertically above the area of Earth's surface would have at that point, if the point were located at the mean sea level. It is calculated over all surfaces - land, sea and in-land water.Maps of mean sea level pressure are used to identify the locations of low and high pressure systems, often referred to as cyclones and anticyclones. Contours of mean sea level pressure also indicate the strength of the wind. Tightly packed contours show stronger winds.The units of this parameter are pascals (Pa).  Mean sea level pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb = 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Orography(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Orography","parameter_ID":"228002","product_type":"forecast","shortName":"orog","standard_name":"Orography","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/228002"},"dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Potential_vorticity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Potential vorticity","parameter_ID":"60","product_type":"forecast","shortName":"pv","standard_name":"Potential_vorticity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/60"},"description":"Potential vorticity is a measure of the capacity for air to rotate in the atmosphere. If we ignore the effects of heating and friction, potential vorticity is conserved following an air parcel. It is used to look for places where large wind storms are likely to originate and develop.  Potential vorticity increases strongly above the tropopause and therefore, it can also be used in studies related to the stratosphere and stratosphere-troposphere exchanges.Large wind storms develop when a column of air in the atmosphere starts to rotate. Potential vorticity is calculated from the wind, temperature and pressure across a column of air in the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K m2kg-1s-1"},"Relative_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Relative humidity","parameter_ID":"157","product_type":"forecast","shortName":"r","standard_name":"Relative_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/157"},"description":"This parameter is the water vapour pressure as a percentage of the value at which the air becomes saturated (the point at which water vapour begins to condense into liquid water or deposition into ice).For temperatures over 0°C (273.15 K) it is calculated for saturation over water. At temperatures below -23°C it is calculated for saturation over ice.  Between -23°C and 0°C this parameter is calculated by interpolating between the ice and water values using a quadratic function.See more information about the model's relative humidity calculation.","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Skin_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Skin temperature","parameter_ID":"235","product_type":"forecast","shortName":"skt","standard_name":"Skin_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235"},"description":"This parameter is the temperature of the surface of the Earth.The skin temperature is the theoretical temperature that is required to satisfy the surface energy balance. It represents the temperature of the uppermost surface layer, which has no heat capacity and so can respond instantaneously to changes in surface fluxes. Skin temperature is calculated differently over land and sea.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.See further information about the skin temperatureover landandover sea.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean skin temperature. The specific encoding for Mean skin temperature can be found in 235079.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Snow_depth_water_equivalent(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Snow depth water equivalent","parameter_ID":"228141","product_type":"forecast","shortName":"sd","standard_name":"Snow_depth_water_equivalent","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228141"},"description":"Snow depth water equivalent in kg m**-2 (mm) water equivalent.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean snow depth water equivalent. The specific encoding for Time-mean snow depth water equivalent can be found in 235078.[NOTE: See 141 for the equivalent parameter in \"m of water equivalent\"]","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Specific_cloud_liquid_water_content(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific cloud liquid water content","parameter_ID":"246","product_type":"forecast","shortName":"clwc","standard_name":"Specific_cloud_liquid_water_content","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/246"},"description":"This parameter is the mass of cloud liquid water droplets per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for agrid box.Water within clouds can be liquid or ice, or a combination of the two.See further information about the cloud formulation.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Specific_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific humidity","parameter_ID":"133","product_type":"forecast","shortName":"q","standard_name":"Specific_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/133"},"description":"This parameter is the mass of water vapour per kilogram of moist air.The total mass of moist air is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Surface_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Surface pressure","parameter_ID":"134","product_type":"forecast","shortName":"sp","standard_name":"Surface_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/134"},"description":"This parameter is the pressure (force per unit area) of the atmosphere on the surface of land, sea and in-land water.It is a measure of the weight of all the air in a column vertically above the area of the Earth's surface represented at a fixed point.Surface pressure is often used in combination with temperature to calculate air density.The strong variation of pressure with altitude makes it difficult to see the low and high pressure systems over mountainous areas, so mean sea level pressure, rather than surface pressure, is normally used for this purpose.The units of this parameter are Pascals (Pa). Surface pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb= 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Temperature(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Temperature","parameter_ID":"130","product_type":"forecast","shortName":"t","standard_name":"Temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/130"},"description":"This parameter is the temperature in the atmosphere.It has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.This parameter is available on multiple levels through the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_10_metre_U_wind_component(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre U wind component","parameter_ID":"235165","product_type":"forecast","shortName":"avg_10u","standard_name":"Time-mean_10_metre_U_wind_component","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235165"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_10_metre_V_wind_component(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre V wind 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temperature","parameter_ID":"235168","product_type":"forecast","shortName":"avg_2d","standard_name":"Time-mean_2_metre_dewpoint_temperature","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235168"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_2_metre_temperature(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"2","levtype":"sfc","long_name":"Time-mean 2 metre temperature","parameter_ID":"228004","product_type":"forecast","shortName":"avg_2t","standard_name":"Time-mean_2_metre_temperature","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/228004"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_U_component_of_wind(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean U component of wind","parameter_ID":"235131","product_type":"forecast","shortName":"avg_u","standard_name":"Time-mean_U_component_of_wind","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235131"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_V_component_of_wind(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean V component of wind","parameter_ID":"235132","product_type":"forecast","shortName":"avg_v","standard_name":"Time-mean_V_component_of_wind","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235132"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_ice_velocity(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean eastward sea ice velocity","parameter_ID":"263003","product_type":"forecast","shortName":"avg_siue","standard_name":"Time-mean_eastward_sea_ice_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263003"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_ice_velocity(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean eastward sea ice velocity","parameter_ID":"263003","product_type":"forecast","shortName":"avg_siue","standard_name":"Time-mean_eastward_sea_ice_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263003"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_water_velocity(clmn_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean eastward sea water velocity","parameter_ID":"263506","product_type":"forecast","shortName":"avg_uoe","standard_name":"Time-mean_eastward_sea_water_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263506"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean eastward sea water velocity","parameter_ID":"263506","product_type":"forecast","shortName":"avg_uoe","standard_name":"Time-mean_eastward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263506"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_turbulent_surface_stress(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean eastward turbulent surface stress","parameter_ID":"235041","product_type":"forecast","shortName":"avg_iews","standard_name":"Time-mean_eastward_turbulent_surface_stress","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235041"},"dimensions":["lat","lon","time"],"type":"data","unit":"N m-2"},"Time-mean_eastward_turbulent_surface_stress(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean eastward turbulent surface stress","parameter_ID":"235041","product_type":"forecast","shortName":"avg_iews","standard_name":"Time-mean_eastward_turbulent_surface_stress","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235041"},"dimensions":["lat","lon","time"],"type":"data","unit":"N m-2"},"Time-mean_geopotential(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean geopotential","parameter_ID":"235129","product_type":"forecast","shortName":"avg_z","standard_name":"Time-mean_geopotential","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235129"},"dimensions":["lat","lon","time"],"type":"data","unit":"m2s-2"},"Time-mean_mean_sea_level_pressure(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean mean sea level pressure","parameter_ID":"235151","product_type":"forecast","shortName":"avg_msl","standard_name":"Time-mean_mean_sea_level_pressure","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235151"},"dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Time-mean_moisture_flux(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean moisture flux","parameter_ID":"235043","product_type":"forecast","shortName":"avg_ie","standard_name":"Time-mean_moisture_flux","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235043"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_moisture_flux(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean moisture flux","parameter_ID":"235043","product_type":"forecast","shortName":"avg_ie","standard_name":"Time-mean_moisture_flux","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235043"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_northward_sea_ice_velocity(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean northward sea ice velocity","parameter_ID":"263004","product_type":"forecast","shortName":"avg_sivn","standard_name":"Time-mean_northward_sea_ice_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263004"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_sea_ice_velocity(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean northward sea ice velocity","parameter_ID":"263004","product_type":"forecast","shortName":"avg_sivn","standard_name":"Time-mean_northward_sea_ice_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263004"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_sea_water_velocity(clmn_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean northward sea water velocity","parameter_ID":"263505","product_type":"forecast","shortName":"avg_von","standard_name":"Time-mean_northward_sea_water_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263505"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_turbulent_surface_stress(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean northward turbulent surface stress","parameter_ID":"235042","product_type":"forecast","shortName":"avg_inss","standard_name":"Time-mean_northward_turbulent_surface_stress","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235042"},"dimensions":["lat","lon","time"],"type":"data","unit":"N m-2"},"Time-mean_northward_turbulent_surface_stress(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean northward turbulent surface stress","parameter_ID":"235042","product_type":"forecast","shortName":"avg_inss","standard_name":"Time-mean_northward_turbulent_surface_stress","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235042"},"dimensions":["lat","lon","time"],"type":"data","unit":"N m-2"},"Time-mean_potential_vorticity(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean potential vorticity","parameter_ID":"235100","product_type":"forecast","shortName":"avg_pv","standard_name":"Time-mean_potential_vorticity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235100"},"dimensions":["lat","lon","time"],"type":"data","unit":"K m2kg-1s-1"},"Time-mean_relative_humidity(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean relative humidity","parameter_ID":"235157","product_type":"forecast","shortName":"avg_r","standard_name":"Time-mean_relative_humidity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235157"},"dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Time-mean_sea_ice_area_fraction(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea ice area fraction","parameter_ID":"263001","product_type":"forecast","shortName":"avg_siconc","standard_name":"Time-mean_sea_ice_area_fraction","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263001"},"dimensions":["lat","lon","time"],"type":"data","unit":"Fraction"},"Time-mean_sea_ice_area_fraction(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea ice area fraction","parameter_ID":"263001","product_type":"forecast","shortName":"avg_siconc","standard_name":"Time-mean_sea_ice_area_fraction","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263001"},"dimensions":["lat","lon","time"],"type":"data","unit":"Fraction"},"Time-mean_sea_ice_thickness(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea ice thickness","parameter_ID":"263000","product_type":"forecast","shortName":"avg_sithick","standard_name":"Time-mean_sea_ice_thickness","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263000"},"dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Time-mean_sea_ice_thickness(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea ice thickness","parameter_ID":"263000","product_type":"forecast","shortName":"avg_sithick","standard_name":"Time-mean_sea_ice_thickness","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263000"},"dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Time-mean_sea_ice_volume_per_unit_area(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea ice volume per unit area","parameter_ID":"263008","product_type":"forecast","shortName":"avg_sivol","standard_name":"Time-mean_sea_ice_volume_per_unit_area","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263008"},"dimensions":["lat","lon","time"],"type":"data","unit":"m3m-2"},"Time-mean_sea_ice_volume_per_unit_area(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea ice volume per unit area","parameter_ID":"263008","product_type":"forecast","shortName":"avg_sivol","standard_name":"Time-mean_sea_ice_volume_per_unit_area","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263008"},"dimensions":["lat","lon","time"],"type":"data","unit":"m3m-2"},"Time-mean_sea_surface_height(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea surface height","parameter_ID":"263124","product_type":"forecast","shortName":"avg_zos","standard_name":"Time-mean_sea_surface_height","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263124"},"dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Time-mean_sea_surface_height(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea surface height","parameter_ID":"263124","product_type":"forecast","shortName":"avg_zos","standard_name":"Time-mean_sea_surface_height","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263124"},"dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Time-mean_sea_surface_practical_salinity(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea surface practical salinity","parameter_ID":"263100","product_type":"forecast","shortName":"avg_sos","standard_name":"Time-mean_sea_surface_practical_salinity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263100"},"dimensions":["lat","lon","time"],"type":"data","unit":"g kg-1"},"Time-mean_sea_surface_practical_salinity(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea surface practical salinity","parameter_ID":"263100","product_type":"forecast","shortName":"avg_sos","standard_name":"Time-mean_sea_surface_practical_salinity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263100"},"dimensions":["lat","lon","time"],"type":"data","unit":"g kg-1"},"Time-mean_sea_surface_temperature(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea surface temperature","parameter_ID":"263101","product_type":"forecast","shortName":"avg_tos","standard_name":"Time-mean_sea_surface_temperature","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263101"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_sea_surface_temperature(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea surface 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For this parameter on other levels, please use 235013.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_total_snowfall_rate_water_equivalent(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total snowfall rate water equivalent","parameter_ID":"235031","product_type":"forecast","shortName":"avg_tsrwe","standard_name":"Time-mean_total_snowfall_rate_water_equivalent","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235031"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_total_snowfall_rate_water_equivalent(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total snowfall rate water equivalent","parameter_ID":"235031","product_type":"forecast","shortName":"avg_tsrwe","standard_name":"Time-mean_total_snowfall_rate_water_equivalent","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235031"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_vertical_velocity(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean vertical velocity","parameter_ID":"235135","product_type":"forecast","shortName":"avg_w","standard_name":"Time-mean_vertical_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235135"},"dimensions":["lat","lon","time"],"type":"data","unit":"Pa s-1"},"Time-mean_vertically-integrated_heat_content_in_the_upper_300_m(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"300","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 300 m","parameter_ID":"263121","product_type":"forecast","shortName":"avg_hc300m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_300_m","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263121"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_vertically-integrated_heat_content_in_the_upper_300_m(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"300","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 300 m","parameter_ID":"263121","product_type":"forecast","shortName":"avg_hc300m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_300_m","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263121"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"700","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 700 m","parameter_ID":"263122","product_type":"forecast","shortName":"avg_hc700m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263122"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"700","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 700 m","parameter_ID":"263122","product_type":"forecast","shortName":"avg_hc700m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263122"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_volumetric_soil_moisture(clmn_sol)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Time-mean volumetric soil moisture","parameter_ID":"235077","product_type":"forecast","shortName":"avg_vsw","standard_name":"Time-mean_volumetric_soil_moisture","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235077"},"dimensions":["lat","lon","time"],"type":"data","unit":"m3m-3"},"Time_mean_top_downward_short-wave_radiation_flux(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time mean top downward short-wave radiation flux","parameter_ID":"235053","product_type":"forecast","shortName":"avg_tdswrf","standard_name":"Time_mean_top_downward_short-wave_radiation_flux","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235053"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Time_mean_top_downward_short-wave_radiation_flux(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time mean top downward short-wave radiation flux","parameter_ID":"235053","product_type":"forecast","shortName":"avg_tdswrf","standard_name":"Time_mean_top_downward_short-wave_radiation_flux","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235053"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Total_Cloud_Cover(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total Cloud Cover","parameter_ID":"228164","product_type":"forecast","shortName":"tcc","standard_name":"Total_Cloud_Cover","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228164"},"description":"[NOTE: See 164 for the equivalent parameter in \"(0-1)\"]","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Total_column_cloud_ice_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud ice water","parameter_ID":"79","product_type":"forecast","shortName":"tciw","standard_name":"Total_column_cloud_ice_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/79"},"description":"This parameter is the amount of ice contained within clouds in a column extending from the surface of the Earth to the top of the atmosphere. Snow (aggregated ice crystals) is not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The  ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_cloud_liquid_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud liquid water","parameter_ID":"78","product_type":"forecast","shortName":"tclw","standard_name":"Total_column_cloud_liquid_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/78"},"description":"This parameter is the amount of liquid water contained within cloud droplets in a column extending from the surface of the Earth to the top of the atmosphere. Rain water droplets, which are much larger in size (and mass), are not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_vertically-integrated_water_vapour(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column vertically-integrated water vapour","parameter_ID":"137","product_type":"forecast","shortName":"tcwv","standard_name":"Total_column_vertically-integrated_water_vapour","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/137"},"description":"This parameter is the total amount of water vapour in a column extending from the surface of the Earth to the top of the atmosphere.This parameter represents the area averaged value for agrid box.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column water","parameter_ID":"136","product_type":"forecast","shortName":"tcw","standard_name":"Total_column_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/136"},"description":"This parameter is the sum of water vapour, liquid water, cloud ice, rain and snow in a column extending from the surface of the Earth to the top of the atmosphere. In old versions of the ECMWF model (IFS), rain and snow were not accounted for.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"U_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"U component of wind","parameter_ID":"131","product_type":"forecast","shortName":"u","standard_name":"U_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/131"},"description":"This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east, in metres per second. A negative sign thus indicates air movement towards the west.This parameter can be combined with the V component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"V_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"V component of wind","parameter_ID":"132","product_type":"forecast","shortName":"v","standard_name":"V_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/132"},"description":"This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north, in metres per second. A negative sign thus indicates air movement towards the south.This parameter can be combined with the U component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Vertical_velocity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Vertical velocity","parameter_ID":"135","product_type":"forecast","shortName":"w","standard_name":"Vertical_velocity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/135"},"description":"This parameter is the speed of air motion in the upward or downward direction. The ECMWF Integrated Forecasting System (IFS) uses a pressure based vertical co-ordinate system and pressure decreases with height, therefore negative values of vertical velocity indicate upward motion.Vertical velocity can be useful to understand the large-scale dynamics of the atmosphere, including areas of upward motion/ascent (negative values) and downward motion/subsidence (positive values).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa s-1"},"Volumetric_soil_moisture(clte_sol)":{"attrs":{"encoding":"instantaneous","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Volumetric soil moisture","parameter_ID":"260199","product_type":"forecast","shortName":"vsw","standard_name":"Volumetric_soil_moisture","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/260199"},"description":"Please note that the encoding listed here for uerra (which includes carra/cerra) includes entries for Time-mean volumetric soil moisture. The specific encoding for Time-mean volumetric soil moisture can be found in 235077.","dimensions":["lat","lon","time"],"type":"data","unit":"m3m-3"}},"sci:publications":[{"doi":"10.1109/MCSE.2023.3260519","citation":"N. Wedi et al., Destination Earth: High-Performance Computing for Weather and Climate, in Computing in Science \u0026 Engineering, vol. 24, no. 6, pp. 29-37, Nov.-Dec. 2022,"},{"doi":"10.21957/d3f982672e","citation":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"dedl:short_description":"The Climate Change Adaptation Digital Twin provides global climate projections and sector-specific information over multiple decades at high resolution via a unified framework combining advanced Earth system models, impact assessments, and observations. This Generation-2, Realization-1 Collection gives access to 'Control Simulation' data based on the 'ICON' model."},{"type":"Collection","title":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Control Simulation - IFS-FESOM - Generation-2 - Realization-1","id":"EO.ECMWF.DAT.D1.DT_CLIMATE.G2.BASELINE_CONT_IFS-FESOM.R1","description":"The DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities by providing innovative climate information on multi-decadal timescales, globally, at scales at which many impacts of climate change are observed. It combines cutting-edge global Earth-system models, impact-sector applications and observations into a unified framework to provide global climate projections and impact-sector information on multi-decadal timescales (1990 to ~2050), at very high spatial resolutions (5 to 10 km).\n\nThe Climate DT represents the first ever attempt to operationalise the production of global multi-decadal climate projections, leveraging the world-leading supercomputing facilities of the EuroHPC Joint Undertaking along with some of the leading European climate models. A concise overview of what the Climate DT aims to achieve, and of the different concepts essential for an understanding of the Digital Twin’s characteristics, is included in the [Climate DT factsheet](https://destine.ecmwf.int/wp-content/uploads/2024/06/2024.06.07_Climate-DT-Fact-Sheet_V7-2.pdf)\n\n## Control Simulation \n\n Repetitive 1990 forcing with no change in forcing over time. These simulations allow to quantify model drift and simulated inter-annual variability and provides relevant context for interpreting historical and scenario simulations.\n\n## Models\n\nThe Climate DT exploits and further evolves a new generation of global storm-resolving and eddy-rich models built through a cooperative model development approach. For more information on models please click [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#models)\n\n## Simulations\n\nThe Climate DT team carries out several types of digital twin simulations on the EuroHPC supercomputers.  Multi-decadal simulations are produced to cover the recent past (from 1990) and possible future evolutions of the climate up to 2050. See [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#simulations) for more information on Simulations\n\n## Parameters\n\nBelow we see the list of parameters extracted from the 'DestinE Climate DT data portfolio', for more information please refer to the pages [Climate DT Phase2 CLTE Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters) and [Climate DT Phase2 CLMN Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters)","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.BASELINE_CONT_IFS-FESOM.R1/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.BASELINE_CONT_IFS-FESOM.R1/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.BASELINE_CONT_IFS-FESOM.R1/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.BASELINE_CONT_IFS-FESOM.R1","title":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Control Simulation - IFS-FESOM - Generation-2 - Realization-1"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters","title":"DestinE ClimateDT Phase 2 CLTE Parameters"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters","title":"DestinE ClimateDT Phase 2 CLMN Parameters"},{"rel":"cite-as","type":"text/html","href":"https://dl.acm.org/doi/abs/10.1109/MCSE.2023.3260519","title":"Destination Earth: High-Performance Computing for Weather and Climate"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.21957/d3f982672e","title":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"assets":{"thumbnail":{"href":"https://s3.central.data.destination-earth.eu/swift/v1/dedl-public/collections/climate-dt-min.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1990-01-01T00:00:00Z","1999-12-31T23:59:59Z"]]}},"license":"CC-BY-4.0","keywords":["Digital Twins","Climate Change","Atmosphere","Ocean","Land","Sea Ice","Snow","Soil","Earth","High Performance Computing","Decision Making","Europe","Meteorology","class:d1","dataset:climate-dt","generation:2","expver:0001","stream:clmn,clte","type:fc","activity:baseline","experiment:cont","realization:1","model:IFS-FESOM","resolution:standard,high","levtype:hl,o2d,o3d,pl,sfc,sol"],"summaries":{"federation:backends":["dedt_lumi"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/datacube/v2.1.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["producer","processor","licensor"],"url":"https://www.ecmwf.int/"}],"created":"2024-04-11T22:31:55Z","updated":"2026-04-24T10:24:59Z","published":"2024-04-11T22:31:55Z","cube:dimensions":{"lat":{"axis":"y","description":"latitude","extent":[-90,90],"type":"spatial"},"lon":{"axis":"x","description":"longitude","extent":[-180,180],"type":"spatial"},"time":{"extent":["1990-01-01T00:00:00Z","1999-12-31T23:59:59Z"],"step":"P0Y0M0DT1H0M0S","type":"temporal"}},"cube:variables":{"10_metre_U_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre U wind component","parameter_ID":"165","product_type":"forecast","shortName":"10u","standard_name":"10_metre_U_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/165"},"description":"This parameter is the eastward component of the 10m wind. It is the horizontal speed of air moving towards the east, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the V component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_V_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre V wind component","parameter_ID":"166","product_type":"forecast","shortName":"10v","standard_name":"10_metre_V_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/166"},"description":"This parameter is the northward component of the 10m wind. It is the horizontal speed of air moving towards the north, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the U component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_wind_speed(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre wind speed","parameter_ID":"207","product_type":"forecast","shortName":"10si","standard_name":"10_metre_wind_speed","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/207"},"description":"This parameter is the horizontal speed of the wind, or movement of air, at a height of ten metres above the surface of the Earth. The units of this parameter are metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.The eastward and northward components of the horizontal wind at 10m are also available as parameters.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"2_metre_dewpoint_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"2","levtype":"sfc","long_name":"2 metre dewpoint temperature","parameter_ID":"168","product_type":"forecast","shortName":"2d","standard_name":"2_metre_dewpoint_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/168"},"description":"This parameter is the temperature to which the air, at 2 metres above the surface of the Earth, would have to be cooled for saturation to occur.It is a measure of the humidity of the air. Combined with temperature and pressure, it can be used to calculate the relative humidity.2m dew point temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"2_metre_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"2","levtype":"sfc","long_name":"2 metre temperature","parameter_ID":"167","product_type":"forecast","shortName":"2t","standard_name":"2_metre_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/167"},"description":"This parameter is the temperature of air at 2m above the surface of land, sea or in-land waters.2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.Please note that the encodings listed here for s2s \u0026 uerra (which includes encodings for carra/cerra) include entries for Mean 2 metre temperature. The specific encoding for Mean 2 metre temperature can be found in 228004.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Geopotential(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Geopotential","parameter_ID":"129","product_type":"forecast","shortName":"z","standard_name":"Geopotential","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/129"},"description":"This parameter is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level.The geopotential height can be calculated by dividing the geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2). The geopotential height plays an important role in synoptic meteorology (analysis of weather patterns). Charts of geopotential height plotted at constant pressure levels (e.g., 300, 500 or 850 hPa) can be used to identify weather systems such as cyclones, anticyclones, troughs and ridges.At the surface of the Earth, this parameter shows the variations in geopotential (height) of the surface, and is often referred to as the orography.","dimensions":["lat","lon","time"],"type":"data","unit":"m2s-2"},"Land-sea_mask(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Land-sea mask","parameter_ID":"172","product_type":"forecast","shortName":"lsm","standard_name":"Land-sea_mask","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/172"},"description":"This parameter is the proportion of land, as opposed to ocean or inland waters (lakes, reservoirs, rivers and coastal waters), in agrid box.This parameter has values ranging between zero and one and is dimensionless.In cycles of the ECMWF Integrated Forecasting System (IFS) from CY41R1 (introduced in May 2015) onwards, grid boxes where this parameter has a value above 0.5 can be comprised of a mixture of land and inland water but not ocean. Grid boxes with a value of 0.5 and below can only be comprised of a water surface. In the latter case, the lake cover is used to determine how much of the water surface is ocean or inland water.In cycles of the IFS before CY41R1, grid boxes where this parameter has a value above 0.5 can only be comprised of land and those grid boxes with a value of 0.5 and below can only be comprised of ocean. In these older model cycles, there is no differentiation between ocean and inland water.","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Mean_sea_level_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Mean sea level pressure","parameter_ID":"151","product_type":"forecast","shortName":"msl","standard_name":"Mean_sea_level_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/151"},"description":"This parameter is the pressure (force per unit area) of the atmosphere adjusted to the height of mean sea level.It is a measure of the weight that all the air in a column vertically above the area of Earth's surface would have at that point, if the point were located at the mean sea level. It is calculated over all surfaces - land, sea and in-land water.Maps of mean sea level pressure are used to identify the locations of low and high pressure systems, often referred to as cyclones and anticyclones. Contours of mean sea level pressure also indicate the strength of the wind. Tightly packed contours show stronger winds.The units of this parameter are pascals (Pa).  Mean sea level pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb = 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Orography(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Orography","parameter_ID":"228002","product_type":"forecast","shortName":"orog","standard_name":"Orography","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/228002"},"dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Potential_vorticity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Potential vorticity","parameter_ID":"60","product_type":"forecast","shortName":"pv","standard_name":"Potential_vorticity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/60"},"description":"Potential vorticity is a measure of the capacity for air to rotate in the atmosphere. If we ignore the effects of heating and friction, potential vorticity is conserved following an air parcel. It is used to look for places where large wind storms are likely to originate and develop.  Potential vorticity increases strongly above the tropopause and therefore, it can also be used in studies related to the stratosphere and stratosphere-troposphere exchanges.Large wind storms develop when a column of air in the atmosphere starts to rotate. Potential vorticity is calculated from the wind, temperature and pressure across a column of air in the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K m2kg-1s-1"},"Relative_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Relative humidity","parameter_ID":"157","product_type":"forecast","shortName":"r","standard_name":"Relative_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/157"},"description":"This parameter is the water vapour pressure as a percentage of the value at which the air becomes saturated (the point at which water vapour begins to condense into liquid water or deposition into ice).For temperatures over 0°C (273.15 K) it is calculated for saturation over water. At temperatures below -23°C it is calculated for saturation over ice.  Between -23°C and 0°C this parameter is calculated by interpolating between the ice and water values using a quadratic function.See more information about the model's relative humidity calculation.","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Skin_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Skin temperature","parameter_ID":"235","product_type":"forecast","shortName":"skt","standard_name":"Skin_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235"},"description":"This parameter is the temperature of the surface of the Earth.The skin temperature is the theoretical temperature that is required to satisfy the surface energy balance. It represents the temperature of the uppermost surface layer, which has no heat capacity and so can respond instantaneously to changes in surface fluxes. Skin temperature is calculated differently over land and sea.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.See further information about the skin temperatureover landandover sea.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean skin temperature. The specific encoding for Mean skin temperature can be found in 235079.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Snow_depth_water_equivalent(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Snow depth water equivalent","parameter_ID":"228141","product_type":"forecast","shortName":"sd","standard_name":"Snow_depth_water_equivalent","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228141"},"description":"Snow depth water equivalent in kg m**-2 (mm) water equivalent.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean snow depth water equivalent. The specific encoding for Time-mean snow depth water equivalent can be found in 235078.[NOTE: See 141 for the equivalent parameter in \"m of water equivalent\"]","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Snow_depth_water_equivalent(clte_sol)":{"attrs":{"encoding":"instantaneous","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Snow depth water equivalent","parameter_ID":"228141","product_type":"forecast","shortName":"sd","standard_name":"Snow_depth_water_equivalent","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228141"},"description":"Snow depth water equivalent in kg m**-2 (mm) water equivalent.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean snow depth water equivalent. The specific encoding for Time-mean snow depth water equivalent can be found in 235078.[NOTE: See 141 for the equivalent parameter in \"m of water equivalent\"]","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Specific_cloud_liquid_water_content(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific cloud liquid water content","parameter_ID":"246","product_type":"forecast","shortName":"clwc","standard_name":"Specific_cloud_liquid_water_content","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/246"},"description":"This parameter is the mass of cloud liquid water droplets per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for agrid box.Water within clouds can be liquid or ice, or a combination of the two.See further information about the cloud formulation.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Specific_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific humidity","parameter_ID":"133","product_type":"forecast","shortName":"q","standard_name":"Specific_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/133"},"description":"This parameter is the mass of water vapour per kilogram of moist air.The total mass of moist air is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Surface_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Surface pressure","parameter_ID":"134","product_type":"forecast","shortName":"sp","standard_name":"Surface_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/134"},"description":"This parameter is the pressure (force per unit area) of the atmosphere on the surface of land, sea and in-land water.It is a measure of the weight of all the air in a column vertically above the area of the Earth's surface represented at a fixed point.Surface pressure is often used in combination with temperature to calculate air density.The strong variation of pressure with altitude makes it difficult to see the low and high pressure systems over mountainous areas, so mean sea level pressure, rather than surface pressure, is normally used for this purpose.The units of this parameter are Pascals (Pa). Surface pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb= 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Temperature(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Temperature","parameter_ID":"130","product_type":"forecast","shortName":"t","standard_name":"Temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/130"},"description":"This parameter is the temperature in the atmosphere.It has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.This parameter is available on multiple levels through the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_10_metre_U_wind_component(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre U wind component","parameter_ID":"235165","product_type":"forecast","shortName":"avg_10u","standard_name":"Time-mean_10_metre_U_wind_component","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235165"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_10_metre_V_wind_component(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre V wind component","parameter_ID":"235166","product_type":"forecast","shortName":"avg_10v","standard_name":"Time-mean_10_metre_V_wind_component","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235166"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_10_metre_wind_speed(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre wind speed","parameter_ID":"228005","product_type":"forecast","shortName":"avg_10ws","standard_name":"Time-mean_10_metre_wind_speed","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/228005"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_2_metre_dewpoint_temperature(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"2","levtype":"sfc","long_name":"Time-mean 2 metre dewpoint temperature","parameter_ID":"235168","product_type":"forecast","shortName":"avg_2d","standard_name":"Time-mean_2_metre_dewpoint_temperature","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235168"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_2_metre_temperature(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"2","levtype":"sfc","long_name":"Time-mean 2 metre temperature","parameter_ID":"228004","product_type":"forecast","shortName":"avg_2t","standard_name":"Time-mean_2_metre_temperature","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/228004"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_U_component_of_wind(clmn_hl)":{"attrs":{"encoding":"mean","levelist":"100","levtype":"hl","long_name":"Time-mean U component of wind","parameter_ID":"235131","product_type":"forecast","shortName":"avg_u","standard_name":"Time-mean_U_component_of_wind","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235131"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_U_component_of_wind(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean U component of wind","parameter_ID":"235131","product_type":"forecast","shortName":"avg_u","standard_name":"Time-mean_U_component_of_wind","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235131"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_V_component_of_wind(clmn_hl)":{"attrs":{"encoding":"mean","levelist":"100","levtype":"hl","long_name":"Time-mean V component of wind","parameter_ID":"235132","product_type":"forecast","shortName":"avg_v","standard_name":"Time-mean_V_component_of_wind","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235132"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_V_component_of_wind(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean V component of wind","parameter_ID":"235132","product_type":"forecast","shortName":"avg_v","standard_name":"Time-mean_V_component_of_wind","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235132"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_ice_velocity(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean eastward sea ice velocity","parameter_ID":"263003","product_type":"forecast","shortName":"avg_siue","standard_name":"Time-mean_eastward_sea_ice_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263003"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_ice_velocity(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean eastward sea ice velocity","parameter_ID":"263003","product_type":"forecast","shortName":"avg_siue","standard_name":"Time-mean_eastward_sea_ice_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263003"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_water_velocity(clmn_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean eastward sea water velocity","parameter_ID":"263506","product_type":"forecast","shortName":"avg_uoe","standard_name":"Time-mean_eastward_sea_water_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263506"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean eastward sea water velocity","parameter_ID":"263506","product_type":"forecast","shortName":"avg_uoe","standard_name":"Time-mean_eastward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263506"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_turbulent_surface_stress(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean eastward turbulent surface stress","parameter_ID":"235041","product_type":"forecast","shortName":"avg_iews","standard_name":"Time-mean_eastward_turbulent_surface_stress","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235041"},"dimensions":["lat","lon","time"],"type":"data","unit":"N m-2"},"Time-mean_eastward_turbulent_surface_stress(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean eastward turbulent surface stress","parameter_ID":"235041","product_type":"forecast","shortName":"avg_iews","standard_name":"Time-mean_eastward_turbulent_surface_stress","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235041"},"dimensions":["lat","lon","time"],"type":"data","unit":"N m-2"},"Time-mean_geopotential(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean geopotential","parameter_ID":"235129","product_type":"forecast","shortName":"avg_z","standard_name":"Time-mean_geopotential","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235129"},"dimensions":["lat","lon","time"],"type":"data","unit":"m2s-2"},"Time-mean_mean_sea_level_pressure(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean mean sea level pressure","parameter_ID":"235151","product_type":"forecast","shortName":"avg_msl","standard_name":"Time-mean_mean_sea_level_pressure","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235151"},"dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Time-mean_moisture_flux(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean moisture flux","parameter_ID":"235043","product_type":"forecast","shortName":"avg_ie","standard_name":"Time-mean_moisture_flux","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235043"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_moisture_flux(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean moisture flux","parameter_ID":"235043","product_type":"forecast","shortName":"avg_ie","standard_name":"Time-mean_moisture_flux","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235043"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_northward_sea_ice_velocity(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean northward sea ice velocity","parameter_ID":"263004","product_type":"forecast","shortName":"avg_sivn","standard_name":"Time-mean_northward_sea_ice_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263004"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_sea_ice_velocity(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean northward sea ice velocity","parameter_ID":"263004","product_type":"forecast","shortName":"avg_sivn","standard_name":"Time-mean_northward_sea_ice_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263004"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_sea_water_velocity(clmn_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean northward sea water velocity","parameter_ID":"263505","product_type":"forecast","shortName":"avg_von","standard_name":"Time-mean_northward_sea_water_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263505"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean northward sea water velocity","parameter_ID":"263505","product_type":"forecast","shortName":"avg_von","standard_name":"Time-mean_northward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263505"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_turbulent_surface_stress(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean northward turbulent surface stress","parameter_ID":"235042","product_type":"forecast","shortName":"avg_inss","standard_name":"Time-mean_northward_turbulent_surface_stress","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235042"},"dimensions":["lat","lon","time"],"type":"data","unit":"N m-2"},"Time-mean_northward_turbulent_surface_stress(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean northward turbulent surface stress","parameter_ID":"235042","product_type":"forecast","shortName":"avg_inss","standard_name":"Time-mean_northward_turbulent_surface_stress","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235042"},"dimensions":["lat","lon","time"],"type":"data","unit":"N m-2"},"Time-mean_potential_vorticity(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean potential vorticity","parameter_ID":"235100","product_type":"forecast","shortName":"avg_pv","standard_name":"Time-mean_potential_vorticity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235100"},"dimensions":["lat","lon","time"],"type":"data","unit":"K m2kg-1s-1"},"Time-mean_relative_humidity(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean relative humidity","parameter_ID":"235157","product_type":"forecast","shortName":"avg_r","standard_name":"Time-mean_relative_humidity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235157"},"dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Time-mean_sea_ice_area_fraction(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea ice area 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flux","parameter_ID":"235039","product_type":"forecast","shortName":"avg_tnswrf","standard_name":"Time-mean_top_net_short-wave_radiation_flux","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235039"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Time-mean_top_net_short-wave_radiation_flux(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean top net short-wave radiation flux","parameter_ID":"235039","product_type":"forecast","shortName":"avg_tnswrf","standard_name":"Time-mean_top_net_short-wave_radiation_flux","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235039"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Time-mean_top_net_short-wave_radiation_flux,_clear_sky(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean top net short-wave radiation flux, clear sky","parameter_ID":"235049","product_type":"forecast","shortName":"avg_tnswrfcs","standard_name":"Time-mean_top_net_short-wave_radiation_flux,_clear_sky","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235049"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Time-mean_top_net_short-wave_radiation_flux,_clear_sky(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean top net short-wave radiation flux, clear sky","parameter_ID":"235049","product_type":"forecast","shortName":"avg_tnswrfcs","standard_name":"Time-mean_top_net_short-wave_radiation_flux,_clear_sky","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235049"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Time-mean_total_cloud_cover(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total cloud cover","parameter_ID":"235288","product_type":"forecast","shortName":"avg_tcc","standard_name":"Time-mean_total_cloud_cover","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235288"},"dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Time-mean_total_column_cloud_ice_water(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total column cloud ice water","parameter_ID":"235088","product_type":"forecast","shortName":"avg_tciw","standard_name":"Time-mean_total_column_cloud_ice_water","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235088"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Time-mean_total_column_heat_content(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean total column heat content","parameter_ID":"263123","product_type":"forecast","shortName":"avg_hcbtm","standard_name":"Time-mean_total_column_heat_content","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263123"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_total_column_heat_content(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean total column heat content","parameter_ID":"263123","product_type":"forecast","shortName":"avg_hcbtm","standard_name":"Time-mean_total_column_heat_content","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263123"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_total_column_liquid_water(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total column liquid water","parameter_ID":"235087","product_type":"forecast","shortName":"avg_tclw","standard_name":"Time-mean_total_column_liquid_water","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235087"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Time-mean_total_column_vertically-integrated_water_vapour(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total column vertically-integrated water vapour","parameter_ID":"235137","product_type":"forecast","shortName":"avg_tcwv","standard_name":"Time-mean_total_column_vertically-integrated_water_vapour","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235137"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Time-mean_total_column_water(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total column water","parameter_ID":"235136","product_type":"forecast","shortName":"avg_tcw","standard_name":"Time-mean_total_column_water","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235136"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Time-mean_total_precipitation_rate(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total precipitation rate","parameter_ID":"235055","product_type":"forecast","shortName":"avg_tprate","standard_name":"Time-mean_total_precipitation_rate","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235055"},"description":"Time-mean total precipitation rate, or time-mean total precipitation flux. This parameter is on level \"Ground or water surface\" (typeOfFirstFixedSurface=1). For this parameter on other levels, please use 235013.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_total_precipitation_rate(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total precipitation rate","parameter_ID":"235055","product_type":"forecast","shortName":"avg_tprate","standard_name":"Time-mean_total_precipitation_rate","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235055"},"description":"Time-mean total precipitation rate, or time-mean total precipitation flux. This parameter is on level \"Ground or water surface\" (typeOfFirstFixedSurface=1). For this parameter on other levels, please use 235013.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_total_snowfall_rate_water_equivalent(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total snowfall rate water equivalent","parameter_ID":"235031","product_type":"forecast","shortName":"avg_tsrwe","standard_name":"Time-mean_total_snowfall_rate_water_equivalent","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235031"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_total_snowfall_rate_water_equivalent(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total snowfall rate water equivalent","parameter_ID":"235031","product_type":"forecast","shortName":"avg_tsrwe","standard_name":"Time-mean_total_snowfall_rate_water_equivalent","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235031"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_upward_sea_water_velocity(clmn_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean upward sea water velocity","parameter_ID":"263507","product_type":"forecast","shortName":"avg_wo","standard_name":"Time-mean_upward_sea_water_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263507"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_upward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean upward sea water velocity","parameter_ID":"263507","product_type":"forecast","shortName":"avg_wo","standard_name":"Time-mean_upward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263507"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_vertical_velocity(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean vertical velocity","parameter_ID":"235135","product_type":"forecast","shortName":"avg_w","standard_name":"Time-mean_vertical_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235135"},"dimensions":["lat","lon","time"],"type":"data","unit":"Pa s-1"},"Time-mean_vertically-integrated_heat_content_in_the_upper_300_m(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"300","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 300 m","parameter_ID":"263121","product_type":"forecast","shortName":"avg_hc300m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_300_m","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263121"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_vertically-integrated_heat_content_in_the_upper_300_m(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"300","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 300 m","parameter_ID":"263121","product_type":"forecast","shortName":"avg_hc300m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_300_m","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263121"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"700","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 700 m","parameter_ID":"263122","product_type":"forecast","shortName":"avg_hc700m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263122"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"700","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 700 m","parameter_ID":"263122","product_type":"forecast","shortName":"avg_hc700m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263122"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_volumetric_soil_moisture(clmn_sol)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Time-mean volumetric soil moisture","parameter_ID":"235077","product_type":"forecast","shortName":"avg_vsw","standard_name":"Time-mean_volumetric_soil_moisture","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235077"},"dimensions":["lat","lon","time"],"type":"data","unit":"m3m-3"},"Time_mean_top_downward_short-wave_radiation_flux(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time mean top downward short-wave radiation flux","parameter_ID":"235053","product_type":"forecast","shortName":"avg_tdswrf","standard_name":"Time_mean_top_downward_short-wave_radiation_flux","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235053"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Time_mean_top_downward_short-wave_radiation_flux(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time mean top downward short-wave radiation flux","parameter_ID":"235053","product_type":"forecast","shortName":"avg_tdswrf","standard_name":"Time_mean_top_downward_short-wave_radiation_flux","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235053"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Total_Cloud_Cover(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total Cloud Cover","parameter_ID":"228164","product_type":"forecast","shortName":"tcc","standard_name":"Total_Cloud_Cover","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228164"},"description":"[NOTE: See 164 for the equivalent parameter in \"(0-1)\"]","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Total_column_cloud_ice_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud ice water","parameter_ID":"79","product_type":"forecast","shortName":"tciw","standard_name":"Total_column_cloud_ice_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/79"},"description":"This parameter is the amount of ice contained within clouds in a column extending from the surface of the Earth to the top of the atmosphere. Snow (aggregated ice crystals) is not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The  ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_cloud_liquid_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud liquid water","parameter_ID":"78","product_type":"forecast","shortName":"tclw","standard_name":"Total_column_cloud_liquid_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/78"},"description":"This parameter is the amount of liquid water contained within cloud droplets in a column extending from the surface of the Earth to the top of the atmosphere. Rain water droplets, which are much larger in size (and mass), are not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_vertically-integrated_water_vapour(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column vertically-integrated water vapour","parameter_ID":"137","product_type":"forecast","shortName":"tcwv","standard_name":"Total_column_vertically-integrated_water_vapour","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/137"},"description":"This parameter is the total amount of water vapour in a column extending from the surface of the Earth to the top of the atmosphere.This parameter represents the area averaged value for agrid box.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column water","parameter_ID":"136","product_type":"forecast","shortName":"tcw","standard_name":"Total_column_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/136"},"description":"This parameter is the sum of water vapour, liquid water, cloud ice, rain and snow in a column extending from the surface of the Earth to the top of the atmosphere. In old versions of the ECMWF model (IFS), rain and snow were not accounted for.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"U_component_of_wind(clte_hl)":{"attrs":{"encoding":"instantaneous","levelist":"100","levtype":"hl","long_name":"U component of wind","parameter_ID":"131","product_type":"forecast","shortName":"u","standard_name":"U_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/131"},"description":"This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east, in metres per second. A negative sign thus indicates air movement towards the west.This parameter can be combined with the V component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"U_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"U component of wind","parameter_ID":"131","product_type":"forecast","shortName":"u","standard_name":"U_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/131"},"description":"This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east, in metres per second. A negative sign thus indicates air movement towards the west.This parameter can be combined with the V component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"V_component_of_wind(clte_hl)":{"attrs":{"encoding":"instantaneous","levelist":"100","levtype":"hl","long_name":"V component of wind","parameter_ID":"132","product_type":"forecast","shortName":"v","standard_name":"V_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/132"},"description":"This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north, in metres per second. A negative sign thus indicates air movement towards the south.This parameter can be combined with the U component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"V_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"V component of wind","parameter_ID":"132","product_type":"forecast","shortName":"v","standard_name":"V_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/132"},"description":"This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north, in metres per second. A negative sign thus indicates air movement towards the south.This parameter can be combined with the U component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Vertical_velocity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Vertical velocity","parameter_ID":"135","product_type":"forecast","shortName":"w","standard_name":"Vertical_velocity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/135"},"description":"This parameter is the speed of air motion in the upward or downward direction. The ECMWF Integrated Forecasting System (IFS) uses a pressure based vertical co-ordinate system and pressure decreases with height, therefore negative values of vertical velocity indicate upward motion.Vertical velocity can be useful to understand the large-scale dynamics of the atmosphere, including areas of upward motion/ascent (negative values) and downward motion/subsidence (positive values).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa s-1"},"Volumetric_soil_moisture(clte_sol)":{"attrs":{"encoding":"instantaneous","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Volumetric soil moisture","parameter_ID":"260199","product_type":"forecast","shortName":"vsw","standard_name":"Volumetric_soil_moisture","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/260199"},"description":"Please note that the encoding listed here for uerra (which includes carra/cerra) includes entries for Time-mean volumetric soil moisture. The specific encoding for Time-mean volumetric soil moisture can be found in 235077.","dimensions":["lat","lon","time"],"type":"data","unit":"m3m-3"}},"sci:publications":[{"doi":"10.1109/MCSE.2023.3260519","citation":"N. Wedi et al., Destination Earth: High-Performance Computing for Weather and Climate, in Computing in Science \u0026 Engineering, vol. 24, no. 6, pp. 29-37, Nov.-Dec. 2022,"},{"doi":"10.21957/d3f982672e","citation":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"dedl:short_description":"The Climate Change Adaptation Digital Twin provides global climate projections and sector-specific information over multiple decades at high resolution via a unified framework combining advanced Earth system models, impact assessments, and observations. This Generation-2, Realization-1 Collection gives access to 'Control Simulation' data based on the 'IFS-FESOM' model."},{"type":"Collection","title":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Control Simulation - IFS-NEMO - Generation-2 - Realization-1","id":"EO.ECMWF.DAT.D1.DT_CLIMATE.G2.BASELINE_CONT_IFS-NEMO.R1","description":"The DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities by providing innovative climate information on multi-decadal timescales, globally, at scales at which many impacts of climate change are observed. It combines cutting-edge global Earth-system models, impact-sector applications and observations into a unified framework to provide global climate projections and impact-sector information on multi-decadal timescales (1990 to ~2050), at very high spatial resolutions (5 to 10 km).\n\nThe Climate DT represents the first ever attempt to operationalise the production of global multi-decadal climate projections, leveraging the world-leading supercomputing facilities of the EuroHPC Joint Undertaking along with some of the leading European climate models. A concise overview of what the Climate DT aims to achieve, and of the different concepts essential for an understanding of the Digital Twin’s characteristics, is included in the [Climate DT factsheet](https://destine.ecmwf.int/wp-content/uploads/2024/06/2024.06.07_Climate-DT-Fact-Sheet_V7-2.pdf)\n\n## Control Simulation \n\n Repetitive 1990 forcing with no change in forcing over time. These simulations allow to quantify model drift and simulated inter-annual variability and provides relevant context for interpreting historical and scenario simulations.\n\n## Models\n\nThe Climate DT exploits and further evolves a new generation of global storm-resolving and eddy-rich models built through a cooperative model development approach. For more information on models please click [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#models)\n\n## Simulations\n\nThe Climate DT team carries out several types of digital twin simulations on the EuroHPC supercomputers.  Multi-decadal simulations are produced to cover the recent past (from 1990) and possible future evolutions of the climate up to 2050. See [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#simulations) for more information on Simulations\n\n## Parameters\n\nBelow we see the list of parameters extracted from the 'DestinE Climate DT data portfolio', for more information please refer to the pages [Climate DT Phase2 CLTE Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters) and [Climate DT Phase2 CLMN Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters)","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.BASELINE_CONT_IFS-NEMO.R1/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.BASELINE_CONT_IFS-NEMO.R1/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.BASELINE_CONT_IFS-NEMO.R1/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.BASELINE_CONT_IFS-NEMO.R1","title":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Control Simulation - IFS-NEMO - Generation-2 - Realization-1"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters","title":"DestinE ClimateDT Phase 2 CLTE Parameters"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters","title":"DestinE ClimateDT Phase 2 CLMN Parameters"},{"rel":"cite-as","type":"text/html","href":"https://dl.acm.org/doi/abs/10.1109/MCSE.2023.3260519","title":"Destination Earth: High-Performance Computing for Weather and Climate"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.21957/d3f982672e","title":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"assets":{"thumbnail":{"href":"https://s3.central.data.destination-earth.eu/swift/v1/dedl-public/collections/climate-dt-min.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1990-01-01T00:00:00Z","1999-12-31T23:59:59Z"]]}},"license":"CC-BY-4.0","keywords":["Digital Twins","Climate Change","Atmosphere","Ocean","Land","Sea Ice","Snow","Soil","Earth","High Performance Computing","Decision Making","Europe","Meteorology","class:d1","dataset:climate-dt","generation:2","expver:0001","stream:clmn,clte","type:fc","activity:baseline","experiment:cont","realization:1","model:IFS-NEMO","resolution:standard,high","levtype:hl,o2d,o3d,pl,sfc,sol"],"summaries":{"federation:backends":["dedt_mn5"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/datacube/v2.1.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["producer","processor","licensor"],"url":"https://www.ecmwf.int/"}],"created":"2024-04-11T22:31:55Z","updated":"2026-04-24T10:24:59Z","published":"2024-04-11T22:31:55Z","cube:dimensions":{"lat":{"axis":"y","description":"latitude","extent":[-90,90],"type":"spatial"},"lon":{"axis":"x","description":"longitude","extent":[-180,180],"type":"spatial"},"time":{"extent":["1990-01-01T00:00:00Z","1999-12-31T23:59:59Z"],"step":"P0Y0M0DT1H0M0S","type":"temporal"}},"cube:variables":{"10_metre_U_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre U wind component","parameter_ID":"165","product_type":"forecast","shortName":"10u","standard_name":"10_metre_U_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/165"},"description":"This parameter is the eastward component of the 10m wind. It is the horizontal speed of air moving towards the east, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the V component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_V_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre V wind component","parameter_ID":"166","product_type":"forecast","shortName":"10v","standard_name":"10_metre_V_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/166"},"description":"This parameter is the northward component of the 10m wind. It is the horizontal speed of air moving towards the north, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the U component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_wind_speed(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre wind speed","parameter_ID":"207","product_type":"forecast","shortName":"10si","standard_name":"10_metre_wind_speed","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/207"},"description":"This parameter is the horizontal speed of the wind, or movement of air, at a height of ten metres above the surface of the Earth. The units of this parameter are metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.The eastward and northward components of the horizontal wind at 10m are also available as parameters.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"2_metre_dewpoint_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"2","levtype":"sfc","long_name":"2 metre dewpoint temperature","parameter_ID":"168","product_type":"forecast","shortName":"2d","standard_name":"2_metre_dewpoint_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/168"},"description":"This parameter is the temperature to which the air, at 2 metres above the surface of the Earth, would have to be cooled for saturation to occur.It is a measure of the humidity of the air. Combined with temperature and pressure, it can be used to calculate the relative humidity.2m dew point temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"2_metre_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"2","levtype":"sfc","long_name":"2 metre temperature","parameter_ID":"167","product_type":"forecast","shortName":"2t","standard_name":"2_metre_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/167"},"description":"This parameter is the temperature of air at 2m above the surface of land, sea or in-land waters.2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.Please note that the encodings listed here for s2s \u0026 uerra (which includes encodings for carra/cerra) include entries for Mean 2 metre temperature. The specific encoding for Mean 2 metre temperature can be found in 228004.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Geopotential(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Geopotential","parameter_ID":"129","product_type":"forecast","shortName":"z","standard_name":"Geopotential","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/129"},"description":"This parameter is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level.The geopotential height can be calculated by dividing the geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2). The geopotential height plays an important role in synoptic meteorology (analysis of weather patterns). Charts of geopotential height plotted at constant pressure levels (e.g., 300, 500 or 850 hPa) can be used to identify weather systems such as cyclones, anticyclones, troughs and ridges.At the surface of the Earth, this parameter shows the variations in geopotential (height) of the surface, and is often referred to as the orography.","dimensions":["lat","lon","time"],"type":"data","unit":"m2s-2"},"Land-sea_mask(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Land-sea mask","parameter_ID":"172","product_type":"forecast","shortName":"lsm","standard_name":"Land-sea_mask","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/172"},"description":"This parameter is the proportion of land, as opposed to ocean or inland waters (lakes, reservoirs, rivers and coastal waters), in agrid box.This parameter has values ranging between zero and one and is dimensionless.In cycles of the ECMWF Integrated Forecasting System (IFS) from CY41R1 (introduced in May 2015) onwards, grid boxes where this parameter has a value above 0.5 can be comprised of a mixture of land and inland water but not ocean. Grid boxes with a value of 0.5 and below can only be comprised of a water surface. In the latter case, the lake cover is used to determine how much of the water surface is ocean or inland water.In cycles of the IFS before CY41R1, grid boxes where this parameter has a value above 0.5 can only be comprised of land and those grid boxes with a value of 0.5 and below can only be comprised of ocean. In these older model cycles, there is no differentiation between ocean and inland water.","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Mean_sea_level_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Mean sea level pressure","parameter_ID":"151","product_type":"forecast","shortName":"msl","standard_name":"Mean_sea_level_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/151"},"description":"This parameter is the pressure (force per unit area) of the atmosphere adjusted to the height of mean sea level.It is a measure of the weight that all the air in a column vertically above the area of Earth's surface would have at that point, if the point were located at the mean sea level. It is calculated over all surfaces - land, sea and in-land water.Maps of mean sea level pressure are used to identify the locations of low and high pressure systems, often referred to as cyclones and anticyclones. Contours of mean sea level pressure also indicate the strength of the wind. Tightly packed contours show stronger winds.The units of this parameter are pascals (Pa).  Mean sea level pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb = 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Orography(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Orography","parameter_ID":"228002","product_type":"forecast","shortName":"orog","standard_name":"Orography","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/228002"},"dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Potential_vorticity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Potential vorticity","parameter_ID":"60","product_type":"forecast","shortName":"pv","standard_name":"Potential_vorticity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/60"},"description":"Potential vorticity is a measure of the capacity for air to rotate in the atmosphere. If we ignore the effects of heating and friction, potential vorticity is conserved following an air parcel. It is used to look for places where large wind storms are likely to originate and develop.  Potential vorticity increases strongly above the tropopause and therefore, it can also be used in studies related to the stratosphere and stratosphere-troposphere exchanges.Large wind storms develop when a column of air in the atmosphere starts to rotate. Potential vorticity is calculated from the wind, temperature and pressure across a column of air in the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K m2kg-1s-1"},"Relative_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Relative humidity","parameter_ID":"157","product_type":"forecast","shortName":"r","standard_name":"Relative_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/157"},"description":"This parameter is the water vapour pressure as a percentage of the value at which the air becomes saturated (the point at which water vapour begins to condense into liquid water or deposition into ice).For temperatures over 0°C (273.15 K) it is calculated for saturation over water. At temperatures below -23°C it is calculated for saturation over ice.  Between -23°C and 0°C this parameter is calculated by interpolating between the ice and water values using a quadratic function.See more information about the model's relative humidity calculation.","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Skin_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Skin temperature","parameter_ID":"235","product_type":"forecast","shortName":"skt","standard_name":"Skin_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235"},"description":"This parameter is the temperature of the surface of the Earth.The skin temperature is the theoretical temperature that is required to satisfy the surface energy balance. It represents the temperature of the uppermost surface layer, which has no heat capacity and so can respond instantaneously to changes in surface fluxes. Skin temperature is calculated differently over land and sea.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.See further information about the skin temperatureover landandover sea.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean skin temperature. The specific encoding for Mean skin temperature can be found in 235079.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Snow_depth_water_equivalent(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Snow depth water equivalent","parameter_ID":"228141","product_type":"forecast","shortName":"sd","standard_name":"Snow_depth_water_equivalent","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228141"},"description":"Snow depth water equivalent in kg m**-2 (mm) water equivalent.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean snow depth water equivalent. The specific encoding for Time-mean snow depth water equivalent can be found in 235078.[NOTE: See 141 for the equivalent parameter in \"m of water equivalent\"]","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Snow_depth_water_equivalent(clte_sol)":{"attrs":{"encoding":"instantaneous","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Snow depth water equivalent","parameter_ID":"228141","product_type":"forecast","shortName":"sd","standard_name":"Snow_depth_water_equivalent","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228141"},"description":"Snow depth water equivalent in kg m**-2 (mm) water equivalent.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean snow depth water equivalent. The specific encoding for Time-mean snow depth water equivalent can be found in 235078.[NOTE: See 141 for the equivalent parameter in \"m of water equivalent\"]","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Specific_cloud_liquid_water_content(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific cloud liquid water content","parameter_ID":"246","product_type":"forecast","shortName":"clwc","standard_name":"Specific_cloud_liquid_water_content","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/246"},"description":"This parameter is the mass of cloud liquid water droplets per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for agrid box.Water within clouds can be liquid or ice, or a combination of the two.See further information about the cloud formulation.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Specific_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific humidity","parameter_ID":"133","product_type":"forecast","shortName":"q","standard_name":"Specific_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/133"},"description":"This parameter is the mass of water vapour per kilogram of moist air.The total mass of moist air is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Surface_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Surface pressure","parameter_ID":"134","product_type":"forecast","shortName":"sp","standard_name":"Surface_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/134"},"description":"This parameter is the pressure (force per unit area) of the atmosphere on the surface of land, sea and in-land water.It is a measure of the weight of all the air in a column vertically above the area of the Earth's surface represented at a fixed point.Surface pressure is often used in combination with temperature to calculate air density.The strong variation of pressure with altitude makes it difficult to see the low and high pressure systems over mountainous areas, so mean sea level pressure, rather than surface pressure, is normally used for this purpose.The units of this parameter are Pascals (Pa). Surface pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb= 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Temperature(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Temperature","parameter_ID":"130","product_type":"forecast","shortName":"t","standard_name":"Temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/130"},"description":"This parameter is the temperature in the atmosphere.It has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.This parameter is available on multiple levels through the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_10_metre_U_wind_component(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre U wind component","parameter_ID":"235165","product_type":"forecast","shortName":"avg_10u","standard_name":"Time-mean_10_metre_U_wind_component","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235165"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_10_metre_V_wind_component(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre V wind component","parameter_ID":"235166","product_type":"forecast","shortName":"avg_10v","standard_name":"Time-mean_10_metre_V_wind_component","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235166"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_10_metre_wind_speed(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre wind speed","parameter_ID":"228005","product_type":"forecast","shortName":"avg_10ws","standard_name":"Time-mean_10_metre_wind_speed","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/228005"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_2_metre_dewpoint_temperature(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"2","levtype":"sfc","long_name":"Time-mean 2 metre dewpoint temperature","parameter_ID":"235168","product_type":"forecast","shortName":"avg_2d","standard_name":"Time-mean_2_metre_dewpoint_temperature","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235168"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_2_metre_temperature(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"2","levtype":"sfc","long_name":"Time-mean 2 metre temperature","parameter_ID":"228004","product_type":"forecast","shortName":"avg_2t","standard_name":"Time-mean_2_metre_temperature","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/228004"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_U_component_of_wind(clmn_hl)":{"attrs":{"encoding":"mean","levelist":"100","levtype":"hl","long_name":"Time-mean U component of wind","parameter_ID":"235131","product_type":"forecast","shortName":"avg_u","standard_name":"Time-mean_U_component_of_wind","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235131"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_U_component_of_wind(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean U component of wind","parameter_ID":"235131","product_type":"forecast","shortName":"avg_u","standard_name":"Time-mean_U_component_of_wind","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235131"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_V_component_of_wind(clmn_hl)":{"attrs":{"encoding":"mean","levelist":"100","levtype":"hl","long_name":"Time-mean V component of 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the upper 300 m","parameter_ID":"263121","product_type":"forecast","shortName":"avg_hc300m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_300_m","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263121"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"700","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 700 m","parameter_ID":"263122","product_type":"forecast","shortName":"avg_hc700m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263122"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"700","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 700 m","parameter_ID":"263122","product_type":"forecast","shortName":"avg_hc700m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263122"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_volumetric_soil_moisture(clmn_sol)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Time-mean volumetric soil moisture","parameter_ID":"235077","product_type":"forecast","shortName":"avg_vsw","standard_name":"Time-mean_volumetric_soil_moisture","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235077"},"dimensions":["lat","lon","time"],"type":"data","unit":"m3m-3"},"Time_mean_top_downward_short-wave_radiation_flux(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time mean top downward short-wave radiation flux","parameter_ID":"235053","product_type":"forecast","shortName":"avg_tdswrf","standard_name":"Time_mean_top_downward_short-wave_radiation_flux","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235053"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Time_mean_top_downward_short-wave_radiation_flux(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time mean top downward short-wave radiation flux","parameter_ID":"235053","product_type":"forecast","shortName":"avg_tdswrf","standard_name":"Time_mean_top_downward_short-wave_radiation_flux","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235053"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Total_Cloud_Cover(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total Cloud Cover","parameter_ID":"228164","product_type":"forecast","shortName":"tcc","standard_name":"Total_Cloud_Cover","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228164"},"description":"[NOTE: See 164 for the equivalent parameter in \"(0-1)\"]","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Total_column_cloud_ice_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud ice water","parameter_ID":"79","product_type":"forecast","shortName":"tciw","standard_name":"Total_column_cloud_ice_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/79"},"description":"This parameter is the amount of ice contained within clouds in a column extending from the surface of the Earth to the top of the atmosphere. Snow (aggregated ice crystals) is not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The  ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_cloud_liquid_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud liquid water","parameter_ID":"78","product_type":"forecast","shortName":"tclw","standard_name":"Total_column_cloud_liquid_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/78"},"description":"This parameter is the amount of liquid water contained within cloud droplets in a column extending from the surface of the Earth to the top of the atmosphere. Rain water droplets, which are much larger in size (and mass), are not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_vertically-integrated_water_vapour(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column vertically-integrated water vapour","parameter_ID":"137","product_type":"forecast","shortName":"tcwv","standard_name":"Total_column_vertically-integrated_water_vapour","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/137"},"description":"This parameter is the total amount of water vapour in a column extending from the surface of the Earth to the top of the atmosphere.This parameter represents the area averaged value for agrid box.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column water","parameter_ID":"136","product_type":"forecast","shortName":"tcw","standard_name":"Total_column_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/136"},"description":"This parameter is the sum of water vapour, liquid water, cloud ice, rain and snow in a column extending from the surface of the Earth to the top of the atmosphere. In old versions of the ECMWF model (IFS), rain and snow were not accounted for.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"U_component_of_wind(clte_hl)":{"attrs":{"encoding":"instantaneous","levelist":"100","levtype":"hl","long_name":"U component of wind","parameter_ID":"131","product_type":"forecast","shortName":"u","standard_name":"U_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/131"},"description":"This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east, in metres per second. A negative sign thus indicates air movement towards the west.This parameter can be combined with the V component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"U_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"U component of wind","parameter_ID":"131","product_type":"forecast","shortName":"u","standard_name":"U_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/131"},"description":"This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east, in metres per second. A negative sign thus indicates air movement towards the west.This parameter can be combined with the V component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"V_component_of_wind(clte_hl)":{"attrs":{"encoding":"instantaneous","levelist":"100","levtype":"hl","long_name":"V component of wind","parameter_ID":"132","product_type":"forecast","shortName":"v","standard_name":"V_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/132"},"description":"This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north, in metres per second. A negative sign thus indicates air movement towards the south.This parameter can be combined with the U component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"V_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"V component of wind","parameter_ID":"132","product_type":"forecast","shortName":"v","standard_name":"V_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/132"},"description":"This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north, in metres per second. A negative sign thus indicates air movement towards the south.This parameter can be combined with the U component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Vertical_velocity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Vertical velocity","parameter_ID":"135","product_type":"forecast","shortName":"w","standard_name":"Vertical_velocity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/135"},"description":"This parameter is the speed of air motion in the upward or downward direction. The ECMWF Integrated Forecasting System (IFS) uses a pressure based vertical co-ordinate system and pressure decreases with height, therefore negative values of vertical velocity indicate upward motion.Vertical velocity can be useful to understand the large-scale dynamics of the atmosphere, including areas of upward motion/ascent (negative values) and downward motion/subsidence (positive values).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa s-1"},"Volumetric_soil_moisture(clte_sol)":{"attrs":{"encoding":"instantaneous","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Volumetric soil moisture","parameter_ID":"260199","product_type":"forecast","shortName":"vsw","standard_name":"Volumetric_soil_moisture","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/260199"},"description":"Please note that the encoding listed here for uerra (which includes carra/cerra) includes entries for Time-mean volumetric soil moisture. The specific encoding for Time-mean volumetric soil moisture can be found in 235077.","dimensions":["lat","lon","time"],"type":"data","unit":"m3m-3"}},"sci:publications":[{"doi":"10.1109/MCSE.2023.3260519","citation":"N. Wedi et al., Destination Earth: High-Performance Computing for Weather and Climate, in Computing in Science \u0026 Engineering, vol. 24, no. 6, pp. 29-37, Nov.-Dec. 2022,"},{"doi":"10.21957/d3f982672e","citation":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"dedl:short_description":"The Climate Change Adaptation Digital Twin provides global climate projections and sector-specific information over multiple decades at high resolution via a unified framework combining advanced Earth system models, impact assessments, and observations. This Generation-2, Realization-1 Collection gives access to 'Control Simulation' data based on the 'IFS-NEMO' model."},{"type":"Collection","title":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Historical Simulation - ICON - Generation-2 - Realization-1","id":"EO.ECMWF.DAT.D1.DT_CLIMATE.G2.BASELINE_HIST_ICON.R1","description":"The DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities by providing innovative climate information on multi-decadal timescales, globally, at scales at which many impacts of climate change are observed. It combines cutting-edge global Earth-system models, impact-sector applications and observations into a unified framework to provide global climate projections and impact-sector information on multi-decadal timescales (1990 to ~2050), at very high spatial resolutions (5 to 10 km).\n\nThe Climate DT represents the first ever attempt to operationalise the production of global multi-decadal climate projections, leveraging the world-leading supercomputing facilities of the EuroHPC Joint Undertaking along with some of the leading European climate models. A concise overview of what the Climate DT aims to achieve, and of the different concepts essential for an understanding of the Digital Twin’s characteristics, is included in the [Climate DT factsheet](https://destine.ecmwf.int/wp-content/uploads/2024/06/2024.06.07_Climate-DT-Fact-Sheet_V7-2.pdf)\n\n## Historical Simulation \n\n Starting in 1990, the forcing follows observed changes in greenhouse gases, aerosols etc. until 2020. The simulations are using standardised CMIP6 forcing. Historical simulations are essential for model evaluation and quality control as they allow a comparison to observations.\n\n## Models\n\nThe Climate DT exploits and further evolves a new generation of global storm-resolving and eddy-rich models built through a cooperative model development approach. For more information on models please click [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#models)\n\n## Simulations\n\nThe Climate DT team carries out several types of digital twin simulations on the EuroHPC supercomputers.  Multi-decadal simulations are produced to cover the recent past (from 1990) and possible future evolutions of the climate up to 2050. See [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#simulations) for more information on Simulations\n\n## Parameters\n\nBelow we see the list of parameters extracted from the 'DestinE Climate DT data portfolio', for more information please refer to the pages [Climate DT Phase2 CLTE Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters) and [Climate DT Phase2 CLMN Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters)","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.BASELINE_HIST_ICON.R1/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.BASELINE_HIST_ICON.R1/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.BASELINE_HIST_ICON.R1/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.BASELINE_HIST_ICON.R1","title":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Historical Simulation - ICON - Generation-2 - Realization-1"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters","title":"DestinE ClimateDT Phase 2 CLTE Parameters"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters","title":"DestinE ClimateDT Phase 2 CLMN Parameters"},{"rel":"cite-as","type":"text/html","href":"https://dl.acm.org/doi/abs/10.1109/MCSE.2023.3260519","title":"Destination Earth: High-Performance Computing for Weather and Climate"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.21957/d3f982672e","title":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"assets":{"thumbnail":{"href":"https://s3.central.data.destination-earth.eu/swift/v1/dedl-public/collections/climate-dt-min.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1990-01-01T00:00:00Z","2014-12-31T23:59:59Z"]]}},"license":"CC-BY-4.0","keywords":["Digital Twins","Climate Change","Atmosphere","Ocean","Land","Sea Ice","Snow","Soil","Earth","High Performance Computing","Decision Making","Europe","Meteorology","class:d1","dataset:climate-dt","generation:2","expver:0001","stream:clmn,clte","type:fc","activity:baseline","experiment:hist","realization:1","model:ICON","resolution:standard,high","levtype:o2d,o3d,pl,sfc,sol"],"summaries":{"federation:backends":["dedt_lumi"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/datacube/v2.1.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["producer","processor","licensor"],"url":"https://www.ecmwf.int/"}],"created":"2024-04-11T22:31:55Z","updated":"2026-04-24T10:24:59Z","published":"2024-04-11T22:31:55Z","cube:dimensions":{"lat":{"axis":"y","description":"latitude","extent":[-90,90],"type":"spatial"},"lon":{"axis":"x","description":"longitude","extent":[-180,180],"type":"spatial"},"time":{"extent":["1990-01-01T00:00:00Z","2014-12-31T23:59:59Z"],"step":"P0Y0M0DT1H0M0S","type":"temporal"}},"cube:variables":{"10_metre_U_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre U wind component","parameter_ID":"165","product_type":"forecast","shortName":"10u","standard_name":"10_metre_U_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/165"},"description":"This parameter is the eastward component of the 10m wind. It is the horizontal speed of air moving towards the east, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the V component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_V_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre V wind component","parameter_ID":"166","product_type":"forecast","shortName":"10v","standard_name":"10_metre_V_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/166"},"description":"This parameter is the northward component of the 10m wind. It is the horizontal speed of air moving towards the north, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the U component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_wind_speed(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre wind speed","parameter_ID":"207","product_type":"forecast","shortName":"10si","standard_name":"10_metre_wind_speed","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/207"},"description":"This parameter is the horizontal speed of the wind, or movement of air, at a height of ten metres above the surface of the Earth. The units of this parameter are metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.The eastward and northward components of the horizontal wind at 10m are also available as parameters.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"2_metre_dewpoint_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"2","levtype":"sfc","long_name":"2 metre dewpoint temperature","parameter_ID":"168","product_type":"forecast","shortName":"2d","standard_name":"2_metre_dewpoint_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/168"},"description":"This parameter is the temperature to which the air, at 2 metres above the surface of the Earth, would have to be cooled for saturation to occur.It is a measure of the humidity of the air. Combined with temperature and pressure, it can be used to calculate the relative humidity.2m dew point temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"2_metre_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"2","levtype":"sfc","long_name":"2 metre temperature","parameter_ID":"167","product_type":"forecast","shortName":"2t","standard_name":"2_metre_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/167"},"description":"This parameter is the temperature of air at 2m above the surface of land, sea or in-land waters.2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.Please note that the encodings listed here for s2s \u0026 uerra (which includes encodings for carra/cerra) include entries for Mean 2 metre temperature. The specific encoding for Mean 2 metre temperature can be found in 228004.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Geopotential(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Geopotential","parameter_ID":"129","product_type":"forecast","shortName":"z","standard_name":"Geopotential","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/129"},"description":"This parameter is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level.The geopotential height can be calculated by dividing the geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2). The geopotential height plays an important role in synoptic meteorology (analysis of weather patterns). Charts of geopotential height plotted at constant pressure levels (e.g., 300, 500 or 850 hPa) can be used to identify weather systems such as cyclones, anticyclones, troughs and ridges.At the surface of the Earth, this parameter shows the variations in geopotential (height) of the surface, and is often referred to as the orography.","dimensions":["lat","lon","time"],"type":"data","unit":"m2s-2"},"Land-sea_mask(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Land-sea mask","parameter_ID":"172","product_type":"forecast","shortName":"lsm","standard_name":"Land-sea_mask","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/172"},"description":"This parameter is the proportion of land, as opposed to ocean or inland waters (lakes, reservoirs, rivers and coastal waters), in agrid box.This parameter has values ranging between zero and one and is dimensionless.In cycles of the ECMWF Integrated Forecasting System (IFS) from CY41R1 (introduced in May 2015) onwards, grid boxes where this parameter has a value above 0.5 can be comprised of a mixture of land and inland water but not ocean. Grid boxes with a value of 0.5 and below can only be comprised of a water surface. In the latter case, the lake cover is used to determine how much of the water surface is ocean or inland water.In cycles of the IFS before CY41R1, grid boxes where this parameter has a value above 0.5 can only be comprised of land and those grid boxes with a value of 0.5 and below can only be comprised of ocean. In these older model cycles, there is no differentiation between ocean and inland water.","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Mean_sea_level_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Mean sea level pressure","parameter_ID":"151","product_type":"forecast","shortName":"msl","standard_name":"Mean_sea_level_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/151"},"description":"This parameter is the pressure (force per unit area) of the atmosphere adjusted to the height of mean sea level.It is a measure of the weight that all the air in a column vertically above the area of Earth's surface would have at that point, if the point were located at the mean sea level. It is calculated over all surfaces - land, sea and in-land water.Maps of mean sea level pressure are used to identify the locations of low and high pressure systems, often referred to as cyclones and anticyclones. Contours of mean sea level pressure also indicate the strength of the wind. Tightly packed contours show stronger winds.The units of this parameter are pascals (Pa).  Mean sea level pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb = 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Orography(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Orography","parameter_ID":"228002","product_type":"forecast","shortName":"orog","standard_name":"Orography","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/228002"},"dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Potential_vorticity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Potential vorticity","parameter_ID":"60","product_type":"forecast","shortName":"pv","standard_name":"Potential_vorticity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/60"},"description":"Potential vorticity is a measure of the capacity for air to rotate in the atmosphere. If we ignore the effects of heating and friction, potential vorticity is conserved following an air parcel. It is used to look for places where large wind storms are likely to originate and develop.  Potential vorticity increases strongly above the tropopause and therefore, it can also be used in studies related to the stratosphere and stratosphere-troposphere exchanges.Large wind storms develop when a column of air in the atmosphere starts to rotate. Potential vorticity is calculated from the wind, temperature and pressure across a column of air in the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K m2kg-1s-1"},"Relative_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Relative humidity","parameter_ID":"157","product_type":"forecast","shortName":"r","standard_name":"Relative_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/157"},"description":"This parameter is the water vapour pressure as a percentage of the value at which the air becomes saturated (the point at which water vapour begins to condense into liquid water or deposition into ice).For temperatures over 0°C (273.15 K) it is calculated for saturation over water. At temperatures below -23°C it is calculated for saturation over ice.  Between -23°C and 0°C this parameter is calculated by interpolating between the ice and water values using a quadratic function.See more information about the model's relative humidity calculation.","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Skin_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Skin temperature","parameter_ID":"235","product_type":"forecast","shortName":"skt","standard_name":"Skin_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235"},"description":"This parameter is the temperature of the surface of the Earth.The skin temperature is the theoretical temperature that is required to satisfy the surface energy balance. It represents the temperature of the uppermost surface layer, which has no heat capacity and so can respond instantaneously to changes in surface fluxes. Skin temperature is calculated differently over land and sea.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.See further information about the skin temperatureover landandover sea.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean skin temperature. The specific encoding for Mean skin temperature can be found in 235079.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Snow_depth_water_equivalent(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Snow depth water equivalent","parameter_ID":"228141","product_type":"forecast","shortName":"sd","standard_name":"Snow_depth_water_equivalent","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228141"},"description":"Snow depth water equivalent in kg m**-2 (mm) water equivalent.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean snow depth water equivalent. The specific encoding for Time-mean snow depth water equivalent can be found in 235078.[NOTE: See 141 for the equivalent parameter in \"m of water equivalent\"]","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Specific_cloud_liquid_water_content(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific cloud liquid water content","parameter_ID":"246","product_type":"forecast","shortName":"clwc","standard_name":"Specific_cloud_liquid_water_content","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/246"},"description":"This parameter is the mass of cloud liquid water droplets per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for agrid box.Water within clouds can be liquid or ice, or a combination of the two.See further information about the cloud formulation.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Specific_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific humidity","parameter_ID":"133","product_type":"forecast","shortName":"q","standard_name":"Specific_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/133"},"description":"This parameter is the mass of water vapour per kilogram of moist air.The total mass of moist air is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Surface_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Surface pressure","parameter_ID":"134","product_type":"forecast","shortName":"sp","standard_name":"Surface_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/134"},"description":"This parameter is the pressure (force per unit area) of the atmosphere on the surface of land, sea and in-land water.It is a measure of the weight of all the air in a column vertically above the area of the Earth's surface represented at a fixed point.Surface pressure is often used in combination with temperature to calculate air density.The strong variation of pressure with altitude makes it difficult to see the low and high pressure systems over mountainous areas, so mean sea level pressure, rather than surface pressure, is normally used for this purpose.The units of this parameter are Pascals (Pa). Surface pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb= 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Temperature(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Temperature","parameter_ID":"130","product_type":"forecast","shortName":"t","standard_name":"Temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/130"},"description":"This parameter is the temperature in the atmosphere.It has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.This parameter is available on multiple levels through the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_10_metre_U_wind_component(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre U wind component","parameter_ID":"235165","product_type":"forecast","shortName":"avg_10u","standard_name":"Time-mean_10_metre_U_wind_component","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235165"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_10_metre_V_wind_component(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre V wind component","parameter_ID":"235166","product_type":"forecast","shortName":"avg_10v","standard_name":"Time-mean_10_metre_V_wind_component","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235166"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_10_metre_wind_speed(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre wind speed","parameter_ID":"228005","product_type":"forecast","shortName":"avg_10ws","standard_name":"Time-mean_10_metre_wind_speed","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/228005"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_2_metre_dewpoint_temperature(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"2","levtype":"sfc","long_name":"Time-mean 2 metre dewpoint temperature","parameter_ID":"235168","product_type":"forecast","shortName":"avg_2d","standard_name":"Time-mean_2_metre_dewpoint_temperature","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235168"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_2_metre_temperature(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"2","levtype":"sfc","long_name":"Time-mean 2 metre temperature","parameter_ID":"228004","product_type":"forecast","shortName":"avg_2t","standard_name":"Time-mean_2_metre_temperature","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/228004"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_U_component_of_wind(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean U component of wind","parameter_ID":"235131","product_type":"forecast","shortName":"avg_u","standard_name":"Time-mean_U_component_of_wind","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235131"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_V_component_of_wind(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean V component of wind","parameter_ID":"235132","product_type":"forecast","shortName":"avg_v","standard_name":"Time-mean_V_component_of_wind","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235132"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_ice_velocity(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean eastward sea ice velocity","parameter_ID":"263003","product_type":"forecast","shortName":"avg_siue","standard_name":"Time-mean_eastward_sea_ice_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263003"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_ice_velocity(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean eastward sea ice velocity","parameter_ID":"263003","product_type":"forecast","shortName":"avg_siue","standard_name":"Time-mean_eastward_sea_ice_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263003"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_water_velocity(clmn_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean eastward sea water velocity","parameter_ID":"263506","product_type":"forecast","shortName":"avg_uoe","standard_name":"Time-mean_eastward_sea_water_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263506"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean eastward sea water velocity","parameter_ID":"263506","product_type":"forecast","shortName":"avg_uoe","standard_name":"Time-mean_eastward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263506"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_turbulent_surface_stress(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean eastward turbulent surface stress","parameter_ID":"235041","product_type":"forecast","shortName":"avg_iews","standard_name":"Time-mean_eastward_turbulent_surface_stress","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235041"},"dimensions":["lat","lon","time"],"type":"data","unit":"N m-2"},"Time-mean_eastward_turbulent_surface_stress(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean eastward turbulent surface stress","parameter_ID":"235041","product_type":"forecast","shortName":"avg_iews","standard_name":"Time-mean_eastward_turbulent_surface_stress","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235041"},"dimensions":["lat","lon","time"],"type":"data","unit":"N m-2"},"Time-mean_geopotential(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean geopotential","parameter_ID":"235129","product_type":"forecast","shortName":"avg_z","standard_name":"Time-mean_geopotential","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235129"},"dimensions":["lat","lon","time"],"type":"data","unit":"m2s-2"},"Time-mean_mean_sea_level_pressure(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean mean sea level pressure","parameter_ID":"235151","product_type":"forecast","shortName":"avg_msl","standard_name":"Time-mean_mean_sea_level_pressure","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235151"},"dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Time-mean_moisture_flux(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean moisture flux","parameter_ID":"235043","product_type":"forecast","shortName":"avg_ie","standard_name":"Time-mean_moisture_flux","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235043"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_moisture_flux(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean moisture flux","parameter_ID":"235043","product_type":"forecast","shortName":"avg_ie","standard_name":"Time-mean_moisture_flux","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235043"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_northward_sea_ice_velocity(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean northward sea ice velocity","parameter_ID":"263004","product_type":"forecast","shortName":"avg_sivn","standard_name":"Time-mean_northward_sea_ice_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263004"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_sea_ice_velocity(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean northward sea ice velocity","parameter_ID":"263004","product_type":"forecast","shortName":"avg_sivn","standard_name":"Time-mean_northward_sea_ice_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263004"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_sea_water_velocity(clmn_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean northward sea water velocity","parameter_ID":"263505","product_type":"forecast","shortName":"avg_von","standard_name":"Time-mean_northward_sea_water_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263505"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_turbulent_surface_stress(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean northward turbulent surface stress","parameter_ID":"235042","product_type":"forecast","shortName":"avg_inss","standard_name":"Time-mean_northward_turbulent_surface_stress","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235042"},"dimensions":["lat","lon","time"],"type":"data","unit":"N m-2"},"Time-mean_northward_turbulent_surface_stress(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean northward turbulent surface stress","parameter_ID":"235042","product_type":"forecast","shortName":"avg_inss","standard_name":"Time-mean_northward_turbulent_surface_stress","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235042"},"dimensions":["lat","lon","time"],"type":"data","unit":"N m-2"},"Time-mean_potential_vorticity(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean potential vorticity","parameter_ID":"235100","product_type":"forecast","shortName":"avg_pv","standard_name":"Time-mean_potential_vorticity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235100"},"dimensions":["lat","lon","time"],"type":"data","unit":"K m2kg-1s-1"},"Time-mean_relative_humidity(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean relative humidity","parameter_ID":"235157","product_type":"forecast","shortName":"avg_r","standard_name":"Time-mean_relative_humidity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235157"},"dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Time-mean_sea_ice_area_fraction(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea ice area fraction","parameter_ID":"263001","product_type":"forecast","shortName":"avg_siconc","standard_name":"Time-mean_sea_ice_area_fraction","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263001"},"dimensions":["lat","lon","time"],"type":"data","unit":"Fraction"},"Time-mean_sea_ice_area_fraction(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea ice area fraction","parameter_ID":"263001","product_type":"forecast","shortName":"avg_siconc","standard_name":"Time-mean_sea_ice_area_fraction","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263001"},"dimensions":["lat","lon","time"],"type":"data","unit":"Fraction"},"Time-mean_sea_ice_thickness(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea ice thickness","parameter_ID":"263000","product_type":"forecast","shortName":"avg_sithick","standard_name":"Time-mean_sea_ice_thickness","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263000"},"dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Time-mean_sea_ice_thickness(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea ice thickness","parameter_ID":"263000","product_type":"forecast","shortName":"avg_sithick","standard_name":"Time-mean_sea_ice_thickness","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263000"},"dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Time-mean_sea_ice_volume_per_unit_area(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea ice volume per unit area","parameter_ID":"263008","product_type":"forecast","shortName":"avg_sivol","standard_name":"Time-mean_sea_ice_volume_per_unit_area","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263008"},"dimensions":["lat","lon","time"],"type":"data","unit":"m3m-2"},"Time-mean_sea_ice_volume_per_unit_area(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea ice volume per unit area","parameter_ID":"263008","product_type":"forecast","shortName":"avg_sivol","standard_name":"Time-mean_sea_ice_volume_per_unit_area","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263008"},"dimensions":["lat","lon","time"],"type":"data","unit":"m3m-2"},"Time-mean_sea_surface_height(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea surface height","parameter_ID":"263124","product_type":"forecast","shortName":"avg_zos","standard_name":"Time-mean_sea_surface_height","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263124"},"dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Time-mean_sea_surface_height(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea surface height","parameter_ID":"263124","product_type":"forecast","shortName":"avg_zos","standard_name":"Time-mean_sea_surface_height","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263124"},"dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Time-mean_sea_surface_practical_salinity(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea surface practical salinity","parameter_ID":"263100","product_type":"forecast","shortName":"avg_sos","standard_name":"Time-mean_sea_surface_practical_salinity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263100"},"dimensions":["lat","lon","time"],"type":"data","unit":"g kg-1"},"Time-mean_sea_surface_practical_salinity(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea surface practical 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rate","parameter_ID":"235055","product_type":"forecast","shortName":"avg_tprate","standard_name":"Time-mean_total_precipitation_rate","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235055"},"description":"Time-mean total precipitation rate, or time-mean total precipitation flux. This parameter is on level \"Ground or water surface\" (typeOfFirstFixedSurface=1). For this parameter on other levels, please use 235013.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_total_precipitation_rate(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total precipitation rate","parameter_ID":"235055","product_type":"forecast","shortName":"avg_tprate","standard_name":"Time-mean_total_precipitation_rate","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235055"},"description":"Time-mean total precipitation rate, or time-mean total precipitation flux. This parameter is on level \"Ground or water surface\" (typeOfFirstFixedSurface=1). For this parameter on other levels, please use 235013.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_total_snowfall_rate_water_equivalent(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total snowfall rate water equivalent","parameter_ID":"235031","product_type":"forecast","shortName":"avg_tsrwe","standard_name":"Time-mean_total_snowfall_rate_water_equivalent","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235031"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_total_snowfall_rate_water_equivalent(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total snowfall rate water equivalent","parameter_ID":"235031","product_type":"forecast","shortName":"avg_tsrwe","standard_name":"Time-mean_total_snowfall_rate_water_equivalent","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235031"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_vertical_velocity(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean vertical velocity","parameter_ID":"235135","product_type":"forecast","shortName":"avg_w","standard_name":"Time-mean_vertical_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235135"},"dimensions":["lat","lon","time"],"type":"data","unit":"Pa s-1"},"Time-mean_vertically-integrated_heat_content_in_the_upper_300_m(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"300","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 300 m","parameter_ID":"263121","product_type":"forecast","shortName":"avg_hc300m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_300_m","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263121"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_vertically-integrated_heat_content_in_the_upper_300_m(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"300","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 300 m","parameter_ID":"263121","product_type":"forecast","shortName":"avg_hc300m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_300_m","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263121"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"700","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 700 m","parameter_ID":"263122","product_type":"forecast","shortName":"avg_hc700m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263122"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"700","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 700 m","parameter_ID":"263122","product_type":"forecast","shortName":"avg_hc700m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263122"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_volumetric_soil_moisture(clmn_sol)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Time-mean volumetric soil moisture","parameter_ID":"235077","product_type":"forecast","shortName":"avg_vsw","standard_name":"Time-mean_volumetric_soil_moisture","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235077"},"dimensions":["lat","lon","time"],"type":"data","unit":"m3m-3"},"Time_mean_top_downward_short-wave_radiation_flux(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time mean top downward short-wave radiation flux","parameter_ID":"235053","product_type":"forecast","shortName":"avg_tdswrf","standard_name":"Time_mean_top_downward_short-wave_radiation_flux","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235053"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Time_mean_top_downward_short-wave_radiation_flux(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time mean top downward short-wave radiation flux","parameter_ID":"235053","product_type":"forecast","shortName":"avg_tdswrf","standard_name":"Time_mean_top_downward_short-wave_radiation_flux","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235053"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Total_Cloud_Cover(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total Cloud Cover","parameter_ID":"228164","product_type":"forecast","shortName":"tcc","standard_name":"Total_Cloud_Cover","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228164"},"description":"[NOTE: See 164 for the equivalent parameter in \"(0-1)\"]","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Total_column_cloud_ice_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud ice water","parameter_ID":"79","product_type":"forecast","shortName":"tciw","standard_name":"Total_column_cloud_ice_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/79"},"description":"This parameter is the amount of ice contained within clouds in a column extending from the surface of the Earth to the top of the atmosphere. Snow (aggregated ice crystals) is not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The  ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_cloud_liquid_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud liquid water","parameter_ID":"78","product_type":"forecast","shortName":"tclw","standard_name":"Total_column_cloud_liquid_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/78"},"description":"This parameter is the amount of liquid water contained within cloud droplets in a column extending from the surface of the Earth to the top of the atmosphere. Rain water droplets, which are much larger in size (and mass), are not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_vertically-integrated_water_vapour(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column vertically-integrated water vapour","parameter_ID":"137","product_type":"forecast","shortName":"tcwv","standard_name":"Total_column_vertically-integrated_water_vapour","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/137"},"description":"This parameter is the total amount of water vapour in a column extending from the surface of the Earth to the top of the atmosphere.This parameter represents the area averaged value for agrid box.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column water","parameter_ID":"136","product_type":"forecast","shortName":"tcw","standard_name":"Total_column_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/136"},"description":"This parameter is the sum of water vapour, liquid water, cloud ice, rain and snow in a column extending from the surface of the Earth to the top of the atmosphere. In old versions of the ECMWF model (IFS), rain and snow were not accounted for.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"U_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"U component of wind","parameter_ID":"131","product_type":"forecast","shortName":"u","standard_name":"U_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/131"},"description":"This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east, in metres per second. A negative sign thus indicates air movement towards the west.This parameter can be combined with the V component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"V_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"V component of wind","parameter_ID":"132","product_type":"forecast","shortName":"v","standard_name":"V_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/132"},"description":"This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north, in metres per second. A negative sign thus indicates air movement towards the south.This parameter can be combined with the U component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Vertical_velocity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Vertical velocity","parameter_ID":"135","product_type":"forecast","shortName":"w","standard_name":"Vertical_velocity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/135"},"description":"This parameter is the speed of air motion in the upward or downward direction. The ECMWF Integrated Forecasting System (IFS) uses a pressure based vertical co-ordinate system and pressure decreases with height, therefore negative values of vertical velocity indicate upward motion.Vertical velocity can be useful to understand the large-scale dynamics of the atmosphere, including areas of upward motion/ascent (negative values) and downward motion/subsidence (positive values).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa s-1"},"Volumetric_soil_moisture(clte_sol)":{"attrs":{"encoding":"instantaneous","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Volumetric soil moisture","parameter_ID":"260199","product_type":"forecast","shortName":"vsw","standard_name":"Volumetric_soil_moisture","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/260199"},"description":"Please note that the encoding listed here for uerra (which includes carra/cerra) includes entries for Time-mean volumetric soil moisture. The specific encoding for Time-mean volumetric soil moisture can be found in 235077.","dimensions":["lat","lon","time"],"type":"data","unit":"m3m-3"}},"sci:publications":[{"doi":"10.1109/MCSE.2023.3260519","citation":"N. Wedi et al., Destination Earth: High-Performance Computing for Weather and Climate, in Computing in Science \u0026 Engineering, vol. 24, no. 6, pp. 29-37, Nov.-Dec. 2022,"},{"doi":"10.21957/d3f982672e","citation":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"dedl:short_description":"The Climate Change Adaptation Digital Twin provides global climate projections and sector-specific information over multiple decades at high resolution via a unified framework combining advanced Earth system models, impact assessments, and observations. This Generation-2, Realization-1 Collection gives access to 'Historical Simulation' data based on the 'ICON' model."},{"type":"Collection","title":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Historical Simulation - IFS-FESOM - Generation-2 - Realization-1","id":"EO.ECMWF.DAT.D1.DT_CLIMATE.G2.BASELINE_HIST_IFS-FESOM.R1","description":"The DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities by providing innovative climate information on multi-decadal timescales, globally, at scales at which many impacts of climate change are observed. It combines cutting-edge global Earth-system models, impact-sector applications and observations into a unified framework to provide global climate projections and impact-sector information on multi-decadal timescales (1990 to ~2050), at very high spatial resolutions (5 to 10 km).\n\nThe Climate DT represents the first ever attempt to operationalise the production of global multi-decadal climate projections, leveraging the world-leading supercomputing facilities of the EuroHPC Joint Undertaking along with some of the leading European climate models. A concise overview of what the Climate DT aims to achieve, and of the different concepts essential for an understanding of the Digital Twin’s characteristics, is included in the [Climate DT factsheet](https://destine.ecmwf.int/wp-content/uploads/2024/06/2024.06.07_Climate-DT-Fact-Sheet_V7-2.pdf)\n\n## Historical Simulation \n\n Starting in 1990, the forcing follows observed changes in greenhouse gases, aerosols etc. until 2020. The simulations are using standardised CMIP6 forcing. Historical simulations are essential for model evaluation and quality control as they allow a comparison to observations.\n\n## Models\n\nThe Climate DT exploits and further evolves a new generation of global storm-resolving and eddy-rich models built through a cooperative model development approach. For more information on models please click [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#models)\n\n## Simulations\n\nThe Climate DT team carries out several types of digital twin simulations on the EuroHPC supercomputers.  Multi-decadal simulations are produced to cover the recent past (from 1990) and possible future evolutions of the climate up to 2050. See [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#simulations) for more information on Simulations\n\n## Parameters\n\nBelow we see the list of parameters extracted from the 'DestinE Climate DT data portfolio', for more information please refer to the pages [Climate DT Phase2 CLTE Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters) and [Climate DT Phase2 CLMN Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters)","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.BASELINE_HIST_IFS-FESOM.R1/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.BASELINE_HIST_IFS-FESOM.R1/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.BASELINE_HIST_IFS-FESOM.R1/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.BASELINE_HIST_IFS-FESOM.R1","title":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Historical Simulation - IFS-FESOM - Generation-2 - Realization-1"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters","title":"DestinE ClimateDT Phase 2 CLTE Parameters"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters","title":"DestinE ClimateDT Phase 2 CLMN Parameters"},{"rel":"cite-as","type":"text/html","href":"https://dl.acm.org/doi/abs/10.1109/MCSE.2023.3260519","title":"Destination Earth: High-Performance Computing for Weather and Climate"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.21957/d3f982672e","title":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"assets":{"thumbnail":{"href":"https://s3.central.data.destination-earth.eu/swift/v1/dedl-public/collections/climate-dt-min.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1990-01-01T00:00:00Z","2014-12-31T23:59:59Z"]]}},"license":"CC-BY-4.0","keywords":["Digital Twins","Climate Change","Atmosphere","Ocean","Land","Sea Ice","Snow","Soil","Earth","High Performance Computing","Decision Making","Europe","Meteorology","class:d1","dataset:climate-dt","generation:2","expver:0001","stream:clmn,clte","type:fc","activity:baseline","experiment:hist","realization:1","model:IFS-FESOM","resolution:standard,high","levtype:hl,o2d,o3d,pl,sfc,sol"],"summaries":{"federation:backends":["dedt_lumi"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/datacube/v2.1.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["producer","processor","licensor"],"url":"https://www.ecmwf.int/"}],"created":"2024-04-11T22:31:55Z","updated":"2026-04-24T10:24:59Z","published":"2024-04-11T22:31:55Z","cube:dimensions":{"lat":{"axis":"y","description":"latitude","extent":[-90,90],"type":"spatial"},"lon":{"axis":"x","description":"longitude","extent":[-180,180],"type":"spatial"},"time":{"extent":["1990-01-01T00:00:00Z","2014-12-31T23:59:59Z"],"step":"P0Y0M0DT1H0M0S","type":"temporal"}},"cube:variables":{"10_metre_U_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre U wind component","parameter_ID":"165","product_type":"forecast","shortName":"10u","standard_name":"10_metre_U_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/165"},"description":"This parameter is the eastward component of the 10m wind. It is the horizontal speed of air moving towards the east, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the V component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_V_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre V wind component","parameter_ID":"166","product_type":"forecast","shortName":"10v","standard_name":"10_metre_V_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/166"},"description":"This parameter is the northward component of the 10m wind. It is the horizontal speed of air moving towards the north, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the U component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_wind_speed(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre wind speed","parameter_ID":"207","product_type":"forecast","shortName":"10si","standard_name":"10_metre_wind_speed","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/207"},"description":"This parameter is the horizontal speed of the wind, or movement of air, at a height of ten metres above the surface of the Earth. The units of this parameter are metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.The eastward and northward components of the horizontal wind at 10m are also available as parameters.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"2_metre_dewpoint_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"2","levtype":"sfc","long_name":"2 metre dewpoint temperature","parameter_ID":"168","product_type":"forecast","shortName":"2d","standard_name":"2_metre_dewpoint_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/168"},"description":"This parameter is the temperature to which the air, at 2 metres above the surface of the Earth, would have to be cooled for saturation to occur.It is a measure of the humidity of the air. Combined with temperature and pressure, it can be used to calculate the relative humidity.2m dew point temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"2_metre_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"2","levtype":"sfc","long_name":"2 metre temperature","parameter_ID":"167","product_type":"forecast","shortName":"2t","standard_name":"2_metre_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/167"},"description":"This parameter is the temperature of air at 2m above the surface of land, sea or in-land waters.2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.Please note that the encodings listed here for s2s \u0026 uerra (which includes encodings for carra/cerra) include entries for Mean 2 metre temperature. The specific encoding for Mean 2 metre temperature can be found in 228004.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Geopotential(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Geopotential","parameter_ID":"129","product_type":"forecast","shortName":"z","standard_name":"Geopotential","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/129"},"description":"This parameter is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level.The geopotential height can be calculated by dividing the geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2). The geopotential height plays an important role in synoptic meteorology (analysis of weather patterns). Charts of geopotential height plotted at constant pressure levels (e.g., 300, 500 or 850 hPa) can be used to identify weather systems such as cyclones, anticyclones, troughs and ridges.At the surface of the Earth, this parameter shows the variations in geopotential (height) of the surface, and is often referred to as the orography.","dimensions":["lat","lon","time"],"type":"data","unit":"m2s-2"},"Land-sea_mask(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Land-sea mask","parameter_ID":"172","product_type":"forecast","shortName":"lsm","standard_name":"Land-sea_mask","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/172"},"description":"This parameter is the proportion of land, as opposed to ocean or inland waters (lakes, reservoirs, rivers and coastal waters), in agrid box.This parameter has values ranging between zero and one and is dimensionless.In cycles of the ECMWF Integrated Forecasting System (IFS) from CY41R1 (introduced in May 2015) onwards, grid boxes where this parameter has a value above 0.5 can be comprised of a mixture of land and inland water but not ocean. Grid boxes with a value of 0.5 and below can only be comprised of a water surface. In the latter case, the lake cover is used to determine how much of the water surface is ocean or inland water.In cycles of the IFS before CY41R1, grid boxes where this parameter has a value above 0.5 can only be comprised of land and those grid boxes with a value of 0.5 and below can only be comprised of ocean. In these older model cycles, there is no differentiation between ocean and inland water.","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Mean_sea_level_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Mean sea level pressure","parameter_ID":"151","product_type":"forecast","shortName":"msl","standard_name":"Mean_sea_level_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/151"},"description":"This parameter is the pressure (force per unit area) of the atmosphere adjusted to the height of mean sea level.It is a measure of the weight that all the air in a column vertically above the area of Earth's surface would have at that point, if the point were located at the mean sea level. It is calculated over all surfaces - land, sea and in-land water.Maps of mean sea level pressure are used to identify the locations of low and high pressure systems, often referred to as cyclones and anticyclones. Contours of mean sea level pressure also indicate the strength of the wind. Tightly packed contours show stronger winds.The units of this parameter are pascals (Pa).  Mean sea level pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb = 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Orography(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Orography","parameter_ID":"228002","product_type":"forecast","shortName":"orog","standard_name":"Orography","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/228002"},"dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Potential_vorticity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Potential vorticity","parameter_ID":"60","product_type":"forecast","shortName":"pv","standard_name":"Potential_vorticity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/60"},"description":"Potential vorticity is a measure of the capacity for air to rotate in the atmosphere. If we ignore the effects of heating and friction, potential vorticity is conserved following an air parcel. It is used to look for places where large wind storms are likely to originate and develop.  Potential vorticity increases strongly above the tropopause and therefore, it can also be used in studies related to the stratosphere and stratosphere-troposphere exchanges.Large wind storms develop when a column of air in the atmosphere starts to rotate. Potential vorticity is calculated from the wind, temperature and pressure across a column of air in the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K m2kg-1s-1"},"Relative_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Relative humidity","parameter_ID":"157","product_type":"forecast","shortName":"r","standard_name":"Relative_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/157"},"description":"This parameter is the water vapour pressure as a percentage of the value at which the air becomes saturated (the point at which water vapour begins to condense into liquid water or deposition into ice).For temperatures over 0°C (273.15 K) it is calculated for saturation over water. At temperatures below -23°C it is calculated for saturation over ice.  Between -23°C and 0°C this parameter is calculated by interpolating between the ice and water values using a quadratic function.See more information about the model's relative humidity calculation.","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Skin_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Skin temperature","parameter_ID":"235","product_type":"forecast","shortName":"skt","standard_name":"Skin_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235"},"description":"This parameter is the temperature of the surface of the Earth.The skin temperature is the theoretical temperature that is required to satisfy the surface energy balance. It represents the temperature of the uppermost surface layer, which has no heat capacity and so can respond instantaneously to changes in surface fluxes. Skin temperature is calculated differently over land and sea.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.See further information about the skin temperatureover landandover sea.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean skin temperature. The specific encoding for Mean skin temperature can be found in 235079.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Snow_depth_water_equivalent(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Snow depth water equivalent","parameter_ID":"228141","product_type":"forecast","shortName":"sd","standard_name":"Snow_depth_water_equivalent","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228141"},"description":"Snow depth water equivalent in kg m**-2 (mm) water equivalent.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean snow depth water equivalent. The specific encoding for Time-mean snow depth water equivalent can be found in 235078.[NOTE: See 141 for the equivalent parameter in \"m of water equivalent\"]","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Snow_depth_water_equivalent(clte_sol)":{"attrs":{"encoding":"instantaneous","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Snow depth water equivalent","parameter_ID":"228141","product_type":"forecast","shortName":"sd","standard_name":"Snow_depth_water_equivalent","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228141"},"description":"Snow depth water equivalent in kg m**-2 (mm) water equivalent.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean snow depth water equivalent. The specific encoding for Time-mean snow depth water equivalent can be found in 235078.[NOTE: See 141 for the equivalent parameter in \"m of water equivalent\"]","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Specific_cloud_liquid_water_content(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific cloud liquid water content","parameter_ID":"246","product_type":"forecast","shortName":"clwc","standard_name":"Specific_cloud_liquid_water_content","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/246"},"description":"This parameter is the mass of cloud liquid water droplets per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for agrid box.Water within clouds can be liquid or ice, or a combination of the two.See further information about the cloud formulation.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Specific_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific humidity","parameter_ID":"133","product_type":"forecast","shortName":"q","standard_name":"Specific_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/133"},"description":"This parameter is the mass of water vapour per kilogram of moist air.The total mass of moist air is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Surface_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Surface pressure","parameter_ID":"134","product_type":"forecast","shortName":"sp","standard_name":"Surface_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/134"},"description":"This parameter is the pressure (force per unit area) of the atmosphere on the surface of land, sea and in-land water.It is a measure of the weight of all the air in a column vertically above the area of the Earth's surface represented at a fixed point.Surface pressure is often used in combination with temperature to calculate air density.The strong variation of pressure with altitude makes it difficult to see the low and high pressure systems over mountainous areas, so mean sea level pressure, rather than surface pressure, is normally used for this purpose.The units of this parameter are Pascals (Pa). Surface pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb= 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Temperature(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Temperature","parameter_ID":"130","product_type":"forecast","shortName":"t","standard_name":"Temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/130"},"description":"This parameter is the temperature in the atmosphere.It has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.This parameter is available on multiple levels through the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_10_metre_U_wind_component(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre U wind component","parameter_ID":"235165","product_type":"forecast","shortName":"avg_10u","standard_name":"Time-mean_10_metre_U_wind_component","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235165"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_10_metre_V_wind_component(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre V wind component","parameter_ID":"235166","product_type":"forecast","shortName":"avg_10v","standard_name":"Time-mean_10_metre_V_wind_component","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235166"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_10_metre_wind_speed(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre wind speed","parameter_ID":"228005","product_type":"forecast","shortName":"avg_10ws","standard_name":"Time-mean_10_metre_wind_speed","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/228005"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_2_metre_dewpoint_temperature(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"2","levtype":"sfc","long_name":"Time-mean 2 metre dewpoint temperature","parameter_ID":"235168","product_type":"forecast","shortName":"avg_2d","standard_name":"Time-mean_2_metre_dewpoint_temperature","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235168"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_2_metre_temperature(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"2","levtype":"sfc","long_name":"Time-mean 2 metre temperature","parameter_ID":"228004","product_type":"forecast","shortName":"avg_2t","standard_name":"Time-mean_2_metre_temperature","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/228004"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_U_component_of_wind(clmn_hl)":{"attrs":{"encoding":"mean","levelist":"100","levtype":"hl","long_name":"Time-mean U component of wind","parameter_ID":"235131","product_type":"forecast","shortName":"avg_u","standard_name":"Time-mean_U_component_of_wind","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235131"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_U_component_of_wind(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean U component of wind","parameter_ID":"235131","product_type":"forecast","shortName":"avg_u","standard_name":"Time-mean_U_component_of_wind","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235131"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_V_component_of_wind(clmn_hl)":{"attrs":{"encoding":"mean","levelist":"100","levtype":"hl","long_name":"Time-mean V component of wind","parameter_ID":"235132","product_type":"forecast","shortName":"avg_v","standard_name":"Time-mean_V_component_of_wind","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235132"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_V_component_of_wind(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean V component of wind","parameter_ID":"235132","product_type":"forecast","shortName":"avg_v","standard_name":"Time-mean_V_component_of_wind","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235132"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_ice_velocity(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean eastward sea ice velocity","parameter_ID":"263003","product_type":"forecast","shortName":"avg_siue","standard_name":"Time-mean_eastward_sea_ice_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263003"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_ice_velocity(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean eastward sea ice velocity","parameter_ID":"263003","product_type":"forecast","shortName":"avg_siue","standard_name":"Time-mean_eastward_sea_ice_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263003"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_water_velocity(clmn_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean eastward sea water velocity","parameter_ID":"263506","product_type":"forecast","shortName":"avg_uoe","standard_name":"Time-mean_eastward_sea_water_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263506"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean eastward sea water velocity","parameter_ID":"263506","product_type":"forecast","shortName":"avg_uoe","standard_name":"Time-mean_eastward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263506"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_turbulent_surface_stress(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean eastward turbulent surface stress","parameter_ID":"235041","product_type":"forecast","shortName":"avg_iews","standard_name":"Time-mean_eastward_turbulent_surface_stress","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235041"},"dimensions":["lat","lon","time"],"type":"data","unit":"N m-2"},"Time-mean_eastward_turbulent_surface_stress(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean eastward turbulent surface stress","parameter_ID":"235041","product_type":"forecast","shortName":"avg_iews","standard_name":"Time-mean_eastward_turbulent_surface_stress","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235041"},"dimensions":["lat","lon","time"],"type":"data","unit":"N m-2"},"Time-mean_geopotential(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean geopotential","parameter_ID":"235129","product_type":"forecast","shortName":"avg_z","standard_name":"Time-mean_geopotential","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235129"},"dimensions":["lat","lon","time"],"type":"data","unit":"m2s-2"},"Time-mean_mean_sea_level_pressure(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean mean sea level pressure","parameter_ID":"235151","product_type":"forecast","shortName":"avg_msl","standard_name":"Time-mean_mean_sea_level_pressure","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235151"},"dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Time-mean_moisture_flux(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean moisture flux","parameter_ID":"235043","product_type":"forecast","shortName":"avg_ie","standard_name":"Time-mean_moisture_flux","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235043"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_moisture_flux(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean moisture flux","parameter_ID":"235043","product_type":"forecast","shortName":"avg_ie","standard_name":"Time-mean_moisture_flux","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235043"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_northward_sea_ice_velocity(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean northward sea ice velocity","parameter_ID":"263004","product_type":"forecast","shortName":"avg_sivn","standard_name":"Time-mean_northward_sea_ice_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263004"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_sea_ice_velocity(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean northward sea ice velocity","parameter_ID":"263004","product_type":"forecast","shortName":"avg_sivn","standard_name":"Time-mean_northward_sea_ice_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263004"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_sea_water_velocity(clmn_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean northward sea water velocity","parameter_ID":"263505","product_type":"forecast","shortName":"avg_von","standard_name":"Time-mean_northward_sea_water_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263505"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean northward sea water velocity","parameter_ID":"263505","product_type":"forecast","shortName":"avg_von","standard_name":"Time-mean_northward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263505"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_turbulent_surface_stress(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean northward turbulent surface 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flux","parameter_ID":"235040","product_type":"forecast","shortName":"avg_tnlwrf","standard_name":"Time-mean_top_net_long-wave_radiation_flux","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235040"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Time-mean_top_net_long-wave_radiation_flux(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean top net long-wave radiation flux","parameter_ID":"235040","product_type":"forecast","shortName":"avg_tnlwrf","standard_name":"Time-mean_top_net_long-wave_radiation_flux","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235040"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Time-mean_top_net_long-wave_radiation_flux,_clear_sky(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean top net long-wave radiation flux, clear sky","parameter_ID":"235050","product_type":"forecast","shortName":"avg_tnlwrfcs","standard_name":"Time-mean_top_net_long-wave_radiation_flux,_clear_sky","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235050"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Time-mean_top_net_long-wave_radiation_flux,_clear_sky(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean top net long-wave radiation flux, clear sky","parameter_ID":"235050","product_type":"forecast","shortName":"avg_tnlwrfcs","standard_name":"Time-mean_top_net_long-wave_radiation_flux,_clear_sky","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235050"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Time-mean_top_net_short-wave_radiation_flux(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean top net short-wave radiation flux","parameter_ID":"235039","product_type":"forecast","shortName":"avg_tnswrf","standard_name":"Time-mean_top_net_short-wave_radiation_flux","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235039"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Time-mean_top_net_short-wave_radiation_flux(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean top net short-wave radiation flux","parameter_ID":"235039","product_type":"forecast","shortName":"avg_tnswrf","standard_name":"Time-mean_top_net_short-wave_radiation_flux","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235039"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Time-mean_top_net_short-wave_radiation_flux,_clear_sky(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean top net short-wave radiation flux, clear sky","parameter_ID":"235049","product_type":"forecast","shortName":"avg_tnswrfcs","standard_name":"Time-mean_top_net_short-wave_radiation_flux,_clear_sky","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235049"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Time-mean_top_net_short-wave_radiation_flux,_clear_sky(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean top net short-wave radiation flux, clear sky","parameter_ID":"235049","product_type":"forecast","shortName":"avg_tnswrfcs","standard_name":"Time-mean_top_net_short-wave_radiation_flux,_clear_sky","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235049"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Time-mean_total_cloud_cover(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total cloud cover","parameter_ID":"235288","product_type":"forecast","shortName":"avg_tcc","standard_name":"Time-mean_total_cloud_cover","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235288"},"dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Time-mean_total_column_cloud_ice_water(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total column cloud ice water","parameter_ID":"235088","product_type":"forecast","shortName":"avg_tciw","standard_name":"Time-mean_total_column_cloud_ice_water","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235088"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Time-mean_total_column_heat_content(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean total column heat content","parameter_ID":"263123","product_type":"forecast","shortName":"avg_hcbtm","standard_name":"Time-mean_total_column_heat_content","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263123"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_total_column_heat_content(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean total column heat content","parameter_ID":"263123","product_type":"forecast","shortName":"avg_hcbtm","standard_name":"Time-mean_total_column_heat_content","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263123"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_total_column_liquid_water(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total column liquid water","parameter_ID":"235087","product_type":"forecast","shortName":"avg_tclw","standard_name":"Time-mean_total_column_liquid_water","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235087"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Time-mean_total_column_vertically-integrated_water_vapour(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total column vertically-integrated water vapour","parameter_ID":"235137","product_type":"forecast","shortName":"avg_tcwv","standard_name":"Time-mean_total_column_vertically-integrated_water_vapour","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235137"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Time-mean_total_column_water(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total column water","parameter_ID":"235136","product_type":"forecast","shortName":"avg_tcw","standard_name":"Time-mean_total_column_water","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235136"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Time-mean_total_precipitation_rate(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total precipitation rate","parameter_ID":"235055","product_type":"forecast","shortName":"avg_tprate","standard_name":"Time-mean_total_precipitation_rate","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235055"},"description":"Time-mean total precipitation rate, or time-mean total precipitation flux. This parameter is on level \"Ground or water surface\" (typeOfFirstFixedSurface=1). For this parameter on other levels, please use 235013.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_total_precipitation_rate(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total precipitation rate","parameter_ID":"235055","product_type":"forecast","shortName":"avg_tprate","standard_name":"Time-mean_total_precipitation_rate","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235055"},"description":"Time-mean total precipitation rate, or time-mean total precipitation flux. This parameter is on level \"Ground or water surface\" (typeOfFirstFixedSurface=1). For this parameter on other levels, please use 235013.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_total_snowfall_rate_water_equivalent(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total snowfall rate water equivalent","parameter_ID":"235031","product_type":"forecast","shortName":"avg_tsrwe","standard_name":"Time-mean_total_snowfall_rate_water_equivalent","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235031"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_total_snowfall_rate_water_equivalent(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total snowfall rate water equivalent","parameter_ID":"235031","product_type":"forecast","shortName":"avg_tsrwe","standard_name":"Time-mean_total_snowfall_rate_water_equivalent","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235031"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_upward_sea_water_velocity(clmn_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean upward sea water velocity","parameter_ID":"263507","product_type":"forecast","shortName":"avg_wo","standard_name":"Time-mean_upward_sea_water_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263507"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_upward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean upward sea water velocity","parameter_ID":"263507","product_type":"forecast","shortName":"avg_wo","standard_name":"Time-mean_upward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263507"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_vertical_velocity(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean vertical velocity","parameter_ID":"235135","product_type":"forecast","shortName":"avg_w","standard_name":"Time-mean_vertical_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235135"},"dimensions":["lat","lon","time"],"type":"data","unit":"Pa s-1"},"Time-mean_vertically-integrated_heat_content_in_the_upper_300_m(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"300","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 300 m","parameter_ID":"263121","product_type":"forecast","shortName":"avg_hc300m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_300_m","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263121"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_vertically-integrated_heat_content_in_the_upper_300_m(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"300","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 300 m","parameter_ID":"263121","product_type":"forecast","shortName":"avg_hc300m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_300_m","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263121"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"700","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 700 m","parameter_ID":"263122","product_type":"forecast","shortName":"avg_hc700m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263122"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"700","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 700 m","parameter_ID":"263122","product_type":"forecast","shortName":"avg_hc700m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263122"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_volumetric_soil_moisture(clmn_sol)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Time-mean volumetric soil moisture","parameter_ID":"235077","product_type":"forecast","shortName":"avg_vsw","standard_name":"Time-mean_volumetric_soil_moisture","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235077"},"dimensions":["lat","lon","time"],"type":"data","unit":"m3m-3"},"Time_mean_top_downward_short-wave_radiation_flux(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time mean top downward short-wave radiation flux","parameter_ID":"235053","product_type":"forecast","shortName":"avg_tdswrf","standard_name":"Time_mean_top_downward_short-wave_radiation_flux","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235053"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Time_mean_top_downward_short-wave_radiation_flux(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time mean top downward short-wave radiation flux","parameter_ID":"235053","product_type":"forecast","shortName":"avg_tdswrf","standard_name":"Time_mean_top_downward_short-wave_radiation_flux","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235053"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Total_Cloud_Cover(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total Cloud Cover","parameter_ID":"228164","product_type":"forecast","shortName":"tcc","standard_name":"Total_Cloud_Cover","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228164"},"description":"[NOTE: See 164 for the equivalent parameter in \"(0-1)\"]","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Total_column_cloud_ice_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud ice water","parameter_ID":"79","product_type":"forecast","shortName":"tciw","standard_name":"Total_column_cloud_ice_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/79"},"description":"This parameter is the amount of ice contained within clouds in a column extending from the surface of the Earth to the top of the atmosphere. Snow (aggregated ice crystals) is not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The  ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_cloud_liquid_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud liquid water","parameter_ID":"78","product_type":"forecast","shortName":"tclw","standard_name":"Total_column_cloud_liquid_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/78"},"description":"This parameter is the amount of liquid water contained within cloud droplets in a column extending from the surface of the Earth to the top of the atmosphere. Rain water droplets, which are much larger in size (and mass), are not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_vertically-integrated_water_vapour(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column vertically-integrated water vapour","parameter_ID":"137","product_type":"forecast","shortName":"tcwv","standard_name":"Total_column_vertically-integrated_water_vapour","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/137"},"description":"This parameter is the total amount of water vapour in a column extending from the surface of the Earth to the top of the atmosphere.This parameter represents the area averaged value for agrid box.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column water","parameter_ID":"136","product_type":"forecast","shortName":"tcw","standard_name":"Total_column_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/136"},"description":"This parameter is the sum of water vapour, liquid water, cloud ice, rain and snow in a column extending from the surface of the Earth to the top of the atmosphere. In old versions of the ECMWF model (IFS), rain and snow were not accounted for.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"U_component_of_wind(clte_hl)":{"attrs":{"encoding":"instantaneous","levelist":"100","levtype":"hl","long_name":"U component of wind","parameter_ID":"131","product_type":"forecast","shortName":"u","standard_name":"U_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/131"},"description":"This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east, in metres per second. A negative sign thus indicates air movement towards the west.This parameter can be combined with the V component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"U_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"U component of wind","parameter_ID":"131","product_type":"forecast","shortName":"u","standard_name":"U_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/131"},"description":"This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east, in metres per second. A negative sign thus indicates air movement towards the west.This parameter can be combined with the V component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"V_component_of_wind(clte_hl)":{"attrs":{"encoding":"instantaneous","levelist":"100","levtype":"hl","long_name":"V component of wind","parameter_ID":"132","product_type":"forecast","shortName":"v","standard_name":"V_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/132"},"description":"This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north, in metres per second. A negative sign thus indicates air movement towards the south.This parameter can be combined with the U component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"V_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"V component of wind","parameter_ID":"132","product_type":"forecast","shortName":"v","standard_name":"V_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/132"},"description":"This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north, in metres per second. A negative sign thus indicates air movement towards the south.This parameter can be combined with the U component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Vertical_velocity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Vertical velocity","parameter_ID":"135","product_type":"forecast","shortName":"w","standard_name":"Vertical_velocity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/135"},"description":"This parameter is the speed of air motion in the upward or downward direction. The ECMWF Integrated Forecasting System (IFS) uses a pressure based vertical co-ordinate system and pressure decreases with height, therefore negative values of vertical velocity indicate upward motion.Vertical velocity can be useful to understand the large-scale dynamics of the atmosphere, including areas of upward motion/ascent (negative values) and downward motion/subsidence (positive values).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa s-1"},"Volumetric_soil_moisture(clte_sol)":{"attrs":{"encoding":"instantaneous","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Volumetric soil moisture","parameter_ID":"260199","product_type":"forecast","shortName":"vsw","standard_name":"Volumetric_soil_moisture","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/260199"},"description":"Please note that the encoding listed here for uerra (which includes carra/cerra) includes entries for Time-mean volumetric soil moisture. The specific encoding for Time-mean volumetric soil moisture can be found in 235077.","dimensions":["lat","lon","time"],"type":"data","unit":"m3m-3"}},"sci:publications":[{"doi":"10.1109/MCSE.2023.3260519","citation":"N. Wedi et al., Destination Earth: High-Performance Computing for Weather and Climate, in Computing in Science \u0026 Engineering, vol. 24, no. 6, pp. 29-37, Nov.-Dec. 2022,"},{"doi":"10.21957/d3f982672e","citation":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"dedl:short_description":"The Climate Change Adaptation Digital Twin provides global climate projections and sector-specific information over multiple decades at high resolution via a unified framework combining advanced Earth system models, impact assessments, and observations. This Generation-2, Realization-1 Collection gives access to 'Historical Simulation' data based on the 'IFS-FESOM' model."},{"type":"Collection","title":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Historical Simulation - IFS-NEMO - Generation-2 - Realization-1","id":"EO.ECMWF.DAT.D1.DT_CLIMATE.G2.BASELINE_HIST_IFS-NEMO.R1","description":"The DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities by providing innovative climate information on multi-decadal timescales, globally, at scales at which many impacts of climate change are observed. It combines cutting-edge global Earth-system models, impact-sector applications and observations into a unified framework to provide global climate projections and impact-sector information on multi-decadal timescales (1990 to ~2050), at very high spatial resolutions (5 to 10 km).\n\nThe Climate DT represents the first ever attempt to operationalise the production of global multi-decadal climate projections, leveraging the world-leading supercomputing facilities of the EuroHPC Joint Undertaking along with some of the leading European climate models. A concise overview of what the Climate DT aims to achieve, and of the different concepts essential for an understanding of the Digital Twin’s characteristics, is included in the [Climate DT factsheet](https://destine.ecmwf.int/wp-content/uploads/2024/06/2024.06.07_Climate-DT-Fact-Sheet_V7-2.pdf)\n\n## Historical Simulation \n\n Starting in 1990, the forcing follows observed changes in greenhouse gases, aerosols etc. until 2020. The simulations are using standardised CMIP6 forcing. Historical simulations are essential for model evaluation and quality control as they allow a comparison to observations.\n\n## Models\n\nThe Climate DT exploits and further evolves a new generation of global storm-resolving and eddy-rich models built through a cooperative model development approach. For more information on models please click [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#models)\n\n## Simulations\n\nThe Climate DT team carries out several types of digital twin simulations on the EuroHPC supercomputers.  Multi-decadal simulations are produced to cover the recent past (from 1990) and possible future evolutions of the climate up to 2050. See [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#simulations) for more information on Simulations\n\n## Parameters\n\nBelow we see the list of parameters extracted from the 'DestinE Climate DT data portfolio', for more information please refer to the pages [Climate DT Phase2 CLTE Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters) and [Climate DT Phase2 CLMN Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters)","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.BASELINE_HIST_IFS-NEMO.R1/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.BASELINE_HIST_IFS-NEMO.R1/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.BASELINE_HIST_IFS-NEMO.R1/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.BASELINE_HIST_IFS-NEMO.R1","title":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Historical Simulation - IFS-NEMO - Generation-2 - Realization-1"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters","title":"DestinE ClimateDT Phase 2 CLTE Parameters"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters","title":"DestinE ClimateDT Phase 2 CLMN Parameters"},{"rel":"cite-as","type":"text/html","href":"https://dl.acm.org/doi/abs/10.1109/MCSE.2023.3260519","title":"Destination Earth: High-Performance Computing for Weather and Climate"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.21957/d3f982672e","title":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"assets":{"thumbnail":{"href":"https://s3.central.data.destination-earth.eu/swift/v1/dedl-public/collections/climate-dt-min.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1990-01-01T00:00:00Z","2014-12-31T23:59:59Z"]]}},"license":"CC-BY-4.0","keywords":["Digital Twins","Climate Change","Atmosphere","Ocean","Land","Sea Ice","Snow","Soil","Earth","High Performance Computing","Decision Making","Europe","Meteorology","class:d1","dataset:climate-dt","generation:2","expver:0001","stream:clmn,clte","type:fc","activity:baseline","experiment:hist","realization:1","model:IFS-NEMO","resolution:standard,high","levtype:hl,o2d,o3d,pl,sfc,sol"],"summaries":{"federation:backends":["dedt_mn5"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/datacube/v2.1.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["producer","processor","licensor"],"url":"https://www.ecmwf.int/"}],"created":"2024-04-11T22:31:55Z","updated":"2026-04-24T10:24:59Z","published":"2024-04-11T22:31:55Z","cube:dimensions":{"lat":{"axis":"y","description":"latitude","extent":[-90,90],"type":"spatial"},"lon":{"axis":"x","description":"longitude","extent":[-180,180],"type":"spatial"},"time":{"extent":["1990-01-01T00:00:00Z","2014-12-31T23:59:59Z"],"step":"P0Y0M0DT1H0M0S","type":"temporal"}},"cube:variables":{"10_metre_U_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre U wind component","parameter_ID":"165","product_type":"forecast","shortName":"10u","standard_name":"10_metre_U_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/165"},"description":"This parameter is the eastward component of the 10m wind. It is the horizontal speed of air moving towards the east, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the V component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_V_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre V wind component","parameter_ID":"166","product_type":"forecast","shortName":"10v","standard_name":"10_metre_V_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/166"},"description":"This parameter is the northward component of the 10m wind. It is the horizontal speed of air moving towards the north, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the U component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_wind_speed(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre wind speed","parameter_ID":"207","product_type":"forecast","shortName":"10si","standard_name":"10_metre_wind_speed","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/207"},"description":"This parameter is the horizontal speed of the wind, or movement of air, at a height of ten metres above the surface of the Earth. The units of this parameter are metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.The eastward and northward components of the horizontal wind at 10m are also available as parameters.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"2_metre_dewpoint_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"2","levtype":"sfc","long_name":"2 metre dewpoint temperature","parameter_ID":"168","product_type":"forecast","shortName":"2d","standard_name":"2_metre_dewpoint_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/168"},"description":"This parameter is the temperature to which the air, at 2 metres above the surface of the Earth, would have to be cooled for saturation to occur.It is a measure of the humidity of the air. Combined with temperature and pressure, it can be used to calculate the relative humidity.2m dew point temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"2_metre_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"2","levtype":"sfc","long_name":"2 metre temperature","parameter_ID":"167","product_type":"forecast","shortName":"2t","standard_name":"2_metre_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/167"},"description":"This parameter is the temperature of air at 2m above the surface of land, sea or in-land waters.2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.Please note that the encodings listed here for s2s \u0026 uerra (which includes encodings for carra/cerra) include entries for Mean 2 metre temperature. The specific encoding for Mean 2 metre temperature can be found in 228004.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Geopotential(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Geopotential","parameter_ID":"129","product_type":"forecast","shortName":"z","standard_name":"Geopotential","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/129"},"description":"This parameter is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level.The geopotential height can be calculated by dividing the geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2). The geopotential height plays an important role in synoptic meteorology (analysis of weather patterns). Charts of geopotential height plotted at constant pressure levels (e.g., 300, 500 or 850 hPa) can be used to identify weather systems such as cyclones, anticyclones, troughs and ridges.At the surface of the Earth, this parameter shows the variations in geopotential (height) of the surface, and is often referred to as the orography.","dimensions":["lat","lon","time"],"type":"data","unit":"m2s-2"},"Land-sea_mask(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Land-sea mask","parameter_ID":"172","product_type":"forecast","shortName":"lsm","standard_name":"Land-sea_mask","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/172"},"description":"This parameter is the proportion of land, as opposed to ocean or inland waters (lakes, reservoirs, rivers and coastal waters), in agrid box.This parameter has values ranging between zero and one and is dimensionless.In cycles of the ECMWF Integrated Forecasting System (IFS) from CY41R1 (introduced in May 2015) onwards, grid boxes where this parameter has a value above 0.5 can be comprised of a mixture of land and inland water but not ocean. Grid boxes with a value of 0.5 and below can only be comprised of a water surface. In the latter case, the lake cover is used to determine how much of the water surface is ocean or inland water.In cycles of the IFS before CY41R1, grid boxes where this parameter has a value above 0.5 can only be comprised of land and those grid boxes with a value of 0.5 and below can only be comprised of ocean. In these older model cycles, there is no differentiation between ocean and inland water.","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Mean_sea_level_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Mean sea level pressure","parameter_ID":"151","product_type":"forecast","shortName":"msl","standard_name":"Mean_sea_level_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/151"},"description":"This parameter is the pressure (force per unit area) of the atmosphere adjusted to the height of mean sea level.It is a measure of the weight that all the air in a column vertically above the area of Earth's surface would have at that point, if the point were located at the mean sea level. It is calculated over all surfaces - land, sea and in-land water.Maps of mean sea level pressure are used to identify the locations of low and high pressure systems, often referred to as cyclones and anticyclones. Contours of mean sea level pressure also indicate the strength of the wind. Tightly packed contours show stronger winds.The units of this parameter are pascals (Pa).  Mean sea level pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb = 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Orography(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Orography","parameter_ID":"228002","product_type":"forecast","shortName":"orog","standard_name":"Orography","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/228002"},"dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Potential_vorticity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Potential vorticity","parameter_ID":"60","product_type":"forecast","shortName":"pv","standard_name":"Potential_vorticity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/60"},"description":"Potential vorticity is a measure of the capacity for air to rotate in the atmosphere. If we ignore the effects of heating and friction, potential vorticity is conserved following an air parcel. It is used to look for places where large wind storms are likely to originate and develop.  Potential vorticity increases strongly above the tropopause and therefore, it can also be used in studies related to the stratosphere and stratosphere-troposphere exchanges.Large wind storms develop when a column of air in the atmosphere starts to rotate. Potential vorticity is calculated from the wind, temperature and pressure across a column of air in the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K m2kg-1s-1"},"Relative_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Relative humidity","parameter_ID":"157","product_type":"forecast","shortName":"r","standard_name":"Relative_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/157"},"description":"This parameter is the water vapour pressure as a percentage of the value at which the air becomes saturated (the point at which water vapour begins to condense into liquid water or deposition into ice).For temperatures over 0°C (273.15 K) it is calculated for saturation over water. At temperatures below -23°C it is calculated for saturation over ice.  Between -23°C and 0°C this parameter is calculated by interpolating between the ice and water values using a quadratic function.See more information about the model's relative humidity calculation.","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Skin_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Skin temperature","parameter_ID":"235","product_type":"forecast","shortName":"skt","standard_name":"Skin_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235"},"description":"This parameter is the temperature of the surface of the Earth.The skin temperature is the theoretical temperature that is required to satisfy the surface energy balance. It represents the temperature of the uppermost surface layer, which has no heat capacity and so can respond instantaneously to changes in surface fluxes. Skin temperature is calculated differently over land and sea.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.See further information about the skin temperatureover landandover sea.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean skin temperature. The specific encoding for Mean skin temperature can be found in 235079.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Snow_depth_water_equivalent(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Snow depth water equivalent","parameter_ID":"228141","product_type":"forecast","shortName":"sd","standard_name":"Snow_depth_water_equivalent","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228141"},"description":"Snow depth water equivalent in kg m**-2 (mm) water equivalent.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean snow depth water equivalent. The specific encoding for Time-mean snow depth water equivalent can be found in 235078.[NOTE: See 141 for the equivalent parameter in \"m of water equivalent\"]","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Snow_depth_water_equivalent(clte_sol)":{"attrs":{"encoding":"instantaneous","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Snow depth water equivalent","parameter_ID":"228141","product_type":"forecast","shortName":"sd","standard_name":"Snow_depth_water_equivalent","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228141"},"description":"Snow depth water equivalent in kg m**-2 (mm) water equivalent.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean snow depth water equivalent. The specific encoding for Time-mean snow depth water equivalent can be found in 235078.[NOTE: See 141 for the equivalent parameter in \"m of water equivalent\"]","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Specific_cloud_liquid_water_content(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific cloud liquid water content","parameter_ID":"246","product_type":"forecast","shortName":"clwc","standard_name":"Specific_cloud_liquid_water_content","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/246"},"description":"This parameter is the mass of cloud liquid water droplets per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for agrid box.Water within clouds can be liquid or ice, or a combination of the two.See further information about the cloud formulation.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Specific_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific humidity","parameter_ID":"133","product_type":"forecast","shortName":"q","standard_name":"Specific_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/133"},"description":"This parameter is the mass of water vapour per kilogram of moist air.The total mass of moist air is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Surface_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Surface pressure","parameter_ID":"134","product_type":"forecast","shortName":"sp","standard_name":"Surface_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/134"},"description":"This parameter is the pressure (force per unit area) of the atmosphere on the surface of land, sea and in-land water.It is a measure of the weight of all the air in a column vertically above the area of the Earth's surface represented at a fixed point.Surface pressure is often used in combination with temperature to calculate air density.The strong variation of pressure with altitude makes it difficult to see the low and high pressure systems over mountainous areas, so mean sea level pressure, rather than surface pressure, is normally used for this purpose.The units of this parameter are Pascals (Pa). Surface pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb= 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Temperature(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Temperature","parameter_ID":"130","product_type":"forecast","shortName":"t","standard_name":"Temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/130"},"description":"This parameter is the temperature in the atmosphere.It has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.This parameter is available on multiple levels through the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_10_metre_U_wind_component(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre U wind component","parameter_ID":"235165","product_type":"forecast","shortName":"avg_10u","standard_name":"Time-mean_10_metre_U_wind_component","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235165"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_10_metre_V_wind_component(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre V wind component","parameter_ID":"235166","product_type":"forecast","shortName":"avg_10v","standard_name":"Time-mean_10_metre_V_wind_component","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235166"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_10_metre_wind_speed(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre wind speed","parameter_ID":"228005","product_type":"forecast","shortName":"avg_10ws","standard_name":"Time-mean_10_metre_wind_speed","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/228005"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_2_metre_dewpoint_temperature(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"2","levtype":"sfc","long_name":"Time-mean 2 metre dewpoint 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s-1"},"Time-mean_upward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean upward sea water velocity","parameter_ID":"263507","product_type":"forecast","shortName":"avg_wo","standard_name":"Time-mean_upward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263507"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_vertical_velocity(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean vertical velocity","parameter_ID":"235135","product_type":"forecast","shortName":"avg_w","standard_name":"Time-mean_vertical_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235135"},"dimensions":["lat","lon","time"],"type":"data","unit":"Pa s-1"},"Time-mean_vertically-integrated_heat_content_in_the_upper_300_m(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"300","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 300 m","parameter_ID":"263121","product_type":"forecast","shortName":"avg_hc300m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_300_m","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263121"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_vertically-integrated_heat_content_in_the_upper_300_m(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"300","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 300 m","parameter_ID":"263121","product_type":"forecast","shortName":"avg_hc300m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_300_m","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263121"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"700","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 700 m","parameter_ID":"263122","product_type":"forecast","shortName":"avg_hc700m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263122"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"700","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 700 m","parameter_ID":"263122","product_type":"forecast","shortName":"avg_hc700m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263122"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_volumetric_soil_moisture(clmn_sol)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Time-mean volumetric soil moisture","parameter_ID":"235077","product_type":"forecast","shortName":"avg_vsw","standard_name":"Time-mean_volumetric_soil_moisture","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235077"},"dimensions":["lat","lon","time"],"type":"data","unit":"m3m-3"},"Time_mean_top_downward_short-wave_radiation_flux(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time mean top downward short-wave radiation flux","parameter_ID":"235053","product_type":"forecast","shortName":"avg_tdswrf","standard_name":"Time_mean_top_downward_short-wave_radiation_flux","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235053"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Time_mean_top_downward_short-wave_radiation_flux(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time mean top downward short-wave radiation flux","parameter_ID":"235053","product_type":"forecast","shortName":"avg_tdswrf","standard_name":"Time_mean_top_downward_short-wave_radiation_flux","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235053"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Total_Cloud_Cover(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total Cloud Cover","parameter_ID":"228164","product_type":"forecast","shortName":"tcc","standard_name":"Total_Cloud_Cover","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228164"},"description":"[NOTE: See 164 for the equivalent parameter in \"(0-1)\"]","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Total_column_cloud_ice_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud ice water","parameter_ID":"79","product_type":"forecast","shortName":"tciw","standard_name":"Total_column_cloud_ice_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/79"},"description":"This parameter is the amount of ice contained within clouds in a column extending from the surface of the Earth to the top of the atmosphere. Snow (aggregated ice crystals) is not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The  ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_cloud_liquid_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud liquid water","parameter_ID":"78","product_type":"forecast","shortName":"tclw","standard_name":"Total_column_cloud_liquid_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/78"},"description":"This parameter is the amount of liquid water contained within cloud droplets in a column extending from the surface of the Earth to the top of the atmosphere. Rain water droplets, which are much larger in size (and mass), are not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_vertically-integrated_water_vapour(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column vertically-integrated water vapour","parameter_ID":"137","product_type":"forecast","shortName":"tcwv","standard_name":"Total_column_vertically-integrated_water_vapour","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/137"},"description":"This parameter is the total amount of water vapour in a column extending from the surface of the Earth to the top of the atmosphere.This parameter represents the area averaged value for agrid box.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column water","parameter_ID":"136","product_type":"forecast","shortName":"tcw","standard_name":"Total_column_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/136"},"description":"This parameter is the sum of water vapour, liquid water, cloud ice, rain and snow in a column extending from the surface of the Earth to the top of the atmosphere. In old versions of the ECMWF model (IFS), rain and snow were not accounted for.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"U_component_of_wind(clte_hl)":{"attrs":{"encoding":"instantaneous","levelist":"100","levtype":"hl","long_name":"U component of wind","parameter_ID":"131","product_type":"forecast","shortName":"u","standard_name":"U_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/131"},"description":"This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east, in metres per second. A negative sign thus indicates air movement towards the west.This parameter can be combined with the V component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"U_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"U component of wind","parameter_ID":"131","product_type":"forecast","shortName":"u","standard_name":"U_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/131"},"description":"This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east, in metres per second. A negative sign thus indicates air movement towards the west.This parameter can be combined with the V component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"V_component_of_wind(clte_hl)":{"attrs":{"encoding":"instantaneous","levelist":"100","levtype":"hl","long_name":"V component of wind","parameter_ID":"132","product_type":"forecast","shortName":"v","standard_name":"V_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/132"},"description":"This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north, in metres per second. A negative sign thus indicates air movement towards the south.This parameter can be combined with the U component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"V_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"V component of wind","parameter_ID":"132","product_type":"forecast","shortName":"v","standard_name":"V_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/132"},"description":"This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north, in metres per second. A negative sign thus indicates air movement towards the south.This parameter can be combined with the U component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Vertical_velocity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Vertical velocity","parameter_ID":"135","product_type":"forecast","shortName":"w","standard_name":"Vertical_velocity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/135"},"description":"This parameter is the speed of air motion in the upward or downward direction. The ECMWF Integrated Forecasting System (IFS) uses a pressure based vertical co-ordinate system and pressure decreases with height, therefore negative values of vertical velocity indicate upward motion.Vertical velocity can be useful to understand the large-scale dynamics of the atmosphere, including areas of upward motion/ascent (negative values) and downward motion/subsidence (positive values).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa s-1"},"Volumetric_soil_moisture(clte_sol)":{"attrs":{"encoding":"instantaneous","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Volumetric soil moisture","parameter_ID":"260199","product_type":"forecast","shortName":"vsw","standard_name":"Volumetric_soil_moisture","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/260199"},"description":"Please note that the encoding listed here for uerra (which includes carra/cerra) includes entries for Time-mean volumetric soil moisture. The specific encoding for Time-mean volumetric soil moisture can be found in 235077.","dimensions":["lat","lon","time"],"type":"data","unit":"m3m-3"}},"sci:publications":[{"doi":"10.1109/MCSE.2023.3260519","citation":"N. Wedi et al., Destination Earth: High-Performance Computing for Weather and Climate, in Computing in Science \u0026 Engineering, vol. 24, no. 6, pp. 29-37, Nov.-Dec. 2022,"},{"doi":"10.21957/d3f982672e","citation":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"dedl:short_description":"The Climate Change Adaptation Digital Twin provides global climate projections and sector-specific information over multiple decades at high resolution via a unified framework combining advanced Earth system models, impact assessments, and observations. This Generation-2, Realization-1 Collection gives access to 'Historical Simulation' data based on the 'IFS-NEMO' model."},{"type":"Collection","title":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Future Projection - ICON - Generation-2 - Realization-1","id":"EO.ECMWF.DAT.D1.DT_CLIMATE.G2.PROJECTIONS_SSP3-7.0_ICON.R1","description":"The DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities by providing innovative climate information on multi-decadal timescales, globally, at scales at which many impacts of climate change are observed. It combines cutting-edge global Earth-system models, impact-sector applications and observations into a unified framework to provide global climate projections and impact-sector information on multi-decadal timescales (1990 to ~2050), at very high spatial resolutions (5 to 10 km).\n\nThe Climate DT represents the first ever attempt to operationalise the production of global multi-decadal climate projections, leveraging the world-leading supercomputing facilities of the EuroHPC Joint Undertaking along with some of the leading European climate models. A concise overview of what the Climate DT aims to achieve, and of the different concepts essential for an understanding of the Digital Twin’s characteristics, is included in the [Climate DT factsheet](https://destine.ecmwf.int/wp-content/uploads/2024/06/2024.06.07_Climate-DT-Fact-Sheet_V7-2.pdf)\n\n## Future Projection \n\n To project how climate will change on a global and local scale in the future, forcing changes according to the Shared Socioeconomic Pathway (SSP) 3-7.0 scenario from ScenarioMIP. The SSP3-7.0 scenario explores a future with a continuous increase in CO2 emissions with no strong mitigation efforts. All DestinE projections carried out so far follow this scenario. In the future, alternative future scenarios will be explored. The projections carried out so far are initialised in 2020 from reanalysis followed by a 5-year ocean spin-up. For the upcoming simulations carried out in phase 2 of DestinE, scenario simulations will extend the historical simulations.\n\n## Models\n\nThe Climate DT exploits and further evolves a new generation of global storm-resolving and eddy-rich models built through a cooperative model development approach. For more information on models please click [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#models)\n\n## Simulations\n\nThe Climate DT team carries out several types of digital twin simulations on the EuroHPC supercomputers.  Multi-decadal simulations are produced to cover the recent past (from 1990) and possible future evolutions of the climate up to 2050. See [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#simulations) for more information on Simulations\n\n## Parameters\n\nBelow we see the list of parameters extracted from the 'DestinE Climate DT data portfolio', for more information please refer to the pages [Climate DT Phase2 CLTE Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters) and [Climate DT Phase2 CLMN Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters)","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.PROJECTIONS_SSP3-7.0_ICON.R1/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.PROJECTIONS_SSP3-7.0_ICON.R1/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.PROJECTIONS_SSP3-7.0_ICON.R1/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.PROJECTIONS_SSP3-7.0_ICON.R1","title":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Future Projection - ICON - Generation-2 - Realization-1"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters","title":"DestinE ClimateDT Phase 2 CLTE Parameters"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters","title":"DestinE ClimateDT Phase 2 CLMN Parameters"},{"rel":"cite-as","type":"text/html","href":"https://dl.acm.org/doi/abs/10.1109/MCSE.2023.3260519","title":"Destination Earth: High-Performance Computing for Weather and Climate"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.21957/d3f982672e","title":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"assets":{"thumbnail":{"href":"https://s3.central.data.destination-earth.eu/swift/v1/dedl-public/collections/climate-dt-min.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2015-01-01T00:00:00Z","2049-12-31T23:59:59Z"]]}},"license":"CC-BY-4.0","keywords":["Digital Twins","Climate Change","Atmosphere","Ocean","Land","Sea Ice","Snow","Soil","Earth","High Performance Computing","Decision Making","Europe","Meteorology","class:d1","dataset:climate-dt","generation:2","expver:0001","stream:clmn,clte","type:fc","activity:projections","experiment:SSP3-7.0","realization:1","model:ICON","resolution:standard,high","levtype:o2d,o3d,pl,sfc,sol"],"summaries":{"federation:backends":["dedt_lumi"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/datacube/v2.1.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["producer","processor","licensor"],"url":"https://www.ecmwf.int/"}],"created":"2024-04-11T22:31:55Z","updated":"2026-04-24T10:24:59Z","published":"2024-04-11T22:31:55Z","cube:dimensions":{"lat":{"axis":"y","description":"latitude","extent":[-90,90],"type":"spatial"},"lon":{"axis":"x","description":"longitude","extent":[-180,180],"type":"spatial"},"time":{"extent":["2015-01-01T00:00:00Z","2049-12-31T23:59:59Z"],"step":"P0Y0M0DT1H0M0S","type":"temporal"}},"cube:variables":{"10_metre_U_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre U wind component","parameter_ID":"165","product_type":"forecast","shortName":"10u","standard_name":"10_metre_U_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/165"},"description":"This parameter is the eastward component of the 10m wind. It is the horizontal speed of air moving towards the east, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the V component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_V_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre V wind component","parameter_ID":"166","product_type":"forecast","shortName":"10v","standard_name":"10_metre_V_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/166"},"description":"This parameter is the northward component of the 10m wind. It is the horizontal speed of air moving towards the north, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the U component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_wind_speed(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre wind speed","parameter_ID":"207","product_type":"forecast","shortName":"10si","standard_name":"10_metre_wind_speed","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/207"},"description":"This parameter is the horizontal speed of the wind, or movement of air, at a height of ten metres above the surface of the Earth. The units of this parameter are metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.The eastward and northward components of the horizontal wind at 10m are also available as parameters.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"2_metre_dewpoint_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"2","levtype":"sfc","long_name":"2 metre dewpoint temperature","parameter_ID":"168","product_type":"forecast","shortName":"2d","standard_name":"2_metre_dewpoint_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/168"},"description":"This parameter is the temperature to which the air, at 2 metres above the surface of the Earth, would have to be cooled for saturation to occur.It is a measure of the humidity of the air. Combined with temperature and pressure, it can be used to calculate the relative humidity.2m dew point temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"2_metre_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"2","levtype":"sfc","long_name":"2 metre temperature","parameter_ID":"167","product_type":"forecast","shortName":"2t","standard_name":"2_metre_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/167"},"description":"This parameter is the temperature of air at 2m above the surface of land, sea or in-land waters.2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.Please note that the encodings listed here for s2s \u0026 uerra (which includes encodings for carra/cerra) include entries for Mean 2 metre temperature. The specific encoding for Mean 2 metre temperature can be found in 228004.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Geopotential(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Geopotential","parameter_ID":"129","product_type":"forecast","shortName":"z","standard_name":"Geopotential","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/129"},"description":"This parameter is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level.The geopotential height can be calculated by dividing the geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2). The geopotential height plays an important role in synoptic meteorology (analysis of weather patterns). Charts of geopotential height plotted at constant pressure levels (e.g., 300, 500 or 850 hPa) can be used to identify weather systems such as cyclones, anticyclones, troughs and ridges.At the surface of the Earth, this parameter shows the variations in geopotential (height) of the surface, and is often referred to as the orography.","dimensions":["lat","lon","time"],"type":"data","unit":"m2s-2"},"Land-sea_mask(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Land-sea mask","parameter_ID":"172","product_type":"forecast","shortName":"lsm","standard_name":"Land-sea_mask","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/172"},"description":"This parameter is the proportion of land, as opposed to ocean or inland waters (lakes, reservoirs, rivers and coastal waters), in agrid box.This parameter has values ranging between zero and one and is dimensionless.In cycles of the ECMWF Integrated Forecasting System (IFS) from CY41R1 (introduced in May 2015) onwards, grid boxes where this parameter has a value above 0.5 can be comprised of a mixture of land and inland water but not ocean. Grid boxes with a value of 0.5 and below can only be comprised of a water surface. In the latter case, the lake cover is used to determine how much of the water surface is ocean or inland water.In cycles of the IFS before CY41R1, grid boxes where this parameter has a value above 0.5 can only be comprised of land and those grid boxes with a value of 0.5 and below can only be comprised of ocean. In these older model cycles, there is no differentiation between ocean and inland water.","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Mean_sea_level_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Mean sea level pressure","parameter_ID":"151","product_type":"forecast","shortName":"msl","standard_name":"Mean_sea_level_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/151"},"description":"This parameter is the pressure (force per unit area) of the atmosphere adjusted to the height of mean sea level.It is a measure of the weight that all the air in a column vertically above the area of Earth's surface would have at that point, if the point were located at the mean sea level. It is calculated over all surfaces - land, sea and in-land water.Maps of mean sea level pressure are used to identify the locations of low and high pressure systems, often referred to as cyclones and anticyclones. Contours of mean sea level pressure also indicate the strength of the wind. Tightly packed contours show stronger winds.The units of this parameter are pascals (Pa).  Mean sea level pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb = 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Orography(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Orography","parameter_ID":"228002","product_type":"forecast","shortName":"orog","standard_name":"Orography","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/228002"},"dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Potential_vorticity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Potential vorticity","parameter_ID":"60","product_type":"forecast","shortName":"pv","standard_name":"Potential_vorticity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/60"},"description":"Potential vorticity is a measure of the capacity for air to rotate in the atmosphere. If we ignore the effects of heating and friction, potential vorticity is conserved following an air parcel. It is used to look for places where large wind storms are likely to originate and develop.  Potential vorticity increases strongly above the tropopause and therefore, it can also be used in studies related to the stratosphere and stratosphere-troposphere exchanges.Large wind storms develop when a column of air in the atmosphere starts to rotate. Potential vorticity is calculated from the wind, temperature and pressure across a column of air in the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K m2kg-1s-1"},"Relative_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Relative humidity","parameter_ID":"157","product_type":"forecast","shortName":"r","standard_name":"Relative_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/157"},"description":"This parameter is the water vapour pressure as a percentage of the value at which the air becomes saturated (the point at which water vapour begins to condense into liquid water or deposition into ice).For temperatures over 0°C (273.15 K) it is calculated for saturation over water. At temperatures below -23°C it is calculated for saturation over ice.  Between -23°C and 0°C this parameter is calculated by interpolating between the ice and water values using a quadratic function.See more information about the model's relative humidity calculation.","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Skin_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Skin temperature","parameter_ID":"235","product_type":"forecast","shortName":"skt","standard_name":"Skin_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235"},"description":"This parameter is the temperature of the surface of the Earth.The skin temperature is the theoretical temperature that is required to satisfy the surface energy balance. It represents the temperature of the uppermost surface layer, which has no heat capacity and so can respond instantaneously to changes in surface fluxes. Skin temperature is calculated differently over land and sea.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.See further information about the skin temperatureover landandover sea.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean skin temperature. The specific encoding for Mean skin temperature can be found in 235079.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Snow_depth_water_equivalent(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Snow depth water equivalent","parameter_ID":"228141","product_type":"forecast","shortName":"sd","standard_name":"Snow_depth_water_equivalent","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228141"},"description":"Snow depth water equivalent in kg m**-2 (mm) water equivalent.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean snow depth water equivalent. The specific encoding for Time-mean snow depth water equivalent can be found in 235078.[NOTE: See 141 for the equivalent parameter in \"m of water equivalent\"]","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Specific_cloud_liquid_water_content(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific cloud liquid water content","parameter_ID":"246","product_type":"forecast","shortName":"clwc","standard_name":"Specific_cloud_liquid_water_content","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/246"},"description":"This parameter is the mass of cloud liquid water droplets per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for agrid box.Water within clouds can be liquid or ice, or a combination of the two.See further information about the cloud formulation.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Specific_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific humidity","parameter_ID":"133","product_type":"forecast","shortName":"q","standard_name":"Specific_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/133"},"description":"This parameter is the mass of water vapour per kilogram of moist air.The total mass of moist air is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Surface_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Surface pressure","parameter_ID":"134","product_type":"forecast","shortName":"sp","standard_name":"Surface_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/134"},"description":"This parameter is the pressure (force per unit area) of the atmosphere on the surface of land, sea and in-land water.It is a measure of the weight of all the air in a column vertically above the area of the Earth's surface represented at a fixed point.Surface pressure is often used in combination with temperature to calculate air density.The strong variation of pressure with altitude makes it difficult to see the low and high pressure systems over mountainous areas, so mean sea level pressure, rather than surface pressure, is normally used for this purpose.The units of this parameter are Pascals (Pa). Surface pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb= 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Temperature(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Temperature","parameter_ID":"130","product_type":"forecast","shortName":"t","standard_name":"Temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/130"},"description":"This parameter is the temperature in the atmosphere.It has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.This parameter is available on multiple levels through the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_10_metre_U_wind_component(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre U wind component","parameter_ID":"235165","product_type":"forecast","shortName":"avg_10u","standard_name":"Time-mean_10_metre_U_wind_component","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235165"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_10_metre_V_wind_component(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre V wind component","parameter_ID":"235166","product_type":"forecast","shortName":"avg_10v","standard_name":"Time-mean_10_metre_V_wind_component","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235166"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_10_metre_wind_speed(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre wind speed","parameter_ID":"228005","product_type":"forecast","shortName":"avg_10ws","standard_name":"Time-mean_10_metre_wind_speed","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/228005"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_2_metre_dewpoint_temperature(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"2","levtype":"sfc","long_name":"Time-mean 2 metre dewpoint temperature","parameter_ID":"235168","product_type":"forecast","shortName":"avg_2d","standard_name":"Time-mean_2_metre_dewpoint_temperature","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235168"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_2_metre_temperature(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"2","levtype":"sfc","long_name":"Time-mean 2 metre temperature","parameter_ID":"228004","product_type":"forecast","shortName":"avg_2t","standard_name":"Time-mean_2_metre_temperature","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/228004"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_U_component_of_wind(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean U component of wind","parameter_ID":"235131","product_type":"forecast","shortName":"avg_u","standard_name":"Time-mean_U_component_of_wind","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235131"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_V_component_of_wind(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean V component of wind","parameter_ID":"235132","product_type":"forecast","shortName":"avg_v","standard_name":"Time-mean_V_component_of_wind","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235132"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_ice_velocity(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean eastward sea ice velocity","parameter_ID":"263003","product_type":"forecast","shortName":"avg_siue","standard_name":"Time-mean_eastward_sea_ice_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263003"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_ice_velocity(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean eastward sea ice velocity","parameter_ID":"263003","product_type":"forecast","shortName":"avg_siue","standard_name":"Time-mean_eastward_sea_ice_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263003"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_water_velocity(clmn_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean eastward sea water velocity","parameter_ID":"263506","product_type":"forecast","shortName":"avg_uoe","standard_name":"Time-mean_eastward_sea_water_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263506"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean eastward sea water velocity","parameter_ID":"263506","product_type":"forecast","shortName":"avg_uoe","standard_name":"Time-mean_eastward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263506"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_turbulent_surface_stress(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean eastward turbulent surface stress","parameter_ID":"235041","product_type":"forecast","shortName":"avg_iews","standard_name":"Time-mean_eastward_turbulent_surface_stress","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235041"},"dimensions":["lat","lon","time"],"type":"data","unit":"N m-2"},"Time-mean_eastward_turbulent_surface_stress(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean eastward turbulent surface stress","parameter_ID":"235041","product_type":"forecast","shortName":"avg_iews","standard_name":"Time-mean_eastward_turbulent_surface_stress","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235041"},"dimensions":["lat","lon","time"],"type":"data","unit":"N m-2"},"Time-mean_geopotential(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean geopotential","parameter_ID":"235129","product_type":"forecast","shortName":"avg_z","standard_name":"Time-mean_geopotential","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235129"},"dimensions":["lat","lon","time"],"type":"data","unit":"m2s-2"},"Time-mean_mean_sea_level_pressure(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean mean sea level pressure","parameter_ID":"235151","product_type":"forecast","shortName":"avg_msl","standard_name":"Time-mean_mean_sea_level_pressure","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235151"},"dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Time-mean_moisture_flux(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean moisture flux","parameter_ID":"235043","product_type":"forecast","shortName":"avg_ie","standard_name":"Time-mean_moisture_flux","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235043"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_moisture_flux(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean moisture flux","parameter_ID":"235043","product_type":"forecast","shortName":"avg_ie","standard_name":"Time-mean_moisture_flux","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235043"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_northward_sea_ice_velocity(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean northward sea ice velocity","parameter_ID":"263004","product_type":"forecast","shortName":"avg_sivn","standard_name":"Time-mean_northward_sea_ice_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263004"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_sea_ice_velocity(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean northward sea ice velocity","parameter_ID":"263004","product_type":"forecast","shortName":"avg_sivn","standard_name":"Time-mean_northward_sea_ice_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263004"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_sea_water_velocity(clmn_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean northward sea water velocity","parameter_ID":"263505","product_type":"forecast","shortName":"avg_von","standard_name":"Time-mean_northward_sea_water_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263505"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_turbulent_surface_stress(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean northward turbulent surface stress","parameter_ID":"235042","product_type":"forecast","shortName":"avg_inss","standard_name":"Time-mean_northward_turbulent_surface_stress","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235042"},"dimensions":["lat","lon","time"],"type":"data","unit":"N m-2"},"Time-mean_northward_turbulent_surface_stress(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean northward turbulent surface stress","parameter_ID":"235042","product_type":"forecast","shortName":"avg_inss","standard_name":"Time-mean_northward_turbulent_surface_stress","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235042"},"dimensions":["lat","lon","time"],"type":"data","unit":"N m-2"},"Time-mean_potential_vorticity(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean potential vorticity","parameter_ID":"235100","product_type":"forecast","shortName":"avg_pv","standard_name":"Time-mean_potential_vorticity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235100"},"dimensions":["lat","lon","time"],"type":"data","unit":"K m2kg-1s-1"},"Time-mean_relative_humidity(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean relative humidity","parameter_ID":"235157","product_type":"forecast","shortName":"avg_r","standard_name":"Time-mean_relative_humidity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235157"},"dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Time-mean_sea_ice_area_fraction(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea ice area fraction","parameter_ID":"263001","product_type":"forecast","shortName":"avg_siconc","standard_name":"Time-mean_sea_ice_area_fraction","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263001"},"dimensions":["lat","lon","time"],"type":"data","unit":"Fraction"},"Time-mean_sea_ice_area_fraction(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea ice area 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sky","parameter_ID":"235049","product_type":"forecast","shortName":"avg_tnswrfcs","standard_name":"Time-mean_top_net_short-wave_radiation_flux,_clear_sky","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235049"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Time-mean_top_net_short-wave_radiation_flux,_clear_sky(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean top net short-wave radiation flux, clear sky","parameter_ID":"235049","product_type":"forecast","shortName":"avg_tnswrfcs","standard_name":"Time-mean_top_net_short-wave_radiation_flux,_clear_sky","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235049"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Time-mean_total_column_cloud_ice_water(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total column cloud ice water","parameter_ID":"235088","product_type":"forecast","shortName":"avg_tciw","standard_name":"Time-mean_total_column_cloud_ice_water","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235088"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Time-mean_total_column_heat_content(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean total column heat content","parameter_ID":"263123","product_type":"forecast","shortName":"avg_hcbtm","standard_name":"Time-mean_total_column_heat_content","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263123"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_total_column_heat_content(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean total column heat content","parameter_ID":"263123","product_type":"forecast","shortName":"avg_hcbtm","standard_name":"Time-mean_total_column_heat_content","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263123"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_total_column_liquid_water(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total column liquid water","parameter_ID":"235087","product_type":"forecast","shortName":"avg_tclw","standard_name":"Time-mean_total_column_liquid_water","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235087"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Time-mean_total_column_vertically-integrated_water_vapour(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total column vertically-integrated water vapour","parameter_ID":"235137","product_type":"forecast","shortName":"avg_tcwv","standard_name":"Time-mean_total_column_vertically-integrated_water_vapour","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235137"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Time-mean_total_column_water(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total column water","parameter_ID":"235136","product_type":"forecast","shortName":"avg_tcw","standard_name":"Time-mean_total_column_water","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235136"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Time-mean_total_precipitation_rate(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total precipitation rate","parameter_ID":"235055","product_type":"forecast","shortName":"avg_tprate","standard_name":"Time-mean_total_precipitation_rate","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235055"},"description":"Time-mean total precipitation rate, or time-mean total precipitation flux. This parameter is on level \"Ground or water surface\" (typeOfFirstFixedSurface=1). For this parameter on other levels, please use 235013.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_total_precipitation_rate(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total precipitation rate","parameter_ID":"235055","product_type":"forecast","shortName":"avg_tprate","standard_name":"Time-mean_total_precipitation_rate","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235055"},"description":"Time-mean total precipitation rate, or time-mean total precipitation flux. This parameter is on level \"Ground or water surface\" (typeOfFirstFixedSurface=1). For this parameter on other levels, please use 235013.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_total_snowfall_rate_water_equivalent(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total snowfall rate water equivalent","parameter_ID":"235031","product_type":"forecast","shortName":"avg_tsrwe","standard_name":"Time-mean_total_snowfall_rate_water_equivalent","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235031"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_total_snowfall_rate_water_equivalent(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total snowfall rate water 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vertically-integrated heat content in the upper 700 m","parameter_ID":"263122","product_type":"forecast","shortName":"avg_hc700m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263122"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"700","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 700 m","parameter_ID":"263122","product_type":"forecast","shortName":"avg_hc700m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263122"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_volumetric_soil_moisture(clmn_sol)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Time-mean volumetric soil moisture","parameter_ID":"235077","product_type":"forecast","shortName":"avg_vsw","standard_name":"Time-mean_volumetric_soil_moisture","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235077"},"dimensions":["lat","lon","time"],"type":"data","unit":"m3m-3"},"Time_mean_top_downward_short-wave_radiation_flux(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time mean top downward short-wave radiation flux","parameter_ID":"235053","product_type":"forecast","shortName":"avg_tdswrf","standard_name":"Time_mean_top_downward_short-wave_radiation_flux","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235053"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Time_mean_top_downward_short-wave_radiation_flux(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time mean top downward short-wave radiation flux","parameter_ID":"235053","product_type":"forecast","shortName":"avg_tdswrf","standard_name":"Time_mean_top_downward_short-wave_radiation_flux","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235053"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Total_Cloud_Cover(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total Cloud Cover","parameter_ID":"228164","product_type":"forecast","shortName":"tcc","standard_name":"Total_Cloud_Cover","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228164"},"description":"[NOTE: See 164 for the equivalent parameter in \"(0-1)\"]","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Total_column_cloud_ice_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud ice water","parameter_ID":"79","product_type":"forecast","shortName":"tciw","standard_name":"Total_column_cloud_ice_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/79"},"description":"This parameter is the amount of ice contained within clouds in a column extending from the surface of the Earth to the top of the atmosphere. Snow (aggregated ice crystals) is not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The  ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_cloud_liquid_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud liquid water","parameter_ID":"78","product_type":"forecast","shortName":"tclw","standard_name":"Total_column_cloud_liquid_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/78"},"description":"This parameter is the amount of liquid water contained within cloud droplets in a column extending from the surface of the Earth to the top of the atmosphere. Rain water droplets, which are much larger in size (and mass), are not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_vertically-integrated_water_vapour(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column vertically-integrated water vapour","parameter_ID":"137","product_type":"forecast","shortName":"tcwv","standard_name":"Total_column_vertically-integrated_water_vapour","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/137"},"description":"This parameter is the total amount of water vapour in a column extending from the surface of the Earth to the top of the atmosphere.This parameter represents the area averaged value for agrid box.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column water","parameter_ID":"136","product_type":"forecast","shortName":"tcw","standard_name":"Total_column_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/136"},"description":"This parameter is the sum of water vapour, liquid water, cloud ice, rain and snow in a column extending from the surface of the Earth to the top of the atmosphere. In old versions of the ECMWF model (IFS), rain and snow were not accounted for.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"U_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"U component of wind","parameter_ID":"131","product_type":"forecast","shortName":"u","standard_name":"U_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/131"},"description":"This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east, in metres per second. A negative sign thus indicates air movement towards the west.This parameter can be combined with the V component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"V_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"V component of wind","parameter_ID":"132","product_type":"forecast","shortName":"v","standard_name":"V_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/132"},"description":"This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north, in metres per second. A negative sign thus indicates air movement towards the south.This parameter can be combined with the U component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Vertical_velocity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Vertical velocity","parameter_ID":"135","product_type":"forecast","shortName":"w","standard_name":"Vertical_velocity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/135"},"description":"This parameter is the speed of air motion in the upward or downward direction. The ECMWF Integrated Forecasting System (IFS) uses a pressure based vertical co-ordinate system and pressure decreases with height, therefore negative values of vertical velocity indicate upward motion.Vertical velocity can be useful to understand the large-scale dynamics of the atmosphere, including areas of upward motion/ascent (negative values) and downward motion/subsidence (positive values).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa s-1"},"Volumetric_soil_moisture(clte_sol)":{"attrs":{"encoding":"instantaneous","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Volumetric soil moisture","parameter_ID":"260199","product_type":"forecast","shortName":"vsw","standard_name":"Volumetric_soil_moisture","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/260199"},"description":"Please note that the encoding listed here for uerra (which includes carra/cerra) includes entries for Time-mean volumetric soil moisture. The specific encoding for Time-mean volumetric soil moisture can be found in 235077.","dimensions":["lat","lon","time"],"type":"data","unit":"m3m-3"}},"sci:publications":[{"doi":"10.1109/MCSE.2023.3260519","citation":"N. Wedi et al., Destination Earth: High-Performance Computing for Weather and Climate, in Computing in Science \u0026 Engineering, vol. 24, no. 6, pp. 29-37, Nov.-Dec. 2022,"},{"doi":"10.21957/d3f982672e","citation":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"dedl:short_description":"The Climate Change Adaptation Digital Twin provides global climate projections and sector-specific information over multiple decades at high resolution via a unified framework combining advanced Earth system models, impact assessments, and observations. This Generation-2, Realization-1 Collection gives access to 'Future Projection' data based on the 'ICON' model."},{"type":"Collection","title":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Future Projection - IFS-FESOM - Generation-2 - Realization-1","id":"EO.ECMWF.DAT.D1.DT_CLIMATE.G2.PROJECTIONS_SSP3-7.0_IFS-FESOM.R1","description":"The DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities by providing innovative climate information on multi-decadal timescales, globally, at scales at which many impacts of climate change are observed. It combines cutting-edge global Earth-system models, impact-sector applications and observations into a unified framework to provide global climate projections and impact-sector information on multi-decadal timescales (1990 to ~2050), at very high spatial resolutions (5 to 10 km).\n\nThe Climate DT represents the first ever attempt to operationalise the production of global multi-decadal climate projections, leveraging the world-leading supercomputing facilities of the EuroHPC Joint Undertaking along with some of the leading European climate models. A concise overview of what the Climate DT aims to achieve, and of the different concepts essential for an understanding of the Digital Twin’s characteristics, is included in the [Climate DT factsheet](https://destine.ecmwf.int/wp-content/uploads/2024/06/2024.06.07_Climate-DT-Fact-Sheet_V7-2.pdf)\n\n## Future Projection \n\n To project how climate will change on a global and local scale in the future, forcing changes according to the Shared Socioeconomic Pathway (SSP) 3-7.0 scenario from ScenarioMIP. The SSP3-7.0 scenario explores a future with a continuous increase in CO2 emissions with no strong mitigation efforts. All DestinE projections carried out so far follow this scenario. In the future, alternative future scenarios will be explored. The projections carried out so far are initialised in 2020 from reanalysis followed by a 5-year ocean spin-up. For the upcoming simulations carried out in phase 2 of DestinE, scenario simulations will extend the historical simulations.\n\n## Models\n\nThe Climate DT exploits and further evolves a new generation of global storm-resolving and eddy-rich models built through a cooperative model development approach. For more information on models please click [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#models)\n\n## Simulations\n\nThe Climate DT team carries out several types of digital twin simulations on the EuroHPC supercomputers.  Multi-decadal simulations are produced to cover the recent past (from 1990) and possible future evolutions of the climate up to 2050. See [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#simulations) for more information on Simulations\n\n## Parameters\n\nBelow we see the list of parameters extracted from the 'DestinE Climate DT data portfolio', for more information please refer to the pages [Climate DT Phase2 CLTE Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters) and [Climate DT Phase2 CLMN Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters)","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.PROJECTIONS_SSP3-7.0_IFS-FESOM.R1/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.PROJECTIONS_SSP3-7.0_IFS-FESOM.R1/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.PROJECTIONS_SSP3-7.0_IFS-FESOM.R1/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.PROJECTIONS_SSP3-7.0_IFS-FESOM.R1","title":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Future Projection - IFS-FESOM - Generation-2 - Realization-1"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters","title":"DestinE ClimateDT Phase 2 CLTE Parameters"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters","title":"DestinE ClimateDT Phase 2 CLMN Parameters"},{"rel":"cite-as","type":"text/html","href":"https://dl.acm.org/doi/abs/10.1109/MCSE.2023.3260519","title":"Destination Earth: High-Performance Computing for Weather and Climate"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.21957/d3f982672e","title":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"assets":{"thumbnail":{"href":"https://s3.central.data.destination-earth.eu/swift/v1/dedl-public/collections/climate-dt-min.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2015-01-01T00:00:00Z","2049-12-31T23:59:59Z"]]}},"license":"CC-BY-4.0","keywords":["Digital Twins","Climate Change","Atmosphere","Ocean","Land","Sea Ice","Snow","Soil","Earth","High Performance Computing","Decision Making","Europe","Meteorology","class:d1","dataset:climate-dt","generation:2","expver:0001","stream:clmn,clte","type:fc","activity:projections","experiment:SSP3-7.0","realization:1","model:IFS-FESOM","resolution:standard,high","levtype:hl,o2d,o3d,pl,sfc,sol"],"summaries":{"federation:backends":["dedt_lumi"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/datacube/v2.1.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["producer","processor","licensor"],"url":"https://www.ecmwf.int/"}],"created":"2024-04-11T22:31:55Z","updated":"2026-04-24T10:24:59Z","published":"2024-04-11T22:31:55Z","cube:dimensions":{"lat":{"axis":"y","description":"latitude","extent":[-90,90],"type":"spatial"},"lon":{"axis":"x","description":"longitude","extent":[-180,180],"type":"spatial"},"time":{"extent":["2015-01-01T00:00:00Z","2049-12-31T23:59:59Z"],"step":"P0Y0M0DT1H0M0S","type":"temporal"}},"cube:variables":{"10_metre_U_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre U wind component","parameter_ID":"165","product_type":"forecast","shortName":"10u","standard_name":"10_metre_U_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/165"},"description":"This parameter is the eastward component of the 10m wind. It is the horizontal speed of air moving towards the east, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the V component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_V_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre V wind component","parameter_ID":"166","product_type":"forecast","shortName":"10v","standard_name":"10_metre_V_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/166"},"description":"This parameter is the northward component of the 10m wind. It is the horizontal speed of air moving towards the north, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the U component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_wind_speed(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre wind speed","parameter_ID":"207","product_type":"forecast","shortName":"10si","standard_name":"10_metre_wind_speed","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/207"},"description":"This parameter is the horizontal speed of the wind, or movement of air, at a height of ten metres above the surface of the Earth. The units of this parameter are metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.The eastward and northward components of the horizontal wind at 10m are also available as parameters.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"2_metre_dewpoint_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"2","levtype":"sfc","long_name":"2 metre dewpoint temperature","parameter_ID":"168","product_type":"forecast","shortName":"2d","standard_name":"2_metre_dewpoint_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/168"},"description":"This parameter is the temperature to which the air, at 2 metres above the surface of the Earth, would have to be cooled for saturation to occur.It is a measure of the humidity of the air. Combined with temperature and pressure, it can be used to calculate the relative humidity.2m dew point temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"2_metre_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"2","levtype":"sfc","long_name":"2 metre temperature","parameter_ID":"167","product_type":"forecast","shortName":"2t","standard_name":"2_metre_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/167"},"description":"This parameter is the temperature of air at 2m above the surface of land, sea or in-land waters.2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.Please note that the encodings listed here for s2s \u0026 uerra (which includes encodings for carra/cerra) include entries for Mean 2 metre temperature. The specific encoding for Mean 2 metre temperature can be found in 228004.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Geopotential(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Geopotential","parameter_ID":"129","product_type":"forecast","shortName":"z","standard_name":"Geopotential","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/129"},"description":"This parameter is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level.The geopotential height can be calculated by dividing the geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2). The geopotential height plays an important role in synoptic meteorology (analysis of weather patterns). Charts of geopotential height plotted at constant pressure levels (e.g., 300, 500 or 850 hPa) can be used to identify weather systems such as cyclones, anticyclones, troughs and ridges.At the surface of the Earth, this parameter shows the variations in geopotential (height) of the surface, and is often referred to as the orography.","dimensions":["lat","lon","time"],"type":"data","unit":"m2s-2"},"Land-sea_mask(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Land-sea mask","parameter_ID":"172","product_type":"forecast","shortName":"lsm","standard_name":"Land-sea_mask","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/172"},"description":"This parameter is the proportion of land, as opposed to ocean or inland waters (lakes, reservoirs, rivers and coastal waters), in agrid box.This parameter has values ranging between zero and one and is dimensionless.In cycles of the ECMWF Integrated Forecasting System (IFS) from CY41R1 (introduced in May 2015) onwards, grid boxes where this parameter has a value above 0.5 can be comprised of a mixture of land and inland water but not ocean. Grid boxes with a value of 0.5 and below can only be comprised of a water surface. In the latter case, the lake cover is used to determine how much of the water surface is ocean or inland water.In cycles of the IFS before CY41R1, grid boxes where this parameter has a value above 0.5 can only be comprised of land and those grid boxes with a value of 0.5 and below can only be comprised of ocean. In these older model cycles, there is no differentiation between ocean and inland water.","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Mean_sea_level_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Mean sea level pressure","parameter_ID":"151","product_type":"forecast","shortName":"msl","standard_name":"Mean_sea_level_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/151"},"description":"This parameter is the pressure (force per unit area) of the atmosphere adjusted to the height of mean sea level.It is a measure of the weight that all the air in a column vertically above the area of Earth's surface would have at that point, if the point were located at the mean sea level. It is calculated over all surfaces - land, sea and in-land water.Maps of mean sea level pressure are used to identify the locations of low and high pressure systems, often referred to as cyclones and anticyclones. Contours of mean sea level pressure also indicate the strength of the wind. Tightly packed contours show stronger winds.The units of this parameter are pascals (Pa).  Mean sea level pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb = 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Orography(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Orography","parameter_ID":"228002","product_type":"forecast","shortName":"orog","standard_name":"Orography","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/228002"},"dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Potential_vorticity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Potential vorticity","parameter_ID":"60","product_type":"forecast","shortName":"pv","standard_name":"Potential_vorticity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/60"},"description":"Potential vorticity is a measure of the capacity for air to rotate in the atmosphere. If we ignore the effects of heating and friction, potential vorticity is conserved following an air parcel. It is used to look for places where large wind storms are likely to originate and develop.  Potential vorticity increases strongly above the tropopause and therefore, it can also be used in studies related to the stratosphere and stratosphere-troposphere exchanges.Large wind storms develop when a column of air in the atmosphere starts to rotate. Potential vorticity is calculated from the wind, temperature and pressure across a column of air in the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K m2kg-1s-1"},"Relative_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Relative humidity","parameter_ID":"157","product_type":"forecast","shortName":"r","standard_name":"Relative_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/157"},"description":"This parameter is the water vapour pressure as a percentage of the value at which the air becomes saturated (the point at which water vapour begins to condense into liquid water or deposition into ice).For temperatures over 0°C (273.15 K) it is calculated for saturation over water. At temperatures below -23°C it is calculated for saturation over ice.  Between -23°C and 0°C this parameter is calculated by interpolating between the ice and water values using a quadratic function.See more information about the model's relative humidity calculation.","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Skin_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Skin temperature","parameter_ID":"235","product_type":"forecast","shortName":"skt","standard_name":"Skin_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235"},"description":"This parameter is the temperature of the surface of the Earth.The skin temperature is the theoretical temperature that is required to satisfy the surface energy balance. It represents the temperature of the uppermost surface layer, which has no heat capacity and so can respond instantaneously to changes in surface fluxes. Skin temperature is calculated differently over land and sea.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.See further information about the skin temperatureover landandover sea.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean skin temperature. The specific encoding for Mean skin temperature can be found in 235079.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Snow_depth_water_equivalent(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Snow depth water equivalent","parameter_ID":"228141","product_type":"forecast","shortName":"sd","standard_name":"Snow_depth_water_equivalent","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228141"},"description":"Snow depth water equivalent in kg m**-2 (mm) water equivalent.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean snow depth water equivalent. The specific encoding for Time-mean snow depth water equivalent can be found in 235078.[NOTE: See 141 for the equivalent parameter in \"m of water equivalent\"]","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Snow_depth_water_equivalent(clte_sol)":{"attrs":{"encoding":"instantaneous","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Snow depth water equivalent","parameter_ID":"228141","product_type":"forecast","shortName":"sd","standard_name":"Snow_depth_water_equivalent","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228141"},"description":"Snow depth water equivalent in kg m**-2 (mm) water equivalent.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean snow depth water equivalent. The specific encoding for Time-mean snow depth water equivalent can be found in 235078.[NOTE: See 141 for the equivalent parameter in \"m of water equivalent\"]","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Specific_cloud_liquid_water_content(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific cloud liquid water content","parameter_ID":"246","product_type":"forecast","shortName":"clwc","standard_name":"Specific_cloud_liquid_water_content","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/246"},"description":"This parameter is the mass of cloud liquid water droplets per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for agrid box.Water within clouds can be liquid or ice, or a combination of the two.See further information about the cloud formulation.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Specific_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific humidity","parameter_ID":"133","product_type":"forecast","shortName":"q","standard_name":"Specific_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/133"},"description":"This parameter is the mass of water vapour per kilogram of moist air.The total mass of moist air is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Surface_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Surface pressure","parameter_ID":"134","product_type":"forecast","shortName":"sp","standard_name":"Surface_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/134"},"description":"This parameter is the pressure (force per unit area) of the atmosphere on the surface of land, sea and in-land water.It is a measure of the weight of all the air in a column vertically above the area of the Earth's surface represented at a fixed point.Surface pressure is often used in combination with temperature to calculate air density.The strong variation of pressure with altitude makes it difficult to see the low and high pressure systems over mountainous areas, so mean sea level pressure, rather than surface pressure, is normally used for this purpose.The units of this parameter are Pascals (Pa). Surface pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb= 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Temperature(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Temperature","parameter_ID":"130","product_type":"forecast","shortName":"t","standard_name":"Temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/130"},"description":"This parameter is the temperature in the atmosphere.It has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.This parameter is available on multiple levels through the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_10_metre_U_wind_component(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre U wind component","parameter_ID":"235165","product_type":"forecast","shortName":"avg_10u","standard_name":"Time-mean_10_metre_U_wind_component","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235165"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_10_metre_V_wind_component(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre V wind component","parameter_ID":"235166","product_type":"forecast","shortName":"avg_10v","standard_name":"Time-mean_10_metre_V_wind_component","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235166"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_10_metre_wind_speed(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre wind speed","parameter_ID":"228005","product_type":"forecast","shortName":"avg_10ws","standard_name":"Time-mean_10_metre_wind_speed","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/228005"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_2_metre_dewpoint_temperature(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"2","levtype":"sfc","long_name":"Time-mean 2 metre dewpoint temperature","parameter_ID":"235168","product_type":"forecast","shortName":"avg_2d","standard_name":"Time-mean_2_metre_dewpoint_temperature","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235168"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_2_metre_temperature(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"2","levtype":"sfc","long_name":"Time-mean 2 metre temperature","parameter_ID":"228004","product_type":"forecast","shortName":"avg_2t","standard_name":"Time-mean_2_metre_temperature","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/228004"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_U_component_of_wind(clmn_hl)":{"attrs":{"encoding":"mean","levelist":"100","levtype":"hl","long_name":"Time-mean U component of wind","parameter_ID":"235131","product_type":"forecast","shortName":"avg_u","standard_name":"Time-mean_U_component_of_wind","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235131"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_U_component_of_wind(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean U component of wind","parameter_ID":"235131","product_type":"forecast","shortName":"avg_u","standard_name":"Time-mean_U_component_of_wind","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235131"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_V_component_of_wind(clmn_hl)":{"attrs":{"encoding":"mean","levelist":"100","levtype":"hl","long_name":"Time-mean V component of 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the upper 300 m","parameter_ID":"263121","product_type":"forecast","shortName":"avg_hc300m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_300_m","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263121"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"700","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 700 m","parameter_ID":"263122","product_type":"forecast","shortName":"avg_hc700m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263122"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"700","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 700 m","parameter_ID":"263122","product_type":"forecast","shortName":"avg_hc700m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263122"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_volumetric_soil_moisture(clmn_sol)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Time-mean volumetric soil moisture","parameter_ID":"235077","product_type":"forecast","shortName":"avg_vsw","standard_name":"Time-mean_volumetric_soil_moisture","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235077"},"dimensions":["lat","lon","time"],"type":"data","unit":"m3m-3"},"Time_mean_top_downward_short-wave_radiation_flux(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time mean top downward short-wave radiation flux","parameter_ID":"235053","product_type":"forecast","shortName":"avg_tdswrf","standard_name":"Time_mean_top_downward_short-wave_radiation_flux","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235053"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Time_mean_top_downward_short-wave_radiation_flux(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time mean top downward short-wave radiation flux","parameter_ID":"235053","product_type":"forecast","shortName":"avg_tdswrf","standard_name":"Time_mean_top_downward_short-wave_radiation_flux","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235053"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Total_Cloud_Cover(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total Cloud Cover","parameter_ID":"228164","product_type":"forecast","shortName":"tcc","standard_name":"Total_Cloud_Cover","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228164"},"description":"[NOTE: See 164 for the equivalent parameter in \"(0-1)\"]","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Total_column_cloud_ice_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud ice water","parameter_ID":"79","product_type":"forecast","shortName":"tciw","standard_name":"Total_column_cloud_ice_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/79"},"description":"This parameter is the amount of ice contained within clouds in a column extending from the surface of the Earth to the top of the atmosphere. Snow (aggregated ice crystals) is not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The  ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_cloud_liquid_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud liquid water","parameter_ID":"78","product_type":"forecast","shortName":"tclw","standard_name":"Total_column_cloud_liquid_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/78"},"description":"This parameter is the amount of liquid water contained within cloud droplets in a column extending from the surface of the Earth to the top of the atmosphere. Rain water droplets, which are much larger in size (and mass), are not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_vertically-integrated_water_vapour(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column vertically-integrated water vapour","parameter_ID":"137","product_type":"forecast","shortName":"tcwv","standard_name":"Total_column_vertically-integrated_water_vapour","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/137"},"description":"This parameter is the total amount of water vapour in a column extending from the surface of the Earth to the top of the atmosphere.This parameter represents the area averaged value for agrid box.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column water","parameter_ID":"136","product_type":"forecast","shortName":"tcw","standard_name":"Total_column_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/136"},"description":"This parameter is the sum of water vapour, liquid water, cloud ice, rain and snow in a column extending from the surface of the Earth to the top of the atmosphere. In old versions of the ECMWF model (IFS), rain and snow were not accounted for.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"U_component_of_wind(clte_hl)":{"attrs":{"encoding":"instantaneous","levelist":"100","levtype":"hl","long_name":"U component of wind","parameter_ID":"131","product_type":"forecast","shortName":"u","standard_name":"U_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/131"},"description":"This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east, in metres per second. A negative sign thus indicates air movement towards the west.This parameter can be combined with the V component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"U_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"U component of wind","parameter_ID":"131","product_type":"forecast","shortName":"u","standard_name":"U_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/131"},"description":"This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east, in metres per second. A negative sign thus indicates air movement towards the west.This parameter can be combined with the V component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"V_component_of_wind(clte_hl)":{"attrs":{"encoding":"instantaneous","levelist":"100","levtype":"hl","long_name":"V component of wind","parameter_ID":"132","product_type":"forecast","shortName":"v","standard_name":"V_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/132"},"description":"This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north, in metres per second. A negative sign thus indicates air movement towards the south.This parameter can be combined with the U component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"V_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"V component of wind","parameter_ID":"132","product_type":"forecast","shortName":"v","standard_name":"V_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/132"},"description":"This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north, in metres per second. A negative sign thus indicates air movement towards the south.This parameter can be combined with the U component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Vertical_velocity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Vertical velocity","parameter_ID":"135","product_type":"forecast","shortName":"w","standard_name":"Vertical_velocity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/135"},"description":"This parameter is the speed of air motion in the upward or downward direction. The ECMWF Integrated Forecasting System (IFS) uses a pressure based vertical co-ordinate system and pressure decreases with height, therefore negative values of vertical velocity indicate upward motion.Vertical velocity can be useful to understand the large-scale dynamics of the atmosphere, including areas of upward motion/ascent (negative values) and downward motion/subsidence (positive values).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa s-1"},"Volumetric_soil_moisture(clte_sol)":{"attrs":{"encoding":"instantaneous","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Volumetric soil moisture","parameter_ID":"260199","product_type":"forecast","shortName":"vsw","standard_name":"Volumetric_soil_moisture","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/260199"},"description":"Please note that the encoding listed here for uerra (which includes carra/cerra) includes entries for Time-mean volumetric soil moisture. The specific encoding for Time-mean volumetric soil moisture can be found in 235077.","dimensions":["lat","lon","time"],"type":"data","unit":"m3m-3"}},"sci:publications":[{"doi":"10.1109/MCSE.2023.3260519","citation":"N. Wedi et al., Destination Earth: High-Performance Computing for Weather and Climate, in Computing in Science \u0026 Engineering, vol. 24, no. 6, pp. 29-37, Nov.-Dec. 2022,"},{"doi":"10.21957/d3f982672e","citation":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"dedl:short_description":"The Climate Change Adaptation Digital Twin provides global climate projections and sector-specific information over multiple decades at high resolution via a unified framework combining advanced Earth system models, impact assessments, and observations. This Generation-2, Realization-1 Collection gives access to 'Future Projection' data based on the 'IFS-FESOM' model."},{"type":"Collection","title":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Future Projection - IFS-NEMO - Generation-2 - Realization-1","id":"EO.ECMWF.DAT.D1.DT_CLIMATE.G2.PROJECTIONS_SSP3-7.0_IFS-NEMO.R1","description":"The DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities by providing innovative climate information on multi-decadal timescales, globally, at scales at which many impacts of climate change are observed. It combines cutting-edge global Earth-system models, impact-sector applications and observations into a unified framework to provide global climate projections and impact-sector information on multi-decadal timescales (1990 to ~2050), at very high spatial resolutions (5 to 10 km).\n\nThe Climate DT represents the first ever attempt to operationalise the production of global multi-decadal climate projections, leveraging the world-leading supercomputing facilities of the EuroHPC Joint Undertaking along with some of the leading European climate models. A concise overview of what the Climate DT aims to achieve, and of the different concepts essential for an understanding of the Digital Twin’s characteristics, is included in the [Climate DT factsheet](https://destine.ecmwf.int/wp-content/uploads/2024/06/2024.06.07_Climate-DT-Fact-Sheet_V7-2.pdf)\n\n## Future Projection \n\n To project how climate will change on a global and local scale in the future, forcing changes according to the Shared Socioeconomic Pathway (SSP) 3-7.0 scenario from ScenarioMIP. The SSP3-7.0 scenario explores a future with a continuous increase in CO2 emissions with no strong mitigation efforts. All DestinE projections carried out so far follow this scenario. In the future, alternative future scenarios will be explored. The projections carried out so far are initialised in 2020 from reanalysis followed by a 5-year ocean spin-up. For the upcoming simulations carried out in phase 2 of DestinE, scenario simulations will extend the historical simulations.\n\n## Models\n\nThe Climate DT exploits and further evolves a new generation of global storm-resolving and eddy-rich models built through a cooperative model development approach. For more information on models please click [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#models)\n\n## Simulations\n\nThe Climate DT team carries out several types of digital twin simulations on the EuroHPC supercomputers.  Multi-decadal simulations are produced to cover the recent past (from 1990) and possible future evolutions of the climate up to 2050. See [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#simulations) for more information on Simulations\n\n## Parameters\n\nBelow we see the list of parameters extracted from the 'DestinE Climate DT data portfolio', for more information please refer to the pages [Climate DT Phase2 CLTE Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters) and [Climate DT Phase2 CLMN Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters)","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.PROJECTIONS_SSP3-7.0_IFS-NEMO.R1/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.PROJECTIONS_SSP3-7.0_IFS-NEMO.R1/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.PROJECTIONS_SSP3-7.0_IFS-NEMO.R1/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.PROJECTIONS_SSP3-7.0_IFS-NEMO.R1","title":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Future Projection - IFS-NEMO - Generation-2 - Realization-1"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters","title":"DestinE ClimateDT Phase 2 CLTE Parameters"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters","title":"DestinE ClimateDT Phase 2 CLMN Parameters"},{"rel":"cite-as","type":"text/html","href":"https://dl.acm.org/doi/abs/10.1109/MCSE.2023.3260519","title":"Destination Earth: High-Performance Computing for Weather and Climate"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.21957/d3f982672e","title":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"assets":{"thumbnail":{"href":"https://s3.central.data.destination-earth.eu/swift/v1/dedl-public/collections/climate-dt-min.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2015-01-01T00:00:00Z","2049-12-31T23:59:59Z"]]}},"license":"CC-BY-4.0","keywords":["Digital Twins","Climate Change","Atmosphere","Ocean","Land","Sea Ice","Snow","Soil","Earth","High Performance Computing","Decision Making","Europe","Meteorology","class:d1","dataset:climate-dt","generation:2","expver:0001","stream:clmn,clte","type:fc","activity:projections","experiment:SSP3-7.0","realization:1","model:IFS-NEMO","resolution:standard,high","levtype:hl,o2d,o3d,pl,sfc,sol"],"summaries":{"federation:backends":["dedt_mn5"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/datacube/v2.1.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["producer","processor","licensor"],"url":"https://www.ecmwf.int/"}],"created":"2024-04-11T22:31:55Z","updated":"2026-04-24T10:24:59Z","published":"2024-04-11T22:31:55Z","cube:dimensions":{"lat":{"axis":"y","description":"latitude","extent":[-90,90],"type":"spatial"},"lon":{"axis":"x","description":"longitude","extent":[-180,180],"type":"spatial"},"time":{"extent":["2015-01-01T00:00:00Z","2049-12-31T23:59:59Z"],"step":"P0Y0M0DT1H0M0S","type":"temporal"}},"cube:variables":{"10_metre_U_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre U wind component","parameter_ID":"165","product_type":"forecast","shortName":"10u","standard_name":"10_metre_U_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/165"},"description":"This parameter is the eastward component of the 10m wind. It is the horizontal speed of air moving towards the east, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the V component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_V_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre V wind component","parameter_ID":"166","product_type":"forecast","shortName":"10v","standard_name":"10_metre_V_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/166"},"description":"This parameter is the northward component of the 10m wind. It is the horizontal speed of air moving towards the north, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the U component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_wind_speed(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre wind speed","parameter_ID":"207","product_type":"forecast","shortName":"10si","standard_name":"10_metre_wind_speed","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/207"},"description":"This parameter is the horizontal speed of the wind, or movement of air, at a height of ten metres above the surface of the Earth. The units of this parameter are metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.The eastward and northward components of the horizontal wind at 10m are also available as parameters.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"2_metre_dewpoint_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"2","levtype":"sfc","long_name":"2 metre dewpoint temperature","parameter_ID":"168","product_type":"forecast","shortName":"2d","standard_name":"2_metre_dewpoint_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/168"},"description":"This parameter is the temperature to which the air, at 2 metres above the surface of the Earth, would have to be cooled for saturation to occur.It is a measure of the humidity of the air. Combined with temperature and pressure, it can be used to calculate the relative humidity.2m dew point temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"2_metre_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"2","levtype":"sfc","long_name":"2 metre temperature","parameter_ID":"167","product_type":"forecast","shortName":"2t","standard_name":"2_metre_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/167"},"description":"This parameter is the temperature of air at 2m above the surface of land, sea or in-land waters.2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.Please note that the encodings listed here for s2s \u0026 uerra (which includes encodings for carra/cerra) include entries for Mean 2 metre temperature. The specific encoding for Mean 2 metre temperature can be found in 228004.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Geopotential(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Geopotential","parameter_ID":"129","product_type":"forecast","shortName":"z","standard_name":"Geopotential","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/129"},"description":"This parameter is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level.The geopotential height can be calculated by dividing the geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2). The geopotential height plays an important role in synoptic meteorology (analysis of weather patterns). Charts of geopotential height plotted at constant pressure levels (e.g., 300, 500 or 850 hPa) can be used to identify weather systems such as cyclones, anticyclones, troughs and ridges.At the surface of the Earth, this parameter shows the variations in geopotential (height) of the surface, and is often referred to as the orography.","dimensions":["lat","lon","time"],"type":"data","unit":"m2s-2"},"Land-sea_mask(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Land-sea mask","parameter_ID":"172","product_type":"forecast","shortName":"lsm","standard_name":"Land-sea_mask","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/172"},"description":"This parameter is the proportion of land, as opposed to ocean or inland waters (lakes, reservoirs, rivers and coastal waters), in agrid box.This parameter has values ranging between zero and one and is dimensionless.In cycles of the ECMWF Integrated Forecasting System (IFS) from CY41R1 (introduced in May 2015) onwards, grid boxes where this parameter has a value above 0.5 can be comprised of a mixture of land and inland water but not ocean. Grid boxes with a value of 0.5 and below can only be comprised of a water surface. In the latter case, the lake cover is used to determine how much of the water surface is ocean or inland water.In cycles of the IFS before CY41R1, grid boxes where this parameter has a value above 0.5 can only be comprised of land and those grid boxes with a value of 0.5 and below can only be comprised of ocean. In these older model cycles, there is no differentiation between ocean and inland water.","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Mean_sea_level_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Mean sea level pressure","parameter_ID":"151","product_type":"forecast","shortName":"msl","standard_name":"Mean_sea_level_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/151"},"description":"This parameter is the pressure (force per unit area) of the atmosphere adjusted to the height of mean sea level.It is a measure of the weight that all the air in a column vertically above the area of Earth's surface would have at that point, if the point were located at the mean sea level. It is calculated over all surfaces - land, sea and in-land water.Maps of mean sea level pressure are used to identify the locations of low and high pressure systems, often referred to as cyclones and anticyclones. Contours of mean sea level pressure also indicate the strength of the wind. Tightly packed contours show stronger winds.The units of this parameter are pascals (Pa).  Mean sea level pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb = 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Orography(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Orography","parameter_ID":"228002","product_type":"forecast","shortName":"orog","standard_name":"Orography","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/228002"},"dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Potential_vorticity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Potential vorticity","parameter_ID":"60","product_type":"forecast","shortName":"pv","standard_name":"Potential_vorticity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/60"},"description":"Potential vorticity is a measure of the capacity for air to rotate in the atmosphere. If we ignore the effects of heating and friction, potential vorticity is conserved following an air parcel. It is used to look for places where large wind storms are likely to originate and develop.  Potential vorticity increases strongly above the tropopause and therefore, it can also be used in studies related to the stratosphere and stratosphere-troposphere exchanges.Large wind storms develop when a column of air in the atmosphere starts to rotate. Potential vorticity is calculated from the wind, temperature and pressure across a column of air in the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K m2kg-1s-1"},"Relative_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Relative humidity","parameter_ID":"157","product_type":"forecast","shortName":"r","standard_name":"Relative_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/157"},"description":"This parameter is the water vapour pressure as a percentage of the value at which the air becomes saturated (the point at which water vapour begins to condense into liquid water or deposition into ice).For temperatures over 0°C (273.15 K) it is calculated for saturation over water. At temperatures below -23°C it is calculated for saturation over ice.  Between -23°C and 0°C this parameter is calculated by interpolating between the ice and water values using a quadratic function.See more information about the model's relative humidity calculation.","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Skin_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Skin temperature","parameter_ID":"235","product_type":"forecast","shortName":"skt","standard_name":"Skin_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235"},"description":"This parameter is the temperature of the surface of the Earth.The skin temperature is the theoretical temperature that is required to satisfy the surface energy balance. It represents the temperature of the uppermost surface layer, which has no heat capacity and so can respond instantaneously to changes in surface fluxes. Skin temperature is calculated differently over land and sea.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.See further information about the skin temperatureover landandover sea.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean skin temperature. The specific encoding for Mean skin temperature can be found in 235079.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Snow_depth_water_equivalent(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Snow depth water equivalent","parameter_ID":"228141","product_type":"forecast","shortName":"sd","standard_name":"Snow_depth_water_equivalent","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228141"},"description":"Snow depth water equivalent in kg m**-2 (mm) water equivalent.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean snow depth water equivalent. The specific encoding for Time-mean snow depth water equivalent can be found in 235078.[NOTE: See 141 for the equivalent parameter in \"m of water equivalent\"]","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Snow_depth_water_equivalent(clte_sol)":{"attrs":{"encoding":"instantaneous","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Snow depth water equivalent","parameter_ID":"228141","product_type":"forecast","shortName":"sd","standard_name":"Snow_depth_water_equivalent","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228141"},"description":"Snow depth water equivalent in kg m**-2 (mm) water equivalent.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean snow depth water equivalent. The specific encoding for Time-mean snow depth water equivalent can be found in 235078.[NOTE: See 141 for the equivalent parameter in \"m of water equivalent\"]","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Specific_cloud_liquid_water_content(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific cloud liquid water content","parameter_ID":"246","product_type":"forecast","shortName":"clwc","standard_name":"Specific_cloud_liquid_water_content","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/246"},"description":"This parameter is the mass of cloud liquid water droplets per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for agrid box.Water within clouds can be liquid or ice, or a combination of the two.See further information about the cloud formulation.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Specific_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific humidity","parameter_ID":"133","product_type":"forecast","shortName":"q","standard_name":"Specific_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/133"},"description":"This parameter is the mass of water vapour per kilogram of moist air.The total mass of moist air is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Surface_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Surface pressure","parameter_ID":"134","product_type":"forecast","shortName":"sp","standard_name":"Surface_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/134"},"description":"This parameter is the pressure (force per unit area) of the atmosphere on the surface of land, sea and in-land water.It is a measure of the weight of all the air in a column vertically above the area of the Earth's surface represented at a fixed point.Surface pressure is often used in combination with temperature to calculate air density.The strong variation of pressure with altitude makes it difficult to see the low and high pressure systems over mountainous areas, so mean sea level pressure, rather than surface pressure, is normally used for this purpose.The units of this parameter are Pascals (Pa). Surface pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb= 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Temperature(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Temperature","parameter_ID":"130","product_type":"forecast","shortName":"t","standard_name":"Temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/130"},"description":"This parameter is the temperature in the atmosphere.It has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.This parameter is available on multiple levels through the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_10_metre_U_wind_component(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre U wind component","parameter_ID":"235165","product_type":"forecast","shortName":"avg_10u","standard_name":"Time-mean_10_metre_U_wind_component","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235165"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_10_metre_V_wind_component(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre V wind component","parameter_ID":"235166","product_type":"forecast","shortName":"avg_10v","standard_name":"Time-mean_10_metre_V_wind_component","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235166"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_10_metre_wind_speed(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre wind speed","parameter_ID":"228005","product_type":"forecast","shortName":"avg_10ws","standard_name":"Time-mean_10_metre_wind_speed","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/228005"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_2_metre_dewpoint_temperature(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"2","levtype":"sfc","long_name":"Time-mean 2 metre dewpoint temperature","parameter_ID":"235168","product_type":"forecast","shortName":"avg_2d","standard_name":"Time-mean_2_metre_dewpoint_temperature","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235168"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_2_metre_temperature(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"2","levtype":"sfc","long_name":"Time-mean 2 metre temperature","parameter_ID":"228004","product_type":"forecast","shortName":"avg_2t","standard_name":"Time-mean_2_metre_temperature","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/228004"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_U_component_of_wind(clmn_hl)":{"attrs":{"encoding":"mean","levelist":"100","levtype":"hl","long_name":"Time-mean U component of wind","parameter_ID":"235131","product_type":"forecast","shortName":"avg_u","standard_name":"Time-mean_U_component_of_wind","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235131"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_U_component_of_wind(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean U component of wind","parameter_ID":"235131","product_type":"forecast","shortName":"avg_u","standard_name":"Time-mean_U_component_of_wind","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235131"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_V_component_of_wind(clmn_hl)":{"attrs":{"encoding":"mean","levelist":"100","levtype":"hl","long_name":"Time-mean V component of wind","parameter_ID":"235132","product_type":"forecast","shortName":"avg_v","standard_name":"Time-mean_V_component_of_wind","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235132"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_V_component_of_wind(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean V component of wind","parameter_ID":"235132","product_type":"forecast","shortName":"avg_v","standard_name":"Time-mean_V_component_of_wind","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235132"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_ice_velocity(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean eastward sea ice velocity","parameter_ID":"263003","product_type":"forecast","shortName":"avg_siue","standard_name":"Time-mean_eastward_sea_ice_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263003"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_ice_velocity(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean eastward sea ice velocity","parameter_ID":"263003","product_type":"forecast","shortName":"avg_siue","standard_name":"Time-mean_eastward_sea_ice_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263003"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_water_velocity(clmn_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean eastward sea water velocity","parameter_ID":"263506","product_type":"forecast","shortName":"avg_uoe","standard_name":"Time-mean_eastward_sea_water_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263506"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean eastward sea water velocity","parameter_ID":"263506","product_type":"forecast","shortName":"avg_uoe","standard_name":"Time-mean_eastward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263506"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_turbulent_surface_stress(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean eastward turbulent surface stress","parameter_ID":"235041","product_type":"forecast","shortName":"avg_iews","standard_name":"Time-mean_eastward_turbulent_surface_stress","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235041"},"dimensions":["lat","lon","time"],"type":"data","unit":"N m-2"},"Time-mean_eastward_turbulent_surface_stress(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean eastward turbulent surface stress","parameter_ID":"235041","product_type":"forecast","shortName":"avg_iews","standard_name":"Time-mean_eastward_turbulent_surface_stress","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235041"},"dimensions":["lat","lon","time"],"type":"data","unit":"N m-2"},"Time-mean_geopotential(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean geopotential","parameter_ID":"235129","product_type":"forecast","shortName":"avg_z","standard_name":"Time-mean_geopotential","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235129"},"dimensions":["lat","lon","time"],"type":"data","unit":"m2s-2"},"Time-mean_mean_sea_level_pressure(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean mean sea level pressure","parameter_ID":"235151","product_type":"forecast","shortName":"avg_msl","standard_name":"Time-mean_mean_sea_level_pressure","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235151"},"dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Time-mean_moisture_flux(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean moisture flux","parameter_ID":"235043","product_type":"forecast","shortName":"avg_ie","standard_name":"Time-mean_moisture_flux","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235043"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_moisture_flux(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean moisture flux","parameter_ID":"235043","product_type":"forecast","shortName":"avg_ie","standard_name":"Time-mean_moisture_flux","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235043"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_northward_sea_ice_velocity(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean northward sea ice velocity","parameter_ID":"263004","product_type":"forecast","shortName":"avg_sivn","standard_name":"Time-mean_northward_sea_ice_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263004"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_sea_ice_velocity(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean northward sea ice velocity","parameter_ID":"263004","product_type":"forecast","shortName":"avg_sivn","standard_name":"Time-mean_northward_sea_ice_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263004"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_sea_water_velocity(clmn_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean northward sea water velocity","parameter_ID":"263505","product_type":"forecast","shortName":"avg_von","standard_name":"Time-mean_northward_sea_water_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263505"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean northward sea water velocity","parameter_ID":"263505","product_type":"forecast","shortName":"avg_von","standard_name":"Time-mean_northward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263505"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_turbulent_surface_stress(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean northward turbulent surface stress","parameter_ID":"235042","product_type":"forecast","shortName":"avg_inss","standard_name":"Time-mean_northward_turbulent_surface_stress","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235042"},"dimensions":["lat","lon","time"],"type":"data","unit":"N m-2"},"Time-mean_northward_turbulent_surface_stress(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean northward turbulent surface stress","parameter_ID":"235042","product_type":"forecast","shortName":"avg_inss","standard_name":"Time-mean_northward_turbulent_surface_stress","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235042"},"dimensions":["lat","lon","time"],"type":"data","unit":"N m-2"},"Time-mean_potential_vorticity(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean potential vorticity","parameter_ID":"235100","product_type":"forecast","shortName":"avg_pv","standard_name":"Time-mean_potential_vorticity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235100"},"dimensions":["lat","lon","time"],"type":"data","unit":"K m2kg-1s-1"},"Time-mean_relative_humidity(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean relative humidity","parameter_ID":"235157","product_type":"forecast","shortName":"avg_r","standard_name":"Time-mean_relative_humidity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235157"},"dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Time-mean_sea_ice_area_fraction(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea ice area fraction","parameter_ID":"263001","product_type":"forecast","shortName":"avg_siconc","standard_name":"Time-mean_sea_ice_area_fraction","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263001"},"dimensions":["lat","lon","time"],"type":"data","unit":"Fraction"},"Time-mean_sea_ice_area_fraction(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea ice area fraction","parameter_ID":"263001","product_type":"forecast","shortName":"avg_siconc","standard_name":"Time-mean_sea_ice_area_fraction","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263001"},"dimensions":["lat","lon","time"],"type":"data","unit":"Fraction"},"Time-mean_sea_ice_thickness(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea ice 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sky","parameter_ID":"235049","product_type":"forecast","shortName":"avg_tnswrfcs","standard_name":"Time-mean_top_net_short-wave_radiation_flux,_clear_sky","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235049"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Time-mean_top_net_short-wave_radiation_flux,_clear_sky(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean top net short-wave radiation flux, clear sky","parameter_ID":"235049","product_type":"forecast","shortName":"avg_tnswrfcs","standard_name":"Time-mean_top_net_short-wave_radiation_flux,_clear_sky","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235049"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Time-mean_total_cloud_cover(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total cloud cover","parameter_ID":"235288","product_type":"forecast","shortName":"avg_tcc","standard_name":"Time-mean_total_cloud_cover","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235288"},"dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Time-mean_total_column_cloud_ice_water(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total column cloud ice water","parameter_ID":"235088","product_type":"forecast","shortName":"avg_tciw","standard_name":"Time-mean_total_column_cloud_ice_water","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235088"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Time-mean_total_column_heat_content(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean total column heat content","parameter_ID":"263123","product_type":"forecast","shortName":"avg_hcbtm","standard_name":"Time-mean_total_column_heat_content","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263123"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_total_column_heat_content(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean total column heat content","parameter_ID":"263123","product_type":"forecast","shortName":"avg_hcbtm","standard_name":"Time-mean_total_column_heat_content","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263123"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_total_column_liquid_water(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total column liquid water","parameter_ID":"235087","product_type":"forecast","shortName":"avg_tclw","standard_name":"Time-mean_total_column_liquid_water","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235087"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Time-mean_total_column_vertically-integrated_water_vapour(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total column vertically-integrated water vapour","parameter_ID":"235137","product_type":"forecast","shortName":"avg_tcwv","standard_name":"Time-mean_total_column_vertically-integrated_water_vapour","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235137"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Time-mean_total_column_water(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total column water","parameter_ID":"235136","product_type":"forecast","shortName":"avg_tcw","standard_name":"Time-mean_total_column_water","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235136"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Time-mean_total_precipitation_rate(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total precipitation rate","parameter_ID":"235055","product_type":"forecast","shortName":"avg_tprate","standard_name":"Time-mean_total_precipitation_rate","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235055"},"description":"Time-mean total precipitation rate, or time-mean total precipitation flux. This parameter is on level \"Ground or water surface\" (typeOfFirstFixedSurface=1). For this parameter on other levels, please use 235013.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_total_precipitation_rate(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total precipitation rate","parameter_ID":"235055","product_type":"forecast","shortName":"avg_tprate","standard_name":"Time-mean_total_precipitation_rate","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235055"},"description":"Time-mean total precipitation rate, or time-mean total precipitation flux. This parameter is on level \"Ground or water surface\" (typeOfFirstFixedSurface=1). For this parameter on other levels, please use 235013.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_total_snowfall_rate_water_equivalent(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total snowfall rate water equivalent","parameter_ID":"235031","product_type":"forecast","shortName":"avg_tsrwe","standard_name":"Time-mean_total_snowfall_rate_water_equivalent","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235031"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_total_snowfall_rate_water_equivalent(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total snowfall rate water equivalent","parameter_ID":"235031","product_type":"forecast","shortName":"avg_tsrwe","standard_name":"Time-mean_total_snowfall_rate_water_equivalent","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235031"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_upward_sea_water_velocity(clmn_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean upward sea water velocity","parameter_ID":"263507","product_type":"forecast","shortName":"avg_wo","standard_name":"Time-mean_upward_sea_water_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263507"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_upward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean upward sea water velocity","parameter_ID":"263507","product_type":"forecast","shortName":"avg_wo","standard_name":"Time-mean_upward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263507"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_vertical_velocity(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean vertical velocity","parameter_ID":"235135","product_type":"forecast","shortName":"avg_w","standard_name":"Time-mean_vertical_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235135"},"dimensions":["lat","lon","time"],"type":"data","unit":"Pa s-1"},"Time-mean_vertically-integrated_heat_content_in_the_upper_300_m(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"300","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 300 m","parameter_ID":"263121","product_type":"forecast","shortName":"avg_hc300m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_300_m","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263121"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_vertically-integrated_heat_content_in_the_upper_300_m(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"300","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 300 m","parameter_ID":"263121","product_type":"forecast","shortName":"avg_hc300m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_300_m","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263121"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"700","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 700 m","parameter_ID":"263122","product_type":"forecast","shortName":"avg_hc700m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263122"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"700","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 700 m","parameter_ID":"263122","product_type":"forecast","shortName":"avg_hc700m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263122"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_volumetric_soil_moisture(clmn_sol)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Time-mean volumetric soil moisture","parameter_ID":"235077","product_type":"forecast","shortName":"avg_vsw","standard_name":"Time-mean_volumetric_soil_moisture","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235077"},"dimensions":["lat","lon","time"],"type":"data","unit":"m3m-3"},"Time_mean_top_downward_short-wave_radiation_flux(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time mean top downward short-wave radiation flux","parameter_ID":"235053","product_type":"forecast","shortName":"avg_tdswrf","standard_name":"Time_mean_top_downward_short-wave_radiation_flux","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235053"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Time_mean_top_downward_short-wave_radiation_flux(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time mean top downward short-wave radiation flux","parameter_ID":"235053","product_type":"forecast","shortName":"avg_tdswrf","standard_name":"Time_mean_top_downward_short-wave_radiation_flux","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235053"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Total_Cloud_Cover(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total Cloud Cover","parameter_ID":"228164","product_type":"forecast","shortName":"tcc","standard_name":"Total_Cloud_Cover","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228164"},"description":"[NOTE: See 164 for the equivalent parameter in \"(0-1)\"]","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Total_column_cloud_ice_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud ice water","parameter_ID":"79","product_type":"forecast","shortName":"tciw","standard_name":"Total_column_cloud_ice_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/79"},"description":"This parameter is the amount of ice contained within clouds in a column extending from the surface of the Earth to the top of the atmosphere. Snow (aggregated ice crystals) is not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The  ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_cloud_liquid_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud liquid water","parameter_ID":"78","product_type":"forecast","shortName":"tclw","standard_name":"Total_column_cloud_liquid_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/78"},"description":"This parameter is the amount of liquid water contained within cloud droplets in a column extending from the surface of the Earth to the top of the atmosphere. Rain water droplets, which are much larger in size (and mass), are not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_vertically-integrated_water_vapour(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column vertically-integrated water vapour","parameter_ID":"137","product_type":"forecast","shortName":"tcwv","standard_name":"Total_column_vertically-integrated_water_vapour","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/137"},"description":"This parameter is the total amount of water vapour in a column extending from the surface of the Earth to the top of the atmosphere.This parameter represents the area averaged value for agrid box.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column water","parameter_ID":"136","product_type":"forecast","shortName":"tcw","standard_name":"Total_column_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/136"},"description":"This parameter is the sum of water vapour, liquid water, cloud ice, rain and snow in a column extending from the surface of the Earth to the top of the atmosphere. In old versions of the ECMWF model (IFS), rain and snow were not accounted for.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"U_component_of_wind(clte_hl)":{"attrs":{"encoding":"instantaneous","levelist":"100","levtype":"hl","long_name":"U component of wind","parameter_ID":"131","product_type":"forecast","shortName":"u","standard_name":"U_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/131"},"description":"This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east, in metres per second. A negative sign thus indicates air movement towards the west.This parameter can be combined with the V component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"U_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"U component of wind","parameter_ID":"131","product_type":"forecast","shortName":"u","standard_name":"U_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/131"},"description":"This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east, in metres per second. A negative sign thus indicates air movement towards the west.This parameter can be combined with the V component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"V_component_of_wind(clte_hl)":{"attrs":{"encoding":"instantaneous","levelist":"100","levtype":"hl","long_name":"V component of wind","parameter_ID":"132","product_type":"forecast","shortName":"v","standard_name":"V_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/132"},"description":"This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north, in metres per second. A negative sign thus indicates air movement towards the south.This parameter can be combined with the U component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"V_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"V component of wind","parameter_ID":"132","product_type":"forecast","shortName":"v","standard_name":"V_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/132"},"description":"This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north, in metres per second. A negative sign thus indicates air movement towards the south.This parameter can be combined with the U component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Vertical_velocity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Vertical velocity","parameter_ID":"135","product_type":"forecast","shortName":"w","standard_name":"Vertical_velocity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/135"},"description":"This parameter is the speed of air motion in the upward or downward direction. The ECMWF Integrated Forecasting System (IFS) uses a pressure based vertical co-ordinate system and pressure decreases with height, therefore negative values of vertical velocity indicate upward motion.Vertical velocity can be useful to understand the large-scale dynamics of the atmosphere, including areas of upward motion/ascent (negative values) and downward motion/subsidence (positive values).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa s-1"},"Volumetric_soil_moisture(clte_sol)":{"attrs":{"encoding":"instantaneous","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Volumetric soil moisture","parameter_ID":"260199","product_type":"forecast","shortName":"vsw","standard_name":"Volumetric_soil_moisture","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/260199"},"description":"Please note that the encoding listed here for uerra (which includes carra/cerra) includes entries for Time-mean volumetric soil moisture. The specific encoding for Time-mean volumetric soil moisture can be found in 235077.","dimensions":["lat","lon","time"],"type":"data","unit":"m3m-3"}},"sci:publications":[{"doi":"10.1109/MCSE.2023.3260519","citation":"N. Wedi et al., Destination Earth: High-Performance Computing for Weather and Climate, in Computing in Science \u0026 Engineering, vol. 24, no. 6, pp. 29-37, Nov.-Dec. 2022,"},{"doi":"10.21957/d3f982672e","citation":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"dedl:short_description":"The Climate Change Adaptation Digital Twin provides global climate projections and sector-specific information over multiple decades at high resolution via a unified framework combining advanced Earth system models, impact assessments, and observations. This Generation-2, Realization-1 Collection gives access to 'Future Projection' data based on the 'IFS-NEMO' model."},{"type":"Collection","title":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Storyline Simulation Present Climate - IFS-FESOM - Generation-2 - Realization-1","id":"EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_CONT_IFS-FESOM.R1","description":"The DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities by providing innovative climate information on multi-decadal timescales, globally, at scales at which many impacts of climate change are observed. It combines cutting-edge global Earth-system models, impact-sector applications and observations into a unified framework to provide global climate projections and impact-sector information on multi-decadal timescales (1990 to ~2050), at very high spatial resolutions (5 to 10 km).\n\nThe Climate DT represents the first ever attempt to operationalise the production of global multi-decadal climate projections, leveraging the world-leading supercomputing facilities of the EuroHPC Joint Undertaking along with some of the leading European climate models. A concise overview of what the Climate DT aims to achieve, and of the different concepts essential for an understanding of the Digital Twin’s characteristics, is included in the [Climate DT factsheet](https://destine.ecmwf.int/wp-content/uploads/2024/06/2024.06.07_Climate-DT-Fact-Sheet_V7-2.pdf)\n\n## Storyline Simulation Present Climate \n\n Storyline simulations offer a what-if capability to explore how a weather event we experienced in the recent past would change in a warmer climate and how it would have looked in past pre-industrial conditions. To constrain simulated weather events, a nudging approach is used to keep the simulated large-scale flow close to the ERA5 reanalysis for the period 2017 – 2023. Processes on smaller scales and thermodynamic processes are free to evolve, which allows scientists to study e.g. how extreme precipitation would change in a warmer world for a weather event observed in present-day conditions. Initial conditions are taken from ocean-only spinup simulations for 1950 and 2017, and from the IFS-FESOM projection for a +2 °C world. Storyline simulations will be updated regularly to include most recent months. The next update will extend all storylines to include 2024.\n\n## Models\n\nThe Climate DT exploits and further evolves a new generation of global storm-resolving and eddy-rich models built through a cooperative model development approach. For more information on models please click [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#models)\n\n## Simulations\n\nThe Climate DT team carries out several types of digital twin simulations on the EuroHPC supercomputers.  Multi-decadal simulations are produced to cover the recent past (from 1990) and possible future evolutions of the climate up to 2050. See [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#simulations) for more information on Simulations\n\n## Parameters\n\nBelow we see the list of parameters extracted from the 'DestinE Climate DT data portfolio', for more information please refer to the pages [Climate DT Phase2 CLTE Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters) and [Climate DT Phase2 CLMN Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters)","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_CONT_IFS-FESOM.R1/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_CONT_IFS-FESOM.R1/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_CONT_IFS-FESOM.R1/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_CONT_IFS-FESOM.R1","title":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Storyline Simulation Present Climate - IFS-FESOM - Generation-2 - Realization-1"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters","title":"DestinE ClimateDT Phase 2 CLTE Parameters"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters","title":"DestinE ClimateDT Phase 2 CLMN Parameters"},{"rel":"cite-as","type":"text/html","href":"https://dl.acm.org/doi/abs/10.1109/MCSE.2023.3260519","title":"Destination Earth: High-Performance Computing for Weather and Climate"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.21957/d3f982672e","title":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"assets":{"thumbnail":{"href":"https://s3.central.data.destination-earth.eu/swift/v1/dedl-public/collections/climate-dt-min.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2017-01-01T00:00:00Z","2025-12-31T23:59:59Z"]]}},"license":"CC-BY-4.0","keywords":["Digital Twins","Climate Change","Atmosphere","Ocean","Land","Sea Ice","Snow","Soil","Earth","High Performance Computing","Decision Making","Europe","Meteorology","class:d1","dataset:climate-dt","generation:2","expver:0001","stream:clmn,clte","type:fc","activity:story-nudging","experiment:cont","realization:1","model:IFS-FESOM","resolution:standard,high","levtype:hl,o2d,o3d,pl,sfc,sol"],"summaries":{"federation:backends":["dedt_mn5"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/datacube/v2.1.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["producer","processor","licensor"],"url":"https://www.ecmwf.int/"}],"created":"2024-04-11T22:31:55Z","updated":"2026-04-24T10:24:59Z","published":"2024-04-11T22:31:55Z","cube:dimensions":{"lat":{"axis":"y","description":"latitude","extent":[-90,90],"type":"spatial"},"lon":{"axis":"x","description":"longitude","extent":[-180,180],"type":"spatial"},"time":{"extent":["2017-01-01T00:00:00Z","2025-12-31T23:59:59Z"],"step":"P0Y0M0DT1H0M0S","type":"temporal"}},"cube:variables":{"10_metre_U_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre U wind component","parameter_ID":"165","product_type":"forecast","shortName":"10u","standard_name":"10_metre_U_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/165"},"description":"This parameter is the eastward component of the 10m wind. It is the horizontal speed of air moving towards the east, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the V component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_V_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre V wind component","parameter_ID":"166","product_type":"forecast","shortName":"10v","standard_name":"10_metre_V_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/166"},"description":"This parameter is the northward component of the 10m wind. It is the horizontal speed of air moving towards the north, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the U component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_wind_speed(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre wind speed","parameter_ID":"207","product_type":"forecast","shortName":"10si","standard_name":"10_metre_wind_speed","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/207"},"description":"This parameter is the horizontal speed of the wind, or movement of air, at a height of ten metres above the surface of the Earth. The units of this parameter are metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.The eastward and northward components of the horizontal wind at 10m are also available as parameters.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"2_metre_dewpoint_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"2","levtype":"sfc","long_name":"2 metre dewpoint temperature","parameter_ID":"168","product_type":"forecast","shortName":"2d","standard_name":"2_metre_dewpoint_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/168"},"description":"This parameter is the temperature to which the air, at 2 metres above the surface of the Earth, would have to be cooled for saturation to occur.It is a measure of the humidity of the air. Combined with temperature and pressure, it can be used to calculate the relative humidity.2m dew point temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"2_metre_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"2","levtype":"sfc","long_name":"2 metre temperature","parameter_ID":"167","product_type":"forecast","shortName":"2t","standard_name":"2_metre_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/167"},"description":"This parameter is the temperature of air at 2m above the surface of land, sea or in-land waters.2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.Please note that the encodings listed here for s2s \u0026 uerra (which includes encodings for carra/cerra) include entries for Mean 2 metre temperature. The specific encoding for Mean 2 metre temperature can be found in 228004.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Geopotential(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Geopotential","parameter_ID":"129","product_type":"forecast","shortName":"z","standard_name":"Geopotential","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/129"},"description":"This parameter is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level.The geopotential height can be calculated by dividing the geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2). The geopotential height plays an important role in synoptic meteorology (analysis of weather patterns). Charts of geopotential height plotted at constant pressure levels (e.g., 300, 500 or 850 hPa) can be used to identify weather systems such as cyclones, anticyclones, troughs and ridges.At the surface of the Earth, this parameter shows the variations in geopotential (height) of the surface, and is often referred to as the orography.","dimensions":["lat","lon","time"],"type":"data","unit":"m2s-2"},"Land-sea_mask(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Land-sea mask","parameter_ID":"172","product_type":"forecast","shortName":"lsm","standard_name":"Land-sea_mask","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/172"},"description":"This parameter is the proportion of land, as opposed to ocean or inland waters (lakes, reservoirs, rivers and coastal waters), in agrid box.This parameter has values ranging between zero and one and is dimensionless.In cycles of the ECMWF Integrated Forecasting System (IFS) from CY41R1 (introduced in May 2015) onwards, grid boxes where this parameter has a value above 0.5 can be comprised of a mixture of land and inland water but not ocean. Grid boxes with a value of 0.5 and below can only be comprised of a water surface. In the latter case, the lake cover is used to determine how much of the water surface is ocean or inland water.In cycles of the IFS before CY41R1, grid boxes where this parameter has a value above 0.5 can only be comprised of land and those grid boxes with a value of 0.5 and below can only be comprised of ocean. In these older model cycles, there is no differentiation between ocean and inland water.","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Mean_sea_level_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Mean sea level pressure","parameter_ID":"151","product_type":"forecast","shortName":"msl","standard_name":"Mean_sea_level_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/151"},"description":"This parameter is the pressure (force per unit area) of the atmosphere adjusted to the height of mean sea level.It is a measure of the weight that all the air in a column vertically above the area of Earth's surface would have at that point, if the point were located at the mean sea level. It is calculated over all surfaces - land, sea and in-land water.Maps of mean sea level pressure are used to identify the locations of low and high pressure systems, often referred to as cyclones and anticyclones. Contours of mean sea level pressure also indicate the strength of the wind. Tightly packed contours show stronger winds.The units of this parameter are pascals (Pa).  Mean sea level pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb = 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Orography(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Orography","parameter_ID":"228002","product_type":"forecast","shortName":"orog","standard_name":"Orography","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/228002"},"dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Potential_vorticity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Potential vorticity","parameter_ID":"60","product_type":"forecast","shortName":"pv","standard_name":"Potential_vorticity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/60"},"description":"Potential vorticity is a measure of the capacity for air to rotate in the atmosphere. If we ignore the effects of heating and friction, potential vorticity is conserved following an air parcel. It is used to look for places where large wind storms are likely to originate and develop.  Potential vorticity increases strongly above the tropopause and therefore, it can also be used in studies related to the stratosphere and stratosphere-troposphere exchanges.Large wind storms develop when a column of air in the atmosphere starts to rotate. Potential vorticity is calculated from the wind, temperature and pressure across a column of air in the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K m2kg-1s-1"},"Relative_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Relative humidity","parameter_ID":"157","product_type":"forecast","shortName":"r","standard_name":"Relative_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/157"},"description":"This parameter is the water vapour pressure as a percentage of the value at which the air becomes saturated (the point at which water vapour begins to condense into liquid water or deposition into ice).For temperatures over 0°C (273.15 K) it is calculated for saturation over water. At temperatures below -23°C it is calculated for saturation over ice.  Between -23°C and 0°C this parameter is calculated by interpolating between the ice and water values using a quadratic function.See more information about the model's relative humidity calculation.","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Skin_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Skin temperature","parameter_ID":"235","product_type":"forecast","shortName":"skt","standard_name":"Skin_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235"},"description":"This parameter is the temperature of the surface of the Earth.The skin temperature is the theoretical temperature that is required to satisfy the surface energy balance. It represents the temperature of the uppermost surface layer, which has no heat capacity and so can respond instantaneously to changes in surface fluxes. Skin temperature is calculated differently over land and sea.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.See further information about the skin temperatureover landandover sea.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean skin temperature. The specific encoding for Mean skin temperature can be found in 235079.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Snow_depth_water_equivalent(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Snow depth water equivalent","parameter_ID":"228141","product_type":"forecast","shortName":"sd","standard_name":"Snow_depth_water_equivalent","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228141"},"description":"Snow depth water equivalent in kg m**-2 (mm) water equivalent.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean snow depth water equivalent. The specific encoding for Time-mean snow depth water equivalent can be found in 235078.[NOTE: See 141 for the equivalent parameter in \"m of water equivalent\"]","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Snow_depth_water_equivalent(clte_sol)":{"attrs":{"encoding":"instantaneous","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Snow depth water equivalent","parameter_ID":"228141","product_type":"forecast","shortName":"sd","standard_name":"Snow_depth_water_equivalent","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228141"},"description":"Snow depth water equivalent in kg m**-2 (mm) water equivalent.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean snow depth water equivalent. The specific encoding for Time-mean snow depth water equivalent can be found in 235078.[NOTE: See 141 for the equivalent parameter in \"m of water equivalent\"]","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Specific_cloud_liquid_water_content(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific cloud liquid water content","parameter_ID":"246","product_type":"forecast","shortName":"clwc","standard_name":"Specific_cloud_liquid_water_content","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/246"},"description":"This parameter is the mass of cloud liquid water droplets per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for agrid box.Water within clouds can be liquid or ice, or a combination of the two.See further information about the cloud formulation.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Specific_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific humidity","parameter_ID":"133","product_type":"forecast","shortName":"q","standard_name":"Specific_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/133"},"description":"This parameter is the mass of water vapour per kilogram of moist air.The total mass of moist air is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Surface_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Surface pressure","parameter_ID":"134","product_type":"forecast","shortName":"sp","standard_name":"Surface_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/134"},"description":"This parameter is the pressure (force per unit area) of the atmosphere on the surface of land, sea and in-land water.It is a measure of the weight of all the air in a column vertically above the area of the Earth's surface represented at a fixed point.Surface pressure is often used in combination with temperature to calculate air density.The strong variation of pressure with altitude makes it difficult to see the low and high pressure systems over mountainous areas, so mean sea level pressure, rather than surface pressure, is normally used for this purpose.The units of this parameter are Pascals (Pa). Surface pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb= 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Temperature(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Temperature","parameter_ID":"130","product_type":"forecast","shortName":"t","standard_name":"Temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/130"},"description":"This parameter is the temperature in the atmosphere.It has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.This parameter is available on multiple levels through the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_10_metre_U_wind_component(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre U wind component","parameter_ID":"235165","product_type":"forecast","shortName":"avg_10u","standard_name":"Time-mean_10_metre_U_wind_component","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235165"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_10_metre_V_wind_component(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre V wind component","parameter_ID":"235166","product_type":"forecast","shortName":"avg_10v","standard_name":"Time-mean_10_metre_V_wind_component","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235166"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_10_metre_wind_speed(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre wind speed","parameter_ID":"228005","product_type":"forecast","shortName":"avg_10ws","standard_name":"Time-mean_10_metre_wind_speed","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/228005"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_2_metre_dewpoint_temperature(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"2","levtype":"sfc","long_name":"Time-mean 2 metre dewpoint temperature","parameter_ID":"235168","product_type":"forecast","shortName":"avg_2d","standard_name":"Time-mean_2_metre_dewpoint_temperature","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235168"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_2_metre_temperature(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"2","levtype":"sfc","long_name":"Time-mean 2 metre temperature","parameter_ID":"228004","product_type":"forecast","shortName":"avg_2t","standard_name":"Time-mean_2_metre_temperature","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/228004"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_U_component_of_wind(clmn_hl)":{"attrs":{"encoding":"mean","levelist":"100","levtype":"hl","long_name":"Time-mean U component of wind","parameter_ID":"235131","product_type":"forecast","shortName":"avg_u","standard_name":"Time-mean_U_component_of_wind","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235131"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_U_component_of_wind(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean U component of wind","parameter_ID":"235131","product_type":"forecast","shortName":"avg_u","standard_name":"Time-mean_U_component_of_wind","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235131"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_V_component_of_wind(clmn_hl)":{"attrs":{"encoding":"mean","levelist":"100","levtype":"hl","long_name":"Time-mean V component of 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m-2"},"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"700","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 700 m","parameter_ID":"263122","product_type":"forecast","shortName":"avg_hc700m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263122"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_volumetric_soil_moisture(clmn_sol)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Time-mean volumetric soil moisture","parameter_ID":"235077","product_type":"forecast","shortName":"avg_vsw","standard_name":"Time-mean_volumetric_soil_moisture","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235077"},"dimensions":["lat","lon","time"],"type":"data","unit":"m3m-3"},"Time_mean_top_downward_short-wave_radiation_flux(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time mean top downward short-wave radiation flux","parameter_ID":"235053","product_type":"forecast","shortName":"avg_tdswrf","standard_name":"Time_mean_top_downward_short-wave_radiation_flux","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235053"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Time_mean_top_downward_short-wave_radiation_flux(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time mean top downward short-wave radiation flux","parameter_ID":"235053","product_type":"forecast","shortName":"avg_tdswrf","standard_name":"Time_mean_top_downward_short-wave_radiation_flux","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235053"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Total_Cloud_Cover(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total Cloud Cover","parameter_ID":"228164","product_type":"forecast","shortName":"tcc","standard_name":"Total_Cloud_Cover","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228164"},"description":"[NOTE: See 164 for the equivalent parameter in \"(0-1)\"]","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Total_column_cloud_ice_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud ice water","parameter_ID":"79","product_type":"forecast","shortName":"tciw","standard_name":"Total_column_cloud_ice_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/79"},"description":"This parameter is the amount of ice contained within clouds in a column extending from the surface of the Earth to the top of the atmosphere. Snow (aggregated ice crystals) is not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The  ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_cloud_liquid_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud liquid water","parameter_ID":"78","product_type":"forecast","shortName":"tclw","standard_name":"Total_column_cloud_liquid_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/78"},"description":"This parameter is the amount of liquid water contained within cloud droplets in a column extending from the surface of the Earth to the top of the atmosphere. Rain water droplets, which are much larger in size (and mass), are not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_vertically-integrated_water_vapour(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column vertically-integrated water vapour","parameter_ID":"137","product_type":"forecast","shortName":"tcwv","standard_name":"Total_column_vertically-integrated_water_vapour","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/137"},"description":"This parameter is the total amount of water vapour in a column extending from the surface of the Earth to the top of the atmosphere.This parameter represents the area averaged value for agrid box.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column water","parameter_ID":"136","product_type":"forecast","shortName":"tcw","standard_name":"Total_column_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/136"},"description":"This parameter is the sum of water vapour, liquid water, cloud ice, rain and snow in a column extending from the surface of the Earth to the top of the atmosphere. In old versions of the ECMWF model (IFS), rain and snow were not accounted for.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"U_component_of_wind(clte_hl)":{"attrs":{"encoding":"instantaneous","levelist":"100","levtype":"hl","long_name":"U component of wind","parameter_ID":"131","product_type":"forecast","shortName":"u","standard_name":"U_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/131"},"description":"This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east, in metres per second. A negative sign thus indicates air movement towards the west.This parameter can be combined with the V component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"U_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"U component of wind","parameter_ID":"131","product_type":"forecast","shortName":"u","standard_name":"U_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/131"},"description":"This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east, in metres per second. A negative sign thus indicates air movement towards the west.This parameter can be combined with the V component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"V_component_of_wind(clte_hl)":{"attrs":{"encoding":"instantaneous","levelist":"100","levtype":"hl","long_name":"V component of wind","parameter_ID":"132","product_type":"forecast","shortName":"v","standard_name":"V_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/132"},"description":"This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north, in metres per second. A negative sign thus indicates air movement towards the south.This parameter can be combined with the U component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"V_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"V component of wind","parameter_ID":"132","product_type":"forecast","shortName":"v","standard_name":"V_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/132"},"description":"This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north, in metres per second. A negative sign thus indicates air movement towards the south.This parameter can be combined with the U component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Vertical_velocity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Vertical velocity","parameter_ID":"135","product_type":"forecast","shortName":"w","standard_name":"Vertical_velocity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/135"},"description":"This parameter is the speed of air motion in the upward or downward direction. The ECMWF Integrated Forecasting System (IFS) uses a pressure based vertical co-ordinate system and pressure decreases with height, therefore negative values of vertical velocity indicate upward motion.Vertical velocity can be useful to understand the large-scale dynamics of the atmosphere, including areas of upward motion/ascent (negative values) and downward motion/subsidence (positive values).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa s-1"},"Volumetric_soil_moisture(clte_sol)":{"attrs":{"encoding":"instantaneous","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Volumetric soil moisture","parameter_ID":"260199","product_type":"forecast","shortName":"vsw","standard_name":"Volumetric_soil_moisture","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/260199"},"description":"Please note that the encoding listed here for uerra (which includes carra/cerra) includes entries for Time-mean volumetric soil moisture. The specific encoding for Time-mean volumetric soil moisture can be found in 235077.","dimensions":["lat","lon","time"],"type":"data","unit":"m3m-3"}},"sci:publications":[{"doi":"10.1109/MCSE.2023.3260519","citation":"N. Wedi et al., Destination Earth: High-Performance Computing for Weather and Climate, in Computing in Science \u0026 Engineering, vol. 24, no. 6, pp. 29-37, Nov.-Dec. 2022,"},{"doi":"10.21957/d3f982672e","citation":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"dedl:short_description":"The Climate Change Adaptation Digital Twin provides global climate projections and sector-specific information over multiple decades at high resolution via a unified framework combining advanced Earth system models, impact assessments, and observations. This Generation-2, Realization-1 Collection gives access to 'Storyline Simulation Present Climate' data based on the 'IFS-FESOM' model."},{"type":"Collection","title":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Storyline Simulation Present Climate - IFS-FESOM - Generation-2 - Realization-2","id":"EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_CONT_IFS-FESOM.R2","description":"The DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities by providing innovative climate information on multi-decadal timescales, globally, at scales at which many impacts of climate change are observed. It combines cutting-edge global Earth-system models, impact-sector applications and observations into a unified framework to provide global climate projections and impact-sector information on multi-decadal timescales (1990 to ~2050), at very high spatial resolutions (5 to 10 km).\n\nThe Climate DT represents the first ever attempt to operationalise the production of global multi-decadal climate projections, leveraging the world-leading supercomputing facilities of the EuroHPC Joint Undertaking along with some of the leading European climate models. A concise overview of what the Climate DT aims to achieve, and of the different concepts essential for an understanding of the Digital Twin’s characteristics, is included in the [Climate DT factsheet](https://destine.ecmwf.int/wp-content/uploads/2024/06/2024.06.07_Climate-DT-Fact-Sheet_V7-2.pdf)\n\n## Storyline Simulation Present Climate \n\n Storyline simulations offer a what-if capability to explore how a weather event we experienced in the recent past would change in a warmer climate and how it would have looked in past pre-industrial conditions. To constrain simulated weather events, a nudging approach is used to keep the simulated large-scale flow close to the ERA5 reanalysis for the period 2017 – 2023. Processes on smaller scales and thermodynamic processes are free to evolve, which allows scientists to study e.g. how extreme precipitation would change in a warmer world for a weather event observed in present-day conditions. Initial conditions are taken from ocean-only spinup simulations for 1950 and 2017, and from the IFS-FESOM projection for a +2 °C world. Storyline simulations will be updated regularly to include most recent months. The next update will extend all storylines to include 2024.\n\n## Models\n\nThe Climate DT exploits and further evolves a new generation of global storm-resolving and eddy-rich models built through a cooperative model development approach. For more information on models please click [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#models)\n\n## Simulations\n\nThe Climate DT team carries out several types of digital twin simulations on the EuroHPC supercomputers.  Multi-decadal simulations are produced to cover the recent past (from 1990) and possible future evolutions of the climate up to 2050. See [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#simulations) for more information on Simulations\n\n## Parameters\n\nBelow we see the list of parameters extracted from the 'DestinE Climate DT data portfolio', for more information please refer to the pages [Climate DT Phase2 CLTE Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters) and [Climate DT Phase2 CLMN Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters)","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_CONT_IFS-FESOM.R2/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_CONT_IFS-FESOM.R2/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_CONT_IFS-FESOM.R2/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_CONT_IFS-FESOM.R2","title":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Storyline Simulation Present Climate - IFS-FESOM - Generation-2 - Realization-2"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters","title":"DestinE ClimateDT Phase 2 CLTE Parameters"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters","title":"DestinE ClimateDT Phase 2 CLMN Parameters"},{"rel":"cite-as","type":"text/html","href":"https://dl.acm.org/doi/abs/10.1109/MCSE.2023.3260519","title":"Destination Earth: High-Performance Computing for Weather and Climate"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.21957/d3f982672e","title":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"assets":{"thumbnail":{"href":"https://s3.central.data.destination-earth.eu/swift/v1/dedl-public/collections/climate-dt-min.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2017-01-01T00:00:00Z","2025-12-31T23:59:59Z"]]}},"license":"CC-BY-4.0","keywords":["Digital Twins","Climate Change","Atmosphere","Ocean","Land","Sea Ice","Snow","Soil","Earth","High Performance Computing","Decision Making","Europe","Meteorology","class:d1","dataset:climate-dt","generation:2","expver:0001","stream:clmn,clte","type:fc","activity:story-nudging","experiment:cont","realization:2","model:IFS-FESOM","resolution:standard,high","levtype:hl,o2d,o3d,pl,sfc,sol"],"summaries":{"federation:backends":["dedt_mn5"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/datacube/v2.1.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["producer","processor","licensor"],"url":"https://www.ecmwf.int/"}],"created":"2024-04-11T22:31:55Z","updated":"2026-04-24T10:24:59Z","published":"2024-04-11T22:31:55Z","cube:dimensions":{"lat":{"axis":"y","description":"latitude","extent":[-90,90],"type":"spatial"},"lon":{"axis":"x","description":"longitude","extent":[-180,180],"type":"spatial"},"time":{"extent":["2017-01-01T00:00:00Z","2025-12-31T23:59:59Z"],"step":"P0Y0M0DT1H0M0S","type":"temporal"}},"cube:variables":{"10_metre_U_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre U wind component","parameter_ID":"165","product_type":"forecast","shortName":"10u","standard_name":"10_metre_U_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/165"},"description":"This parameter is the eastward component of the 10m wind. It is the horizontal speed of air moving towards the east, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the V component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_V_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre V wind component","parameter_ID":"166","product_type":"forecast","shortName":"10v","standard_name":"10_metre_V_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/166"},"description":"This parameter is the northward component of the 10m wind. It is the horizontal speed of air moving towards the north, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the U component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_wind_speed(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre wind speed","parameter_ID":"207","product_type":"forecast","shortName":"10si","standard_name":"10_metre_wind_speed","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/207"},"description":"This parameter is the horizontal speed of the wind, or movement of air, at a height of ten metres above the surface of the Earth. The units of this parameter are metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.The eastward and northward components of the horizontal wind at 10m are also available as parameters.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"2_metre_dewpoint_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"2","levtype":"sfc","long_name":"2 metre dewpoint temperature","parameter_ID":"168","product_type":"forecast","shortName":"2d","standard_name":"2_metre_dewpoint_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/168"},"description":"This parameter is the temperature to which the air, at 2 metres above the surface of the Earth, would have to be cooled for saturation to occur.It is a measure of the humidity of the air. Combined with temperature and pressure, it can be used to calculate the relative humidity.2m dew point temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"2_metre_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"2","levtype":"sfc","long_name":"2 metre temperature","parameter_ID":"167","product_type":"forecast","shortName":"2t","standard_name":"2_metre_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/167"},"description":"This parameter is the temperature of air at 2m above the surface of land, sea or in-land waters.2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.Please note that the encodings listed here for s2s \u0026 uerra (which includes encodings for carra/cerra) include entries for Mean 2 metre temperature. The specific encoding for Mean 2 metre temperature can be found in 228004.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Geopotential(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Geopotential","parameter_ID":"129","product_type":"forecast","shortName":"z","standard_name":"Geopotential","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/129"},"description":"This parameter is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level.The geopotential height can be calculated by dividing the geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2). The geopotential height plays an important role in synoptic meteorology (analysis of weather patterns). Charts of geopotential height plotted at constant pressure levels (e.g., 300, 500 or 850 hPa) can be used to identify weather systems such as cyclones, anticyclones, troughs and ridges.At the surface of the Earth, this parameter shows the variations in geopotential (height) of the surface, and is often referred to as the orography.","dimensions":["lat","lon","time"],"type":"data","unit":"m2s-2"},"Land-sea_mask(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Land-sea mask","parameter_ID":"172","product_type":"forecast","shortName":"lsm","standard_name":"Land-sea_mask","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/172"},"description":"This parameter is the proportion of land, as opposed to ocean or inland waters (lakes, reservoirs, rivers and coastal waters), in agrid box.This parameter has values ranging between zero and one and is dimensionless.In cycles of the ECMWF Integrated Forecasting System (IFS) from CY41R1 (introduced in May 2015) onwards, grid boxes where this parameter has a value above 0.5 can be comprised of a mixture of land and inland water but not ocean. Grid boxes with a value of 0.5 and below can only be comprised of a water surface. In the latter case, the lake cover is used to determine how much of the water surface is ocean or inland water.In cycles of the IFS before CY41R1, grid boxes where this parameter has a value above 0.5 can only be comprised of land and those grid boxes with a value of 0.5 and below can only be comprised of ocean. In these older model cycles, there is no differentiation between ocean and inland water.","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Mean_sea_level_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Mean sea level pressure","parameter_ID":"151","product_type":"forecast","shortName":"msl","standard_name":"Mean_sea_level_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/151"},"description":"This parameter is the pressure (force per unit area) of the atmosphere adjusted to the height of mean sea level.It is a measure of the weight that all the air in a column vertically above the area of Earth's surface would have at that point, if the point were located at the mean sea level. It is calculated over all surfaces - land, sea and in-land water.Maps of mean sea level pressure are used to identify the locations of low and high pressure systems, often referred to as cyclones and anticyclones. Contours of mean sea level pressure also indicate the strength of the wind. Tightly packed contours show stronger winds.The units of this parameter are pascals (Pa).  Mean sea level pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb = 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Orography(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Orography","parameter_ID":"228002","product_type":"forecast","shortName":"orog","standard_name":"Orography","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/228002"},"dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Potential_vorticity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Potential vorticity","parameter_ID":"60","product_type":"forecast","shortName":"pv","standard_name":"Potential_vorticity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/60"},"description":"Potential vorticity is a measure of the capacity for air to rotate in the atmosphere. If we ignore the effects of heating and friction, potential vorticity is conserved following an air parcel. It is used to look for places where large wind storms are likely to originate and develop.  Potential vorticity increases strongly above the tropopause and therefore, it can also be used in studies related to the stratosphere and stratosphere-troposphere exchanges.Large wind storms develop when a column of air in the atmosphere starts to rotate. Potential vorticity is calculated from the wind, temperature and pressure across a column of air in the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K m2kg-1s-1"},"Relative_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Relative humidity","parameter_ID":"157","product_type":"forecast","shortName":"r","standard_name":"Relative_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/157"},"description":"This parameter is the water vapour pressure as a percentage of the value at which the air becomes saturated (the point at which water vapour begins to condense into liquid water or deposition into ice).For temperatures over 0°C (273.15 K) it is calculated for saturation over water. At temperatures below -23°C it is calculated for saturation over ice.  Between -23°C and 0°C this parameter is calculated by interpolating between the ice and water values using a quadratic function.See more information about the model's relative humidity calculation.","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Skin_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Skin temperature","parameter_ID":"235","product_type":"forecast","shortName":"skt","standard_name":"Skin_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235"},"description":"This parameter is the temperature of the surface of the Earth.The skin temperature is the theoretical temperature that is required to satisfy the surface energy balance. It represents the temperature of the uppermost surface layer, which has no heat capacity and so can respond instantaneously to changes in surface fluxes. Skin temperature is calculated differently over land and sea.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.See further information about the skin temperatureover landandover sea.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean skin temperature. The specific encoding for Mean skin temperature can be found in 235079.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Snow_depth_water_equivalent(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Snow depth water equivalent","parameter_ID":"228141","product_type":"forecast","shortName":"sd","standard_name":"Snow_depth_water_equivalent","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228141"},"description":"Snow depth water equivalent in kg m**-2 (mm) water equivalent.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean snow depth water equivalent. The specific encoding for Time-mean snow depth water equivalent can be found in 235078.[NOTE: See 141 for the equivalent parameter in \"m of water equivalent\"]","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Snow_depth_water_equivalent(clte_sol)":{"attrs":{"encoding":"instantaneous","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Snow depth water equivalent","parameter_ID":"228141","product_type":"forecast","shortName":"sd","standard_name":"Snow_depth_water_equivalent","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228141"},"description":"Snow depth water equivalent in kg m**-2 (mm) water equivalent.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean snow depth water equivalent. The specific encoding for Time-mean snow depth water equivalent can be found in 235078.[NOTE: See 141 for the equivalent parameter in \"m of water equivalent\"]","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Specific_cloud_liquid_water_content(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific cloud liquid water content","parameter_ID":"246","product_type":"forecast","shortName":"clwc","standard_name":"Specific_cloud_liquid_water_content","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/246"},"description":"This parameter is the mass of cloud liquid water droplets per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for agrid box.Water within clouds can be liquid or ice, or a combination of the two.See further information about the cloud formulation.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Specific_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific humidity","parameter_ID":"133","product_type":"forecast","shortName":"q","standard_name":"Specific_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/133"},"description":"This parameter is the mass of water vapour per kilogram of moist air.The total mass of moist air is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Surface_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Surface pressure","parameter_ID":"134","product_type":"forecast","shortName":"sp","standard_name":"Surface_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/134"},"description":"This parameter is the pressure (force per unit area) of the atmosphere on the surface of land, sea and in-land water.It is a measure of the weight of all the air in a column vertically above the area of the Earth's surface represented at a fixed point.Surface pressure is often used in combination with temperature to calculate air density.The strong variation of pressure with altitude makes it difficult to see the low and high pressure systems over mountainous areas, so mean sea level pressure, rather than surface pressure, is normally used for this purpose.The units of this parameter are Pascals (Pa). Surface pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb= 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Temperature(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Temperature","parameter_ID":"130","product_type":"forecast","shortName":"t","standard_name":"Temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/130"},"description":"This parameter is the temperature in the atmosphere.It has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.This parameter is available on multiple levels through the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_10_metre_U_wind_component(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre U wind component","parameter_ID":"235165","product_type":"forecast","shortName":"avg_10u","standard_name":"Time-mean_10_metre_U_wind_component","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235165"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_10_metre_V_wind_component(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre V wind component","parameter_ID":"235166","product_type":"forecast","shortName":"avg_10v","standard_name":"Time-mean_10_metre_V_wind_component","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235166"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_10_metre_wind_speed(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre wind speed","parameter_ID":"228005","product_type":"forecast","shortName":"avg_10ws","standard_name":"Time-mean_10_metre_wind_speed","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/228005"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_2_metre_dewpoint_temperature(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"2","levtype":"sfc","long_name":"Time-mean 2 metre dewpoint temperature","parameter_ID":"235168","product_type":"forecast","shortName":"avg_2d","standard_name":"Time-mean_2_metre_dewpoint_temperature","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235168"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_2_metre_temperature(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"2","levtype":"sfc","long_name":"Time-mean 2 metre temperature","parameter_ID":"228004","product_type":"forecast","shortName":"avg_2t","standard_name":"Time-mean_2_metre_temperature","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/228004"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_U_component_of_wind(clmn_hl)":{"attrs":{"encoding":"mean","levelist":"100","levtype":"hl","long_name":"Time-mean U component of wind","parameter_ID":"235131","product_type":"forecast","shortName":"avg_u","standard_name":"Time-mean_U_component_of_wind","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235131"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_U_component_of_wind(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean U component of wind","parameter_ID":"235131","product_type":"forecast","shortName":"avg_u","standard_name":"Time-mean_U_component_of_wind","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235131"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_V_component_of_wind(clmn_hl)":{"attrs":{"encoding":"mean","levelist":"100","levtype":"hl","long_name":"Time-mean V component of wind","parameter_ID":"235132","product_type":"forecast","shortName":"avg_v","standard_name":"Time-mean_V_component_of_wind","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235132"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_V_component_of_wind(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean V component of wind","parameter_ID":"235132","product_type":"forecast","shortName":"avg_v","standard_name":"Time-mean_V_component_of_wind","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235132"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_ice_velocity(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean eastward sea ice velocity","parameter_ID":"263003","product_type":"forecast","shortName":"avg_siue","standard_name":"Time-mean_eastward_sea_ice_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263003"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_ice_velocity(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean eastward sea ice velocity","parameter_ID":"263003","product_type":"forecast","shortName":"avg_siue","standard_name":"Time-mean_eastward_sea_ice_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263003"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_water_velocity(clmn_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean eastward sea water velocity","parameter_ID":"263506","product_type":"forecast","shortName":"avg_uoe","standard_name":"Time-mean_eastward_sea_water_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263506"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean eastward sea water velocity","parameter_ID":"263506","product_type":"forecast","shortName":"avg_uoe","standard_name":"Time-mean_eastward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263506"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_turbulent_surface_stress(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean eastward turbulent surface stress","parameter_ID":"235041","product_type":"forecast","shortName":"avg_iews","standard_name":"Time-mean_eastward_turbulent_surface_stress","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235041"},"dimensions":["lat","lon","time"],"type":"data","unit":"N m-2"},"Time-mean_eastward_turbulent_surface_stress(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean eastward turbulent surface stress","parameter_ID":"235041","product_type":"forecast","shortName":"avg_iews","standard_name":"Time-mean_eastward_turbulent_surface_stress","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235041"},"dimensions":["lat","lon","time"],"type":"data","unit":"N m-2"},"Time-mean_geopotential(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean geopotential","parameter_ID":"235129","product_type":"forecast","shortName":"avg_z","standard_name":"Time-mean_geopotential","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235129"},"dimensions":["lat","lon","time"],"type":"data","unit":"m2s-2"},"Time-mean_mean_sea_level_pressure(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean mean sea level pressure","parameter_ID":"235151","product_type":"forecast","shortName":"avg_msl","standard_name":"Time-mean_mean_sea_level_pressure","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235151"},"dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Time-mean_moisture_flux(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean moisture flux","parameter_ID":"235043","product_type":"forecast","shortName":"avg_ie","standard_name":"Time-mean_moisture_flux","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235043"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_moisture_flux(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean moisture flux","parameter_ID":"235043","product_type":"forecast","shortName":"avg_ie","standard_name":"Time-mean_moisture_flux","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235043"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_northward_sea_ice_velocity(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean northward sea ice velocity","parameter_ID":"263004","product_type":"forecast","shortName":"avg_sivn","standard_name":"Time-mean_northward_sea_ice_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263004"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_sea_ice_velocity(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean northward sea ice velocity","parameter_ID":"263004","product_type":"forecast","shortName":"avg_sivn","standard_name":"Time-mean_northward_sea_ice_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263004"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_sea_water_velocity(clmn_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean northward sea water velocity","parameter_ID":"263505","product_type":"forecast","shortName":"avg_von","standard_name":"Time-mean_northward_sea_water_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263505"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean northward sea water velocity","parameter_ID":"263505","product_type":"forecast","shortName":"avg_von","standard_name":"Time-mean_northward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263505"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_turbulent_surface_stress(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean northward turbulent surface stress","parameter_ID":"235042","product_type":"forecast","shortName":"avg_inss","standard_name":"Time-mean_northward_turbulent_surface_stress","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235042"},"dimensions":["lat","lon","time"],"type":"data","unit":"N m-2"},"Time-mean_northward_turbulent_surface_stress(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean northward turbulent surface stress","parameter_ID":"235042","product_type":"forecast","shortName":"avg_inss","standard_name":"Time-mean_northward_turbulent_surface_stress","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235042"},"dimensions":["lat","lon","time"],"type":"data","unit":"N m-2"},"Time-mean_potential_vorticity(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean potential vorticity","parameter_ID":"235100","product_type":"forecast","shortName":"avg_pv","standard_name":"Time-mean_potential_vorticity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235100"},"dimensions":["lat","lon","time"],"type":"data","unit":"K m2kg-1s-1"},"Time-mean_relative_humidity(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean relative humidity","parameter_ID":"235157","product_type":"forecast","shortName":"avg_r","standard_name":"Time-mean_relative_humidity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235157"},"dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Time-mean_sea_ice_area_fraction(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea ice area fraction","parameter_ID":"263001","product_type":"forecast","shortName":"avg_siconc","standard_name":"Time-mean_sea_ice_area_fraction","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263001"},"dimensions":["lat","lon","time"],"type":"data","unit":"Fraction"},"Time-mean_sea_ice_area_fraction(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea ice area fraction","parameter_ID":"263001","product_type":"forecast","shortName":"avg_siconc","standard_name":"Time-mean_sea_ice_area_fraction","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263001"},"dimensions":["lat","lon","time"],"type":"data","unit":"Fraction"},"Time-mean_sea_ice_thickness(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea ice 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sky","parameter_ID":"235049","product_type":"forecast","shortName":"avg_tnswrfcs","standard_name":"Time-mean_top_net_short-wave_radiation_flux,_clear_sky","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235049"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Time-mean_top_net_short-wave_radiation_flux,_clear_sky(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean top net short-wave radiation flux, clear sky","parameter_ID":"235049","product_type":"forecast","shortName":"avg_tnswrfcs","standard_name":"Time-mean_top_net_short-wave_radiation_flux,_clear_sky","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235049"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Time-mean_total_cloud_cover(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total cloud cover","parameter_ID":"235288","product_type":"forecast","shortName":"avg_tcc","standard_name":"Time-mean_total_cloud_cover","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235288"},"dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Time-mean_total_column_cloud_ice_water(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total column cloud ice water","parameter_ID":"235088","product_type":"forecast","shortName":"avg_tciw","standard_name":"Time-mean_total_column_cloud_ice_water","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235088"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Time-mean_total_column_heat_content(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean total column heat content","parameter_ID":"263123","product_type":"forecast","shortName":"avg_hcbtm","standard_name":"Time-mean_total_column_heat_content","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263123"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_total_column_heat_content(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean total column heat content","parameter_ID":"263123","product_type":"forecast","shortName":"avg_hcbtm","standard_name":"Time-mean_total_column_heat_content","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263123"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_total_column_liquid_water(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total column liquid water","parameter_ID":"235087","product_type":"forecast","shortName":"avg_tclw","standard_name":"Time-mean_total_column_liquid_water","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235087"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Time-mean_total_column_vertically-integrated_water_vapour(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total column vertically-integrated water vapour","parameter_ID":"235137","product_type":"forecast","shortName":"avg_tcwv","standard_name":"Time-mean_total_column_vertically-integrated_water_vapour","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235137"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Time-mean_total_column_water(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total column water","parameter_ID":"235136","product_type":"forecast","shortName":"avg_tcw","standard_name":"Time-mean_total_column_water","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235136"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Time-mean_total_precipitation_rate(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total precipitation rate","parameter_ID":"235055","product_type":"forecast","shortName":"avg_tprate","standard_name":"Time-mean_total_precipitation_rate","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235055"},"description":"Time-mean total precipitation rate, or time-mean total precipitation flux. This parameter is on level \"Ground or water surface\" (typeOfFirstFixedSurface=1). For this parameter on other levels, please use 235013.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_total_precipitation_rate(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total precipitation rate","parameter_ID":"235055","product_type":"forecast","shortName":"avg_tprate","standard_name":"Time-mean_total_precipitation_rate","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235055"},"description":"Time-mean total precipitation rate, or time-mean total precipitation flux. This parameter is on level \"Ground or water surface\" (typeOfFirstFixedSurface=1). For this parameter on other levels, please use 235013.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_total_snowfall_rate_water_equivalent(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total snowfall rate water equivalent","parameter_ID":"235031","product_type":"forecast","shortName":"avg_tsrwe","standard_name":"Time-mean_total_snowfall_rate_water_equivalent","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235031"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_total_snowfall_rate_water_equivalent(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total snowfall rate water equivalent","parameter_ID":"235031","product_type":"forecast","shortName":"avg_tsrwe","standard_name":"Time-mean_total_snowfall_rate_water_equivalent","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235031"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_upward_sea_water_velocity(clmn_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean upward sea water velocity","parameter_ID":"263507","product_type":"forecast","shortName":"avg_wo","standard_name":"Time-mean_upward_sea_water_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263507"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_upward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean upward sea water velocity","parameter_ID":"263507","product_type":"forecast","shortName":"avg_wo","standard_name":"Time-mean_upward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263507"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_vertical_velocity(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean vertical velocity","parameter_ID":"235135","product_type":"forecast","shortName":"avg_w","standard_name":"Time-mean_vertical_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235135"},"dimensions":["lat","lon","time"],"type":"data","unit":"Pa s-1"},"Time-mean_vertically-integrated_heat_content_in_the_upper_300_m(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"300","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 300 m","parameter_ID":"263121","product_type":"forecast","shortName":"avg_hc300m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_300_m","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263121"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_vertically-integrated_heat_content_in_the_upper_300_m(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"300","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 300 m","parameter_ID":"263121","product_type":"forecast","shortName":"avg_hc300m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_300_m","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263121"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"700","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 700 m","parameter_ID":"263122","product_type":"forecast","shortName":"avg_hc700m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263122"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"700","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 700 m","parameter_ID":"263122","product_type":"forecast","shortName":"avg_hc700m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263122"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_volumetric_soil_moisture(clmn_sol)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Time-mean volumetric soil moisture","parameter_ID":"235077","product_type":"forecast","shortName":"avg_vsw","standard_name":"Time-mean_volumetric_soil_moisture","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235077"},"dimensions":["lat","lon","time"],"type":"data","unit":"m3m-3"},"Time_mean_top_downward_short-wave_radiation_flux(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time mean top downward short-wave radiation flux","parameter_ID":"235053","product_type":"forecast","shortName":"avg_tdswrf","standard_name":"Time_mean_top_downward_short-wave_radiation_flux","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235053"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Time_mean_top_downward_short-wave_radiation_flux(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time mean top downward short-wave radiation flux","parameter_ID":"235053","product_type":"forecast","shortName":"avg_tdswrf","standard_name":"Time_mean_top_downward_short-wave_radiation_flux","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235053"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Total_Cloud_Cover(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total Cloud Cover","parameter_ID":"228164","product_type":"forecast","shortName":"tcc","standard_name":"Total_Cloud_Cover","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228164"},"description":"[NOTE: See 164 for the equivalent parameter in \"(0-1)\"]","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Total_column_cloud_ice_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud ice water","parameter_ID":"79","product_type":"forecast","shortName":"tciw","standard_name":"Total_column_cloud_ice_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/79"},"description":"This parameter is the amount of ice contained within clouds in a column extending from the surface of the Earth to the top of the atmosphere. Snow (aggregated ice crystals) is not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The  ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_cloud_liquid_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud liquid water","parameter_ID":"78","product_type":"forecast","shortName":"tclw","standard_name":"Total_column_cloud_liquid_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/78"},"description":"This parameter is the amount of liquid water contained within cloud droplets in a column extending from the surface of the Earth to the top of the atmosphere. Rain water droplets, which are much larger in size (and mass), are not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_vertically-integrated_water_vapour(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column vertically-integrated water vapour","parameter_ID":"137","product_type":"forecast","shortName":"tcwv","standard_name":"Total_column_vertically-integrated_water_vapour","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/137"},"description":"This parameter is the total amount of water vapour in a column extending from the surface of the Earth to the top of the atmosphere.This parameter represents the area averaged value for agrid box.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column water","parameter_ID":"136","product_type":"forecast","shortName":"tcw","standard_name":"Total_column_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/136"},"description":"This parameter is the sum of water vapour, liquid water, cloud ice, rain and snow in a column extending from the surface of the Earth to the top of the atmosphere. In old versions of the ECMWF model (IFS), rain and snow were not accounted for.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"U_component_of_wind(clte_hl)":{"attrs":{"encoding":"instantaneous","levelist":"100","levtype":"hl","long_name":"U component of wind","parameter_ID":"131","product_type":"forecast","shortName":"u","standard_name":"U_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/131"},"description":"This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east, in metres per second. A negative sign thus indicates air movement towards the west.This parameter can be combined with the V component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"U_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"U component of wind","parameter_ID":"131","product_type":"forecast","shortName":"u","standard_name":"U_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/131"},"description":"This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east, in metres per second. A negative sign thus indicates air movement towards the west.This parameter can be combined with the V component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"V_component_of_wind(clte_hl)":{"attrs":{"encoding":"instantaneous","levelist":"100","levtype":"hl","long_name":"V component of wind","parameter_ID":"132","product_type":"forecast","shortName":"v","standard_name":"V_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/132"},"description":"This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north, in metres per second. A negative sign thus indicates air movement towards the south.This parameter can be combined with the U component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"V_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"V component of wind","parameter_ID":"132","product_type":"forecast","shortName":"v","standard_name":"V_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/132"},"description":"This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north, in metres per second. A negative sign thus indicates air movement towards the south.This parameter can be combined with the U component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Vertical_velocity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Vertical velocity","parameter_ID":"135","product_type":"forecast","shortName":"w","standard_name":"Vertical_velocity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/135"},"description":"This parameter is the speed of air motion in the upward or downward direction. The ECMWF Integrated Forecasting System (IFS) uses a pressure based vertical co-ordinate system and pressure decreases with height, therefore negative values of vertical velocity indicate upward motion.Vertical velocity can be useful to understand the large-scale dynamics of the atmosphere, including areas of upward motion/ascent (negative values) and downward motion/subsidence (positive values).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa s-1"},"Volumetric_soil_moisture(clte_sol)":{"attrs":{"encoding":"instantaneous","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Volumetric soil moisture","parameter_ID":"260199","product_type":"forecast","shortName":"vsw","standard_name":"Volumetric_soil_moisture","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/260199"},"description":"Please note that the encoding listed here for uerra (which includes carra/cerra) includes entries for Time-mean volumetric soil moisture. The specific encoding for Time-mean volumetric soil moisture can be found in 235077.","dimensions":["lat","lon","time"],"type":"data","unit":"m3m-3"}},"sci:publications":[{"doi":"10.1109/MCSE.2023.3260519","citation":"N. Wedi et al., Destination Earth: High-Performance Computing for Weather and Climate, in Computing in Science \u0026 Engineering, vol. 24, no. 6, pp. 29-37, Nov.-Dec. 2022,"},{"doi":"10.21957/d3f982672e","citation":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"dedl:short_description":"The Climate Change Adaptation Digital Twin provides global climate projections and sector-specific information over multiple decades at high resolution via a unified framework combining advanced Earth system models, impact assessments, and observations. This Generation-2, Realization-2 Collection gives access to 'Storyline Simulation Present Climate' data based on the 'IFS-FESOM' model."},{"type":"Collection","title":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Storyline Simulation Present Climate - IFS-FESOM - Generation-2 - Realization-3","id":"EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_CONT_IFS-FESOM.R3","description":"The DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities by providing innovative climate information on multi-decadal timescales, globally, at scales at which many impacts of climate change are observed. It combines cutting-edge global Earth-system models, impact-sector applications and observations into a unified framework to provide global climate projections and impact-sector information on multi-decadal timescales (1990 to ~2050), at very high spatial resolutions (5 to 10 km).\n\nThe Climate DT represents the first ever attempt to operationalise the production of global multi-decadal climate projections, leveraging the world-leading supercomputing facilities of the EuroHPC Joint Undertaking along with some of the leading European climate models. A concise overview of what the Climate DT aims to achieve, and of the different concepts essential for an understanding of the Digital Twin’s characteristics, is included in the [Climate DT factsheet](https://destine.ecmwf.int/wp-content/uploads/2024/06/2024.06.07_Climate-DT-Fact-Sheet_V7-2.pdf)\n\n## Storyline Simulation Present Climate \n\n Storyline simulations offer a what-if capability to explore how a weather event we experienced in the recent past would change in a warmer climate and how it would have looked in past pre-industrial conditions. To constrain simulated weather events, a nudging approach is used to keep the simulated large-scale flow close to the ERA5 reanalysis for the period 2017 – 2023. Processes on smaller scales and thermodynamic processes are free to evolve, which allows scientists to study e.g. how extreme precipitation would change in a warmer world for a weather event observed in present-day conditions. Initial conditions are taken from ocean-only spinup simulations for 1950 and 2017, and from the IFS-FESOM projection for a +2 °C world. Storyline simulations will be updated regularly to include most recent months. The next update will extend all storylines to include 2024.\n\n## Models\n\nThe Climate DT exploits and further evolves a new generation of global storm-resolving and eddy-rich models built through a cooperative model development approach. For more information on models please click [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#models)\n\n## Simulations\n\nThe Climate DT team carries out several types of digital twin simulations on the EuroHPC supercomputers.  Multi-decadal simulations are produced to cover the recent past (from 1990) and possible future evolutions of the climate up to 2050. See [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#simulations) for more information on Simulations\n\n## Parameters\n\nBelow we see the list of parameters extracted from the 'DestinE Climate DT data portfolio', for more information please refer to the pages [Climate DT Phase2 CLTE Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters) and [Climate DT Phase2 CLMN Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters)","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_CONT_IFS-FESOM.R3/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_CONT_IFS-FESOM.R3/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_CONT_IFS-FESOM.R3/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_CONT_IFS-FESOM.R3","title":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Storyline Simulation Present Climate - IFS-FESOM - Generation-2 - Realization-3"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters","title":"DestinE ClimateDT Phase 2 CLTE Parameters"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters","title":"DestinE ClimateDT Phase 2 CLMN Parameters"},{"rel":"cite-as","type":"text/html","href":"https://dl.acm.org/doi/abs/10.1109/MCSE.2023.3260519","title":"Destination Earth: High-Performance Computing for Weather and Climate"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.21957/d3f982672e","title":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"assets":{"thumbnail":{"href":"https://s3.central.data.destination-earth.eu/swift/v1/dedl-public/collections/climate-dt-min.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2017-01-01T00:00:00Z","2025-12-31T23:59:59Z"]]}},"license":"CC-BY-4.0","keywords":["Digital Twins","Climate Change","Atmosphere","Ocean","Land","Sea Ice","Snow","Soil","Earth","High Performance Computing","Decision Making","Europe","Meteorology","class:d1","dataset:climate-dt","generation:2","expver:0001","stream:clmn,clte","type:fc","activity:story-nudging","experiment:cont","realization:3","model:IFS-FESOM","resolution:standard,high","levtype:hl,o2d,o3d,pl,sfc,sol"],"summaries":{"federation:backends":["dedt_mn5"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/datacube/v2.1.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["producer","processor","licensor"],"url":"https://www.ecmwf.int/"}],"created":"2024-04-11T22:31:55Z","updated":"2026-04-24T10:24:59Z","published":"2024-04-11T22:31:55Z","cube:dimensions":{"lat":{"axis":"y","description":"latitude","extent":[-90,90],"type":"spatial"},"lon":{"axis":"x","description":"longitude","extent":[-180,180],"type":"spatial"},"time":{"extent":["2017-01-01T00:00:00Z","2025-12-31T23:59:59Z"],"step":"P0Y0M0DT1H0M0S","type":"temporal"}},"cube:variables":{"10_metre_U_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre U wind component","parameter_ID":"165","product_type":"forecast","shortName":"10u","standard_name":"10_metre_U_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/165"},"description":"This parameter is the eastward component of the 10m wind. It is the horizontal speed of air moving towards the east, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the V component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_V_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre V wind component","parameter_ID":"166","product_type":"forecast","shortName":"10v","standard_name":"10_metre_V_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/166"},"description":"This parameter is the northward component of the 10m wind. It is the horizontal speed of air moving towards the north, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the U component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_wind_speed(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre wind speed","parameter_ID":"207","product_type":"forecast","shortName":"10si","standard_name":"10_metre_wind_speed","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/207"},"description":"This parameter is the horizontal speed of the wind, or movement of air, at a height of ten metres above the surface of the Earth. The units of this parameter are metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.The eastward and northward components of the horizontal wind at 10m are also available as parameters.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"2_metre_dewpoint_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"2","levtype":"sfc","long_name":"2 metre dewpoint temperature","parameter_ID":"168","product_type":"forecast","shortName":"2d","standard_name":"2_metre_dewpoint_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/168"},"description":"This parameter is the temperature to which the air, at 2 metres above the surface of the Earth, would have to be cooled for saturation to occur.It is a measure of the humidity of the air. Combined with temperature and pressure, it can be used to calculate the relative humidity.2m dew point temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"2_metre_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"2","levtype":"sfc","long_name":"2 metre temperature","parameter_ID":"167","product_type":"forecast","shortName":"2t","standard_name":"2_metre_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/167"},"description":"This parameter is the temperature of air at 2m above the surface of land, sea or in-land waters.2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.Please note that the encodings listed here for s2s \u0026 uerra (which includes encodings for carra/cerra) include entries for Mean 2 metre temperature. The specific encoding for Mean 2 metre temperature can be found in 228004.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Geopotential(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Geopotential","parameter_ID":"129","product_type":"forecast","shortName":"z","standard_name":"Geopotential","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/129"},"description":"This parameter is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level.The geopotential height can be calculated by dividing the geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2). The geopotential height plays an important role in synoptic meteorology (analysis of weather patterns). Charts of geopotential height plotted at constant pressure levels (e.g., 300, 500 or 850 hPa) can be used to identify weather systems such as cyclones, anticyclones, troughs and ridges.At the surface of the Earth, this parameter shows the variations in geopotential (height) of the surface, and is often referred to as the orography.","dimensions":["lat","lon","time"],"type":"data","unit":"m2s-2"},"Land-sea_mask(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Land-sea mask","parameter_ID":"172","product_type":"forecast","shortName":"lsm","standard_name":"Land-sea_mask","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/172"},"description":"This parameter is the proportion of land, as opposed to ocean or inland waters (lakes, reservoirs, rivers and coastal waters), in agrid box.This parameter has values ranging between zero and one and is dimensionless.In cycles of the ECMWF Integrated Forecasting System (IFS) from CY41R1 (introduced in May 2015) onwards, grid boxes where this parameter has a value above 0.5 can be comprised of a mixture of land and inland water but not ocean. Grid boxes with a value of 0.5 and below can only be comprised of a water surface. In the latter case, the lake cover is used to determine how much of the water surface is ocean or inland water.In cycles of the IFS before CY41R1, grid boxes where this parameter has a value above 0.5 can only be comprised of land and those grid boxes with a value of 0.5 and below can only be comprised of ocean. In these older model cycles, there is no differentiation between ocean and inland water.","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Mean_sea_level_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Mean sea level pressure","parameter_ID":"151","product_type":"forecast","shortName":"msl","standard_name":"Mean_sea_level_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/151"},"description":"This parameter is the pressure (force per unit area) of the atmosphere adjusted to the height of mean sea level.It is a measure of the weight that all the air in a column vertically above the area of Earth's surface would have at that point, if the point were located at the mean sea level. It is calculated over all surfaces - land, sea and in-land water.Maps of mean sea level pressure are used to identify the locations of low and high pressure systems, often referred to as cyclones and anticyclones. Contours of mean sea level pressure also indicate the strength of the wind. Tightly packed contours show stronger winds.The units of this parameter are pascals (Pa).  Mean sea level pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb = 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Orography(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Orography","parameter_ID":"228002","product_type":"forecast","shortName":"orog","standard_name":"Orography","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/228002"},"dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Potential_vorticity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Potential vorticity","parameter_ID":"60","product_type":"forecast","shortName":"pv","standard_name":"Potential_vorticity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/60"},"description":"Potential vorticity is a measure of the capacity for air to rotate in the atmosphere. If we ignore the effects of heating and friction, potential vorticity is conserved following an air parcel. It is used to look for places where large wind storms are likely to originate and develop.  Potential vorticity increases strongly above the tropopause and therefore, it can also be used in studies related to the stratosphere and stratosphere-troposphere exchanges.Large wind storms develop when a column of air in the atmosphere starts to rotate. Potential vorticity is calculated from the wind, temperature and pressure across a column of air in the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K m2kg-1s-1"},"Relative_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Relative humidity","parameter_ID":"157","product_type":"forecast","shortName":"r","standard_name":"Relative_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/157"},"description":"This parameter is the water vapour pressure as a percentage of the value at which the air becomes saturated (the point at which water vapour begins to condense into liquid water or deposition into ice).For temperatures over 0°C (273.15 K) it is calculated for saturation over water. At temperatures below -23°C it is calculated for saturation over ice.  Between -23°C and 0°C this parameter is calculated by interpolating between the ice and water values using a quadratic function.See more information about the model's relative humidity calculation.","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Skin_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Skin temperature","parameter_ID":"235","product_type":"forecast","shortName":"skt","standard_name":"Skin_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235"},"description":"This parameter is the temperature of the surface of the Earth.The skin temperature is the theoretical temperature that is required to satisfy the surface energy balance. It represents the temperature of the uppermost surface layer, which has no heat capacity and so can respond instantaneously to changes in surface fluxes. Skin temperature is calculated differently over land and sea.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.See further information about the skin temperatureover landandover sea.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean skin temperature. The specific encoding for Mean skin temperature can be found in 235079.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Snow_depth_water_equivalent(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Snow depth water equivalent","parameter_ID":"228141","product_type":"forecast","shortName":"sd","standard_name":"Snow_depth_water_equivalent","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228141"},"description":"Snow depth water equivalent in kg m**-2 (mm) water equivalent.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean snow depth water equivalent. The specific encoding for Time-mean snow depth water equivalent can be found in 235078.[NOTE: See 141 for the equivalent parameter in \"m of water equivalent\"]","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Snow_depth_water_equivalent(clte_sol)":{"attrs":{"encoding":"instantaneous","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Snow depth water equivalent","parameter_ID":"228141","product_type":"forecast","shortName":"sd","standard_name":"Snow_depth_water_equivalent","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228141"},"description":"Snow depth water equivalent in kg m**-2 (mm) water equivalent.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean snow depth water equivalent. The specific encoding for Time-mean snow depth water equivalent can be found in 235078.[NOTE: See 141 for the equivalent parameter in \"m of water equivalent\"]","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Specific_cloud_liquid_water_content(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific cloud liquid water content","parameter_ID":"246","product_type":"forecast","shortName":"clwc","standard_name":"Specific_cloud_liquid_water_content","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/246"},"description":"This parameter is the mass of cloud liquid water droplets per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for agrid box.Water within clouds can be liquid or ice, or a combination of the two.See further information about the cloud formulation.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Specific_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific humidity","parameter_ID":"133","product_type":"forecast","shortName":"q","standard_name":"Specific_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/133"},"description":"This parameter is the mass of water vapour per kilogram of moist air.The total mass of moist air is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Surface_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Surface pressure","parameter_ID":"134","product_type":"forecast","shortName":"sp","standard_name":"Surface_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/134"},"description":"This parameter is the pressure (force per unit area) of the atmosphere on the surface of land, sea and in-land water.It is a measure of the weight of all the air in a column vertically above the area of the Earth's surface represented at a fixed point.Surface pressure is often used in combination with temperature to calculate air density.The strong variation of pressure with altitude makes it difficult to see the low and high pressure systems over mountainous areas, so mean sea level pressure, rather than surface pressure, is normally used for this purpose.The units of this parameter are Pascals (Pa). Surface pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb= 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Temperature(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Temperature","parameter_ID":"130","product_type":"forecast","shortName":"t","standard_name":"Temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/130"},"description":"This parameter is the temperature in the atmosphere.It has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.This parameter is available on multiple levels through the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_10_metre_U_wind_component(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre U wind component","parameter_ID":"235165","product_type":"forecast","shortName":"avg_10u","standard_name":"Time-mean_10_metre_U_wind_component","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235165"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_10_metre_V_wind_component(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre V wind component","parameter_ID":"235166","product_type":"forecast","shortName":"avg_10v","standard_name":"Time-mean_10_metre_V_wind_component","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235166"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_10_metre_wind_speed(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre wind speed","parameter_ID":"228005","product_type":"forecast","shortName":"avg_10ws","standard_name":"Time-mean_10_metre_wind_speed","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/228005"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_2_metre_dewpoint_temperature(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"2","levtype":"sfc","long_name":"Time-mean 2 metre dewpoint temperature","parameter_ID":"235168","product_type":"forecast","shortName":"avg_2d","standard_name":"Time-mean_2_metre_dewpoint_temperature","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235168"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_2_metre_temperature(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"2","levtype":"sfc","long_name":"Time-mean 2 metre temperature","parameter_ID":"228004","product_type":"forecast","shortName":"avg_2t","standard_name":"Time-mean_2_metre_temperature","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/228004"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_U_component_of_wind(clmn_hl)":{"attrs":{"encoding":"mean","levelist":"100","levtype":"hl","long_name":"Time-mean U component of wind","parameter_ID":"235131","product_type":"forecast","shortName":"avg_u","standard_name":"Time-mean_U_component_of_wind","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235131"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_U_component_of_wind(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean U component of wind","parameter_ID":"235131","product_type":"forecast","shortName":"avg_u","standard_name":"Time-mean_U_component_of_wind","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235131"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_V_component_of_wind(clmn_hl)":{"attrs":{"encoding":"mean","levelist":"100","levtype":"hl","long_name":"Time-mean V component of 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the upper 300 m","parameter_ID":"263121","product_type":"forecast","shortName":"avg_hc300m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_300_m","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263121"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"700","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 700 m","parameter_ID":"263122","product_type":"forecast","shortName":"avg_hc700m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263122"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"700","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 700 m","parameter_ID":"263122","product_type":"forecast","shortName":"avg_hc700m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263122"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_volumetric_soil_moisture(clmn_sol)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Time-mean volumetric soil moisture","parameter_ID":"235077","product_type":"forecast","shortName":"avg_vsw","standard_name":"Time-mean_volumetric_soil_moisture","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235077"},"dimensions":["lat","lon","time"],"type":"data","unit":"m3m-3"},"Time_mean_top_downward_short-wave_radiation_flux(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time mean top downward short-wave radiation flux","parameter_ID":"235053","product_type":"forecast","shortName":"avg_tdswrf","standard_name":"Time_mean_top_downward_short-wave_radiation_flux","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235053"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Time_mean_top_downward_short-wave_radiation_flux(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time mean top downward short-wave radiation flux","parameter_ID":"235053","product_type":"forecast","shortName":"avg_tdswrf","standard_name":"Time_mean_top_downward_short-wave_radiation_flux","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235053"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Total_Cloud_Cover(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total Cloud Cover","parameter_ID":"228164","product_type":"forecast","shortName":"tcc","standard_name":"Total_Cloud_Cover","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228164"},"description":"[NOTE: See 164 for the equivalent parameter in \"(0-1)\"]","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Total_column_cloud_ice_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud ice water","parameter_ID":"79","product_type":"forecast","shortName":"tciw","standard_name":"Total_column_cloud_ice_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/79"},"description":"This parameter is the amount of ice contained within clouds in a column extending from the surface of the Earth to the top of the atmosphere. Snow (aggregated ice crystals) is not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The  ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_cloud_liquid_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud liquid water","parameter_ID":"78","product_type":"forecast","shortName":"tclw","standard_name":"Total_column_cloud_liquid_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/78"},"description":"This parameter is the amount of liquid water contained within cloud droplets in a column extending from the surface of the Earth to the top of the atmosphere. Rain water droplets, which are much larger in size (and mass), are not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_vertically-integrated_water_vapour(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column vertically-integrated water vapour","parameter_ID":"137","product_type":"forecast","shortName":"tcwv","standard_name":"Total_column_vertically-integrated_water_vapour","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/137"},"description":"This parameter is the total amount of water vapour in a column extending from the surface of the Earth to the top of the atmosphere.This parameter represents the area averaged value for agrid box.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column water","parameter_ID":"136","product_type":"forecast","shortName":"tcw","standard_name":"Total_column_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/136"},"description":"This parameter is the sum of water vapour, liquid water, cloud ice, rain and snow in a column extending from the surface of the Earth to the top of the atmosphere. In old versions of the ECMWF model (IFS), rain and snow were not accounted for.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"U_component_of_wind(clte_hl)":{"attrs":{"encoding":"instantaneous","levelist":"100","levtype":"hl","long_name":"U component of wind","parameter_ID":"131","product_type":"forecast","shortName":"u","standard_name":"U_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/131"},"description":"This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east, in metres per second. A negative sign thus indicates air movement towards the west.This parameter can be combined with the V component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"U_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"U component of wind","parameter_ID":"131","product_type":"forecast","shortName":"u","standard_name":"U_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/131"},"description":"This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east, in metres per second. A negative sign thus indicates air movement towards the west.This parameter can be combined with the V component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"V_component_of_wind(clte_hl)":{"attrs":{"encoding":"instantaneous","levelist":"100","levtype":"hl","long_name":"V component of wind","parameter_ID":"132","product_type":"forecast","shortName":"v","standard_name":"V_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/132"},"description":"This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north, in metres per second. A negative sign thus indicates air movement towards the south.This parameter can be combined with the U component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"V_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"V component of wind","parameter_ID":"132","product_type":"forecast","shortName":"v","standard_name":"V_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/132"},"description":"This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north, in metres per second. A negative sign thus indicates air movement towards the south.This parameter can be combined with the U component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Vertical_velocity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Vertical velocity","parameter_ID":"135","product_type":"forecast","shortName":"w","standard_name":"Vertical_velocity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/135"},"description":"This parameter is the speed of air motion in the upward or downward direction. The ECMWF Integrated Forecasting System (IFS) uses a pressure based vertical co-ordinate system and pressure decreases with height, therefore negative values of vertical velocity indicate upward motion.Vertical velocity can be useful to understand the large-scale dynamics of the atmosphere, including areas of upward motion/ascent (negative values) and downward motion/subsidence (positive values).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa s-1"},"Volumetric_soil_moisture(clte_sol)":{"attrs":{"encoding":"instantaneous","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Volumetric soil moisture","parameter_ID":"260199","product_type":"forecast","shortName":"vsw","standard_name":"Volumetric_soil_moisture","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/260199"},"description":"Please note that the encoding listed here for uerra (which includes carra/cerra) includes entries for Time-mean volumetric soil moisture. The specific encoding for Time-mean volumetric soil moisture can be found in 235077.","dimensions":["lat","lon","time"],"type":"data","unit":"m3m-3"}},"sci:publications":[{"doi":"10.1109/MCSE.2023.3260519","citation":"N. Wedi et al., Destination Earth: High-Performance Computing for Weather and Climate, in Computing in Science \u0026 Engineering, vol. 24, no. 6, pp. 29-37, Nov.-Dec. 2022,"},{"doi":"10.21957/d3f982672e","citation":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"dedl:short_description":"The Climate Change Adaptation Digital Twin provides global climate projections and sector-specific information over multiple decades at high resolution via a unified framework combining advanced Earth system models, impact assessments, and observations. This Generation-2, Realization-3 Collection gives access to 'Storyline Simulation Present Climate' data based on the 'IFS-FESOM' model."},{"type":"Collection","title":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Storyline Simulation Present Climate - IFS-FESOM - Generation-2 - Realization-4","id":"EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_CONT_IFS-FESOM.R4","description":"The DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities by providing innovative climate information on multi-decadal timescales, globally, at scales at which many impacts of climate change are observed. It combines cutting-edge global Earth-system models, impact-sector applications and observations into a unified framework to provide global climate projections and impact-sector information on multi-decadal timescales (1990 to ~2050), at very high spatial resolutions (5 to 10 km).\n\nThe Climate DT represents the first ever attempt to operationalise the production of global multi-decadal climate projections, leveraging the world-leading supercomputing facilities of the EuroHPC Joint Undertaking along with some of the leading European climate models. A concise overview of what the Climate DT aims to achieve, and of the different concepts essential for an understanding of the Digital Twin’s characteristics, is included in the [Climate DT factsheet](https://destine.ecmwf.int/wp-content/uploads/2024/06/2024.06.07_Climate-DT-Fact-Sheet_V7-2.pdf)\n\n## Storyline Simulation Present Climate \n\n Storyline simulations offer a what-if capability to explore how a weather event we experienced in the recent past would change in a warmer climate and how it would have looked in past pre-industrial conditions. To constrain simulated weather events, a nudging approach is used to keep the simulated large-scale flow close to the ERA5 reanalysis for the period 2017 – 2023. Processes on smaller scales and thermodynamic processes are free to evolve, which allows scientists to study e.g. how extreme precipitation would change in a warmer world for a weather event observed in present-day conditions. Initial conditions are taken from ocean-only spinup simulations for 1950 and 2017, and from the IFS-FESOM projection for a +2 °C world. Storyline simulations will be updated regularly to include most recent months. The next update will extend all storylines to include 2024.\n\n## Models\n\nThe Climate DT exploits and further evolves a new generation of global storm-resolving and eddy-rich models built through a cooperative model development approach. For more information on models please click [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#models)\n\n## Simulations\n\nThe Climate DT team carries out several types of digital twin simulations on the EuroHPC supercomputers.  Multi-decadal simulations are produced to cover the recent past (from 1990) and possible future evolutions of the climate up to 2050. See [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#simulations) for more information on Simulations\n\n## Parameters\n\nBelow we see the list of parameters extracted from the 'DestinE Climate DT data portfolio', for more information please refer to the pages [Climate DT Phase2 CLTE Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters) and [Climate DT Phase2 CLMN Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters)","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_CONT_IFS-FESOM.R4/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_CONT_IFS-FESOM.R4/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_CONT_IFS-FESOM.R4/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_CONT_IFS-FESOM.R4","title":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Storyline Simulation Present Climate - IFS-FESOM - Generation-2 - Realization-4"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters","title":"DestinE ClimateDT Phase 2 CLTE Parameters"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters","title":"DestinE ClimateDT Phase 2 CLMN Parameters"},{"rel":"cite-as","type":"text/html","href":"https://dl.acm.org/doi/abs/10.1109/MCSE.2023.3260519","title":"Destination Earth: High-Performance Computing for Weather and Climate"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.21957/d3f982672e","title":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"assets":{"thumbnail":{"href":"https://s3.central.data.destination-earth.eu/swift/v1/dedl-public/collections/climate-dt-min.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2017-01-01T00:00:00Z","2025-12-31T23:59:59Z"]]}},"license":"CC-BY-4.0","keywords":["Digital Twins","Climate Change","Atmosphere","Ocean","Land","Sea Ice","Snow","Soil","Earth","High Performance Computing","Decision Making","Europe","Meteorology","class:d1","dataset:climate-dt","generation:2","expver:0001","stream:clmn,clte","type:fc","activity:story-nudging","experiment:cont","realization:4","model:IFS-FESOM","resolution:standard,high","levtype:hl,o2d,o3d,pl,sfc,sol"],"summaries":{"federation:backends":["dedt_mn5"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/datacube/v2.1.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["producer","processor","licensor"],"url":"https://www.ecmwf.int/"}],"created":"2024-04-11T22:31:55Z","updated":"2026-04-24T10:24:59Z","published":"2024-04-11T22:31:55Z","cube:dimensions":{"lat":{"axis":"y","description":"latitude","extent":[-90,90],"type":"spatial"},"lon":{"axis":"x","description":"longitude","extent":[-180,180],"type":"spatial"},"time":{"extent":["2017-01-01T00:00:00Z","2025-12-31T23:59:59Z"],"step":"P0Y0M0DT1H0M0S","type":"temporal"}},"cube:variables":{"10_metre_U_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre U wind component","parameter_ID":"165","product_type":"forecast","shortName":"10u","standard_name":"10_metre_U_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/165"},"description":"This parameter is the eastward component of the 10m wind. It is the horizontal speed of air moving towards the east, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the V component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_V_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre V wind component","parameter_ID":"166","product_type":"forecast","shortName":"10v","standard_name":"10_metre_V_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/166"},"description":"This parameter is the northward component of the 10m wind. It is the horizontal speed of air moving towards the north, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the U component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_wind_speed(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre wind speed","parameter_ID":"207","product_type":"forecast","shortName":"10si","standard_name":"10_metre_wind_speed","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/207"},"description":"This parameter is the horizontal speed of the wind, or movement of air, at a height of ten metres above the surface of the Earth. The units of this parameter are metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.The eastward and northward components of the horizontal wind at 10m are also available as parameters.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"2_metre_dewpoint_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"2","levtype":"sfc","long_name":"2 metre dewpoint temperature","parameter_ID":"168","product_type":"forecast","shortName":"2d","standard_name":"2_metre_dewpoint_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/168"},"description":"This parameter is the temperature to which the air, at 2 metres above the surface of the Earth, would have to be cooled for saturation to occur.It is a measure of the humidity of the air. Combined with temperature and pressure, it can be used to calculate the relative humidity.2m dew point temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"2_metre_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"2","levtype":"sfc","long_name":"2 metre temperature","parameter_ID":"167","product_type":"forecast","shortName":"2t","standard_name":"2_metre_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/167"},"description":"This parameter is the temperature of air at 2m above the surface of land, sea or in-land waters.2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.Please note that the encodings listed here for s2s \u0026 uerra (which includes encodings for carra/cerra) include entries for Mean 2 metre temperature. The specific encoding for Mean 2 metre temperature can be found in 228004.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Geopotential(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Geopotential","parameter_ID":"129","product_type":"forecast","shortName":"z","standard_name":"Geopotential","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/129"},"description":"This parameter is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level.The geopotential height can be calculated by dividing the geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2). The geopotential height plays an important role in synoptic meteorology (analysis of weather patterns). Charts of geopotential height plotted at constant pressure levels (e.g., 300, 500 or 850 hPa) can be used to identify weather systems such as cyclones, anticyclones, troughs and ridges.At the surface of the Earth, this parameter shows the variations in geopotential (height) of the surface, and is often referred to as the orography.","dimensions":["lat","lon","time"],"type":"data","unit":"m2s-2"},"Land-sea_mask(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Land-sea mask","parameter_ID":"172","product_type":"forecast","shortName":"lsm","standard_name":"Land-sea_mask","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/172"},"description":"This parameter is the proportion of land, as opposed to ocean or inland waters (lakes, reservoirs, rivers and coastal waters), in agrid box.This parameter has values ranging between zero and one and is dimensionless.In cycles of the ECMWF Integrated Forecasting System (IFS) from CY41R1 (introduced in May 2015) onwards, grid boxes where this parameter has a value above 0.5 can be comprised of a mixture of land and inland water but not ocean. Grid boxes with a value of 0.5 and below can only be comprised of a water surface. In the latter case, the lake cover is used to determine how much of the water surface is ocean or inland water.In cycles of the IFS before CY41R1, grid boxes where this parameter has a value above 0.5 can only be comprised of land and those grid boxes with a value of 0.5 and below can only be comprised of ocean. In these older model cycles, there is no differentiation between ocean and inland water.","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Mean_sea_level_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Mean sea level pressure","parameter_ID":"151","product_type":"forecast","shortName":"msl","standard_name":"Mean_sea_level_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/151"},"description":"This parameter is the pressure (force per unit area) of the atmosphere adjusted to the height of mean sea level.It is a measure of the weight that all the air in a column vertically above the area of Earth's surface would have at that point, if the point were located at the mean sea level. It is calculated over all surfaces - land, sea and in-land water.Maps of mean sea level pressure are used to identify the locations of low and high pressure systems, often referred to as cyclones and anticyclones. Contours of mean sea level pressure also indicate the strength of the wind. Tightly packed contours show stronger winds.The units of this parameter are pascals (Pa).  Mean sea level pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb = 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Orography(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Orography","parameter_ID":"228002","product_type":"forecast","shortName":"orog","standard_name":"Orography","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/228002"},"dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Potential_vorticity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Potential vorticity","parameter_ID":"60","product_type":"forecast","shortName":"pv","standard_name":"Potential_vorticity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/60"},"description":"Potential vorticity is a measure of the capacity for air to rotate in the atmosphere. If we ignore the effects of heating and friction, potential vorticity is conserved following an air parcel. It is used to look for places where large wind storms are likely to originate and develop.  Potential vorticity increases strongly above the tropopause and therefore, it can also be used in studies related to the stratosphere and stratosphere-troposphere exchanges.Large wind storms develop when a column of air in the atmosphere starts to rotate. Potential vorticity is calculated from the wind, temperature and pressure across a column of air in the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K m2kg-1s-1"},"Relative_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Relative humidity","parameter_ID":"157","product_type":"forecast","shortName":"r","standard_name":"Relative_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/157"},"description":"This parameter is the water vapour pressure as a percentage of the value at which the air becomes saturated (the point at which water vapour begins to condense into liquid water or deposition into ice).For temperatures over 0°C (273.15 K) it is calculated for saturation over water. At temperatures below -23°C it is calculated for saturation over ice.  Between -23°C and 0°C this parameter is calculated by interpolating between the ice and water values using a quadratic function.See more information about the model's relative humidity calculation.","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Skin_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Skin temperature","parameter_ID":"235","product_type":"forecast","shortName":"skt","standard_name":"Skin_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235"},"description":"This parameter is the temperature of the surface of the Earth.The skin temperature is the theoretical temperature that is required to satisfy the surface energy balance. It represents the temperature of the uppermost surface layer, which has no heat capacity and so can respond instantaneously to changes in surface fluxes. Skin temperature is calculated differently over land and sea.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.See further information about the skin temperatureover landandover sea.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean skin temperature. The specific encoding for Mean skin temperature can be found in 235079.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Snow_depth_water_equivalent(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Snow depth water equivalent","parameter_ID":"228141","product_type":"forecast","shortName":"sd","standard_name":"Snow_depth_water_equivalent","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228141"},"description":"Snow depth water equivalent in kg m**-2 (mm) water equivalent.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean snow depth water equivalent. The specific encoding for Time-mean snow depth water equivalent can be found in 235078.[NOTE: See 141 for the equivalent parameter in \"m of water equivalent\"]","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Snow_depth_water_equivalent(clte_sol)":{"attrs":{"encoding":"instantaneous","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Snow depth water equivalent","parameter_ID":"228141","product_type":"forecast","shortName":"sd","standard_name":"Snow_depth_water_equivalent","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228141"},"description":"Snow depth water equivalent in kg m**-2 (mm) water equivalent.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean snow depth water equivalent. The specific encoding for Time-mean snow depth water equivalent can be found in 235078.[NOTE: See 141 for the equivalent parameter in \"m of water equivalent\"]","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Specific_cloud_liquid_water_content(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific cloud liquid water content","parameter_ID":"246","product_type":"forecast","shortName":"clwc","standard_name":"Specific_cloud_liquid_water_content","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/246"},"description":"This parameter is the mass of cloud liquid water droplets per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for agrid box.Water within clouds can be liquid or ice, or a combination of the two.See further information about the cloud formulation.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Specific_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific humidity","parameter_ID":"133","product_type":"forecast","shortName":"q","standard_name":"Specific_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/133"},"description":"This parameter is the mass of water vapour per kilogram of moist air.The total mass of moist air is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Surface_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Surface pressure","parameter_ID":"134","product_type":"forecast","shortName":"sp","standard_name":"Surface_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/134"},"description":"This parameter is the pressure (force per unit area) of the atmosphere on the surface of land, sea and in-land water.It is a measure of the weight of all the air in a column vertically above the area of the Earth's surface represented at a fixed point.Surface pressure is often used in combination with temperature to calculate air density.The strong variation of pressure with altitude makes it difficult to see the low and high pressure systems over mountainous areas, so mean sea level pressure, rather than surface pressure, is normally used for this purpose.The units of this parameter are Pascals (Pa). Surface pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb= 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Temperature(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Temperature","parameter_ID":"130","product_type":"forecast","shortName":"t","standard_name":"Temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/130"},"description":"This parameter is the temperature in the atmosphere.It has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.This parameter is available on multiple levels through the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_10_metre_U_wind_component(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre U wind component","parameter_ID":"235165","product_type":"forecast","shortName":"avg_10u","standard_name":"Time-mean_10_metre_U_wind_component","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235165"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_10_metre_V_wind_component(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre V wind component","parameter_ID":"235166","product_type":"forecast","shortName":"avg_10v","standard_name":"Time-mean_10_metre_V_wind_component","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235166"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_10_metre_wind_speed(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre wind speed","parameter_ID":"228005","product_type":"forecast","shortName":"avg_10ws","standard_name":"Time-mean_10_metre_wind_speed","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/228005"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_2_metre_dewpoint_temperature(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"2","levtype":"sfc","long_name":"Time-mean 2 metre dewpoint temperature","parameter_ID":"235168","product_type":"forecast","shortName":"avg_2d","standard_name":"Time-mean_2_metre_dewpoint_temperature","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235168"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_2_metre_temperature(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"2","levtype":"sfc","long_name":"Time-mean 2 metre temperature","parameter_ID":"228004","product_type":"forecast","shortName":"avg_2t","standard_name":"Time-mean_2_metre_temperature","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/228004"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_U_component_of_wind(clmn_hl)":{"attrs":{"encoding":"mean","levelist":"100","levtype":"hl","long_name":"Time-mean U component of wind","parameter_ID":"235131","product_type":"forecast","shortName":"avg_u","standard_name":"Time-mean_U_component_of_wind","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235131"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_U_component_of_wind(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean U component of wind","parameter_ID":"235131","product_type":"forecast","shortName":"avg_u","standard_name":"Time-mean_U_component_of_wind","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235131"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_V_component_of_wind(clmn_hl)":{"attrs":{"encoding":"mean","levelist":"100","levtype":"hl","long_name":"Time-mean V component of wind","parameter_ID":"235132","product_type":"forecast","shortName":"avg_v","standard_name":"Time-mean_V_component_of_wind","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235132"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_V_component_of_wind(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean V component of wind","parameter_ID":"235132","product_type":"forecast","shortName":"avg_v","standard_name":"Time-mean_V_component_of_wind","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235132"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_ice_velocity(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean eastward sea ice velocity","parameter_ID":"263003","product_type":"forecast","shortName":"avg_siue","standard_name":"Time-mean_eastward_sea_ice_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263003"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_ice_velocity(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean eastward sea ice velocity","parameter_ID":"263003","product_type":"forecast","shortName":"avg_siue","standard_name":"Time-mean_eastward_sea_ice_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263003"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_water_velocity(clmn_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean eastward sea water velocity","parameter_ID":"263506","product_type":"forecast","shortName":"avg_uoe","standard_name":"Time-mean_eastward_sea_water_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263506"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean eastward sea water velocity","parameter_ID":"263506","product_type":"forecast","shortName":"avg_uoe","standard_name":"Time-mean_eastward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263506"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_turbulent_surface_stress(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean eastward turbulent surface stress","parameter_ID":"235041","product_type":"forecast","shortName":"avg_iews","standard_name":"Time-mean_eastward_turbulent_surface_stress","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235041"},"dimensions":["lat","lon","time"],"type":"data","unit":"N m-2"},"Time-mean_eastward_turbulent_surface_stress(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean eastward turbulent surface stress","parameter_ID":"235041","product_type":"forecast","shortName":"avg_iews","standard_name":"Time-mean_eastward_turbulent_surface_stress","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235041"},"dimensions":["lat","lon","time"],"type":"data","unit":"N m-2"},"Time-mean_geopotential(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean geopotential","parameter_ID":"235129","product_type":"forecast","shortName":"avg_z","standard_name":"Time-mean_geopotential","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235129"},"dimensions":["lat","lon","time"],"type":"data","unit":"m2s-2"},"Time-mean_mean_sea_level_pressure(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean mean sea level pressure","parameter_ID":"235151","product_type":"forecast","shortName":"avg_msl","standard_name":"Time-mean_mean_sea_level_pressure","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235151"},"dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Time-mean_moisture_flux(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean moisture flux","parameter_ID":"235043","product_type":"forecast","shortName":"avg_ie","standard_name":"Time-mean_moisture_flux","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235043"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_moisture_flux(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean moisture flux","parameter_ID":"235043","product_type":"forecast","shortName":"avg_ie","standard_name":"Time-mean_moisture_flux","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235043"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_northward_sea_ice_velocity(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean northward sea ice velocity","parameter_ID":"263004","product_type":"forecast","shortName":"avg_sivn","standard_name":"Time-mean_northward_sea_ice_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263004"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_sea_ice_velocity(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean northward sea ice velocity","parameter_ID":"263004","product_type":"forecast","shortName":"avg_sivn","standard_name":"Time-mean_northward_sea_ice_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263004"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_sea_water_velocity(clmn_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean northward sea water velocity","parameter_ID":"263505","product_type":"forecast","shortName":"avg_von","standard_name":"Time-mean_northward_sea_water_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263505"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean northward sea water velocity","parameter_ID":"263505","product_type":"forecast","shortName":"avg_von","standard_name":"Time-mean_northward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263505"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_turbulent_surface_stress(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean northward turbulent surface stress","parameter_ID":"235042","product_type":"forecast","shortName":"avg_inss","standard_name":"Time-mean_northward_turbulent_surface_stress","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235042"},"dimensions":["lat","lon","time"],"type":"data","unit":"N m-2"},"Time-mean_northward_turbulent_surface_stress(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean northward turbulent surface stress","parameter_ID":"235042","product_type":"forecast","shortName":"avg_inss","standard_name":"Time-mean_northward_turbulent_surface_stress","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235042"},"dimensions":["lat","lon","time"],"type":"data","unit":"N m-2"},"Time-mean_potential_vorticity(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean potential vorticity","parameter_ID":"235100","product_type":"forecast","shortName":"avg_pv","standard_name":"Time-mean_potential_vorticity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235100"},"dimensions":["lat","lon","time"],"type":"data","unit":"K m2kg-1s-1"},"Time-mean_relative_humidity(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean relative humidity","parameter_ID":"235157","product_type":"forecast","shortName":"avg_r","standard_name":"Time-mean_relative_humidity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235157"},"dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Time-mean_sea_ice_area_fraction(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea ice area fraction","parameter_ID":"263001","product_type":"forecast","shortName":"avg_siconc","standard_name":"Time-mean_sea_ice_area_fraction","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263001"},"dimensions":["lat","lon","time"],"type":"data","unit":"Fraction"},"Time-mean_sea_ice_area_fraction(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea ice area fraction","parameter_ID":"263001","product_type":"forecast","shortName":"avg_siconc","standard_name":"Time-mean_sea_ice_area_fraction","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263001"},"dimensions":["lat","lon","time"],"type":"data","unit":"Fraction"},"Time-mean_sea_ice_thickness(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea ice 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sky","parameter_ID":"235049","product_type":"forecast","shortName":"avg_tnswrfcs","standard_name":"Time-mean_top_net_short-wave_radiation_flux,_clear_sky","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235049"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Time-mean_top_net_short-wave_radiation_flux,_clear_sky(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean top net short-wave radiation flux, clear sky","parameter_ID":"235049","product_type":"forecast","shortName":"avg_tnswrfcs","standard_name":"Time-mean_top_net_short-wave_radiation_flux,_clear_sky","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235049"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Time-mean_total_cloud_cover(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total cloud cover","parameter_ID":"235288","product_type":"forecast","shortName":"avg_tcc","standard_name":"Time-mean_total_cloud_cover","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235288"},"dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Time-mean_total_column_cloud_ice_water(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total column cloud ice water","parameter_ID":"235088","product_type":"forecast","shortName":"avg_tciw","standard_name":"Time-mean_total_column_cloud_ice_water","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235088"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Time-mean_total_column_heat_content(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean total column heat content","parameter_ID":"263123","product_type":"forecast","shortName":"avg_hcbtm","standard_name":"Time-mean_total_column_heat_content","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263123"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_total_column_heat_content(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean total column heat content","parameter_ID":"263123","product_type":"forecast","shortName":"avg_hcbtm","standard_name":"Time-mean_total_column_heat_content","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263123"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_total_column_liquid_water(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total column liquid water","parameter_ID":"235087","product_type":"forecast","shortName":"avg_tclw","standard_name":"Time-mean_total_column_liquid_water","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235087"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Time-mean_total_column_vertically-integrated_water_vapour(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total column vertically-integrated water vapour","parameter_ID":"235137","product_type":"forecast","shortName":"avg_tcwv","standard_name":"Time-mean_total_column_vertically-integrated_water_vapour","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235137"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Time-mean_total_column_water(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total column water","parameter_ID":"235136","product_type":"forecast","shortName":"avg_tcw","standard_name":"Time-mean_total_column_water","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235136"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Time-mean_total_precipitation_rate(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total precipitation rate","parameter_ID":"235055","product_type":"forecast","shortName":"avg_tprate","standard_name":"Time-mean_total_precipitation_rate","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235055"},"description":"Time-mean total precipitation rate, or time-mean total precipitation flux. This parameter is on level \"Ground or water surface\" (typeOfFirstFixedSurface=1). For this parameter on other levels, please use 235013.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_total_precipitation_rate(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total precipitation rate","parameter_ID":"235055","product_type":"forecast","shortName":"avg_tprate","standard_name":"Time-mean_total_precipitation_rate","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235055"},"description":"Time-mean total precipitation rate, or time-mean total precipitation flux. This parameter is on level \"Ground or water surface\" (typeOfFirstFixedSurface=1). For this parameter on other levels, please use 235013.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_total_snowfall_rate_water_equivalent(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total snowfall rate water equivalent","parameter_ID":"235031","product_type":"forecast","shortName":"avg_tsrwe","standard_name":"Time-mean_total_snowfall_rate_water_equivalent","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235031"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_total_snowfall_rate_water_equivalent(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total snowfall rate water equivalent","parameter_ID":"235031","product_type":"forecast","shortName":"avg_tsrwe","standard_name":"Time-mean_total_snowfall_rate_water_equivalent","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235031"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_upward_sea_water_velocity(clmn_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean upward sea water velocity","parameter_ID":"263507","product_type":"forecast","shortName":"avg_wo","standard_name":"Time-mean_upward_sea_water_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263507"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_upward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean upward sea water velocity","parameter_ID":"263507","product_type":"forecast","shortName":"avg_wo","standard_name":"Time-mean_upward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263507"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_vertical_velocity(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean vertical velocity","parameter_ID":"235135","product_type":"forecast","shortName":"avg_w","standard_name":"Time-mean_vertical_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235135"},"dimensions":["lat","lon","time"],"type":"data","unit":"Pa s-1"},"Time-mean_vertically-integrated_heat_content_in_the_upper_300_m(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"300","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 300 m","parameter_ID":"263121","product_type":"forecast","shortName":"avg_hc300m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_300_m","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263121"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_vertically-integrated_heat_content_in_the_upper_300_m(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"300","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 300 m","parameter_ID":"263121","product_type":"forecast","shortName":"avg_hc300m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_300_m","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263121"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"700","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 700 m","parameter_ID":"263122","product_type":"forecast","shortName":"avg_hc700m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263122"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"700","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 700 m","parameter_ID":"263122","product_type":"forecast","shortName":"avg_hc700m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263122"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_volumetric_soil_moisture(clmn_sol)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Time-mean volumetric soil moisture","parameter_ID":"235077","product_type":"forecast","shortName":"avg_vsw","standard_name":"Time-mean_volumetric_soil_moisture","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235077"},"dimensions":["lat","lon","time"],"type":"data","unit":"m3m-3"},"Time_mean_top_downward_short-wave_radiation_flux(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time mean top downward short-wave radiation flux","parameter_ID":"235053","product_type":"forecast","shortName":"avg_tdswrf","standard_name":"Time_mean_top_downward_short-wave_radiation_flux","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235053"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Time_mean_top_downward_short-wave_radiation_flux(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time mean top downward short-wave radiation flux","parameter_ID":"235053","product_type":"forecast","shortName":"avg_tdswrf","standard_name":"Time_mean_top_downward_short-wave_radiation_flux","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235053"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Total_Cloud_Cover(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total Cloud Cover","parameter_ID":"228164","product_type":"forecast","shortName":"tcc","standard_name":"Total_Cloud_Cover","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228164"},"description":"[NOTE: See 164 for the equivalent parameter in \"(0-1)\"]","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Total_column_cloud_ice_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud ice water","parameter_ID":"79","product_type":"forecast","shortName":"tciw","standard_name":"Total_column_cloud_ice_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/79"},"description":"This parameter is the amount of ice contained within clouds in a column extending from the surface of the Earth to the top of the atmosphere. Snow (aggregated ice crystals) is not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The  ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_cloud_liquid_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud liquid water","parameter_ID":"78","product_type":"forecast","shortName":"tclw","standard_name":"Total_column_cloud_liquid_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/78"},"description":"This parameter is the amount of liquid water contained within cloud droplets in a column extending from the surface of the Earth to the top of the atmosphere. Rain water droplets, which are much larger in size (and mass), are not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_vertically-integrated_water_vapour(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column vertically-integrated water vapour","parameter_ID":"137","product_type":"forecast","shortName":"tcwv","standard_name":"Total_column_vertically-integrated_water_vapour","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/137"},"description":"This parameter is the total amount of water vapour in a column extending from the surface of the Earth to the top of the atmosphere.This parameter represents the area averaged value for agrid box.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column water","parameter_ID":"136","product_type":"forecast","shortName":"tcw","standard_name":"Total_column_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/136"},"description":"This parameter is the sum of water vapour, liquid water, cloud ice, rain and snow in a column extending from the surface of the Earth to the top of the atmosphere. In old versions of the ECMWF model (IFS), rain and snow were not accounted for.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"U_component_of_wind(clte_hl)":{"attrs":{"encoding":"instantaneous","levelist":"100","levtype":"hl","long_name":"U component of wind","parameter_ID":"131","product_type":"forecast","shortName":"u","standard_name":"U_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/131"},"description":"This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east, in metres per second. A negative sign thus indicates air movement towards the west.This parameter can be combined with the V component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"U_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"U component of wind","parameter_ID":"131","product_type":"forecast","shortName":"u","standard_name":"U_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/131"},"description":"This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east, in metres per second. A negative sign thus indicates air movement towards the west.This parameter can be combined with the V component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"V_component_of_wind(clte_hl)":{"attrs":{"encoding":"instantaneous","levelist":"100","levtype":"hl","long_name":"V component of wind","parameter_ID":"132","product_type":"forecast","shortName":"v","standard_name":"V_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/132"},"description":"This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north, in metres per second. A negative sign thus indicates air movement towards the south.This parameter can be combined with the U component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"V_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"V component of wind","parameter_ID":"132","product_type":"forecast","shortName":"v","standard_name":"V_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/132"},"description":"This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north, in metres per second. A negative sign thus indicates air movement towards the south.This parameter can be combined with the U component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Vertical_velocity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Vertical velocity","parameter_ID":"135","product_type":"forecast","shortName":"w","standard_name":"Vertical_velocity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/135"},"description":"This parameter is the speed of air motion in the upward or downward direction. The ECMWF Integrated Forecasting System (IFS) uses a pressure based vertical co-ordinate system and pressure decreases with height, therefore negative values of vertical velocity indicate upward motion.Vertical velocity can be useful to understand the large-scale dynamics of the atmosphere, including areas of upward motion/ascent (negative values) and downward motion/subsidence (positive values).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa s-1"},"Volumetric_soil_moisture(clte_sol)":{"attrs":{"encoding":"instantaneous","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Volumetric soil moisture","parameter_ID":"260199","product_type":"forecast","shortName":"vsw","standard_name":"Volumetric_soil_moisture","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/260199"},"description":"Please note that the encoding listed here for uerra (which includes carra/cerra) includes entries for Time-mean volumetric soil moisture. The specific encoding for Time-mean volumetric soil moisture can be found in 235077.","dimensions":["lat","lon","time"],"type":"data","unit":"m3m-3"}},"sci:publications":[{"doi":"10.1109/MCSE.2023.3260519","citation":"N. Wedi et al., Destination Earth: High-Performance Computing for Weather and Climate, in Computing in Science \u0026 Engineering, vol. 24, no. 6, pp. 29-37, Nov.-Dec. 2022,"},{"doi":"10.21957/d3f982672e","citation":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"dedl:short_description":"The Climate Change Adaptation Digital Twin provides global climate projections and sector-specific information over multiple decades at high resolution via a unified framework combining advanced Earth system models, impact assessments, and observations. This Generation-2, Realization-4 Collection gives access to 'Storyline Simulation Present Climate' data based on the 'IFS-FESOM' model."},{"type":"Collection","title":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Storyline Simulation Present Climate - IFS-FESOM - Generation-2 - Realization-5","id":"EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_CONT_IFS-FESOM.R5","description":"The DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities by providing innovative climate information on multi-decadal timescales, globally, at scales at which many impacts of climate change are observed. It combines cutting-edge global Earth-system models, impact-sector applications and observations into a unified framework to provide global climate projections and impact-sector information on multi-decadal timescales (1990 to ~2050), at very high spatial resolutions (5 to 10 km).\n\nThe Climate DT represents the first ever attempt to operationalise the production of global multi-decadal climate projections, leveraging the world-leading supercomputing facilities of the EuroHPC Joint Undertaking along with some of the leading European climate models. A concise overview of what the Climate DT aims to achieve, and of the different concepts essential for an understanding of the Digital Twin’s characteristics, is included in the [Climate DT factsheet](https://destine.ecmwf.int/wp-content/uploads/2024/06/2024.06.07_Climate-DT-Fact-Sheet_V7-2.pdf)\n\n## Storyline Simulation Present Climate \n\n Storyline simulations offer a what-if capability to explore how a weather event we experienced in the recent past would change in a warmer climate and how it would have looked in past pre-industrial conditions. To constrain simulated weather events, a nudging approach is used to keep the simulated large-scale flow close to the ERA5 reanalysis for the period 2017 – 2023. Processes on smaller scales and thermodynamic processes are free to evolve, which allows scientists to study e.g. how extreme precipitation would change in a warmer world for a weather event observed in present-day conditions. Initial conditions are taken from ocean-only spinup simulations for 1950 and 2017, and from the IFS-FESOM projection for a +2 °C world. Storyline simulations will be updated regularly to include most recent months. The next update will extend all storylines to include 2024.\n\n## Models\n\nThe Climate DT exploits and further evolves a new generation of global storm-resolving and eddy-rich models built through a cooperative model development approach. For more information on models please click [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#models)\n\n## Simulations\n\nThe Climate DT team carries out several types of digital twin simulations on the EuroHPC supercomputers.  Multi-decadal simulations are produced to cover the recent past (from 1990) and possible future evolutions of the climate up to 2050. See [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#simulations) for more information on Simulations\n\n## Parameters\n\nBelow we see the list of parameters extracted from the 'DestinE Climate DT data portfolio', for more information please refer to the pages [Climate DT Phase2 CLTE Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters) and [Climate DT Phase2 CLMN Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters)","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_CONT_IFS-FESOM.R5/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_CONT_IFS-FESOM.R5/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_CONT_IFS-FESOM.R5/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_CONT_IFS-FESOM.R5","title":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Storyline Simulation Present Climate - IFS-FESOM - Generation-2 - Realization-5"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters","title":"DestinE ClimateDT Phase 2 CLTE Parameters"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters","title":"DestinE ClimateDT Phase 2 CLMN Parameters"},{"rel":"cite-as","type":"text/html","href":"https://dl.acm.org/doi/abs/10.1109/MCSE.2023.3260519","title":"Destination Earth: High-Performance Computing for Weather and Climate"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.21957/d3f982672e","title":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"assets":{"thumbnail":{"href":"https://s3.central.data.destination-earth.eu/swift/v1/dedl-public/collections/climate-dt-min.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2017-01-01T00:00:00Z","2025-12-31T23:59:59Z"]]}},"license":"CC-BY-4.0","keywords":["Digital Twins","Climate Change","Atmosphere","Ocean","Land","Sea Ice","Snow","Soil","Earth","High Performance Computing","Decision Making","Europe","Meteorology","class:d1","dataset:climate-dt","generation:2","expver:0001","stream:clmn,clte","type:fc","activity:story-nudging","experiment:cont","realization:5","model:IFS-FESOM","resolution:standard,high","levtype:hl,o2d,o3d,pl,sfc,sol"],"summaries":{"federation:backends":["dedt_mn5"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/datacube/v2.1.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["producer","processor","licensor"],"url":"https://www.ecmwf.int/"}],"created":"2024-04-11T22:31:55Z","updated":"2026-04-24T10:24:59Z","published":"2024-04-11T22:31:55Z","cube:dimensions":{"lat":{"axis":"y","description":"latitude","extent":[-90,90],"type":"spatial"},"lon":{"axis":"x","description":"longitude","extent":[-180,180],"type":"spatial"},"time":{"extent":["2017-01-01T00:00:00Z","2025-12-31T23:59:59Z"],"step":"P0Y0M0DT1H0M0S","type":"temporal"}},"cube:variables":{"10_metre_U_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre U wind component","parameter_ID":"165","product_type":"forecast","shortName":"10u","standard_name":"10_metre_U_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/165"},"description":"This parameter is the eastward component of the 10m wind. It is the horizontal speed of air moving towards the east, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the V component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_V_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre V wind component","parameter_ID":"166","product_type":"forecast","shortName":"10v","standard_name":"10_metre_V_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/166"},"description":"This parameter is the northward component of the 10m wind. It is the horizontal speed of air moving towards the north, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the U component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_wind_speed(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre wind speed","parameter_ID":"207","product_type":"forecast","shortName":"10si","standard_name":"10_metre_wind_speed","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/207"},"description":"This parameter is the horizontal speed of the wind, or movement of air, at a height of ten metres above the surface of the Earth. The units of this parameter are metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.The eastward and northward components of the horizontal wind at 10m are also available as parameters.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"2_metre_dewpoint_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"2","levtype":"sfc","long_name":"2 metre dewpoint temperature","parameter_ID":"168","product_type":"forecast","shortName":"2d","standard_name":"2_metre_dewpoint_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/168"},"description":"This parameter is the temperature to which the air, at 2 metres above the surface of the Earth, would have to be cooled for saturation to occur.It is a measure of the humidity of the air. Combined with temperature and pressure, it can be used to calculate the relative humidity.2m dew point temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"2_metre_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"2","levtype":"sfc","long_name":"2 metre temperature","parameter_ID":"167","product_type":"forecast","shortName":"2t","standard_name":"2_metre_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/167"},"description":"This parameter is the temperature of air at 2m above the surface of land, sea or in-land waters.2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.Please note that the encodings listed here for s2s \u0026 uerra (which includes encodings for carra/cerra) include entries for Mean 2 metre temperature. The specific encoding for Mean 2 metre temperature can be found in 228004.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Geopotential(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Geopotential","parameter_ID":"129","product_type":"forecast","shortName":"z","standard_name":"Geopotential","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/129"},"description":"This parameter is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level.The geopotential height can be calculated by dividing the geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2). The geopotential height plays an important role in synoptic meteorology (analysis of weather patterns). Charts of geopotential height plotted at constant pressure levels (e.g., 300, 500 or 850 hPa) can be used to identify weather systems such as cyclones, anticyclones, troughs and ridges.At the surface of the Earth, this parameter shows the variations in geopotential (height) of the surface, and is often referred to as the orography.","dimensions":["lat","lon","time"],"type":"data","unit":"m2s-2"},"Land-sea_mask(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Land-sea mask","parameter_ID":"172","product_type":"forecast","shortName":"lsm","standard_name":"Land-sea_mask","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/172"},"description":"This parameter is the proportion of land, as opposed to ocean or inland waters (lakes, reservoirs, rivers and coastal waters), in agrid box.This parameter has values ranging between zero and one and is dimensionless.In cycles of the ECMWF Integrated Forecasting System (IFS) from CY41R1 (introduced in May 2015) onwards, grid boxes where this parameter has a value above 0.5 can be comprised of a mixture of land and inland water but not ocean. Grid boxes with a value of 0.5 and below can only be comprised of a water surface. In the latter case, the lake cover is used to determine how much of the water surface is ocean or inland water.In cycles of the IFS before CY41R1, grid boxes where this parameter has a value above 0.5 can only be comprised of land and those grid boxes with a value of 0.5 and below can only be comprised of ocean. In these older model cycles, there is no differentiation between ocean and inland water.","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Mean_sea_level_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Mean sea level pressure","parameter_ID":"151","product_type":"forecast","shortName":"msl","standard_name":"Mean_sea_level_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/151"},"description":"This parameter is the pressure (force per unit area) of the atmosphere adjusted to the height of mean sea level.It is a measure of the weight that all the air in a column vertically above the area of Earth's surface would have at that point, if the point were located at the mean sea level. It is calculated over all surfaces - land, sea and in-land water.Maps of mean sea level pressure are used to identify the locations of low and high pressure systems, often referred to as cyclones and anticyclones. Contours of mean sea level pressure also indicate the strength of the wind. Tightly packed contours show stronger winds.The units of this parameter are pascals (Pa).  Mean sea level pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb = 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Orography(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Orography","parameter_ID":"228002","product_type":"forecast","shortName":"orog","standard_name":"Orography","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/228002"},"dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Potential_vorticity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Potential vorticity","parameter_ID":"60","product_type":"forecast","shortName":"pv","standard_name":"Potential_vorticity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/60"},"description":"Potential vorticity is a measure of the capacity for air to rotate in the atmosphere. If we ignore the effects of heating and friction, potential vorticity is conserved following an air parcel. It is used to look for places where large wind storms are likely to originate and develop.  Potential vorticity increases strongly above the tropopause and therefore, it can also be used in studies related to the stratosphere and stratosphere-troposphere exchanges.Large wind storms develop when a column of air in the atmosphere starts to rotate. Potential vorticity is calculated from the wind, temperature and pressure across a column of air in the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K m2kg-1s-1"},"Relative_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Relative humidity","parameter_ID":"157","product_type":"forecast","shortName":"r","standard_name":"Relative_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/157"},"description":"This parameter is the water vapour pressure as a percentage of the value at which the air becomes saturated (the point at which water vapour begins to condense into liquid water or deposition into ice).For temperatures over 0°C (273.15 K) it is calculated for saturation over water. At temperatures below -23°C it is calculated for saturation over ice.  Between -23°C and 0°C this parameter is calculated by interpolating between the ice and water values using a quadratic function.See more information about the model's relative humidity calculation.","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Skin_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Skin temperature","parameter_ID":"235","product_type":"forecast","shortName":"skt","standard_name":"Skin_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235"},"description":"This parameter is the temperature of the surface of the Earth.The skin temperature is the theoretical temperature that is required to satisfy the surface energy balance. It represents the temperature of the uppermost surface layer, which has no heat capacity and so can respond instantaneously to changes in surface fluxes. Skin temperature is calculated differently over land and sea.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.See further information about the skin temperatureover landandover sea.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean skin temperature. The specific encoding for Mean skin temperature can be found in 235079.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Snow_depth_water_equivalent(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Snow depth water equivalent","parameter_ID":"228141","product_type":"forecast","shortName":"sd","standard_name":"Snow_depth_water_equivalent","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228141"},"description":"Snow depth water equivalent in kg m**-2 (mm) water equivalent.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean snow depth water equivalent. The specific encoding for Time-mean snow depth water equivalent can be found in 235078.[NOTE: See 141 for the equivalent parameter in \"m of water equivalent\"]","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Snow_depth_water_equivalent(clte_sol)":{"attrs":{"encoding":"instantaneous","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Snow depth water equivalent","parameter_ID":"228141","product_type":"forecast","shortName":"sd","standard_name":"Snow_depth_water_equivalent","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228141"},"description":"Snow depth water equivalent in kg m**-2 (mm) water equivalent.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean snow depth water equivalent. The specific encoding for Time-mean snow depth water equivalent can be found in 235078.[NOTE: See 141 for the equivalent parameter in \"m of water equivalent\"]","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Specific_cloud_liquid_water_content(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific cloud liquid water content","parameter_ID":"246","product_type":"forecast","shortName":"clwc","standard_name":"Specific_cloud_liquid_water_content","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/246"},"description":"This parameter is the mass of cloud liquid water droplets per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for agrid box.Water within clouds can be liquid or ice, or a combination of the two.See further information about the cloud formulation.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Specific_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific humidity","parameter_ID":"133","product_type":"forecast","shortName":"q","standard_name":"Specific_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/133"},"description":"This parameter is the mass of water vapour per kilogram of moist air.The total mass of moist air is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Surface_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Surface pressure","parameter_ID":"134","product_type":"forecast","shortName":"sp","standard_name":"Surface_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/134"},"description":"This parameter is the pressure (force per unit area) of the atmosphere on the surface of land, sea and in-land water.It is a measure of the weight of all the air in a column vertically above the area of the Earth's surface represented at a fixed point.Surface pressure is often used in combination with temperature to calculate air density.The strong variation of pressure with altitude makes it difficult to see the low and high pressure systems over mountainous areas, so mean sea level pressure, rather than surface pressure, is normally used for this purpose.The units of this parameter are Pascals (Pa). Surface pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb= 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Temperature(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Temperature","parameter_ID":"130","product_type":"forecast","shortName":"t","standard_name":"Temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/130"},"description":"This parameter is the temperature in the atmosphere.It has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.This parameter is available on multiple levels through the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_10_metre_U_wind_component(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre U wind component","parameter_ID":"235165","product_type":"forecast","shortName":"avg_10u","standard_name":"Time-mean_10_metre_U_wind_component","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235165"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_10_metre_V_wind_component(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre V wind component","parameter_ID":"235166","product_type":"forecast","shortName":"avg_10v","standard_name":"Time-mean_10_metre_V_wind_component","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235166"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_10_metre_wind_speed(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre wind speed","parameter_ID":"228005","product_type":"forecast","shortName":"avg_10ws","standard_name":"Time-mean_10_metre_wind_speed","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/228005"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_2_metre_dewpoint_temperature(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"2","levtype":"sfc","long_name":"Time-mean 2 metre dewpoint temperature","parameter_ID":"235168","product_type":"forecast","shortName":"avg_2d","standard_name":"Time-mean_2_metre_dewpoint_temperature","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235168"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_2_metre_temperature(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"2","levtype":"sfc","long_name":"Time-mean 2 metre temperature","parameter_ID":"228004","product_type":"forecast","shortName":"avg_2t","standard_name":"Time-mean_2_metre_temperature","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/228004"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_U_component_of_wind(clmn_hl)":{"attrs":{"encoding":"mean","levelist":"100","levtype":"hl","long_name":"Time-mean U component of wind","parameter_ID":"235131","product_type":"forecast","shortName":"avg_u","standard_name":"Time-mean_U_component_of_wind","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235131"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_U_component_of_wind(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean U component of wind","parameter_ID":"235131","product_type":"forecast","shortName":"avg_u","standard_name":"Time-mean_U_component_of_wind","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235131"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_V_component_of_wind(clmn_hl)":{"attrs":{"encoding":"mean","levelist":"100","levtype":"hl","long_name":"Time-mean V component of 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m-2"},"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"700","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 700 m","parameter_ID":"263122","product_type":"forecast","shortName":"avg_hc700m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263122"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_volumetric_soil_moisture(clmn_sol)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Time-mean volumetric soil moisture","parameter_ID":"235077","product_type":"forecast","shortName":"avg_vsw","standard_name":"Time-mean_volumetric_soil_moisture","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235077"},"dimensions":["lat","lon","time"],"type":"data","unit":"m3m-3"},"Time_mean_top_downward_short-wave_radiation_flux(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time mean top downward short-wave radiation flux","parameter_ID":"235053","product_type":"forecast","shortName":"avg_tdswrf","standard_name":"Time_mean_top_downward_short-wave_radiation_flux","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235053"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Time_mean_top_downward_short-wave_radiation_flux(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time mean top downward short-wave radiation flux","parameter_ID":"235053","product_type":"forecast","shortName":"avg_tdswrf","standard_name":"Time_mean_top_downward_short-wave_radiation_flux","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235053"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Total_Cloud_Cover(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total Cloud Cover","parameter_ID":"228164","product_type":"forecast","shortName":"tcc","standard_name":"Total_Cloud_Cover","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228164"},"description":"[NOTE: See 164 for the equivalent parameter in \"(0-1)\"]","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Total_column_cloud_ice_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud ice water","parameter_ID":"79","product_type":"forecast","shortName":"tciw","standard_name":"Total_column_cloud_ice_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/79"},"description":"This parameter is the amount of ice contained within clouds in a column extending from the surface of the Earth to the top of the atmosphere. Snow (aggregated ice crystals) is not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The  ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_cloud_liquid_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud liquid water","parameter_ID":"78","product_type":"forecast","shortName":"tclw","standard_name":"Total_column_cloud_liquid_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/78"},"description":"This parameter is the amount of liquid water contained within cloud droplets in a column extending from the surface of the Earth to the top of the atmosphere. Rain water droplets, which are much larger in size (and mass), are not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_vertically-integrated_water_vapour(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column vertically-integrated water vapour","parameter_ID":"137","product_type":"forecast","shortName":"tcwv","standard_name":"Total_column_vertically-integrated_water_vapour","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/137"},"description":"This parameter is the total amount of water vapour in a column extending from the surface of the Earth to the top of the atmosphere.This parameter represents the area averaged value for agrid box.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column water","parameter_ID":"136","product_type":"forecast","shortName":"tcw","standard_name":"Total_column_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/136"},"description":"This parameter is the sum of water vapour, liquid water, cloud ice, rain and snow in a column extending from the surface of the Earth to the top of the atmosphere. In old versions of the ECMWF model (IFS), rain and snow were not accounted for.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"U_component_of_wind(clte_hl)":{"attrs":{"encoding":"instantaneous","levelist":"100","levtype":"hl","long_name":"U component of wind","parameter_ID":"131","product_type":"forecast","shortName":"u","standard_name":"U_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/131"},"description":"This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east, in metres per second. A negative sign thus indicates air movement towards the west.This parameter can be combined with the V component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"U_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"U component of wind","parameter_ID":"131","product_type":"forecast","shortName":"u","standard_name":"U_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/131"},"description":"This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east, in metres per second. A negative sign thus indicates air movement towards the west.This parameter can be combined with the V component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"V_component_of_wind(clte_hl)":{"attrs":{"encoding":"instantaneous","levelist":"100","levtype":"hl","long_name":"V component of wind","parameter_ID":"132","product_type":"forecast","shortName":"v","standard_name":"V_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/132"},"description":"This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north, in metres per second. A negative sign thus indicates air movement towards the south.This parameter can be combined with the U component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"V_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"V component of wind","parameter_ID":"132","product_type":"forecast","shortName":"v","standard_name":"V_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/132"},"description":"This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north, in metres per second. A negative sign thus indicates air movement towards the south.This parameter can be combined with the U component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Vertical_velocity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Vertical velocity","parameter_ID":"135","product_type":"forecast","shortName":"w","standard_name":"Vertical_velocity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/135"},"description":"This parameter is the speed of air motion in the upward or downward direction. The ECMWF Integrated Forecasting System (IFS) uses a pressure based vertical co-ordinate system and pressure decreases with height, therefore negative values of vertical velocity indicate upward motion.Vertical velocity can be useful to understand the large-scale dynamics of the atmosphere, including areas of upward motion/ascent (negative values) and downward motion/subsidence (positive values).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa s-1"},"Volumetric_soil_moisture(clte_sol)":{"attrs":{"encoding":"instantaneous","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Volumetric soil moisture","parameter_ID":"260199","product_type":"forecast","shortName":"vsw","standard_name":"Volumetric_soil_moisture","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/260199"},"description":"Please note that the encoding listed here for uerra (which includes carra/cerra) includes entries for Time-mean volumetric soil moisture. The specific encoding for Time-mean volumetric soil moisture can be found in 235077.","dimensions":["lat","lon","time"],"type":"data","unit":"m3m-3"}},"sci:publications":[{"doi":"10.1109/MCSE.2023.3260519","citation":"N. Wedi et al., Destination Earth: High-Performance Computing for Weather and Climate, in Computing in Science \u0026 Engineering, vol. 24, no. 6, pp. 29-37, Nov.-Dec. 2022,"},{"doi":"10.21957/d3f982672e","citation":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"dedl:short_description":"The Climate Change Adaptation Digital Twin provides global climate projections and sector-specific information over multiple decades at high resolution via a unified framework combining advanced Earth system models, impact assessments, and observations. This Generation-2, Realization-5 Collection gives access to 'Storyline Simulation Present Climate' data based on the 'IFS-FESOM' model."},{"type":"Collection","title":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Storyline Simulation Past Climate - IFS-FESOM - Generation-2 - Realization-1","id":"EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_HIST_IFS-FESOM.R1","description":"The DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities by providing innovative climate information on multi-decadal timescales, globally, at scales at which many impacts of climate change are observed. It combines cutting-edge global Earth-system models, impact-sector applications and observations into a unified framework to provide global climate projections and impact-sector information on multi-decadal timescales (1990 to ~2050), at very high spatial resolutions (5 to 10 km).\n\nThe Climate DT represents the first ever attempt to operationalise the production of global multi-decadal climate projections, leveraging the world-leading supercomputing facilities of the EuroHPC Joint Undertaking along with some of the leading European climate models. A concise overview of what the Climate DT aims to achieve, and of the different concepts essential for an understanding of the Digital Twin’s characteristics, is included in the [Climate DT factsheet](https://destine.ecmwf.int/wp-content/uploads/2024/06/2024.06.07_Climate-DT-Fact-Sheet_V7-2.pdf)\n\n## Storyline Simulation Past Climate \n\n Storyline simulations offer a what-if capability to explore how a weather event we experienced in the recent past would change in a warmer climate and how it would have looked in past pre-industrial conditions. To constrain simulated weather events, a nudging approach is used to keep the simulated large-scale flow close to the ERA5 reanalysis for the period 2017 – 2023. Processes on smaller scales and thermodynamic processes are free to evolve, which allows scientists to study e.g. how extreme precipitation would change in a warmer world for a weather event observed in present-day conditions. Initial conditions are taken from ocean-only spinup simulations for 1950 and 2017, and from the IFS-FESOM projection for a +2 °C world. Storyline simulations will be updated regularly to include most recent months. The next update will extend all storylines to include 2024.\n\n## Models\n\nThe Climate DT exploits and further evolves a new generation of global storm-resolving and eddy-rich models built through a cooperative model development approach. For more information on models please click [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#models)\n\n## Simulations\n\nThe Climate DT team carries out several types of digital twin simulations on the EuroHPC supercomputers.  Multi-decadal simulations are produced to cover the recent past (from 1990) and possible future evolutions of the climate up to 2050. See [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#simulations) for more information on Simulations\n\n## Parameters\n\nBelow we see the list of parameters extracted from the 'DestinE Climate DT data portfolio', for more information please refer to the pages [Climate DT Phase2 CLTE Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters) and [Climate DT Phase2 CLMN Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters)","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_HIST_IFS-FESOM.R1/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_HIST_IFS-FESOM.R1/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_HIST_IFS-FESOM.R1/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_HIST_IFS-FESOM.R1","title":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Storyline Simulation Past Climate - IFS-FESOM - Generation-2 - Realization-1"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters","title":"DestinE ClimateDT Phase 2 CLTE Parameters"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters","title":"DestinE ClimateDT Phase 2 CLMN Parameters"},{"rel":"cite-as","type":"text/html","href":"https://dl.acm.org/doi/abs/10.1109/MCSE.2023.3260519","title":"Destination Earth: High-Performance Computing for Weather and Climate"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.21957/d3f982672e","title":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"assets":{"thumbnail":{"href":"https://s3.central.data.destination-earth.eu/swift/v1/dedl-public/collections/climate-dt-min.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2017-01-01T00:00:00Z","2025-12-31T23:59:59Z"]]}},"license":"CC-BY-4.0","keywords":["Digital Twins","Climate Change","Atmosphere","Ocean","Land","Sea Ice","Snow","Soil","Earth","High Performance Computing","Decision Making","Europe","Meteorology","class:d1","dataset:climate-dt","generation:2","expver:0001","stream:clmn,clte","type:fc","activity:story-nudging","experiment:hist","realization:1","model:IFS-FESOM","resolution:standard,high","levtype:hl,o2d,o3d,pl,sfc,sol"],"summaries":{"federation:backends":["dedt_mn5"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/datacube/v2.1.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["producer","processor","licensor"],"url":"https://www.ecmwf.int/"}],"created":"2024-04-11T22:31:55Z","updated":"2026-04-24T10:24:59Z","published":"2024-04-11T22:31:55Z","cube:dimensions":{"lat":{"axis":"y","description":"latitude","extent":[-90,90],"type":"spatial"},"lon":{"axis":"x","description":"longitude","extent":[-180,180],"type":"spatial"},"time":{"extent":["2017-01-01T00:00:00Z","2025-12-31T23:59:59Z"],"step":"P0Y0M0DT1H0M0S","type":"temporal"}},"cube:variables":{"10_metre_U_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre U wind component","parameter_ID":"165","product_type":"forecast","shortName":"10u","standard_name":"10_metre_U_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/165"},"description":"This parameter is the eastward component of the 10m wind. It is the horizontal speed of air moving towards the east, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the V component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_V_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre V wind component","parameter_ID":"166","product_type":"forecast","shortName":"10v","standard_name":"10_metre_V_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/166"},"description":"This parameter is the northward component of the 10m wind. It is the horizontal speed of air moving towards the north, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the U component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_wind_speed(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre wind speed","parameter_ID":"207","product_type":"forecast","shortName":"10si","standard_name":"10_metre_wind_speed","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/207"},"description":"This parameter is the horizontal speed of the wind, or movement of air, at a height of ten metres above the surface of the Earth. The units of this parameter are metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.The eastward and northward components of the horizontal wind at 10m are also available as parameters.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"2_metre_dewpoint_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"2","levtype":"sfc","long_name":"2 metre dewpoint temperature","parameter_ID":"168","product_type":"forecast","shortName":"2d","standard_name":"2_metre_dewpoint_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/168"},"description":"This parameter is the temperature to which the air, at 2 metres above the surface of the Earth, would have to be cooled for saturation to occur.It is a measure of the humidity of the air. Combined with temperature and pressure, it can be used to calculate the relative humidity.2m dew point temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"2_metre_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"2","levtype":"sfc","long_name":"2 metre temperature","parameter_ID":"167","product_type":"forecast","shortName":"2t","standard_name":"2_metre_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/167"},"description":"This parameter is the temperature of air at 2m above the surface of land, sea or in-land waters.2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.Please note that the encodings listed here for s2s \u0026 uerra (which includes encodings for carra/cerra) include entries for Mean 2 metre temperature. The specific encoding for Mean 2 metre temperature can be found in 228004.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Geopotential(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Geopotential","parameter_ID":"129","product_type":"forecast","shortName":"z","standard_name":"Geopotential","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/129"},"description":"This parameter is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level.The geopotential height can be calculated by dividing the geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2). The geopotential height plays an important role in synoptic meteorology (analysis of weather patterns). Charts of geopotential height plotted at constant pressure levels (e.g., 300, 500 or 850 hPa) can be used to identify weather systems such as cyclones, anticyclones, troughs and ridges.At the surface of the Earth, this parameter shows the variations in geopotential (height) of the surface, and is often referred to as the orography.","dimensions":["lat","lon","time"],"type":"data","unit":"m2s-2"},"Land-sea_mask(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Land-sea mask","parameter_ID":"172","product_type":"forecast","shortName":"lsm","standard_name":"Land-sea_mask","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/172"},"description":"This parameter is the proportion of land, as opposed to ocean or inland waters (lakes, reservoirs, rivers and coastal waters), in agrid box.This parameter has values ranging between zero and one and is dimensionless.In cycles of the ECMWF Integrated Forecasting System (IFS) from CY41R1 (introduced in May 2015) onwards, grid boxes where this parameter has a value above 0.5 can be comprised of a mixture of land and inland water but not ocean. Grid boxes with a value of 0.5 and below can only be comprised of a water surface. In the latter case, the lake cover is used to determine how much of the water surface is ocean or inland water.In cycles of the IFS before CY41R1, grid boxes where this parameter has a value above 0.5 can only be comprised of land and those grid boxes with a value of 0.5 and below can only be comprised of ocean. In these older model cycles, there is no differentiation between ocean and inland water.","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Mean_sea_level_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Mean sea level pressure","parameter_ID":"151","product_type":"forecast","shortName":"msl","standard_name":"Mean_sea_level_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/151"},"description":"This parameter is the pressure (force per unit area) of the atmosphere adjusted to the height of mean sea level.It is a measure of the weight that all the air in a column vertically above the area of Earth's surface would have at that point, if the point were located at the mean sea level. It is calculated over all surfaces - land, sea and in-land water.Maps of mean sea level pressure are used to identify the locations of low and high pressure systems, often referred to as cyclones and anticyclones. Contours of mean sea level pressure also indicate the strength of the wind. Tightly packed contours show stronger winds.The units of this parameter are pascals (Pa).  Mean sea level pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb = 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Orography(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Orography","parameter_ID":"228002","product_type":"forecast","shortName":"orog","standard_name":"Orography","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/228002"},"dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Potential_vorticity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Potential vorticity","parameter_ID":"60","product_type":"forecast","shortName":"pv","standard_name":"Potential_vorticity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/60"},"description":"Potential vorticity is a measure of the capacity for air to rotate in the atmosphere. If we ignore the effects of heating and friction, potential vorticity is conserved following an air parcel. It is used to look for places where large wind storms are likely to originate and develop.  Potential vorticity increases strongly above the tropopause and therefore, it can also be used in studies related to the stratosphere and stratosphere-troposphere exchanges.Large wind storms develop when a column of air in the atmosphere starts to rotate. Potential vorticity is calculated from the wind, temperature and pressure across a column of air in the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K m2kg-1s-1"},"Relative_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Relative humidity","parameter_ID":"157","product_type":"forecast","shortName":"r","standard_name":"Relative_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/157"},"description":"This parameter is the water vapour pressure as a percentage of the value at which the air becomes saturated (the point at which water vapour begins to condense into liquid water or deposition into ice).For temperatures over 0°C (273.15 K) it is calculated for saturation over water. At temperatures below -23°C it is calculated for saturation over ice.  Between -23°C and 0°C this parameter is calculated by interpolating between the ice and water values using a quadratic function.See more information about the model's relative humidity calculation.","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Skin_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Skin temperature","parameter_ID":"235","product_type":"forecast","shortName":"skt","standard_name":"Skin_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235"},"description":"This parameter is the temperature of the surface of the Earth.The skin temperature is the theoretical temperature that is required to satisfy the surface energy balance. It represents the temperature of the uppermost surface layer, which has no heat capacity and so can respond instantaneously to changes in surface fluxes. Skin temperature is calculated differently over land and sea.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.See further information about the skin temperatureover landandover sea.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean skin temperature. The specific encoding for Mean skin temperature can be found in 235079.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Snow_depth_water_equivalent(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Snow depth water equivalent","parameter_ID":"228141","product_type":"forecast","shortName":"sd","standard_name":"Snow_depth_water_equivalent","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228141"},"description":"Snow depth water equivalent in kg m**-2 (mm) water equivalent.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean snow depth water equivalent. The specific encoding for Time-mean snow depth water equivalent can be found in 235078.[NOTE: See 141 for the equivalent parameter in \"m of water equivalent\"]","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Snow_depth_water_equivalent(clte_sol)":{"attrs":{"encoding":"instantaneous","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Snow depth water equivalent","parameter_ID":"228141","product_type":"forecast","shortName":"sd","standard_name":"Snow_depth_water_equivalent","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228141"},"description":"Snow depth water equivalent in kg m**-2 (mm) water equivalent.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean snow depth water equivalent. The specific encoding for Time-mean snow depth water equivalent can be found in 235078.[NOTE: See 141 for the equivalent parameter in \"m of water equivalent\"]","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Specific_cloud_liquid_water_content(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific cloud liquid water content","parameter_ID":"246","product_type":"forecast","shortName":"clwc","standard_name":"Specific_cloud_liquid_water_content","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/246"},"description":"This parameter is the mass of cloud liquid water droplets per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for agrid box.Water within clouds can be liquid or ice, or a combination of the two.See further information about the cloud formulation.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Specific_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific humidity","parameter_ID":"133","product_type":"forecast","shortName":"q","standard_name":"Specific_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/133"},"description":"This parameter is the mass of water vapour per kilogram of moist air.The total mass of moist air is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Surface_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Surface pressure","parameter_ID":"134","product_type":"forecast","shortName":"sp","standard_name":"Surface_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/134"},"description":"This parameter is the pressure (force per unit area) of the atmosphere on the surface of land, sea and in-land water.It is a measure of the weight of all the air in a column vertically above the area of the Earth's surface represented at a fixed point.Surface pressure is often used in combination with temperature to calculate air density.The strong variation of pressure with altitude makes it difficult to see the low and high pressure systems over mountainous areas, so mean sea level pressure, rather than surface pressure, is normally used for this purpose.The units of this parameter are Pascals (Pa). Surface pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb= 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Temperature(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Temperature","parameter_ID":"130","product_type":"forecast","shortName":"t","standard_name":"Temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/130"},"description":"This parameter is the temperature in the atmosphere.It has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.This parameter is available on multiple levels through the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_10_metre_U_wind_component(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre U wind component","parameter_ID":"235165","product_type":"forecast","shortName":"avg_10u","standard_name":"Time-mean_10_metre_U_wind_component","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235165"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_10_metre_V_wind_component(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre V wind component","parameter_ID":"235166","product_type":"forecast","shortName":"avg_10v","standard_name":"Time-mean_10_metre_V_wind_component","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235166"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_10_metre_wind_speed(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre wind speed","parameter_ID":"228005","product_type":"forecast","shortName":"avg_10ws","standard_name":"Time-mean_10_metre_wind_speed","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/228005"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_2_metre_dewpoint_temperature(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"2","levtype":"sfc","long_name":"Time-mean 2 metre dewpoint temperature","parameter_ID":"235168","product_type":"forecast","shortName":"avg_2d","standard_name":"Time-mean_2_metre_dewpoint_temperature","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235168"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_2_metre_temperature(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"2","levtype":"sfc","long_name":"Time-mean 2 metre temperature","parameter_ID":"228004","product_type":"forecast","shortName":"avg_2t","standard_name":"Time-mean_2_metre_temperature","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/228004"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_U_component_of_wind(clmn_hl)":{"attrs":{"encoding":"mean","levelist":"100","levtype":"hl","long_name":"Time-mean U component of wind","parameter_ID":"235131","product_type":"forecast","shortName":"avg_u","standard_name":"Time-mean_U_component_of_wind","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235131"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_U_component_of_wind(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean U component of wind","parameter_ID":"235131","product_type":"forecast","shortName":"avg_u","standard_name":"Time-mean_U_component_of_wind","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235131"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_V_component_of_wind(clmn_hl)":{"attrs":{"encoding":"mean","levelist":"100","levtype":"hl","long_name":"Time-mean V component of wind","parameter_ID":"235132","product_type":"forecast","shortName":"avg_v","standard_name":"Time-mean_V_component_of_wind","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235132"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_V_component_of_wind(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean V component of wind","parameter_ID":"235132","product_type":"forecast","shortName":"avg_v","standard_name":"Time-mean_V_component_of_wind","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235132"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_ice_velocity(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean eastward sea ice velocity","parameter_ID":"263003","product_type":"forecast","shortName":"avg_siue","standard_name":"Time-mean_eastward_sea_ice_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263003"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_ice_velocity(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean eastward sea ice velocity","parameter_ID":"263003","product_type":"forecast","shortName":"avg_siue","standard_name":"Time-mean_eastward_sea_ice_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263003"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_water_velocity(clmn_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean eastward sea water velocity","parameter_ID":"263506","product_type":"forecast","shortName":"avg_uoe","standard_name":"Time-mean_eastward_sea_water_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263506"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean eastward sea water velocity","parameter_ID":"263506","product_type":"forecast","shortName":"avg_uoe","standard_name":"Time-mean_eastward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263506"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_turbulent_surface_stress(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean eastward turbulent surface stress","parameter_ID":"235041","product_type":"forecast","shortName":"avg_iews","standard_name":"Time-mean_eastward_turbulent_surface_stress","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235041"},"dimensions":["lat","lon","time"],"type":"data","unit":"N m-2"},"Time-mean_eastward_turbulent_surface_stress(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean eastward turbulent surface stress","parameter_ID":"235041","product_type":"forecast","shortName":"avg_iews","standard_name":"Time-mean_eastward_turbulent_surface_stress","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235041"},"dimensions":["lat","lon","time"],"type":"data","unit":"N m-2"},"Time-mean_geopotential(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean geopotential","parameter_ID":"235129","product_type":"forecast","shortName":"avg_z","standard_name":"Time-mean_geopotential","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235129"},"dimensions":["lat","lon","time"],"type":"data","unit":"m2s-2"},"Time-mean_mean_sea_level_pressure(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean mean sea level pressure","parameter_ID":"235151","product_type":"forecast","shortName":"avg_msl","standard_name":"Time-mean_mean_sea_level_pressure","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235151"},"dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Time-mean_moisture_flux(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean moisture flux","parameter_ID":"235043","product_type":"forecast","shortName":"avg_ie","standard_name":"Time-mean_moisture_flux","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235043"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_moisture_flux(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean moisture flux","parameter_ID":"235043","product_type":"forecast","shortName":"avg_ie","standard_name":"Time-mean_moisture_flux","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235043"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_northward_sea_ice_velocity(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean northward sea ice velocity","parameter_ID":"263004","product_type":"forecast","shortName":"avg_sivn","standard_name":"Time-mean_northward_sea_ice_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263004"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_sea_ice_velocity(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean northward sea ice velocity","parameter_ID":"263004","product_type":"forecast","shortName":"avg_sivn","standard_name":"Time-mean_northward_sea_ice_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263004"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_sea_water_velocity(clmn_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean northward sea water velocity","parameter_ID":"263505","product_type":"forecast","shortName":"avg_von","standard_name":"Time-mean_northward_sea_water_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263505"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean northward sea water velocity","parameter_ID":"263505","product_type":"forecast","shortName":"avg_von","standard_name":"Time-mean_northward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263505"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_turbulent_surface_stress(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean northward turbulent surface stress","parameter_ID":"235042","product_type":"forecast","shortName":"avg_inss","standard_name":"Time-mean_northward_turbulent_surface_stress","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235042"},"dimensions":["lat","lon","time"],"type":"data","unit":"N m-2"},"Time-mean_northward_turbulent_surface_stress(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean northward turbulent surface stress","parameter_ID":"235042","product_type":"forecast","shortName":"avg_inss","standard_name":"Time-mean_northward_turbulent_surface_stress","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235042"},"dimensions":["lat","lon","time"],"type":"data","unit":"N m-2"},"Time-mean_potential_vorticity(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean potential vorticity","parameter_ID":"235100","product_type":"forecast","shortName":"avg_pv","standard_name":"Time-mean_potential_vorticity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235100"},"dimensions":["lat","lon","time"],"type":"data","unit":"K m2kg-1s-1"},"Time-mean_relative_humidity(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean relative humidity","parameter_ID":"235157","product_type":"forecast","shortName":"avg_r","standard_name":"Time-mean_relative_humidity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235157"},"dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Time-mean_sea_ice_area_fraction(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea ice area fraction","parameter_ID":"263001","product_type":"forecast","shortName":"avg_siconc","standard_name":"Time-mean_sea_ice_area_fraction","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263001"},"dimensions":["lat","lon","time"],"type":"data","unit":"Fraction"},"Time-mean_sea_ice_area_fraction(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea ice area fraction","parameter_ID":"263001","product_type":"forecast","shortName":"avg_siconc","standard_name":"Time-mean_sea_ice_area_fraction","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263001"},"dimensions":["lat","lon","time"],"type":"data","unit":"Fraction"},"Time-mean_sea_ice_thickness(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea ice 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sky","parameter_ID":"235049","product_type":"forecast","shortName":"avg_tnswrfcs","standard_name":"Time-mean_top_net_short-wave_radiation_flux,_clear_sky","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235049"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Time-mean_top_net_short-wave_radiation_flux,_clear_sky(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean top net short-wave radiation flux, clear sky","parameter_ID":"235049","product_type":"forecast","shortName":"avg_tnswrfcs","standard_name":"Time-mean_top_net_short-wave_radiation_flux,_clear_sky","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235049"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Time-mean_total_cloud_cover(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total cloud cover","parameter_ID":"235288","product_type":"forecast","shortName":"avg_tcc","standard_name":"Time-mean_total_cloud_cover","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235288"},"dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Time-mean_total_column_cloud_ice_water(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total column cloud ice water","parameter_ID":"235088","product_type":"forecast","shortName":"avg_tciw","standard_name":"Time-mean_total_column_cloud_ice_water","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235088"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Time-mean_total_column_heat_content(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean total column heat content","parameter_ID":"263123","product_type":"forecast","shortName":"avg_hcbtm","standard_name":"Time-mean_total_column_heat_content","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263123"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_total_column_heat_content(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean total column heat content","parameter_ID":"263123","product_type":"forecast","shortName":"avg_hcbtm","standard_name":"Time-mean_total_column_heat_content","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263123"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_total_column_liquid_water(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total column liquid water","parameter_ID":"235087","product_type":"forecast","shortName":"avg_tclw","standard_name":"Time-mean_total_column_liquid_water","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235087"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Time-mean_total_column_vertically-integrated_water_vapour(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total column vertically-integrated water vapour","parameter_ID":"235137","product_type":"forecast","shortName":"avg_tcwv","standard_name":"Time-mean_total_column_vertically-integrated_water_vapour","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235137"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Time-mean_total_column_water(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total column water","parameter_ID":"235136","product_type":"forecast","shortName":"avg_tcw","standard_name":"Time-mean_total_column_water","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235136"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Time-mean_total_precipitation_rate(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total precipitation rate","parameter_ID":"235055","product_type":"forecast","shortName":"avg_tprate","standard_name":"Time-mean_total_precipitation_rate","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235055"},"description":"Time-mean total precipitation rate, or time-mean total precipitation flux. This parameter is on level \"Ground or water surface\" (typeOfFirstFixedSurface=1). For this parameter on other levels, please use 235013.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_total_precipitation_rate(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total precipitation rate","parameter_ID":"235055","product_type":"forecast","shortName":"avg_tprate","standard_name":"Time-mean_total_precipitation_rate","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235055"},"description":"Time-mean total precipitation rate, or time-mean total precipitation flux. This parameter is on level \"Ground or water surface\" (typeOfFirstFixedSurface=1). For this parameter on other levels, please use 235013.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_total_snowfall_rate_water_equivalent(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total snowfall rate water equivalent","parameter_ID":"235031","product_type":"forecast","shortName":"avg_tsrwe","standard_name":"Time-mean_total_snowfall_rate_water_equivalent","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235031"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_total_snowfall_rate_water_equivalent(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total snowfall rate water equivalent","parameter_ID":"235031","product_type":"forecast","shortName":"avg_tsrwe","standard_name":"Time-mean_total_snowfall_rate_water_equivalent","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235031"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_upward_sea_water_velocity(clmn_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean upward sea water velocity","parameter_ID":"263507","product_type":"forecast","shortName":"avg_wo","standard_name":"Time-mean_upward_sea_water_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263507"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_upward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean upward sea water velocity","parameter_ID":"263507","product_type":"forecast","shortName":"avg_wo","standard_name":"Time-mean_upward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263507"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_vertical_velocity(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean vertical velocity","parameter_ID":"235135","product_type":"forecast","shortName":"avg_w","standard_name":"Time-mean_vertical_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235135"},"dimensions":["lat","lon","time"],"type":"data","unit":"Pa s-1"},"Time-mean_vertically-integrated_heat_content_in_the_upper_300_m(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"300","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 300 m","parameter_ID":"263121","product_type":"forecast","shortName":"avg_hc300m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_300_m","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263121"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_vertically-integrated_heat_content_in_the_upper_300_m(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"300","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 300 m","parameter_ID":"263121","product_type":"forecast","shortName":"avg_hc300m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_300_m","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263121"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"700","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 700 m","parameter_ID":"263122","product_type":"forecast","shortName":"avg_hc700m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263122"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"700","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 700 m","parameter_ID":"263122","product_type":"forecast","shortName":"avg_hc700m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263122"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_volumetric_soil_moisture(clmn_sol)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Time-mean volumetric soil moisture","parameter_ID":"235077","product_type":"forecast","shortName":"avg_vsw","standard_name":"Time-mean_volumetric_soil_moisture","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235077"},"dimensions":["lat","lon","time"],"type":"data","unit":"m3m-3"},"Time_mean_top_downward_short-wave_radiation_flux(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time mean top downward short-wave radiation flux","parameter_ID":"235053","product_type":"forecast","shortName":"avg_tdswrf","standard_name":"Time_mean_top_downward_short-wave_radiation_flux","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235053"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Time_mean_top_downward_short-wave_radiation_flux(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time mean top downward short-wave radiation flux","parameter_ID":"235053","product_type":"forecast","shortName":"avg_tdswrf","standard_name":"Time_mean_top_downward_short-wave_radiation_flux","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235053"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Total_Cloud_Cover(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total Cloud Cover","parameter_ID":"228164","product_type":"forecast","shortName":"tcc","standard_name":"Total_Cloud_Cover","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228164"},"description":"[NOTE: See 164 for the equivalent parameter in \"(0-1)\"]","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Total_column_cloud_ice_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud ice water","parameter_ID":"79","product_type":"forecast","shortName":"tciw","standard_name":"Total_column_cloud_ice_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/79"},"description":"This parameter is the amount of ice contained within clouds in a column extending from the surface of the Earth to the top of the atmosphere. Snow (aggregated ice crystals) is not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The  ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_cloud_liquid_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud liquid water","parameter_ID":"78","product_type":"forecast","shortName":"tclw","standard_name":"Total_column_cloud_liquid_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/78"},"description":"This parameter is the amount of liquid water contained within cloud droplets in a column extending from the surface of the Earth to the top of the atmosphere. Rain water droplets, which are much larger in size (and mass), are not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_vertically-integrated_water_vapour(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column vertically-integrated water vapour","parameter_ID":"137","product_type":"forecast","shortName":"tcwv","standard_name":"Total_column_vertically-integrated_water_vapour","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/137"},"description":"This parameter is the total amount of water vapour in a column extending from the surface of the Earth to the top of the atmosphere.This parameter represents the area averaged value for agrid box.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column water","parameter_ID":"136","product_type":"forecast","shortName":"tcw","standard_name":"Total_column_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/136"},"description":"This parameter is the sum of water vapour, liquid water, cloud ice, rain and snow in a column extending from the surface of the Earth to the top of the atmosphere. In old versions of the ECMWF model (IFS), rain and snow were not accounted for.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"U_component_of_wind(clte_hl)":{"attrs":{"encoding":"instantaneous","levelist":"100","levtype":"hl","long_name":"U component of wind","parameter_ID":"131","product_type":"forecast","shortName":"u","standard_name":"U_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/131"},"description":"This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east, in metres per second. A negative sign thus indicates air movement towards the west.This parameter can be combined with the V component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"U_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"U component of wind","parameter_ID":"131","product_type":"forecast","shortName":"u","standard_name":"U_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/131"},"description":"This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east, in metres per second. A negative sign thus indicates air movement towards the west.This parameter can be combined with the V component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"V_component_of_wind(clte_hl)":{"attrs":{"encoding":"instantaneous","levelist":"100","levtype":"hl","long_name":"V component of wind","parameter_ID":"132","product_type":"forecast","shortName":"v","standard_name":"V_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/132"},"description":"This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north, in metres per second. A negative sign thus indicates air movement towards the south.This parameter can be combined with the U component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"V_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"V component of wind","parameter_ID":"132","product_type":"forecast","shortName":"v","standard_name":"V_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/132"},"description":"This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north, in metres per second. A negative sign thus indicates air movement towards the south.This parameter can be combined with the U component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Vertical_velocity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Vertical velocity","parameter_ID":"135","product_type":"forecast","shortName":"w","standard_name":"Vertical_velocity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/135"},"description":"This parameter is the speed of air motion in the upward or downward direction. The ECMWF Integrated Forecasting System (IFS) uses a pressure based vertical co-ordinate system and pressure decreases with height, therefore negative values of vertical velocity indicate upward motion.Vertical velocity can be useful to understand the large-scale dynamics of the atmosphere, including areas of upward motion/ascent (negative values) and downward motion/subsidence (positive values).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa s-1"},"Volumetric_soil_moisture(clte_sol)":{"attrs":{"encoding":"instantaneous","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Volumetric soil moisture","parameter_ID":"260199","product_type":"forecast","shortName":"vsw","standard_name":"Volumetric_soil_moisture","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/260199"},"description":"Please note that the encoding listed here for uerra (which includes carra/cerra) includes entries for Time-mean volumetric soil moisture. The specific encoding for Time-mean volumetric soil moisture can be found in 235077.","dimensions":["lat","lon","time"],"type":"data","unit":"m3m-3"}},"sci:publications":[{"doi":"10.1109/MCSE.2023.3260519","citation":"N. Wedi et al., Destination Earth: High-Performance Computing for Weather and Climate, in Computing in Science \u0026 Engineering, vol. 24, no. 6, pp. 29-37, Nov.-Dec. 2022,"},{"doi":"10.21957/d3f982672e","citation":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"dedl:short_description":"The Climate Change Adaptation Digital Twin provides global climate projections and sector-specific information over multiple decades at high resolution via a unified framework combining advanced Earth system models, impact assessments, and observations. This Generation-2, Realization-1 Collection gives access to 'Storyline Simulation Past Climate' data based on the 'IFS-FESOM' model."},{"type":"Collection","title":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Storyline Simulation Past Climate - IFS-FESOM - Generation-2 - Realization-2","id":"EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_HIST_IFS-FESOM.R2","description":"The DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities by providing innovative climate information on multi-decadal timescales, globally, at scales at which many impacts of climate change are observed. It combines cutting-edge global Earth-system models, impact-sector applications and observations into a unified framework to provide global climate projections and impact-sector information on multi-decadal timescales (1990 to ~2050), at very high spatial resolutions (5 to 10 km).\n\nThe Climate DT represents the first ever attempt to operationalise the production of global multi-decadal climate projections, leveraging the world-leading supercomputing facilities of the EuroHPC Joint Undertaking along with some of the leading European climate models. A concise overview of what the Climate DT aims to achieve, and of the different concepts essential for an understanding of the Digital Twin’s characteristics, is included in the [Climate DT factsheet](https://destine.ecmwf.int/wp-content/uploads/2024/06/2024.06.07_Climate-DT-Fact-Sheet_V7-2.pdf)\n\n## Storyline Simulation Past Climate \n\n Storyline simulations offer a what-if capability to explore how a weather event we experienced in the recent past would change in a warmer climate and how it would have looked in past pre-industrial conditions. To constrain simulated weather events, a nudging approach is used to keep the simulated large-scale flow close to the ERA5 reanalysis for the period 2017 – 2023. Processes on smaller scales and thermodynamic processes are free to evolve, which allows scientists to study e.g. how extreme precipitation would change in a warmer world for a weather event observed in present-day conditions. Initial conditions are taken from ocean-only spinup simulations for 1950 and 2017, and from the IFS-FESOM projection for a +2 °C world. Storyline simulations will be updated regularly to include most recent months. The next update will extend all storylines to include 2024.\n\n## Models\n\nThe Climate DT exploits and further evolves a new generation of global storm-resolving and eddy-rich models built through a cooperative model development approach. For more information on models please click [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#models)\n\n## Simulations\n\nThe Climate DT team carries out several types of digital twin simulations on the EuroHPC supercomputers.  Multi-decadal simulations are produced to cover the recent past (from 1990) and possible future evolutions of the climate up to 2050. See [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#simulations) for more information on Simulations\n\n## Parameters\n\nBelow we see the list of parameters extracted from the 'DestinE Climate DT data portfolio', for more information please refer to the pages [Climate DT Phase2 CLTE Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters) and [Climate DT Phase2 CLMN Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters)","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_HIST_IFS-FESOM.R2/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_HIST_IFS-FESOM.R2/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_HIST_IFS-FESOM.R2/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_HIST_IFS-FESOM.R2","title":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Storyline Simulation Past Climate - IFS-FESOM - Generation-2 - Realization-2"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters","title":"DestinE ClimateDT Phase 2 CLTE Parameters"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters","title":"DestinE ClimateDT Phase 2 CLMN Parameters"},{"rel":"cite-as","type":"text/html","href":"https://dl.acm.org/doi/abs/10.1109/MCSE.2023.3260519","title":"Destination Earth: High-Performance Computing for Weather and Climate"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.21957/d3f982672e","title":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"assets":{"thumbnail":{"href":"https://s3.central.data.destination-earth.eu/swift/v1/dedl-public/collections/climate-dt-min.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2017-01-01T00:00:00Z","2025-12-31T23:59:59Z"]]}},"license":"CC-BY-4.0","keywords":["Digital Twins","Climate Change","Atmosphere","Ocean","Land","Sea Ice","Snow","Soil","Earth","High Performance Computing","Decision Making","Europe","Meteorology","class:d1","dataset:climate-dt","generation:2","expver:0001","stream:clmn,clte","type:fc","activity:story-nudging","experiment:hist","realization:2","model:IFS-FESOM","resolution:standard,high","levtype:hl,o2d,o3d,pl,sfc,sol"],"summaries":{"federation:backends":["dedt_mn5"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/datacube/v2.1.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["producer","processor","licensor"],"url":"https://www.ecmwf.int/"}],"created":"2024-04-11T22:31:55Z","updated":"2026-04-24T10:24:59Z","published":"2024-04-11T22:31:55Z","cube:dimensions":{"lat":{"axis":"y","description":"latitude","extent":[-90,90],"type":"spatial"},"lon":{"axis":"x","description":"longitude","extent":[-180,180],"type":"spatial"},"time":{"extent":["2017-01-01T00:00:00Z","2025-12-31T23:59:59Z"],"step":"P0Y0M0DT1H0M0S","type":"temporal"}},"cube:variables":{"10_metre_U_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre U wind component","parameter_ID":"165","product_type":"forecast","shortName":"10u","standard_name":"10_metre_U_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/165"},"description":"This parameter is the eastward component of the 10m wind. It is the horizontal speed of air moving towards the east, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the V component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_V_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre V wind component","parameter_ID":"166","product_type":"forecast","shortName":"10v","standard_name":"10_metre_V_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/166"},"description":"This parameter is the northward component of the 10m wind. It is the horizontal speed of air moving towards the north, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the U component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_wind_speed(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre wind speed","parameter_ID":"207","product_type":"forecast","shortName":"10si","standard_name":"10_metre_wind_speed","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/207"},"description":"This parameter is the horizontal speed of the wind, or movement of air, at a height of ten metres above the surface of the Earth. The units of this parameter are metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.The eastward and northward components of the horizontal wind at 10m are also available as parameters.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"2_metre_dewpoint_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"2","levtype":"sfc","long_name":"2 metre dewpoint temperature","parameter_ID":"168","product_type":"forecast","shortName":"2d","standard_name":"2_metre_dewpoint_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/168"},"description":"This parameter is the temperature to which the air, at 2 metres above the surface of the Earth, would have to be cooled for saturation to occur.It is a measure of the humidity of the air. Combined with temperature and pressure, it can be used to calculate the relative humidity.2m dew point temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"2_metre_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"2","levtype":"sfc","long_name":"2 metre temperature","parameter_ID":"167","product_type":"forecast","shortName":"2t","standard_name":"2_metre_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/167"},"description":"This parameter is the temperature of air at 2m above the surface of land, sea or in-land waters.2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.Please note that the encodings listed here for s2s \u0026 uerra (which includes encodings for carra/cerra) include entries for Mean 2 metre temperature. The specific encoding for Mean 2 metre temperature can be found in 228004.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Geopotential(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Geopotential","parameter_ID":"129","product_type":"forecast","shortName":"z","standard_name":"Geopotential","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/129"},"description":"This parameter is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level.The geopotential height can be calculated by dividing the geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2). The geopotential height plays an important role in synoptic meteorology (analysis of weather patterns). Charts of geopotential height plotted at constant pressure levels (e.g., 300, 500 or 850 hPa) can be used to identify weather systems such as cyclones, anticyclones, troughs and ridges.At the surface of the Earth, this parameter shows the variations in geopotential (height) of the surface, and is often referred to as the orography.","dimensions":["lat","lon","time"],"type":"data","unit":"m2s-2"},"Land-sea_mask(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Land-sea mask","parameter_ID":"172","product_type":"forecast","shortName":"lsm","standard_name":"Land-sea_mask","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/172"},"description":"This parameter is the proportion of land, as opposed to ocean or inland waters (lakes, reservoirs, rivers and coastal waters), in agrid box.This parameter has values ranging between zero and one and is dimensionless.In cycles of the ECMWF Integrated Forecasting System (IFS) from CY41R1 (introduced in May 2015) onwards, grid boxes where this parameter has a value above 0.5 can be comprised of a mixture of land and inland water but not ocean. Grid boxes with a value of 0.5 and below can only be comprised of a water surface. In the latter case, the lake cover is used to determine how much of the water surface is ocean or inland water.In cycles of the IFS before CY41R1, grid boxes where this parameter has a value above 0.5 can only be comprised of land and those grid boxes with a value of 0.5 and below can only be comprised of ocean. In these older model cycles, there is no differentiation between ocean and inland water.","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Mean_sea_level_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Mean sea level pressure","parameter_ID":"151","product_type":"forecast","shortName":"msl","standard_name":"Mean_sea_level_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/151"},"description":"This parameter is the pressure (force per unit area) of the atmosphere adjusted to the height of mean sea level.It is a measure of the weight that all the air in a column vertically above the area of Earth's surface would have at that point, if the point were located at the mean sea level. It is calculated over all surfaces - land, sea and in-land water.Maps of mean sea level pressure are used to identify the locations of low and high pressure systems, often referred to as cyclones and anticyclones. Contours of mean sea level pressure also indicate the strength of the wind. Tightly packed contours show stronger winds.The units of this parameter are pascals (Pa).  Mean sea level pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb = 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Orography(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Orography","parameter_ID":"228002","product_type":"forecast","shortName":"orog","standard_name":"Orography","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/228002"},"dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Potential_vorticity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Potential vorticity","parameter_ID":"60","product_type":"forecast","shortName":"pv","standard_name":"Potential_vorticity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/60"},"description":"Potential vorticity is a measure of the capacity for air to rotate in the atmosphere. If we ignore the effects of heating and friction, potential vorticity is conserved following an air parcel. It is used to look for places where large wind storms are likely to originate and develop.  Potential vorticity increases strongly above the tropopause and therefore, it can also be used in studies related to the stratosphere and stratosphere-troposphere exchanges.Large wind storms develop when a column of air in the atmosphere starts to rotate. Potential vorticity is calculated from the wind, temperature and pressure across a column of air in the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K m2kg-1s-1"},"Relative_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Relative humidity","parameter_ID":"157","product_type":"forecast","shortName":"r","standard_name":"Relative_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/157"},"description":"This parameter is the water vapour pressure as a percentage of the value at which the air becomes saturated (the point at which water vapour begins to condense into liquid water or deposition into ice).For temperatures over 0°C (273.15 K) it is calculated for saturation over water. At temperatures below -23°C it is calculated for saturation over ice.  Between -23°C and 0°C this parameter is calculated by interpolating between the ice and water values using a quadratic function.See more information about the model's relative humidity calculation.","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Skin_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Skin temperature","parameter_ID":"235","product_type":"forecast","shortName":"skt","standard_name":"Skin_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235"},"description":"This parameter is the temperature of the surface of the Earth.The skin temperature is the theoretical temperature that is required to satisfy the surface energy balance. It represents the temperature of the uppermost surface layer, which has no heat capacity and so can respond instantaneously to changes in surface fluxes. Skin temperature is calculated differently over land and sea.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.See further information about the skin temperatureover landandover sea.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean skin temperature. The specific encoding for Mean skin temperature can be found in 235079.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Snow_depth_water_equivalent(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Snow depth water equivalent","parameter_ID":"228141","product_type":"forecast","shortName":"sd","standard_name":"Snow_depth_water_equivalent","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228141"},"description":"Snow depth water equivalent in kg m**-2 (mm) water equivalent.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean snow depth water equivalent. The specific encoding for Time-mean snow depth water equivalent can be found in 235078.[NOTE: See 141 for the equivalent parameter in \"m of water equivalent\"]","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Snow_depth_water_equivalent(clte_sol)":{"attrs":{"encoding":"instantaneous","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Snow depth water equivalent","parameter_ID":"228141","product_type":"forecast","shortName":"sd","standard_name":"Snow_depth_water_equivalent","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228141"},"description":"Snow depth water equivalent in kg m**-2 (mm) water equivalent.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean snow depth water equivalent. The specific encoding for Time-mean snow depth water equivalent can be found in 235078.[NOTE: See 141 for the equivalent parameter in \"m of water equivalent\"]","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Specific_cloud_liquid_water_content(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific cloud liquid water content","parameter_ID":"246","product_type":"forecast","shortName":"clwc","standard_name":"Specific_cloud_liquid_water_content","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/246"},"description":"This parameter is the mass of cloud liquid water droplets per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for agrid box.Water within clouds can be liquid or ice, or a combination of the two.See further information about the cloud formulation.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Specific_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific humidity","parameter_ID":"133","product_type":"forecast","shortName":"q","standard_name":"Specific_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/133"},"description":"This parameter is the mass of water vapour per kilogram of moist air.The total mass of moist air is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Surface_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Surface pressure","parameter_ID":"134","product_type":"forecast","shortName":"sp","standard_name":"Surface_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/134"},"description":"This parameter is the pressure (force per unit area) of the atmosphere on the surface of land, sea and in-land water.It is a measure of the weight of all the air in a column vertically above the area of the Earth's surface represented at a fixed point.Surface pressure is often used in combination with temperature to calculate air density.The strong variation of pressure with altitude makes it difficult to see the low and high pressure systems over mountainous areas, so mean sea level pressure, rather than surface pressure, is normally used for this purpose.The units of this parameter are Pascals (Pa). Surface pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb= 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Temperature(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Temperature","parameter_ID":"130","product_type":"forecast","shortName":"t","standard_name":"Temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/130"},"description":"This parameter is the temperature in the atmosphere.It has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.This parameter is available on multiple levels through the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_10_metre_U_wind_component(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre U wind component","parameter_ID":"235165","product_type":"forecast","shortName":"avg_10u","standard_name":"Time-mean_10_metre_U_wind_component","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235165"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_10_metre_V_wind_component(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre V wind component","parameter_ID":"235166","product_type":"forecast","shortName":"avg_10v","standard_name":"Time-mean_10_metre_V_wind_component","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235166"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_10_metre_wind_speed(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre wind speed","parameter_ID":"228005","product_type":"forecast","shortName":"avg_10ws","standard_name":"Time-mean_10_metre_wind_speed","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/228005"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_2_metre_dewpoint_temperature(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"2","levtype":"sfc","long_name":"Time-mean 2 metre dewpoint temperature","parameter_ID":"235168","product_type":"forecast","shortName":"avg_2d","standard_name":"Time-mean_2_metre_dewpoint_temperature","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235168"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_2_metre_temperature(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"2","levtype":"sfc","long_name":"Time-mean 2 metre temperature","parameter_ID":"228004","product_type":"forecast","shortName":"avg_2t","standard_name":"Time-mean_2_metre_temperature","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/228004"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_U_component_of_wind(clmn_hl)":{"attrs":{"encoding":"mean","levelist":"100","levtype":"hl","long_name":"Time-mean U component of wind","parameter_ID":"235131","product_type":"forecast","shortName":"avg_u","standard_name":"Time-mean_U_component_of_wind","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235131"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_U_component_of_wind(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean U component of wind","parameter_ID":"235131","product_type":"forecast","shortName":"avg_u","standard_name":"Time-mean_U_component_of_wind","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235131"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_V_component_of_wind(clmn_hl)":{"attrs":{"encoding":"mean","levelist":"100","levtype":"hl","long_name":"Time-mean V component of 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m-2"},"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"700","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 700 m","parameter_ID":"263122","product_type":"forecast","shortName":"avg_hc700m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263122"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_volumetric_soil_moisture(clmn_sol)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Time-mean volumetric soil moisture","parameter_ID":"235077","product_type":"forecast","shortName":"avg_vsw","standard_name":"Time-mean_volumetric_soil_moisture","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235077"},"dimensions":["lat","lon","time"],"type":"data","unit":"m3m-3"},"Time_mean_top_downward_short-wave_radiation_flux(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time mean top downward short-wave radiation flux","parameter_ID":"235053","product_type":"forecast","shortName":"avg_tdswrf","standard_name":"Time_mean_top_downward_short-wave_radiation_flux","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235053"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Time_mean_top_downward_short-wave_radiation_flux(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time mean top downward short-wave radiation flux","parameter_ID":"235053","product_type":"forecast","shortName":"avg_tdswrf","standard_name":"Time_mean_top_downward_short-wave_radiation_flux","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235053"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Total_Cloud_Cover(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total Cloud Cover","parameter_ID":"228164","product_type":"forecast","shortName":"tcc","standard_name":"Total_Cloud_Cover","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228164"},"description":"[NOTE: See 164 for the equivalent parameter in \"(0-1)\"]","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Total_column_cloud_ice_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud ice water","parameter_ID":"79","product_type":"forecast","shortName":"tciw","standard_name":"Total_column_cloud_ice_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/79"},"description":"This parameter is the amount of ice contained within clouds in a column extending from the surface of the Earth to the top of the atmosphere. Snow (aggregated ice crystals) is not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The  ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_cloud_liquid_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud liquid water","parameter_ID":"78","product_type":"forecast","shortName":"tclw","standard_name":"Total_column_cloud_liquid_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/78"},"description":"This parameter is the amount of liquid water contained within cloud droplets in a column extending from the surface of the Earth to the top of the atmosphere. Rain water droplets, which are much larger in size (and mass), are not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_vertically-integrated_water_vapour(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column vertically-integrated water vapour","parameter_ID":"137","product_type":"forecast","shortName":"tcwv","standard_name":"Total_column_vertically-integrated_water_vapour","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/137"},"description":"This parameter is the total amount of water vapour in a column extending from the surface of the Earth to the top of the atmosphere.This parameter represents the area averaged value for agrid box.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column water","parameter_ID":"136","product_type":"forecast","shortName":"tcw","standard_name":"Total_column_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/136"},"description":"This parameter is the sum of water vapour, liquid water, cloud ice, rain and snow in a column extending from the surface of the Earth to the top of the atmosphere. In old versions of the ECMWF model (IFS), rain and snow were not accounted for.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"U_component_of_wind(clte_hl)":{"attrs":{"encoding":"instantaneous","levelist":"100","levtype":"hl","long_name":"U component of wind","parameter_ID":"131","product_type":"forecast","shortName":"u","standard_name":"U_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/131"},"description":"This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east, in metres per second. A negative sign thus indicates air movement towards the west.This parameter can be combined with the V component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"U_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"U component of wind","parameter_ID":"131","product_type":"forecast","shortName":"u","standard_name":"U_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/131"},"description":"This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east, in metres per second. A negative sign thus indicates air movement towards the west.This parameter can be combined with the V component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"V_component_of_wind(clte_hl)":{"attrs":{"encoding":"instantaneous","levelist":"100","levtype":"hl","long_name":"V component of wind","parameter_ID":"132","product_type":"forecast","shortName":"v","standard_name":"V_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/132"},"description":"This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north, in metres per second. A negative sign thus indicates air movement towards the south.This parameter can be combined with the U component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"V_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"V component of wind","parameter_ID":"132","product_type":"forecast","shortName":"v","standard_name":"V_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/132"},"description":"This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north, in metres per second. A negative sign thus indicates air movement towards the south.This parameter can be combined with the U component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Vertical_velocity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Vertical velocity","parameter_ID":"135","product_type":"forecast","shortName":"w","standard_name":"Vertical_velocity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/135"},"description":"This parameter is the speed of air motion in the upward or downward direction. The ECMWF Integrated Forecasting System (IFS) uses a pressure based vertical co-ordinate system and pressure decreases with height, therefore negative values of vertical velocity indicate upward motion.Vertical velocity can be useful to understand the large-scale dynamics of the atmosphere, including areas of upward motion/ascent (negative values) and downward motion/subsidence (positive values).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa s-1"},"Volumetric_soil_moisture(clte_sol)":{"attrs":{"encoding":"instantaneous","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Volumetric soil moisture","parameter_ID":"260199","product_type":"forecast","shortName":"vsw","standard_name":"Volumetric_soil_moisture","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/260199"},"description":"Please note that the encoding listed here for uerra (which includes carra/cerra) includes entries for Time-mean volumetric soil moisture. The specific encoding for Time-mean volumetric soil moisture can be found in 235077.","dimensions":["lat","lon","time"],"type":"data","unit":"m3m-3"}},"sci:publications":[{"doi":"10.1109/MCSE.2023.3260519","citation":"N. Wedi et al., Destination Earth: High-Performance Computing for Weather and Climate, in Computing in Science \u0026 Engineering, vol. 24, no. 6, pp. 29-37, Nov.-Dec. 2022,"},{"doi":"10.21957/d3f982672e","citation":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"dedl:short_description":"The Climate Change Adaptation Digital Twin provides global climate projections and sector-specific information over multiple decades at high resolution via a unified framework combining advanced Earth system models, impact assessments, and observations. This Generation-2, Realization-2 Collection gives access to 'Storyline Simulation Past Climate' data based on the 'IFS-FESOM' model."},{"type":"Collection","title":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Storyline Simulation Past Climate - IFS-FESOM - Generation-2 - Realization-3","id":"EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_HIST_IFS-FESOM.R3","description":"The DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities by providing innovative climate information on multi-decadal timescales, globally, at scales at which many impacts of climate change are observed. It combines cutting-edge global Earth-system models, impact-sector applications and observations into a unified framework to provide global climate projections and impact-sector information on multi-decadal timescales (1990 to ~2050), at very high spatial resolutions (5 to 10 km).\n\nThe Climate DT represents the first ever attempt to operationalise the production of global multi-decadal climate projections, leveraging the world-leading supercomputing facilities of the EuroHPC Joint Undertaking along with some of the leading European climate models. A concise overview of what the Climate DT aims to achieve, and of the different concepts essential for an understanding of the Digital Twin’s characteristics, is included in the [Climate DT factsheet](https://destine.ecmwf.int/wp-content/uploads/2024/06/2024.06.07_Climate-DT-Fact-Sheet_V7-2.pdf)\n\n## Storyline Simulation Past Climate \n\n Storyline simulations offer a what-if capability to explore how a weather event we experienced in the recent past would change in a warmer climate and how it would have looked in past pre-industrial conditions. To constrain simulated weather events, a nudging approach is used to keep the simulated large-scale flow close to the ERA5 reanalysis for the period 2017 – 2023. Processes on smaller scales and thermodynamic processes are free to evolve, which allows scientists to study e.g. how extreme precipitation would change in a warmer world for a weather event observed in present-day conditions. Initial conditions are taken from ocean-only spinup simulations for 1950 and 2017, and from the IFS-FESOM projection for a +2 °C world. Storyline simulations will be updated regularly to include most recent months. The next update will extend all storylines to include 2024.\n\n## Models\n\nThe Climate DT exploits and further evolves a new generation of global storm-resolving and eddy-rich models built through a cooperative model development approach. For more information on models please click [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#models)\n\n## Simulations\n\nThe Climate DT team carries out several types of digital twin simulations on the EuroHPC supercomputers.  Multi-decadal simulations are produced to cover the recent past (from 1990) and possible future evolutions of the climate up to 2050. See [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#simulations) for more information on Simulations\n\n## Parameters\n\nBelow we see the list of parameters extracted from the 'DestinE Climate DT data portfolio', for more information please refer to the pages [Climate DT Phase2 CLTE Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters) and [Climate DT Phase2 CLMN Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters)","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_HIST_IFS-FESOM.R3/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_HIST_IFS-FESOM.R3/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_HIST_IFS-FESOM.R3/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_HIST_IFS-FESOM.R3","title":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Storyline Simulation Past Climate - IFS-FESOM - Generation-2 - Realization-3"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters","title":"DestinE ClimateDT Phase 2 CLTE Parameters"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters","title":"DestinE ClimateDT Phase 2 CLMN Parameters"},{"rel":"cite-as","type":"text/html","href":"https://dl.acm.org/doi/abs/10.1109/MCSE.2023.3260519","title":"Destination Earth: High-Performance Computing for Weather and Climate"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.21957/d3f982672e","title":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"assets":{"thumbnail":{"href":"https://s3.central.data.destination-earth.eu/swift/v1/dedl-public/collections/climate-dt-min.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2017-01-01T00:00:00Z","2025-12-31T23:59:59Z"]]}},"license":"CC-BY-4.0","keywords":["Digital Twins","Climate Change","Atmosphere","Ocean","Land","Sea Ice","Snow","Soil","Earth","High Performance Computing","Decision Making","Europe","Meteorology","class:d1","dataset:climate-dt","generation:2","expver:0001","stream:clmn,clte","type:fc","activity:story-nudging","experiment:hist","realization:3","model:IFS-FESOM","resolution:standard,high","levtype:hl,o2d,o3d,pl,sfc,sol"],"summaries":{"federation:backends":["dedt_mn5"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/datacube/v2.1.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["producer","processor","licensor"],"url":"https://www.ecmwf.int/"}],"created":"2024-04-11T22:31:55Z","updated":"2026-04-24T10:24:59Z","published":"2024-04-11T22:31:55Z","cube:dimensions":{"lat":{"axis":"y","description":"latitude","extent":[-90,90],"type":"spatial"},"lon":{"axis":"x","description":"longitude","extent":[-180,180],"type":"spatial"},"time":{"extent":["2017-01-01T00:00:00Z","2025-12-31T23:59:59Z"],"step":"P0Y0M0DT1H0M0S","type":"temporal"}},"cube:variables":{"10_metre_U_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre U wind component","parameter_ID":"165","product_type":"forecast","shortName":"10u","standard_name":"10_metre_U_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/165"},"description":"This parameter is the eastward component of the 10m wind. It is the horizontal speed of air moving towards the east, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the V component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_V_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre V wind component","parameter_ID":"166","product_type":"forecast","shortName":"10v","standard_name":"10_metre_V_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/166"},"description":"This parameter is the northward component of the 10m wind. It is the horizontal speed of air moving towards the north, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the U component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_wind_speed(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre wind speed","parameter_ID":"207","product_type":"forecast","shortName":"10si","standard_name":"10_metre_wind_speed","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/207"},"description":"This parameter is the horizontal speed of the wind, or movement of air, at a height of ten metres above the surface of the Earth. The units of this parameter are metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.The eastward and northward components of the horizontal wind at 10m are also available as parameters.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"2_metre_dewpoint_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"2","levtype":"sfc","long_name":"2 metre dewpoint temperature","parameter_ID":"168","product_type":"forecast","shortName":"2d","standard_name":"2_metre_dewpoint_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/168"},"description":"This parameter is the temperature to which the air, at 2 metres above the surface of the Earth, would have to be cooled for saturation to occur.It is a measure of the humidity of the air. Combined with temperature and pressure, it can be used to calculate the relative humidity.2m dew point temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"2_metre_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"2","levtype":"sfc","long_name":"2 metre temperature","parameter_ID":"167","product_type":"forecast","shortName":"2t","standard_name":"2_metre_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/167"},"description":"This parameter is the temperature of air at 2m above the surface of land, sea or in-land waters.2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.Please note that the encodings listed here for s2s \u0026 uerra (which includes encodings for carra/cerra) include entries for Mean 2 metre temperature. The specific encoding for Mean 2 metre temperature can be found in 228004.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Geopotential(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Geopotential","parameter_ID":"129","product_type":"forecast","shortName":"z","standard_name":"Geopotential","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/129"},"description":"This parameter is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level.The geopotential height can be calculated by dividing the geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2). The geopotential height plays an important role in synoptic meteorology (analysis of weather patterns). Charts of geopotential height plotted at constant pressure levels (e.g., 300, 500 or 850 hPa) can be used to identify weather systems such as cyclones, anticyclones, troughs and ridges.At the surface of the Earth, this parameter shows the variations in geopotential (height) of the surface, and is often referred to as the orography.","dimensions":["lat","lon","time"],"type":"data","unit":"m2s-2"},"Land-sea_mask(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Land-sea mask","parameter_ID":"172","product_type":"forecast","shortName":"lsm","standard_name":"Land-sea_mask","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/172"},"description":"This parameter is the proportion of land, as opposed to ocean or inland waters (lakes, reservoirs, rivers and coastal waters), in agrid box.This parameter has values ranging between zero and one and is dimensionless.In cycles of the ECMWF Integrated Forecasting System (IFS) from CY41R1 (introduced in May 2015) onwards, grid boxes where this parameter has a value above 0.5 can be comprised of a mixture of land and inland water but not ocean. Grid boxes with a value of 0.5 and below can only be comprised of a water surface. In the latter case, the lake cover is used to determine how much of the water surface is ocean or inland water.In cycles of the IFS before CY41R1, grid boxes where this parameter has a value above 0.5 can only be comprised of land and those grid boxes with a value of 0.5 and below can only be comprised of ocean. In these older model cycles, there is no differentiation between ocean and inland water.","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Mean_sea_level_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Mean sea level pressure","parameter_ID":"151","product_type":"forecast","shortName":"msl","standard_name":"Mean_sea_level_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/151"},"description":"This parameter is the pressure (force per unit area) of the atmosphere adjusted to the height of mean sea level.It is a measure of the weight that all the air in a column vertically above the area of Earth's surface would have at that point, if the point were located at the mean sea level. It is calculated over all surfaces - land, sea and in-land water.Maps of mean sea level pressure are used to identify the locations of low and high pressure systems, often referred to as cyclones and anticyclones. Contours of mean sea level pressure also indicate the strength of the wind. Tightly packed contours show stronger winds.The units of this parameter are pascals (Pa).  Mean sea level pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb = 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Orography(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Orography","parameter_ID":"228002","product_type":"forecast","shortName":"orog","standard_name":"Orography","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/228002"},"dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Potential_vorticity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Potential vorticity","parameter_ID":"60","product_type":"forecast","shortName":"pv","standard_name":"Potential_vorticity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/60"},"description":"Potential vorticity is a measure of the capacity for air to rotate in the atmosphere. If we ignore the effects of heating and friction, potential vorticity is conserved following an air parcel. It is used to look for places where large wind storms are likely to originate and develop.  Potential vorticity increases strongly above the tropopause and therefore, it can also be used in studies related to the stratosphere and stratosphere-troposphere exchanges.Large wind storms develop when a column of air in the atmosphere starts to rotate. Potential vorticity is calculated from the wind, temperature and pressure across a column of air in the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K m2kg-1s-1"},"Relative_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Relative humidity","parameter_ID":"157","product_type":"forecast","shortName":"r","standard_name":"Relative_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/157"},"description":"This parameter is the water vapour pressure as a percentage of the value at which the air becomes saturated (the point at which water vapour begins to condense into liquid water or deposition into ice).For temperatures over 0°C (273.15 K) it is calculated for saturation over water. At temperatures below -23°C it is calculated for saturation over ice.  Between -23°C and 0°C this parameter is calculated by interpolating between the ice and water values using a quadratic function.See more information about the model's relative humidity calculation.","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Skin_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Skin temperature","parameter_ID":"235","product_type":"forecast","shortName":"skt","standard_name":"Skin_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235"},"description":"This parameter is the temperature of the surface of the Earth.The skin temperature is the theoretical temperature that is required to satisfy the surface energy balance. It represents the temperature of the uppermost surface layer, which has no heat capacity and so can respond instantaneously to changes in surface fluxes. Skin temperature is calculated differently over land and sea.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.See further information about the skin temperatureover landandover sea.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean skin temperature. The specific encoding for Mean skin temperature can be found in 235079.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Snow_depth_water_equivalent(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Snow depth water equivalent","parameter_ID":"228141","product_type":"forecast","shortName":"sd","standard_name":"Snow_depth_water_equivalent","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228141"},"description":"Snow depth water equivalent in kg m**-2 (mm) water equivalent.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean snow depth water equivalent. The specific encoding for Time-mean snow depth water equivalent can be found in 235078.[NOTE: See 141 for the equivalent parameter in \"m of water equivalent\"]","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Snow_depth_water_equivalent(clte_sol)":{"attrs":{"encoding":"instantaneous","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Snow depth water equivalent","parameter_ID":"228141","product_type":"forecast","shortName":"sd","standard_name":"Snow_depth_water_equivalent","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228141"},"description":"Snow depth water equivalent in kg m**-2 (mm) water equivalent.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean snow depth water equivalent. The specific encoding for Time-mean snow depth water equivalent can be found in 235078.[NOTE: See 141 for the equivalent parameter in \"m of water equivalent\"]","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Specific_cloud_liquid_water_content(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific cloud liquid water content","parameter_ID":"246","product_type":"forecast","shortName":"clwc","standard_name":"Specific_cloud_liquid_water_content","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/246"},"description":"This parameter is the mass of cloud liquid water droplets per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for agrid box.Water within clouds can be liquid or ice, or a combination of the two.See further information about the cloud formulation.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Specific_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific humidity","parameter_ID":"133","product_type":"forecast","shortName":"q","standard_name":"Specific_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/133"},"description":"This parameter is the mass of water vapour per kilogram of moist air.The total mass of moist air is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Surface_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Surface pressure","parameter_ID":"134","product_type":"forecast","shortName":"sp","standard_name":"Surface_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/134"},"description":"This parameter is the pressure (force per unit area) of the atmosphere on the surface of land, sea and in-land water.It is a measure of the weight of all the air in a column vertically above the area of the Earth's surface represented at a fixed point.Surface pressure is often used in combination with temperature to calculate air density.The strong variation of pressure with altitude makes it difficult to see the low and high pressure systems over mountainous areas, so mean sea level pressure, rather than surface pressure, is normally used for this purpose.The units of this parameter are Pascals (Pa). Surface pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb= 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Temperature(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Temperature","parameter_ID":"130","product_type":"forecast","shortName":"t","standard_name":"Temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/130"},"description":"This parameter is the temperature in the atmosphere.It has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.This parameter is available on multiple levels through the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_10_metre_U_wind_component(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre U wind component","parameter_ID":"235165","product_type":"forecast","shortName":"avg_10u","standard_name":"Time-mean_10_metre_U_wind_component","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235165"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_10_metre_V_wind_component(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre V wind component","parameter_ID":"235166","product_type":"forecast","shortName":"avg_10v","standard_name":"Time-mean_10_metre_V_wind_component","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235166"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_10_metre_wind_speed(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre wind speed","parameter_ID":"228005","product_type":"forecast","shortName":"avg_10ws","standard_name":"Time-mean_10_metre_wind_speed","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/228005"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_2_metre_dewpoint_temperature(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"2","levtype":"sfc","long_name":"Time-mean 2 metre dewpoint temperature","parameter_ID":"235168","product_type":"forecast","shortName":"avg_2d","standard_name":"Time-mean_2_metre_dewpoint_temperature","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235168"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_2_metre_temperature(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"2","levtype":"sfc","long_name":"Time-mean 2 metre temperature","parameter_ID":"228004","product_type":"forecast","shortName":"avg_2t","standard_name":"Time-mean_2_metre_temperature","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/228004"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_U_component_of_wind(clmn_hl)":{"attrs":{"encoding":"mean","levelist":"100","levtype":"hl","long_name":"Time-mean U component of wind","parameter_ID":"235131","product_type":"forecast","shortName":"avg_u","standard_name":"Time-mean_U_component_of_wind","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235131"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_U_component_of_wind(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean U component of wind","parameter_ID":"235131","product_type":"forecast","shortName":"avg_u","standard_name":"Time-mean_U_component_of_wind","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235131"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_V_component_of_wind(clmn_hl)":{"attrs":{"encoding":"mean","levelist":"100","levtype":"hl","long_name":"Time-mean V component of wind","parameter_ID":"235132","product_type":"forecast","shortName":"avg_v","standard_name":"Time-mean_V_component_of_wind","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235132"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_V_component_of_wind(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean V component of wind","parameter_ID":"235132","product_type":"forecast","shortName":"avg_v","standard_name":"Time-mean_V_component_of_wind","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235132"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_ice_velocity(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean eastward sea ice velocity","parameter_ID":"263003","product_type":"forecast","shortName":"avg_siue","standard_name":"Time-mean_eastward_sea_ice_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263003"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_ice_velocity(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean eastward sea ice velocity","parameter_ID":"263003","product_type":"forecast","shortName":"avg_siue","standard_name":"Time-mean_eastward_sea_ice_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263003"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_water_velocity(clmn_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean eastward sea water velocity","parameter_ID":"263506","product_type":"forecast","shortName":"avg_uoe","standard_name":"Time-mean_eastward_sea_water_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263506"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean eastward sea water velocity","parameter_ID":"263506","product_type":"forecast","shortName":"avg_uoe","standard_name":"Time-mean_eastward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263506"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_turbulent_surface_stress(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean eastward turbulent surface stress","parameter_ID":"235041","product_type":"forecast","shortName":"avg_iews","standard_name":"Time-mean_eastward_turbulent_surface_stress","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235041"},"dimensions":["lat","lon","time"],"type":"data","unit":"N m-2"},"Time-mean_eastward_turbulent_surface_stress(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean eastward turbulent surface stress","parameter_ID":"235041","product_type":"forecast","shortName":"avg_iews","standard_name":"Time-mean_eastward_turbulent_surface_stress","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235041"},"dimensions":["lat","lon","time"],"type":"data","unit":"N m-2"},"Time-mean_geopotential(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean geopotential","parameter_ID":"235129","product_type":"forecast","shortName":"avg_z","standard_name":"Time-mean_geopotential","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235129"},"dimensions":["lat","lon","time"],"type":"data","unit":"m2s-2"},"Time-mean_mean_sea_level_pressure(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean mean sea level pressure","parameter_ID":"235151","product_type":"forecast","shortName":"avg_msl","standard_name":"Time-mean_mean_sea_level_pressure","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235151"},"dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Time-mean_moisture_flux(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean moisture flux","parameter_ID":"235043","product_type":"forecast","shortName":"avg_ie","standard_name":"Time-mean_moisture_flux","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235043"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_moisture_flux(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean moisture flux","parameter_ID":"235043","product_type":"forecast","shortName":"avg_ie","standard_name":"Time-mean_moisture_flux","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235043"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_northward_sea_ice_velocity(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean northward sea ice velocity","parameter_ID":"263004","product_type":"forecast","shortName":"avg_sivn","standard_name":"Time-mean_northward_sea_ice_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263004"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_sea_ice_velocity(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean northward sea ice velocity","parameter_ID":"263004","product_type":"forecast","shortName":"avg_sivn","standard_name":"Time-mean_northward_sea_ice_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263004"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_sea_water_velocity(clmn_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean northward sea water velocity","parameter_ID":"263505","product_type":"forecast","shortName":"avg_von","standard_name":"Time-mean_northward_sea_water_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263505"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean northward sea water velocity","parameter_ID":"263505","product_type":"forecast","shortName":"avg_von","standard_name":"Time-mean_northward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263505"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_turbulent_surface_stress(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean northward turbulent surface stress","parameter_ID":"235042","product_type":"forecast","shortName":"avg_inss","standard_name":"Time-mean_northward_turbulent_surface_stress","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235042"},"dimensions":["lat","lon","time"],"type":"data","unit":"N m-2"},"Time-mean_northward_turbulent_surface_stress(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean northward turbulent surface stress","parameter_ID":"235042","product_type":"forecast","shortName":"avg_inss","standard_name":"Time-mean_northward_turbulent_surface_stress","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235042"},"dimensions":["lat","lon","time"],"type":"data","unit":"N m-2"},"Time-mean_potential_vorticity(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean potential vorticity","parameter_ID":"235100","product_type":"forecast","shortName":"avg_pv","standard_name":"Time-mean_potential_vorticity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235100"},"dimensions":["lat","lon","time"],"type":"data","unit":"K m2kg-1s-1"},"Time-mean_relative_humidity(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean relative humidity","parameter_ID":"235157","product_type":"forecast","shortName":"avg_r","standard_name":"Time-mean_relative_humidity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235157"},"dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Time-mean_sea_ice_area_fraction(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea ice area fraction","parameter_ID":"263001","product_type":"forecast","shortName":"avg_siconc","standard_name":"Time-mean_sea_ice_area_fraction","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263001"},"dimensions":["lat","lon","time"],"type":"data","unit":"Fraction"},"Time-mean_sea_ice_area_fraction(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea ice area fraction","parameter_ID":"263001","product_type":"forecast","shortName":"avg_siconc","standard_name":"Time-mean_sea_ice_area_fraction","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263001"},"dimensions":["lat","lon","time"],"type":"data","unit":"Fraction"},"Time-mean_sea_ice_thickness(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea ice 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sky","parameter_ID":"235049","product_type":"forecast","shortName":"avg_tnswrfcs","standard_name":"Time-mean_top_net_short-wave_radiation_flux,_clear_sky","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235049"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Time-mean_top_net_short-wave_radiation_flux,_clear_sky(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean top net short-wave radiation flux, clear sky","parameter_ID":"235049","product_type":"forecast","shortName":"avg_tnswrfcs","standard_name":"Time-mean_top_net_short-wave_radiation_flux,_clear_sky","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235049"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Time-mean_total_cloud_cover(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total cloud cover","parameter_ID":"235288","product_type":"forecast","shortName":"avg_tcc","standard_name":"Time-mean_total_cloud_cover","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235288"},"dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Time-mean_total_column_cloud_ice_water(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total column cloud ice water","parameter_ID":"235088","product_type":"forecast","shortName":"avg_tciw","standard_name":"Time-mean_total_column_cloud_ice_water","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235088"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Time-mean_total_column_heat_content(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean total column heat content","parameter_ID":"263123","product_type":"forecast","shortName":"avg_hcbtm","standard_name":"Time-mean_total_column_heat_content","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263123"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_total_column_heat_content(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean total column heat content","parameter_ID":"263123","product_type":"forecast","shortName":"avg_hcbtm","standard_name":"Time-mean_total_column_heat_content","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263123"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_total_column_liquid_water(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total column liquid water","parameter_ID":"235087","product_type":"forecast","shortName":"avg_tclw","standard_name":"Time-mean_total_column_liquid_water","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235087"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Time-mean_total_column_vertically-integrated_water_vapour(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total column vertically-integrated water vapour","parameter_ID":"235137","product_type":"forecast","shortName":"avg_tcwv","standard_name":"Time-mean_total_column_vertically-integrated_water_vapour","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235137"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Time-mean_total_column_water(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total column water","parameter_ID":"235136","product_type":"forecast","shortName":"avg_tcw","standard_name":"Time-mean_total_column_water","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235136"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Time-mean_total_precipitation_rate(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total precipitation rate","parameter_ID":"235055","product_type":"forecast","shortName":"avg_tprate","standard_name":"Time-mean_total_precipitation_rate","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235055"},"description":"Time-mean total precipitation rate, or time-mean total precipitation flux. This parameter is on level \"Ground or water surface\" (typeOfFirstFixedSurface=1). For this parameter on other levels, please use 235013.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_total_precipitation_rate(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total precipitation rate","parameter_ID":"235055","product_type":"forecast","shortName":"avg_tprate","standard_name":"Time-mean_total_precipitation_rate","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235055"},"description":"Time-mean total precipitation rate, or time-mean total precipitation flux. This parameter is on level \"Ground or water surface\" (typeOfFirstFixedSurface=1). For this parameter on other levels, please use 235013.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_total_snowfall_rate_water_equivalent(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total snowfall rate water equivalent","parameter_ID":"235031","product_type":"forecast","shortName":"avg_tsrwe","standard_name":"Time-mean_total_snowfall_rate_water_equivalent","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235031"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_total_snowfall_rate_water_equivalent(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total snowfall rate water equivalent","parameter_ID":"235031","product_type":"forecast","shortName":"avg_tsrwe","standard_name":"Time-mean_total_snowfall_rate_water_equivalent","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235031"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_upward_sea_water_velocity(clmn_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean upward sea water velocity","parameter_ID":"263507","product_type":"forecast","shortName":"avg_wo","standard_name":"Time-mean_upward_sea_water_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263507"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_upward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean upward sea water velocity","parameter_ID":"263507","product_type":"forecast","shortName":"avg_wo","standard_name":"Time-mean_upward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263507"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_vertical_velocity(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean vertical velocity","parameter_ID":"235135","product_type":"forecast","shortName":"avg_w","standard_name":"Time-mean_vertical_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235135"},"dimensions":["lat","lon","time"],"type":"data","unit":"Pa s-1"},"Time-mean_vertically-integrated_heat_content_in_the_upper_300_m(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"300","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 300 m","parameter_ID":"263121","product_type":"forecast","shortName":"avg_hc300m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_300_m","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263121"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_vertically-integrated_heat_content_in_the_upper_300_m(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"300","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 300 m","parameter_ID":"263121","product_type":"forecast","shortName":"avg_hc300m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_300_m","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263121"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"700","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 700 m","parameter_ID":"263122","product_type":"forecast","shortName":"avg_hc700m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263122"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"700","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 700 m","parameter_ID":"263122","product_type":"forecast","shortName":"avg_hc700m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263122"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_volumetric_soil_moisture(clmn_sol)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Time-mean volumetric soil moisture","parameter_ID":"235077","product_type":"forecast","shortName":"avg_vsw","standard_name":"Time-mean_volumetric_soil_moisture","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235077"},"dimensions":["lat","lon","time"],"type":"data","unit":"m3m-3"},"Time_mean_top_downward_short-wave_radiation_flux(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time mean top downward short-wave radiation flux","parameter_ID":"235053","product_type":"forecast","shortName":"avg_tdswrf","standard_name":"Time_mean_top_downward_short-wave_radiation_flux","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235053"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Time_mean_top_downward_short-wave_radiation_flux(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time mean top downward short-wave radiation flux","parameter_ID":"235053","product_type":"forecast","shortName":"avg_tdswrf","standard_name":"Time_mean_top_downward_short-wave_radiation_flux","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235053"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Total_Cloud_Cover(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total Cloud Cover","parameter_ID":"228164","product_type":"forecast","shortName":"tcc","standard_name":"Total_Cloud_Cover","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228164"},"description":"[NOTE: See 164 for the equivalent parameter in \"(0-1)\"]","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Total_column_cloud_ice_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud ice water","parameter_ID":"79","product_type":"forecast","shortName":"tciw","standard_name":"Total_column_cloud_ice_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/79"},"description":"This parameter is the amount of ice contained within clouds in a column extending from the surface of the Earth to the top of the atmosphere. Snow (aggregated ice crystals) is not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The  ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_cloud_liquid_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud liquid water","parameter_ID":"78","product_type":"forecast","shortName":"tclw","standard_name":"Total_column_cloud_liquid_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/78"},"description":"This parameter is the amount of liquid water contained within cloud droplets in a column extending from the surface of the Earth to the top of the atmosphere. Rain water droplets, which are much larger in size (and mass), are not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_vertically-integrated_water_vapour(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column vertically-integrated water vapour","parameter_ID":"137","product_type":"forecast","shortName":"tcwv","standard_name":"Total_column_vertically-integrated_water_vapour","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/137"},"description":"This parameter is the total amount of water vapour in a column extending from the surface of the Earth to the top of the atmosphere.This parameter represents the area averaged value for agrid box.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column water","parameter_ID":"136","product_type":"forecast","shortName":"tcw","standard_name":"Total_column_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/136"},"description":"This parameter is the sum of water vapour, liquid water, cloud ice, rain and snow in a column extending from the surface of the Earth to the top of the atmosphere. In old versions of the ECMWF model (IFS), rain and snow were not accounted for.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"U_component_of_wind(clte_hl)":{"attrs":{"encoding":"instantaneous","levelist":"100","levtype":"hl","long_name":"U component of wind","parameter_ID":"131","product_type":"forecast","shortName":"u","standard_name":"U_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/131"},"description":"This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east, in metres per second. A negative sign thus indicates air movement towards the west.This parameter can be combined with the V component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"U_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"U component of wind","parameter_ID":"131","product_type":"forecast","shortName":"u","standard_name":"U_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/131"},"description":"This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east, in metres per second. A negative sign thus indicates air movement towards the west.This parameter can be combined with the V component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"V_component_of_wind(clte_hl)":{"attrs":{"encoding":"instantaneous","levelist":"100","levtype":"hl","long_name":"V component of wind","parameter_ID":"132","product_type":"forecast","shortName":"v","standard_name":"V_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/132"},"description":"This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north, in metres per second. A negative sign thus indicates air movement towards the south.This parameter can be combined with the U component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"V_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"V component of wind","parameter_ID":"132","product_type":"forecast","shortName":"v","standard_name":"V_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/132"},"description":"This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north, in metres per second. A negative sign thus indicates air movement towards the south.This parameter can be combined with the U component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Vertical_velocity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Vertical velocity","parameter_ID":"135","product_type":"forecast","shortName":"w","standard_name":"Vertical_velocity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/135"},"description":"This parameter is the speed of air motion in the upward or downward direction. The ECMWF Integrated Forecasting System (IFS) uses a pressure based vertical co-ordinate system and pressure decreases with height, therefore negative values of vertical velocity indicate upward motion.Vertical velocity can be useful to understand the large-scale dynamics of the atmosphere, including areas of upward motion/ascent (negative values) and downward motion/subsidence (positive values).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa s-1"},"Volumetric_soil_moisture(clte_sol)":{"attrs":{"encoding":"instantaneous","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Volumetric soil moisture","parameter_ID":"260199","product_type":"forecast","shortName":"vsw","standard_name":"Volumetric_soil_moisture","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/260199"},"description":"Please note that the encoding listed here for uerra (which includes carra/cerra) includes entries for Time-mean volumetric soil moisture. The specific encoding for Time-mean volumetric soil moisture can be found in 235077.","dimensions":["lat","lon","time"],"type":"data","unit":"m3m-3"}},"sci:publications":[{"doi":"10.1109/MCSE.2023.3260519","citation":"N. Wedi et al., Destination Earth: High-Performance Computing for Weather and Climate, in Computing in Science \u0026 Engineering, vol. 24, no. 6, pp. 29-37, Nov.-Dec. 2022,"},{"doi":"10.21957/d3f982672e","citation":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"dedl:short_description":"The Climate Change Adaptation Digital Twin provides global climate projections and sector-specific information over multiple decades at high resolution via a unified framework combining advanced Earth system models, impact assessments, and observations. This Generation-2, Realization-3 Collection gives access to 'Storyline Simulation Past Climate' data based on the 'IFS-FESOM' model."},{"type":"Collection","title":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Storyline Simulation Past Climate - IFS-FESOM - Generation-2 - Realization-4","id":"EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_HIST_IFS-FESOM.R4","description":"The DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities by providing innovative climate information on multi-decadal timescales, globally, at scales at which many impacts of climate change are observed. It combines cutting-edge global Earth-system models, impact-sector applications and observations into a unified framework to provide global climate projections and impact-sector information on multi-decadal timescales (1990 to ~2050), at very high spatial resolutions (5 to 10 km).\n\nThe Climate DT represents the first ever attempt to operationalise the production of global multi-decadal climate projections, leveraging the world-leading supercomputing facilities of the EuroHPC Joint Undertaking along with some of the leading European climate models. A concise overview of what the Climate DT aims to achieve, and of the different concepts essential for an understanding of the Digital Twin’s characteristics, is included in the [Climate DT factsheet](https://destine.ecmwf.int/wp-content/uploads/2024/06/2024.06.07_Climate-DT-Fact-Sheet_V7-2.pdf)\n\n## Storyline Simulation Past Climate \n\n Storyline simulations offer a what-if capability to explore how a weather event we experienced in the recent past would change in a warmer climate and how it would have looked in past pre-industrial conditions. To constrain simulated weather events, a nudging approach is used to keep the simulated large-scale flow close to the ERA5 reanalysis for the period 2017 – 2023. Processes on smaller scales and thermodynamic processes are free to evolve, which allows scientists to study e.g. how extreme precipitation would change in a warmer world for a weather event observed in present-day conditions. Initial conditions are taken from ocean-only spinup simulations for 1950 and 2017, and from the IFS-FESOM projection for a +2 °C world. Storyline simulations will be updated regularly to include most recent months. The next update will extend all storylines to include 2024.\n\n## Models\n\nThe Climate DT exploits and further evolves a new generation of global storm-resolving and eddy-rich models built through a cooperative model development approach. For more information on models please click [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#models)\n\n## Simulations\n\nThe Climate DT team carries out several types of digital twin simulations on the EuroHPC supercomputers.  Multi-decadal simulations are produced to cover the recent past (from 1990) and possible future evolutions of the climate up to 2050. See [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#simulations) for more information on Simulations\n\n## Parameters\n\nBelow we see the list of parameters extracted from the 'DestinE Climate DT data portfolio', for more information please refer to the pages [Climate DT Phase2 CLTE Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters) and [Climate DT Phase2 CLMN Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters)","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_HIST_IFS-FESOM.R4/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_HIST_IFS-FESOM.R4/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_HIST_IFS-FESOM.R4/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_HIST_IFS-FESOM.R4","title":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Storyline Simulation Past Climate - IFS-FESOM - Generation-2 - Realization-4"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters","title":"DestinE ClimateDT Phase 2 CLTE Parameters"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters","title":"DestinE ClimateDT Phase 2 CLMN Parameters"},{"rel":"cite-as","type":"text/html","href":"https://dl.acm.org/doi/abs/10.1109/MCSE.2023.3260519","title":"Destination Earth: High-Performance Computing for Weather and Climate"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.21957/d3f982672e","title":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"assets":{"thumbnail":{"href":"https://s3.central.data.destination-earth.eu/swift/v1/dedl-public/collections/climate-dt-min.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2017-01-01T00:00:00Z","2025-12-31T23:59:59Z"]]}},"license":"CC-BY-4.0","keywords":["Digital Twins","Climate Change","Atmosphere","Ocean","Land","Sea Ice","Snow","Soil","Earth","High Performance Computing","Decision Making","Europe","Meteorology","class:d1","dataset:climate-dt","generation:2","expver:0001","stream:clmn,clte","type:fc","activity:story-nudging","experiment:hist","realization:4","model:IFS-FESOM","resolution:standard,high","levtype:hl,o2d,o3d,pl,sfc,sol"],"summaries":{"federation:backends":["dedt_mn5"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/datacube/v2.1.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["producer","processor","licensor"],"url":"https://www.ecmwf.int/"}],"created":"2024-04-11T22:31:55Z","updated":"2026-04-24T10:24:59Z","published":"2024-04-11T22:31:55Z","cube:dimensions":{"lat":{"axis":"y","description":"latitude","extent":[-90,90],"type":"spatial"},"lon":{"axis":"x","description":"longitude","extent":[-180,180],"type":"spatial"},"time":{"extent":["2017-01-01T00:00:00Z","2025-12-31T23:59:59Z"],"step":"P0Y0M0DT1H0M0S","type":"temporal"}},"cube:variables":{"10_metre_U_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre U wind component","parameter_ID":"165","product_type":"forecast","shortName":"10u","standard_name":"10_metre_U_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/165"},"description":"This parameter is the eastward component of the 10m wind. It is the horizontal speed of air moving towards the east, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the V component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_V_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre V wind component","parameter_ID":"166","product_type":"forecast","shortName":"10v","standard_name":"10_metre_V_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/166"},"description":"This parameter is the northward component of the 10m wind. It is the horizontal speed of air moving towards the north, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the U component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_wind_speed(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre wind speed","parameter_ID":"207","product_type":"forecast","shortName":"10si","standard_name":"10_metre_wind_speed","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/207"},"description":"This parameter is the horizontal speed of the wind, or movement of air, at a height of ten metres above the surface of the Earth. The units of this parameter are metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.The eastward and northward components of the horizontal wind at 10m are also available as parameters.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"2_metre_dewpoint_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"2","levtype":"sfc","long_name":"2 metre dewpoint temperature","parameter_ID":"168","product_type":"forecast","shortName":"2d","standard_name":"2_metre_dewpoint_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/168"},"description":"This parameter is the temperature to which the air, at 2 metres above the surface of the Earth, would have to be cooled for saturation to occur.It is a measure of the humidity of the air. Combined with temperature and pressure, it can be used to calculate the relative humidity.2m dew point temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"2_metre_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"2","levtype":"sfc","long_name":"2 metre temperature","parameter_ID":"167","product_type":"forecast","shortName":"2t","standard_name":"2_metre_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/167"},"description":"This parameter is the temperature of air at 2m above the surface of land, sea or in-land waters.2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.Please note that the encodings listed here for s2s \u0026 uerra (which includes encodings for carra/cerra) include entries for Mean 2 metre temperature. The specific encoding for Mean 2 metre temperature can be found in 228004.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Geopotential(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Geopotential","parameter_ID":"129","product_type":"forecast","shortName":"z","standard_name":"Geopotential","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/129"},"description":"This parameter is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level.The geopotential height can be calculated by dividing the geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2). The geopotential height plays an important role in synoptic meteorology (analysis of weather patterns). Charts of geopotential height plotted at constant pressure levels (e.g., 300, 500 or 850 hPa) can be used to identify weather systems such as cyclones, anticyclones, troughs and ridges.At the surface of the Earth, this parameter shows the variations in geopotential (height) of the surface, and is often referred to as the orography.","dimensions":["lat","lon","time"],"type":"data","unit":"m2s-2"},"Land-sea_mask(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Land-sea mask","parameter_ID":"172","product_type":"forecast","shortName":"lsm","standard_name":"Land-sea_mask","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/172"},"description":"This parameter is the proportion of land, as opposed to ocean or inland waters (lakes, reservoirs, rivers and coastal waters), in agrid box.This parameter has values ranging between zero and one and is dimensionless.In cycles of the ECMWF Integrated Forecasting System (IFS) from CY41R1 (introduced in May 2015) onwards, grid boxes where this parameter has a value above 0.5 can be comprised of a mixture of land and inland water but not ocean. Grid boxes with a value of 0.5 and below can only be comprised of a water surface. In the latter case, the lake cover is used to determine how much of the water surface is ocean or inland water.In cycles of the IFS before CY41R1, grid boxes where this parameter has a value above 0.5 can only be comprised of land and those grid boxes with a value of 0.5 and below can only be comprised of ocean. In these older model cycles, there is no differentiation between ocean and inland water.","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Mean_sea_level_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Mean sea level pressure","parameter_ID":"151","product_type":"forecast","shortName":"msl","standard_name":"Mean_sea_level_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/151"},"description":"This parameter is the pressure (force per unit area) of the atmosphere adjusted to the height of mean sea level.It is a measure of the weight that all the air in a column vertically above the area of Earth's surface would have at that point, if the point were located at the mean sea level. It is calculated over all surfaces - land, sea and in-land water.Maps of mean sea level pressure are used to identify the locations of low and high pressure systems, often referred to as cyclones and anticyclones. Contours of mean sea level pressure also indicate the strength of the wind. Tightly packed contours show stronger winds.The units of this parameter are pascals (Pa).  Mean sea level pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb = 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Orography(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Orography","parameter_ID":"228002","product_type":"forecast","shortName":"orog","standard_name":"Orography","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/228002"},"dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Potential_vorticity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Potential vorticity","parameter_ID":"60","product_type":"forecast","shortName":"pv","standard_name":"Potential_vorticity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/60"},"description":"Potential vorticity is a measure of the capacity for air to rotate in the atmosphere. If we ignore the effects of heating and friction, potential vorticity is conserved following an air parcel. It is used to look for places where large wind storms are likely to originate and develop.  Potential vorticity increases strongly above the tropopause and therefore, it can also be used in studies related to the stratosphere and stratosphere-troposphere exchanges.Large wind storms develop when a column of air in the atmosphere starts to rotate. Potential vorticity is calculated from the wind, temperature and pressure across a column of air in the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K m2kg-1s-1"},"Relative_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Relative humidity","parameter_ID":"157","product_type":"forecast","shortName":"r","standard_name":"Relative_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/157"},"description":"This parameter is the water vapour pressure as a percentage of the value at which the air becomes saturated (the point at which water vapour begins to condense into liquid water or deposition into ice).For temperatures over 0°C (273.15 K) it is calculated for saturation over water. At temperatures below -23°C it is calculated for saturation over ice.  Between -23°C and 0°C this parameter is calculated by interpolating between the ice and water values using a quadratic function.See more information about the model's relative humidity calculation.","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Skin_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Skin temperature","parameter_ID":"235","product_type":"forecast","shortName":"skt","standard_name":"Skin_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235"},"description":"This parameter is the temperature of the surface of the Earth.The skin temperature is the theoretical temperature that is required to satisfy the surface energy balance. It represents the temperature of the uppermost surface layer, which has no heat capacity and so can respond instantaneously to changes in surface fluxes. Skin temperature is calculated differently over land and sea.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.See further information about the skin temperatureover landandover sea.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean skin temperature. The specific encoding for Mean skin temperature can be found in 235079.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Snow_depth_water_equivalent(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Snow depth water equivalent","parameter_ID":"228141","product_type":"forecast","shortName":"sd","standard_name":"Snow_depth_water_equivalent","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228141"},"description":"Snow depth water equivalent in kg m**-2 (mm) water equivalent.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean snow depth water equivalent. The specific encoding for Time-mean snow depth water equivalent can be found in 235078.[NOTE: See 141 for the equivalent parameter in \"m of water equivalent\"]","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Snow_depth_water_equivalent(clte_sol)":{"attrs":{"encoding":"instantaneous","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Snow depth water equivalent","parameter_ID":"228141","product_type":"forecast","shortName":"sd","standard_name":"Snow_depth_water_equivalent","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228141"},"description":"Snow depth water equivalent in kg m**-2 (mm) water equivalent.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean snow depth water equivalent. The specific encoding for Time-mean snow depth water equivalent can be found in 235078.[NOTE: See 141 for the equivalent parameter in \"m of water equivalent\"]","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Specific_cloud_liquid_water_content(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific cloud liquid water content","parameter_ID":"246","product_type":"forecast","shortName":"clwc","standard_name":"Specific_cloud_liquid_water_content","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/246"},"description":"This parameter is the mass of cloud liquid water droplets per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for agrid box.Water within clouds can be liquid or ice, or a combination of the two.See further information about the cloud formulation.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Specific_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific humidity","parameter_ID":"133","product_type":"forecast","shortName":"q","standard_name":"Specific_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/133"},"description":"This parameter is the mass of water vapour per kilogram of moist air.The total mass of moist air is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Surface_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Surface pressure","parameter_ID":"134","product_type":"forecast","shortName":"sp","standard_name":"Surface_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/134"},"description":"This parameter is the pressure (force per unit area) of the atmosphere on the surface of land, sea and in-land water.It is a measure of the weight of all the air in a column vertically above the area of the Earth's surface represented at a fixed point.Surface pressure is often used in combination with temperature to calculate air density.The strong variation of pressure with altitude makes it difficult to see the low and high pressure systems over mountainous areas, so mean sea level pressure, rather than surface pressure, is normally used for this purpose.The units of this parameter are Pascals (Pa). Surface pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb= 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Temperature(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Temperature","parameter_ID":"130","product_type":"forecast","shortName":"t","standard_name":"Temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/130"},"description":"This parameter is the temperature in the atmosphere.It has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.This parameter is available on multiple levels through the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_10_metre_U_wind_component(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre U wind component","parameter_ID":"235165","product_type":"forecast","shortName":"avg_10u","standard_name":"Time-mean_10_metre_U_wind_component","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235165"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_10_metre_V_wind_component(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre V wind component","parameter_ID":"235166","product_type":"forecast","shortName":"avg_10v","standard_name":"Time-mean_10_metre_V_wind_component","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235166"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_10_metre_wind_speed(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre wind speed","parameter_ID":"228005","product_type":"forecast","shortName":"avg_10ws","standard_name":"Time-mean_10_metre_wind_speed","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/228005"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_2_metre_dewpoint_temperature(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"2","levtype":"sfc","long_name":"Time-mean 2 metre dewpoint temperature","parameter_ID":"235168","product_type":"forecast","shortName":"avg_2d","standard_name":"Time-mean_2_metre_dewpoint_temperature","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235168"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_2_metre_temperature(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"2","levtype":"sfc","long_name":"Time-mean 2 metre temperature","parameter_ID":"228004","product_type":"forecast","shortName":"avg_2t","standard_name":"Time-mean_2_metre_temperature","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/228004"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_U_component_of_wind(clmn_hl)":{"attrs":{"encoding":"mean","levelist":"100","levtype":"hl","long_name":"Time-mean U component of wind","parameter_ID":"235131","product_type":"forecast","shortName":"avg_u","standard_name":"Time-mean_U_component_of_wind","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235131"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_U_component_of_wind(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean U component of wind","parameter_ID":"235131","product_type":"forecast","shortName":"avg_u","standard_name":"Time-mean_U_component_of_wind","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235131"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_V_component_of_wind(clmn_hl)":{"attrs":{"encoding":"mean","levelist":"100","levtype":"hl","long_name":"Time-mean V component of 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m-2"},"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"700","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 700 m","parameter_ID":"263122","product_type":"forecast","shortName":"avg_hc700m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263122"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_volumetric_soil_moisture(clmn_sol)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Time-mean volumetric soil moisture","parameter_ID":"235077","product_type":"forecast","shortName":"avg_vsw","standard_name":"Time-mean_volumetric_soil_moisture","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235077"},"dimensions":["lat","lon","time"],"type":"data","unit":"m3m-3"},"Time_mean_top_downward_short-wave_radiation_flux(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time mean top downward short-wave radiation flux","parameter_ID":"235053","product_type":"forecast","shortName":"avg_tdswrf","standard_name":"Time_mean_top_downward_short-wave_radiation_flux","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235053"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Time_mean_top_downward_short-wave_radiation_flux(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time mean top downward short-wave radiation flux","parameter_ID":"235053","product_type":"forecast","shortName":"avg_tdswrf","standard_name":"Time_mean_top_downward_short-wave_radiation_flux","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235053"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Total_Cloud_Cover(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total Cloud Cover","parameter_ID":"228164","product_type":"forecast","shortName":"tcc","standard_name":"Total_Cloud_Cover","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228164"},"description":"[NOTE: See 164 for the equivalent parameter in \"(0-1)\"]","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Total_column_cloud_ice_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud ice water","parameter_ID":"79","product_type":"forecast","shortName":"tciw","standard_name":"Total_column_cloud_ice_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/79"},"description":"This parameter is the amount of ice contained within clouds in a column extending from the surface of the Earth to the top of the atmosphere. Snow (aggregated ice crystals) is not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The  ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_cloud_liquid_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud liquid water","parameter_ID":"78","product_type":"forecast","shortName":"tclw","standard_name":"Total_column_cloud_liquid_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/78"},"description":"This parameter is the amount of liquid water contained within cloud droplets in a column extending from the surface of the Earth to the top of the atmosphere. Rain water droplets, which are much larger in size (and mass), are not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_vertically-integrated_water_vapour(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column vertically-integrated water vapour","parameter_ID":"137","product_type":"forecast","shortName":"tcwv","standard_name":"Total_column_vertically-integrated_water_vapour","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/137"},"description":"This parameter is the total amount of water vapour in a column extending from the surface of the Earth to the top of the atmosphere.This parameter represents the area averaged value for agrid box.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column water","parameter_ID":"136","product_type":"forecast","shortName":"tcw","standard_name":"Total_column_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/136"},"description":"This parameter is the sum of water vapour, liquid water, cloud ice, rain and snow in a column extending from the surface of the Earth to the top of the atmosphere. In old versions of the ECMWF model (IFS), rain and snow were not accounted for.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"U_component_of_wind(clte_hl)":{"attrs":{"encoding":"instantaneous","levelist":"100","levtype":"hl","long_name":"U component of wind","parameter_ID":"131","product_type":"forecast","shortName":"u","standard_name":"U_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/131"},"description":"This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east, in metres per second. A negative sign thus indicates air movement towards the west.This parameter can be combined with the V component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"U_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"U component of wind","parameter_ID":"131","product_type":"forecast","shortName":"u","standard_name":"U_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/131"},"description":"This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east, in metres per second. A negative sign thus indicates air movement towards the west.This parameter can be combined with the V component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"V_component_of_wind(clte_hl)":{"attrs":{"encoding":"instantaneous","levelist":"100","levtype":"hl","long_name":"V component of wind","parameter_ID":"132","product_type":"forecast","shortName":"v","standard_name":"V_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/132"},"description":"This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north, in metres per second. A negative sign thus indicates air movement towards the south.This parameter can be combined with the U component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"V_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"V component of wind","parameter_ID":"132","product_type":"forecast","shortName":"v","standard_name":"V_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/132"},"description":"This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north, in metres per second. A negative sign thus indicates air movement towards the south.This parameter can be combined with the U component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Vertical_velocity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Vertical velocity","parameter_ID":"135","product_type":"forecast","shortName":"w","standard_name":"Vertical_velocity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/135"},"description":"This parameter is the speed of air motion in the upward or downward direction. The ECMWF Integrated Forecasting System (IFS) uses a pressure based vertical co-ordinate system and pressure decreases with height, therefore negative values of vertical velocity indicate upward motion.Vertical velocity can be useful to understand the large-scale dynamics of the atmosphere, including areas of upward motion/ascent (negative values) and downward motion/subsidence (positive values).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa s-1"},"Volumetric_soil_moisture(clte_sol)":{"attrs":{"encoding":"instantaneous","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Volumetric soil moisture","parameter_ID":"260199","product_type":"forecast","shortName":"vsw","standard_name":"Volumetric_soil_moisture","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/260199"},"description":"Please note that the encoding listed here for uerra (which includes carra/cerra) includes entries for Time-mean volumetric soil moisture. The specific encoding for Time-mean volumetric soil moisture can be found in 235077.","dimensions":["lat","lon","time"],"type":"data","unit":"m3m-3"}},"sci:publications":[{"doi":"10.1109/MCSE.2023.3260519","citation":"N. Wedi et al., Destination Earth: High-Performance Computing for Weather and Climate, in Computing in Science \u0026 Engineering, vol. 24, no. 6, pp. 29-37, Nov.-Dec. 2022,"},{"doi":"10.21957/d3f982672e","citation":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"dedl:short_description":"The Climate Change Adaptation Digital Twin provides global climate projections and sector-specific information over multiple decades at high resolution via a unified framework combining advanced Earth system models, impact assessments, and observations. This Generation-2, Realization-4 Collection gives access to 'Storyline Simulation Past Climate' data based on the 'IFS-FESOM' model."},{"type":"Collection","title":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Storyline Simulation Past Climate - IFS-FESOM - Generation-2 - Realization-5","id":"EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_HIST_IFS-FESOM.R5","description":"The DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities by providing innovative climate information on multi-decadal timescales, globally, at scales at which many impacts of climate change are observed. It combines cutting-edge global Earth-system models, impact-sector applications and observations into a unified framework to provide global climate projections and impact-sector information on multi-decadal timescales (1990 to ~2050), at very high spatial resolutions (5 to 10 km).\n\nThe Climate DT represents the first ever attempt to operationalise the production of global multi-decadal climate projections, leveraging the world-leading supercomputing facilities of the EuroHPC Joint Undertaking along with some of the leading European climate models. A concise overview of what the Climate DT aims to achieve, and of the different concepts essential for an understanding of the Digital Twin’s characteristics, is included in the [Climate DT factsheet](https://destine.ecmwf.int/wp-content/uploads/2024/06/2024.06.07_Climate-DT-Fact-Sheet_V7-2.pdf)\n\n## Storyline Simulation Past Climate \n\n Storyline simulations offer a what-if capability to explore how a weather event we experienced in the recent past would change in a warmer climate and how it would have looked in past pre-industrial conditions. To constrain simulated weather events, a nudging approach is used to keep the simulated large-scale flow close to the ERA5 reanalysis for the period 2017 – 2023. Processes on smaller scales and thermodynamic processes are free to evolve, which allows scientists to study e.g. how extreme precipitation would change in a warmer world for a weather event observed in present-day conditions. Initial conditions are taken from ocean-only spinup simulations for 1950 and 2017, and from the IFS-FESOM projection for a +2 °C world. Storyline simulations will be updated regularly to include most recent months. The next update will extend all storylines to include 2024.\n\n## Models\n\nThe Climate DT exploits and further evolves a new generation of global storm-resolving and eddy-rich models built through a cooperative model development approach. For more information on models please click [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#models)\n\n## Simulations\n\nThe Climate DT team carries out several types of digital twin simulations on the EuroHPC supercomputers.  Multi-decadal simulations are produced to cover the recent past (from 1990) and possible future evolutions of the climate up to 2050. See [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#simulations) for more information on Simulations\n\n## Parameters\n\nBelow we see the list of parameters extracted from the 'DestinE Climate DT data portfolio', for more information please refer to the pages [Climate DT Phase2 CLTE Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters) and [Climate DT Phase2 CLMN Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters)","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_HIST_IFS-FESOM.R5/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_HIST_IFS-FESOM.R5/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_HIST_IFS-FESOM.R5/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_HIST_IFS-FESOM.R5","title":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Storyline Simulation Past Climate - IFS-FESOM - Generation-2 - Realization-5"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters","title":"DestinE ClimateDT Phase 2 CLTE Parameters"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters","title":"DestinE ClimateDT Phase 2 CLMN Parameters"},{"rel":"cite-as","type":"text/html","href":"https://dl.acm.org/doi/abs/10.1109/MCSE.2023.3260519","title":"Destination Earth: High-Performance Computing for Weather and Climate"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.21957/d3f982672e","title":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"assets":{"thumbnail":{"href":"https://s3.central.data.destination-earth.eu/swift/v1/dedl-public/collections/climate-dt-min.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2017-01-01T00:00:00Z","2025-12-31T23:59:59Z"]]}},"license":"CC-BY-4.0","keywords":["Digital Twins","Climate Change","Atmosphere","Ocean","Land","Sea Ice","Snow","Soil","Earth","High Performance Computing","Decision Making","Europe","Meteorology","class:d1","dataset:climate-dt","generation:2","expver:0001","stream:clmn,clte","type:fc","activity:story-nudging","experiment:hist","realization:5","model:IFS-FESOM","resolution:standard,high","levtype:hl,o2d,o3d,pl,sfc,sol"],"summaries":{"federation:backends":["dedt_mn5"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/datacube/v2.1.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["producer","processor","licensor"],"url":"https://www.ecmwf.int/"}],"created":"2024-04-11T22:31:55Z","updated":"2026-04-24T10:24:59Z","published":"2024-04-11T22:31:55Z","cube:dimensions":{"lat":{"axis":"y","description":"latitude","extent":[-90,90],"type":"spatial"},"lon":{"axis":"x","description":"longitude","extent":[-180,180],"type":"spatial"},"time":{"extent":["2017-01-01T00:00:00Z","2025-12-31T23:59:59Z"],"step":"P0Y0M0DT1H0M0S","type":"temporal"}},"cube:variables":{"10_metre_U_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre U wind component","parameter_ID":"165","product_type":"forecast","shortName":"10u","standard_name":"10_metre_U_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/165"},"description":"This parameter is the eastward component of the 10m wind. It is the horizontal speed of air moving towards the east, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the V component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_V_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre V wind component","parameter_ID":"166","product_type":"forecast","shortName":"10v","standard_name":"10_metre_V_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/166"},"description":"This parameter is the northward component of the 10m wind. It is the horizontal speed of air moving towards the north, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the U component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_wind_speed(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre wind speed","parameter_ID":"207","product_type":"forecast","shortName":"10si","standard_name":"10_metre_wind_speed","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/207"},"description":"This parameter is the horizontal speed of the wind, or movement of air, at a height of ten metres above the surface of the Earth. The units of this parameter are metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.The eastward and northward components of the horizontal wind at 10m are also available as parameters.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"2_metre_dewpoint_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"2","levtype":"sfc","long_name":"2 metre dewpoint temperature","parameter_ID":"168","product_type":"forecast","shortName":"2d","standard_name":"2_metre_dewpoint_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/168"},"description":"This parameter is the temperature to which the air, at 2 metres above the surface of the Earth, would have to be cooled for saturation to occur.It is a measure of the humidity of the air. Combined with temperature and pressure, it can be used to calculate the relative humidity.2m dew point temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"2_metre_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"2","levtype":"sfc","long_name":"2 metre temperature","parameter_ID":"167","product_type":"forecast","shortName":"2t","standard_name":"2_metre_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/167"},"description":"This parameter is the temperature of air at 2m above the surface of land, sea or in-land waters.2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.Please note that the encodings listed here for s2s \u0026 uerra (which includes encodings for carra/cerra) include entries for Mean 2 metre temperature. The specific encoding for Mean 2 metre temperature can be found in 228004.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Geopotential(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Geopotential","parameter_ID":"129","product_type":"forecast","shortName":"z","standard_name":"Geopotential","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/129"},"description":"This parameter is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level.The geopotential height can be calculated by dividing the geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2). The geopotential height plays an important role in synoptic meteorology (analysis of weather patterns). Charts of geopotential height plotted at constant pressure levels (e.g., 300, 500 or 850 hPa) can be used to identify weather systems such as cyclones, anticyclones, troughs and ridges.At the surface of the Earth, this parameter shows the variations in geopotential (height) of the surface, and is often referred to as the orography.","dimensions":["lat","lon","time"],"type":"data","unit":"m2s-2"},"Land-sea_mask(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Land-sea mask","parameter_ID":"172","product_type":"forecast","shortName":"lsm","standard_name":"Land-sea_mask","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/172"},"description":"This parameter is the proportion of land, as opposed to ocean or inland waters (lakes, reservoirs, rivers and coastal waters), in agrid box.This parameter has values ranging between zero and one and is dimensionless.In cycles of the ECMWF Integrated Forecasting System (IFS) from CY41R1 (introduced in May 2015) onwards, grid boxes where this parameter has a value above 0.5 can be comprised of a mixture of land and inland water but not ocean. Grid boxes with a value of 0.5 and below can only be comprised of a water surface. In the latter case, the lake cover is used to determine how much of the water surface is ocean or inland water.In cycles of the IFS before CY41R1, grid boxes where this parameter has a value above 0.5 can only be comprised of land and those grid boxes with a value of 0.5 and below can only be comprised of ocean. In these older model cycles, there is no differentiation between ocean and inland water.","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Mean_sea_level_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Mean sea level pressure","parameter_ID":"151","product_type":"forecast","shortName":"msl","standard_name":"Mean_sea_level_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/151"},"description":"This parameter is the pressure (force per unit area) of the atmosphere adjusted to the height of mean sea level.It is a measure of the weight that all the air in a column vertically above the area of Earth's surface would have at that point, if the point were located at the mean sea level. It is calculated over all surfaces - land, sea and in-land water.Maps of mean sea level pressure are used to identify the locations of low and high pressure systems, often referred to as cyclones and anticyclones. Contours of mean sea level pressure also indicate the strength of the wind. Tightly packed contours show stronger winds.The units of this parameter are pascals (Pa).  Mean sea level pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb = 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Orography(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Orography","parameter_ID":"228002","product_type":"forecast","shortName":"orog","standard_name":"Orography","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/228002"},"dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Potential_vorticity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Potential vorticity","parameter_ID":"60","product_type":"forecast","shortName":"pv","standard_name":"Potential_vorticity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/60"},"description":"Potential vorticity is a measure of the capacity for air to rotate in the atmosphere. If we ignore the effects of heating and friction, potential vorticity is conserved following an air parcel. It is used to look for places where large wind storms are likely to originate and develop.  Potential vorticity increases strongly above the tropopause and therefore, it can also be used in studies related to the stratosphere and stratosphere-troposphere exchanges.Large wind storms develop when a column of air in the atmosphere starts to rotate. Potential vorticity is calculated from the wind, temperature and pressure across a column of air in the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K m2kg-1s-1"},"Relative_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Relative humidity","parameter_ID":"157","product_type":"forecast","shortName":"r","standard_name":"Relative_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/157"},"description":"This parameter is the water vapour pressure as a percentage of the value at which the air becomes saturated (the point at which water vapour begins to condense into liquid water or deposition into ice).For temperatures over 0°C (273.15 K) it is calculated for saturation over water. At temperatures below -23°C it is calculated for saturation over ice.  Between -23°C and 0°C this parameter is calculated by interpolating between the ice and water values using a quadratic function.See more information about the model's relative humidity calculation.","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Skin_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Skin temperature","parameter_ID":"235","product_type":"forecast","shortName":"skt","standard_name":"Skin_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235"},"description":"This parameter is the temperature of the surface of the Earth.The skin temperature is the theoretical temperature that is required to satisfy the surface energy balance. It represents the temperature of the uppermost surface layer, which has no heat capacity and so can respond instantaneously to changes in surface fluxes. Skin temperature is calculated differently over land and sea.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.See further information about the skin temperatureover landandover sea.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean skin temperature. The specific encoding for Mean skin temperature can be found in 235079.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Snow_depth_water_equivalent(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Snow depth water equivalent","parameter_ID":"228141","product_type":"forecast","shortName":"sd","standard_name":"Snow_depth_water_equivalent","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228141"},"description":"Snow depth water equivalent in kg m**-2 (mm) water equivalent.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean snow depth water equivalent. The specific encoding for Time-mean snow depth water equivalent can be found in 235078.[NOTE: See 141 for the equivalent parameter in \"m of water equivalent\"]","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Snow_depth_water_equivalent(clte_sol)":{"attrs":{"encoding":"instantaneous","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Snow depth water equivalent","parameter_ID":"228141","product_type":"forecast","shortName":"sd","standard_name":"Snow_depth_water_equivalent","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228141"},"description":"Snow depth water equivalent in kg m**-2 (mm) water equivalent.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean snow depth water equivalent. The specific encoding for Time-mean snow depth water equivalent can be found in 235078.[NOTE: See 141 for the equivalent parameter in \"m of water equivalent\"]","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Specific_cloud_liquid_water_content(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific cloud liquid water content","parameter_ID":"246","product_type":"forecast","shortName":"clwc","standard_name":"Specific_cloud_liquid_water_content","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/246"},"description":"This parameter is the mass of cloud liquid water droplets per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for agrid box.Water within clouds can be liquid or ice, or a combination of the two.See further information about the cloud formulation.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Specific_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific humidity","parameter_ID":"133","product_type":"forecast","shortName":"q","standard_name":"Specific_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/133"},"description":"This parameter is the mass of water vapour per kilogram of moist air.The total mass of moist air is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Surface_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Surface pressure","parameter_ID":"134","product_type":"forecast","shortName":"sp","standard_name":"Surface_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/134"},"description":"This parameter is the pressure (force per unit area) of the atmosphere on the surface of land, sea and in-land water.It is a measure of the weight of all the air in a column vertically above the area of the Earth's surface represented at a fixed point.Surface pressure is often used in combination with temperature to calculate air density.The strong variation of pressure with altitude makes it difficult to see the low and high pressure systems over mountainous areas, so mean sea level pressure, rather than surface pressure, is normally used for this purpose.The units of this parameter are Pascals (Pa). Surface pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb= 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Temperature(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Temperature","parameter_ID":"130","product_type":"forecast","shortName":"t","standard_name":"Temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/130"},"description":"This parameter is the temperature in the atmosphere.It has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.This parameter is available on multiple levels through the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_10_metre_U_wind_component(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre U wind component","parameter_ID":"235165","product_type":"forecast","shortName":"avg_10u","standard_name":"Time-mean_10_metre_U_wind_component","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235165"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_10_metre_V_wind_component(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre V wind component","parameter_ID":"235166","product_type":"forecast","shortName":"avg_10v","standard_name":"Time-mean_10_metre_V_wind_component","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235166"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_10_metre_wind_speed(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre wind speed","parameter_ID":"228005","product_type":"forecast","shortName":"avg_10ws","standard_name":"Time-mean_10_metre_wind_speed","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/228005"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_2_metre_dewpoint_temperature(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"2","levtype":"sfc","long_name":"Time-mean 2 metre dewpoint temperature","parameter_ID":"235168","product_type":"forecast","shortName":"avg_2d","standard_name":"Time-mean_2_metre_dewpoint_temperature","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235168"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_2_metre_temperature(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"2","levtype":"sfc","long_name":"Time-mean 2 metre temperature","parameter_ID":"228004","product_type":"forecast","shortName":"avg_2t","standard_name":"Time-mean_2_metre_temperature","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/228004"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_U_component_of_wind(clmn_hl)":{"attrs":{"encoding":"mean","levelist":"100","levtype":"hl","long_name":"Time-mean U component of wind","parameter_ID":"235131","product_type":"forecast","shortName":"avg_u","standard_name":"Time-mean_U_component_of_wind","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235131"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_U_component_of_wind(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean U component of wind","parameter_ID":"235131","product_type":"forecast","shortName":"avg_u","standard_name":"Time-mean_U_component_of_wind","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235131"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_V_component_of_wind(clmn_hl)":{"attrs":{"encoding":"mean","levelist":"100","levtype":"hl","long_name":"Time-mean V component of wind","parameter_ID":"235132","product_type":"forecast","shortName":"avg_v","standard_name":"Time-mean_V_component_of_wind","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235132"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_V_component_of_wind(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean V component of wind","parameter_ID":"235132","product_type":"forecast","shortName":"avg_v","standard_name":"Time-mean_V_component_of_wind","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235132"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_ice_velocity(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean eastward sea ice velocity","parameter_ID":"263003","product_type":"forecast","shortName":"avg_siue","standard_name":"Time-mean_eastward_sea_ice_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263003"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_ice_velocity(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean eastward sea ice velocity","parameter_ID":"263003","product_type":"forecast","shortName":"avg_siue","standard_name":"Time-mean_eastward_sea_ice_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263003"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_water_velocity(clmn_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean eastward sea water velocity","parameter_ID":"263506","product_type":"forecast","shortName":"avg_uoe","standard_name":"Time-mean_eastward_sea_water_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263506"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean eastward sea water velocity","parameter_ID":"263506","product_type":"forecast","shortName":"avg_uoe","standard_name":"Time-mean_eastward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263506"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_turbulent_surface_stress(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean eastward turbulent surface stress","parameter_ID":"235041","product_type":"forecast","shortName":"avg_iews","standard_name":"Time-mean_eastward_turbulent_surface_stress","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235041"},"dimensions":["lat","lon","time"],"type":"data","unit":"N m-2"},"Time-mean_eastward_turbulent_surface_stress(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean eastward turbulent surface stress","parameter_ID":"235041","product_type":"forecast","shortName":"avg_iews","standard_name":"Time-mean_eastward_turbulent_surface_stress","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235041"},"dimensions":["lat","lon","time"],"type":"data","unit":"N m-2"},"Time-mean_geopotential(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean geopotential","parameter_ID":"235129","product_type":"forecast","shortName":"avg_z","standard_name":"Time-mean_geopotential","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235129"},"dimensions":["lat","lon","time"],"type":"data","unit":"m2s-2"},"Time-mean_mean_sea_level_pressure(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean mean sea level pressure","parameter_ID":"235151","product_type":"forecast","shortName":"avg_msl","standard_name":"Time-mean_mean_sea_level_pressure","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235151"},"dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Time-mean_moisture_flux(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean moisture flux","parameter_ID":"235043","product_type":"forecast","shortName":"avg_ie","standard_name":"Time-mean_moisture_flux","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235043"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_moisture_flux(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean moisture flux","parameter_ID":"235043","product_type":"forecast","shortName":"avg_ie","standard_name":"Time-mean_moisture_flux","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235043"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_northward_sea_ice_velocity(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean northward sea ice velocity","parameter_ID":"263004","product_type":"forecast","shortName":"avg_sivn","standard_name":"Time-mean_northward_sea_ice_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263004"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_sea_ice_velocity(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean northward sea ice velocity","parameter_ID":"263004","product_type":"forecast","shortName":"avg_sivn","standard_name":"Time-mean_northward_sea_ice_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263004"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_sea_water_velocity(clmn_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean northward sea water velocity","parameter_ID":"263505","product_type":"forecast","shortName":"avg_von","standard_name":"Time-mean_northward_sea_water_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263505"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean northward sea water velocity","parameter_ID":"263505","product_type":"forecast","shortName":"avg_von","standard_name":"Time-mean_northward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263505"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_turbulent_surface_stress(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean northward turbulent surface stress","parameter_ID":"235042","product_type":"forecast","shortName":"avg_inss","standard_name":"Time-mean_northward_turbulent_surface_stress","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235042"},"dimensions":["lat","lon","time"],"type":"data","unit":"N m-2"},"Time-mean_northward_turbulent_surface_stress(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean northward turbulent surface stress","parameter_ID":"235042","product_type":"forecast","shortName":"avg_inss","standard_name":"Time-mean_northward_turbulent_surface_stress","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235042"},"dimensions":["lat","lon","time"],"type":"data","unit":"N m-2"},"Time-mean_potential_vorticity(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean potential vorticity","parameter_ID":"235100","product_type":"forecast","shortName":"avg_pv","standard_name":"Time-mean_potential_vorticity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235100"},"dimensions":["lat","lon","time"],"type":"data","unit":"K m2kg-1s-1"},"Time-mean_relative_humidity(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean relative humidity","parameter_ID":"235157","product_type":"forecast","shortName":"avg_r","standard_name":"Time-mean_relative_humidity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235157"},"dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Time-mean_sea_ice_area_fraction(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea ice area fraction","parameter_ID":"263001","product_type":"forecast","shortName":"avg_siconc","standard_name":"Time-mean_sea_ice_area_fraction","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263001"},"dimensions":["lat","lon","time"],"type":"data","unit":"Fraction"},"Time-mean_sea_ice_area_fraction(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea ice area fraction","parameter_ID":"263001","product_type":"forecast","shortName":"avg_siconc","standard_name":"Time-mean_sea_ice_area_fraction","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263001"},"dimensions":["lat","lon","time"],"type":"data","unit":"Fraction"},"Time-mean_sea_ice_thickness(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea ice 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sky","parameter_ID":"235049","product_type":"forecast","shortName":"avg_tnswrfcs","standard_name":"Time-mean_top_net_short-wave_radiation_flux,_clear_sky","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235049"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Time-mean_top_net_short-wave_radiation_flux,_clear_sky(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean top net short-wave radiation flux, clear sky","parameter_ID":"235049","product_type":"forecast","shortName":"avg_tnswrfcs","standard_name":"Time-mean_top_net_short-wave_radiation_flux,_clear_sky","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235049"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Time-mean_total_cloud_cover(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total cloud cover","parameter_ID":"235288","product_type":"forecast","shortName":"avg_tcc","standard_name":"Time-mean_total_cloud_cover","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235288"},"dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Time-mean_total_column_cloud_ice_water(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total column cloud ice water","parameter_ID":"235088","product_type":"forecast","shortName":"avg_tciw","standard_name":"Time-mean_total_column_cloud_ice_water","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235088"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Time-mean_total_column_heat_content(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean total column heat content","parameter_ID":"263123","product_type":"forecast","shortName":"avg_hcbtm","standard_name":"Time-mean_total_column_heat_content","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263123"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_total_column_heat_content(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean total column heat content","parameter_ID":"263123","product_type":"forecast","shortName":"avg_hcbtm","standard_name":"Time-mean_total_column_heat_content","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263123"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_total_column_liquid_water(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total column liquid water","parameter_ID":"235087","product_type":"forecast","shortName":"avg_tclw","standard_name":"Time-mean_total_column_liquid_water","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235087"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Time-mean_total_column_vertically-integrated_water_vapour(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total column vertically-integrated water vapour","parameter_ID":"235137","product_type":"forecast","shortName":"avg_tcwv","standard_name":"Time-mean_total_column_vertically-integrated_water_vapour","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235137"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Time-mean_total_column_water(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total column water","parameter_ID":"235136","product_type":"forecast","shortName":"avg_tcw","standard_name":"Time-mean_total_column_water","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235136"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Time-mean_total_precipitation_rate(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total precipitation rate","parameter_ID":"235055","product_type":"forecast","shortName":"avg_tprate","standard_name":"Time-mean_total_precipitation_rate","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235055"},"description":"Time-mean total precipitation rate, or time-mean total precipitation flux. This parameter is on level \"Ground or water surface\" (typeOfFirstFixedSurface=1). For this parameter on other levels, please use 235013.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_total_precipitation_rate(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total precipitation rate","parameter_ID":"235055","product_type":"forecast","shortName":"avg_tprate","standard_name":"Time-mean_total_precipitation_rate","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235055"},"description":"Time-mean total precipitation rate, or time-mean total precipitation flux. This parameter is on level \"Ground or water surface\" (typeOfFirstFixedSurface=1). For this parameter on other levels, please use 235013.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_total_snowfall_rate_water_equivalent(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total snowfall rate water equivalent","parameter_ID":"235031","product_type":"forecast","shortName":"avg_tsrwe","standard_name":"Time-mean_total_snowfall_rate_water_equivalent","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235031"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_total_snowfall_rate_water_equivalent(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total snowfall rate water equivalent","parameter_ID":"235031","product_type":"forecast","shortName":"avg_tsrwe","standard_name":"Time-mean_total_snowfall_rate_water_equivalent","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235031"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_upward_sea_water_velocity(clmn_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean upward sea water velocity","parameter_ID":"263507","product_type":"forecast","shortName":"avg_wo","standard_name":"Time-mean_upward_sea_water_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263507"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_upward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean upward sea water velocity","parameter_ID":"263507","product_type":"forecast","shortName":"avg_wo","standard_name":"Time-mean_upward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263507"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_vertical_velocity(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean vertical velocity","parameter_ID":"235135","product_type":"forecast","shortName":"avg_w","standard_name":"Time-mean_vertical_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235135"},"dimensions":["lat","lon","time"],"type":"data","unit":"Pa s-1"},"Time-mean_vertically-integrated_heat_content_in_the_upper_300_m(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"300","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 300 m","parameter_ID":"263121","product_type":"forecast","shortName":"avg_hc300m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_300_m","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263121"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_vertically-integrated_heat_content_in_the_upper_300_m(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"300","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 300 m","parameter_ID":"263121","product_type":"forecast","shortName":"avg_hc300m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_300_m","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263121"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"700","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 700 m","parameter_ID":"263122","product_type":"forecast","shortName":"avg_hc700m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263122"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"700","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 700 m","parameter_ID":"263122","product_type":"forecast","shortName":"avg_hc700m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263122"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_volumetric_soil_moisture(clmn_sol)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Time-mean volumetric soil moisture","parameter_ID":"235077","product_type":"forecast","shortName":"avg_vsw","standard_name":"Time-mean_volumetric_soil_moisture","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235077"},"dimensions":["lat","lon","time"],"type":"data","unit":"m3m-3"},"Time_mean_top_downward_short-wave_radiation_flux(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time mean top downward short-wave radiation flux","parameter_ID":"235053","product_type":"forecast","shortName":"avg_tdswrf","standard_name":"Time_mean_top_downward_short-wave_radiation_flux","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235053"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Time_mean_top_downward_short-wave_radiation_flux(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time mean top downward short-wave radiation flux","parameter_ID":"235053","product_type":"forecast","shortName":"avg_tdswrf","standard_name":"Time_mean_top_downward_short-wave_radiation_flux","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235053"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Total_Cloud_Cover(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total Cloud Cover","parameter_ID":"228164","product_type":"forecast","shortName":"tcc","standard_name":"Total_Cloud_Cover","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228164"},"description":"[NOTE: See 164 for the equivalent parameter in \"(0-1)\"]","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Total_column_cloud_ice_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud ice water","parameter_ID":"79","product_type":"forecast","shortName":"tciw","standard_name":"Total_column_cloud_ice_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/79"},"description":"This parameter is the amount of ice contained within clouds in a column extending from the surface of the Earth to the top of the atmosphere. Snow (aggregated ice crystals) is not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The  ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_cloud_liquid_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud liquid water","parameter_ID":"78","product_type":"forecast","shortName":"tclw","standard_name":"Total_column_cloud_liquid_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/78"},"description":"This parameter is the amount of liquid water contained within cloud droplets in a column extending from the surface of the Earth to the top of the atmosphere. Rain water droplets, which are much larger in size (and mass), are not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_vertically-integrated_water_vapour(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column vertically-integrated water vapour","parameter_ID":"137","product_type":"forecast","shortName":"tcwv","standard_name":"Total_column_vertically-integrated_water_vapour","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/137"},"description":"This parameter is the total amount of water vapour in a column extending from the surface of the Earth to the top of the atmosphere.This parameter represents the area averaged value for agrid box.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column water","parameter_ID":"136","product_type":"forecast","shortName":"tcw","standard_name":"Total_column_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/136"},"description":"This parameter is the sum of water vapour, liquid water, cloud ice, rain and snow in a column extending from the surface of the Earth to the top of the atmosphere. In old versions of the ECMWF model (IFS), rain and snow were not accounted for.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"U_component_of_wind(clte_hl)":{"attrs":{"encoding":"instantaneous","levelist":"100","levtype":"hl","long_name":"U component of wind","parameter_ID":"131","product_type":"forecast","shortName":"u","standard_name":"U_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/131"},"description":"This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east, in metres per second. A negative sign thus indicates air movement towards the west.This parameter can be combined with the V component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"U_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"U component of wind","parameter_ID":"131","product_type":"forecast","shortName":"u","standard_name":"U_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/131"},"description":"This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east, in metres per second. A negative sign thus indicates air movement towards the west.This parameter can be combined with the V component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"V_component_of_wind(clte_hl)":{"attrs":{"encoding":"instantaneous","levelist":"100","levtype":"hl","long_name":"V component of wind","parameter_ID":"132","product_type":"forecast","shortName":"v","standard_name":"V_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/132"},"description":"This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north, in metres per second. A negative sign thus indicates air movement towards the south.This parameter can be combined with the U component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"V_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"V component of wind","parameter_ID":"132","product_type":"forecast","shortName":"v","standard_name":"V_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/132"},"description":"This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north, in metres per second. A negative sign thus indicates air movement towards the south.This parameter can be combined with the U component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Vertical_velocity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Vertical velocity","parameter_ID":"135","product_type":"forecast","shortName":"w","standard_name":"Vertical_velocity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/135"},"description":"This parameter is the speed of air motion in the upward or downward direction. The ECMWF Integrated Forecasting System (IFS) uses a pressure based vertical co-ordinate system and pressure decreases with height, therefore negative values of vertical velocity indicate upward motion.Vertical velocity can be useful to understand the large-scale dynamics of the atmosphere, including areas of upward motion/ascent (negative values) and downward motion/subsidence (positive values).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa s-1"},"Volumetric_soil_moisture(clte_sol)":{"attrs":{"encoding":"instantaneous","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Volumetric soil moisture","parameter_ID":"260199","product_type":"forecast","shortName":"vsw","standard_name":"Volumetric_soil_moisture","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/260199"},"description":"Please note that the encoding listed here for uerra (which includes carra/cerra) includes entries for Time-mean volumetric soil moisture. The specific encoding for Time-mean volumetric soil moisture can be found in 235077.","dimensions":["lat","lon","time"],"type":"data","unit":"m3m-3"}},"sci:publications":[{"doi":"10.1109/MCSE.2023.3260519","citation":"N. Wedi et al., Destination Earth: High-Performance Computing for Weather and Climate, in Computing in Science \u0026 Engineering, vol. 24, no. 6, pp. 29-37, Nov.-Dec. 2022,"},{"doi":"10.21957/d3f982672e","citation":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"dedl:short_description":"The Climate Change Adaptation Digital Twin provides global climate projections and sector-specific information over multiple decades at high resolution via a unified framework combining advanced Earth system models, impact assessments, and observations. This Generation-2, Realization-5 Collection gives access to 'Storyline Simulation Past Climate' data based on the 'IFS-FESOM' model."},{"type":"Collection","title":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Storyline Simulation Future Climate - IFS-FESOM - Generation-2 - Realization-1","id":"EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_TPLUS2.0K_IFS-FESOM.R1","description":"The DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities by providing innovative climate information on multi-decadal timescales, globally, at scales at which many impacts of climate change are observed. It combines cutting-edge global Earth-system models, impact-sector applications and observations into a unified framework to provide global climate projections and impact-sector information on multi-decadal timescales (1990 to ~2050), at very high spatial resolutions (5 to 10 km).\n\nThe Climate DT represents the first ever attempt to operationalise the production of global multi-decadal climate projections, leveraging the world-leading supercomputing facilities of the EuroHPC Joint Undertaking along with some of the leading European climate models. A concise overview of what the Climate DT aims to achieve, and of the different concepts essential for an understanding of the Digital Twin’s characteristics, is included in the [Climate DT factsheet](https://destine.ecmwf.int/wp-content/uploads/2024/06/2024.06.07_Climate-DT-Fact-Sheet_V7-2.pdf)\n\n## Storyline Simulation Future Climate \n\n Storyline simulations offer a what-if capability to explore how a weather event we experienced in the recent past would change in a warmer climate and how it would have looked in past pre-industrial conditions. To constrain simulated weather events, a nudging approach is used to keep the simulated large-scale flow close to the ERA5 reanalysis for the period 2017 – 2023. Processes on smaller scales and thermodynamic processes are free to evolve, which allows scientists to study e.g. how extreme precipitation would change in a warmer world for a weather event observed in present-day conditions. Initial conditions are taken from ocean-only spinup simulations for 1950 and 2017, and from the IFS-FESOM projection for a +2 °C world. Storyline simulations will be updated regularly to include most recent months. The next update will extend all storylines to include 2024.\n\n## Models\n\nThe Climate DT exploits and further evolves a new generation of global storm-resolving and eddy-rich models built through a cooperative model development approach. For more information on models please click [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#models)\n\n## Simulations\n\nThe Climate DT team carries out several types of digital twin simulations on the EuroHPC supercomputers.  Multi-decadal simulations are produced to cover the recent past (from 1990) and possible future evolutions of the climate up to 2050. See [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#simulations) for more information on Simulations\n\n## Parameters\n\nBelow we see the list of parameters extracted from the 'DestinE Climate DT data portfolio', for more information please refer to the pages [Climate DT Phase2 CLTE Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters) and [Climate DT Phase2 CLMN Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters)","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_TPLUS2.0K_IFS-FESOM.R1/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_TPLUS2.0K_IFS-FESOM.R1/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_TPLUS2.0K_IFS-FESOM.R1/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_TPLUS2.0K_IFS-FESOM.R1","title":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Storyline Simulation Future Climate - IFS-FESOM - Generation-2 - Realization-1"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters","title":"DestinE ClimateDT Phase 2 CLTE Parameters"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters","title":"DestinE ClimateDT Phase 2 CLMN Parameters"},{"rel":"cite-as","type":"text/html","href":"https://dl.acm.org/doi/abs/10.1109/MCSE.2023.3260519","title":"Destination Earth: High-Performance Computing for Weather and Climate"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.21957/d3f982672e","title":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"assets":{"thumbnail":{"href":"https://s3.central.data.destination-earth.eu/swift/v1/dedl-public/collections/climate-dt-min.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2017-01-01T00:00:00Z","2025-12-31T23:59:59Z"]]}},"license":"CC-BY-4.0","keywords":["Digital Twins","Climate Change","Atmosphere","Ocean","Land","Sea Ice","Snow","Soil","Earth","High Performance Computing","Decision Making","Europe","Meteorology","class:d1","dataset:climate-dt","generation:2","expver:0001","stream:clmn,clte","type:fc","activity:story-nudging","experiment:Tplus2.0K","realization:1","model:IFS-FESOM","resolution:standard,high","levtype:hl,o2d,o3d,pl,sfc,sol"],"summaries":{"federation:backends":["dedt_mn5"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/datacube/v2.1.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["producer","processor","licensor"],"url":"https://www.ecmwf.int/"}],"created":"2024-04-11T22:31:55Z","updated":"2026-04-24T10:24:59Z","published":"2024-04-11T22:31:55Z","cube:dimensions":{"lat":{"axis":"y","description":"latitude","extent":[-90,90],"type":"spatial"},"lon":{"axis":"x","description":"longitude","extent":[-180,180],"type":"spatial"},"time":{"extent":["2017-01-01T00:00:00Z","2025-12-31T23:59:59Z"],"step":"P0Y0M0DT1H0M0S","type":"temporal"}},"cube:variables":{"10_metre_U_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre U wind component","parameter_ID":"165","product_type":"forecast","shortName":"10u","standard_name":"10_metre_U_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/165"},"description":"This parameter is the eastward component of the 10m wind. It is the horizontal speed of air moving towards the east, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the V component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_V_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre V wind component","parameter_ID":"166","product_type":"forecast","shortName":"10v","standard_name":"10_metre_V_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/166"},"description":"This parameter is the northward component of the 10m wind. It is the horizontal speed of air moving towards the north, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the U component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_wind_speed(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre wind speed","parameter_ID":"207","product_type":"forecast","shortName":"10si","standard_name":"10_metre_wind_speed","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/207"},"description":"This parameter is the horizontal speed of the wind, or movement of air, at a height of ten metres above the surface of the Earth. The units of this parameter are metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.The eastward and northward components of the horizontal wind at 10m are also available as parameters.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"2_metre_dewpoint_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"2","levtype":"sfc","long_name":"2 metre dewpoint temperature","parameter_ID":"168","product_type":"forecast","shortName":"2d","standard_name":"2_metre_dewpoint_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/168"},"description":"This parameter is the temperature to which the air, at 2 metres above the surface of the Earth, would have to be cooled for saturation to occur.It is a measure of the humidity of the air. Combined with temperature and pressure, it can be used to calculate the relative humidity.2m dew point temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"2_metre_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"2","levtype":"sfc","long_name":"2 metre temperature","parameter_ID":"167","product_type":"forecast","shortName":"2t","standard_name":"2_metre_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/167"},"description":"This parameter is the temperature of air at 2m above the surface of land, sea or in-land waters.2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.Please note that the encodings listed here for s2s \u0026 uerra (which includes encodings for carra/cerra) include entries for Mean 2 metre temperature. The specific encoding for Mean 2 metre temperature can be found in 228004.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Geopotential(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Geopotential","parameter_ID":"129","product_type":"forecast","shortName":"z","standard_name":"Geopotential","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/129"},"description":"This parameter is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level.The geopotential height can be calculated by dividing the geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2). The geopotential height plays an important role in synoptic meteorology (analysis of weather patterns). Charts of geopotential height plotted at constant pressure levels (e.g., 300, 500 or 850 hPa) can be used to identify weather systems such as cyclones, anticyclones, troughs and ridges.At the surface of the Earth, this parameter shows the variations in geopotential (height) of the surface, and is often referred to as the orography.","dimensions":["lat","lon","time"],"type":"data","unit":"m2s-2"},"Land-sea_mask(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Land-sea mask","parameter_ID":"172","product_type":"forecast","shortName":"lsm","standard_name":"Land-sea_mask","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/172"},"description":"This parameter is the proportion of land, as opposed to ocean or inland waters (lakes, reservoirs, rivers and coastal waters), in agrid box.This parameter has values ranging between zero and one and is dimensionless.In cycles of the ECMWF Integrated Forecasting System (IFS) from CY41R1 (introduced in May 2015) onwards, grid boxes where this parameter has a value above 0.5 can be comprised of a mixture of land and inland water but not ocean. Grid boxes with a value of 0.5 and below can only be comprised of a water surface. In the latter case, the lake cover is used to determine how much of the water surface is ocean or inland water.In cycles of the IFS before CY41R1, grid boxes where this parameter has a value above 0.5 can only be comprised of land and those grid boxes with a value of 0.5 and below can only be comprised of ocean. In these older model cycles, there is no differentiation between ocean and inland water.","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Mean_sea_level_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Mean sea level pressure","parameter_ID":"151","product_type":"forecast","shortName":"msl","standard_name":"Mean_sea_level_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/151"},"description":"This parameter is the pressure (force per unit area) of the atmosphere adjusted to the height of mean sea level.It is a measure of the weight that all the air in a column vertically above the area of Earth's surface would have at that point, if the point were located at the mean sea level. It is calculated over all surfaces - land, sea and in-land water.Maps of mean sea level pressure are used to identify the locations of low and high pressure systems, often referred to as cyclones and anticyclones. Contours of mean sea level pressure also indicate the strength of the wind. Tightly packed contours show stronger winds.The units of this parameter are pascals (Pa).  Mean sea level pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb = 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Orography(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Orography","parameter_ID":"228002","product_type":"forecast","shortName":"orog","standard_name":"Orography","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/228002"},"dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Potential_vorticity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Potential vorticity","parameter_ID":"60","product_type":"forecast","shortName":"pv","standard_name":"Potential_vorticity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/60"},"description":"Potential vorticity is a measure of the capacity for air to rotate in the atmosphere. If we ignore the effects of heating and friction, potential vorticity is conserved following an air parcel. It is used to look for places where large wind storms are likely to originate and develop.  Potential vorticity increases strongly above the tropopause and therefore, it can also be used in studies related to the stratosphere and stratosphere-troposphere exchanges.Large wind storms develop when a column of air in the atmosphere starts to rotate. Potential vorticity is calculated from the wind, temperature and pressure across a column of air in the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K m2kg-1s-1"},"Relative_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Relative humidity","parameter_ID":"157","product_type":"forecast","shortName":"r","standard_name":"Relative_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/157"},"description":"This parameter is the water vapour pressure as a percentage of the value at which the air becomes saturated (the point at which water vapour begins to condense into liquid water or deposition into ice).For temperatures over 0°C (273.15 K) it is calculated for saturation over water. At temperatures below -23°C it is calculated for saturation over ice.  Between -23°C and 0°C this parameter is calculated by interpolating between the ice and water values using a quadratic function.See more information about the model's relative humidity calculation.","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Skin_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Skin temperature","parameter_ID":"235","product_type":"forecast","shortName":"skt","standard_name":"Skin_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235"},"description":"This parameter is the temperature of the surface of the Earth.The skin temperature is the theoretical temperature that is required to satisfy the surface energy balance. It represents the temperature of the uppermost surface layer, which has no heat capacity and so can respond instantaneously to changes in surface fluxes. Skin temperature is calculated differently over land and sea.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.See further information about the skin temperatureover landandover sea.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean skin temperature. The specific encoding for Mean skin temperature can be found in 235079.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Snow_depth_water_equivalent(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Snow depth water equivalent","parameter_ID":"228141","product_type":"forecast","shortName":"sd","standard_name":"Snow_depth_water_equivalent","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228141"},"description":"Snow depth water equivalent in kg m**-2 (mm) water equivalent.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean snow depth water equivalent. The specific encoding for Time-mean snow depth water equivalent can be found in 235078.[NOTE: See 141 for the equivalent parameter in \"m of water equivalent\"]","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Snow_depth_water_equivalent(clte_sol)":{"attrs":{"encoding":"instantaneous","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Snow depth water equivalent","parameter_ID":"228141","product_type":"forecast","shortName":"sd","standard_name":"Snow_depth_water_equivalent","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228141"},"description":"Snow depth water equivalent in kg m**-2 (mm) water equivalent.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean snow depth water equivalent. The specific encoding for Time-mean snow depth water equivalent can be found in 235078.[NOTE: See 141 for the equivalent parameter in \"m of water equivalent\"]","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Specific_cloud_liquid_water_content(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific cloud liquid water content","parameter_ID":"246","product_type":"forecast","shortName":"clwc","standard_name":"Specific_cloud_liquid_water_content","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/246"},"description":"This parameter is the mass of cloud liquid water droplets per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for agrid box.Water within clouds can be liquid or ice, or a combination of the two.See further information about the cloud formulation.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Specific_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific humidity","parameter_ID":"133","product_type":"forecast","shortName":"q","standard_name":"Specific_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/133"},"description":"This parameter is the mass of water vapour per kilogram of moist air.The total mass of moist air is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Surface_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Surface pressure","parameter_ID":"134","product_type":"forecast","shortName":"sp","standard_name":"Surface_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/134"},"description":"This parameter is the pressure (force per unit area) of the atmosphere on the surface of land, sea and in-land water.It is a measure of the weight of all the air in a column vertically above the area of the Earth's surface represented at a fixed point.Surface pressure is often used in combination with temperature to calculate air density.The strong variation of pressure with altitude makes it difficult to see the low and high pressure systems over mountainous areas, so mean sea level pressure, rather than surface pressure, is normally used for this purpose.The units of this parameter are Pascals (Pa). Surface pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb= 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Temperature(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Temperature","parameter_ID":"130","product_type":"forecast","shortName":"t","standard_name":"Temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/130"},"description":"This parameter is the temperature in the atmosphere.It has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.This parameter is available on multiple levels through the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_10_metre_U_wind_component(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre U wind component","parameter_ID":"235165","product_type":"forecast","shortName":"avg_10u","standard_name":"Time-mean_10_metre_U_wind_component","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235165"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_10_metre_V_wind_component(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre V wind component","parameter_ID":"235166","product_type":"forecast","shortName":"avg_10v","standard_name":"Time-mean_10_metre_V_wind_component","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235166"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_10_metre_wind_speed(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre wind speed","parameter_ID":"228005","product_type":"forecast","shortName":"avg_10ws","standard_name":"Time-mean_10_metre_wind_speed","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/228005"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_2_metre_dewpoint_temperature(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"2","levtype":"sfc","long_name":"Time-mean 2 metre dewpoint temperature","parameter_ID":"235168","product_type":"forecast","shortName":"avg_2d","standard_name":"Time-mean_2_metre_dewpoint_temperature","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235168"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_2_metre_temperature(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"2","levtype":"sfc","long_name":"Time-mean 2 metre temperature","parameter_ID":"228004","product_type":"forecast","shortName":"avg_2t","standard_name":"Time-mean_2_metre_temperature","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/228004"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_U_component_of_wind(clmn_hl)":{"attrs":{"encoding":"mean","levelist":"100","levtype":"hl","long_name":"Time-mean U component of wind","parameter_ID":"235131","product_type":"forecast","shortName":"avg_u","standard_name":"Time-mean_U_component_of_wind","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235131"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_U_component_of_wind(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean U component of wind","parameter_ID":"235131","product_type":"forecast","shortName":"avg_u","standard_name":"Time-mean_U_component_of_wind","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235131"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_V_component_of_wind(clmn_hl)":{"attrs":{"encoding":"mean","levelist":"100","levtype":"hl","long_name":"Time-mean V component of 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m-2"},"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"700","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 700 m","parameter_ID":"263122","product_type":"forecast","shortName":"avg_hc700m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263122"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_volumetric_soil_moisture(clmn_sol)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Time-mean volumetric soil moisture","parameter_ID":"235077","product_type":"forecast","shortName":"avg_vsw","standard_name":"Time-mean_volumetric_soil_moisture","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235077"},"dimensions":["lat","lon","time"],"type":"data","unit":"m3m-3"},"Time_mean_top_downward_short-wave_radiation_flux(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time mean top downward short-wave radiation flux","parameter_ID":"235053","product_type":"forecast","shortName":"avg_tdswrf","standard_name":"Time_mean_top_downward_short-wave_radiation_flux","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235053"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Time_mean_top_downward_short-wave_radiation_flux(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time mean top downward short-wave radiation flux","parameter_ID":"235053","product_type":"forecast","shortName":"avg_tdswrf","standard_name":"Time_mean_top_downward_short-wave_radiation_flux","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235053"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Total_Cloud_Cover(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total Cloud Cover","parameter_ID":"228164","product_type":"forecast","shortName":"tcc","standard_name":"Total_Cloud_Cover","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228164"},"description":"[NOTE: See 164 for the equivalent parameter in \"(0-1)\"]","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Total_column_cloud_ice_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud ice water","parameter_ID":"79","product_type":"forecast","shortName":"tciw","standard_name":"Total_column_cloud_ice_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/79"},"description":"This parameter is the amount of ice contained within clouds in a column extending from the surface of the Earth to the top of the atmosphere. Snow (aggregated ice crystals) is not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The  ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_cloud_liquid_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud liquid water","parameter_ID":"78","product_type":"forecast","shortName":"tclw","standard_name":"Total_column_cloud_liquid_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/78"},"description":"This parameter is the amount of liquid water contained within cloud droplets in a column extending from the surface of the Earth to the top of the atmosphere. Rain water droplets, which are much larger in size (and mass), are not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_vertically-integrated_water_vapour(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column vertically-integrated water vapour","parameter_ID":"137","product_type":"forecast","shortName":"tcwv","standard_name":"Total_column_vertically-integrated_water_vapour","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/137"},"description":"This parameter is the total amount of water vapour in a column extending from the surface of the Earth to the top of the atmosphere.This parameter represents the area averaged value for agrid box.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column water","parameter_ID":"136","product_type":"forecast","shortName":"tcw","standard_name":"Total_column_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/136"},"description":"This parameter is the sum of water vapour, liquid water, cloud ice, rain and snow in a column extending from the surface of the Earth to the top of the atmosphere. In old versions of the ECMWF model (IFS), rain and snow were not accounted for.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"U_component_of_wind(clte_hl)":{"attrs":{"encoding":"instantaneous","levelist":"100","levtype":"hl","long_name":"U component of wind","parameter_ID":"131","product_type":"forecast","shortName":"u","standard_name":"U_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/131"},"description":"This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east, in metres per second. A negative sign thus indicates air movement towards the west.This parameter can be combined with the V component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"U_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"U component of wind","parameter_ID":"131","product_type":"forecast","shortName":"u","standard_name":"U_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/131"},"description":"This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east, in metres per second. A negative sign thus indicates air movement towards the west.This parameter can be combined with the V component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"V_component_of_wind(clte_hl)":{"attrs":{"encoding":"instantaneous","levelist":"100","levtype":"hl","long_name":"V component of wind","parameter_ID":"132","product_type":"forecast","shortName":"v","standard_name":"V_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/132"},"description":"This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north, in metres per second. A negative sign thus indicates air movement towards the south.This parameter can be combined with the U component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"V_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"V component of wind","parameter_ID":"132","product_type":"forecast","shortName":"v","standard_name":"V_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/132"},"description":"This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north, in metres per second. A negative sign thus indicates air movement towards the south.This parameter can be combined with the U component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Vertical_velocity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Vertical velocity","parameter_ID":"135","product_type":"forecast","shortName":"w","standard_name":"Vertical_velocity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/135"},"description":"This parameter is the speed of air motion in the upward or downward direction. The ECMWF Integrated Forecasting System (IFS) uses a pressure based vertical co-ordinate system and pressure decreases with height, therefore negative values of vertical velocity indicate upward motion.Vertical velocity can be useful to understand the large-scale dynamics of the atmosphere, including areas of upward motion/ascent (negative values) and downward motion/subsidence (positive values).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa s-1"},"Volumetric_soil_moisture(clte_sol)":{"attrs":{"encoding":"instantaneous","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Volumetric soil moisture","parameter_ID":"260199","product_type":"forecast","shortName":"vsw","standard_name":"Volumetric_soil_moisture","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/260199"},"description":"Please note that the encoding listed here for uerra (which includes carra/cerra) includes entries for Time-mean volumetric soil moisture. The specific encoding for Time-mean volumetric soil moisture can be found in 235077.","dimensions":["lat","lon","time"],"type":"data","unit":"m3m-3"}},"sci:publications":[{"doi":"10.1109/MCSE.2023.3260519","citation":"N. Wedi et al., Destination Earth: High-Performance Computing for Weather and Climate, in Computing in Science \u0026 Engineering, vol. 24, no. 6, pp. 29-37, Nov.-Dec. 2022,"},{"doi":"10.21957/d3f982672e","citation":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"dedl:short_description":"The Climate Change Adaptation Digital Twin provides global climate projections and sector-specific information over multiple decades at high resolution via a unified framework combining advanced Earth system models, impact assessments, and observations. This Generation-2, Realization-1 Collection gives access to 'Storyline Simulation Future Climate' data based on the 'IFS-FESOM' model."},{"type":"Collection","title":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Storyline Simulation Future Climate - IFS-FESOM - Generation-2 - Realization-2","id":"EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_TPLUS2.0K_IFS-FESOM.R2","description":"The DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities by providing innovative climate information on multi-decadal timescales, globally, at scales at which many impacts of climate change are observed. It combines cutting-edge global Earth-system models, impact-sector applications and observations into a unified framework to provide global climate projections and impact-sector information on multi-decadal timescales (1990 to ~2050), at very high spatial resolutions (5 to 10 km).\n\nThe Climate DT represents the first ever attempt to operationalise the production of global multi-decadal climate projections, leveraging the world-leading supercomputing facilities of the EuroHPC Joint Undertaking along with some of the leading European climate models. A concise overview of what the Climate DT aims to achieve, and of the different concepts essential for an understanding of the Digital Twin’s characteristics, is included in the [Climate DT factsheet](https://destine.ecmwf.int/wp-content/uploads/2024/06/2024.06.07_Climate-DT-Fact-Sheet_V7-2.pdf)\n\n## Storyline Simulation Future Climate \n\n Storyline simulations offer a what-if capability to explore how a weather event we experienced in the recent past would change in a warmer climate and how it would have looked in past pre-industrial conditions. To constrain simulated weather events, a nudging approach is used to keep the simulated large-scale flow close to the ERA5 reanalysis for the period 2017 – 2023. Processes on smaller scales and thermodynamic processes are free to evolve, which allows scientists to study e.g. how extreme precipitation would change in a warmer world for a weather event observed in present-day conditions. Initial conditions are taken from ocean-only spinup simulations for 1950 and 2017, and from the IFS-FESOM projection for a +2 °C world. Storyline simulations will be updated regularly to include most recent months. The next update will extend all storylines to include 2024.\n\n## Models\n\nThe Climate DT exploits and further evolves a new generation of global storm-resolving and eddy-rich models built through a cooperative model development approach. For more information on models please click [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#models)\n\n## Simulations\n\nThe Climate DT team carries out several types of digital twin simulations on the EuroHPC supercomputers.  Multi-decadal simulations are produced to cover the recent past (from 1990) and possible future evolutions of the climate up to 2050. See [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#simulations) for more information on Simulations\n\n## Parameters\n\nBelow we see the list of parameters extracted from the 'DestinE Climate DT data portfolio', for more information please refer to the pages [Climate DT Phase2 CLTE Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters) and [Climate DT Phase2 CLMN Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters)","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_TPLUS2.0K_IFS-FESOM.R2/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_TPLUS2.0K_IFS-FESOM.R2/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_TPLUS2.0K_IFS-FESOM.R2/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_TPLUS2.0K_IFS-FESOM.R2","title":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Storyline Simulation Future Climate - IFS-FESOM - Generation-2 - Realization-2"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters","title":"DestinE ClimateDT Phase 2 CLTE Parameters"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters","title":"DestinE ClimateDT Phase 2 CLMN Parameters"},{"rel":"cite-as","type":"text/html","href":"https://dl.acm.org/doi/abs/10.1109/MCSE.2023.3260519","title":"Destination Earth: High-Performance Computing for Weather and Climate"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.21957/d3f982672e","title":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"assets":{"thumbnail":{"href":"https://s3.central.data.destination-earth.eu/swift/v1/dedl-public/collections/climate-dt-min.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2017-01-01T00:00:00Z","2025-12-31T23:59:59Z"]]}},"license":"CC-BY-4.0","keywords":["Digital Twins","Climate Change","Atmosphere","Ocean","Land","Sea Ice","Snow","Soil","Earth","High Performance Computing","Decision Making","Europe","Meteorology","class:d1","dataset:climate-dt","generation:2","expver:0001","stream:clmn,clte","type:fc","activity:story-nudging","experiment:Tplus2.0K","realization:2","model:IFS-FESOM","resolution:standard,high","levtype:hl,o2d,o3d,pl,sfc,sol"],"summaries":{"federation:backends":["dedt_mn5"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/datacube/v2.1.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["producer","processor","licensor"],"url":"https://www.ecmwf.int/"}],"created":"2024-04-11T22:31:55Z","updated":"2026-04-24T10:24:59Z","published":"2024-04-11T22:31:55Z","cube:dimensions":{"lat":{"axis":"y","description":"latitude","extent":[-90,90],"type":"spatial"},"lon":{"axis":"x","description":"longitude","extent":[-180,180],"type":"spatial"},"time":{"extent":["2017-01-01T00:00:00Z","2025-12-31T23:59:59Z"],"step":"P0Y0M0DT1H0M0S","type":"temporal"}},"cube:variables":{"10_metre_U_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre U wind component","parameter_ID":"165","product_type":"forecast","shortName":"10u","standard_name":"10_metre_U_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/165"},"description":"This parameter is the eastward component of the 10m wind. It is the horizontal speed of air moving towards the east, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the V component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_V_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre V wind component","parameter_ID":"166","product_type":"forecast","shortName":"10v","standard_name":"10_metre_V_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/166"},"description":"This parameter is the northward component of the 10m wind. It is the horizontal speed of air moving towards the north, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the U component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_wind_speed(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre wind speed","parameter_ID":"207","product_type":"forecast","shortName":"10si","standard_name":"10_metre_wind_speed","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/207"},"description":"This parameter is the horizontal speed of the wind, or movement of air, at a height of ten metres above the surface of the Earth. The units of this parameter are metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.The eastward and northward components of the horizontal wind at 10m are also available as parameters.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"2_metre_dewpoint_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"2","levtype":"sfc","long_name":"2 metre dewpoint temperature","parameter_ID":"168","product_type":"forecast","shortName":"2d","standard_name":"2_metre_dewpoint_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/168"},"description":"This parameter is the temperature to which the air, at 2 metres above the surface of the Earth, would have to be cooled for saturation to occur.It is a measure of the humidity of the air. Combined with temperature and pressure, it can be used to calculate the relative humidity.2m dew point temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"2_metre_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"2","levtype":"sfc","long_name":"2 metre temperature","parameter_ID":"167","product_type":"forecast","shortName":"2t","standard_name":"2_metre_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/167"},"description":"This parameter is the temperature of air at 2m above the surface of land, sea or in-land waters.2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.Please note that the encodings listed here for s2s \u0026 uerra (which includes encodings for carra/cerra) include entries for Mean 2 metre temperature. The specific encoding for Mean 2 metre temperature can be found in 228004.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Geopotential(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Geopotential","parameter_ID":"129","product_type":"forecast","shortName":"z","standard_name":"Geopotential","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/129"},"description":"This parameter is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level.The geopotential height can be calculated by dividing the geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2). The geopotential height plays an important role in synoptic meteorology (analysis of weather patterns). Charts of geopotential height plotted at constant pressure levels (e.g., 300, 500 or 850 hPa) can be used to identify weather systems such as cyclones, anticyclones, troughs and ridges.At the surface of the Earth, this parameter shows the variations in geopotential (height) of the surface, and is often referred to as the orography.","dimensions":["lat","lon","time"],"type":"data","unit":"m2s-2"},"Land-sea_mask(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Land-sea mask","parameter_ID":"172","product_type":"forecast","shortName":"lsm","standard_name":"Land-sea_mask","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/172"},"description":"This parameter is the proportion of land, as opposed to ocean or inland waters (lakes, reservoirs, rivers and coastal waters), in agrid box.This parameter has values ranging between zero and one and is dimensionless.In cycles of the ECMWF Integrated Forecasting System (IFS) from CY41R1 (introduced in May 2015) onwards, grid boxes where this parameter has a value above 0.5 can be comprised of a mixture of land and inland water but not ocean. Grid boxes with a value of 0.5 and below can only be comprised of a water surface. In the latter case, the lake cover is used to determine how much of the water surface is ocean or inland water.In cycles of the IFS before CY41R1, grid boxes where this parameter has a value above 0.5 can only be comprised of land and those grid boxes with a value of 0.5 and below can only be comprised of ocean. In these older model cycles, there is no differentiation between ocean and inland water.","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Mean_sea_level_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Mean sea level pressure","parameter_ID":"151","product_type":"forecast","shortName":"msl","standard_name":"Mean_sea_level_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/151"},"description":"This parameter is the pressure (force per unit area) of the atmosphere adjusted to the height of mean sea level.It is a measure of the weight that all the air in a column vertically above the area of Earth's surface would have at that point, if the point were located at the mean sea level. It is calculated over all surfaces - land, sea and in-land water.Maps of mean sea level pressure are used to identify the locations of low and high pressure systems, often referred to as cyclones and anticyclones. Contours of mean sea level pressure also indicate the strength of the wind. Tightly packed contours show stronger winds.The units of this parameter are pascals (Pa).  Mean sea level pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb = 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Orography(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Orography","parameter_ID":"228002","product_type":"forecast","shortName":"orog","standard_name":"Orography","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/228002"},"dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Potential_vorticity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Potential vorticity","parameter_ID":"60","product_type":"forecast","shortName":"pv","standard_name":"Potential_vorticity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/60"},"description":"Potential vorticity is a measure of the capacity for air to rotate in the atmosphere. If we ignore the effects of heating and friction, potential vorticity is conserved following an air parcel. It is used to look for places where large wind storms are likely to originate and develop.  Potential vorticity increases strongly above the tropopause and therefore, it can also be used in studies related to the stratosphere and stratosphere-troposphere exchanges.Large wind storms develop when a column of air in the atmosphere starts to rotate. Potential vorticity is calculated from the wind, temperature and pressure across a column of air in the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K m2kg-1s-1"},"Relative_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Relative humidity","parameter_ID":"157","product_type":"forecast","shortName":"r","standard_name":"Relative_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/157"},"description":"This parameter is the water vapour pressure as a percentage of the value at which the air becomes saturated (the point at which water vapour begins to condense into liquid water or deposition into ice).For temperatures over 0°C (273.15 K) it is calculated for saturation over water. At temperatures below -23°C it is calculated for saturation over ice.  Between -23°C and 0°C this parameter is calculated by interpolating between the ice and water values using a quadratic function.See more information about the model's relative humidity calculation.","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Skin_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Skin temperature","parameter_ID":"235","product_type":"forecast","shortName":"skt","standard_name":"Skin_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235"},"description":"This parameter is the temperature of the surface of the Earth.The skin temperature is the theoretical temperature that is required to satisfy the surface energy balance. It represents the temperature of the uppermost surface layer, which has no heat capacity and so can respond instantaneously to changes in surface fluxes. Skin temperature is calculated differently over land and sea.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.See further information about the skin temperatureover landandover sea.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean skin temperature. The specific encoding for Mean skin temperature can be found in 235079.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Snow_depth_water_equivalent(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Snow depth water equivalent","parameter_ID":"228141","product_type":"forecast","shortName":"sd","standard_name":"Snow_depth_water_equivalent","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228141"},"description":"Snow depth water equivalent in kg m**-2 (mm) water equivalent.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean snow depth water equivalent. The specific encoding for Time-mean snow depth water equivalent can be found in 235078.[NOTE: See 141 for the equivalent parameter in \"m of water equivalent\"]","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Snow_depth_water_equivalent(clte_sol)":{"attrs":{"encoding":"instantaneous","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Snow depth water equivalent","parameter_ID":"228141","product_type":"forecast","shortName":"sd","standard_name":"Snow_depth_water_equivalent","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228141"},"description":"Snow depth water equivalent in kg m**-2 (mm) water equivalent.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean snow depth water equivalent. The specific encoding for Time-mean snow depth water equivalent can be found in 235078.[NOTE: See 141 for the equivalent parameter in \"m of water equivalent\"]","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Specific_cloud_liquid_water_content(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific cloud liquid water content","parameter_ID":"246","product_type":"forecast","shortName":"clwc","standard_name":"Specific_cloud_liquid_water_content","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/246"},"description":"This parameter is the mass of cloud liquid water droplets per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for agrid box.Water within clouds can be liquid or ice, or a combination of the two.See further information about the cloud formulation.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Specific_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific humidity","parameter_ID":"133","product_type":"forecast","shortName":"q","standard_name":"Specific_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/133"},"description":"This parameter is the mass of water vapour per kilogram of moist air.The total mass of moist air is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Surface_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Surface pressure","parameter_ID":"134","product_type":"forecast","shortName":"sp","standard_name":"Surface_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/134"},"description":"This parameter is the pressure (force per unit area) of the atmosphere on the surface of land, sea and in-land water.It is a measure of the weight of all the air in a column vertically above the area of the Earth's surface represented at a fixed point.Surface pressure is often used in combination with temperature to calculate air density.The strong variation of pressure with altitude makes it difficult to see the low and high pressure systems over mountainous areas, so mean sea level pressure, rather than surface pressure, is normally used for this purpose.The units of this parameter are Pascals (Pa). Surface pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb= 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Temperature(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Temperature","parameter_ID":"130","product_type":"forecast","shortName":"t","standard_name":"Temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/130"},"description":"This parameter is the temperature in the atmosphere.It has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.This parameter is available on multiple levels through the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_10_metre_U_wind_component(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre U wind component","parameter_ID":"235165","product_type":"forecast","shortName":"avg_10u","standard_name":"Time-mean_10_metre_U_wind_component","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235165"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_10_metre_V_wind_component(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre V wind component","parameter_ID":"235166","product_type":"forecast","shortName":"avg_10v","standard_name":"Time-mean_10_metre_V_wind_component","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235166"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_10_metre_wind_speed(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre wind speed","parameter_ID":"228005","product_type":"forecast","shortName":"avg_10ws","standard_name":"Time-mean_10_metre_wind_speed","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/228005"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_2_metre_dewpoint_temperature(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"2","levtype":"sfc","long_name":"Time-mean 2 metre dewpoint temperature","parameter_ID":"235168","product_type":"forecast","shortName":"avg_2d","standard_name":"Time-mean_2_metre_dewpoint_temperature","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235168"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_2_metre_temperature(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"2","levtype":"sfc","long_name":"Time-mean 2 metre temperature","parameter_ID":"228004","product_type":"forecast","shortName":"avg_2t","standard_name":"Time-mean_2_metre_temperature","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/228004"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_U_component_of_wind(clmn_hl)":{"attrs":{"encoding":"mean","levelist":"100","levtype":"hl","long_name":"Time-mean U component of wind","parameter_ID":"235131","product_type":"forecast","shortName":"avg_u","standard_name":"Time-mean_U_component_of_wind","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235131"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_U_component_of_wind(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean U component of wind","parameter_ID":"235131","product_type":"forecast","shortName":"avg_u","standard_name":"Time-mean_U_component_of_wind","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235131"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_V_component_of_wind(clmn_hl)":{"attrs":{"encoding":"mean","levelist":"100","levtype":"hl","long_name":"Time-mean V component of wind","parameter_ID":"235132","product_type":"forecast","shortName":"avg_v","standard_name":"Time-mean_V_component_of_wind","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235132"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_V_component_of_wind(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean V component of wind","parameter_ID":"235132","product_type":"forecast","shortName":"avg_v","standard_name":"Time-mean_V_component_of_wind","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235132"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_ice_velocity(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean eastward sea ice velocity","parameter_ID":"263003","product_type":"forecast","shortName":"avg_siue","standard_name":"Time-mean_eastward_sea_ice_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263003"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_ice_velocity(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean eastward sea ice velocity","parameter_ID":"263003","product_type":"forecast","shortName":"avg_siue","standard_name":"Time-mean_eastward_sea_ice_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263003"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_water_velocity(clmn_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean eastward sea water velocity","parameter_ID":"263506","product_type":"forecast","shortName":"avg_uoe","standard_name":"Time-mean_eastward_sea_water_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263506"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean eastward sea water velocity","parameter_ID":"263506","product_type":"forecast","shortName":"avg_uoe","standard_name":"Time-mean_eastward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263506"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_turbulent_surface_stress(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean eastward turbulent surface stress","parameter_ID":"235041","product_type":"forecast","shortName":"avg_iews","standard_name":"Time-mean_eastward_turbulent_surface_stress","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235041"},"dimensions":["lat","lon","time"],"type":"data","unit":"N m-2"},"Time-mean_eastward_turbulent_surface_stress(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean eastward turbulent surface stress","parameter_ID":"235041","product_type":"forecast","shortName":"avg_iews","standard_name":"Time-mean_eastward_turbulent_surface_stress","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235041"},"dimensions":["lat","lon","time"],"type":"data","unit":"N m-2"},"Time-mean_geopotential(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean geopotential","parameter_ID":"235129","product_type":"forecast","shortName":"avg_z","standard_name":"Time-mean_geopotential","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235129"},"dimensions":["lat","lon","time"],"type":"data","unit":"m2s-2"},"Time-mean_mean_sea_level_pressure(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean mean sea level pressure","parameter_ID":"235151","product_type":"forecast","shortName":"avg_msl","standard_name":"Time-mean_mean_sea_level_pressure","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235151"},"dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Time-mean_moisture_flux(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean moisture flux","parameter_ID":"235043","product_type":"forecast","shortName":"avg_ie","standard_name":"Time-mean_moisture_flux","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235043"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_moisture_flux(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean moisture flux","parameter_ID":"235043","product_type":"forecast","shortName":"avg_ie","standard_name":"Time-mean_moisture_flux","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235043"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_northward_sea_ice_velocity(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean northward sea ice velocity","parameter_ID":"263004","product_type":"forecast","shortName":"avg_sivn","standard_name":"Time-mean_northward_sea_ice_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263004"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_sea_ice_velocity(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean northward sea ice velocity","parameter_ID":"263004","product_type":"forecast","shortName":"avg_sivn","standard_name":"Time-mean_northward_sea_ice_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263004"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_sea_water_velocity(clmn_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean northward sea water velocity","parameter_ID":"263505","product_type":"forecast","shortName":"avg_von","standard_name":"Time-mean_northward_sea_water_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263505"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean northward sea water velocity","parameter_ID":"263505","product_type":"forecast","shortName":"avg_von","standard_name":"Time-mean_northward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263505"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_turbulent_surface_stress(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean northward turbulent surface stress","parameter_ID":"235042","product_type":"forecast","shortName":"avg_inss","standard_name":"Time-mean_northward_turbulent_surface_stress","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235042"},"dimensions":["lat","lon","time"],"type":"data","unit":"N m-2"},"Time-mean_northward_turbulent_surface_stress(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean northward turbulent surface stress","parameter_ID":"235042","product_type":"forecast","shortName":"avg_inss","standard_name":"Time-mean_northward_turbulent_surface_stress","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235042"},"dimensions":["lat","lon","time"],"type":"data","unit":"N m-2"},"Time-mean_potential_vorticity(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean potential vorticity","parameter_ID":"235100","product_type":"forecast","shortName":"avg_pv","standard_name":"Time-mean_potential_vorticity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235100"},"dimensions":["lat","lon","time"],"type":"data","unit":"K m2kg-1s-1"},"Time-mean_relative_humidity(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean relative humidity","parameter_ID":"235157","product_type":"forecast","shortName":"avg_r","standard_name":"Time-mean_relative_humidity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235157"},"dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Time-mean_sea_ice_area_fraction(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea ice area fraction","parameter_ID":"263001","product_type":"forecast","shortName":"avg_siconc","standard_name":"Time-mean_sea_ice_area_fraction","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263001"},"dimensions":["lat","lon","time"],"type":"data","unit":"Fraction"},"Time-mean_sea_ice_area_fraction(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea ice area fraction","parameter_ID":"263001","product_type":"forecast","shortName":"avg_siconc","standard_name":"Time-mean_sea_ice_area_fraction","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263001"},"dimensions":["lat","lon","time"],"type":"data","unit":"Fraction"},"Time-mean_sea_ice_thickness(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea ice 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sky","parameter_ID":"235049","product_type":"forecast","shortName":"avg_tnswrfcs","standard_name":"Time-mean_top_net_short-wave_radiation_flux,_clear_sky","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235049"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Time-mean_top_net_short-wave_radiation_flux,_clear_sky(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean top net short-wave radiation flux, clear sky","parameter_ID":"235049","product_type":"forecast","shortName":"avg_tnswrfcs","standard_name":"Time-mean_top_net_short-wave_radiation_flux,_clear_sky","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235049"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Time-mean_total_cloud_cover(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total cloud cover","parameter_ID":"235288","product_type":"forecast","shortName":"avg_tcc","standard_name":"Time-mean_total_cloud_cover","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235288"},"dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Time-mean_total_column_cloud_ice_water(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total column cloud ice water","parameter_ID":"235088","product_type":"forecast","shortName":"avg_tciw","standard_name":"Time-mean_total_column_cloud_ice_water","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235088"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Time-mean_total_column_heat_content(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean total column heat content","parameter_ID":"263123","product_type":"forecast","shortName":"avg_hcbtm","standard_name":"Time-mean_total_column_heat_content","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263123"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_total_column_heat_content(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean total column heat content","parameter_ID":"263123","product_type":"forecast","shortName":"avg_hcbtm","standard_name":"Time-mean_total_column_heat_content","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263123"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_total_column_liquid_water(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total column liquid water","parameter_ID":"235087","product_type":"forecast","shortName":"avg_tclw","standard_name":"Time-mean_total_column_liquid_water","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235087"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Time-mean_total_column_vertically-integrated_water_vapour(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total column vertically-integrated water vapour","parameter_ID":"235137","product_type":"forecast","shortName":"avg_tcwv","standard_name":"Time-mean_total_column_vertically-integrated_water_vapour","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235137"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Time-mean_total_column_water(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total column water","parameter_ID":"235136","product_type":"forecast","shortName":"avg_tcw","standard_name":"Time-mean_total_column_water","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235136"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Time-mean_total_precipitation_rate(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total precipitation rate","parameter_ID":"235055","product_type":"forecast","shortName":"avg_tprate","standard_name":"Time-mean_total_precipitation_rate","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235055"},"description":"Time-mean total precipitation rate, or time-mean total precipitation flux. This parameter is on level \"Ground or water surface\" (typeOfFirstFixedSurface=1). For this parameter on other levels, please use 235013.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_total_precipitation_rate(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total precipitation rate","parameter_ID":"235055","product_type":"forecast","shortName":"avg_tprate","standard_name":"Time-mean_total_precipitation_rate","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235055"},"description":"Time-mean total precipitation rate, or time-mean total precipitation flux. This parameter is on level \"Ground or water surface\" (typeOfFirstFixedSurface=1). For this parameter on other levels, please use 235013.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_total_snowfall_rate_water_equivalent(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total snowfall rate water equivalent","parameter_ID":"235031","product_type":"forecast","shortName":"avg_tsrwe","standard_name":"Time-mean_total_snowfall_rate_water_equivalent","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235031"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_total_snowfall_rate_water_equivalent(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total snowfall rate water equivalent","parameter_ID":"235031","product_type":"forecast","shortName":"avg_tsrwe","standard_name":"Time-mean_total_snowfall_rate_water_equivalent","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235031"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_upward_sea_water_velocity(clmn_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean upward sea water velocity","parameter_ID":"263507","product_type":"forecast","shortName":"avg_wo","standard_name":"Time-mean_upward_sea_water_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263507"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_upward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean upward sea water velocity","parameter_ID":"263507","product_type":"forecast","shortName":"avg_wo","standard_name":"Time-mean_upward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263507"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_vertical_velocity(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean vertical velocity","parameter_ID":"235135","product_type":"forecast","shortName":"avg_w","standard_name":"Time-mean_vertical_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235135"},"dimensions":["lat","lon","time"],"type":"data","unit":"Pa s-1"},"Time-mean_vertically-integrated_heat_content_in_the_upper_300_m(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"300","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 300 m","parameter_ID":"263121","product_type":"forecast","shortName":"avg_hc300m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_300_m","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263121"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_vertically-integrated_heat_content_in_the_upper_300_m(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"300","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 300 m","parameter_ID":"263121","product_type":"forecast","shortName":"avg_hc300m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_300_m","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263121"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"700","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 700 m","parameter_ID":"263122","product_type":"forecast","shortName":"avg_hc700m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263122"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"700","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 700 m","parameter_ID":"263122","product_type":"forecast","shortName":"avg_hc700m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263122"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_volumetric_soil_moisture(clmn_sol)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Time-mean volumetric soil moisture","parameter_ID":"235077","product_type":"forecast","shortName":"avg_vsw","standard_name":"Time-mean_volumetric_soil_moisture","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235077"},"dimensions":["lat","lon","time"],"type":"data","unit":"m3m-3"},"Time_mean_top_downward_short-wave_radiation_flux(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time mean top downward short-wave radiation flux","parameter_ID":"235053","product_type":"forecast","shortName":"avg_tdswrf","standard_name":"Time_mean_top_downward_short-wave_radiation_flux","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235053"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Time_mean_top_downward_short-wave_radiation_flux(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time mean top downward short-wave radiation flux","parameter_ID":"235053","product_type":"forecast","shortName":"avg_tdswrf","standard_name":"Time_mean_top_downward_short-wave_radiation_flux","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235053"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Total_Cloud_Cover(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total Cloud Cover","parameter_ID":"228164","product_type":"forecast","shortName":"tcc","standard_name":"Total_Cloud_Cover","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228164"},"description":"[NOTE: See 164 for the equivalent parameter in \"(0-1)\"]","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Total_column_cloud_ice_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud ice water","parameter_ID":"79","product_type":"forecast","shortName":"tciw","standard_name":"Total_column_cloud_ice_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/79"},"description":"This parameter is the amount of ice contained within clouds in a column extending from the surface of the Earth to the top of the atmosphere. Snow (aggregated ice crystals) is not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The  ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_cloud_liquid_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud liquid water","parameter_ID":"78","product_type":"forecast","shortName":"tclw","standard_name":"Total_column_cloud_liquid_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/78"},"description":"This parameter is the amount of liquid water contained within cloud droplets in a column extending from the surface of the Earth to the top of the atmosphere. Rain water droplets, which are much larger in size (and mass), are not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_vertically-integrated_water_vapour(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column vertically-integrated water vapour","parameter_ID":"137","product_type":"forecast","shortName":"tcwv","standard_name":"Total_column_vertically-integrated_water_vapour","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/137"},"description":"This parameter is the total amount of water vapour in a column extending from the surface of the Earth to the top of the atmosphere.This parameter represents the area averaged value for agrid box.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column water","parameter_ID":"136","product_type":"forecast","shortName":"tcw","standard_name":"Total_column_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/136"},"description":"This parameter is the sum of water vapour, liquid water, cloud ice, rain and snow in a column extending from the surface of the Earth to the top of the atmosphere. In old versions of the ECMWF model (IFS), rain and snow were not accounted for.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"U_component_of_wind(clte_hl)":{"attrs":{"encoding":"instantaneous","levelist":"100","levtype":"hl","long_name":"U component of wind","parameter_ID":"131","product_type":"forecast","shortName":"u","standard_name":"U_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/131"},"description":"This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east, in metres per second. A negative sign thus indicates air movement towards the west.This parameter can be combined with the V component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"U_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"U component of wind","parameter_ID":"131","product_type":"forecast","shortName":"u","standard_name":"U_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/131"},"description":"This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east, in metres per second. A negative sign thus indicates air movement towards the west.This parameter can be combined with the V component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"V_component_of_wind(clte_hl)":{"attrs":{"encoding":"instantaneous","levelist":"100","levtype":"hl","long_name":"V component of wind","parameter_ID":"132","product_type":"forecast","shortName":"v","standard_name":"V_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/132"},"description":"This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north, in metres per second. A negative sign thus indicates air movement towards the south.This parameter can be combined with the U component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"V_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"V component of wind","parameter_ID":"132","product_type":"forecast","shortName":"v","standard_name":"V_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/132"},"description":"This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north, in metres per second. A negative sign thus indicates air movement towards the south.This parameter can be combined with the U component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Vertical_velocity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Vertical velocity","parameter_ID":"135","product_type":"forecast","shortName":"w","standard_name":"Vertical_velocity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/135"},"description":"This parameter is the speed of air motion in the upward or downward direction. The ECMWF Integrated Forecasting System (IFS) uses a pressure based vertical co-ordinate system and pressure decreases with height, therefore negative values of vertical velocity indicate upward motion.Vertical velocity can be useful to understand the large-scale dynamics of the atmosphere, including areas of upward motion/ascent (negative values) and downward motion/subsidence (positive values).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa s-1"},"Volumetric_soil_moisture(clte_sol)":{"attrs":{"encoding":"instantaneous","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Volumetric soil moisture","parameter_ID":"260199","product_type":"forecast","shortName":"vsw","standard_name":"Volumetric_soil_moisture","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/260199"},"description":"Please note that the encoding listed here for uerra (which includes carra/cerra) includes entries for Time-mean volumetric soil moisture. The specific encoding for Time-mean volumetric soil moisture can be found in 235077.","dimensions":["lat","lon","time"],"type":"data","unit":"m3m-3"}},"sci:publications":[{"doi":"10.1109/MCSE.2023.3260519","citation":"N. Wedi et al., Destination Earth: High-Performance Computing for Weather and Climate, in Computing in Science \u0026 Engineering, vol. 24, no. 6, pp. 29-37, Nov.-Dec. 2022,"},{"doi":"10.21957/d3f982672e","citation":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"dedl:short_description":"The Climate Change Adaptation Digital Twin provides global climate projections and sector-specific information over multiple decades at high resolution via a unified framework combining advanced Earth system models, impact assessments, and observations. This Generation-2, Realization-2 Collection gives access to 'Storyline Simulation Future Climate' data based on the 'IFS-FESOM' model."},{"type":"Collection","title":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Storyline Simulation Future Climate - IFS-FESOM - Generation-2 - Realization-3","id":"EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_TPLUS2.0K_IFS-FESOM.R3","description":"The DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities by providing innovative climate information on multi-decadal timescales, globally, at scales at which many impacts of climate change are observed. It combines cutting-edge global Earth-system models, impact-sector applications and observations into a unified framework to provide global climate projections and impact-sector information on multi-decadal timescales (1990 to ~2050), at very high spatial resolutions (5 to 10 km).\n\nThe Climate DT represents the first ever attempt to operationalise the production of global multi-decadal climate projections, leveraging the world-leading supercomputing facilities of the EuroHPC Joint Undertaking along with some of the leading European climate models. A concise overview of what the Climate DT aims to achieve, and of the different concepts essential for an understanding of the Digital Twin’s characteristics, is included in the [Climate DT factsheet](https://destine.ecmwf.int/wp-content/uploads/2024/06/2024.06.07_Climate-DT-Fact-Sheet_V7-2.pdf)\n\n## Storyline Simulation Future Climate \n\n Storyline simulations offer a what-if capability to explore how a weather event we experienced in the recent past would change in a warmer climate and how it would have looked in past pre-industrial conditions. To constrain simulated weather events, a nudging approach is used to keep the simulated large-scale flow close to the ERA5 reanalysis for the period 2017 – 2023. Processes on smaller scales and thermodynamic processes are free to evolve, which allows scientists to study e.g. how extreme precipitation would change in a warmer world for a weather event observed in present-day conditions. Initial conditions are taken from ocean-only spinup simulations for 1950 and 2017, and from the IFS-FESOM projection for a +2 °C world. Storyline simulations will be updated regularly to include most recent months. The next update will extend all storylines to include 2024.\n\n## Models\n\nThe Climate DT exploits and further evolves a new generation of global storm-resolving and eddy-rich models built through a cooperative model development approach. For more information on models please click [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#models)\n\n## Simulations\n\nThe Climate DT team carries out several types of digital twin simulations on the EuroHPC supercomputers.  Multi-decadal simulations are produced to cover the recent past (from 1990) and possible future evolutions of the climate up to 2050. See [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#simulations) for more information on Simulations\n\n## Parameters\n\nBelow we see the list of parameters extracted from the 'DestinE Climate DT data portfolio', for more information please refer to the pages [Climate DT Phase2 CLTE Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters) and [Climate DT Phase2 CLMN Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters)","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_TPLUS2.0K_IFS-FESOM.R3/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_TPLUS2.0K_IFS-FESOM.R3/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_TPLUS2.0K_IFS-FESOM.R3/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_TPLUS2.0K_IFS-FESOM.R3","title":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Storyline Simulation Future Climate - IFS-FESOM - Generation-2 - Realization-3"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters","title":"DestinE ClimateDT Phase 2 CLTE Parameters"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters","title":"DestinE ClimateDT Phase 2 CLMN Parameters"},{"rel":"cite-as","type":"text/html","href":"https://dl.acm.org/doi/abs/10.1109/MCSE.2023.3260519","title":"Destination Earth: High-Performance Computing for Weather and Climate"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.21957/d3f982672e","title":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"assets":{"thumbnail":{"href":"https://s3.central.data.destination-earth.eu/swift/v1/dedl-public/collections/climate-dt-min.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2017-01-01T00:00:00Z","2025-12-31T23:59:59Z"]]}},"license":"CC-BY-4.0","keywords":["Digital Twins","Climate Change","Atmosphere","Ocean","Land","Sea Ice","Snow","Soil","Earth","High Performance Computing","Decision Making","Europe","Meteorology","class:d1","dataset:climate-dt","generation:2","expver:0001","stream:clmn,clte","type:fc","activity:story-nudging","experiment:Tplus2.0K","realization:3","model:IFS-FESOM","resolution:standard,high","levtype:hl,o2d,o3d,pl,sfc,sol"],"summaries":{"federation:backends":["dedt_mn5"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/datacube/v2.1.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["producer","processor","licensor"],"url":"https://www.ecmwf.int/"}],"created":"2024-04-11T22:31:55Z","updated":"2026-04-24T10:24:59Z","published":"2024-04-11T22:31:55Z","cube:dimensions":{"lat":{"axis":"y","description":"latitude","extent":[-90,90],"type":"spatial"},"lon":{"axis":"x","description":"longitude","extent":[-180,180],"type":"spatial"},"time":{"extent":["2017-01-01T00:00:00Z","2025-12-31T23:59:59Z"],"step":"P0Y0M0DT1H0M0S","type":"temporal"}},"cube:variables":{"10_metre_U_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre U wind component","parameter_ID":"165","product_type":"forecast","shortName":"10u","standard_name":"10_metre_U_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/165"},"description":"This parameter is the eastward component of the 10m wind. It is the horizontal speed of air moving towards the east, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the V component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_V_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre V wind component","parameter_ID":"166","product_type":"forecast","shortName":"10v","standard_name":"10_metre_V_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/166"},"description":"This parameter is the northward component of the 10m wind. It is the horizontal speed of air moving towards the north, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the U component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_wind_speed(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre wind speed","parameter_ID":"207","product_type":"forecast","shortName":"10si","standard_name":"10_metre_wind_speed","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/207"},"description":"This parameter is the horizontal speed of the wind, or movement of air, at a height of ten metres above the surface of the Earth. The units of this parameter are metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.The eastward and northward components of the horizontal wind at 10m are also available as parameters.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"2_metre_dewpoint_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"2","levtype":"sfc","long_name":"2 metre dewpoint temperature","parameter_ID":"168","product_type":"forecast","shortName":"2d","standard_name":"2_metre_dewpoint_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/168"},"description":"This parameter is the temperature to which the air, at 2 metres above the surface of the Earth, would have to be cooled for saturation to occur.It is a measure of the humidity of the air. Combined with temperature and pressure, it can be used to calculate the relative humidity.2m dew point temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"2_metre_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"2","levtype":"sfc","long_name":"2 metre temperature","parameter_ID":"167","product_type":"forecast","shortName":"2t","standard_name":"2_metre_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/167"},"description":"This parameter is the temperature of air at 2m above the surface of land, sea or in-land waters.2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.Please note that the encodings listed here for s2s \u0026 uerra (which includes encodings for carra/cerra) include entries for Mean 2 metre temperature. The specific encoding for Mean 2 metre temperature can be found in 228004.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Geopotential(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Geopotential","parameter_ID":"129","product_type":"forecast","shortName":"z","standard_name":"Geopotential","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/129"},"description":"This parameter is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level.The geopotential height can be calculated by dividing the geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2). The geopotential height plays an important role in synoptic meteorology (analysis of weather patterns). Charts of geopotential height plotted at constant pressure levels (e.g., 300, 500 or 850 hPa) can be used to identify weather systems such as cyclones, anticyclones, troughs and ridges.At the surface of the Earth, this parameter shows the variations in geopotential (height) of the surface, and is often referred to as the orography.","dimensions":["lat","lon","time"],"type":"data","unit":"m2s-2"},"Land-sea_mask(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Land-sea mask","parameter_ID":"172","product_type":"forecast","shortName":"lsm","standard_name":"Land-sea_mask","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/172"},"description":"This parameter is the proportion of land, as opposed to ocean or inland waters (lakes, reservoirs, rivers and coastal waters), in agrid box.This parameter has values ranging between zero and one and is dimensionless.In cycles of the ECMWF Integrated Forecasting System (IFS) from CY41R1 (introduced in May 2015) onwards, grid boxes where this parameter has a value above 0.5 can be comprised of a mixture of land and inland water but not ocean. Grid boxes with a value of 0.5 and below can only be comprised of a water surface. In the latter case, the lake cover is used to determine how much of the water surface is ocean or inland water.In cycles of the IFS before CY41R1, grid boxes where this parameter has a value above 0.5 can only be comprised of land and those grid boxes with a value of 0.5 and below can only be comprised of ocean. In these older model cycles, there is no differentiation between ocean and inland water.","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Mean_sea_level_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Mean sea level pressure","parameter_ID":"151","product_type":"forecast","shortName":"msl","standard_name":"Mean_sea_level_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/151"},"description":"This parameter is the pressure (force per unit area) of the atmosphere adjusted to the height of mean sea level.It is a measure of the weight that all the air in a column vertically above the area of Earth's surface would have at that point, if the point were located at the mean sea level. It is calculated over all surfaces - land, sea and in-land water.Maps of mean sea level pressure are used to identify the locations of low and high pressure systems, often referred to as cyclones and anticyclones. Contours of mean sea level pressure also indicate the strength of the wind. Tightly packed contours show stronger winds.The units of this parameter are pascals (Pa).  Mean sea level pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb = 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Orography(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Orography","parameter_ID":"228002","product_type":"forecast","shortName":"orog","standard_name":"Orography","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/228002"},"dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Potential_vorticity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Potential vorticity","parameter_ID":"60","product_type":"forecast","shortName":"pv","standard_name":"Potential_vorticity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/60"},"description":"Potential vorticity is a measure of the capacity for air to rotate in the atmosphere. If we ignore the effects of heating and friction, potential vorticity is conserved following an air parcel. It is used to look for places where large wind storms are likely to originate and develop.  Potential vorticity increases strongly above the tropopause and therefore, it can also be used in studies related to the stratosphere and stratosphere-troposphere exchanges.Large wind storms develop when a column of air in the atmosphere starts to rotate. Potential vorticity is calculated from the wind, temperature and pressure across a column of air in the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K m2kg-1s-1"},"Relative_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Relative humidity","parameter_ID":"157","product_type":"forecast","shortName":"r","standard_name":"Relative_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/157"},"description":"This parameter is the water vapour pressure as a percentage of the value at which the air becomes saturated (the point at which water vapour begins to condense into liquid water or deposition into ice).For temperatures over 0°C (273.15 K) it is calculated for saturation over water. At temperatures below -23°C it is calculated for saturation over ice.  Between -23°C and 0°C this parameter is calculated by interpolating between the ice and water values using a quadratic function.See more information about the model's relative humidity calculation.","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Skin_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Skin temperature","parameter_ID":"235","product_type":"forecast","shortName":"skt","standard_name":"Skin_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235"},"description":"This parameter is the temperature of the surface of the Earth.The skin temperature is the theoretical temperature that is required to satisfy the surface energy balance. It represents the temperature of the uppermost surface layer, which has no heat capacity and so can respond instantaneously to changes in surface fluxes. Skin temperature is calculated differently over land and sea.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.See further information about the skin temperatureover landandover sea.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean skin temperature. The specific encoding for Mean skin temperature can be found in 235079.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Snow_depth_water_equivalent(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Snow depth water equivalent","parameter_ID":"228141","product_type":"forecast","shortName":"sd","standard_name":"Snow_depth_water_equivalent","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228141"},"description":"Snow depth water equivalent in kg m**-2 (mm) water equivalent.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean snow depth water equivalent. The specific encoding for Time-mean snow depth water equivalent can be found in 235078.[NOTE: See 141 for the equivalent parameter in \"m of water equivalent\"]","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Snow_depth_water_equivalent(clte_sol)":{"attrs":{"encoding":"instantaneous","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Snow depth water equivalent","parameter_ID":"228141","product_type":"forecast","shortName":"sd","standard_name":"Snow_depth_water_equivalent","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228141"},"description":"Snow depth water equivalent in kg m**-2 (mm) water equivalent.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean snow depth water equivalent. The specific encoding for Time-mean snow depth water equivalent can be found in 235078.[NOTE: See 141 for the equivalent parameter in \"m of water equivalent\"]","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Specific_cloud_liquid_water_content(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific cloud liquid water content","parameter_ID":"246","product_type":"forecast","shortName":"clwc","standard_name":"Specific_cloud_liquid_water_content","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/246"},"description":"This parameter is the mass of cloud liquid water droplets per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for agrid box.Water within clouds can be liquid or ice, or a combination of the two.See further information about the cloud formulation.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Specific_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific humidity","parameter_ID":"133","product_type":"forecast","shortName":"q","standard_name":"Specific_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/133"},"description":"This parameter is the mass of water vapour per kilogram of moist air.The total mass of moist air is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Surface_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Surface pressure","parameter_ID":"134","product_type":"forecast","shortName":"sp","standard_name":"Surface_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/134"},"description":"This parameter is the pressure (force per unit area) of the atmosphere on the surface of land, sea and in-land water.It is a measure of the weight of all the air in a column vertically above the area of the Earth's surface represented at a fixed point.Surface pressure is often used in combination with temperature to calculate air density.The strong variation of pressure with altitude makes it difficult to see the low and high pressure systems over mountainous areas, so mean sea level pressure, rather than surface pressure, is normally used for this purpose.The units of this parameter are Pascals (Pa). Surface pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb= 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Temperature(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Temperature","parameter_ID":"130","product_type":"forecast","shortName":"t","standard_name":"Temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/130"},"description":"This parameter is the temperature in the atmosphere.It has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.This parameter is available on multiple levels through the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_10_metre_U_wind_component(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre U wind component","parameter_ID":"235165","product_type":"forecast","shortName":"avg_10u","standard_name":"Time-mean_10_metre_U_wind_component","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235165"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_10_metre_V_wind_component(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre V wind component","parameter_ID":"235166","product_type":"forecast","shortName":"avg_10v","standard_name":"Time-mean_10_metre_V_wind_component","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235166"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_10_metre_wind_speed(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre wind speed","parameter_ID":"228005","product_type":"forecast","shortName":"avg_10ws","standard_name":"Time-mean_10_metre_wind_speed","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/228005"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_2_metre_dewpoint_temperature(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"2","levtype":"sfc","long_name":"Time-mean 2 metre dewpoint temperature","parameter_ID":"235168","product_type":"forecast","shortName":"avg_2d","standard_name":"Time-mean_2_metre_dewpoint_temperature","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235168"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_2_metre_temperature(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"2","levtype":"sfc","long_name":"Time-mean 2 metre temperature","parameter_ID":"228004","product_type":"forecast","shortName":"avg_2t","standard_name":"Time-mean_2_metre_temperature","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/228004"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_U_component_of_wind(clmn_hl)":{"attrs":{"encoding":"mean","levelist":"100","levtype":"hl","long_name":"Time-mean U component of wind","parameter_ID":"235131","product_type":"forecast","shortName":"avg_u","standard_name":"Time-mean_U_component_of_wind","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235131"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_U_component_of_wind(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean U component of wind","parameter_ID":"235131","product_type":"forecast","shortName":"avg_u","standard_name":"Time-mean_U_component_of_wind","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235131"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_V_component_of_wind(clmn_hl)":{"attrs":{"encoding":"mean","levelist":"100","levtype":"hl","long_name":"Time-mean V component of 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m-2"},"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"700","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 700 m","parameter_ID":"263122","product_type":"forecast","shortName":"avg_hc700m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263122"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_volumetric_soil_moisture(clmn_sol)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Time-mean volumetric soil moisture","parameter_ID":"235077","product_type":"forecast","shortName":"avg_vsw","standard_name":"Time-mean_volumetric_soil_moisture","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235077"},"dimensions":["lat","lon","time"],"type":"data","unit":"m3m-3"},"Time_mean_top_downward_short-wave_radiation_flux(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time mean top downward short-wave radiation flux","parameter_ID":"235053","product_type":"forecast","shortName":"avg_tdswrf","standard_name":"Time_mean_top_downward_short-wave_radiation_flux","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235053"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Time_mean_top_downward_short-wave_radiation_flux(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time mean top downward short-wave radiation flux","parameter_ID":"235053","product_type":"forecast","shortName":"avg_tdswrf","standard_name":"Time_mean_top_downward_short-wave_radiation_flux","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235053"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Total_Cloud_Cover(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total Cloud Cover","parameter_ID":"228164","product_type":"forecast","shortName":"tcc","standard_name":"Total_Cloud_Cover","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228164"},"description":"[NOTE: See 164 for the equivalent parameter in \"(0-1)\"]","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Total_column_cloud_ice_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud ice water","parameter_ID":"79","product_type":"forecast","shortName":"tciw","standard_name":"Total_column_cloud_ice_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/79"},"description":"This parameter is the amount of ice contained within clouds in a column extending from the surface of the Earth to the top of the atmosphere. Snow (aggregated ice crystals) is not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The  ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_cloud_liquid_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud liquid water","parameter_ID":"78","product_type":"forecast","shortName":"tclw","standard_name":"Total_column_cloud_liquid_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/78"},"description":"This parameter is the amount of liquid water contained within cloud droplets in a column extending from the surface of the Earth to the top of the atmosphere. Rain water droplets, which are much larger in size (and mass), are not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_vertically-integrated_water_vapour(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column vertically-integrated water vapour","parameter_ID":"137","product_type":"forecast","shortName":"tcwv","standard_name":"Total_column_vertically-integrated_water_vapour","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/137"},"description":"This parameter is the total amount of water vapour in a column extending from the surface of the Earth to the top of the atmosphere.This parameter represents the area averaged value for agrid box.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column water","parameter_ID":"136","product_type":"forecast","shortName":"tcw","standard_name":"Total_column_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/136"},"description":"This parameter is the sum of water vapour, liquid water, cloud ice, rain and snow in a column extending from the surface of the Earth to the top of the atmosphere. In old versions of the ECMWF model (IFS), rain and snow were not accounted for.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"U_component_of_wind(clte_hl)":{"attrs":{"encoding":"instantaneous","levelist":"100","levtype":"hl","long_name":"U component of wind","parameter_ID":"131","product_type":"forecast","shortName":"u","standard_name":"U_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/131"},"description":"This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east, in metres per second. A negative sign thus indicates air movement towards the west.This parameter can be combined with the V component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"U_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"U component of wind","parameter_ID":"131","product_type":"forecast","shortName":"u","standard_name":"U_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/131"},"description":"This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east, in metres per second. A negative sign thus indicates air movement towards the west.This parameter can be combined with the V component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"V_component_of_wind(clte_hl)":{"attrs":{"encoding":"instantaneous","levelist":"100","levtype":"hl","long_name":"V component of wind","parameter_ID":"132","product_type":"forecast","shortName":"v","standard_name":"V_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/132"},"description":"This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north, in metres per second. A negative sign thus indicates air movement towards the south.This parameter can be combined with the U component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"V_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"V component of wind","parameter_ID":"132","product_type":"forecast","shortName":"v","standard_name":"V_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/132"},"description":"This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north, in metres per second. A negative sign thus indicates air movement towards the south.This parameter can be combined with the U component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Vertical_velocity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Vertical velocity","parameter_ID":"135","product_type":"forecast","shortName":"w","standard_name":"Vertical_velocity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/135"},"description":"This parameter is the speed of air motion in the upward or downward direction. The ECMWF Integrated Forecasting System (IFS) uses a pressure based vertical co-ordinate system and pressure decreases with height, therefore negative values of vertical velocity indicate upward motion.Vertical velocity can be useful to understand the large-scale dynamics of the atmosphere, including areas of upward motion/ascent (negative values) and downward motion/subsidence (positive values).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa s-1"},"Volumetric_soil_moisture(clte_sol)":{"attrs":{"encoding":"instantaneous","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Volumetric soil moisture","parameter_ID":"260199","product_type":"forecast","shortName":"vsw","standard_name":"Volumetric_soil_moisture","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/260199"},"description":"Please note that the encoding listed here for uerra (which includes carra/cerra) includes entries for Time-mean volumetric soil moisture. The specific encoding for Time-mean volumetric soil moisture can be found in 235077.","dimensions":["lat","lon","time"],"type":"data","unit":"m3m-3"}},"sci:publications":[{"doi":"10.1109/MCSE.2023.3260519","citation":"N. Wedi et al., Destination Earth: High-Performance Computing for Weather and Climate, in Computing in Science \u0026 Engineering, vol. 24, no. 6, pp. 29-37, Nov.-Dec. 2022,"},{"doi":"10.21957/d3f982672e","citation":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"dedl:short_description":"The Climate Change Adaptation Digital Twin provides global climate projections and sector-specific information over multiple decades at high resolution via a unified framework combining advanced Earth system models, impact assessments, and observations. This Generation-2, Realization-3 Collection gives access to 'Storyline Simulation Future Climate' data based on the 'IFS-FESOM' model."},{"type":"Collection","title":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Storyline Simulation Future Climate - IFS-FESOM - Generation-2 - Realization-4","id":"EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_TPLUS2.0K_IFS-FESOM.R4","description":"The DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities by providing innovative climate information on multi-decadal timescales, globally, at scales at which many impacts of climate change are observed. It combines cutting-edge global Earth-system models, impact-sector applications and observations into a unified framework to provide global climate projections and impact-sector information on multi-decadal timescales (1990 to ~2050), at very high spatial resolutions (5 to 10 km).\n\nThe Climate DT represents the first ever attempt to operationalise the production of global multi-decadal climate projections, leveraging the world-leading supercomputing facilities of the EuroHPC Joint Undertaking along with some of the leading European climate models. A concise overview of what the Climate DT aims to achieve, and of the different concepts essential for an understanding of the Digital Twin’s characteristics, is included in the [Climate DT factsheet](https://destine.ecmwf.int/wp-content/uploads/2024/06/2024.06.07_Climate-DT-Fact-Sheet_V7-2.pdf)\n\n## Storyline Simulation Future Climate \n\n Storyline simulations offer a what-if capability to explore how a weather event we experienced in the recent past would change in a warmer climate and how it would have looked in past pre-industrial conditions. To constrain simulated weather events, a nudging approach is used to keep the simulated large-scale flow close to the ERA5 reanalysis for the period 2017 – 2023. Processes on smaller scales and thermodynamic processes are free to evolve, which allows scientists to study e.g. how extreme precipitation would change in a warmer world for a weather event observed in present-day conditions. Initial conditions are taken from ocean-only spinup simulations for 1950 and 2017, and from the IFS-FESOM projection for a +2 °C world. Storyline simulations will be updated regularly to include most recent months. The next update will extend all storylines to include 2024.\n\n## Models\n\nThe Climate DT exploits and further evolves a new generation of global storm-resolving and eddy-rich models built through a cooperative model development approach. For more information on models please click [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#models)\n\n## Simulations\n\nThe Climate DT team carries out several types of digital twin simulations on the EuroHPC supercomputers.  Multi-decadal simulations are produced to cover the recent past (from 1990) and possible future evolutions of the climate up to 2050. See [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#simulations) for more information on Simulations\n\n## Parameters\n\nBelow we see the list of parameters extracted from the 'DestinE Climate DT data portfolio', for more information please refer to the pages [Climate DT Phase2 CLTE Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters) and [Climate DT Phase2 CLMN Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters)","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_TPLUS2.0K_IFS-FESOM.R4/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_TPLUS2.0K_IFS-FESOM.R4/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_TPLUS2.0K_IFS-FESOM.R4/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_TPLUS2.0K_IFS-FESOM.R4","title":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Storyline Simulation Future Climate - IFS-FESOM - Generation-2 - Realization-4"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters","title":"DestinE ClimateDT Phase 2 CLTE Parameters"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters","title":"DestinE ClimateDT Phase 2 CLMN Parameters"},{"rel":"cite-as","type":"text/html","href":"https://dl.acm.org/doi/abs/10.1109/MCSE.2023.3260519","title":"Destination Earth: High-Performance Computing for Weather and Climate"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.21957/d3f982672e","title":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"assets":{"thumbnail":{"href":"https://s3.central.data.destination-earth.eu/swift/v1/dedl-public/collections/climate-dt-min.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2017-01-01T00:00:00Z","2025-12-31T23:59:59Z"]]}},"license":"CC-BY-4.0","keywords":["Digital Twins","Climate Change","Atmosphere","Ocean","Land","Sea Ice","Snow","Soil","Earth","High Performance Computing","Decision Making","Europe","Meteorology","class:d1","dataset:climate-dt","generation:2","expver:0001","stream:clmn,clte","type:fc","activity:story-nudging","experiment:Tplus2.0K","realization:4","model:IFS-FESOM","resolution:standard,high","levtype:hl,o2d,o3d,pl,sfc,sol"],"summaries":{"federation:backends":["dedt_mn5"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/datacube/v2.1.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["producer","processor","licensor"],"url":"https://www.ecmwf.int/"}],"created":"2024-04-11T22:31:55Z","updated":"2026-04-24T10:24:59Z","published":"2024-04-11T22:31:55Z","cube:dimensions":{"lat":{"axis":"y","description":"latitude","extent":[-90,90],"type":"spatial"},"lon":{"axis":"x","description":"longitude","extent":[-180,180],"type":"spatial"},"time":{"extent":["2017-01-01T00:00:00Z","2025-12-31T23:59:59Z"],"step":"P0Y0M0DT1H0M0S","type":"temporal"}},"cube:variables":{"10_metre_U_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre U wind component","parameter_ID":"165","product_type":"forecast","shortName":"10u","standard_name":"10_metre_U_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/165"},"description":"This parameter is the eastward component of the 10m wind. It is the horizontal speed of air moving towards the east, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the V component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_V_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre V wind component","parameter_ID":"166","product_type":"forecast","shortName":"10v","standard_name":"10_metre_V_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/166"},"description":"This parameter is the northward component of the 10m wind. It is the horizontal speed of air moving towards the north, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the U component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_wind_speed(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre wind speed","parameter_ID":"207","product_type":"forecast","shortName":"10si","standard_name":"10_metre_wind_speed","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/207"},"description":"This parameter is the horizontal speed of the wind, or movement of air, at a height of ten metres above the surface of the Earth. The units of this parameter are metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.The eastward and northward components of the horizontal wind at 10m are also available as parameters.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"2_metre_dewpoint_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"2","levtype":"sfc","long_name":"2 metre dewpoint temperature","parameter_ID":"168","product_type":"forecast","shortName":"2d","standard_name":"2_metre_dewpoint_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/168"},"description":"This parameter is the temperature to which the air, at 2 metres above the surface of the Earth, would have to be cooled for saturation to occur.It is a measure of the humidity of the air. Combined with temperature and pressure, it can be used to calculate the relative humidity.2m dew point temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"2_metre_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"2","levtype":"sfc","long_name":"2 metre temperature","parameter_ID":"167","product_type":"forecast","shortName":"2t","standard_name":"2_metre_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/167"},"description":"This parameter is the temperature of air at 2m above the surface of land, sea or in-land waters.2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.Please note that the encodings listed here for s2s \u0026 uerra (which includes encodings for carra/cerra) include entries for Mean 2 metre temperature. The specific encoding for Mean 2 metre temperature can be found in 228004.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Geopotential(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Geopotential","parameter_ID":"129","product_type":"forecast","shortName":"z","standard_name":"Geopotential","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/129"},"description":"This parameter is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level.The geopotential height can be calculated by dividing the geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2). The geopotential height plays an important role in synoptic meteorology (analysis of weather patterns). Charts of geopotential height plotted at constant pressure levels (e.g., 300, 500 or 850 hPa) can be used to identify weather systems such as cyclones, anticyclones, troughs and ridges.At the surface of the Earth, this parameter shows the variations in geopotential (height) of the surface, and is often referred to as the orography.","dimensions":["lat","lon","time"],"type":"data","unit":"m2s-2"},"Land-sea_mask(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Land-sea mask","parameter_ID":"172","product_type":"forecast","shortName":"lsm","standard_name":"Land-sea_mask","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/172"},"description":"This parameter is the proportion of land, as opposed to ocean or inland waters (lakes, reservoirs, rivers and coastal waters), in agrid box.This parameter has values ranging between zero and one and is dimensionless.In cycles of the ECMWF Integrated Forecasting System (IFS) from CY41R1 (introduced in May 2015) onwards, grid boxes where this parameter has a value above 0.5 can be comprised of a mixture of land and inland water but not ocean. Grid boxes with a value of 0.5 and below can only be comprised of a water surface. In the latter case, the lake cover is used to determine how much of the water surface is ocean or inland water.In cycles of the IFS before CY41R1, grid boxes where this parameter has a value above 0.5 can only be comprised of land and those grid boxes with a value of 0.5 and below can only be comprised of ocean. In these older model cycles, there is no differentiation between ocean and inland water.","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Mean_sea_level_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Mean sea level pressure","parameter_ID":"151","product_type":"forecast","shortName":"msl","standard_name":"Mean_sea_level_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/151"},"description":"This parameter is the pressure (force per unit area) of the atmosphere adjusted to the height of mean sea level.It is a measure of the weight that all the air in a column vertically above the area of Earth's surface would have at that point, if the point were located at the mean sea level. It is calculated over all surfaces - land, sea and in-land water.Maps of mean sea level pressure are used to identify the locations of low and high pressure systems, often referred to as cyclones and anticyclones. Contours of mean sea level pressure also indicate the strength of the wind. Tightly packed contours show stronger winds.The units of this parameter are pascals (Pa).  Mean sea level pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb = 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Orography(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Orography","parameter_ID":"228002","product_type":"forecast","shortName":"orog","standard_name":"Orography","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/228002"},"dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Potential_vorticity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Potential vorticity","parameter_ID":"60","product_type":"forecast","shortName":"pv","standard_name":"Potential_vorticity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/60"},"description":"Potential vorticity is a measure of the capacity for air to rotate in the atmosphere. If we ignore the effects of heating and friction, potential vorticity is conserved following an air parcel. It is used to look for places where large wind storms are likely to originate and develop.  Potential vorticity increases strongly above the tropopause and therefore, it can also be used in studies related to the stratosphere and stratosphere-troposphere exchanges.Large wind storms develop when a column of air in the atmosphere starts to rotate. Potential vorticity is calculated from the wind, temperature and pressure across a column of air in the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K m2kg-1s-1"},"Relative_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Relative humidity","parameter_ID":"157","product_type":"forecast","shortName":"r","standard_name":"Relative_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/157"},"description":"This parameter is the water vapour pressure as a percentage of the value at which the air becomes saturated (the point at which water vapour begins to condense into liquid water or deposition into ice).For temperatures over 0°C (273.15 K) it is calculated for saturation over water. At temperatures below -23°C it is calculated for saturation over ice.  Between -23°C and 0°C this parameter is calculated by interpolating between the ice and water values using a quadratic function.See more information about the model's relative humidity calculation.","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Skin_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Skin temperature","parameter_ID":"235","product_type":"forecast","shortName":"skt","standard_name":"Skin_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235"},"description":"This parameter is the temperature of the surface of the Earth.The skin temperature is the theoretical temperature that is required to satisfy the surface energy balance. It represents the temperature of the uppermost surface layer, which has no heat capacity and so can respond instantaneously to changes in surface fluxes. Skin temperature is calculated differently over land and sea.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.See further information about the skin temperatureover landandover sea.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean skin temperature. The specific encoding for Mean skin temperature can be found in 235079.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Snow_depth_water_equivalent(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Snow depth water equivalent","parameter_ID":"228141","product_type":"forecast","shortName":"sd","standard_name":"Snow_depth_water_equivalent","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228141"},"description":"Snow depth water equivalent in kg m**-2 (mm) water equivalent.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean snow depth water equivalent. The specific encoding for Time-mean snow depth water equivalent can be found in 235078.[NOTE: See 141 for the equivalent parameter in \"m of water equivalent\"]","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Snow_depth_water_equivalent(clte_sol)":{"attrs":{"encoding":"instantaneous","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Snow depth water equivalent","parameter_ID":"228141","product_type":"forecast","shortName":"sd","standard_name":"Snow_depth_water_equivalent","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228141"},"description":"Snow depth water equivalent in kg m**-2 (mm) water equivalent.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean snow depth water equivalent. The specific encoding for Time-mean snow depth water equivalent can be found in 235078.[NOTE: See 141 for the equivalent parameter in \"m of water equivalent\"]","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Specific_cloud_liquid_water_content(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific cloud liquid water content","parameter_ID":"246","product_type":"forecast","shortName":"clwc","standard_name":"Specific_cloud_liquid_water_content","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/246"},"description":"This parameter is the mass of cloud liquid water droplets per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for agrid box.Water within clouds can be liquid or ice, or a combination of the two.See further information about the cloud formulation.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Specific_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific humidity","parameter_ID":"133","product_type":"forecast","shortName":"q","standard_name":"Specific_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/133"},"description":"This parameter is the mass of water vapour per kilogram of moist air.The total mass of moist air is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Surface_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Surface pressure","parameter_ID":"134","product_type":"forecast","shortName":"sp","standard_name":"Surface_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/134"},"description":"This parameter is the pressure (force per unit area) of the atmosphere on the surface of land, sea and in-land water.It is a measure of the weight of all the air in a column vertically above the area of the Earth's surface represented at a fixed point.Surface pressure is often used in combination with temperature to calculate air density.The strong variation of pressure with altitude makes it difficult to see the low and high pressure systems over mountainous areas, so mean sea level pressure, rather than surface pressure, is normally used for this purpose.The units of this parameter are Pascals (Pa). Surface pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb= 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Temperature(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Temperature","parameter_ID":"130","product_type":"forecast","shortName":"t","standard_name":"Temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/130"},"description":"This parameter is the temperature in the atmosphere.It has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.This parameter is available on multiple levels through the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_10_metre_U_wind_component(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre U wind component","parameter_ID":"235165","product_type":"forecast","shortName":"avg_10u","standard_name":"Time-mean_10_metre_U_wind_component","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235165"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_10_metre_V_wind_component(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre V wind component","parameter_ID":"235166","product_type":"forecast","shortName":"avg_10v","standard_name":"Time-mean_10_metre_V_wind_component","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235166"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_10_metre_wind_speed(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre wind speed","parameter_ID":"228005","product_type":"forecast","shortName":"avg_10ws","standard_name":"Time-mean_10_metre_wind_speed","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/228005"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_2_metre_dewpoint_temperature(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"2","levtype":"sfc","long_name":"Time-mean 2 metre dewpoint temperature","parameter_ID":"235168","product_type":"forecast","shortName":"avg_2d","standard_name":"Time-mean_2_metre_dewpoint_temperature","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235168"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_2_metre_temperature(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"2","levtype":"sfc","long_name":"Time-mean 2 metre temperature","parameter_ID":"228004","product_type":"forecast","shortName":"avg_2t","standard_name":"Time-mean_2_metre_temperature","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/228004"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_U_component_of_wind(clmn_hl)":{"attrs":{"encoding":"mean","levelist":"100","levtype":"hl","long_name":"Time-mean U component of wind","parameter_ID":"235131","product_type":"forecast","shortName":"avg_u","standard_name":"Time-mean_U_component_of_wind","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235131"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_U_component_of_wind(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean U component of wind","parameter_ID":"235131","product_type":"forecast","shortName":"avg_u","standard_name":"Time-mean_U_component_of_wind","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235131"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_V_component_of_wind(clmn_hl)":{"attrs":{"encoding":"mean","levelist":"100","levtype":"hl","long_name":"Time-mean V component of wind","parameter_ID":"235132","product_type":"forecast","shortName":"avg_v","standard_name":"Time-mean_V_component_of_wind","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235132"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_V_component_of_wind(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean V component of wind","parameter_ID":"235132","product_type":"forecast","shortName":"avg_v","standard_name":"Time-mean_V_component_of_wind","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235132"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_ice_velocity(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean eastward sea ice velocity","parameter_ID":"263003","product_type":"forecast","shortName":"avg_siue","standard_name":"Time-mean_eastward_sea_ice_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263003"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_ice_velocity(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean eastward sea ice velocity","parameter_ID":"263003","product_type":"forecast","shortName":"avg_siue","standard_name":"Time-mean_eastward_sea_ice_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263003"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_water_velocity(clmn_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean eastward sea water velocity","parameter_ID":"263506","product_type":"forecast","shortName":"avg_uoe","standard_name":"Time-mean_eastward_sea_water_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263506"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean eastward sea water velocity","parameter_ID":"263506","product_type":"forecast","shortName":"avg_uoe","standard_name":"Time-mean_eastward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263506"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_turbulent_surface_stress(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean eastward turbulent surface stress","parameter_ID":"235041","product_type":"forecast","shortName":"avg_iews","standard_name":"Time-mean_eastward_turbulent_surface_stress","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235041"},"dimensions":["lat","lon","time"],"type":"data","unit":"N m-2"},"Time-mean_eastward_turbulent_surface_stress(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean eastward turbulent surface stress","parameter_ID":"235041","product_type":"forecast","shortName":"avg_iews","standard_name":"Time-mean_eastward_turbulent_surface_stress","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235041"},"dimensions":["lat","lon","time"],"type":"data","unit":"N m-2"},"Time-mean_geopotential(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean geopotential","parameter_ID":"235129","product_type":"forecast","shortName":"avg_z","standard_name":"Time-mean_geopotential","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235129"},"dimensions":["lat","lon","time"],"type":"data","unit":"m2s-2"},"Time-mean_mean_sea_level_pressure(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean mean sea level pressure","parameter_ID":"235151","product_type":"forecast","shortName":"avg_msl","standard_name":"Time-mean_mean_sea_level_pressure","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235151"},"dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Time-mean_moisture_flux(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean moisture flux","parameter_ID":"235043","product_type":"forecast","shortName":"avg_ie","standard_name":"Time-mean_moisture_flux","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235043"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_moisture_flux(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean moisture flux","parameter_ID":"235043","product_type":"forecast","shortName":"avg_ie","standard_name":"Time-mean_moisture_flux","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235043"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_northward_sea_ice_velocity(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean northward sea ice velocity","parameter_ID":"263004","product_type":"forecast","shortName":"avg_sivn","standard_name":"Time-mean_northward_sea_ice_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263004"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_sea_ice_velocity(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean northward sea ice velocity","parameter_ID":"263004","product_type":"forecast","shortName":"avg_sivn","standard_name":"Time-mean_northward_sea_ice_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263004"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_sea_water_velocity(clmn_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean northward sea water velocity","parameter_ID":"263505","product_type":"forecast","shortName":"avg_von","standard_name":"Time-mean_northward_sea_water_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263505"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean northward sea water velocity","parameter_ID":"263505","product_type":"forecast","shortName":"avg_von","standard_name":"Time-mean_northward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263505"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_turbulent_surface_stress(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean northward turbulent surface stress","parameter_ID":"235042","product_type":"forecast","shortName":"avg_inss","standard_name":"Time-mean_northward_turbulent_surface_stress","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235042"},"dimensions":["lat","lon","time"],"type":"data","unit":"N m-2"},"Time-mean_northward_turbulent_surface_stress(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean northward turbulent surface stress","parameter_ID":"235042","product_type":"forecast","shortName":"avg_inss","standard_name":"Time-mean_northward_turbulent_surface_stress","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235042"},"dimensions":["lat","lon","time"],"type":"data","unit":"N m-2"},"Time-mean_potential_vorticity(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean potential vorticity","parameter_ID":"235100","product_type":"forecast","shortName":"avg_pv","standard_name":"Time-mean_potential_vorticity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235100"},"dimensions":["lat","lon","time"],"type":"data","unit":"K m2kg-1s-1"},"Time-mean_relative_humidity(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean relative humidity","parameter_ID":"235157","product_type":"forecast","shortName":"avg_r","standard_name":"Time-mean_relative_humidity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235157"},"dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Time-mean_sea_ice_area_fraction(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea ice area fraction","parameter_ID":"263001","product_type":"forecast","shortName":"avg_siconc","standard_name":"Time-mean_sea_ice_area_fraction","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263001"},"dimensions":["lat","lon","time"],"type":"data","unit":"Fraction"},"Time-mean_sea_ice_area_fraction(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea ice area fraction","parameter_ID":"263001","product_type":"forecast","shortName":"avg_siconc","standard_name":"Time-mean_sea_ice_area_fraction","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263001"},"dimensions":["lat","lon","time"],"type":"data","unit":"Fraction"},"Time-mean_sea_ice_thickness(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea ice 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sky","parameter_ID":"235049","product_type":"forecast","shortName":"avg_tnswrfcs","standard_name":"Time-mean_top_net_short-wave_radiation_flux,_clear_sky","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235049"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Time-mean_top_net_short-wave_radiation_flux,_clear_sky(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean top net short-wave radiation flux, clear sky","parameter_ID":"235049","product_type":"forecast","shortName":"avg_tnswrfcs","standard_name":"Time-mean_top_net_short-wave_radiation_flux,_clear_sky","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235049"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Time-mean_total_cloud_cover(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total cloud cover","parameter_ID":"235288","product_type":"forecast","shortName":"avg_tcc","standard_name":"Time-mean_total_cloud_cover","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235288"},"dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Time-mean_total_column_cloud_ice_water(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total column cloud ice water","parameter_ID":"235088","product_type":"forecast","shortName":"avg_tciw","standard_name":"Time-mean_total_column_cloud_ice_water","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235088"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Time-mean_total_column_heat_content(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean total column heat content","parameter_ID":"263123","product_type":"forecast","shortName":"avg_hcbtm","standard_name":"Time-mean_total_column_heat_content","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263123"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_total_column_heat_content(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean total column heat content","parameter_ID":"263123","product_type":"forecast","shortName":"avg_hcbtm","standard_name":"Time-mean_total_column_heat_content","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263123"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_total_column_liquid_water(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total column liquid water","parameter_ID":"235087","product_type":"forecast","shortName":"avg_tclw","standard_name":"Time-mean_total_column_liquid_water","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235087"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Time-mean_total_column_vertically-integrated_water_vapour(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total column vertically-integrated water vapour","parameter_ID":"235137","product_type":"forecast","shortName":"avg_tcwv","standard_name":"Time-mean_total_column_vertically-integrated_water_vapour","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235137"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Time-mean_total_column_water(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total column water","parameter_ID":"235136","product_type":"forecast","shortName":"avg_tcw","standard_name":"Time-mean_total_column_water","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235136"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Time-mean_total_precipitation_rate(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total precipitation rate","parameter_ID":"235055","product_type":"forecast","shortName":"avg_tprate","standard_name":"Time-mean_total_precipitation_rate","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235055"},"description":"Time-mean total precipitation rate, or time-mean total precipitation flux. This parameter is on level \"Ground or water surface\" (typeOfFirstFixedSurface=1). For this parameter on other levels, please use 235013.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_total_precipitation_rate(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total precipitation rate","parameter_ID":"235055","product_type":"forecast","shortName":"avg_tprate","standard_name":"Time-mean_total_precipitation_rate","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235055"},"description":"Time-mean total precipitation rate, or time-mean total precipitation flux. This parameter is on level \"Ground or water surface\" (typeOfFirstFixedSurface=1). For this parameter on other levels, please use 235013.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_total_snowfall_rate_water_equivalent(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total snowfall rate water equivalent","parameter_ID":"235031","product_type":"forecast","shortName":"avg_tsrwe","standard_name":"Time-mean_total_snowfall_rate_water_equivalent","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235031"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_total_snowfall_rate_water_equivalent(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time-mean total snowfall rate water equivalent","parameter_ID":"235031","product_type":"forecast","shortName":"avg_tsrwe","standard_name":"Time-mean_total_snowfall_rate_water_equivalent","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235031"},"dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"Time-mean_upward_sea_water_velocity(clmn_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean upward sea water velocity","parameter_ID":"263507","product_type":"forecast","shortName":"avg_wo","standard_name":"Time-mean_upward_sea_water_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263507"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_upward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72","levtype":"o3d","long_name":"Time-mean upward sea water velocity","parameter_ID":"263507","product_type":"forecast","shortName":"avg_wo","standard_name":"Time-mean_upward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263507"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_vertical_velocity(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean vertical velocity","parameter_ID":"235135","product_type":"forecast","shortName":"avg_w","standard_name":"Time-mean_vertical_velocity","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235135"},"dimensions":["lat","lon","time"],"type":"data","unit":"Pa s-1"},"Time-mean_vertically-integrated_heat_content_in_the_upper_300_m(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"300","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 300 m","parameter_ID":"263121","product_type":"forecast","shortName":"avg_hc300m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_300_m","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263121"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_vertically-integrated_heat_content_in_the_upper_300_m(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"300","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 300 m","parameter_ID":"263121","product_type":"forecast","shortName":"avg_hc300m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_300_m","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263121"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m(clmn_o2d)":{"attrs":{"encoding":"mean","levelist":"700","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 700 m","parameter_ID":"263122","product_type":"forecast","shortName":"avg_hc700m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/263122"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"700","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 700 m","parameter_ID":"263122","product_type":"forecast","shortName":"avg_hc700m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263122"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_volumetric_soil_moisture(clmn_sol)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Time-mean volumetric soil moisture","parameter_ID":"235077","product_type":"forecast","shortName":"avg_vsw","standard_name":"Time-mean_volumetric_soil_moisture","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235077"},"dimensions":["lat","lon","time"],"type":"data","unit":"m3m-3"},"Time_mean_top_downward_short-wave_radiation_flux(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time mean top downward short-wave radiation flux","parameter_ID":"235053","product_type":"forecast","shortName":"avg_tdswrf","standard_name":"Time_mean_top_downward_short-wave_radiation_flux","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235053"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Time_mean_top_downward_short-wave_radiation_flux(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time mean top downward short-wave radiation flux","parameter_ID":"235053","product_type":"forecast","shortName":"avg_tdswrf","standard_name":"Time_mean_top_downward_short-wave_radiation_flux","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235053"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Total_Cloud_Cover(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total Cloud Cover","parameter_ID":"228164","product_type":"forecast","shortName":"tcc","standard_name":"Total_Cloud_Cover","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228164"},"description":"[NOTE: See 164 for the equivalent parameter in \"(0-1)\"]","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Total_column_cloud_ice_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud ice water","parameter_ID":"79","product_type":"forecast","shortName":"tciw","standard_name":"Total_column_cloud_ice_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/79"},"description":"This parameter is the amount of ice contained within clouds in a column extending from the surface of the Earth to the top of the atmosphere. Snow (aggregated ice crystals) is not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The  ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_cloud_liquid_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud liquid water","parameter_ID":"78","product_type":"forecast","shortName":"tclw","standard_name":"Total_column_cloud_liquid_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/78"},"description":"This parameter is the amount of liquid water contained within cloud droplets in a column extending from the surface of the Earth to the top of the atmosphere. Rain water droplets, which are much larger in size (and mass), are not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_vertically-integrated_water_vapour(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column vertically-integrated water vapour","parameter_ID":"137","product_type":"forecast","shortName":"tcwv","standard_name":"Total_column_vertically-integrated_water_vapour","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/137"},"description":"This parameter is the total amount of water vapour in a column extending from the surface of the Earth to the top of the atmosphere.This parameter represents the area averaged value for agrid box.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column water","parameter_ID":"136","product_type":"forecast","shortName":"tcw","standard_name":"Total_column_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/136"},"description":"This parameter is the sum of water vapour, liquid water, cloud ice, rain and snow in a column extending from the surface of the Earth to the top of the atmosphere. In old versions of the ECMWF model (IFS), rain and snow were not accounted for.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"U_component_of_wind(clte_hl)":{"attrs":{"encoding":"instantaneous","levelist":"100","levtype":"hl","long_name":"U component of wind","parameter_ID":"131","product_type":"forecast","shortName":"u","standard_name":"U_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/131"},"description":"This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east, in metres per second. A negative sign thus indicates air movement towards the west.This parameter can be combined with the V component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"U_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"U component of wind","parameter_ID":"131","product_type":"forecast","shortName":"u","standard_name":"U_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/131"},"description":"This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east, in metres per second. A negative sign thus indicates air movement towards the west.This parameter can be combined with the V component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"V_component_of_wind(clte_hl)":{"attrs":{"encoding":"instantaneous","levelist":"100","levtype":"hl","long_name":"V component of wind","parameter_ID":"132","product_type":"forecast","shortName":"v","standard_name":"V_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/132"},"description":"This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north, in metres per second. A negative sign thus indicates air movement towards the south.This parameter can be combined with the U component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"V_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"V component of wind","parameter_ID":"132","product_type":"forecast","shortName":"v","standard_name":"V_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/132"},"description":"This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north, in metres per second. A negative sign thus indicates air movement towards the south.This parameter can be combined with the U component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Vertical_velocity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Vertical velocity","parameter_ID":"135","product_type":"forecast","shortName":"w","standard_name":"Vertical_velocity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/135"},"description":"This parameter is the speed of air motion in the upward or downward direction. The ECMWF Integrated Forecasting System (IFS) uses a pressure based vertical co-ordinate system and pressure decreases with height, therefore negative values of vertical velocity indicate upward motion.Vertical velocity can be useful to understand the large-scale dynamics of the atmosphere, including areas of upward motion/ascent (negative values) and downward motion/subsidence (positive values).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa s-1"},"Volumetric_soil_moisture(clte_sol)":{"attrs":{"encoding":"instantaneous","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Volumetric soil moisture","parameter_ID":"260199","product_type":"forecast","shortName":"vsw","standard_name":"Volumetric_soil_moisture","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/260199"},"description":"Please note that the encoding listed here for uerra (which includes carra/cerra) includes entries for Time-mean volumetric soil moisture. The specific encoding for Time-mean volumetric soil moisture can be found in 235077.","dimensions":["lat","lon","time"],"type":"data","unit":"m3m-3"}},"sci:publications":[{"doi":"10.1109/MCSE.2023.3260519","citation":"N. Wedi et al., Destination Earth: High-Performance Computing for Weather and Climate, in Computing in Science \u0026 Engineering, vol. 24, no. 6, pp. 29-37, Nov.-Dec. 2022,"},{"doi":"10.21957/d3f982672e","citation":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"dedl:short_description":"The Climate Change Adaptation Digital Twin provides global climate projections and sector-specific information over multiple decades at high resolution via a unified framework combining advanced Earth system models, impact assessments, and observations. This Generation-2, Realization-4 Collection gives access to 'Storyline Simulation Future Climate' data based on the 'IFS-FESOM' model."},{"type":"Collection","title":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Storyline Simulation Future Climate - IFS-FESOM - Generation-2 - Realization-5","id":"EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_TPLUS2.0K_IFS-FESOM.R5","description":"The DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities by providing innovative climate information on multi-decadal timescales, globally, at scales at which many impacts of climate change are observed. It combines cutting-edge global Earth-system models, impact-sector applications and observations into a unified framework to provide global climate projections and impact-sector information on multi-decadal timescales (1990 to ~2050), at very high spatial resolutions (5 to 10 km).\n\nThe Climate DT represents the first ever attempt to operationalise the production of global multi-decadal climate projections, leveraging the world-leading supercomputing facilities of the EuroHPC Joint Undertaking along with some of the leading European climate models. A concise overview of what the Climate DT aims to achieve, and of the different concepts essential for an understanding of the Digital Twin’s characteristics, is included in the [Climate DT factsheet](https://destine.ecmwf.int/wp-content/uploads/2024/06/2024.06.07_Climate-DT-Fact-Sheet_V7-2.pdf)\n\n## Storyline Simulation Future Climate \n\n Storyline simulations offer a what-if capability to explore how a weather event we experienced in the recent past would change in a warmer climate and how it would have looked in past pre-industrial conditions. To constrain simulated weather events, a nudging approach is used to keep the simulated large-scale flow close to the ERA5 reanalysis for the period 2017 – 2023. Processes on smaller scales and thermodynamic processes are free to evolve, which allows scientists to study e.g. how extreme precipitation would change in a warmer world for a weather event observed in present-day conditions. Initial conditions are taken from ocean-only spinup simulations for 1950 and 2017, and from the IFS-FESOM projection for a +2 °C world. Storyline simulations will be updated regularly to include most recent months. The next update will extend all storylines to include 2024.\n\n## Models\n\nThe Climate DT exploits and further evolves a new generation of global storm-resolving and eddy-rich models built through a cooperative model development approach. For more information on models please click [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#models)\n\n## Simulations\n\nThe Climate DT team carries out several types of digital twin simulations on the EuroHPC supercomputers.  Multi-decadal simulations are produced to cover the recent past (from 1990) and possible future evolutions of the climate up to 2050. See [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#simulations) for more information on Simulations\n\n## Parameters\n\nBelow we see the list of parameters extracted from the 'DestinE Climate DT data portfolio', for more information please refer to the pages [Climate DT Phase2 CLTE Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters) and [Climate DT Phase2 CLMN Parameters](https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters)","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_TPLUS2.0K_IFS-FESOM.R5/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_TPLUS2.0K_IFS-FESOM.R5/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_TPLUS2.0K_IFS-FESOM.R5/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.D1.DT_CLIMATE.G2.STORY-NUDGING_TPLUS2.0K_IFS-FESOM.R5","title":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Storyline Simulation Future Climate - IFS-FESOM - Generation-2 - Realization-5"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clte+Parameters","title":"DestinE ClimateDT Phase 2 CLTE Parameters"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Phase+2+clmn+Parameters","title":"DestinE ClimateDT Phase 2 CLMN Parameters"},{"rel":"cite-as","type":"text/html","href":"https://dl.acm.org/doi/abs/10.1109/MCSE.2023.3260519","title":"Destination Earth: High-Performance Computing for Weather and Climate"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.21957/d3f982672e","title":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"assets":{"thumbnail":{"href":"https://s3.central.data.destination-earth.eu/swift/v1/dedl-public/collections/climate-dt-min.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2017-01-01T00:00:00Z","2025-12-31T23:59:59Z"]]}},"license":"CC-BY-4.0","keywords":["Digital Twins","Climate Change","Atmosphere","Ocean","Land","Sea Ice","Snow","Soil","Earth","High Performance Computing","Decision Making","Europe","Meteorology","class:d1","dataset:climate-dt","generation:2","expver:0001","stream:clmn,clte","type:fc","activity:story-nudging","experiment:Tplus2.0K","realization:5","model:IFS-FESOM","resolution:standard,high","levtype:hl,o2d,o3d,pl,sfc,sol"],"summaries":{"federation:backends":["dedt_mn5"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/datacube/v2.1.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["producer","processor","licensor"],"url":"https://www.ecmwf.int/"}],"created":"2024-04-11T22:31:55Z","updated":"2026-04-24T10:24:59Z","published":"2024-04-11T22:31:55Z","cube:dimensions":{"lat":{"axis":"y","description":"latitude","extent":[-90,90],"type":"spatial"},"lon":{"axis":"x","description":"longitude","extent":[-180,180],"type":"spatial"},"time":{"extent":["2017-01-01T00:00:00Z","2025-12-31T23:59:59Z"],"step":"P0Y0M0DT1H0M0S","type":"temporal"}},"cube:variables":{"10_metre_U_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre U wind component","parameter_ID":"165","product_type":"forecast","shortName":"10u","standard_name":"10_metre_U_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/165"},"description":"This parameter is the eastward component of the 10m wind. It is the horizontal speed of air moving towards the east, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the V component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_V_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre V wind component","parameter_ID":"166","product_type":"forecast","shortName":"10v","standard_name":"10_metre_V_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/166"},"description":"This parameter is the northward component of the 10m wind. It is the horizontal speed of air moving towards the north, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the U component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_wind_speed(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"10","levtype":"sfc","long_name":"10 metre wind speed","parameter_ID":"207","product_type":"forecast","shortName":"10si","standard_name":"10_metre_wind_speed","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/207"},"description":"This parameter is the horizontal speed of the wind, or movement of air, at a height of ten metres above the surface of the Earth. The units of this parameter are metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.The eastward and northward components of the horizontal wind at 10m are also available as parameters.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"2_metre_dewpoint_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"2","levtype":"sfc","long_name":"2 metre dewpoint temperature","parameter_ID":"168","product_type":"forecast","shortName":"2d","standard_name":"2_metre_dewpoint_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/168"},"description":"This parameter is the temperature to which the air, at 2 metres above the surface of the Earth, would have to be cooled for saturation to occur.It is a measure of the humidity of the air. Combined with temperature and pressure, it can be used to calculate the relative humidity.2m dew point temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"2_metre_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"2","levtype":"sfc","long_name":"2 metre temperature","parameter_ID":"167","product_type":"forecast","shortName":"2t","standard_name":"2_metre_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/167"},"description":"This parameter is the temperature of air at 2m above the surface of land, sea or in-land waters.2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.Please note that the encodings listed here for s2s \u0026 uerra (which includes encodings for carra/cerra) include entries for Mean 2 metre temperature. The specific encoding for Mean 2 metre temperature can be found in 228004.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Geopotential(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Geopotential","parameter_ID":"129","product_type":"forecast","shortName":"z","standard_name":"Geopotential","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/129"},"description":"This parameter is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level.The geopotential height can be calculated by dividing the geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2). The geopotential height plays an important role in synoptic meteorology (analysis of weather patterns). Charts of geopotential height plotted at constant pressure levels (e.g., 300, 500 or 850 hPa) can be used to identify weather systems such as cyclones, anticyclones, troughs and ridges.At the surface of the Earth, this parameter shows the variations in geopotential (height) of the surface, and is often referred to as the orography.","dimensions":["lat","lon","time"],"type":"data","unit":"m2s-2"},"Land-sea_mask(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Land-sea mask","parameter_ID":"172","product_type":"forecast","shortName":"lsm","standard_name":"Land-sea_mask","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/172"},"description":"This parameter is the proportion of land, as opposed to ocean or inland waters (lakes, reservoirs, rivers and coastal waters), in agrid box.This parameter has values ranging between zero and one and is dimensionless.In cycles of the ECMWF Integrated Forecasting System (IFS) from CY41R1 (introduced in May 2015) onwards, grid boxes where this parameter has a value above 0.5 can be comprised of a mixture of land and inland water but not ocean. Grid boxes with a value of 0.5 and below can only be comprised of a water surface. In the latter case, the lake cover is used to determine how much of the water surface is ocean or inland water.In cycles of the IFS before CY41R1, grid boxes where this parameter has a value above 0.5 can only be comprised of land and those grid boxes with a value of 0.5 and below can only be comprised of ocean. In these older model cycles, there is no differentiation between ocean and inland water.","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Mean_sea_level_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Mean sea level pressure","parameter_ID":"151","product_type":"forecast","shortName":"msl","standard_name":"Mean_sea_level_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/151"},"description":"This parameter is the pressure (force per unit area) of the atmosphere adjusted to the height of mean sea level.It is a measure of the weight that all the air in a column vertically above the area of Earth's surface would have at that point, if the point were located at the mean sea level. It is calculated over all surfaces - land, sea and in-land water.Maps of mean sea level pressure are used to identify the locations of low and high pressure systems, often referred to as cyclones and anticyclones. Contours of mean sea level pressure also indicate the strength of the wind. Tightly packed contours show stronger winds.The units of this parameter are pascals (Pa).  Mean sea level pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb = 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Orography(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Orography","parameter_ID":"228002","product_type":"forecast","shortName":"orog","standard_name":"Orography","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/228002"},"dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Potential_vorticity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Potential vorticity","parameter_ID":"60","product_type":"forecast","shortName":"pv","standard_name":"Potential_vorticity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/60"},"description":"Potential vorticity is a measure of the capacity for air to rotate in the atmosphere. If we ignore the effects of heating and friction, potential vorticity is conserved following an air parcel. It is used to look for places where large wind storms are likely to originate and develop.  Potential vorticity increases strongly above the tropopause and therefore, it can also be used in studies related to the stratosphere and stratosphere-troposphere exchanges.Large wind storms develop when a column of air in the atmosphere starts to rotate. Potential vorticity is calculated from the wind, temperature and pressure across a column of air in the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K m2kg-1s-1"},"Relative_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Relative humidity","parameter_ID":"157","product_type":"forecast","shortName":"r","standard_name":"Relative_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/157"},"description":"This parameter is the water vapour pressure as a percentage of the value at which the air becomes saturated (the point at which water vapour begins to condense into liquid water or deposition into ice).For temperatures over 0°C (273.15 K) it is calculated for saturation over water. At temperatures below -23°C it is calculated for saturation over ice.  Between -23°C and 0°C this parameter is calculated by interpolating between the ice and water values using a quadratic function.See more information about the model's relative humidity calculation.","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Skin_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Skin temperature","parameter_ID":"235","product_type":"forecast","shortName":"skt","standard_name":"Skin_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235"},"description":"This parameter is the temperature of the surface of the Earth.The skin temperature is the theoretical temperature that is required to satisfy the surface energy balance. It represents the temperature of the uppermost surface layer, which has no heat capacity and so can respond instantaneously to changes in surface fluxes. Skin temperature is calculated differently over land and sea.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.See further information about the skin temperatureover landandover sea.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean skin temperature. The specific encoding for Mean skin temperature can be found in 235079.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Snow_depth_water_equivalent(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Snow depth water equivalent","parameter_ID":"228141","product_type":"forecast","shortName":"sd","standard_name":"Snow_depth_water_equivalent","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228141"},"description":"Snow depth water equivalent in kg m**-2 (mm) water equivalent.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean snow depth water equivalent. The specific encoding for Time-mean snow depth water equivalent can be found in 235078.[NOTE: See 141 for the equivalent parameter in \"m of water equivalent\"]","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Snow_depth_water_equivalent(clte_sol)":{"attrs":{"encoding":"instantaneous","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Snow depth water equivalent","parameter_ID":"228141","product_type":"forecast","shortName":"sd","standard_name":"Snow_depth_water_equivalent","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228141"},"description":"Snow depth water equivalent in kg m**-2 (mm) water equivalent.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean snow depth water equivalent. The specific encoding for Time-mean snow depth water equivalent can be found in 235078.[NOTE: See 141 for the equivalent parameter in \"m of water equivalent\"]","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Specific_cloud_liquid_water_content(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific cloud liquid water content","parameter_ID":"246","product_type":"forecast","shortName":"clwc","standard_name":"Specific_cloud_liquid_water_content","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/246"},"description":"This parameter is the mass of cloud liquid water droplets per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for agrid box.Water within clouds can be liquid or ice, or a combination of the two.See further information about the cloud formulation.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Specific_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific humidity","parameter_ID":"133","product_type":"forecast","shortName":"q","standard_name":"Specific_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/133"},"description":"This parameter is the mass of water vapour per kilogram of moist air.The total mass of moist air is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Surface_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Surface pressure","parameter_ID":"134","product_type":"forecast","shortName":"sp","standard_name":"Surface_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/134"},"description":"This parameter is the pressure (force per unit area) of the atmosphere on the surface of land, sea and in-land water.It is a measure of the weight of all the air in a column vertically above the area of the Earth's surface represented at a fixed point.Surface pressure is often used in combination with temperature to calculate air density.The strong variation of pressure with altitude makes it difficult to see the low and high pressure systems over mountainous areas, so mean sea level pressure, rather than surface pressure, is normally used for this purpose.The units of this parameter are Pascals (Pa). Surface pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb= 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Temperature(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Temperature","parameter_ID":"130","product_type":"forecast","shortName":"t","standard_name":"Temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/130"},"description":"This parameter is the temperature in the atmosphere.It has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.This parameter is available on multiple levels through the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_10_metre_U_wind_component(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre U wind component","parameter_ID":"235165","product_type":"forecast","shortName":"avg_10u","standard_name":"Time-mean_10_metre_U_wind_component","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235165"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_10_metre_V_wind_component(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre V wind component","parameter_ID":"235166","product_type":"forecast","shortName":"avg_10v","standard_name":"Time-mean_10_metre_V_wind_component","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235166"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_10_metre_wind_speed(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"10","levtype":"sfc","long_name":"Time-mean 10 metre wind speed","parameter_ID":"228005","product_type":"forecast","shortName":"avg_10ws","standard_name":"Time-mean_10_metre_wind_speed","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/228005"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_2_metre_dewpoint_temperature(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"2","levtype":"sfc","long_name":"Time-mean 2 metre dewpoint temperature","parameter_ID":"235168","product_type":"forecast","shortName":"avg_2d","standard_name":"Time-mean_2_metre_dewpoint_temperature","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235168"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_2_metre_temperature(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"2","levtype":"sfc","long_name":"Time-mean 2 metre temperature","parameter_ID":"228004","product_type":"forecast","shortName":"avg_2t","standard_name":"Time-mean_2_metre_temperature","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/228004"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_U_component_of_wind(clmn_hl)":{"attrs":{"encoding":"mean","levelist":"100","levtype":"hl","long_name":"Time-mean U component of wind","parameter_ID":"235131","product_type":"forecast","shortName":"avg_u","standard_name":"Time-mean_U_component_of_wind","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235131"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_U_component_of_wind(clmn_pl)":{"attrs":{"encoding":"mean","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Time-mean U component of wind","parameter_ID":"235131","product_type":"forecast","shortName":"avg_u","standard_name":"Time-mean_U_component_of_wind","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235131"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_V_component_of_wind(clmn_hl)":{"attrs":{"encoding":"mean","levelist":"100","levtype":"hl","long_name":"Time-mean V component of 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m-2"},"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"700","levtype":"o2d","long_name":"Time-mean vertically-integrated heat content in the upper 700 m","parameter_ID":"263122","product_type":"forecast","shortName":"avg_hc700m","standard_name":"Time-mean_vertically-integrated_heat_content_in_the_upper_700_m","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263122"},"dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_volumetric_soil_moisture(clmn_sol)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Time-mean volumetric soil moisture","parameter_ID":"235077","product_type":"forecast","shortName":"avg_vsw","standard_name":"Time-mean_volumetric_soil_moisture","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235077"},"dimensions":["lat","lon","time"],"type":"data","unit":"m3m-3"},"Time_mean_top_downward_short-wave_radiation_flux(clmn_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time mean top downward short-wave radiation flux","parameter_ID":"235053","product_type":"forecast","shortName":"avg_tdswrf","standard_name":"Time_mean_top_downward_short-wave_radiation_flux","stream":"clmn","time":"Monthly","url":"https://codes.ecmwf.int/grib/param-db/235053"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Time_mean_top_downward_short-wave_radiation_flux(clte_sfc)":{"attrs":{"encoding":"mean","levelist":"","levtype":"sfc","long_name":"Time mean top downward short-wave radiation flux","parameter_ID":"235053","product_type":"forecast","shortName":"avg_tdswrf","standard_name":"Time_mean_top_downward_short-wave_radiation_flux","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235053"},"dimensions":["lat","lon","time"],"type":"data","unit":"W m-2"},"Total_Cloud_Cover(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total Cloud Cover","parameter_ID":"228164","product_type":"forecast","shortName":"tcc","standard_name":"Total_Cloud_Cover","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228164"},"description":"[NOTE: See 164 for the equivalent parameter in \"(0-1)\"]","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Total_column_cloud_ice_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud ice water","parameter_ID":"79","product_type":"forecast","shortName":"tciw","standard_name":"Total_column_cloud_ice_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/79"},"description":"This parameter is the amount of ice contained within clouds in a column extending from the surface of the Earth to the top of the atmosphere. Snow (aggregated ice crystals) is not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The  ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_cloud_liquid_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud liquid water","parameter_ID":"78","product_type":"forecast","shortName":"tclw","standard_name":"Total_column_cloud_liquid_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/78"},"description":"This parameter is the amount of liquid water contained within cloud droplets in a column extending from the surface of the Earth to the top of the atmosphere. Rain water droplets, which are much larger in size (and mass), are not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_vertically-integrated_water_vapour(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column vertically-integrated water vapour","parameter_ID":"137","product_type":"forecast","shortName":"tcwv","standard_name":"Total_column_vertically-integrated_water_vapour","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/137"},"description":"This parameter is the total amount of water vapour in a column extending from the surface of the Earth to the top of the atmosphere.This parameter represents the area averaged value for agrid box.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column water","parameter_ID":"136","product_type":"forecast","shortName":"tcw","standard_name":"Total_column_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/136"},"description":"This parameter is the sum of water vapour, liquid water, cloud ice, rain and snow in a column extending from the surface of the Earth to the top of the atmosphere. In old versions of the ECMWF model (IFS), rain and snow were not accounted for.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"U_component_of_wind(clte_hl)":{"attrs":{"encoding":"instantaneous","levelist":"100","levtype":"hl","long_name":"U component of wind","parameter_ID":"131","product_type":"forecast","shortName":"u","standard_name":"U_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/131"},"description":"This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east, in metres per second. A negative sign thus indicates air movement towards the west.This parameter can be combined with the V component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"U_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"U component of wind","parameter_ID":"131","product_type":"forecast","shortName":"u","standard_name":"U_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/131"},"description":"This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east, in metres per second. A negative sign thus indicates air movement towards the west.This parameter can be combined with the V component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"V_component_of_wind(clte_hl)":{"attrs":{"encoding":"instantaneous","levelist":"100","levtype":"hl","long_name":"V component of wind","parameter_ID":"132","product_type":"forecast","shortName":"v","standard_name":"V_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/132"},"description":"This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north, in metres per second. A negative sign thus indicates air movement towards the south.This parameter can be combined with the U component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"V_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"V component of wind","parameter_ID":"132","product_type":"forecast","shortName":"v","standard_name":"V_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/132"},"description":"This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north, in metres per second. A negative sign thus indicates air movement towards the south.This parameter can be combined with the U component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Vertical_velocity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Vertical velocity","parameter_ID":"135","product_type":"forecast","shortName":"w","standard_name":"Vertical_velocity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/135"},"description":"This parameter is the speed of air motion in the upward or downward direction. The ECMWF Integrated Forecasting System (IFS) uses a pressure based vertical co-ordinate system and pressure decreases with height, therefore negative values of vertical velocity indicate upward motion.Vertical velocity can be useful to understand the large-scale dynamics of the atmosphere, including areas of upward motion/ascent (negative values) and downward motion/subsidence (positive values).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa s-1"},"Volumetric_soil_moisture(clte_sol)":{"attrs":{"encoding":"instantaneous","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Volumetric soil moisture","parameter_ID":"260199","product_type":"forecast","shortName":"vsw","standard_name":"Volumetric_soil_moisture","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/260199"},"description":"Please note that the encoding listed here for uerra (which includes carra/cerra) includes entries for Time-mean volumetric soil moisture. The specific encoding for Time-mean volumetric soil moisture can be found in 235077.","dimensions":["lat","lon","time"],"type":"data","unit":"m3m-3"}},"sci:publications":[{"doi":"10.1109/MCSE.2023.3260519","citation":"N. Wedi et al., Destination Earth: High-Performance Computing for Weather and Climate, in Computing in Science \u0026 Engineering, vol. 24, no. 6, pp. 29-37, Nov.-Dec. 2022,"},{"doi":"10.21957/d3f982672e","citation":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"dedl:short_description":"The Climate Change Adaptation Digital Twin provides global climate projections and sector-specific information over multiple decades at high resolution via a unified framework combining advanced Earth system models, impact assessments, and observations. This Generation-2, Realization-5 Collection gives access to 'Storyline Simulation Future Climate' data based on the 'IFS-FESOM' model."},{"type":"Collection","title":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Generation-1","id":"EO.ECMWF.DAT.DT_CLIMATE_ADAPTATION","description":"Note: This Generation 1 Collection has been superseded by Generation 2 Simulation-level Collections\n\nThe DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities by providing innovative climate information on multi-decadal timescales, globally, at scales at which many impacts of climate change are observed. It combines cutting-edge global Earth-system models, impact-sector applications and observations into a unified framework to provide global climate projections and impact-sector information on multi-decadal timescales (1990 to ~2050), at very high spatial resolutions (5 to 10 km).\n\nThe Climate DT represents the first ever attempt to operationalise the production of global multi-decadal climate projections, leveraging the world-leading supercomputing facilities of the EuroHPC Joint Undertaking along with some of the leading European climate models. A concise overview of what the Climate DT aims to achieve, and of the different concepts essential for an understanding of the Digital Twin’s characteristics, is included in the [Climate DT factsheet](https://destine.ecmwf.int/wp-content/uploads/2024/06/2024.06.07_Climate-DT-Fact-Sheet_V7-2.pdf)\n\n## Models\n\nThe Climate DT exploits and further evolves a new generation of global storm-resolving and eddy-rich models built through a cooperative model development approach. For more information on models please click [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#models)\n\n## Simulations\n\nThe Climate DT team carries out several types of digital twin simulations on the EuroHPC supercomputers.  Multi-decadal simulations are produced to cover the recent past (from 1990) and possible future evolutions of the climate up to 2050. See [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#simulations) for more information on Simulations\n\n## Parameters\n\nBelow we see the list of parameters extracted from the 'DestinE Climate DT data portfolio', for more information please refer to the page [Climate DT Parameters](https://confluence.ecmwf.int/display/DDCZ/Climate+DT+Phase+1+data+catalogue)","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.DT_CLIMATE_ADAPTATION/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.DT_CLIMATE_ADAPTATION/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.DT_CLIMATE_ADAPTATION/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.DT_CLIMATE_ADAPTATION","title":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Generation-1"},{"rel":"example","type":"application/x-ipynb+json","href":"https://raw.githubusercontent.com/destination-earth/DestinE-DataLake-Lab/refs/heads/main/HDA/DestinE%20Digital%20Twins/DEDL-HDA-EO.ECMWF.DAT.DT_CLIMATE.ipynb","title":"Destination Earth - Climate DT Parameter - Data Access using DEDL HDA","application:type":"jupyter-notebook","application:embedded":true,"application:language":"Python"},{"rel":"example","type":"application/x-ipynb+json","href":"https://raw.githubusercontent.com/destination-earth/DestinE-DataLake-Lab/refs/heads/main/HDA/DestinE%20Digital%20Twins/ClimateDT-ExtractLocationValues.ipynb","title":"DT Tutorial: Is it going to rain in the next 3 weekends?","application:type":"jupyter-notebook","application:embedded":true,"application:language":"Python"},{"rel":"example","type":"application/x-ipynb+json","href":"https://raw.githubusercontent.com/destination-earth/DestinE-DataLake-Lab/refs/heads/main/HDA/DestinE%20Digital%20Twins/ClimateDT-ParameterPlotter.ipynb","title":"Destination Earth - HDA Climate DT Parameter Plotter Tutorial","application:type":"jupyter-notebook","application:embedded":true,"application:language":"Python"},{"rel":"example","type":"application/x-ipynb+json","href":"https://raw.githubusercontent.com/destination-earth/DestinE-DataLake-Lab/refs/heads/main/HDA/DestinE%20Digital%20Twins/DEDL-HDA-EO.ECMWF.DAT.DT_CLIMATE-Series.ipynb","title":"Destination Earth - Climate DT Parameter Series Plot- Data Access using DEDL HDA","application:type":"jupyter-notebook","application:embedded":true,"application:language":"Python"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Parameters","title":"DestinE ClimateDT Parameters"},{"rel":"cite-as","type":"text/html","href":"https://dl.acm.org/doi/abs/10.1109/MCSE.2023.3260519","title":"Destination Earth: High-Performance Computing for Weather and Climate"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.21957/d3f982672e","title":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"assets":{"thumbnail":{"href":"https://s3.central.data.destination-earth.eu/swift/v1/dedl-public/collections/climate-dt-min.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2020-07-01T00:00:00Z","2039-12-31T23:59:59Z"]]}},"license":"CC-BY-4.0","keywords":["Digital Twins","Climate Change","Atmosphere","Ocean","Land","Sea Ice","Snow","Soil","Earth","High Performance Computing","Decision Making","Europe","Meteorology"],"summaries":{"federation:backends":["dedt_lumi"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/datacube/v2.1.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json","https://stac-extensions.github.io/application/v0.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["producer","processor","licensor"],"url":"https://www.ecmwf.int/"},{"name":"Destination Earth Data Lake (DEDL)","roles":["host"],"url":"https://data.destination-earth.eu/"}],"created":"2024-04-11T22:31:55Z","updated":"2026-04-27T14:04:20Z","published":"2024-04-11T22:31:55Z","cube:dimensions":{"lat":{"axis":"y","description":"latitude","extent":[-90,90],"type":"spatial"},"lon":{"axis":"x","description":"longitude","extent":[-180,180],"type":"spatial"},"time":{"extent":["2020-07-01T00:00:00Z","2025-07-01T00:00:00Z"],"step":"P0Y0M0DT1H0M0S","type":"temporal"}},"cube:variables":{"100_metre_U_wind_component":{"attrs":{"long_name":"100 metre U wind component","parameter_ID":"228246","product_type":"forecast","shortName":"100u","standard_name":"100_metre_U_wind_component"},"description":"This parameter is the eastward component of the 100 m wind. It is the horizontal speed of air moving towards the east, at a height of 100 metres above the surface of the Earth, in metres per second.","dimensions":["lat","lon","time"],"type":"data","unit":"m s**-1"},"100_metre_V_wind_component":{"attrs":{"long_name":"100 metre V wind component","parameter_ID":"228247","product_type":"forecast","shortName":"100v","standard_name":"100_metre_V_wind_component"},"description":"This parameter is the northward component of the 100 m wind. It is the horizontal speed of air moving towards the east, at a height of 100 metres above the surface of the Earth, in metres per second.","dimensions":["lat","lon","time"],"type":"data","unit":"m s**-1"},"10_metre_U_wind_component":{"attrs":{"long_name":"10 metre U wind component","parameter_ID":"165","product_type":"forecast","shortName":"10u","standard_name":"eastward_wind"},"description":"Eastward component of the near-surface (usually, 10 meters) wind","dimensions":["lat","lon","time"],"type":"data","unit":"m s**-1"},"10_metre_V_wind_component":{"attrs":{"long_name":"10 metre V wind component","parameter_ID":"166","product_type":"forecast","shortName":"10v","standard_name":"northward_wind"},"description":"Northward component of the near surface wind","dimensions":["lat","lon","time"],"type":"data","unit":"m s**-1"},"2_metre_dewpoint_temperature":{"attrs":{"long_name":"2 metre dewpoint temperature","parameter_ID":"168","product_type":"forecast","shortName":"2d","standard_name":"dew_point_temperature"},"description":"This parameter is the temperature to which the air, at 2 metres above the surface of the Earth, would have to be cooled for saturation to occur. It is a measure of the humidity of the air. Combined with temperature and pressure, it can be used to calculate the relative humidity.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"2_metre_temperature":{"attrs":{"long_name":"2 metre temperature","parameter_ID":"167","product_type":"forecast","shortName":"2t","standard_name":"air_temperature"},"description":"near-surface (usually, 2 meter) air temperature","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Boundary_layer_height":{"attrs":{"long_name":"Boundary layer height","parameter_ID":"159","product_type":"forecast","shortName":"blh","standard_name":"Boundary_layer_height"},"description":"This parameter is the depth of air next to the Earth's surface which is most affected by the resistance to the transfer of momentum, heat or moisture across the surface. The boundary layer height can be as low as a few tens of metres, such as in cooling air at night, or as high as several kilometres over the desert in the middle of a hot sunny day. When the boundary layer height is low, higher concentrations of pollutants (emitted from the Earth's surface) can develop.","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Charnock":{"attrs":{"long_name":"Charnock","parameter_ID":"148","product_type":"forecast","shortName":"chnk","standard_name":"Charnock"},"description":"This parameter accounts for increased aerodynamic roughness as wave heights grow due to increasing surface stress. It depends on the wind speed, wave age and other aspects of the sea state and is used to calculate how much the waves slow down the wind.","dimensions":["lat","lon","time"],"type":"data","unit":"Numeric"},"Evaporation":{"attrs":{"long_name":"Evaporation","parameter_ID":"182","product_type":"forecast","shortName":"e","standard_name":"Evaporation"},"description":"This parameter is the accumulated amount of water that has evaporated from the Earth's surface, including a simplified representation of transpiration (from vegetation), into vapour in the air above. This parameter is accumulated over a particular time period which depends on the data extracted.","dimensions":["lat","lon","time"],"type":"data","unit":"m of water equivalent"},"Geopotential":{"attrs":{"long_name":"Geopotential","parameter_ID":"129","product_type":"forecast","shortName":"z","standard_name":"Geopotential"},"description":"This parameter is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level.","dimensions":["lat","lon","time"],"type":"data","unit":"m**2 s**-2"},"High_cloud_cover":{"attrs":{"long_name":"High cloud cover","parameter_ID":"188","product_type":"forecast","shortName":"hcc","standard_name":"High_cloud_cover"},"description":"The proportion of a grid box covered by cloud occurring in the high levels of the troposphere. High cloud is a single level field calculated from cloud occurring on model levels with a pressure less than 0.45 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), high cloud would be calculated using levels with a pressure of less than 450 hPa (approximately 6km and above ( assuming a `standard atmosphere`)).","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Land_sea_mask":{"attrs":{"long_name":"Land-sea mask","parameter_ID":"172","product_type":"forecast","shortName":"lsm","standard_name":"Land_sea_mask"},"description":"This parameter is the proportion of land, as opposed to ocean or inland waters (lakes, reservoirs, rivers and coastal waters), in a grid box. This parameter has values ranging between zero and one and is dimensionless.","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Low_cloud_cover":{"attrs":{"long_name":"Low cloud cover","parameter_ID":"186","product_type":"forecast","shortName":"lcc","standard_name":"Low_cloud_cover"},"description":"This parameter is the proportion of a grid box covered by cloud occurring in the lower levels of the troposphere. Low cloud is a single level field calculated from cloud occurring on model levels with a pressure greater than 0.8 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), low cloud would be calculated using levels with a pressure greater than 800 hPa (below approximately 2km (assuming a 'standard atmosphere')).","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Mean_sea_level_pressure":{"attrs":{"long_name":"Mean sea level pressure","parameter_ID":"151","product_type":"forecast","shortName":"msl","standard_name":"Mean_sea_level_pressure"},"description":"This parameter is the pressure (force per unit area) of the atmosphere adjusted to the height of mean sea level.","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Medium_cloud_cover":{"attrs":{"long_name":"Medium cloud cover","parameter_ID":"187","product_type":"forecast","shortName":"mcc","standard_name":"Medium_cloud_cover"},"description":"This parameter is the proportion of a grid box covered by cloud occurring in the middle levels of the troposphere. Medium cloud is a single level field calculated from cloud occurring on model levels with a pressure between 0.45 and 0.8 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), medium cloud would be calculated using levels with a pressure of less than or equal to 800 hPa and greater than or equal to 450 hPa (between approximately 2km and 6km (assuming a 'standard atmosphere')).","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Potential_vorticity":{"attrs":{"long_name":"Potential vorticity","parameter_ID":"60","product_type":"forecast","shortName":"pv","standard_name":"Potential_vorticity"},"description":"Potential vorticity is a measure of the capacity for air to rotate in the atmosphere. If we ignore the effects of heating and friction, potential vorticity is conserved following an air parcel. It is used to look for places where large wind storms are likely to originate and develop. Potential vorticity increases strongly above the tropopause and therefore, it can also be used in studies related to the stratosphere and stratosphere-troposphere exchanges.","dimensions":["lat","lon","time"],"type":"data","unit":"K m**2 kg**-1 s**-1"},"Relative_humidity":{"attrs":{"long_name":"Relative humidity","parameter_ID":"157","product_type":"forecast","shortName":"r","standard_name":"Relative_humidity"},"description":"This parameter is the water vapour pressure as a percentage of the value at which the air becomes saturated (the point at which water vapour begins to condense into liquid water or deposition into ice).","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Skin_temperature":{"attrs":{"long_name":"Skin temperature","parameter_ID":"235","product_type":"forecast","shortName":"skt","standard_name":"Skin_temperature"},"description":"This parameter is the temperature of the surface of the Earth. The skin temperature is the theoretical temperature that is required to satisfy the surface energy balance. It represents the temperature of the uppermost surface layer, which has no heat capacity and so can respond instantaneously to changes in surface fluxes. Skin temperature is calculated differently over land and sea.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Snow_depth":{"attrs":{"long_name":"Snow depth","parameter_ID":"141","product_type":"forecast","shortName":"sd","standard_name":"Snow_depth"},"description":"This parameter is the depth of snow from the snow-covered area of a grid box.","dimensions":["lat","lon","time"],"type":"data","unit":"m of water equivalent"},"Snow_depth_water_equivalent":{"attrs":{"long_name":"Snow depth water equivalent","parameter_ID":"228141","product_type":"forecast","shortName":"sd","standard_name":"Snow_depth_water_equivalent"},"description":"Snow depth water equivalent in kg m**-2 (mm) water equivalent. Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean snow depth water equivalent. The specific encoding for Time-mean snow depth water equivalent can be found in 235078.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m**-2"},"Snowfall":{"attrs":{"long_name":"Snowfall","parameter_ID":"144","product_type":"forecast","shortName":"sf","standard_name":"Snowfall"},"description":"This parameter is the accumulated snow that falls to the Earth's surface. It is the sum of large-scale snowfall and convective snowfall. Large-scale snowfall is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of the grid box or larger.","dimensions":["lat","lon","time"],"type":"data","unit":"m of water equivalent"},"Specific_cloud_liquid_water_content":{"attrs":{"long_name":"Specific cloud liquid water content","parameter_ID":"246","product_type":"forecast","shortName":"clwc","standard_name":"Specific_cloud_liquid_water_content"},"description":"This parameter is the mass of cloud liquid water droplets per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg**-1"},"Specific_humidity":{"attrs":{"long_name":"Specific humidity","parameter_ID":"133","product_type":"forecast","shortName":"q","standard_name":"Specific_humidity"},"description":"This parameter is the mass of water vapour per kilogram of moist air. The total mass of moist air is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg**-1"},"Sub_surface_runoff":{"attrs":{"long_name":"Sub-surface runoff","parameter_ID":"9","product_type":"forecast","shortName":"ssro","standard_name":"Sub_surface_runoff"},"description":"Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is simply called 'runoff'. This parameter is the total amount of water accumulated over a particular time period which depends on the data extracted.The units of runoff are depth in metres. This is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid square area. Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here.","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Surface_latent_heat_flux":{"attrs":{"long_name":"Surface latent heat flux","parameter_ID":"147","product_type":"forecast","shortName":"slhf","standard_name":"Surface_latent_heat_flux"},"description":"This parameter is the transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earth's surface and the atmosphere through the effects of turbulent air motion. Evaporation from the Earth's surface represents a transfer of energy from the surface to the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"J m**-2"},"Surface_long_wave_(thermal)_radiation_downwards":{"attrs":{"long_name":"Surface short-wave (solar) radiation downwards","parameter_ID":"175","product_type":"forecast","shortName":"strd","standard_name":"Surface_long_wave_(thermal)_radiation_downwards"},"description":"This parameter is the amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere and clouds that reaches a horizontal plane at the surface of the Earth. The surface of the Earth emits thermal radiation, some of which is absorbed by the atmosphere and clouds. The atmosphere and clouds likewise emit thermal radiation in all directions, some of which reaches the surface (represented by this parameter).","dimensions":["lat","lon","time"],"type":"data","unit":"J m**-2"},"Surface_net_long_wave_(thermal)_radiation":{"attrs":{"long_name":"Surface net long-wave (thermal) radiation","parameter_ID":"177","product_type":"forecast","shortName":"str","standard_name":"Surface_net_long_wave_(thermal)_radiation"},"description":"Thermal radiation (also known as longwave or terrestrial radiation) refers to radiation emitted by the atmosphere, clouds and the surface of the Earth. This parameter is the difference between downward and upward thermal radiation at the surface of the Earth. It the amount passing through a horizontal plane. The atmosphere and clouds emit thermal radiation in all directions, some of which reaches the surface as downward thermal radiation. The upward thermal radiation at the surface consists of thermal radiation emitted by the surface plus the fraction of downwards thermal radiation reflected upward by the surface.","dimensions":["lat","lon","time"],"type":"data","unit":"J m**-2"},"Surface_net_short_wave_(solar)_radiation":{"attrs":{"long_name":"Surface net short-wave (solar) radiation","parameter_ID":"176","product_type":"forecast","shortName":"ssr","standard_name":"Surface_net_short_wave_(solar)_radiation"},"description":"This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo). Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The remainder is incident on the Earth's surface, where some of it is reflected.","dimensions":["lat","lon","time"],"type":"data","unit":"J m**-2"},"Surface_sensible_heat_flux":{"attrs":{"long_name":"Surface sensible heat flux","parameter_ID":"146","product_type":"forecast","shortName":"sshf","standard_name":"Surface_sensible_heat_flux"},"description":"This parameter is the transfer of heat between the Earth's surface and the atmosphere through the effects of turbulent air motion (but excluding any heat transfer resulting from condensation or evaporation). The magnitude of the sensible heat flux is governed by the difference in temperature between the surface and the overlying atmosphere, wind speed and the surface roughness. For example, cold air overlying a warm surface would produce a sensible heat flux from the land (or ocean) into the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"J m**-2"},"Surface_short_wave_(solar)_radiation_downwards":{"attrs":{"long_name":"Surface short-wave (solar) radiation downwards","parameter_ID":"169","product_type":"forecast","shortName":"ssrd","standard_name":"Surface_short_wave_(solar)_radiation_downwards"},"description":"This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth. This parameter comprises both direct and diffuse solar radiation. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface (represented by this parameter).","dimensions":["lat","lon","time"],"type":"data","unit":"J m**-2"},"TOA_incident_short_wave_(solar)_radiation":{"attrs":{"long_name":"TOA incident short-wave (solar) radiation","parameter_ID":"212","product_type":"forecast","shortName":"tisr","standard_name":"TOA_incident_short_wave_(solar)_radiation"},"description":"Accumulated field","dimensions":["lat","lon","time"],"type":"data","unit":"J m**-2"},"Temperature":{"attrs":{"long_name":"Temperature","parameter_ID":"130","product_type":"forecast","shortName":"t","standard_name":"Temperature"},"description":"This parameter is the temperature in the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-integrated eastward turbulent surface stress due to surface roughness":{"attrs":{"long_name":"Time-integrated eastward turbulent surface stress due to surface roughness","parameter_ID":"260654","product_type":"forecast","shortName":"etsssr","standard_name":"Time-integrated eastward turbulent surface stress due to surface roughness"},"description":" ","dimensions":["lat","lon","time"],"type":"data","unit":"N m**-2 s"},"Time-integrated northward turbulent surface stress due to surface roughness":{"attrs":{"long_name":"Time-integrated northward turbulent surface stress due to surface roughness","parameter_ID":"260655","product_type":"forecast","shortName":"ntsssr","standard_name":"Time-integrated northward turbulent surface stress due to surface roughness"},"description":" ","dimensions":["lat","lon","time"],"type":"data","unit":"N m**-2 s"},"Time-mean_snow_thickness_over_sea_ice":{"attrs":{"long_name":"Time-mean snow thickness over sea ice","parameter_ID":"263002","product_type":"forecast","shortName":"avg_sisnthick","standard_name":"Time-mean_snow_thickness_over_sea_ice"},"description":"","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Time_integrated_eastward_turbulent_surface_stress":{"attrs":{"long_name":"Time-integrated eastward turbulent surface stress","parameter_ID":"180","product_type":"forecast","shortName":"ewss","standard_name":"Time_integrated_eastward_turbulent_surface_stress"},"description":"Air flowing over a surface exerts a stress that transfers momentum to the surface and slows the wind. This parameter is the accumulated stress on the Earth's surface in the eastward direction due to both the turbulent interactions between the atmosphere and the surface, and to turbulent orographic form drag. The turbulent interactions between the atmosphere and the surface are due to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km being derived from land surface data at about 1 km resolution.","dimensions":["lat","lon","time"],"type":"data","unit":"N m**-2 s"},"Time_integrated_northward_turbulent_surface_stress":{"attrs":{"long_name":"Time_integrated_northward_turbulent_surface_stress","parameter_ID":"181","product_type":"forecast","shortName":"nsss","standard_name":"Time_integrated_eastward_turbulent_surface_stress"},"description":"Air flowing over a surface exerts a stress that transfers momentum to the surface and slows the wind. This parameter is the accumulated stress on the Earth's surface in the northward direction due to both the turbulent interactions between the atmosphere and the surface, and to turbulent orographic form drag. The turbulent interactions between the atmosphere and the surface are due to the roughness of the surface.","dimensions":["lat","lon","time"],"type":"data","unit":"N m**-2 s"},"Time_mean_X_component_of_sea_ice_velocity":{"attrs":{"long_name":"Time_mean_X_component_of_sea_ice_velocity","parameter_ID":"263021","product_type":"forecast","shortName":"avg_six","standard_name":"Time_mean_X_component_of_sea_ice_velocity"},"description":" ","dimensions":["lat","lon","time"],"type":"data","unit":"m s**-1"},"Time_mean_Y_component_of_sea_ice_velocity":{"attrs":{"long_name":"Time_mean_Y_component_of_sea_ice_velocity","parameter_ID":"263022","product_type":"forecast","shortName":"avg_siy","standard_name":"Time_mean_Y_component_of_sea_ice_velocity"},"description":" ","dimensions":["lat","lon","time"],"type":"data","unit":"m s**-1"},"Time_mean_eastward_sea_ice_velocity":{"attrs":{"long_name":"Time-mean eastward sea ice velocity","parameter_ID":"263003","product_type":"forecast","shortName":"avg_siue","standard_name":"Time_mean_eastward_sea_ice_velocity"},"description":" ","dimensions":["lat","lon","time"],"type":"data","unit":"m s**-1"},"Time_mean_eastward_sea_water_velocity":{"attrs":{"long_name":"Time-mean eastward sea water velocity","parameter_ID":"263506","product_type":"forecast","shortName":"avg_uoe","standard_name":"Time_mean_eastward_sea_water_velocity"},"description":" ","dimensions":["lat","lon","time"],"type":"data","unit":"m s**-1"},"Time_mean_northward_sea_ice_velocity":{"attrs":{"long_name":"Time-mean northward sea ice velocity","parameter_ID":"263004","product_type":"forecast","shortName":"avg_sivn","standard_name":"Time_mean_northward_sea_ice_velocity"},"description":" ","dimensions":["lat","lon","time"],"type":"data","unit":"m s**-1"},"Time_mean_northward_sea_water_velocity":{"attrs":{"long_name":"Time-mean northward sea water velocity","parameter_ID":"263505","product_type":"forecast","shortName":"avg_von","standard_name":"Time_mean_northward_sea_water_velocity"},"description":" ","dimensions":["lat","lon","time"],"type":"data","unit":"m s**-1"},"Time_mean_ocean_mixed_layer_depth_defined_by_sigma_theta_0.03_kg_m-3":{"attrs":{"long_name":"Time-mean ocean mixed layer depth defined by sigma theta 0.03 kg m-3","parameter_ID":"263114","product_type":"forecast","shortName":"avg_mlotst030","standard_name":"Time_mean_ocean_mixed_layer_depth_defined_by_sigma_theta_0.03_kg_m-3"},"description":" ","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Time_mean_sea_ice_area_fraction":{"attrs":{"long_name":"Time-mean sea ice area fraction","parameter_ID":"263001","product_type":"forecast","shortName":"avg_siconc","standard_name":"Time_mean_sea_ice_area_fraction"},"description":" ","dimensions":["lat","lon","time"],"type":"data","unit":"Fraction"},"Time_mean_sea_ice_thickness":{"attrs":{"long_name":"Time-mean sea ice thickness","parameter_ID":"263000","product_type":"forecast","shortName":"avg_sithick","standard_name":"Time_mean_sea_ice_thickness"},"description":" ","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Time_mean_sea_ice_volume_per_unit_area":{"attrs":{"long_name":"Time-mean sea ice volume per unit area","parameter_ID":"263008","product_type":"forecast","shortName":"avg_sivol","standard_name":"Time_mean_sea_ice_volume_per_unit_area"},"description":" ","dimensions":["lat","lon","time"],"type":"data","unit":"m**3 m**-2"},"Time_mean_sea_surface_height":{"attrs":{"long_name":"Time-mean sea surface height","parameter_ID":"263124","product_type":"forecast","shortName":"avg_zos","standard_name":"Time_mean_sea_surface_height"},"description":" ","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Time_mean_sea_surface_practical_salinity":{"attrs":{"long_name":"Time-mean sea surface practical salinity","parameter_ID":"263100","product_type":"forecast","shortName":"avg_sos","standard_name":"Time_mean_sea_surface_practical_salinity"},"description":" ","dimensions":["lat","lon","time"],"type":"data","unit":"g kg**-1"},"Time_mean_sea_surface_temperature":{"attrs":{"long_name":"Time_mean_sea_surface_temperature","parameter_ID":"263101","product_type":"forecast","shortName":"avg_tos","standard_name":"Time_mean_sea_surface_temperature"},"description":" ","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time_mean_sea_water_potential_temperature":{"attrs":{"long_name":"Time-mean sea water potential temperature","parameter_ID":"263501","product_type":"forecast","shortName":"avg_thetao","standard_name":"Time_mean_sea_water_potential_temperature"},"description":" ","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time_mean_sea_water_practical_salinity":{"attrs":{"long_name":"Time-mean sea water practical salinity","parameter_ID":"263500","product_type":"forecast","shortName":"avg_so","standard_name":"Time_mean_sea_water_practical_salinity"},"description":" ","dimensions":["lat","lon","time"],"type":"data","unit":"g kg**-1"},"Time_mean_snow_volume_over_sea_ice_per_unit_area":{"attrs":{"long_name":"Time-mean snow volume over sea ice per unit area","parameter_ID":"263009","product_type":"forecast","shortName":"avg_snvol","standard_name":"Time_mean_snow_volume_over_sea_ice_per_unit_area"},"description":" ","dimensions":["lat","lon","time"],"type":"data","unit":"m**3 m**-2"},"Time_mean_upward_sea_water_velocity":{"attrs":{"long_name":"Time-mean upward sea water velocity","parameter_ID":"263507","product_type":"forecast","shortName":"avg_wo","standard_name":"Time_mean_upward_sea_water_velocity"},"description":" ","dimensions":["lat","lon","time"],"type":"data","unit":"m s**-1"},"Time_mean_vertically_integrated_heat_content_in_the_upper_300_m":{"attrs":{"long_name":"Time-mean vertically-integrated heat content in the upper 300 m","parameter_ID":"263121","product_type":"forecast","shortName":"avg_hc300m","standard_name":"Time_mean_vertically_integrated_heat_content_in_the_upper_300_m"},"description":" ","dimensions":["lat","lon","time"],"type":"data","unit":"J m**-2"},"Time_mean_vertically_integrated_heat_content_in_the_upper_700_m":{"attrs":{"long_name":"Time-mean vertically-integrated heat content in the upper 700 m","parameter_ID":"263122","product_type":"forecast","shortName":"avg_hc700m","standard_name":"Time_mean_vertically_integrated_heat_content_in_the_upper_700_m"},"description":" ","dimensions":["lat","lon","time"],"type":"data","unit":"J m**-2"},"Top_net_long_wave_(thermal)_radiation":{"attrs":{"long_name":"Top net long-wave (thermal) radiation","parameter_ID":"179","product_type":"forecast","shortName":"ttr","standard_name":"Top_net_long_wave_(thermal)_radiation"},"description":"The thermal (also known as terrestrial or longwave) radiation emitted to space at the top of the atmosphere is commonly known as the Outgoing Longwave Radiation (OLR). The top net thermal radiation (this parameter) is equal to the negative of OLR.","dimensions":["lat","lon","time"],"type":"data","unit":"J m**-2"},"Top_net_short_wave_(solar)_radiation":{"attrs":{"long_name":"Top net short-wave (solar) radiation","parameter_ID":"178","product_type":"forecast","shortName":"tsr","standard_name":"Top_net_short_wave_(solar)_radiation"},"description":"This parameter is the incoming solar radiation (also known as shortwave radiation) minus the outgoing solar radiation at the top of the atmosphere. It is the amount of radiation passing through a horizontal plane. The incoming solar radiation is the amount received from the Sun. The outgoing solar radiation is the amount reflected and scattered by the Earth's atmosphere and surface.","dimensions":["lat","lon","time"],"type":"data","unit":"J m**-2"},"Total_cloud_cover":{"attrs":{"long_name":"Total cloud cover","parameter_ID":"164","product_type":"forecast","shortName":"tcc","standard_name":"Total_cloud_cover"},"description":"This parameter is the proportion of a grid box covered by cloud. Total cloud cover is a single level field calculated from the cloud occurring at different model levels through the atmosphere. Assumptions are made about the degree of overlap/randomness between clouds at different heights.","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Total_cloud_cover_":{"attrs":{"long_name":"Total cloud cover","parameter_ID":"228164","product_type":"forecast","shortName":"tcc_","standard_name":"Total_cloud_cover_"},"description":"[NOTE: See 164 for the equivalent parameter in 0-1]","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Total_column_cloud_ice_water":{"attrs":{"long_name":"Total column cloud ice water","parameter_ID":"79","product_type":"forecast","shortName":"tciw","standard_name":"Total_column_cloud_ice_water"},"description":"the amount of ice contained within clouds in a column extending from the surface of the Earth to the top of the atmosphere. Snow (aggregated ice crystals) is not included in this parameter.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m**-2"},"Total_column_cloud_liquid_water":{"attrs":{"long_name":"Total column cloud liquid water","parameter_ID":"78","product_type":"forecast","shortName":"tclw","standard_name":"Total_column_cloud_liquid_water"},"description":"the amount of liquid water contained within clouds in a column extending from the surface of the Earth to the top of the atmosphere. Snow (aggregated ice crystals) is not included in this parameter.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m**-2"},"Total_column_vertically_integrated_water_vapour":{"attrs":{"long_name":"Total column vertically-integrated water vapour","parameter_ID":"137","product_type":"forecast","shortName":"tcwv","standard_name":"Total_column_vertically_integrated_water_vapour"},"description":"This parameter is the total amount of water vapour in a column extending from the surface of the Earth to the top of the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m**-2"},"Total_precipitation_rate":{"attrs":{"long_name":"Total precipitation rate","parameter_ID":"260048","product_type":"forecast","shortName":"tprate","standard_name":"Total_precipitation_rate"},"description":"This parameter is the rate of total precipitation, at the specified time. In the ECMWF Integrated Forecasting System (IFS), total precipitation is rain and snow that falls to the Earth's surface. It is the sum of large-scale precipitation and convective precipitation. Large-scale precipitation is generated by the cloud scheme in the IFS. The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of a grid box or larger. Convective precipitation is generated by the convection scheme in the IFS. The convection scheme represents convection at spatial scales smaller than the grid box.See further information. Precipitation parameters do not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m**-2 s**-1"},"U_component_of_wind":{"attrs":{"long_name":"U component of wind","parameter_ID":"131","product_type":"forecast","shortName":"u","standard_name":"U_component_of_wind"},"description":"This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east, in metres per second. A negative sign thus indicates air movement towards the west.","dimensions":["lat","lon","time"],"type":"data","unit":"m s**-1"},"V_component_of_wind":{"attrs":{"long_name":"V component of wind","parameter_ID":"132","product_type":"forecast","shortName":"v","standard_name":"V_component_of_wind"},"description":"This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the east, in metres per second. A negative sign thus indicates air movement towards the west.","dimensions":["lat","lon","time"],"type":"data","unit":"m s**-1"},"Vertical_velocity":{"attrs":{"long_name":"Vertical velocity","parameter_ID":"135","product_type":"forecast","shortName":"w","standard_name":"Vertical_velocity"},"description":"This parameter is the speed of air motion in the upward or downward direction. The ECMWF Integrated Forecasting System (IFS) uses a pressure based vertical co-ordinate system and pressure decreases with height, therefore negative values of vertical velocity indicate upward motion.","dimensions":["lat","lon","time"],"type":"data","unit":"Pa s**-1"},"surface_pressure":{"attrs":{"long_name":"Surface pressure","parameter_ID":"134","product_type":"forecast","shortName":"sp","standard_name":"surface_air_pressure"},"description":"Surface pressure (not mean sea-level pressure), 2-D field to calculate the 3-D pressure field from hybrid coordinates","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"surface_runoff":{"attrs":{"long_name":"Surface runoff","parameter_ID":"8","product_type":"forecast","shortName":"sro","standard_name":"surface_runoff_amount"},"description":"Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is simply called 'runoff'. This parameter is the total amount of water accumulated over a particular time period which depends on the data extracted.The units of runoff are depth in metres. This is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid square area. Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here.","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"total_precipitation":{"attrs":{"long_name":"Total precipitation","parameter_ID":"228","product_type":"forecast","shortName":"tp","standard_name":"lwe_thickness_of_precipitation_amount"},"description":"The construction lwe_thickness_of_X_amount or _content means the vertical extent of a layer of liquid water having the same mass per unit area. 'Precipitation' in the Earth's atmosphere means precipitation of water in all phases. The abbreviation 'lwe' means liquid water equivalent.","dimensions":["lat","lon","time"],"type":"data","unit":"m"}},"sci:publications":[{"doi":"10.1109/MCSE.2023.3260519","citation":"N. Wedi et al., Destination Earth: High-Performance Computing for Weather and Climate, in Computing in Science \u0026 Engineering, vol. 24, no. 6, pp. 29-37, Nov.-Dec. 2022,"},{"doi":"10.21957/d3f982672e","citation":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"dedl:short_description":"The Climate Change Adaptation Digital Twin provides global climate projections and sector-specific information over multiple decades at high resolution via a unified framework combining advanced Earth system models, impact assessments, and observations. This Collection exposes Generation-1 simulations."},{"type":"Collection","title":"Weather-Induced Extremes Digital Twin (Extremes DT)","id":"EO.ECMWF.DAT.DT_EXTREMES","description":"The DestinE Digital Twin for Weather-Induced Extremes (Extremes DT) supports responding and adapting to extreme events in a changing world by providing a capability to produce tailored simulations and address what-if scenarios related to extreme events in a past, present and future climate, complementing existing capabilities at national and European level.\n\nThe Extremes DT combines cutting-edge Earth-system models, impact-sector models and observations. It uses a global and a regional component to provide information on extreme events on a timescale of a few days ahead, at very high spatial resolution (4.4 km globally, 500 – 750 m over Europe).\n\nThe Extremes DT sets up a unified, flexible, framework to simulate extreme weather events and their associated impacts at km-scale resolutions, using the world-leading supercomputing facilities of the EuroHPC Joint Undertaking. A concise overview of what the Extremes DT aims to achieve, and the different concepts essential for an understanding of the Digital Twin’s characteristics is included in the [Extremes DT factsheet](https://destine.ecmwf.int/wp-content/uploads/2024/06/Extreme-Events-DT_General.pdf)\n\n## Models\n\nThe global component of the Extremes DT builds on ECMWF’s Integrated Forecasting System. It is based on the configuration used operationally at ECMWF, by further increasing the resolution of several components of the Earth System Model (atmosphere, land and waves) to a resolution of 4.4 km. The ocean component remains unchanged and is NEMO on the ORCA025 grid. For more information on models please click [here](https://destine.ecmwf.int/weather-induced-extremes-digital-twin-1/#models-extremes)\n\n## Simulations\n\nThe global Extremes DT simulations are carried out by ECMWF on EuroHPC supercomputers.\n\nThe global component of the Extremes DT provides four-day global simulations with a resolution of 4.4 km, initialized daily from the 00UTC ECMWF operational analysis at a resolution of 9 km (depending on EuroHPC availability, and queuing times). See [here](https://destine.ecmwf.int/weather-induced-extremes-digital-twin-1/#simulations-extremes) for more information on Simulations\n\n## Parameters\n\nBelow we see the list of parameters extracted from the 'DestinE Extremes DT data portfolio', for more information please refer to the page [Extremes DT Parameters](https://confluence.ecmwf.int/display/DDCZ/Extremes+DT+data+catalogue)","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.DT_EXTREMES/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.DT_EXTREMES/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.DT_EXTREMES/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.DT_EXTREMES","title":"Weather-Induced Extremes Digital Twin (Extremes DT)"},{"rel":"example","type":"application/x-ipynb+json","href":"https://raw.githubusercontent.com/destination-earth/DestinE-DataLake-Lab/refs/heads/main/HDA/DestinE%20Digital%20Twins/DEDL-HDA-EO.ECMWF.DAT.DT_EXTREMES-Series.ipynb","title":"Destination Earth - Weather-Induced Extremes Digital Twin Series - Data Access using DEDL HDA","application:type":"jupyter-notebook","application:embedded":true,"application:language":"Python"},{"rel":"example","type":"application/x-ipynb+json","href":"https://raw.githubusercontent.com/destination-earth/DestinE-DataLake-Lab/refs/heads/main/HDA/DestinE%20Digital%20Twins/DEDL-HDA-EO.ECMWF.DAT.DT_EXTREMES.ipynb","title":"Destination Earth - Weather-Induced Extremes Digital Twin - Data Access using DEDL HDA","application:type":"jupyter-notebook","application:embedded":true,"application:language":"Python"},{"rel":"example","type":"application/x-ipynb+json","href":"https://raw.githubusercontent.com/destination-earth/DestinE-DataLake-Lab/refs/heads/main/HDA/DestinE%20Digital%20Twins/ExtremeDT-ParameterPlotter.ipynb","title":"Destination Earth - Weather-Induced Extremes DT Parameter Plotter Tutorial","application:type":"jupyter-notebook","application:embedded":true,"application:language":"Python"},{"rel":"example","type":"application/x-ipynb+json","href":"https://raw.githubusercontent.com/destination-earth/DestinE-DataLake-Lab/refs/heads/main/HDA/DestinE%20Digital%20Twins/ExtremeDT-dataAvailability.ipynb","title":"Destination Earth - Aviso notification for DT data availability","application:type":"jupyter-notebook","application:embedded":true,"application:language":"Python"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/DestinE+ExtremesDT+Parameters","title":"DestinE ExtremesDT Parameters"},{"rel":"cite-as","type":"text/html","href":"https://dl.acm.org/doi/abs/10.1109/MCSE.2023.3260519","title":"Destination Earth: High-Performance Computing for Weather and Climate"}],"assets":{"thumbnail":{"href":"https://s3.central.data.destination-earth.eu/swift/v1/dedl-public/collections/extremes-dt-min.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2026-08-25T00:00:00Z","2026-09-19T00:00:00Z"]]}},"license":"CC-BY-4.0","keywords":["Digital Twins","Weather","Atmosphere","Ocean","Land","Sea Ice","Snow","Soil","Earth","High Performance Computing","Decision Making","Weather Forecasting","Europe","Meteorology"],"summaries":{"federation:backends":["dedt_lumi"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/datacube/v2.1.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json","https://stac-extensions.github.io/application/v0.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["producer","processor","licensor"],"url":"https://www.ecmwf.int/"},{"name":"Destination Earth Data Lake (DEDL)","roles":["host"],"url":"https://data.destination-earth.eu/"}],"created":"2024-04-11T22:31:55Z","updated":"2026-09-19T11:50:48Z","published":"2024-04-11T22:31:55Z","cube:dimensions":{"lat":{"axis":"y","description":"latitude","extent":[-90,90],"type":"spatial"},"lon":{"axis":"x","description":"longitude","extent":[-180,180],"type":"spatial"},"time":{"extent":[null,null],"step":"P0Y0M0DT1H0M0S","type":"temporal"}},"cube:variables":{"10_metre_U_component_of_wind":{"attrs":{"long_name":"10 metre U wind component","parameter_ID":"165","product_type":"forecast","shortName":"10u","standard_name":"eastward_wind"},"description":"Eastward component of the near-surface (usually, 10 meters) wind","dimensions":["lat","lon","time"],"type":"data","unit":"m s**-1"},"10_metre_V_component_of_wind":{"attrs":{"long_name":"10 metre V wind component","parameter_ID":"166","product_type":"forecast","shortName":"10v","standard_name":"northward_wind"},"description":"Northward component of the near surface wind","dimensions":["lat","lon","time"],"type":"data","unit":"m s**-1"},"2_metre_dewpoint_temperature":{"attrs":{"long_name":"2 metre dewpoint temperature","parameter_ID":"168","product_type":"forecast","shortName":"2d","standard_name":"dew_point_temperature"},"description":"","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"2_metre_temperature":{"attrs":{"long_name":"2 metre temperature","parameter_ID":"167","product_type":"forecast","shortName":"2t","standard_name":"air_temperature"},"description":"near-surface (usually, 2 meter) air temperature","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Accumulated_freezing_rain":{"attrs":{"long_name":"Accumulated freezing rain","parameter_ID":"228216","product_type":"forecast","shortName":"fzra","standard_name":"Accumulated_freezing_rain"},"description":"This parameter is the total amount of precipitation falling as freezing rain, accumulated over a particular time period which depends on the data extracted.","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Averaged_total_lightning_flash_density_in_the_last_6_hours":{"attrs":{"long_name":"Averaged total lightning flash density in the last 6 hours","parameter_ID":"228058","product_type":"forecast","shortName":"litota6","standard_name":"Averaged_total_lightning_flash_density_in_the_last_6_hours"},"description":"This parameter gives the total lightning flash rate averaged over the last 6 hours. Note that this parameter has units of flashes per square kilometre per day. Conversion of this parameter to units of flashes per 100 square kilometres per hour can give values that are easier to interpret.","dimensions":["lat","lon","time"],"type":"data","unit":"km**-2 day**-1"},"Convective_rain_rate":{"attrs":{"long_name":"Convective rain rate","parameter_ID":"228218","product_type":"forecast","shortName":"crr","standard_name":"Convective_rain_rate"},"description":"This parameter is the rate of rainfall (rainfall intensity), at the specified time ","dimensions":["lat","lon","time"],"type":"data","unit":"kg m**-2 s**-1"},"Divergence":{"attrs":{"long_name":"Divergence","parameter_ID":"155","product_type":"forecast","shortName":"d","standard_name":"Divergence"},"description":"This parameter is the horizontal divergence of velocity. It is the rate at which air is spreading out horizontally from a point, per square metre. This parameter is positive for air that is spreading out, or diverging, and negative for the opposite, for air that is concentrating, or converging (convergence).","dimensions":["lat","lon","time"],"type":"data","unit":"s**-1"},"Geopotential":{"attrs":{"long_name":"Geopotential","parameter_ID":"129","product_type":"forecast","shortName":"z","standard_name":"Geopotential"},"description":"This parameter is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level.","dimensions":["lat","lon","time"],"type":"data","unit":"m**2 s**-2"},"Instantaneous_10_metre_wind_gust":{"attrs":{"long_name":"Instantaneous 10 metre wind gust","parameter_ID":"228029","product_type":"forecast","shortName":"i10fg","standard_name":"Instantaneous_10_metre_wind_gust"},"description":"This parameter is the maximum wind gust at the specified time, at a height of ten metres above the surface of the Earth.","dimensions":["lat","lon","time"],"type":"data","unit":"m s**-1"},"Instantaneous_total_lightning_flash_density":{"attrs":{"long_name":"Instantaneous total lightning flash density","parameter_ID":"228050","product_type":"forecast","shortName":"litoti","standard_name":"Instantaneous_total_lightning_flash_density"},"description":"This parameter gives the total lightning flash rate at the specified time.","dimensions":["lat","lon","time"],"type":"data","unit":"km**-2 day**-1"},"Large_scale_precipitation":{"attrs":{"long_name":"Large-scale precipitation","parameter_ID":"142","product_type":"forecast","shortName":"lsp","standard_name":"Large_scale_precipitation"},"description":"This parameter is the accumulated liquid and frozen water, comprising rain and snow, that falls to the Earth's surface and which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of the grid box or larger.","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Large_scale_rain_rate":{"attrs":{"long_name":"Large scale rain rate","parameter_ID":"228219","product_type":"forecast","shortName":"lsrr","standard_name":"Large_scale_rain_rate"},"description":"This parameter is the rate of rainfall (rainfall intensity), at the specified time, generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of a grid box or larger.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m**-2 s**-1"},"Large_scale_snowfall_rate_water_equivalent":{"attrs":{"long_name":"Large scale snowfall rate water equivalent","parameter_ID":"228221","product_type":"forecast","shortName":"lssfr","standard_name":"Large_scale_snowfall_rate_water_equivalent"},"description":"This parameter is the rate of snowfall (snowfall intensity), at the specified time, generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale snowfall due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of a grid box or larger.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m**-2 s**-1"},"Mean_sea_level_pressure":{"attrs":{"long_name":"Mean sea level pressure","parameter_ID":"151","product_type":"forecast","shortName":"msl","standard_name":"Mean_sea_level_pressure"},"description":"This parameter is the pressure (force per unit area) of the atmosphere adjusted to the height of mean sea level.","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Mean_wave_direction":{"attrs":{"long_name":"Mean wave direction","parameter_ID":"140230","product_type":"forecast","shortName":"mwd","standard_name":"Mean_wave_direction"},"description":"This parameter is the mean direction of ocean/sea surface waves. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is a mean over all frequencies and directions of the two-dimensional wave spectrum.","dimensions":["lat","lon","time"],"type":"data","unit":"degrees"},"Mean_wave_period":{"attrs":{"long_name":"Mean wave period","parameter_ID":"140232","product_type":"forecast","shortName":"mwp","standard_name":"Mean_wave_period"},"description":"This parameter is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is a mean over all frequencies and directions of the two-dimensional wave spectrum.","dimensions":["lat","lon","time"],"type":"data","unit":"s"},"Mean_zero_crossing_wave_period":{"attrs":{"long_name":"Mean zero-crossing wave period","parameter_ID":"140221","product_type":"forecast","shortName":"mp2","standard_name":"Mean_zero_crossing_wave_period"},"description":"This parameter represents the mean length of time between occasions where the sea/ocean surface crosses mean sea level. In combination with wave height information, it could be used to assess the length of time that a coastal structure might be under water, for example.","dimensions":["lat","lon","time"],"type":"data","unit":"s"},"Most_unstable_CAPE":{"attrs":{"long_name":"Most-unstable CAPE","parameter_ID":"228235","product_type":"forecast","shortName":"mucape","standard_name":"Most_unstable_CAPE"},"description":"Convective Available Potential Energy (CAPE) is a measure of the amount of energy available for convection. It is related to the maximum potential vertical velocity in the updraught.","dimensions":["lat","lon","time"],"type":"data","unit":"J kg**-1"},"Peak_wave_period":{"attrs":{"long_name":"Peak wave period","parameter_ID":"140231","product_type":"forecast","shortName":"pp1d","standard_name":"Peak_wave_period"},"description":"This parameter represents the period of the most energetic ocean waves generated by local winds and associated with swell. The wave period is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point.","dimensions":["lat","lon","time"],"type":"data","unit":"s"},"Precipitation_type":{"attrs":{"long_name":"Precipitation type","parameter_ID":"260015","product_type":"forecast","shortName":"ptype","standard_name":"Precipitation_type"},"description":"This parameter describes the type of precipitation at the surface, at the specified time. Values of precipitation type defined in the IFS: 0 = No precipitation 1 = Rain 3 = Freezing rain (i.e. supercooled raindrops which freeze on contact with the ground and other surfaces) 5 = Snow 6 = Wet snow (i.e. snow particles which are starting to melt) 7 = Mixture of rain and snow 8 = Ice pellets 12 = Freezing drizzle (i.e. supercooled drizzle which freezes on contact with the ground and other surfaces). These precipitation types are consistent with WMO Code Table 4.201. 2 (thunderstorm), 4 (mixed ice) and 9 (graupel), 10 (hail) and 11 (drizzle) are not diagnosed in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"code table (4.201)"},"Relative_humidity":{"attrs":{"long_name":"Relative humidity","parameter_ID":"157","product_type":"forecast","shortName":"r","standard_name":"Relative_humidity"},"description":"This parameter is the water vapour pressure as a percentage of the value at which the air becomes saturated (the point at which water vapour begins to condense into liquid water or deposition into ice).","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Sea_ice_area_fraction":{"attrs":{"long_name":"Sea ice area fraction","parameter_ID":"31","product_type":"forecast","shortName":"ci","standard_name":"Sea_ice_area_fraction"},"description":"This parameter is the fraction of a grid box which is covered by sea ice. Sea ice can only occur in a grid box which includes ocean or inland water according to the land sea mask and lake cover, at the resolution being used. This parameter can be known as sea-ice (area) fraction, sea-ice concentration and more generally as sea-ice cover.","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Sea_surface_temperature":{"attrs":{"long_name":"Sea surface temperature","parameter_ID":"34","product_type":"forecast","shortName":"sst","standard_name":"Sea_surface_temperature"},"description":"This parameter is the temperature of sea water near the surface.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Significant_height_of_combined_wind_waves_and_swell":{"attrs":{"long_name":"Significant height of combined wind waves and swell","parameter_ID":"140229","product_type":"forecast","shortName":"swh","standard_name":"Significant_height_of_combined_wind_waves_and_swell"},"description":"This parameter represents the average height of the highest third of surface ocean/sea waves generated by wind and swell. It represents the vertical distance between the wave crest and the wave trough.","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Snowfall":{"attrs":{"long_name":"Snowfall","parameter_ID":"144","product_type":"forecast","shortName":"sf","standard_name":"Snowfall"},"description":"This parameter is the accumulated snow that falls to the Earth's surface. It is the sum of large-scale snowfall and convective snowfall. Large-scale snowfall is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of the grid box or larger.","dimensions":["lat","lon","time"],"type":"data","unit":"m of water equivalent"},"Specific_humidity":{"attrs":{"long_name":"Specific humidity","parameter_ID":"133","product_type":"forecast","shortName":"q","standard_name":"Specific_humidity"},"description":"This parameter is the mass of water vapour per kilogram of moist air. The total mass of moist air is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg**-1"},"Surface_direct_short_wave_solar_radiation":{"attrs":{"long_name":"Surface direct short-wave (solar) radiation","parameter_ID":"228021","product_type":"forecast","shortName":"fdir","standard_name":"Surface_direct_short_wave_solar_radiation"},"description":"This parameter is the amount of direct solar radiation (also known as shortwave radiation) reaching the surface of the Earth. It is the amount of radiation passing through a horizontal plane, not a plane perpendicular to the direction of the Sun.","dimensions":["lat","lon","time"],"type":"data","unit":"J m**-2"},"Surface_long_wave_(thermal)_radiation_downwards":{"attrs":{"long_name":"Surface short-wave (solar) radiation downwards","parameter_ID":"175","product_type":"forecast","shortName":"strd","standard_name":"Surface_long_wave_(thermal)_radiation_downwards"},"description":"This parameter is the amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere and clouds that reaches a horizontal plane at the surface of the Earth. The surface of the Earth emits thermal radiation, some of which is absorbed by the atmosphere and clouds. The atmosphere and clouds likewise emit thermal radiation in all directions, some of which reaches the surface (represented by this parameter).","dimensions":["lat","lon","time"],"type":"data","unit":"J m**-2"},"Surface_net_long_wave_(thermal)_radiation":{"attrs":{"long_name":"Surface net long-wave (thermal) radiation","parameter_ID":"177","product_type":"forecast","shortName":"str","standard_name":"Surface_net_long_wave_(thermal)_radiation"},"description":"Thermal radiation (also known as longwave or terrestrial radiation) refers to radiation emitted by the atmosphere, clouds and the surface of the Earth. This parameter is the difference between downward and upward thermal radiation at the surface of the Earth. It the amount passing through a horizontal plane. The atmosphere and clouds emit thermal radiation in all directions, some of which reaches the surface as downward thermal radiation. The upward thermal radiation at the surface consists of thermal radiation emitted by the surface plus the fraction of downwards thermal radiation reflected upward by the surface.","dimensions":["lat","lon","time"],"type":"data","unit":"J m**-2"},"Surface_net_short_wave_(solar)_radiation":{"attrs":{"long_name":"Surface net short-wave (solar) radiation","parameter_ID":"176","product_type":"forecast","shortName":"ssr","standard_name":"Surface_net_short_wave_(solar)_radiation"},"description":"This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo). Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The remainder is incident on the Earth's surface, where some of it is reflected.","dimensions":["lat","lon","time"],"type":"data","unit":"J m**-2"},"Surface_short_wave_(solar)_radiation_downwards":{"attrs":{"long_name":"Surface short-wave (solar) radiation downwards","parameter_ID":"169","product_type":"forecast","shortName":"ssrd","standard_name":"Surface_short_wave_(solar)_radiation_downwards"},"description":"This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth. This parameter comprises both direct and diffuse solar radiation. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface (represented by this parameter).","dimensions":["lat","lon","time"],"type":"data","unit":"J m**-2"},"Temperature":{"attrs":{"long_name":"Temperature","parameter_ID":"130","product_type":"forecast","shortName":"t","standard_name":"Temperature"},"description":"This parameter is the temperature in the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time_integrated_eastward_turbulent_surface_stress":{"attrs":{"long_name":"Time-integrated eastward turbulent surface stress","parameter_ID":"180","product_type":"forecast","shortName":"ewss","standard_name":"Time_integrated_eastward_turbulent_surface_stress"},"description":"Air flowing over a surface exerts a stress that transfers momentum to the surface and slows the wind. This parameter is the accumulated stress on the Earth's surface in the eastward direction due to both the turbulent interactions between the atmosphere and the surface, and to turbulent orographic form drag. The turbulent interactions between the atmosphere and the surface are due to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km being derived from land surface data at about 1 km resolution.","dimensions":["lat","lon","time"],"type":"data","unit":"N m**-2 s"},"Time_integrated_northward_turbulent_surface_stress":{"attrs":{"long_name":"Time_integrated_northward_turbulent_surface_stress","parameter_ID":"181","product_type":"forecast","shortName":"nsss","standard_name":"Time_integrated_eastward_turbulent_surface_stress"},"description":"Air flowing over a surface exerts a stress that transfers momentum to the surface and slows the wind. This parameter is the accumulated stress on the Earth's surface in the northward direction due to both the turbulent interactions between the atmosphere and the surface, and to turbulent orographic form drag. The turbulent interactions between the atmosphere and the surface are due to the roughness of the surface.","dimensions":["lat","lon","time"],"type":"data","unit":"N m**-2 s"},"Top_net_long_wave_(thermal)_radiation":{"attrs":{"long_name":"Top net long-wave (thermal) radiation","parameter_ID":"179","product_type":"forecast","shortName":"ttr","standard_name":"Top_net_long_wave_(thermal)_radiation"},"description":"The thermal (also known as terrestrial or longwave) radiation emitted to space at the top of the atmosphere is commonly known as the Outgoing Longwave Radiation (OLR). The top net thermal radiation (this parameter) is equal to the negative of OLR.","dimensions":["lat","lon","time"],"type":"data","unit":"J m**-2"},"Top_net_short_wave_(solar)_radiation":{"attrs":{"long_name":"Top net short-wave (solar) radiation","parameter_ID":"178","product_type":"forecast","shortName":"tsr","standard_name":"Top_net_short_wave_(solar)_radiation"},"description":"This parameter is the incoming solar radiation (also known as shortwave radiation) minus the outgoing solar radiation at the top of the atmosphere. It is the amount of radiation passing through a horizontal plane. The incoming solar radiation is the amount received from the Sun. The outgoing solar radiation is the amount reflected and scattered by the Earth's atmosphere and surface.","dimensions":["lat","lon","time"],"type":"data","unit":"J m**-2"},"Total_cloud_cover":{"attrs":{"long_name":"Total cloud cover","parameter_ID":"164","product_type":"forecast","shortName":"tcc","standard_name":"Total_cloud_cover"},"description":"This parameter is the proportion of a grid box covered by cloud. Total cloud cover is a single level field calculated from the cloud occurring at different model levels through the atmosphere. Assumptions are made about the degree of overlap/randomness between clouds at different heights.","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Total_column_cloud_liquid_water":{"attrs":{"long_name":"Total column cloud liquid water","parameter_ID":"78","product_type":"forecast","shortName":"tclw","standard_name":"Total_column_cloud_liquid_water"},"description":"the amount of liquid water contained within clouds in a column extending from the surface of the Earth to the top of the atmosphere. Snow (aggregated ice crystals) is not included in this parameter.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m**-2"},"Total_column_vertically_integrated_water_vapour":{"attrs":{"long_name":"Total column vertically-integrated water vapour","parameter_ID":"137","product_type":"forecast","shortName":"tcwv","standard_name":"Total_column_vertically_integrated_water_vapour"},"description":"This parameter is the total amount of water vapour in a column extending from the surface of the Earth to the top of the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m**-2"},"Total_column_water":{"attrs":{"long_name":"Total column water","parameter_ID":"136","product_type":"forecast","shortName":"tcw","standard_name":"Total_column_water"},"description":"This parameter is the sum of water vapour, liquid water, cloud ice, rain and snow in a column extending from the surface of the Earth to the top of the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m**-2"},"U_component_of_wind":{"attrs":{"long_name":"U component of wind","parameter_ID":"131","product_type":"forecast","shortName":"u","standard_name":"U_component_of_wind"},"description":"This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east, in metres per second. A negative sign thus indicates air movement towards the west.","dimensions":["lat","lon","time"],"type":"data","unit":"m s**-1"},"V_component_of_wind":{"attrs":{"long_name":"V component of wind","parameter_ID":"132","product_type":"forecast","shortName":"v","standard_name":"V_component_of_wind"},"description":"This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the east, in metres per second. A negative sign thus indicates air movement towards the west.","dimensions":["lat","lon","time"],"type":"data","unit":"m s**-1"},"Visibility":{"attrs":{"long_name":"Visibility","parameter_ID":"3020","product_type":"forecast","shortName":"vis","standard_name":"Visibility"},"description":"A visibility parameter was introduced in the ECMWF Integrated Forecasting System (IFS) from 12 May 2015. It uses model projections of water vapour, cloud, rain and snow, and climatological aerosol fields to estimate the visibility that would be recorded by weather observers.","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Vorticity_(relative)":{"attrs":{"long_name":"Vorticity (relative)","parameter_ID":"138","product_type":"forecast","shortName":"vo","standard_name":"Vorticity_(relative)"},"description":"This parameter is a measure of the rotation of air in the horizontal, around a vertical axis, relative to a fixed point on the surface of the Earth.","dimensions":["lat","lon","time"],"type":"data","unit":"s**-1"},"runoff":{"attrs":{"long_name":"Runoff","parameter_ID":"205","product_type":"forecast","shortName":"ro","standard_name":"runoff_amount"},"description":"The total run-off (including drainage through the base of the soil model) per unit area leaving the land portion of the grid cell. 'Amount' means mass per unit area. Runoff is the liquid water which drains from land. If not specified, 'runoff' refers to the sum of surface runoff and subsurface drainage.","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"surface_pressure":{"attrs":{"long_name":"Surface pressure","parameter_ID":"134","product_type":"forecast","shortName":"sp","standard_name":"surface_air_pressure"},"description":"Surface pressure (not mean sea-level pressure), 2-D field to calculate the 3-D pressure field from hybrid coordinates","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"total_precipitation":{"attrs":{"long_name":"Total precipitation","parameter_ID":"228","product_type":"forecast","shortName":"tp","standard_name":"lwe_thickness_of_precipitation_amount"},"description":"The construction lwe_thickness_of_X_amount or _content means the vertical extent of a layer of liquid water having the same mass per unit area. 'Precipitation' in the Earth's atmosphere means precipitation of water in all phases. The abbreviation 'lwe' means liquid water equivalent.","dimensions":["lat","lon","time"],"type":"data","unit":"m"}},"sci:publications":[{"doi":"10.1109/MCSE.2023.3260519","citation":"N. Wedi et al., Destination Earth: High-Performance Computing for Weather and Climate, in Computing in Science \u0026 Engineering, vol. 24, no. 6, pp. 29-37, Nov.-Dec. 2022,"}],"dedl:short_description":"The Weather-Induced Extremes Digital Twin (Extremes DT) is a digital twin that simulates extreme weather events and their impacts at km-scale resolutions using combined Earth-system models, impact-sector models, and observations across a global and regional scale."},{"type":"Collection","title":"nextGEMS - Historical Simulation - IFS-FESOM - Generation-1 - Realization-1","id":"EO.ECMWF.DAT.NG.DT_CLIMATE.G1.CMIP6_HIST_IFS-FESOM.R1","description":"Note: This Generation 1 Collection has been superseded by Generation 2 Simulation-level Collections\n\nThe nextGEMS data is aligned with the Climate Change Adaptation Digital Twin. The DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities by providing innovative climate information on multi-decadal timescales, globally, at scales at which many impacts of climate change are observed. It combines cutting-edge global Earth-system models, impact-sector applications and observations into a unified framework to provide global climate projections and impact-sector information on multi-decadal timescales (1990 to ~2050), at very high spatial resolutions (5 to 10 km).\n\nThe Climate DT represents the first ever attempt to operationalise the production of global multi-decadal climate projections, leveraging the world-leading supercomputing facilities of the EuroHPC Joint Undertaking along with some of the leading European climate models. A concise overview of what the Climate DT aims to achieve, and of the different concepts essential for an understanding of the Digital Twin’s characteristics, is included in the [Climate DT factsheet](https://destine.ecmwf.int/wp-content/uploads/2024/06/2024.06.07_Climate-DT-Fact-Sheet_V7-2.pdf)\n\n## Historical Simulation \n\n Starting in 1990, the forcing follows observed changes in greenhouse gases, aerosols etc. until 2020. The simulations are using standardised CMIP6 forcing. Historical simulations are essential for model evaluation and quality control as they allow a comparison to observations.\n\n## Models\n\nThe Climate DT exploits and further evolves a new generation of global storm-resolving and eddy-rich models built through a cooperative model development approach. For more information on models please click [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#models)\n\n## Simulations\n\nThe Climate DT team carries out several types of digital twin simulations on the EuroHPC supercomputers.  Multi-decadal simulations are produced to cover the recent past (from 1990) and possible future evolutions of the climate up to 2050. See [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#simulations) for more information on Simulations\n\n## Parameters\n\nBelow we see the list of parameters extracted from the 'DestinE Climate DT data portfolio', for more information please refer to the page [Climate DT Phase 1 CLTE Parameters](https://confluence.ecmwf.int/display/DDCZ/Climate+DT+Phase+1+data+catalogue)","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.NG.DT_CLIMATE.G1.CMIP6_HIST_IFS-FESOM.R1/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.NG.DT_CLIMATE.G1.CMIP6_HIST_IFS-FESOM.R1/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.NG.DT_CLIMATE.G1.CMIP6_HIST_IFS-FESOM.R1/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.NG.DT_CLIMATE.G1.CMIP6_HIST_IFS-FESOM.R1","title":"nextGEMS - Historical Simulation - IFS-FESOM - Generation-1 - Realization-1"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/NextGEMS+data+catalogue","title":"NextGEMS data catalogue"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.26050/WDCC/nextGEMS_cyc3","title":"Koldunov, Nikolay; Kölling, Tobias; Pedruzo-Bagazgoitia, Xabier; Rackow, Thomas; Redler, René; Sidorenko, Dmitry; Wieners, Karl-Hermann; Ziemen, Florian Andreas (2023). nextGEMS: output of the model development cycle 3 simulations for ICON and IFS. World Data Center for Climate (WDCC) at DKRZ."}],"assets":{"thumbnail":{"href":"https://s3.central.data.destination-earth.eu/swift/v1/dedl-public/collections/climate-dt-min.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1990-01-01T00:00:00Z","2019-12-31T23:59:59Z"]]}},"license":"CC-BY-4.0","keywords":["Digital Twins","Climate Change","Atmosphere","Ocean","Land","Sea Ice","Snow","Soil","Earth","High Performance Computing","Decision Making","Europe","Meteorology","class:ng","generation:1","expver:0001","stream:clte","type:fc","activity:CMIP6","experiment:hist","realization:1","model:IFS-FESOM","resolution:standard,high","levtype:hl,o2d,o3d,pl,sfc,sol"],"summaries":{"federation:backends":["dedt_lumi"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/datacube/v2.1.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["producer","processor","licensor"],"url":"https://www.ecmwf.int/"}],"created":"2024-04-11T22:31:55Z","updated":"2026-04-27T14:04:20Z","published":"2024-04-11T22:31:55Z","cube:dimensions":{"lat":{"axis":"y","description":"latitude","extent":[-90,90],"type":"spatial"},"lon":{"axis":"x","description":"longitude","extent":[-180,180],"type":"spatial"},"time":{"extent":["1990-01-01T00:00:00Z","2019-12-31T23:59:59Z"],"step":"P0Y0M0DT1H0M0S","type":"temporal"}},"cube:variables":{"10_metre_U_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"10 metre U wind component","parameter_ID":"165","product_type":"forecast","shortName":"10u","standard_name":"10_metre_U_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/165"},"description":"This parameter is the eastward component of the 10m wind. It is the horizontal speed of air moving towards the east, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the V component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_V_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"10 metre V wind component","parameter_ID":"166","product_type":"forecast","shortName":"10v","standard_name":"10_metre_V_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/166"},"description":"This parameter is the northward component of the 10m wind. It is the horizontal speed of air moving towards the north, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the U component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"2_metre_dewpoint_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"2 metre dewpoint temperature","parameter_ID":"168","product_type":"forecast","shortName":"2d","standard_name":"2_metre_dewpoint_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/168"},"description":"This parameter is the temperature to which the air, at 2 metres above the surface of the Earth, would have to be cooled for saturation to occur.It is a measure of the humidity of the air. Combined with temperature and pressure, it can be used to calculate the relative humidity.2m dew point temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"2_metre_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"2 metre temperature","parameter_ID":"167","product_type":"forecast","shortName":"2t","standard_name":"2_metre_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/167"},"description":"This parameter is the temperature of air at 2m above the surface of land, sea or in-land waters.2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.Please note that the encodings listed here for s2s \u0026 uerra (which includes encodings for carra/cerra) include entries for Mean 2 metre temperature. The specific encoding for Mean 2 metre temperature can be found in 228004.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Boundary_layer_height(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Boundary layer height","parameter_ID":"159","product_type":"forecast","shortName":"blh","standard_name":"Boundary_layer_height","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/159"},"description":"This parameter is the depth of air next to the Earth's surface which is most affected by the resistance to the transfer of momentum, heat or moisture across the surface.The boundary layer height can be as low as a few tens of metres, such as in cooling air at night, or as high as several kilometres over the desert in the middle of a hot sunny day. When the boundary layer height is low, higher concentrations of pollutants (emitted from the Earth's surface) can develop.The boundary layer height calculation is based on the bulk Richardson number (a measure of the atmospheric conditions) following the conclusions of a 2012 review.See further information.","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Charnock(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Charnock","parameter_ID":"148","product_type":"forecast","shortName":"chnk","standard_name":"Charnock","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/148"},"description":"This parameter accounts for increased aerodynamic roughness as wave heights grow due to increasing surface stress. It depends on the wind speed, wave age and other aspects of the sea state and is used to calculate how much the waves slow down the wind.When the atmospheric model is run without the ocean model, this parameter has a constant value of 0.018. When the atmospheric model is coupled to the ocean model, this parameter is calculated by theECMWF Wave Model.","dimensions":["lat","lon","time"],"type":"data","unit":"Numeric"},"Evaporation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Evaporation","parameter_ID":"182","product_type":"forecast","shortName":"e","standard_name":"Evaporation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/182"},"description":"This parameter is the accumulated amount of water that has evaporated from the Earth's surface, including a simplified representation of transpiration (from vegetation), into vapour in the air above.This parameter is accumulated over aparticular time period which depends on the data extracted.The ECMWF Integrated Forecasting System convention is that downward fluxes are positive. Therefore, negative values indicate evaporation and positive values indicate condensation.[NOTE: See 260259 for the equivalent parameter in \"kg m-2\"]","dimensions":["lat","lon","time"],"type":"data","unit":"m of water equivalent"},"Geopotential(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Geopotential","parameter_ID":"129","product_type":"forecast","shortName":"z","standard_name":"Geopotential","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/129"},"description":"This parameter is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level.The geopotential height can be calculated by dividing the geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2). The geopotential height plays an important role in synoptic meteorology (analysis of weather patterns). Charts of geopotential height plotted at constant pressure levels (e.g., 300, 500 or 850 hPa) can be used to identify weather systems such as cyclones, anticyclones, troughs and ridges.At the surface of the Earth, this parameter shows the variations in geopotential (height) of the surface, and is often referred to as the orography.","dimensions":["lat","lon","time"],"type":"data","unit":"m2s-2"},"High_cloud_cover(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"High cloud cover","parameter_ID":"188","product_type":"forecast","shortName":"hcc","standard_name":"High_cloud_cover","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/188"},"description":"The proportion of agrid boxcovered by cloud occurring in the high levels of the troposphere. High cloud is a single level field calculated from cloud occurring on model levels with a pressure less than 0.45 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), high cloud would be calculated using levels with a pressure of less than 450 hPa (approximately 6km and above (assuming a `standard atmosphere`)).The high cloud cover parameter is calculated from cloud for the appropriate model levels as described above. Assumptions are made about the degree of overlap/randomness between clouds in different model levels.Cloud fractions vary from 0 to 1.[NOTE: See 3075 for the equivalent parameter in \"%\"]","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Low_cloud_cover(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Low cloud cover","parameter_ID":"186","product_type":"forecast","shortName":"lcc","standard_name":"Low_cloud_cover","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/186"},"description":"This parameter is the proportion of agrid boxcovered by cloud occurring in the lower levels of the troposphere. Low cloud is a single level field calculated from cloud occurring on model levels with a pressure greater than 0.8 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), low cloud would be calculated using levels with a pressure greater than 800 hPa (below approximately 2km (assuming a 'standard atmosphere')).The low cloud cover parameter is calculated from cloud cover for the appropriate model levels as described above. Assumptions are made about the degree of overlap/randomness between clouds in different model levels.Cloud fractions vary from 0 to 1.[NOTE: See 3073 for the equivalent parameter in \"%\"]","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Mean_sea_level_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Mean sea level pressure","parameter_ID":"151","product_type":"forecast","shortName":"msl","standard_name":"Mean_sea_level_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/151"},"description":"This parameter is the pressure (force per unit area) of the atmosphere adjusted to the height of mean sea level.It is a measure of the weight that all the air in a column vertically above the area of Earth's surface would have at that point, if the point were located at the mean sea level. It is calculated over all surfaces - land, sea and in-land water.Maps of mean sea level pressure are used to identify the locations of low and high pressure systems, often referred to as cyclones and anticyclones. Contours of mean sea level pressure also indicate the strength of the wind. Tightly packed contours show stronger winds.The units of this parameter are pascals (Pa).  Mean sea level pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb = 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Medium_cloud_cover(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Medium cloud cover","parameter_ID":"187","product_type":"forecast","shortName":"mcc","standard_name":"Medium_cloud_cover","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/187"},"description":"This parameter is the proportion of agrid boxcovered by cloud occurring in the middle levels of the troposphere. Medium cloud is a single level field calculated from cloud occurring on model levels with a pressure between 0.45 and 0.8 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), medium cloud would be calculated using levels with a pressure of less than or equal to 800 hPa and greater than or equal to 450 hPa (between approximately 2km and 6km (assuming a 'standard atmosphere')).The medium cloud parameter is calculated from cloud cover for the appropriate model levels as described above. Assumptions are made about the degree of overlap/randomness between clouds in different model levels.Cloud fractions vary from 0 to 1.[NOTE: See 3074 for the equivalent parameter in \"%\"]","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Potential_vorticity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Potential vorticity","parameter_ID":"60","product_type":"forecast","shortName":"pv","standard_name":"Potential_vorticity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/60"},"description":"Potential vorticity is a measure of the capacity for air to rotate in the atmosphere. If we ignore the effects of heating and friction, potential vorticity is conserved following an air parcel. It is used to look for places where large wind storms are likely to originate and develop.  Potential vorticity increases strongly above the tropopause and therefore, it can also be used in studies related to the stratosphere and stratosphere-troposphere exchanges.Large wind storms develop when a column of air in the atmosphere starts to rotate. Potential vorticity is calculated from the wind, temperature and pressure across a column of air in the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K m2kg-1s-1"},"Relative_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Relative humidity","parameter_ID":"157","product_type":"forecast","shortName":"r","standard_name":"Relative_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/157"},"description":"This parameter is the water vapour pressure as a percentage of the value at which the air becomes saturated (the point at which water vapour begins to condense into liquid water or deposition into ice).For temperatures over 0°C (273.15 K) it is calculated for saturation over water. At temperatures below -23°C it is calculated for saturation over ice.  Between -23°C and 0°C this parameter is calculated by interpolating between the ice and water values using a quadratic function.See more information about the model's relative humidity calculation.","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Skin_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Skin temperature","parameter_ID":"235","product_type":"forecast","shortName":"skt","standard_name":"Skin_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235"},"description":"This parameter is the temperature of the surface of the Earth.The skin temperature is the theoretical temperature that is required to satisfy the surface energy balance. It represents the temperature of the uppermost surface layer, which has no heat capacity and so can respond instantaneously to changes in surface fluxes. Skin temperature is calculated differently over land and sea.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.See further information about the skin temperatureover landandover sea.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean skin temperature. The specific encoding for Mean skin temperature can be found in 235079.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Snow_depth(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Snow depth","parameter_ID":"141","product_type":"forecast","shortName":"sd","standard_name":"Snow_depth","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/141"},"description":"This parameter is the depth of snow from the snow-covered area of agrid box.Its units are metres of water equivalent, so it is the depth the water would have if the snow melted and was spread evenly over the whole grid box. The ECMWF Integrated Forecast System represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box.See further information.\n\n[NOTE: See 228141 for the equivalent parameter in \"kg m-2\"]","dimensions":["lat","lon","time"],"type":"data","unit":"m of water equivalent"},"Snow_depth_water_equivalent(clte_sol)":{"attrs":{"encoding":"instantaneous","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Snow depth water equivalent","parameter_ID":"228141","product_type":"forecast","shortName":"sd","standard_name":"Snow_depth_water_equivalent","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228141"},"description":"Snow depth water equivalent in kg m**-2 (mm) water equivalent.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean snow depth water equivalent. The specific encoding for Time-mean snow depth water equivalent can be found in 235078.[NOTE: See 141 for the equivalent parameter in \"m of water equivalent\"]","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Snowfall(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Snowfall","parameter_ID":"144","product_type":"forecast","shortName":"sf","standard_name":"Snowfall","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/144"},"description":"This parameter is the accumulated snow that falls to the Earth's surface. It is the sum of large-scale snowfall and convective snowfall. Large-scale snowfall is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of thegrid boxor larger. Convective snowfall is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box.See further information.This parameter is the total amount of wateraccumulated over a particular time period which depends on the data extracted. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box.Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box.[NOTE: See 228144 for the equivalent parameter in \"kg m-2\"]","dimensions":["lat","lon","time"],"type":"data","unit":"m of water equivalent"},"Specific_cloud_liquid_water_content(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific cloud liquid water content","parameter_ID":"246","product_type":"forecast","shortName":"clwc","standard_name":"Specific_cloud_liquid_water_content","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/246"},"description":"This parameter is the mass of cloud liquid water droplets per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for agrid box.Water within clouds can be liquid or ice, or a combination of the two.See further information about the cloud formulation.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Specific_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific humidity","parameter_ID":"133","product_type":"forecast","shortName":"q","standard_name":"Specific_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/133"},"description":"This parameter is the mass of water vapour per kilogram of moist air.The total mass of moist air is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Sub-surface_runoff(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Sub-surface runoff","parameter_ID":"9","product_type":"forecast","shortName":"ssro","standard_name":"Sub-surface_runoff","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/9"},"description":"Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is simply called 'runoff'. This parameter is the total amount of water accumulated over aparticular time period which depends on the data extracted.The units of runoff are depth in metres.  This is the depth the water would have if it were spread evenly over thegrid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid square area.  Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here.Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood.  More information about how runoff is calculated is given in theIFS Physical Processes documentation.\n\n[NOTE: See 231012 for the equivalent parameter in \"kg m-2\"]","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Surface_long-wave_(thermal)_radiation_downwards(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Surface long-wave (thermal) radiation downwards","parameter_ID":"175","product_type":"forecast","shortName":"strd","standard_name":"Surface_long-wave_(thermal)_radiation_downwards","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/175"},"description":"This parameter is the amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere and clouds that reaches a horizontal plane at the surface of the Earth.The surface of the Earth emits thermal radiation, some of which is absorbed by the atmosphere and clouds. The atmosphere and clouds likewise emit thermal radiation in all directions, some of which reaches the surface (represented by this parameter).See further documentation.This parameter isaccumulated over a particular time period which depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Surface_net_long-wave_(thermal)_radiation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Surface net long-wave (thermal) radiation","parameter_ID":"177","product_type":"forecast","shortName":"str","standard_name":"Surface_net_long-wave_(thermal)_radiation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/177"},"description":"Thermal radiation (also known as longwave or terrestrial radiation) refers to radiation emitted by the atmosphere, clouds and the surface of the Earth. This parameter is the difference between downward and upward thermal radiation at the surface of the Earth. It the amount passing through a horizontal plane.The atmosphere and clouds emit thermal radiation in all directions, some of which reaches the surface as downward thermal radiation. The upward thermal radiation at the surface consists of thermal radiation emitted by the surface plus the fraction of downwards thermal radiation reflected upward by the surface.See further documentation.This parameter isaccumulated over a particular time periodwhich depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds.The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Surface_net_short-wave_(solar)_radiation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Surface net short-wave (solar) radiation","parameter_ID":"176","product_type":"forecast","shortName":"ssr","standard_name":"Surface_net_short-wave_(solar)_radiation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/176"},"description":"This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo).Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The remainder is incident on the Earth's surface, where some of it is reflected.See further documentation.This parameter isaccumulated over a particular time period which depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Surface_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Surface pressure","parameter_ID":"134","product_type":"forecast","shortName":"sp","standard_name":"Surface_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/134"},"description":"This parameter is the pressure (force per unit area) of the atmosphere on the surface of land, sea and in-land water.It is a measure of the weight of all the air in a column vertically above the area of the Earth's surface represented at a fixed point.Surface pressure is often used in combination with temperature to calculate air density.The strong variation of pressure with altitude makes it difficult to see the low and high pressure systems over mountainous areas, so mean sea level pressure, rather than surface pressure, is normally used for this purpose.The units of this parameter are Pascals (Pa). Surface pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb= 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Surface_runoff(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Surface runoff","parameter_ID":"8","product_type":"forecast","shortName":"sro","standard_name":"Surface_runoff","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/8"},"description":"Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is simply called 'runoff'. This parameter is the total amount of water accumulated over aparticular time period which depends on the data extracted.The units of runoff are depth in metres.  This is the depth the water would have if it were spread evenly over thegrid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid square area.  Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here.Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood.  More information about how runoff is calculated is given in theIFS Physical Processes documentation.\n\n[NOTE: See 231010 for the equivalent parameter in \"kg m-2\"]","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Surface_short-wave_(solar)_radiation_downwards(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Surface short-wave (solar) radiation downwards","parameter_ID":"169","product_type":"forecast","shortName":"ssrd","standard_name":"Surface_short-wave_(solar)_radiation_downwards","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/169"},"description":"This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth. This parameter comprises both direct and diffuse solar radiation.Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface (represented by this parameter).See further documentation.To a reasonably good approximation, this parameter is the model equivalent of what would be measured by a pyranometer (an instrument used for measuring solar radiation) at the surface. However, care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over amodel grid box.This parameter isaccumulated over a particular time period which depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"TOA_incident_short-wave_(solar)_radiation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"TOA incident short-wave (solar) radiation","parameter_ID":"212","product_type":"forecast","shortName":"tisr","standard_name":"TOA_incident_short-wave_(solar)_radiation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/212"},"description":"Accumulated field","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Temperature(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Temperature","parameter_ID":"130","product_type":"forecast","shortName":"t","standard_name":"Temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/130"},"description":"This parameter is the temperature in the atmosphere.It has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.This parameter is available on multiple levels through the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-integrated_eastward_turbulent_surface_stress(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Time-integrated eastward turbulent surface stress","parameter_ID":"180","product_type":"forecast","shortName":"ewss","standard_name":"Time-integrated_eastward_turbulent_surface_stress","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/180"},"description":"Air flowing over a surface exerts a stress that transfers momentum to the surface and slows the wind. This parameter is the accumulated stress on the Earth's surface in the eastward direction due to both the turbulent interactions between the atmosphere and the surface, and to turbulent orographic form drag. The turbulent interactions between the atmosphere and the surface are due to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km being derived from land surface data at about 1 km resolution.See further information.Positive (negative) values denote stress in the eastward (westward) direction.This parameter isaccumulated over a particular time periodwhich depends on the data extracted.","dimensions":["lat","lon","time"],"type":"data","unit":"N m-2s"},"Time-integrated_northward_turbulent_surface_stress(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Time-integrated northward turbulent surface stress","parameter_ID":"181","product_type":"forecast","shortName":"nsss","standard_name":"Time-integrated_northward_turbulent_surface_stress","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/181"},"description":"Air flowing over a surface exerts a stress that transfers momentum to the surface and slows the wind. This parameter is the accumulated stress on the Earth's surface in the northward direction due to both the turbulent interactions between the atmosphere and the surface, and to turbulent orographic form drag.The turbulent interactions between the atmosphere and the surface are due to the roughness of the surface.The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km being derived from land surface data at about 1 km resolution.See further information.Positive (negative) values denote stress in the northward (southward) direction.This parameter isaccumulated over a particular time periodwhich depends on the data extracted.","dimensions":["lat","lon","time"],"type":"data","unit":"N m-2s"},"Time-integrated_surface_latent_heat_net_flux(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Time-integrated surface latent heat net flux","parameter_ID":"147","product_type":"forecast","shortName":"slhf","standard_name":"Time-integrated_surface_latent_heat_net_flux","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/147"},"description":"This parameter is the transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earth's surface and the atmosphere through the effects of turbulent air motion. Evaporation from the Earth's surface represents a transfer of energy from the surface to the atmosphere.See further documentationThis parameter is accumulated over aparticular time period which depends on the data extracted.The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds.The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-integrated_surface_sensible_heat_net_flux(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Time-integrated surface sensible heat net flux","parameter_ID":"146","product_type":"forecast","shortName":"sshf","standard_name":"Time-integrated_surface_sensible_heat_net_flux","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/146"},"description":"This parameter is the transfer of heat between the Earth's surface and the atmosphere through the effects of turbulent air motion (but excluding any heat transfer resulting from condensation or evaporation).The magnitude of the sensible heat flux is governed by the difference in temperature between the surface and the overlying atmosphere, wind speed and the surface roughness. For example, cold air overlying a warm surface would produce a sensible heat flux from the land (or ocean) into the atmosphere.See further documentationThis is a single level parameter and it is accumulated over aparticular time period which depends on the data extracted.The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_eastward_sea_ice_velocity(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean eastward sea ice velocity","parameter_ID":"263003","product_type":"forecast","shortName":"avg_siue","standard_name":"Time-mean_eastward_sea_ice_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263003"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69","levtype":"o3d","long_name":"Time-mean eastward sea water velocity","parameter_ID":"263506","product_type":"forecast","shortName":"avg_uoe","standard_name":"Time-mean_eastward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263506"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_sea_ice_velocity(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean northward sea ice velocity","parameter_ID":"263004","product_type":"forecast","shortName":"avg_sivn","standard_name":"Time-mean_northward_sea_ice_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263004"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69","levtype":"o3d","long_name":"Time-mean northward sea water velocity","parameter_ID":"263505","product_type":"forecast","shortName":"avg_von","standard_name":"Time-mean_northward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263505"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_sea_ice_area_fraction(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea ice area fraction","parameter_ID":"263001","product_type":"forecast","shortName":"avg_siconc","standard_name":"Time-mean_sea_ice_area_fraction","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263001"},"dimensions":["lat","lon","time"],"type":"data","unit":"Fraction"},"Time-mean_sea_ice_thickness(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea ice thickness","parameter_ID":"263000","product_type":"forecast","shortName":"avg_sithick","standard_name":"Time-mean_sea_ice_thickness","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263000"},"dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Time-mean_sea_surface_height(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea surface height","parameter_ID":"263124","product_type":"forecast","shortName":"avg_zos","standard_name":"Time-mean_sea_surface_height","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263124"},"dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Time-mean_sea_surface_practical_salinity(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea surface practical salinity","parameter_ID":"263100","product_type":"forecast","shortName":"avg_sos","standard_name":"Time-mean_sea_surface_practical_salinity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263100"},"dimensions":["lat","lon","time"],"type":"data","unit":"g kg-1"},"Time-mean_sea_surface_temperature(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea surface temperature","parameter_ID":"263101","product_type":"forecast","shortName":"avg_tos","standard_name":"Time-mean_sea_surface_temperature","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263101"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_sea_water_potential_temperature(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69","levtype":"o3d","long_name":"Time-mean sea water potential temperature","parameter_ID":"263501","product_type":"forecast","shortName":"avg_thetao","standard_name":"Time-mean_sea_water_potential_temperature","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263501"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_sea_water_practical_salinity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69","levtype":"o3d","long_name":"Time-mean sea water practical salinity","parameter_ID":"263500","product_type":"forecast","shortName":"avg_so","standard_name":"Time-mean_sea_water_practical_salinity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263500"},"dimensions":["lat","lon","time"],"type":"data","unit":"g kg-1"},"Time-mean_snow_thickness_over_sea_ice(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean snow thickness over sea ice","parameter_ID":"263002","product_type":"forecast","shortName":"avg_sisnthick","standard_name":"Time-mean_snow_thickness_over_sea_ice","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263002"},"dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Time-mean_upward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69","levtype":"o3d","long_name":"Time-mean upward sea water velocity","parameter_ID":"263507","product_type":"forecast","shortName":"avg_wo","standard_name":"Time-mean_upward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263507"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Top_net_long-wave_(thermal)_radiation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Top net long-wave (thermal) radiation","parameter_ID":"179","product_type":"forecast","shortName":"ttr","standard_name":"Top_net_long-wave_(thermal)_radiation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/179"},"description":"The thermal (also known as terrestrial or longwave) radiation emitted to space at the top of the atmosphere is commonly known as the Outgoing Longwave Radiation (OLR). The top net thermal radiation (this parameter) is equal to the negative of OLR.See further documentation.This parameter isaccumulated over a particular time periodwhich depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds.The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Top_net_short-wave_(solar)_radiation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Top net short-wave (solar) radiation","parameter_ID":"178","product_type":"forecast","shortName":"tsr","standard_name":"Top_net_short-wave_(solar)_radiation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/178"},"description":"This parameter is the incoming solar radiation (also known as shortwave radiation) minus the outgoing solar radiation at the top of the atmosphere. It is the amount of radiation passing through a horizontal plane. The incoming solar radiation is the amount received from the Sun. The outgoing solar radiation is the amount reflected and scattered by the Earth's atmosphere and surface.See further documentation.This parameter isaccumulated over a particular time periodwhich depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds.The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Total_cloud_cover(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total cloud cover","parameter_ID":"164","product_type":"forecast","shortName":"tcc","standard_name":"Total_cloud_cover","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/164"},"description":"This parameter is the proportion of agrid boxcovered by cloud. Total cloud cover is a single level field calculated from the cloud occurring at different model levels through the atmosphere. Assumptions are made about the degree of overlap/randomness between clouds at different heights.Cloud fractions vary from 0 to 1.[NOTE: See 228164 for the equivalent parameter in \"%\"]","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Total_column_cloud_ice_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud ice water","parameter_ID":"79","product_type":"forecast","shortName":"tciw","standard_name":"Total_column_cloud_ice_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/79"},"description":"This parameter is the amount of ice contained within clouds in a column extending from the surface of the Earth to the top of the atmosphere. Snow (aggregated ice crystals) is not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The  ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_cloud_liquid_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud liquid water","parameter_ID":"78","product_type":"forecast","shortName":"tclw","standard_name":"Total_column_cloud_liquid_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/78"},"description":"This parameter is the amount of liquid water contained within cloud droplets in a column extending from the surface of the Earth to the top of the atmosphere. Rain water droplets, which are much larger in size (and mass), are not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_vertically-integrated_water_vapour(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column vertically-integrated water vapour","parameter_ID":"137","product_type":"forecast","shortName":"tcwv","standard_name":"Total_column_vertically-integrated_water_vapour","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/137"},"description":"This parameter is the total amount of water vapour in a column extending from the surface of the Earth to the top of the atmosphere.This parameter represents the area averaged value for agrid box.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_precipitation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Total precipitation","parameter_ID":"228","product_type":"forecast","shortName":"tp","standard_name":"Total_precipitation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228"},"description":"This parameter is the accumulated liquid and frozen water, comprising rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation and convective precipitation. Large-scale precipitation is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of thegrid boxor larger. Convective precipitation is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box.See further information.This parameter does not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth.This parameter is the total amount of wateraccumulated over a particular time period which depends on the data extracted. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box.Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box.\n\n[NOTE: See 228228 for the equivalent parameter in \"kg m-2\"]","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Total_precipitation_rate(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total precipitation rate","parameter_ID":"260048","product_type":"forecast","shortName":"tprate","standard_name":"Total_precipitation_rate","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/260048"},"description":"This parameter is the rate of total precipitation,at the specified time.In the ECMWF Integrated Forecasting System (IFS), total precipitation is rain and snow that falls to the Earth's surface.  It is the sum of large-scale precipitation and convective precipitation. Large-scale precipitation is generated by the cloud scheme in the IFS. The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of agrid boxor larger. Convective precipitation is generated by the convection scheme in the IFS. The convection scheme represents convection at spatial scales smaller than the grid box.Seefurther information. Precipitation parameters do not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth.1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm per second.Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box andmodel time step.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"U_component_of_wind(clte_hl)":{"attrs":{"encoding":"instantaneous","levelist":"100","levtype":"hl","long_name":"U component of wind","parameter_ID":"131","product_type":"forecast","shortName":"u","standard_name":"U_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/131"},"description":"This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east, in metres per second. A negative sign thus indicates air movement towards the west.This parameter can be combined with the V component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"U_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"U component of wind","parameter_ID":"131","product_type":"forecast","shortName":"u","standard_name":"U_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/131"},"description":"This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east, in metres per second. A negative sign thus indicates air movement towards the west.This parameter can be combined with the V component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"V_component_of_wind(clte_hl)":{"attrs":{"encoding":"instantaneous","levelist":"100","levtype":"hl","long_name":"V component of wind","parameter_ID":"132","product_type":"forecast","shortName":"v","standard_name":"V_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/132"},"description":"This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north, in metres per second. A negative sign thus indicates air movement towards the south.This parameter can be combined with the U component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"V_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"V component of wind","parameter_ID":"132","product_type":"forecast","shortName":"v","standard_name":"V_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/132"},"description":"This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north, in metres per second. A negative sign thus indicates air movement towards the south.This parameter can be combined with the U component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Vertical_velocity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Vertical velocity","parameter_ID":"135","product_type":"forecast","shortName":"w","standard_name":"Vertical_velocity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/135"},"description":"This parameter is the speed of air motion in the upward or downward direction. The ECMWF Integrated Forecasting System (IFS) uses a pressure based vertical co-ordinate system and pressure decreases with height, therefore negative values of vertical velocity indicate upward motion.Vertical velocity can be useful to understand the large-scale dynamics of the atmosphere, including areas of upward motion/ascent (negative values) and downward motion/subsidence (positive values).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa s-1"}},"sci:publications":[{"doi":"10.26050/WDCC/nextGEMS_cyc3","citation":"Koldunov, Nikolay; Kölling, Tobias; Pedruzo-Bagazgoitia, Xabier; Rackow, Thomas; Redler, René; Sidorenko, Dmitry; Wieners, Karl-Hermann; Ziemen, Florian Andreas (2023). nextGEMS: output of the model development cycle 3 simulations for ICON and IFS. World Data Center for Climate (WDCC) at DKRZ."}],"dedl:short_description":"The nextGEMS data is aligned with the Climate Change Adaptation Digital Twin. The Climate Change Adaptation Digital Twin provides global climate projections and sector-specific information over multiple decades at high resolution via a unified framework combining advanced Earth system models, impact assessments, and observations. This nextGEMS Collection gives access to 'Historical Simulation' data based on the 'IFS-FESOM' model. This is a Generation-1 Collection."},{"type":"Collection","title":"nextGEMS - Future Projection - IFS-FESOM - Generation-1 - Realization-2","id":"EO.ECMWF.DAT.NG.DT_CLIMATE.G1.SCENARIOMIP_SSP3-7.0_IFS-FESOM.R2","description":"Note: This Generation 1 Collection has been superseded by Generation 2 Simulation-level Collections\n\nThe nextGEMS data is aligned with the Climate Change Adaptation Digital Twin. The DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities by providing innovative climate information on multi-decadal timescales, globally, at scales at which many impacts of climate change are observed. It combines cutting-edge global Earth-system models, impact-sector applications and observations into a unified framework to provide global climate projections and impact-sector information on multi-decadal timescales (1990 to ~2050), at very high spatial resolutions (5 to 10 km).\n\nThe Climate DT represents the first ever attempt to operationalise the production of global multi-decadal climate projections, leveraging the world-leading supercomputing facilities of the EuroHPC Joint Undertaking along with some of the leading European climate models. A concise overview of what the Climate DT aims to achieve, and of the different concepts essential for an understanding of the Digital Twin’s characteristics, is included in the [Climate DT factsheet](https://destine.ecmwf.int/wp-content/uploads/2024/06/2024.06.07_Climate-DT-Fact-Sheet_V7-2.pdf)\n\n## Future Projection \n\n To project how climate will change on a global and local scale in the future, forcing changes according to the Shared Socioeconomic Pathway (SSP) 3-7.0 scenario from ScenarioMIP. The SSP3-7.0 scenario explores a future with a continuous increase in CO2 emissions with no strong mitigation efforts. All DestinE projections carried out so far follow this scenario. In the future, alternative future scenarios will be explored. The projections carried out so far are initialised in 2020 from reanalysis followed by a 5-year ocean spin-up. For the upcoming simulations carried out in phase 2 of DestinE, scenario simulations will extend the historical simulations.\n\n## Models\n\nThe Climate DT exploits and further evolves a new generation of global storm-resolving and eddy-rich models built through a cooperative model development approach. For more information on models please click [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#models)\n\n## Simulations\n\nThe Climate DT team carries out several types of digital twin simulations on the EuroHPC supercomputers.  Multi-decadal simulations are produced to cover the recent past (from 1990) and possible future evolutions of the climate up to 2050. See [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#simulations) for more information on Simulations\n\n## Parameters\n\nBelow we see the list of parameters extracted from the 'DestinE Climate DT data portfolio', for more information please refer to the page [Climate DT Phase 1 CLTE Parameters](https://confluence.ecmwf.int/display/DDCZ/Climate+DT+Phase+1+data+catalogue)","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.NG.DT_CLIMATE.G1.SCENARIOMIP_SSP3-7.0_IFS-FESOM.R2/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.NG.DT_CLIMATE.G1.SCENARIOMIP_SSP3-7.0_IFS-FESOM.R2/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.NG.DT_CLIMATE.G1.SCENARIOMIP_SSP3-7.0_IFS-FESOM.R2/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.NG.DT_CLIMATE.G1.SCENARIOMIP_SSP3-7.0_IFS-FESOM.R2","title":"nextGEMS - Future Projection - IFS-FESOM - Generation-1 - Realization-2"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/NextGEMS+data+catalogue","title":"NextGEMS data catalogue"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.26050/WDCC/nextGEMS_cyc3","title":"Koldunov, Nikolay; Kölling, Tobias; Pedruzo-Bagazgoitia, Xabier; Rackow, Thomas; Redler, René; Sidorenko, Dmitry; Wieners, Karl-Hermann; Ziemen, Florian Andreas (2023). nextGEMS: output of the model development cycle 3 simulations for ICON and IFS. World Data Center for Climate (WDCC) at DKRZ."}],"assets":{"thumbnail":{"href":"https://s3.central.data.destination-earth.eu/swift/v1/dedl-public/collections/climate-dt-min.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2020-01-01T00:00:00Z","2049-12-31T23:59:59Z"]]}},"license":"CC-BY-4.0","keywords":["Digital Twins","Climate Change","Atmosphere","Ocean","Land","Sea Ice","Snow","Soil","Earth","High Performance Computing","Decision Making","Europe","Meteorology","class:d1","dataset:climate-dt","generation:1","expver:0001","stream:clte","type:fc","activity:ScenarioMIP","experiment:SSP3-7.0","realization:2","model:IFS-FESOM","resolution:standard,high","levtype:hl,o2d,o3d,pl,sfc,sol"],"summaries":{"federation:backends":["dedt_lumi"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/datacube/v2.1.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["producer","processor","licensor"],"url":"https://www.ecmwf.int/"}],"created":"2024-04-11T22:31:55Z","updated":"2026-04-27T14:04:20Z","published":"2024-04-11T22:31:55Z","cube:dimensions":{"lat":{"axis":"y","description":"latitude","extent":[-90,90],"type":"spatial"},"lon":{"axis":"x","description":"longitude","extent":[-180,180],"type":"spatial"},"time":{"extent":["2020-01-01T00:00:00Z","2049-12-31T23:59:59Z"],"step":"P0Y0M0DT1H0M0S","type":"temporal"}},"cube:variables":{"100_metre_U_wind_component(clte_hl)":{"attrs":{"encoding":"instantaneous","levelist":"100","levtype":"hl","long_name":"100 metre U wind component","parameter_ID":"228246","product_type":"forecast","shortName":"100u","standard_name":"100_metre_U_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228246"},"description":"This parameter is the eastward component of the 100 m wind. It is the horizontal speed of air moving towards the east, at a height of 100 metres above the surface of the Earth, in metres per second.Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over amodel grid box and model time step.This parameter can be combined with the northward component to give the speed and direction of the horizontal 100 m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"100_metre_V_wind_component(clte_hl)":{"attrs":{"encoding":"instantaneous","levelist":"100","levtype":"hl","long_name":"100 metre V wind component","parameter_ID":"228247","product_type":"forecast","shortName":"100v","standard_name":"100_metre_V_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228247"},"description":"This parameter is the northward component of the 100 m wind. It is the horizontal speed of air moving towards the north, at a height of 100 metres above the surface of the Earth, in metres per second.Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over amodel grid box and model time step.This parameter can be combined with the eastward component to give the speed and direction of the horizontal 100 m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_U_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"10 metre U wind component","parameter_ID":"165","product_type":"forecast","shortName":"10u","standard_name":"10_metre_U_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/165"},"description":"This parameter is the eastward component of the 10m wind. It is the horizontal speed of air moving towards the east, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the V component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"10_metre_V_wind_component(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"10 metre V wind component","parameter_ID":"166","product_type":"forecast","shortName":"10v","standard_name":"10_metre_V_wind_component","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/166"},"description":"This parameter is the northward component of the 10m wind. It is the horizontal speed of air moving towards the north, at a height of ten metres above the surface of the Earth, in metres per second.Care should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.This parameter can be combined with the U component of 10m wind to give the speed and direction of the horizontal 10m wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"2_metre_dewpoint_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"2 metre dewpoint temperature","parameter_ID":"168","product_type":"forecast","shortName":"2d","standard_name":"2_metre_dewpoint_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/168"},"description":"This parameter is the temperature to which the air, at 2 metres above the surface of the Earth, would have to be cooled for saturation to occur.It is a measure of the humidity of the air. Combined with temperature and pressure, it can be used to calculate the relative humidity.2m dew point temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"2_metre_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"2 metre temperature","parameter_ID":"167","product_type":"forecast","shortName":"2t","standard_name":"2_metre_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/167"},"description":"This parameter is the temperature of air at 2m above the surface of land, sea or in-land waters.2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.See further information.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.Please note that the encodings listed here for s2s \u0026 uerra (which includes encodings for carra/cerra) include entries for Mean 2 metre temperature. The specific encoding for Mean 2 metre temperature can be found in 228004.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Boundary_layer_height(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Boundary layer height","parameter_ID":"159","product_type":"forecast","shortName":"blh","standard_name":"Boundary_layer_height","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/159"},"description":"This parameter is the depth of air next to the Earth's surface which is most affected by the resistance to the transfer of momentum, heat or moisture across the surface.The boundary layer height can be as low as a few tens of metres, such as in cooling air at night, or as high as several kilometres over the desert in the middle of a hot sunny day. When the boundary layer height is low, higher concentrations of pollutants (emitted from the Earth's surface) can develop.The boundary layer height calculation is based on the bulk Richardson number (a measure of the atmospheric conditions) following the conclusions of a 2012 review.See further information.","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Charnock(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Charnock","parameter_ID":"148","product_type":"forecast","shortName":"chnk","standard_name":"Charnock","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/148"},"description":"This parameter accounts for increased aerodynamic roughness as wave heights grow due to increasing surface stress. It depends on the wind speed, wave age and other aspects of the sea state and is used to calculate how much the waves slow down the wind.When the atmospheric model is run without the ocean model, this parameter has a constant value of 0.018. When the atmospheric model is coupled to the ocean model, this parameter is calculated by theECMWF Wave Model.","dimensions":["lat","lon","time"],"type":"data","unit":"Numeric"},"Evaporation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Evaporation","parameter_ID":"182","product_type":"forecast","shortName":"e","standard_name":"Evaporation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/182"},"description":"This parameter is the accumulated amount of water that has evaporated from the Earth's surface, including a simplified representation of transpiration (from vegetation), into vapour in the air above.This parameter is accumulated over aparticular time period which depends on the data extracted.The ECMWF Integrated Forecasting System convention is that downward fluxes are positive. Therefore, negative values indicate evaporation and positive values indicate condensation.[NOTE: See 260259 for the equivalent parameter in \"kg m-2\"]","dimensions":["lat","lon","time"],"type":"data","unit":"m of water equivalent"},"Geopotential(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Geopotential","parameter_ID":"129","product_type":"forecast","shortName":"z","standard_name":"Geopotential","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/129"},"description":"This parameter is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level.The geopotential height can be calculated by dividing the geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2). The geopotential height plays an important role in synoptic meteorology (analysis of weather patterns). Charts of geopotential height plotted at constant pressure levels (e.g., 300, 500 or 850 hPa) can be used to identify weather systems such as cyclones, anticyclones, troughs and ridges.At the surface of the Earth, this parameter shows the variations in geopotential (height) of the surface, and is often referred to as the orography.","dimensions":["lat","lon","time"],"type":"data","unit":"m2s-2"},"High_cloud_cover(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"High cloud cover","parameter_ID":"188","product_type":"forecast","shortName":"hcc","standard_name":"High_cloud_cover","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/188"},"description":"The proportion of agrid boxcovered by cloud occurring in the high levels of the troposphere. High cloud is a single level field calculated from cloud occurring on model levels with a pressure less than 0.45 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), high cloud would be calculated using levels with a pressure of less than 450 hPa (approximately 6km and above (assuming a `standard atmosphere`)).The high cloud cover parameter is calculated from cloud for the appropriate model levels as described above. Assumptions are made about the degree of overlap/randomness between clouds in different model levels.Cloud fractions vary from 0 to 1.[NOTE: See 3075 for the equivalent parameter in \"%\"]","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Low_cloud_cover(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Low cloud cover","parameter_ID":"186","product_type":"forecast","shortName":"lcc","standard_name":"Low_cloud_cover","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/186"},"description":"This parameter is the proportion of agrid boxcovered by cloud occurring in the lower levels of the troposphere. Low cloud is a single level field calculated from cloud occurring on model levels with a pressure greater than 0.8 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), low cloud would be calculated using levels with a pressure greater than 800 hPa (below approximately 2km (assuming a 'standard atmosphere')).The low cloud cover parameter is calculated from cloud cover for the appropriate model levels as described above. Assumptions are made about the degree of overlap/randomness between clouds in different model levels.Cloud fractions vary from 0 to 1.[NOTE: See 3073 for the equivalent parameter in \"%\"]","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Mean_sea_level_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Mean sea level pressure","parameter_ID":"151","product_type":"forecast","shortName":"msl","standard_name":"Mean_sea_level_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/151"},"description":"This parameter is the pressure (force per unit area) of the atmosphere adjusted to the height of mean sea level.It is a measure of the weight that all the air in a column vertically above the area of Earth's surface would have at that point, if the point were located at the mean sea level. It is calculated over all surfaces - land, sea and in-land water.Maps of mean sea level pressure are used to identify the locations of low and high pressure systems, often referred to as cyclones and anticyclones. Contours of mean sea level pressure also indicate the strength of the wind. Tightly packed contours show stronger winds.The units of this parameter are pascals (Pa).  Mean sea level pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb = 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Medium_cloud_cover(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Medium cloud cover","parameter_ID":"187","product_type":"forecast","shortName":"mcc","standard_name":"Medium_cloud_cover","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/187"},"description":"This parameter is the proportion of agrid boxcovered by cloud occurring in the middle levels of the troposphere. Medium cloud is a single level field calculated from cloud occurring on model levels with a pressure between 0.45 and 0.8 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), medium cloud would be calculated using levels with a pressure of less than or equal to 800 hPa and greater than or equal to 450 hPa (between approximately 2km and 6km (assuming a 'standard atmosphere')).The medium cloud parameter is calculated from cloud cover for the appropriate model levels as described above. Assumptions are made about the degree of overlap/randomness between clouds in different model levels.Cloud fractions vary from 0 to 1.[NOTE: See 3074 for the equivalent parameter in \"%\"]","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Potential_vorticity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Potential vorticity","parameter_ID":"60","product_type":"forecast","shortName":"pv","standard_name":"Potential_vorticity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/60"},"description":"Potential vorticity is a measure of the capacity for air to rotate in the atmosphere. If we ignore the effects of heating and friction, potential vorticity is conserved following an air parcel. It is used to look for places where large wind storms are likely to originate and develop.  Potential vorticity increases strongly above the tropopause and therefore, it can also be used in studies related to the stratosphere and stratosphere-troposphere exchanges.Large wind storms develop when a column of air in the atmosphere starts to rotate. Potential vorticity is calculated from the wind, temperature and pressure across a column of air in the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K m2kg-1s-1"},"Relative_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Relative humidity","parameter_ID":"157","product_type":"forecast","shortName":"r","standard_name":"Relative_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/157"},"description":"This parameter is the water vapour pressure as a percentage of the value at which the air becomes saturated (the point at which water vapour begins to condense into liquid water or deposition into ice).For temperatures over 0°C (273.15 K) it is calculated for saturation over water. At temperatures below -23°C it is calculated for saturation over ice.  Between -23°C and 0°C this parameter is calculated by interpolating between the ice and water values using a quadratic function.See more information about the model's relative humidity calculation.","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Skin_temperature(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Skin temperature","parameter_ID":"235","product_type":"forecast","shortName":"skt","standard_name":"Skin_temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/235"},"description":"This parameter is the temperature of the surface of the Earth.The skin temperature is the theoretical temperature that is required to satisfy the surface energy balance. It represents the temperature of the uppermost surface layer, which has no heat capacity and so can respond instantaneously to changes in surface fluxes. Skin temperature is calculated differently over land and sea.This parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.See further information about the skin temperatureover landandover sea.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean skin temperature. The specific encoding for Mean skin temperature can be found in 235079.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Snow_depth(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Snow depth","parameter_ID":"141","product_type":"forecast","shortName":"sd","standard_name":"Snow_depth","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/141"},"description":"This parameter is the depth of snow from the snow-covered area of agrid box.Its units are metres of water equivalent, so it is the depth the water would have if the snow melted and was spread evenly over the whole grid box. The ECMWF Integrated Forecast System represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box.See further information.\n\n[NOTE: See 228141 for the equivalent parameter in \"kg m-2\"]","dimensions":["lat","lon","time"],"type":"data","unit":"m of water equivalent"},"Snow_depth_water_equivalent(clte_sol)":{"attrs":{"encoding":"instantaneous","levelist":"1,2,3,4,5","levtype":"sol","long_name":"Snow depth water equivalent","parameter_ID":"228141","product_type":"forecast","shortName":"sd","standard_name":"Snow_depth_water_equivalent","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228141"},"description":"Snow depth water equivalent in kg m**-2 (mm) water equivalent.Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean snow depth water equivalent. The specific encoding for Time-mean snow depth water equivalent can be found in 235078.[NOTE: See 141 for the equivalent parameter in \"m of water equivalent\"]","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Snowfall(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Snowfall","parameter_ID":"144","product_type":"forecast","shortName":"sf","standard_name":"Snowfall","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/144"},"description":"This parameter is the accumulated snow that falls to the Earth's surface. It is the sum of large-scale snowfall and convective snowfall. Large-scale snowfall is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of thegrid boxor larger. Convective snowfall is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box.See further information.This parameter is the total amount of wateraccumulated over a particular time period which depends on the data extracted. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box.Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box.[NOTE: See 228144 for the equivalent parameter in \"kg m-2\"]","dimensions":["lat","lon","time"],"type":"data","unit":"m of water equivalent"},"Specific_cloud_liquid_water_content(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific cloud liquid water content","parameter_ID":"246","product_type":"forecast","shortName":"clwc","standard_name":"Specific_cloud_liquid_water_content","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/246"},"description":"This parameter is the mass of cloud liquid water droplets per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for agrid box.Water within clouds can be liquid or ice, or a combination of the two.See further information about the cloud formulation.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Specific_humidity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Specific humidity","parameter_ID":"133","product_type":"forecast","shortName":"q","standard_name":"Specific_humidity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/133"},"description":"This parameter is the mass of water vapour per kilogram of moist air.The total mass of moist air is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg-1"},"Sub-surface_runoff(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Sub-surface runoff","parameter_ID":"9","product_type":"forecast","shortName":"ssro","standard_name":"Sub-surface_runoff","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/9"},"description":"Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is simply called 'runoff'. This parameter is the total amount of water accumulated over aparticular time period which depends on the data extracted.The units of runoff are depth in metres.  This is the depth the water would have if it were spread evenly over thegrid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid square area.  Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here.Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood.  More information about how runoff is calculated is given in theIFS Physical Processes documentation.\n\n[NOTE: See 231012 for the equivalent parameter in \"kg m-2\"]","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Surface_long-wave_(thermal)_radiation_downwards(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Surface long-wave (thermal) radiation downwards","parameter_ID":"175","product_type":"forecast","shortName":"strd","standard_name":"Surface_long-wave_(thermal)_radiation_downwards","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/175"},"description":"This parameter is the amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere and clouds that reaches a horizontal plane at the surface of the Earth.The surface of the Earth emits thermal radiation, some of which is absorbed by the atmosphere and clouds. The atmosphere and clouds likewise emit thermal radiation in all directions, some of which reaches the surface (represented by this parameter).See further documentation.This parameter isaccumulated over a particular time period which depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Surface_net_long-wave_(thermal)_radiation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Surface net long-wave (thermal) radiation","parameter_ID":"177","product_type":"forecast","shortName":"str","standard_name":"Surface_net_long-wave_(thermal)_radiation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/177"},"description":"Thermal radiation (also known as longwave or terrestrial radiation) refers to radiation emitted by the atmosphere, clouds and the surface of the Earth. This parameter is the difference between downward and upward thermal radiation at the surface of the Earth. It the amount passing through a horizontal plane.The atmosphere and clouds emit thermal radiation in all directions, some of which reaches the surface as downward thermal radiation. The upward thermal radiation at the surface consists of thermal radiation emitted by the surface plus the fraction of downwards thermal radiation reflected upward by the surface.See further documentation.This parameter isaccumulated over a particular time periodwhich depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds.The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Surface_net_short-wave_(solar)_radiation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Surface net short-wave (solar) radiation","parameter_ID":"176","product_type":"forecast","shortName":"ssr","standard_name":"Surface_net_short-wave_(solar)_radiation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/176"},"description":"This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo).Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The remainder is incident on the Earth's surface, where some of it is reflected.See further documentation.This parameter isaccumulated over a particular time period which depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Surface_pressure(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Surface pressure","parameter_ID":"134","product_type":"forecast","shortName":"sp","standard_name":"Surface_pressure","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/134"},"description":"This parameter is the pressure (force per unit area) of the atmosphere on the surface of land, sea and in-land water.It is a measure of the weight of all the air in a column vertically above the area of the Earth's surface represented at a fixed point.Surface pressure is often used in combination with temperature to calculate air density.The strong variation of pressure with altitude makes it difficult to see the low and high pressure systems over mountainous areas, so mean sea level pressure, rather than surface pressure, is normally used for this purpose.The units of this parameter are Pascals (Pa). Surface pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb= 100 Pa).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Surface_runoff(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Surface runoff","parameter_ID":"8","product_type":"forecast","shortName":"sro","standard_name":"Surface_runoff","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/8"},"description":"Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is simply called 'runoff'. This parameter is the total amount of water accumulated over aparticular time period which depends on the data extracted.The units of runoff are depth in metres.  This is the depth the water would have if it were spread evenly over thegrid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid square area.  Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here.Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood.  More information about how runoff is calculated is given in theIFS Physical Processes documentation.\n\n[NOTE: See 231010 for the equivalent parameter in \"kg m-2\"]","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Surface_short-wave_(solar)_radiation_downwards(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Surface short-wave (solar) radiation downwards","parameter_ID":"169","product_type":"forecast","shortName":"ssrd","standard_name":"Surface_short-wave_(solar)_radiation_downwards","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/169"},"description":"This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth. This parameter comprises both direct and diffuse solar radiation.Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface (represented by this parameter).See further documentation.To a reasonably good approximation, this parameter is the model equivalent of what would be measured by a pyranometer (an instrument used for measuring solar radiation) at the surface. However, care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over amodel grid box.This parameter isaccumulated over a particular time period which depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"TOA_incident_short-wave_(solar)_radiation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"TOA incident short-wave (solar) radiation","parameter_ID":"212","product_type":"forecast","shortName":"tisr","standard_name":"TOA_incident_short-wave_(solar)_radiation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/212"},"description":"Accumulated field","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Temperature(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Temperature","parameter_ID":"130","product_type":"forecast","shortName":"t","standard_name":"Temperature","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/130"},"description":"This parameter is the temperature in the atmosphere.It has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.This parameter is available on multiple levels through the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-integrated_eastward_turbulent_surface_stress(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Time-integrated eastward turbulent surface stress","parameter_ID":"180","product_type":"forecast","shortName":"ewss","standard_name":"Time-integrated_eastward_turbulent_surface_stress","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/180"},"description":"Air flowing over a surface exerts a stress that transfers momentum to the surface and slows the wind. This parameter is the accumulated stress on the Earth's surface in the eastward direction due to both the turbulent interactions between the atmosphere and the surface, and to turbulent orographic form drag. The turbulent interactions between the atmosphere and the surface are due to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km being derived from land surface data at about 1 km resolution.See further information.Positive (negative) values denote stress in the eastward (westward) direction.This parameter isaccumulated over a particular time periodwhich depends on the data extracted.","dimensions":["lat","lon","time"],"type":"data","unit":"N m-2s"},"Time-integrated_northward_turbulent_surface_stress(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Time-integrated northward turbulent surface stress","parameter_ID":"181","product_type":"forecast","shortName":"nsss","standard_name":"Time-integrated_northward_turbulent_surface_stress","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/181"},"description":"Air flowing over a surface exerts a stress that transfers momentum to the surface and slows the wind. This parameter is the accumulated stress on the Earth's surface in the northward direction due to both the turbulent interactions between the atmosphere and the surface, and to turbulent orographic form drag.The turbulent interactions between the atmosphere and the surface are due to the roughness of the surface.The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km being derived from land surface data at about 1 km resolution.See further information.Positive (negative) values denote stress in the northward (southward) direction.This parameter isaccumulated over a particular time periodwhich depends on the data extracted.","dimensions":["lat","lon","time"],"type":"data","unit":"N m-2s"},"Time-integrated_surface_latent_heat_net_flux(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Time-integrated surface latent heat net flux","parameter_ID":"147","product_type":"forecast","shortName":"slhf","standard_name":"Time-integrated_surface_latent_heat_net_flux","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/147"},"description":"This parameter is the transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earth's surface and the atmosphere through the effects of turbulent air motion. Evaporation from the Earth's surface represents a transfer of energy from the surface to the atmosphere.See further documentationThis parameter is accumulated over aparticular time period which depends on the data extracted.The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds.The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-integrated_surface_sensible_heat_net_flux(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Time-integrated surface sensible heat net flux","parameter_ID":"146","product_type":"forecast","shortName":"sshf","standard_name":"Time-integrated_surface_sensible_heat_net_flux","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/146"},"description":"This parameter is the transfer of heat between the Earth's surface and the atmosphere through the effects of turbulent air motion (but excluding any heat transfer resulting from condensation or evaporation).The magnitude of the sensible heat flux is governed by the difference in temperature between the surface and the overlying atmosphere, wind speed and the surface roughness. For example, cold air overlying a warm surface would produce a sensible heat flux from the land (or ocean) into the atmosphere.See further documentationThis is a single level parameter and it is accumulated over aparticular time period which depends on the data extracted.The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Time-mean_eastward_sea_ice_velocity(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean eastward sea ice velocity","parameter_ID":"263003","product_type":"forecast","shortName":"avg_siue","standard_name":"Time-mean_eastward_sea_ice_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263003"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_eastward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69","levtype":"o3d","long_name":"Time-mean eastward sea water velocity","parameter_ID":"263506","product_type":"forecast","shortName":"avg_uoe","standard_name":"Time-mean_eastward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263506"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_sea_ice_velocity(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean northward sea ice velocity","parameter_ID":"263004","product_type":"forecast","shortName":"avg_sivn","standard_name":"Time-mean_northward_sea_ice_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263004"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_northward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69","levtype":"o3d","long_name":"Time-mean northward sea water velocity","parameter_ID":"263505","product_type":"forecast","shortName":"avg_von","standard_name":"Time-mean_northward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263505"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Time-mean_sea_ice_area_fraction(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea ice area fraction","parameter_ID":"263001","product_type":"forecast","shortName":"avg_siconc","standard_name":"Time-mean_sea_ice_area_fraction","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263001"},"dimensions":["lat","lon","time"],"type":"data","unit":"Fraction"},"Time-mean_sea_ice_thickness(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea ice thickness","parameter_ID":"263000","product_type":"forecast","shortName":"avg_sithick","standard_name":"Time-mean_sea_ice_thickness","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263000"},"dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Time-mean_sea_surface_height(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea surface height","parameter_ID":"263124","product_type":"forecast","shortName":"avg_zos","standard_name":"Time-mean_sea_surface_height","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263124"},"dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Time-mean_sea_surface_practical_salinity(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea surface practical salinity","parameter_ID":"263100","product_type":"forecast","shortName":"avg_sos","standard_name":"Time-mean_sea_surface_practical_salinity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263100"},"dimensions":["lat","lon","time"],"type":"data","unit":"g kg-1"},"Time-mean_sea_surface_temperature(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean sea surface temperature","parameter_ID":"263101","product_type":"forecast","shortName":"avg_tos","standard_name":"Time-mean_sea_surface_temperature","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263101"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_sea_water_potential_temperature(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69","levtype":"o3d","long_name":"Time-mean sea water potential temperature","parameter_ID":"263501","product_type":"forecast","shortName":"avg_thetao","standard_name":"Time-mean_sea_water_potential_temperature","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263501"},"dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-mean_sea_water_practical_salinity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69","levtype":"o3d","long_name":"Time-mean sea water practical salinity","parameter_ID":"263500","product_type":"forecast","shortName":"avg_so","standard_name":"Time-mean_sea_water_practical_salinity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263500"},"dimensions":["lat","lon","time"],"type":"data","unit":"g kg-1"},"Time-mean_snow_thickness_over_sea_ice(clte_o2d)":{"attrs":{"encoding":"mean","levelist":"","levtype":"o2d","long_name":"Time-mean snow thickness over sea ice","parameter_ID":"263002","product_type":"forecast","shortName":"avg_sisnthick","standard_name":"Time-mean_snow_thickness_over_sea_ice","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263002"},"dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Time-mean_upward_sea_water_velocity(clte_o3d)":{"attrs":{"encoding":"mean","levelist":"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69","levtype":"o3d","long_name":"Time-mean upward sea water velocity","parameter_ID":"263507","product_type":"forecast","shortName":"avg_wo","standard_name":"Time-mean_upward_sea_water_velocity","stream":"clte","time":"Daily","url":"https://codes.ecmwf.int/grib/param-db/263507"},"dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Top_net_long-wave_(thermal)_radiation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Top net long-wave (thermal) radiation","parameter_ID":"179","product_type":"forecast","shortName":"ttr","standard_name":"Top_net_long-wave_(thermal)_radiation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/179"},"description":"The thermal (also known as terrestrial or longwave) radiation emitted to space at the top of the atmosphere is commonly known as the Outgoing Longwave Radiation (OLR). The top net thermal radiation (this parameter) is equal to the negative of OLR.See further documentation.This parameter isaccumulated over a particular time periodwhich depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds.The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Top_net_short-wave_(solar)_radiation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Top net short-wave (solar) radiation","parameter_ID":"178","product_type":"forecast","shortName":"tsr","standard_name":"Top_net_short-wave_(solar)_radiation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/178"},"description":"This parameter is the incoming solar radiation (also known as shortwave radiation) minus the outgoing solar radiation at the top of the atmosphere. It is the amount of radiation passing through a horizontal plane. The incoming solar radiation is the amount received from the Sun. The outgoing solar radiation is the amount reflected and scattered by the Earth's atmosphere and surface.See further documentation.This parameter isaccumulated over a particular time periodwhich depends on the data extracted. The units are joules per square metre (J m-2). To convert to watts per square metre (W m-2), the accumulated values should be divided by the accumulation period expressed in seconds.The ECMWF convention for vertical fluxes is positive downwards.","dimensions":["lat","lon","time"],"type":"data","unit":"J m-2"},"Total_cloud_cover(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total cloud cover","parameter_ID":"164","product_type":"forecast","shortName":"tcc","standard_name":"Total_cloud_cover","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/164"},"description":"This parameter is the proportion of agrid boxcovered by cloud. Total cloud cover is a single level field calculated from the cloud occurring at different model levels through the atmosphere. Assumptions are made about the degree of overlap/randomness between clouds at different heights.Cloud fractions vary from 0 to 1.[NOTE: See 228164 for the equivalent parameter in \"%\"]","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Total_column_cloud_ice_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud ice water","parameter_ID":"79","product_type":"forecast","shortName":"tciw","standard_name":"Total_column_cloud_ice_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/79"},"description":"This parameter is the amount of ice contained within clouds in a column extending from the surface of the Earth to the top of the atmosphere. Snow (aggregated ice crystals) is not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The  ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_cloud_liquid_water(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column cloud liquid water","parameter_ID":"78","product_type":"forecast","shortName":"tclw","standard_name":"Total_column_cloud_liquid_water","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/78"},"description":"This parameter is the amount of liquid water contained within cloud droplets in a column extending from the surface of the Earth to the top of the atmosphere. Rain water droplets, which are much larger in size (and mass), are not included in this parameter.This parameter represents the area averaged value for amodel grid box.Clouds contain a continuum of different- sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_column_vertically-integrated_water_vapour(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total column vertically-integrated water vapour","parameter_ID":"137","product_type":"forecast","shortName":"tcwv","standard_name":"Total_column_vertically-integrated_water_vapour","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/137"},"description":"This parameter is the total amount of water vapour in a column extending from the surface of the Earth to the top of the atmosphere.This parameter represents the area averaged value for agrid box.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2"},"Total_precipitation(clte_sfc)":{"attrs":{"encoding":"accumulated","levelist":"","levtype":"sfc","long_name":"Total precipitation","parameter_ID":"228","product_type":"forecast","shortName":"tp","standard_name":"Total_precipitation","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/228"},"description":"This parameter is the accumulated liquid and frozen water, comprising rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation and convective precipitation. Large-scale precipitation is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of thegrid boxor larger. Convective precipitation is generated by the convection scheme in the IFS, which represents convection at spatial scales smaller than the grid box.See further information.This parameter does not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth.This parameter is the total amount of wateraccumulated over a particular time period which depends on the data extracted. The units of this parameter are depth in metres of water equivalent. It is the depth the water would have if it were spread evenly over the grid box.Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box.\n\n[NOTE: See 228228 for the equivalent parameter in \"kg m-2\"]","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Total_precipitation_rate(clte_sfc)":{"attrs":{"encoding":"instantaneous","levelist":"","levtype":"sfc","long_name":"Total precipitation rate","parameter_ID":"260048","product_type":"forecast","shortName":"tprate","standard_name":"Total_precipitation_rate","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/260048"},"description":"This parameter is the rate of total precipitation,at the specified time.In the ECMWF Integrated Forecasting System (IFS), total precipitation is rain and snow that falls to the Earth's surface.  It is the sum of large-scale precipitation and convective precipitation. Large-scale precipitation is generated by the cloud scheme in the IFS. The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of agrid boxor larger. Convective precipitation is generated by the convection scheme in the IFS. The convection scheme represents convection at spatial scales smaller than the grid box.Seefurther information. Precipitation parameters do not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth.1 kg of water spread over 1 square metre of surface is 1 mm deep (neglecting the effects of temperature on the density of water), therefore the units are equivalent to mm per second.Care should be taken when comparing model parameters with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box andmodel time step.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m-2s-1"},"U_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"U component of wind","parameter_ID":"131","product_type":"forecast","shortName":"u","standard_name":"U_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/131"},"description":"This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east, in metres per second. A negative sign thus indicates air movement towards the west.This parameter can be combined with the V component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"V_component_of_wind(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"V component of wind","parameter_ID":"132","product_type":"forecast","shortName":"v","standard_name":"V_component_of_wind","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/132"},"description":"This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north, in metres per second. A negative sign thus indicates air movement towards the south.This parameter can be combined with the U component of wind to give the speed and direction of the horizontal wind.","dimensions":["lat","lon","time"],"type":"data","unit":"m s-1"},"Vertical_velocity(clte_pl)":{"attrs":{"encoding":"instantaneous","levelist":"1000,925,850,700,600,500,400,300,250,200,150,100,70,50,30,20,10,5,1","levtype":"pl","long_name":"Vertical velocity","parameter_ID":"135","product_type":"forecast","shortName":"w","standard_name":"Vertical_velocity","stream":"clte","time":"Hourly","url":"https://codes.ecmwf.int/grib/param-db/135"},"description":"This parameter is the speed of air motion in the upward or downward direction. The ECMWF Integrated Forecasting System (IFS) uses a pressure based vertical co-ordinate system and pressure decreases with height, therefore negative values of vertical velocity indicate upward motion.Vertical velocity can be useful to understand the large-scale dynamics of the atmosphere, including areas of upward motion/ascent (negative values) and downward motion/subsidence (positive values).","dimensions":["lat","lon","time"],"type":"data","unit":"Pa s-1"}},"sci:publications":[{"doi":"10.26050/WDCC/nextGEMS_cyc3","citation":"Koldunov, Nikolay; Kölling, Tobias; Pedruzo-Bagazgoitia, Xabier; Rackow, Thomas; Redler, René; Sidorenko, Dmitry; Wieners, Karl-Hermann; Ziemen, Florian Andreas (2023). nextGEMS: output of the model development cycle 3 simulations for ICON and IFS. World Data Center for Climate (WDCC) at DKRZ."}],"dedl:short_description":"The nextGEMS data is aligned with the Climate Change Adaptation Digital Twin. The Climate Change Adaptation Digital Twin provides global climate projections and sector-specific information over multiple decades at high resolution via a unified framework combining advanced Earth system models, impact assessments, and observations. This nextGEMS Collection gives access to 'Future Projection' data based on the 'IFS-FESOM' model. This is a Generation-1 Collection."},{"type":"Collection","title":"DestinE Climate Adaptation DT, activity ScenarioMIP, experiment SSP3-7.0, model ICON","id":"EO.ECMWF.DAT.DT_CLIMATE_ADAPTATION_ICON","description":"The DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities through the provision of innovative climate information on multi-decadal timescales, at scales at which the impacts of climate change are observed. \nThis initiative presents the first ever attempt to operationalise the production of global multi-decadal climate projections at km-scale resolutions of 5 to 10km, leveraging the world- leading supercomputing facilities of the EuroHPC Joint Undertaking along with some of the leading European climate models. It also enables performing bespoke simulations to assess the impacts of new scenarios or of extreme events in a rapidly warming world and inform adaptation activities. \nHere we propose a selection of datasets derived from the DestinE Climate Change Adaptation Digital Twin according to the output of the ICON model coupled with the Shared Socioeconomic Pathway 3-7.0 (experiment: SSP3-7.0) as defined in the Scenario Model Intercomparison Project (activity: ScenarioMIP). Datasets are presented as Zarr archives regridded from the original HEALPix grid to a regular latitude-longitude grid. The chunking is optimised for time based and regional analysis.","links":[{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.DT_CLIMATE_ADAPTATION_ICON/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.DT_CLIMATE_ADAPTATION_ICON/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.DT_CLIMATE_ADAPTATION_ICON","title":"DestinE Climate Adaptation DT, activity ScenarioMIP, experiment SSP3-7.0, model ICON"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Parameters","title":"DestinE ClimateDT Parameters"},{"rel":"describedby","type":"text/html","href":"https://dl.acm.org/doi/abs/10.1109/MCSE.2023.3260519","title":"Destination Earth: High-Performance Computing for Weather and Climate"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.21957/d3f982672e","title":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"assets":{"thumbnail":{"href":"https://s3.central.data.destination-earth.eu/swift/v1/dedl-public/collections/climate-dt-min.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2021-07-01T00:00:00Z","2030-12-31T00:00:00Z"]]}},"license":"CC-BY-4.0","keywords":["Digital Twins","Climate Change","Atmosphere","Sea Ice","ICON","SSP3-7.0","Socioeconomic","ScenarioMIP"],"summaries":{"federation:backends":["desp_cache"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/datacube/v2.1.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["producer","processor","licensor"],"url":"https://www.ecmwf.int/"}],"created":"2024-04-11T22:31:55Z","updated":"2026-04-24T10:24:59Z","published":"2024-04-11T22:31:55Z","cube:dimensions":{"lat":{"axis":"y","description":"latitude","extent":[-90,90],"type":"spatial"},"lon":{"axis":"x","description":"longitude","extent":[-180,180],"type":"spatial"},"time":{"extent":["2020-01-01T00:00:00Z","2040-01-01T00:00:00Z"],"type":"temporal"}},"cube:variables":{"10_metre_u_component_of_wind":{"attrs":{"long_name":"10 metre U wind component","parameter_ID":"165","product_type":"forecast","shortName":"10u","standard_name":"10_metre_u_component_of_wind"},"description":"This parameter is the eastward component of the 10m wind. It is the horizontal speed of air moving towards the east, at a height of ten metres above the surface of the Earth, in metres per second.","dimensions":["lat","lon","time"],"type":"data","unit":"m s**-1"},"10_metre_v_component_of_wind":{"attrs":{"long_name":"10 metre V wind component","parameter_ID":"166","product_type":"forecast","shortName":"10v","standard_name":"10_metre_v_component_of_wind"},"description":"This parameter is the northward component of the 10m wind. It is the horizontal speed of air moving towards the north, at a height of ten metres above the surface of the Earth, in metres per second.","dimensions":["lat","lon","time"],"type":"data","unit":"m s**-1"},"2_metre_temperature":{"attrs":{"long_name":"2 metre temperature","parameter_ID":"167","product_type":"forecast","shortName":"2t","standard_name":"2_metre_temperature"},"description":"This parameter is the temperature of air at 2m above the surface of land, sea or in-land waters.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Mean_sea_level_pressure":{"attrs":{"long_name":"Mean sea level pressure","parameter_ID":"151","product_type":"forecast","shortName":"msl","standard_name":"Mean_sea_level_pressure"},"description":"This parameter is the pressure (force per unit area) of the atmosphere adjusted to the height of mean sea level.","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Skin_temperature":{"attrs":{"long_name":"Skin temperature","parameter_ID":"235","product_type":"forecast","shortName":"skt","standard_name":"Skin_temperature"},"description":"This parameter is the temperature of the surface of the Earth.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Temperature":{"attrs":{"long_name":"Temperature","parameter_ID":"130","product_type":"forecast","shortName":"t","standard_name":"Temperature"},"description":"This parameter is the temperature in the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Total_Cloud_Cover":{"attrs":{"long_name":"Total Cloud Cover","parameter_ID":"228164","product_type":"forecast","shortName":"tcc","standard_name":"Total_Cloud_Cover"},"description":"This parameter is the proportion of a grid box covered by cloud. Total cloud cover is a single level field calculated from the cloud occurring at different model levels through the atmosphere. Assumptions are made about the degree of overlap/randomness between clouds at different heights.","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Total_column_cloud_ice_water":{"attrs":{"long_name":"Total column cloud ice water","parameter_ID":"79","product_type":"forecast","shortName":"tciw","standard_name":"Total_column_cloud_ice_water"},"description":"This parameter is the amount of ice contained within cloud particles in a column extending from the surface of the Earth to the top of the atmosphere. Snow, which is aggregated ice crystals, is included in this parameter. Rain water droplets, which are much larger in size (and mass), are not included in this parameter. This parameter represents the area averaged value for a model grid box. Clouds contain a continuum of different- sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m**-2"},"Total_column_cloud_liquid_water":{"attrs":{"long_name":"Total column cloud liquid water","parameter_ID":"78","product_type":"forecast","shortName":"tclw","standard_name":"Total_column_cloud_liquid_water"},"description":"This parameter is the amount of ice contained within clouds in a column extending from the surface of the Earth to the top of the atmosphere. Snow (aggregated ice crystals) is not included in this parameter. This parameter represents the area averaged value for a model grid box. Clouds contain a continuum of different- sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m**-2"},"Total_column_vertically_integrated_water_vapour":{"attrs":{"long_name":"Total column vertically-integrated water vapour","parameter_ID":"137","product_type":"forecast","shortName":"tcwv","standard_name":"Total_column_vertically_integrated_water_vapour"},"description":"This parameter is the total amount of water vapour in a column extending from the surface of the Earth to the top of the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m**-2"},"Total_precipitation_rate":{"attrs":{"long_name":"Total precipitation rate","parameter_ID":"260048","product_type":"forecast","shortName":"tprate","standard_name":"Total_precipitation_rate"},"description":"This parameter is the rate of total precipitation, at the specified time.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m**-2 s**-1"},"other":"etc"},"sci:publications":[{"doi":"10.1109/MCSE.2023.3260519","citation":"N. Wedi et al., Destination Earth: High-Performance Computing for Weather and Climate, in Computing in Science \u0026 Engineering, vol. 24, no. 6, pp. 29-37, Nov.-Dec. 2022,"},{"doi":"10.21957/d3f982672e","citation":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"dedl:short_description":"A selection of datasets derived from the DestinE Climate Change Adaptation Digital Twin according to the output of the ICON model coupled with the Shared Socioeconomic Pathway 3-7.0 (experiment: SSP3-7.0) as defined in the Scenario Model Intercomparison Project (activity: ScenarioMIP)"},{"type":"Collection","title":"DestinE Climate Adaptation DT, activity ScenarioMIP, experiment SSP3-7.0, model IFS-NEMO","id":"EO.ECMWF.DAT.DT_CLIMATE_ADAPTATION_IFS-NEMO","description":"The DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities through the provision of innovative climate information on multi-decadal timescales, at scales at which the impacts of climate change are observed. \nThis initiative presents the first ever attempt to operationalise the production of global multi-decadal climate projections at km-scale resolutions of 5 to 10km, leveraging the world- leading supercomputing facilities of the EuroHPC Joint Undertaking along with some of the leading European climate models. It also enables performing bespoke simulations to assess the impacts of new scenarios or of extreme events in a rapidly warming world and inform adaptation activities. \nHere we propose a selection of datasets derived from the DestinE Climate Change Adaptation Digital Twin according to the output of the IFS-NEMO model coupled with the Shared Socioeconomic Pathway 3-7.0 (experiment: SSP3-7.0) as defined in the Scenario Model Intercomparison Project (activity: ScenarioMIP). Datasets are presented as Zarr archives regridded from the original HEALPix grid to a regular latitude-longitude grid. The chunking is optimised for time based and regional analysis.","links":[{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.DT_CLIMATE_ADAPTATION_IFS-NEMO/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.DT_CLIMATE_ADAPTATION_IFS-NEMO/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.DT_CLIMATE_ADAPTATION_IFS-NEMO","title":"DestinE Climate Adaptation DT, activity ScenarioMIP, experiment SSP3-7.0, model IFS-NEMO"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Parameters","title":"DestinE ClimateDT Parameters"},{"rel":"describedby","type":"text/html","href":"https://dl.acm.org/doi/abs/10.1109/MCSE.2023.3260519","title":"Destination Earth: High-Performance Computing for Weather and Climate"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.21957/d3f982672e","title":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"assets":{"thumbnail":{"href":"https://s3.central.data.destination-earth.eu/swift/v1/dedl-public/collections/climate-dt-min.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2020-07-01T00:00:00Z","2040-01-01T00:00:00Z"]]}},"license":"CC-BY-4.0","keywords":["Digital Twins","Climate Change","Atmosphere","Sea Ice","IFS-NEMO","SSP3-7.0","Socioeconomic","ScenarioMIP"],"summaries":{"federation:backends":["desp_cache"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/datacube/v2.1.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["producer","processor","licensor"],"url":"https://www.ecmwf.int/"}],"created":"2024-04-11T22:31:55Z","updated":"2026-04-24T10:24:59Z","published":"2024-04-11T22:31:55Z","cube:dimensions":{"lat":{"axis":"y","description":"latitude","extent":[-90,90],"type":"spatial"},"lon":{"axis":"x","description":"longitude","extent":[-180,180],"type":"spatial"},"time":{"extent":["2020-01-01T00:00:00Z","2040-01-01T00:00:00Z"],"type":"temporal"}},"cube:variables":{"10_metre_u_component_of_wind":{"attrs":{"long_name":"10 metre U wind component","parameter_ID":"165","product_type":"forecast","shortName":"10u","standard_name":"10_metre_u_component_of_wind"},"description":"This parameter is the eastward component of the 10m wind. It is the horizontal speed of air moving towards the east, at a height of ten metres above the surface of the Earth, in metres per second.","dimensions":["lat","lon","time"],"type":"data","unit":"m s**-1"},"10_metre_v_component_of_wind":{"attrs":{"long_name":"10 metre V wind component","parameter_ID":"166","product_type":"forecast","shortName":"10v","standard_name":"10_metre_v_component_of_wind"},"description":"This parameter is the northward component of the 10m wind. It is the horizontal speed of air moving towards the north, at a height of ten metres above the surface of the Earth, in metres per second.","dimensions":["lat","lon","time"],"type":"data","unit":"m s**-1"},"2_metre_dewpoint_temperature":{"attrs":{"long_name":"2 metre dewpoint temperature","parameter_ID":"168","product_type":"forecast","shortName":"2d","standard_name":"2_metre_dewpoint_temperature"},"description":"This parameter is the temperature to which the air, at 2 metres above the surface of the Earth, would have to be cooled for saturation to occur.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"2_metre_temperature":{"attrs":{"long_name":"2 metre temperature","parameter_ID":"167","product_type":"forecast","shortName":"2t","standard_name":"2_metre_temperature"},"description":"This parameter is the temperature of air at 2m above the surface of land, sea or in-land waters.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Boundary_layer_height":{"attrs":{"long_name":"Boundary layer height","parameter_ID":"159","product_type":"forecast","shortName":"blh","standard_name":"Boundary_layer_height"},"description":"This parameter is the depth of air next to the Earth's surface which is most affected by the resistance to the transfer of momentum, heat or moisture across the surface.","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Charnock":{"attrs":{"long_name":"Charnock","parameter_ID":"148","product_type":"forecast","shortName":"chnk","standard_name":"Charnock"},"description":"This parameter accounts for increased aerodynamic roughness as wave heights grow due to increasing surface stress. It depends on the wind speed, wave age and other aspects of the sea state and is used to calculate how much the waves slow down the wind. When the atmospheric model is run without the ocean model, this parameter has a constant value of 0.018. When the atmospheric model is coupled to the ocean model, this parameter is calculated by the ECMWF Wave Model.","dimensions":["lat","lon","time"],"type":"data","unit":"Numeric"},"High_cloud_cover":{"attrs":{"long_name":"high cloud cover","parameter_ID":"188","product_type":"forecast","shortName":"hcc","standard_name":"High_cloud_cover"},"description":"The proportion of a grid box covered by cloud occurring in the high levels of the troposphere. High cloud is a single level field calculated from cloud occurring on model levels with a pressure less than 0.45 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), high cloud would be calculated using levels with a pressure of less than 450 hPa (approximately 6km and above ( assuming a `standard atmosphere`)).","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Low_cloud_cover":{"attrs":{"long_name":"Low cloud cover","parameter_ID":"186","product_type":"forecast","shortName":"lcc","standard_name":"Low_cloud_cover"},"description":"This parameter is the proportion of a grid box covered by cloud occurring in the lower levels of the troposphere. Low cloud is a single level field calculated from cloud occurring on model levels with a pressure greater than 0.8 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), low cloud would be calculated using levels with a pressure greater than 800 hPa (below approximately 2km (assuming a 'standard atmosphere')).","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Mean_sea_level_pressure":{"attrs":{"long_name":"Mean sea level pressure","parameter_ID":"151","product_type":"forecast","shortName":"msl","standard_name":"Mean_sea_level_pressure"},"description":"This parameter is the pressure (force per unit area) of the atmosphere adjusted to the height of mean sea level.","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Medium_cloud_cover":{"attrs":{"long_name":"Medium cloud cover","parameter_ID":"187","product_type":"forecast","shortName":"mcc","standard_name":"Medium_cloud_cover"},"description":"This parameter is the proportion of a grid box covered by cloud occurring in the middle levels of the troposphere. Medium cloud is a single level field calculated from cloud occurring on model levels with a pressure between 0.45 and 0.8 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), medium cloud would be calculated using levels with a pressure of less than or equal to 800 hPa and greater than or equal to 450 hPa (between approximately 2km and 6km (assuming a 'standard atmosphere')).","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Relative_humidity":{"attrs":{"long_name":"Relative humidity","parameter_ID":"157","product_type":"forecast","shortName":"r","standard_name":"Relative_humidity"},"description":"This parameter is the water vapour pressure as a percentage of the value at which the air becomes saturated (the point at which water vapour begins to condense into liquid water or deposition into ice).","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Skin_temperature":{"attrs":{"long_name":"Skin temperature","parameter_ID":"235","product_type":"forecast","shortName":"skt","standard_name":"Skin_temperature"},"description":"This parameter is the temperature of the surface of the Earth.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Snow_depth ":{"attrs":{"long_name":"Snow depth","parameter_ID":"141","product_type":"forecast","shortName":"sd","standard_name":"Snow_depth"},"description":"This parameter is the depth of snow from the snow-covered area of a grid box. Its units are metres of water equivalent, so it is the depth the water would have if the snow melted and was spread evenly over the whole grid box. The ECMWF Integrated Forecast System represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box.","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Surface_pressure":{"attrs":{"long_name":"Surface pressure","parameter_ID":"134","product_type":"forecast","shortName":"sp","standard_name":"Surface_pressure"},"description":"This parameter is the pressure (force per unit area) of the atmosphere on the surface of land, sea and in-land water.","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Temperature":{"attrs":{"long_name":"Temperature","parameter_ID":"130","product_type":"forecast","shortName":"t","standard_name":"Temperature"},"description":"This parameter is the temperature in the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time_mean_sea_ice_thickness":{"attrs":{"long_name":"Time-mean sea ice thickness","parameter_ID":"263000","product_type":"forecast","shortName":"avg_sithick","standard_name":"Time_mean_sea_ice_thickness"},"description":" ","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Total_cloud_cover":{"attrs":{"long_name":"Total cloud cover","parameter_ID":"164","product_type":"forecast","shortName":"tcc","standard_name":"Total_cloud_cover"},"description":"This parameter is the proportion of a grid box covered by cloud. Total cloud cover is a single level field calculated from the cloud occurring at different model levels through the atmosphere. Assumptions are made about the degree of overlap/randomness between clouds at different heights.","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Total_column_cloud_ice_water":{"attrs":{"long_name":"Total column cloud ice water","parameter_ID":"79","product_type":"forecast","shortName":"tciw","standard_name":"Total_column_cloud_ice_water"},"description":"This parameter is the amount of ice contained within cloud particles in a column extending from the surface of the Earth to the top of the atmosphere. Snow, which is aggregated ice crystals, is included in this parameter. Rain water droplets, which are much larger in size (and mass), are not included in this parameter. This parameter represents the area averaged value for a model grid box. Clouds contain a continuum of different- sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m**-2"},"Total_column_cloud_liquid_water":{"attrs":{"long_name":"Total column cloud liquid water","parameter_ID":"78","product_type":"forecast","shortName":"tclw","standard_name":"Total_column_cloud_liquid_water"},"description":"This parameter is the amount of ice contained within clouds in a column extending from the surface of the Earth to the top of the atmosphere. Snow (aggregated ice crystals) is not included in this parameter. This parameter represents the area averaged value for a model grid box. Clouds contain a continuum of different- sized water droplets and ice particles. The ECMWF Integrated Forecasting System (IFS) cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including: cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m**-2"},"Total_column_vertically_integrated_water_vapour":{"attrs":{"long_name":"Total column vertically-integrated water vapour","parameter_ID":"137","product_type":"forecast","shortName":"tcwv","standard_name":"Total_column_vertically_integrated_water_vapour"},"description":"This parameter is the total amount of water vapour in a column extending from the surface of the Earth to the top of the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m**-2"},"Total_precipitation_rate":{"attrs":{"long_name":"Total precipitation rate","parameter_ID":"260048","product_type":"forecast","shortName":"tprate","standard_name":"Total_precipitation_rate"},"description":"This parameter is the rate of total precipitation, at the specified time.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m**-2 s**-1"},"other":"etc"},"sci:publications":[{"doi":"10.1109/MCSE.2023.3260519","citation":"N. Wedi et al., Destination Earth: High-Performance Computing for Weather and Climate, in Computing in Science \u0026 Engineering, vol. 24, no. 6, pp. 29-37, Nov.-Dec. 2022,"},{"doi":"10.21957/d3f982672e","citation":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"}],"dedl:short_description":"A selection of datasets derived from the DestinE Climate Change Adaptation Digital Twin according to the output of the IFS-NEMO model coupled with the Shared Socioeconomic Pathway 3-7.0 (experiment: SSP3-7.0) as defined in the Scenario Model Intercomparison Project (activity: ScenarioMIP)"},{"type":"Collection","title":"In-service Aircraft for a Global Observing System","id":"EO.AERIS.DAT.IAGOS","description":"In-service Aircraft for a Global Observing System (IAGOS) is a European Research Infrastructure for global observations of atmospheric composition from commercial aircraft. IAGOS combines the expertise of scientific institutions with the infrastructure of civil aviation in order to provide essential data on climate change and air quality at a global scale. In order to provide optimal information, two complementary systems have been implemented, (i) IAGOS-CORE providing global coverage on a day-to-day basis of key observables and (ii) IAGOS-CARIBIC providing a more in-depth and complex set of observations with lesser geographical and temporal coverage.\n## How to cite IAGOS data\nUse of the data requires proper reference and citation of the IAGOS data, using the exact citation (including the provided DOI) as provided at the moment of upload from IAGOS, if applicable. For all of the IAGOS data we will provide you with the citation to use through the landing page of the data object; from this page you will always be able to download the data object again. The Digital Object Identifier (DOI) of the data object will always resolve to its landing page.\n ##Aknowledgements\nBy downloading the IAGOS data product you agree to the licencing conditions that apply to the data. Under this license derived products and redistribution are allowed, but you are required to always inform your users of the original source of the data used, refer them to the license text and the original source at IAGOS for possible updates or uploads. We ask you to inform the data providers, traceable through the metadata connected to the provided DOI, when the data is used for publication(s), and to offer them the possibility to comment and/or offer them co-authorship or acknowledgement in the publication when this is justified by the added value of the data for your results. In accordance with the IAGOS data policy, users of IAGOS data products are required to:\n\t 1. include the following acknowledgements in publications: 'MOZAIC/CARIBIC/IAGOS data were created with support from the European Commission, national agencies in Germany (BMBF), France (MESR), and the UK (NERC), and the IAGOS member institutions (http://www.iagos.org/partners). The participating airlines (Lufthansa, Air France, Austrian, China Airlines, Hawaiian Airlines, Air Canada, Iberia, Eurowings Discover, Cathay Pacific, Air Namibia, Sabena) supported IAGOS by carrying the measurement equipment free of charge since 1994. The data are available at http://www.iagos.fr thanks to additional support from AERIS.'\n\t 2. offer co-authorship to the IAGOS Principal Investigators if the IAGOS data play a significant role in the publication\n\t 3. identify themselves and provide contact information (valid email address)\n\t 4. provide a short description of the intended research","links":[{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.AERIS.DAT.IAGOS/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.AERIS.DAT.IAGOS/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.AERIS.DAT.IAGOS","title":"In-service Aircraft for a Global Observing System"},{"rel":"describedby","type":"text/html","href":"https://iagos.aeris-data.fr","title":"IAGOS Data Portal"}],"assets":{"thumbnail":{"href":"https://wp1.aeris-data.fr/wp-content-aeris/uploads/sites/66/2020/09/iagos_core_cut-1024x574.png","roles":["thumbnail"],"title":"IAGOS Core","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1994-08-01T00:00:00Z",null]]}},"license":"CC-BY-4.0","keywords":["IAGOS","AERIS","ATMOSPHERIC","AIRCRAFT"],"summaries":{"federation:backends":["internal_fdp"],"processing:level":["L2"]},"item_assets":{"data":{"roles":["data"],"title":"timeseries","type":"application/netcdf"}},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/processing/v1.2.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"IAGOS-CORE","roles":["producer"],"url":"https://iagos.aeris-data.fr/"},{"name":"FZ Jülich","roles":["producer"],"url":"https://www.fz-juelich.de/portal/EN/Home/home_node.html"},{"name":"IAGOS-AISBL","roles":["licensor","producer","processor"],"url":"https://iagos.aeris-data.fr/"},{"name":"Destination Earth Data Lake (DEDL)","roles":["host"],"url":"https://data.destination-earth.eu/"}],"created":"2024-06-03T09:09:18Z","updated":"2026-04-24T10:24:59Z","published":"2024-06-03T09:09:18Z","sci:publications":[{"doi":"10.25326/06","citation":"Boulanger, D., Blot, R., Bundke, U., Gerbig, C., Hermann, M., Nédélec, P., Rohs, S. \u0026 Ziereis, H. (2018).  IAGOS final quality controlled Observational Data L2 – Time series.  [dataset].  Aeris.  https://doi.org/10.25326/06"},{"doi":"10.25326/07","citation":"Boulanger, D., Blot, R., Bundke, U., Gerbig, C., Hermann, M., Nédélec, P., Rohs, S. \u0026 Ziereis, H. (2018).  IAGOS final quality controlled Observational Data L2 – Vertical profiles.  [dataset].  Aeris.  https://doi.org/10.25326/07"}],"dedl:short_description":"The In-Service Aircraft for a Global Observing System (IAGOS) provides comprehensive global observations of atmospheric composition from commercial flights combining scientific expertise with civil aviation infrastructure for climate and air quality monitoring."},{"type":"Collection","title":"Energy Indicators from the Climate Adaptation Digital Twin","id":"EO.BSC.DAT.ENERGY_INDICATORS","description":"The energy sector is strongly exposed to climate variability and change, from shifts in wind and solar resources to changing heating and cooling demand. Planning a resilient energy system therefore requires indicators that translate climate conditions into sector-relevant quantities. The Energy Indicators application provides wind, solar and energy-demand indicators derived from DestinE Climate Digital Twin (Climate DT) model data, enabling users to assess how energy supply and demand conditions vary in space and time and how they may change under future climate scenarios. More information can be found in the Climate DT User Guide. The application provides indicators for the energy sector: capacity factors for different wind turbines, wind speed histograms, and high- and low-wind events, alongside energy-demand indicators (heating and cooling degree-days) and a solar demand indicator (photovoltaic potential). The indicators are derived from hourly Climate DT output and provided globally at kilometre-scale resolution. By deriving these indicators consistently from Climate DT simulations, the application supports the analysis of climate-driven trends in renewable energy resources and energy demand, aiding adaptation planning across the energy sector. More information can be found in the user guide https://platform.destine.eu/docs/climate-dt-user-guide/doc/index.html","links":[{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.BSC.DAT.ENERGY_INDICATORS/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.BSC.DAT.ENERGY_INDICATORS/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.BSC.DAT.ENERGY_INDICATORS","title":"Energy Indicators from the Climate Adaptation Digital Twin"},{"rel":"describedby","type":"text/html","href":"https://destine.ecmwf.int/harnessing-the-climate-change-adaptation-digital-twin-for-wind-energy/","title":"Energy Indicators from the Climate Adaptation Digital Twin - Further Information"}],"assets":{"thumbnail":{"href":"https://s3.central.data.destination-earth.eu/swift/v1/dedl-public/collections/eo-bsc-dat-energy-indicators.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-5,50.05,5,59.95]]},"temporal":{"interval":[["1990-01-01T00:00:00Z","2049-12-31T23:00:00Z"]]}},"license":"CC-BY-4.0","keywords":["Digital Twins","Climate Change","Atmosphere","Climate","Energy","Wind"],"summaries":{"federation:backends":["internal_fdp"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/datacube/v2.2.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"Barcelona Supercomputing Centre (BSC)","roles":["producer","processor"],"url":"https://www.bsc.es"},{"name":"ECMWF","roles":["licensor"],"url":"https://www.ecmwf.int/"},{"name":"Destination Earth Data Lake (DEDL)","roles":["host"],"url":"https://data.destination-earth.eu/"}],"created":"2026-07-01T14:23:40Z","updated":"2026-08-27T12:15:43Z","published":"2026-07-01T14:23:40Z","cube:dimensions":{"lat":{"axis":"y","description":"latitude","extent":[-89.975,89.975],"type":"spatial"},"lon":{"axis":"x","description":"longitude","extent":[-179.975,179.975],"type":"spatial"},"time":{"extent":["1990-01-01T00:00:00Z","2049-12-31T23:00:00Z"],"step":"P0Y0M0DT1H0M0S","type":"temporal"}},"cube:variables":{"99th Wind Speed Percentile":{"attrs":{"long_name":"99th Wind Speed Percentile","parameter_ID":"-","product_type":"forecast","shortName":"ws_99","standard_name":"99th_Wind_Speed_Percentile"},"description":"The 99th percentile of monthly wind speeds at 100m height.","dimensions":["lat","lon","time"],"type":"data","unit":"ms-1"},"Cooling_Degree-Days":{"attrs":{"long_name":"Cooling 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representing the highest daily wind speed recorded.","dimensions":["lat","lon","time"],"type":"data","unit":"ms-1"},"Heating_Degree-Days":{"attrs":{"long_name":"Heating Degree-Days","parameter_ID":"-","product_type":"forecast","shortName":"hdd","standard_name":"Heating_Degree-Days"},"description":"Heating Degree-Days represent the cumulative degrees by which the daily mean temperature falls below a reference temperature, indicating demand for heating.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"High_Wind_Events":{"attrs":{"long_name":"High Wind Events","parameter_ID":"-","product_type":"forecast","shortName":"hwe","standard_name":"High_Wind_Events"},"description":"High Wind Events indicate periods where wind speeds exceed a defined threshold, which can influence weather hazards and energy generation.","dimensions":["lat","lon","time"],"type":"data","unit":"-"},"Low_Wind_Events":{"attrs":{"long_name":"Low Wind 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As a first assessment, it might be sufficient to look at daily rainfall totals for a given region. However, an in-depth analysis requires local information on severity, affected area and statistical return times to enable the development of fitted adaptation strategies. HydroMet fulfils this need by providing information on the intensity, duration, spatial extent and return time of extreme precipitation events over Europe based on DestinE Climate DT model data. More information can be found in the [Climate DT user guide](https://platform.destine.eu/services/documents-and-api/doc/?service_name=climate-dt-user-guide).\n\nThe extreme precipitation parameters produced by HydroMet are based on existing products of the German Meteorological Service (DWD): KOSTRA2020, the latest version of KOSTRA-DWD (Shehu et al., 2023), and CatRaRE, Catalogue of Radar-based heavy Rainfall Events (Lengfeld et al., 2021). KOSTRA is a coordinated effort across German Federal states to develop methods for evaluating precipitation extremes from gauge-based observations. The system, developed over three decades, provides data about precipitation levels, duration and the annual return interval. CatRaRE is a software package able to detect and define extreme rain events and, at the same time, generate a catalogue with meteorological and geographical information for each event. At DWD, it is applied to radar data. HydroMet combines both products for a consistent dataset on the climatological scale.\n\nShehu, B., Willems, W., Stockel, H., Thiele, L.-B., \u0026 Haberlandt, U. (2023). Regionalisation of rainfall depth–duration–frequency curves with different data types in Germany.\n\nHydrology and Earth System Sciences, 27(5), 1109–1132. https://doi.org/10.5194/hess-27-1109-2023 Lengfeld, K., Walawender, E., Winterrath, T., \u0026 Becker, A. (2021). CatRaRE: A Catalogue of radar-based heavy rainfall events in Germany derived from 20 years of data. Meteorologische Zeitschrift, 30(6), 469–487. https://doi.org/10.1127/metz/2021/1088","links":[{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.DWD.STAT.HYDROMET_EXTREMES/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.DWD.STAT.HYDROMET_EXTREMES/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.DWD.STAT.HYDROMET_EXTREMES","title":"HydroMet: Catalogue of extreme precipitation events"},{"rel":"related","type":"text/html","href":"https://destination-earth.eu/use-cases/simulating-the-future-of-extreme-events/","title":"Simulating the Future of Extreme 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Generalized Extreme Value distribution.","dimensions":["lat","lon"]},"y_RRmax":{"description":"Pixel number on the y-axis of the maximum precipitation within the event","dimensions":["number"]},"y_center":{"description":"Pixel number on y-axis of the event center","dimensions":["number"]}},"sci:publications":[{"doi":"10.1127/metz/2021/1088","citation":"Lengfeld, K., Walawender, E., Winterrath, T., \u0026 Becker, A. (2021). CatRaRE: A Catalogue of radar-based heavy rainfall events in Germany derived from 20 years of data. Meteorologische Zeitschrift, 30(6), 469–487. https://doi.org/10.1127/metz/2021/1088"},{"doi":"10.5194/hess-27-1109-2023","citation":"Shehu, B., Willems, W., Stockel, H., Thiele, L.-B., \u0026 Haberlandt, U. (2023). Regionalisation of rainfall depth–duration–frequency curves with different data types in Germany. Hydrology and Earth System Sciences, 27(5), 1109–1132. https://doi.org/10.5194/hess-27-1109-2023"}],"dedl:short_description":"The HydroMet application provides statistics and an event catalogue of extreme rainfall from the Climate Adaptation Digital Twin."},{"type":"Collection","title":"ASCAT Level 1 SZF Climate Data Record Release 2 - Metop","id":"EO.EUM.CM.METOP.ASCSZFR02","description":"Reprocessed L1B data from the Advanced Scatterometer (ASCAT) on METOP-A, resampled at full resolution (SZF). 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Near real-time distribution discontinued on 29/09/2015 but the product contents are now available in the corresponding Level 2 product 'ASCAT Soil Moisture at 12.5 km Swath Grid'.","links":[{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.EUM.DAT.METOP.ASCSZR1B/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.EUM.DAT.METOP.ASCSZR1B/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.EUM.DAT.METOP.ASCSZR1B","title":"ASCAT Level 1 Sigma0 resampled at 12.5 km Swath Grid - Metop - Global"},{"rel":"license","type":"application/pdf","href":"https://www.eumetsat.int/data-policy/eumetsat-data-policy.pdf","title":"EUMETSAT Data Policy"},{"rel":"describedby","type":"text/html","href":"https://data.eumetsat.int/product/EO:EUM:DAT:METOP:ASCSZR1B","title":"ASCAT Level 1 Sigma0 resampled at 12.5 km Swath Grid - Metop - Global"}],"assets":{"thumbnail":{"href":"https://user.eumetsat.int/s3/eup-strapi-media/metop_Ascat_thumbnail_UP_e18ca9c42a.jpg","roles":["thumbnail"],"title":"ASCAT Level 1 Sigma0 resampled at 12.5 km Swath Grid - Metop - Global","type":"image/jpeg"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2007-03-01T00:00:00Z",null]]}},"license":"other","keywords":["Land","Level 1 Data","Ocean","Radar Backscatter NRCS"],"summaries":{"constellation":["METOP"],"federation:backends":["eumetsat"],"instruments":["ASCAT"],"platform":["METOP-A","METOP-B","METOP-C"],"processing:level":["L1"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/timestamps/v1.1.0/schema.json","https://stac-extensions.github.io/processing/v1.2.0/schema.json"],"providers":[{"name":"European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)","roles":["producer","processor","licensor","host"],"url":"https://www.eumetsat.int/"}],"created":"2024-05-16T20:42:11Z","updated":"2026-05-06T10:04:22Z","published":"2024-05-16T20:42:11Z","dedl:short_description":"The ASCAT Level 1 Sigma0 dataset contains global oceanic wind speed and direction measurements with additional applications including sea ice detection and land parameter analysis at 12.5 km resolution."},{"type":"Collection","title":"AVHRR Global (dual) Atmospheric Motion Vectors Climate Data Record Release 1 - Metop-A/B and -B/A","id":"EO.EUM.DAT.METOP.AVHRDUAL0100","description":"Tropospheric winds derived globally using the tandem configuration of METOP-A and METOP-B AVHRR instruments. Two complementary products are generated considering either Metop-A or Metop-B as the reference platform.\n\nThis is the first release of the global atmospheric wind vectors data record. Data are accessible from the EUMETSAT Data Centre. This uses the version 3.1.1 of the EUMETSAT operational algorithm and forecast from ERA-Interim reanalyses as auxiliary data. The data record includes products generated using IR images (channel 4) taken from two AVHRR/3 imager on-board the Metop-A and Metop-B polar satellites. Each product contains several wind vectors with their associated speed, direction, height, and several quality indicators. The dataset covers the period from the 24 April 2013 until December 2017.\n\nThe coverage is global. The dataset is available in two formats: the EUMETSAT EPS native format and the BUFR format.\n\nAtmospheric motion vectors (AMVs) are a valuable observation type for providing dynamical information for forecast models.\n\nThis is a Thematic Climate Data Record (TCDR) produced for the Copernicus Climate Change Service.","links":[{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.EUM.DAT.METOP.AVHRDUAL0100/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.EUM.DAT.METOP.AVHRDUAL0100/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.EUM.DAT.METOP.AVHRDUAL0100","title":"AVHRR Global (dual) Atmospheric Motion Vectors Climate Data Record Release 1 - Metop-A/B and -B/A"},{"rel":"cite-as","href":"https://doi.org/10.15770/EUM_SEC_CLM_0038","title":"Digital Object Identifier (DOI): 10.15770/EUM_SEC_CLM_0038"},{"rel":"describedby","type":"application/pdf","href":"https://user.eumetsat.int/s3/eup-strapi-media/pdf_cs3_311b_t5_1_pug_metop_avhrr_amv_ecb7f27f2e.pdf","title":"Product User Guide for the Atmospheric Motion Vectors generated from Metop-AVHRR for the period 2007-2017"},{"rel":"describedby","type":"application/pdf","href":"https://user.eumetsat.int/s3/eup-strapi-media/pdf_cs3_311b_t5_1_evalrep_metop_avhrr_amv_4a141b5253.pdf","title":"Validation Report: Quality Evaluation Report for Atmospheric Motion Vectors generated with Metop-AVHRR data for the period 2007-2017"},{"rel":"describedby","type":"application/pdf","href":"https://user.eumetsat.int/s3/eup-strapi-media/pdf_avhrr_l2_wind_product_atbd_877b0c8836.pdf","title":"AVHRR L2 Wind product ATBD"},{"rel":"describedby","type":"text/html","href":"https://data.eumetsat.int/product/EO:EUM:DAT:0151","title":"AVHRR Global (dual) Atmospheric Motion Vectors Climate Data Record Release 1 - Metop-A/B and -B/A"}],"assets":{"thumbnail":{"href":"https://user.eumetsat.int/s3/eup-strapi-media/winds3_8f1f4eafc0.png","roles":["thumbnail"],"title":"AVHRR Global (dual) Atmospheric Motion Vectors Climate Data Record Release 1 - Metop-A/B and -B/A","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2013-04-24T00:00:00Z","2017-12-31T00:00:00Z"]]}},"license":"CC-BY-4.0","keywords":["Atmosphere","climatology","Level 2 Data","Meteorology","Wind"],"summaries":{"constellation":["Metop"],"federation:backends":["eumetsat"],"instruments":["AVHRR"],"platform":["Metop-A","Metop-B"],"processing:level":["L2"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/processing/v1.2.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json","https://stac-extensions.github.io/scientific/v1.0.0/schema.json"],"providers":[{"name":"European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)","roles":["producer","processor","licensor","host"],"url":"https://www.eumetsat.int/"}],"created":"2026-04-30T18:31:54Z","updated":"2026-07-01T12:38:54Z","published":"2026-04-30T18:31:54Z","sci:doi":"10.15770/EUM_SEC_CLM_0038","sci:citation":"Digital Object Identifier (DOI): 10.15770/EUM_SEC_CLM_0038","dedl:short_description":"Global tropospheric winds were derived from METOP-A and METOP-B AVHRR instrument tandem configurations, generating two products per satellite. Version 3.1.1 of the EUMETSAT algorithm was used along with ERA-Interim forecasts to create this TCDR for the Copernicus Climate Change Service. It spans 2013-2017, covering the globe with multiple daily wind vector products containing speed, direction, height, and quality indicators, provided in both EUMETSAT EPS and BUFR formats."},{"type":"Collection","title":"AVHRR Polar Atmospheric Motion Vectors Climate Data Record Release 2 - Metop-A and -B","id":"EO.EUM.DAT.METOP.AVHRPWE0200","description":"Tropospheric satellite winds at all heights below the tropopause in the polar regions (latitudes higher than 45º), derived from the infrared channel of the Metop AVHRR instrument.\n\nThis is the second release of the polar atmospheric motion vectors data record. Data are accessible from the EUMETSAT Data Centre. This uses the version 3.1.1 of the EUMETSAT operational algorithm and forecast from ERA-Interim reanalyses as auxiliary data. The data record includes products generated using IR images (channel 4) taken from the AVHRR/3 imager on-board Metop-A and Metop-B polar satellites. Polar AMVs are derived using 2 AVHRR orbits leading to one product every 100 minutes. Each product contains several wind vectors with their associated speed, direction, height, and several quality indicators. The dataset covers the period from 1 March 2007 until 31 December 2017 for Metop-A, and from 24 April 2013 until 31 December 2017 for Metop-B. \n\nThe coverage boundaries are polar caps poleward of 45 degrees. The dataset is available in two formats: the EUMETSAT EPS native format and the BUFR format.\n\nSee Related Products tab for Release 1.\n\nThis is a Thematic Climate Data Record (TCDR) produced for the Copernicus Climate Change Service.","links":[{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.EUM.DAT.METOP.AVHRPWE0200/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.EUM.DAT.METOP.AVHRPWE0200/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.EUM.DAT.METOP.AVHRPWE0200","title":"AVHRR Polar Atmospheric Motion Vectors Climate Data Record Release 2 - Metop-A and -B"},{"rel":"cite-as","href":"https://doi.org/10.15770/EUM_SEC_CLM_0037","title":"Digital Object Identifier (DOI): 10.15770/EUM_SEC_CLM_0037"},{"rel":"describedby","type":"application/pdf","href":"https://user.eumetsat.int/s3/eup-strapi-media/pdf_cs3_311b_t5_1_pug_metop_avhrr_amv_ecb7f27f2e.pdf","title":"Product User Guide for the Atmospheric Motion Vectors generated from Metop-AVHRR for the period 2007-2017"},{"rel":"describedby","type":"application/pdf","href":"https://user.eumetsat.int/s3/eup-strapi-media/pdf_cs3_311b_t5_1_evalrep_metop_avhrr_amv_4a141b5253.pdf","title":"Validation Report: Quality Evaluation Report for Atmospheric Motion Vectors generated with Metop-AVHRR data for the period 2007-2017"},{"rel":"describedby","type":"application/pdf","href":"https://user.eumetsat.int/s3/eup-strapi-media/pdf_avhrr_l2_wind_product_atbd_877b0c8836.pdf","title":"AVHRR L2 Wind product ATBD"},{"rel":"describedby","type":"text/html","href":"https://data.eumetsat.int/product/EO:EUM:DAT:0152","title":"AVHRR Polar Atmospheric Motion Vectors Climate Data Record Release 2 - Metop-A and -B"}],"assets":{"thumbnail":{"href":"https://user.eumetsat.int/s3/eup-strapi-media/winds2_5e1a6c00e8.png","roles":["thumbnail"],"title":"AVHRR Polar Atmospheric Motion Vectors Climate Data Record Release 2 - Metop-A and -B","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2007-03-01T00:00:00Z","2017-12-31T00:00:00Z"]]}},"license":"CC-BY-4.0","keywords":["Atmosphere","climatology","Level 2 Data","Meteorology","Wind"],"summaries":{"constellation":["Metop"],"federation:backends":["eumetsat"],"instruments":["AVHRR"],"platform":["Metop-A","Metop-B"],"processing:level":["L2"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/processing/v1.2.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json","https://stac-extensions.github.io/scientific/v1.0.0/schema.json"],"providers":[{"name":"European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)","roles":["producer","processor","licensor","host"],"url":"https://www.eumetsat.int/"}],"created":"2026-04-30T18:31:54Z","updated":"2026-07-01T12:38:54Z","published":"2026-04-30T18:31:54Z","sci:doi":"10.15770/EUM_SEC_CLM_0037","sci:citation":"Digital Object Identifier (DOI): 10.15770/EUM_SEC_CLM_0037","dedl:short_description":"Polar Atmospheric Motion Vectors (AMV) derived from Metop AVHRR infrared channels, covering latitudes above 45°, providing tropospheric winds at various heights, processed using v3.1.1 EUMETSAT algorithm and ERA-Interim forecasts; available from 2007 (Metop-A) or 2013 (Metop-B) to 2017 in EPS and BUFR formats."},{"type":"Collection","title":"AVHRR GAC Atmospheric Motion Vectors Climate Data Record Release 2 - Multimission - Polar","id":"EO.EUM.DAT.METOP.AVHRRGACR02","description":"This is the second release of the reprocessed polar Atmospheric Motion Vectors (AMV) Thematic Climate Data Record (TCDR) from the Advanced Very High Resolution Radiometer (AVHRR) in Global Area Coverage (GAC), from TIROS-N, NOAA-06, 07, 08, 09, 10, 11, 12, 14, 15, 16, 17, 18 and 19 and Metop-A and -B. It contains AMVs at all heights below the tropopause, derived from images in the Infrared channel at 10.8 microns. Vectors are retrieved by tracking the motion of clouds in two consecutive images. The height assignment of the AMVs is calculated using the Cross-Correlation Contribution (CCC) function to determine the height using the pixels that contribute the most to the vectors. A quality indicator is derived for each vector to assess the reliability of the retrieval. Products are stored in netCDF4 format and cover the period from January 1979 to September 2019. 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Channel 3 switches between 3a and 3b for daytime and nighttime. As a high-resolution imager (about 1.1 km near nadir) its main purpose is to provide cloud and surface information such as cloud coverage, cloud top temperature, surface temperature over land and sea, and vegetation or snow/ice. 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It was processed using IASI instruments onboard Metop-A and Metop-B polar orbiting satellites and covers the period from 2007 until the end of 2023. It includes several variables but the principal product  is the vertical profile of CO provided on 18 atmospheric layers from the ground to 18 km with an additional layer from 18 km to the top of the atmosphere.","links":[{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.EUM.DAT.METOP.O3M-517/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.EUM.DAT.METOP.O3M-517/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.EUM.DAT.METOP.O3M-517","title":"IASI Carbon Monoxide Profiles FORLI-CO Climate Data Record Release 1 - Metop-A and -B"},{"rel":"cite-as","href":"https://doi.org/10.15770/EUM_SAF_AC_0047","title":"Digital Object Identifier (DOI): 10.15770/EUM_SAF_AC_0047"},{"rel":"describedby","type":"application/pdf","href":"https://user.eumetsat.int/s3/eup-strapi-media/Validation_Report_CO_Metop_AB_20231130_v2_255bce4c37.pdf","title":"Validation report of reprocessed IASI L2 CO CDR for Metop-A and B"},{"rel":"describedby","type":"application/pdf","href":"https://user.eumetsat.int/s3/eup-strapi-media/AC_SAF_IASI_CO_CDR_PUM_08042024_c42c579912.pdf","title":"Product User Manual IASI Reprocessed L2 CO CDR (O3M-517) Metop-A\u0026B"},{"rel":"describedby","type":"text/html","href":"https://data.eumetsat.int/product/EO:EUM:DAT:0959","title":"IASI Carbon Monoxide Profiles FORLI-CO Climate Data Record Release 1 - Metop-A and -B"}],"assets":{"thumbnail":{"href":"https://user.eumetsat.int/s3/eup-strapi-media/IASC_Ox2x0100_f7336aa4b8.png","roles":["thumbnail"],"title":"IASI Carbon Monoxide Profiles FORLI-CO Climate Data Record Release 1 - Metop-A and -B","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2007-07-10T00:00:00Z","2023-12-31T00:00:00Z"]]}},"license":"CC-BY-4.0","keywords":["Atmosphere","Climate","climatology","Level 2 Data","Meteorology"],"summaries":{"constellation":["Metop"],"federation:backends":["eumetsat"],"instruments":["IASI"],"platform":["Metop-A","Metop-B"],"processing:level":["L2"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/processing/v1.2.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json","https://stac-extensions.github.io/scientific/v1.0.0/schema.json"],"providers":[{"name":"Atmospheric Composition Satellite Application Facility (AC SAF - EUMETSAT)","roles":["producer","processor","licensor"],"url":"http://ac-saf.eumetsat.int"}],"created":"2026-04-30T18:31:54Z","updated":"2026-07-01T12:38:54Z","published":"2026-04-30T18:31:54Z","sci:doi":"10.15770/EUM_SAF_AC_0047","sci:citation":"Digital Object Identifier (DOI): 10.15770/EUM_SAF_AC_0047","dedl:short_description":"IASI Carbon Monoxide CDR uses the FORLI algorithm applied to IASI L1C data from Metop-A/B satellites, covering 2007-2023, providing CO profiles across 19 atmospheric layers up to the top of the atmosphere."},{"type":"Collection","title":"ASCAT Winds and Soil Moisture at 25 km Swath Grid - Metop","id":"EO.EUM.DAT.METOP.OAS025","description":"This ASCAT Multi-parameter product contains surface wind vectors over ocean and soil moisture index over land. Additionally, the backscatter values involved in the retrieval of the geophysical parameters above are also included, as well as several quality flags to facilitate the use of the data. For NWP users this product is provided in BUFR format. The netCDF version of this product contains Winds ONLY. For users interested in either ASCAT backscatter, wind or soil moisture index data in other formats, please refer to the relevant product entries available for the specific parameters. Finally, note that although the OSI SAF is identified as the originating centre for this product, it is not responsible for the processing of soil moisture index values, which is currently carried out at EUMETSAT. \n\nThese data are only available in the Data Centre as of 28/02/2011. Archived NRT data before that date can be requested from the OSI SAF helpdesk. 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If users would like to circumvent this issue, they can contact EUMETSAT to obtain the FULL version of the FCDR data that contain original count values and recalibration coefficients which allow for the computation of “toa_bidirectional_reflectance_vis” without running into the aforementioned issue.","links":[{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.EUM.DAT.MFG.MTP15EASY0200_57DEG/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.EUM.DAT.MFG.MTP15EASY0200_57DEG/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.EUM.DAT.MFG.MTP15EASY0200_57DEG","title":"MVIRI Level 1.5 Climate Data Record Release 2 - MFG - 57 degree"},{"rel":"cite-as","href":"https://doi.org/10.15770/EUM_SEC_CLM_0058","title":"Digital Object Identifier (DOI): 10.15770/EUM_SEC_CLM_0058"},{"rel":"describedby","type":"application/pdf","href":"https://user.eumetsat.int/s3/eup-strapi-media/C3_S_311b_T4_2_D4_3_MVIRI_FCDR_Release_2_PUG_1b77ce07a9.pdf","title":"MVIRI FCDR Release 2 Product User Guide"},{"rel":"describedby","type":"application/pdf","href":"https://user.eumetsat.int/s3/eup-strapi-media/C3_S_311b_T4_2_D4_3_MVIRI_FCDR_Release_2_Quality_Evaluation_Report_d0f47ca28e.pdf","title":"MVIRI FCDR Release 2 Quality Evaluation Report"},{"rel":"describedby","type":"text/html","href":"https://data.eumetsat.int/product/EO:EUM:DAT:0881","title":"MVIRI Level 1.5 Climate Data Record Release 2 - MFG - 57 degree"},{"rel":"license","type":"text/html","href":"https://user.eumetsat.int/resources/user-guides/data-registration-and-licensing#ID-Data-Licensing","title":"EUMETSAT Meteosat \u003e1hr latency \u0026 Metop"},{"rel":"license","type":"application/pdf","href":"https://www.eumetsat.int/data-policy/eumetsat-data-policy.pdf","title":"EUMETSAT Data Policy - PDF"}],"assets":{"thumbnail":{"href":"https://user.eumetsat.int/s3/eup-strapi-media/MET_7_FCDR_57degree_cfd4922a3f.png","roles":["thumbnail"],"title":"MVIRI Level 1.5 Climate Data Record Release 2 - MFG - 57 degree","type":"image/png"}},"extent":{"spatial":{"bbox":[[-81.08,-81.08,81.08,81.08]]},"temporal":{"interval":[["2006-11-01T00:00:00Z","2017-04-04T00:00:00Z"]]}},"license":"proprietary","keywords":["Atmosphere","climatology","Level 1 Data","Meteorology"],"summaries":{"constellation":["MFG"],"federation:backends":["eumetsat"],"instruments":["MVIRI"],"platform":["METEOSAT7"],"processing:level":["L1"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/processing/v1.2.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json","https://stac-extensions.github.io/scientific/v1.0.0/schema.json"],"providers":[{"name":"European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)","roles":["producer","processor","licensor","host"],"url":"https://www.eumetsat.int/"}],"created":"2026-04-30T18:31:54Z","updated":"2026-07-01T12:38:54Z","published":"2026-04-30T18:31:54Z","sci:doi":"10.15770/EUM_SEC_CLM_0058","sci:citation":"Digital Object Identifier (DOI): 10.15770/EUM_SEC_CLM_0058","dedl:short_description":"This dataset contains Release 2 of the Fundamental Climate Data Record (FCDR) of the Meteosat Visible and Infrared Imager (MVIRI). 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The final product is then derived from these two intermediate products, and includes information on wind speed, direction, height, and quality. AMVs are extracted from the FCI VIS 0.8, IR 3.8 (night only), IR 10.5, WV 6.3 and WV 7.3 channels. The AMV product is available in BUFR and netCDF format, every 30 minutes.","links":[{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.EUM.DAT.MTG.FCI-AMV-BUFR/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.EUM.DAT.MTG.FCI-AMV-BUFR/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.EUM.DAT.MTG.FCI-AMV-BUFR","title":"Atmospheric Motion Vectors (BUFR) - MTG - 0 degree"},{"rel":"describedby","type":"text/html","href":"https://data.eumetsat.int/product/EO:EUM:DAT:0998","title":"Product Description"},{"rel":"license","type":"text/html","href":"https://user.eumetsat.int/resources/user-guides/data-registration-and-licensing#ID-Data-Licensing","title":"EUMETSAT Data Policy"}],"assets":{"thumbnail":{"href":"https://user.eumetsat.int/s3/eup-strapi-media/MTG_AMV_0248b7514f.png","roles":["thumbnail"],"title":"Atmospheric Motion Vectors (BUFR) - MTG - 0 degree","type":"image/png"}},"extent":{"spatial":{"bbox":[[0,0,0,0]]},"temporal":{"interval":[["2025-01-22T00:00:00Z",null]]}},"license":"other","keywords":["MTG","Level 2 Data","AMV","Clouds","FCI","BUFR"],"summaries":{"constellation":["MTG"],"federation:backends":["eumetsat"],"instruments":["FCI"],"platform":["MTG"],"processing:level":["L2"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/processing/v1.2.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)","roles":["producer","processor","licensor","host"],"url":"https://www.eumetsat.int"}],"created":"2025-02-20T09:37:35Z","updated":"2026-05-06T10:04:22Z","published":"2025-02-20T09:37:35Z","dedl:short_description":"The Atmospheric Motion Vector product combines cloud and water vapor feature tracking across consecutive satellite images to derive wind speed, direction, height, and quality data at 30-minute intervals."},{"type":"Collection","title":"Atmospheric Motion Vectors (netCDF) - MTG - 0 degree","id":"EO.EUM.DAT.MTG.FCI-AMV-NETCDF","description":"The Atmospheric Motion Vector (AMV) product is realised by tracking clouds or water vapour features in consecutive FCI satellite images based on feature tracking between each pair of consecutive repeat cycles, leading to two intermediate AMV products for an image triplet. The final product is then derived from these two intermediate products, and includes information on wind speed, direction, height, and quality. AMVs are extracted from the FCI VIS 0.8, IR 3.8 (night only), IR 10.5, WV 6.3 and WV 7.3 channels. The AMV product is available in BUFR and netCDF format, every 30 minutes.","links":[{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.EUM.DAT.MTG.FCI-AMV-NETCDF/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.EUM.DAT.MTG.FCI-AMV-NETCDF/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.EUM.DAT.MTG.FCI-AMV-NETCDF","title":"Atmospheric Motion Vectors (netCDF) - MTG - 0 degree"},{"rel":"describedby","type":"text/html","href":"https://data.eumetsat.int/product/EO:EUM:DAT:0676","title":"Product Description"},{"rel":"license","type":"text/html","href":"https://user.eumetsat.int/resources/user-guides/data-registration-and-licensing#ID-Data-Licensing","title":"EUMETSAT Data Policy"}],"assets":{"thumbnail":{"href":"https://user.eumetsat.int/s3/eup-strapi-media/MTG_AMV_0248b7514f.png","roles":["thumbnail"],"title":"Atmospheric Motion Vectors (netCDF) - MTG - 0 degree","type":"image/png"}},"extent":{"spatial":{"bbox":[[0,0,0,0]]},"temporal":{"interval":[["2025-01-22T00:00:00Z",null]]}},"license":"other","keywords":["MTG","Level 2 Data","AMV","Clouds","FCI","netCDF"],"summaries":{"constellation":["MTG"],"federation:backends":["eumetsat"],"instruments":["FCI"],"platform":["MTG"],"processing:level":["L2"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/processing/v1.2.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)","roles":["producer","processor","licensor","host"],"url":"https://www.eumetsat.int"}],"created":"2025-02-20T09:37:35Z","updated":"2026-05-06T10:04:22Z","published":"2025-02-20T09:37:35Z","dedl:short_description":"The Atmospheric Motion Vector product tracks cloud and water vapor features across consecutive Meteosat Third Generation (MTG) satellite images at 0 degrees latitude, providing data on wind speed, direction, height, and quality in netCDF format every 30 minutes."},{"type":"Collection","title":"All Sky Radiance (BUFR) - MTG - 0 degree","id":"EO.EUM.DAT.MTG.FCI-ASR-BUFR","description":"The All-Sky Radiance (ASR) product is a segmented product that provides FCI Level 1C data statistics within processing segments referred to as Field-of-Regard (FoR). The statistics are computed on the L1C radiances (for all FCI channels), brightness temperatures (for the eight IR channels) and reflectances (for the eight visible and near-infrared channels) and include the mean value, standard deviation, minimum and maximum values within the FoR. The ASR product is available in BUFR and netCDF format, every 10 minutes, at a spatial resolution of 16x16 pixels (IR) and 32x32 pixels (VIS).","links":[{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.EUM.DAT.MTG.FCI-ASR-BUFR/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.EUM.DAT.MTG.FCI-ASR-BUFR/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.EUM.DAT.MTG.FCI-ASR-BUFR","title":"All Sky Radiance (BUFR) - MTG - 0 degree"},{"rel":"describedby","type":"text/html","href":"https://data.eumetsat.int/product/EO:EUM:DAT:0799","title":"Product Description"},{"rel":"license","type":"text/html","href":"https://user.eumetsat.int/resources/user-guides/data-registration-and-licensing#ID-Data-Licensing","title":"EUMETSAT Data Policy"}],"assets":{"thumbnail":{"href":"https://user.eumetsat.int/s3/eup-strapi-media/MTG_ASR_7c11f1ca7f.png","roles":["thumbnail"],"title":"All Sky Radiance (BUFR) - MTG - 0 degree","type":"image/png"}},"extent":{"spatial":{"bbox":[[0,0,0,0]]},"temporal":{"interval":[["2025-01-22T00:00:00Z",null]]}},"license":"other","keywords":["MTG","Level 2 Data","ASR","FCI","Radiance","BUFR"],"summaries":{"constellation":["MTG"],"federation:backends":["eumetsat"],"instruments":["FCI"],"platform":["MTG"],"processing:level":["L2"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/processing/v1.2.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)","roles":["producer","processor","licensor","host"],"url":"https://www.eumetsat.int"}],"created":"2025-02-20T09:37:35Z","updated":"2026-05-06T10:04:22Z","published":"2025-02-20T09:37:35Z","dedl:short_description":"The All-Sky Radiance product contains FCI Level 1C data statistics for each 16x16 or 32x32 pixel segment with means, stds, mins, maxes calculated from L1C radiances/temps/reflections updated every 10 minutes."},{"type":"Collection","title":"All Sky Radiance (netCDF) - MTG - 0 degree","id":"EO.EUM.DAT.MTG.FCI-ASR-NETCDF","description":"The All-Sky Radiance (ASR) product is a segmented product that provides FCI Level 1C data statistics within processing segments referred to as Field-of-Regard (FoR). The statistics are computed on the L1C radiances (for all FCI channels), brightness temperatures (for the eight IR channels) and reflectances (for the eight visible and near-infrared channels) and include the mean value, standard deviation, minimum and maximum values within the FoR. The ASR product is available in BUFR and netCDF format, every 10 minutes, at a spatial resolution of 16x16 pixels (IR) and 32x32 pixels (VIS).","links":[{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.EUM.DAT.MTG.FCI-ASR-NETCDF/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.EUM.DAT.MTG.FCI-ASR-NETCDF/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.EUM.DAT.MTG.FCI-ASR-NETCDF","title":"All Sky Radiance (netCDF) - MTG - 0 degree"},{"rel":"describedby","type":"text/html","href":"https://data.eumetsat.int/product/EO:EUM:DAT:0677","title":"Product Description"},{"rel":"license","type":"text/html","href":"https://user.eumetsat.int/resources/user-guides/data-registration-and-licensing#ID-Data-Licensing","title":"EUMETSAT Data Policy"}],"assets":{"thumbnail":{"href":"https://user.eumetsat.int/s3/eup-strapi-media/MTG_ASR_7c11f1ca7f.png","roles":["thumbnail"],"title":"All Sky Radiance (netCDF) - MTG - 0 degree","type":"image/png"}},"extent":{"spatial":{"bbox":[[0,0,0,0]]},"temporal":{"interval":[["2025-01-22T00:00:00Z",null]]}},"license":"other","keywords":["MTG","Level 2 Data","ASR","FCI","Radiance","netCDF"],"summaries":{"constellation":["MTG"],"federation:backends":["eumetsat"],"instruments":["FCI"],"platform":["MTG"],"processing:level":["L2"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/processing/v1.2.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)","roles":["producer","processor","licensor","host"],"url":"https://www.eumetsat.int"}],"created":"2025-02-20T09:37:35Z","updated":"2026-05-06T10:04:22Z","published":"2025-02-20T09:37:35Z","dedl:short_description":"The All-Sky Radiance product contains segment-level statistics for FCI Level 1C data including means, deviations, minima, maxima across various channels within each 16/32 pixel field-of-regard area."},{"type":"Collection","title":"Cloud Mask - MTG - 0 degree","id":"EO.EUM.DAT.MTG.FCI-CLM","description":"The central aim of the cloud mask (CLM) product is to identify cloudy and cloud free FCI Level 1c pixels with high confidence. The product also provides information on the presence of snow/sea ice, volcanic ash and dust. This information is crucial both for spatiotemporal analyses of the cloud coverage and for the subsequent retrieval of other meteorological products that are only valid for cloudy (e.g. cloud properties) or clear pixels (e.g. clear sky reflectance maps or global instability indices). The algorithm is based on multispectral threshold techniques applied to each pixel of the image. CLM is available in netCDF and GRIB format, every 10 minutes, at a spatial resolution of 2 km at nadir.","links":[{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.EUM.DAT.MTG.FCI-CLM/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.EUM.DAT.MTG.FCI-CLM/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.EUM.DAT.MTG.FCI-CLM","title":"Cloud Mask - MTG - 0 degree"},{"rel":"describedby","type":"text/html","href":"https://data.eumetsat.int/product/EO:EUM:DAT:0678","title":"Product Description"},{"rel":"license","type":"text/html","href":"https://user.eumetsat.int/resources/user-guides/data-registration-and-licensing#ID-Data-Licensing","title":"EUMETSAT Data Policy"},{"rel":"example","type":"application/x-ipynb+json","href":"https://raw.githubusercontent.com/destination-earth/DestinE-DataLake-Lab/refs/heads/main/HDA/EUM_data/DEDL-HDA-EO.EUM.DAT.MTG.ipynb","title":"MTG FCI CLM through HDA Examples","application:type":"jupyter-notebook","application:embedded":true,"application:language":"Python"}],"assets":{"thumbnail":{"href":"https://user.eumetsat.int/s3/eup-strapi-media/MTG_CLM_78821c04d7.png","roles":["thumbnail"],"title":"Cloud Mask - MTG - 0 degree","type":"image/png"}},"extent":{"spatial":{"bbox":[[0,0,0,0]]},"temporal":{"interval":[["2025-01-22T00:00:00Z",null]]}},"license":"other","keywords":["MTG","Level 2 Data","CLM","Clouds","FCI"],"summaries":{"constellation":["MTG"],"federation:backends":["eumetsat"],"instruments":["FCI"],"platform":["MTG"],"processing:level":["L2"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/processing/v1.2.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json","https://stac-extensions.github.io/application/v0.1.0/schema.json"],"providers":[{"name":"European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)","roles":["producer","processor","licensor","host"],"url":"https://www.eumetsat.int"}],"created":"2025-02-20T09:37:35Z","updated":"2026-05-06T10:04:22Z","published":"2025-02-20T09:37:35Z","dedl:short_description":"The Cloud Mask (CLM) product identifies cloudy/cloud-free pixels with high confidence from MTG data at 0 degrees, providing additional information on snow/ice, volcanic ash, and dust, suitable for various applications including cloud analysis and meteorological retrievals."},{"type":"Collection","title":"Global Instability Indices - MTG - 0 degree","id":"EO.EUM.DAT.MTG.FCI-GII","description":"The Global Instability Index (GII) product provides information about instability of the atmosphere and thus can identify regions of convective potential. GII is a segmented product that uses an optimal estimation scheme to fit clear-sky vertical profiles of temperature and humidity, constrained by NWP forecast products, to FCI observations in the seven channels WV6.3, WV7.3, IR8.7, IR9.7, IR10.5, IR12.3, and IR13.3. The retrieved profiles are then used to compute atmospheric instability indices: Lifted Index, K Index, Layer Precipitable Water, Total Precipitable Water. 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These are closely related to other similar space-born instruments. The definition of a group is shared by LI, GLM and ISS-LIS groups: collections of pixel-based lightning events that are acquired within the same acquisition frame and are spatially clustered. LI groups provide users with information about the time-slicing imaging (over the LI acquisition time, ie one millisecond) of lightning optical emissions. When comparing LI groups with either GLM or ISS-LIS groups, users must consider the differences in design between instruments, such as integration time and spatial sampling/resolution. Both GLM and ISS-LIS acquire over two milliseconds. When observing the same storm, this difference in design can potentially create considerable differences in the total number of groups, as well as differences between the acquisition times of the groups. In addition, differences will be found also for the geolocation of groups. 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Note that as part of this extension, the entire Metop-C record was reprocessed with improved calibration coefficients from PATMOS-X.","links":[{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.EUM.DAT.MULT.AVHGAC1C0100/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.EUM.DAT.MULT.AVHGAC1C0100/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.EUM.DAT.MULT.AVHGAC1C0100","title":"AVHRR Fundamental Data Record - Release 1 - Multimission"},{"rel":"cite-as","href":"https://doi.org/10.15770/EUM_SEC_CLM_0060","title":"Digital Object Identifier (DOI): 10.15770/EUM_SEC_CLM_0060"},{"rel":"describedby","type":"application/pdf","href":"https://user.eumetsat.int/s3/eup-strapi-media/Py_GAC_FDR_ATBD_e7d9b988c2.pdf","title":"PyGAC FDR ATBD"},{"rel":"describedby","type":"application/pdf","href":"https://user.eumetsat.int/s3/eup-strapi-media/Py_GAC_AVHRR_FDR_Release_1_Product_Users_Guide_89d400fc49.pdf","title":"PyGAC AVHRR FDR Release 1 Product Users Guide"},{"rel":"describedby","type":"application/pdf","href":"https://user.eumetsat.int/s3/eup-strapi-media/Py_GAC_AVHRR_FDR_Release_1_Validation_Report_e5b0df726e.pdf","title":"PyGAC AVHRR FDR Release 1 Validation Report"},{"rel":"describedby","type":"application/pdf","href":"https://user.eumetsat.int/s3/eup-strapi-media/Py_GAC_AVHRR_FDR_Release_1_1_extension_Report_ID_1426909_v1_B_01b495ba70.pdf","title":"PyGAC AVHRR FDR Release 1.1 extension Report"},{"rel":"describedby","type":"text/html","href":"https://data.eumetsat.int/product/EO:EUM:DAT:0862","title":"AVHRR Fundamental Data Record - Release 1 - Multimission"}],"assets":{"thumbnail":{"href":"https://user.eumetsat.int/s3/eup-strapi-media/pygac_fdr_r1_2_a2d95338d0.png","roles":["thumbnail"],"title":"AVHRR Fundamental Data Record - Release 1 - Multimission","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1978-11-05T00:00:00Z","2024-06-30T00:00:00Z"]]}},"license":"CC-BY-4.0","keywords":["Atmosphere","climatology","Level 1 Data","Meteorology"],"summaries":{"constellation":["NOAA","Metop","TIROS"],"federation:backends":["eumetsat"],"instruments":["AVHRR"],"platform":["NOAA-15","Metop-B","NOAA-18","Metop-A","NOAA-19","NOAA-16","NOAA-17","NOAA-14","NOAA-12","NOAA-11","Metop-C","NOAA-10","NOAA-09","NOAA-07","NOAA-06","TIROS-N","NOAA-08"],"processing:level":["L1"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/processing/v1.2.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json","https://stac-extensions.github.io/scientific/v1.0.0/schema.json"],"providers":[{"name":"European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)","roles":["producer","processor","licensor","host"],"url":"https://www.eumetsat.int/"}],"created":"2026-04-30T18:31:54Z","updated":"2026-07-01T12:38:54Z","published":"2026-04-30T18:31:54Z","sci:doi":"10.15770/EUM_SEC_CLM_0060","sci:citation":"Digital Object Identifier (DOI): 10.15770/EUM_SEC_CLM_0060","dedl:short_description":"AVHRR Fundamental Data Record containing reflectance and brightness temperature data from 1978-2021, derived from 17 AVHRR instruments onboard NOAA and EUMETSAT satellites. Calibration uses PATMOS-X coefficients for visible channels and nominal operational calibration for infrared channels. Extended coverage includes 2022-June 2024 data from Metop-B, Metop-C, NOAA-15, NOAA-18, and NOAA-19, including reprocessing of Metop-C records with updated PATMOS-X coefficients."},{"type":"Collection","title":"HIRS Level 1B - Metop - Global","id":"EO.EUM.DAT.MULT.HIRSL1","description":"The High Resolution Infrared Sounder (HIRS) operates at 20 channels (19 channels in the infrared and one in the visible). Its main purpose is to provide input for the vertical temperature and humidity profile retrievals. 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The FCDR was generated in the EU Horizon2020 Fidelity and uncertainty in climate data records from Earth Observations (FIDUCEO) project and as further elaborated in the Product User Guide, the FCDR files are provided in FIDUCEO EASY netCDF4 formats.","links":[{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.EUM.DAT.MULT.MHSMWHS0100/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.EUM.DAT.MULT.MHSMWHS0100/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.EUM.DAT.MULT.MHSMWHS0100","title":"MHS Microwave Humidity Sounder Climate Data Record Release 1 - Metop and NOAA"},{"rel":"describedby","type":"application/pdf","href":"https://user.eumetsat.int/s3/eup-strapi-media/pdf_mhs_fcdr_pug_c054e15879.pdf","title":"Product user guide - Microwave FCDR release 4.1"},{"rel":"describedby","type":"application/pdf","href":"https://user.eumetsat.int/s3/eup-strapi-media/pdf_d4_6_ass_fid_fcdr_5c08d60543.pdf","title":"Meteorological assessment of consistency stability and uncertainty of FIDUCEO FCDRs"},{"rel":"describedby","type":"application/pdf","href":"https://user.eumetsat.int/s3/eup-strapi-media/pdf_d2_2_mw_fcdr_f98a9e3b07.pdf","title":"D2.2(Microwave): Report on the MW FCDR: Uncertainty"},{"rel":"cite-as","href":"https://doi.org/10.15770/EUM_SEC_CLM_0045","title":"Digital Object Identifier (DOI): 10.15770/EUM_SEC_CLM_0045"},{"rel":"describedby","type":"text/html","href":"https://data.eumetsat.int/product/EO:EUM:DAT:0305","title":"MHS Microwave Humidity Sounder Climate Data Record Release 1 - Metop and NOAA"}],"assets":{"thumbnail":{"href":"https://user.eumetsat.int/s3/eup-strapi-media/mhs_fcdr_f64bc9121d.png","roles":["thumbnail"],"title":"MHS Microwave Humidity Sounder Climate Data Record Release 1 - Metop and NOAA","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2005-08-30T00:00:00Z","2017-12-31T00:00:00Z"]]}},"license":"CC-BY-4.0","keywords":["Atmosphere","climatology","Fundamental Climate Data Record","Level 1 Data","Meteorology"],"summaries":{"constellation":["NOAA","Metop"],"federation:backends":["eumetsat"],"platform":["NOAA-18","Metop-A","NOAA-19","Metop-B"],"processing:level":["L1"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/processing/v1.2.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json","https://stac-extensions.github.io/scientific/v1.0.0/schema.json"],"providers":[{"name":"European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)","roles":["producer","processor","licensor","host"],"url":"https://www.eumetsat.int/"}],"created":"2026-04-30T18:31:54Z","updated":"2026-07-01T12:38:54Z","published":"2026-04-30T18:31:54Z","sci:doi":"10.15770/EUM_SEC_CLM_0045","sci:citation":"Digital Object Identifier (DOI): 10.15770/EUM_SEC_CLM_0045","dedl:short_description":"The Fundamental Climate Data Record (FCDR) Release 1 for the Microwave Humidity Sounder (MHS) onboard NOAA's N18/N19 and EUMETSAT's Metop-A/B satellites, providing recalibrated brightness temperatures with uncertainties and error correlations in FIDUCEO EASY netCDF4 format."},{"type":"Collection","title":"CLARA-A3: CM SAF cLoud, Albedo and surface RAdiation dataset from AVHRR data - Edition 3","id":"EO.EUM.DAT.MULT.OLR-CM-11342-CM-6321","description":"The CLARA-A3 record provides cloud properties and radiation parameters derived from the AVHRR sensor onboard polar orbiting NOAA and METOP satellites. 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Furthermore, a range of radiation products are included in CLARA-A3: surface black-sky, white-sky and blue-sky albedo; surface downwelling short- and longwave radiation as well as surface net radiation; top-of-atmosphere (TOA) upwelling short- and longwave radiation. Cloud products are available as monthly and daily averages and histograms, as well as daily resampled global products (Level 2b) for individual satellites. Surface albedo is presented as monthly and pentad (5 day) averages. Surface and TOA radiation products are provided as daily and monthly averages. All averages are available on a 0.25° x 0.25° global grid. Surface albedo and selected cloud products are also provided on two equal area grids with a resolution of 25 km x 25 km covering the polar regions. Daily resampled cloud products (level 2b) are provided in a global grid with a resolution of 0.05°x0.05°. CLARA-A3 features a comprehensive set of documentation including User Manuals, Validation Reports and Algorithms Theoretical Baseline Documents. This is a Thematic Climate Data Record (TCDR).","links":[{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.EUM.DAT.MULT.OLR-CM-11342-CM-6321/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.EUM.DAT.MULT.OLR-CM-11342-CM-6321/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.EUM.DAT.MULT.OLR-CM-11342-CM-6321","title":"CLARA-A3: CM SAF cLoud, Albedo and surface RAdiation dataset from AVHRR data - Edition 3"},{"rel":"cite-as","href":"https://doi.org/10.5676/EUM_SAF_CM/CLARA_AVHRR/V003","title":"DOI page with documentation"},{"rel":"describedby","type":"text/html","href":"https://data.eumetsat.int/product/EO:EUM:DAT:0874","title":"CLARA-A3: CM SAF cLoud, Albedo and surface RAdiation dataset from AVHRR data - Edition 3"}],"assets":{"thumbnail":{"href":"https://user.eumetsat.int/s3/eup-strapi-media/clara_a3_example_image_27da6c4054.png","roles":["thumbnail"],"title":"CLARA-A3: CM SAF cLoud, Albedo and surface RAdiation dataset from AVHRR data - Edition 3","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1979-01-01T00:00:00Z",null]]}},"license":"CC-BY-4.0","keywords":["Atmosphere","climatology","Clouds","Meteorology","Radiation","Surface radiation \u0026 albedo","Surface radiation albedo"],"summaries":{"constellation":["NOAA","Metop"],"federation:backends":["eumetsat"],"instruments":["AVHRR"],"platform":["NOAA-07","NOAA-09","NOAA-11","NOAA-12","NOAA-14","NOAA-15","NOAA-16","NOAA-17","NOAA-18","NOAA-19","Metop-A","Metop-B","Metop-C"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/processing/v1.2.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json","https://stac-extensions.github.io/scientific/v1.0.0/schema.json"],"providers":[{"name":"Climate Monitoring Satellite Application Facility (CM SAF - EUMETSAT)","roles":["producer","processor","licensor"],"url":"http://cm-saf.eumetsat.int"}],"created":"2026-04-30T18:31:54Z","updated":"2026-07-01T12:38:54Z","published":"2026-04-30T18:31:54Z","sci:doi":"10.5676/EUM_SAF_CM/CLARA_AVHRR/V003","sci:citation":"DOI page with documentation","dedl:short_description":"CLARA-A3 contains cloud properties and radiation parameters derived from AVHRR sensors on NOAA/METOP satellites, spanning 1979-2020 as CDR and ongoing ICDR with 10-day latency. It includes various cloud products such as cloud masks, thermodynamic phases, optical thicknesses, and radiative fluxes at the surface and TOA, all gridded globally or over polar regions. Products include daily/monthly/pentadal averages/histograms/resamples, supported by extensive documentation."},{"type":"Collection","title":"GIRAFE v1: CM SAF Global Interpolated RAinFall Estimation version 1","id":"EO.EUM.DAT.MULT.PMW_IR-GIRAFE_PRECIP-CDR-V001","description":"The GIRAFE v1 climate data record (CDR) provides precipitation estimates derived from a combination of passive microwave (PMW) observations onboard polar orbiting satellites and infrared (IR) observations onboard geostationary satellites. GIRAFE v1 covers the time period 2002/01/01 until 2022/12/31. The PMW input to GIRAFE v1 is from various microwave imager and sounder instruments. Precipitation rate estimates are retrieved from the observed PMW brightness temperatures by precipitation retrieval algorithms HOAPS, PNPR-CLIM*, and PRPS. The resulting archives of instantaneous precipitation rate estimates are homogenized using quantile mapping. The IR input to GIRAFE comes from the five geostationary positions forming the Geo-Ring, providing observations along all geographical longitudes. The spatially and temporally highly resolved IR input is trained to detect the occurrence of precipitation using the PMW-based instantaneous precipitation rate estimates. Conditional precipitation rates are computed based on PMW observations only. At latitudes higher than 55°N/S where Geo-Ring IR pixels are extremely distorted, GIRAFE v1 relies only on the PMW input. GIRAFE v1 is a gridded product which is available globally at a spatial resolution of 1° x 1°  and at a temporal resolution of 24h as accumulated precipitation computed from the (IR-based) fraction of precipitation and the conditional precipitation rate. Additionally, 1° x 1° monthly mean values of the daily accumulated precipitation are provided. The daily accumulated precipitation features a dedicated sampling uncertainty at the same 1° x 1° x 24 h resolution which is based on the analysis of decorrelation scales in space and time in the IR-based  precipitation fields. This is a Thematic Climate Data Record (TCDR).\n*The PNPR-CLIM algorithm has been developed by CNR-ISAC in the C3S_312b_Lot1 Copernicus project.","links":[{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.EUM.DAT.MULT.PMW_IR-GIRAFE_PRECIP-CDR-V001/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.EUM.DAT.MULT.PMW_IR-GIRAFE_PRECIP-CDR-V001/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.EUM.DAT.MULT.PMW_IR-GIRAFE_PRECIP-CDR-V001","title":"GIRAFE v1: CM SAF Global Interpolated RAinFall Estimation version 1"},{"rel":"cite-as","href":"https://doi.org/10.5676/EUM_SAF_CM/GIRAFE/V001","title":"CM SAF landing page for GIRAFE v1: CM SAF Global Interpolated RAinFall Estimation version 1"},{"rel":"describedby","type":"text/html","href":"https://data.eumetsat.int/product/EO:EUM:DAT:0921","title":"GIRAFE v1: CM SAF Global Interpolated RAinFall Estimation version 1"}],"assets":{"thumbnail":{"href":"https://user.eumetsat.int/s3/eup-strapi-media/girafe_bd1b6a610b.png","roles":["thumbnail"],"title":"GIRAFE v1: CM SAF Global Interpolated RAinFall Estimation version 1","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-01-01T00:00:00Z","2022-12-31T00:00:00Z"]]}},"license":"CC-BY-4.0","keywords":["Atmosphere","Precipitation","Climate Data Record","Level 3 Data","GIRAFE","CDR","MFG","MSG","MULTIMISSION"],"summaries":{"constellation":["GOES","Himawari","MFG","MSG"],"federation:backends":["eumetsat"],"platform":["GOES-12","GOES-13","GOES-14","GOES-15","GOES-16","GOES-17","GOES-18","Himawari-6","Himawari-7","Himawari-8","Himawari-9","Meteosat-3","Meteosat-4","Meteosat-5","Meteosat-6","Meteosat-7","Meteosat-8","Meteosat-9","Meteosat-10","Meteosat-11"],"processing:level":["L3"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/processing/v1.2.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json","https://stac-extensions.github.io/scientific/v1.0.0/schema.json"],"providers":[{"name":"European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)","roles":["producer","processor","licensor","host"],"url":"https://www.eumetsat.int"}],"created":"2025-12-15T14:39:42Z","updated":"2026-06-19T14:48:29Z","published":"2025-12-15T14:39:42Z","sci:doi":"10.5676/EUM_SAF_CM/GIRAFE/V001","sci:citation":"CM SAF landing page for GIRAFE v1: CM SAF Global Interpolated RAinFall Estimation version 1","dedl:short_description":"GIRAFE v1 climate data record combines PMW and IR satellite observations to estimate global precipitation rates from 2002-2022, offering 1°x1° resolution and 24-hour accumulation, including sampling uncertainties."},{"type":"Collection","title":"CM SAF Passive Microwave Upper Tropospheric Humidity (UTH) Data Record - Edition 2","id":"EO.EUM.DAT.MULT.UTH-CM-14712","description":"The second edition of the CM SAF Upper Tropospheric Humidity (UTH) is a satellite-based climate data record. It is a near-global 1°x1° latitude-longitude dataset that is produced with both hourly and daily time steps. The dataset is based on data from twelve passive microwave (MW) sounders operating at 183 GHz in polar orbit that are combined into a single time series covering the period 6 July 1994 to 31 December 2018. The UTH provided typically represents a broad atmospheric layer between 500 and 200 hPa. However, the exact height of this layer depends on the atmospheric conditions at the time of the observation. An optional fixed layer approximation adjustment is supplied that users can apply to provide an estimated mean relative humidity (RH) between ±60° latitude for a fixed layer between 500 and 200 hPa (mean_RH). However, users are advised to take care using this correction, especially outside of the tropics where the mean_RH is of lower quality. Users are also advised to take care using UTH observations above ±60° latitude as the retrieval is sometimes less reliable at high latitudes. Further information describing the dataset in detail can be found in the available product documentation. This is a Thematic Climate Data Record (TCDR).","links":[{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.EUM.DAT.MULT.UTH-CM-14712/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.EUM.DAT.MULT.UTH-CM-14712/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.EUM.DAT.MULT.UTH-CM-14712","title":"CM SAF Passive Microwave Upper Tropospheric Humidity (UTH) Data Record - Edition 2"},{"rel":"cite-as","href":"https://doi.org/10.5676/EUM_SAF_CM/UTH/V002","title":"Digital Object Identifier (DOI): 10.5676/EUM_SAF_CM/UTH/V002"},{"rel":"describedby","type":"text/html","href":"https://data.eumetsat.int/product/EO:EUM:DAT:0910","title":"CM SAF Passive Microwave Upper Tropospheric Humidity (UTH) Data Record - Edition 2"}],"assets":{"thumbnail":{"href":"https://user.eumetsat.int/s3/eup-strapi-media/UTH_example_8d907bcdb0.png","roles":["thumbnail"],"title":"CM SAF Passive Microwave Upper Tropospheric Humidity (UTH) Data Record - Edition 2","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1994-07-05T00:00:00Z","2018-12-31T00:00:00Z"]]}},"license":"CC-BY-4.0","keywords":["Atmosphere","climatology","Level 2 Data","Level 3 Data","Meteorology","Temperature \u0026 humidity","Temperature humidity"],"summaries":{"constellation":["Metop","JPSS","NOAA","DMSP"],"federation:backends":["eumetsat"],"platform":["DMSP-F11","DMSP-F12","DMSP-F14","DMSP-F15","NOAA-15","NOAA-16","NOAA-17","NOAA-18","NOAA-19","Metop-A","Metop-B","Metop-C","Suomi-NPP"],"processing:level":["L2"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/processing/v1.2.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json","https://stac-extensions.github.io/scientific/v1.0.0/schema.json"],"providers":[{"name":"Climate Monitoring Satellite Application Facility (CM SAF - EUMETSAT)","roles":["producer","processor","licensor"],"url":"http://cm-saf.eumetsat.int"}],"created":"2026-04-30T18:31:54Z","updated":"2026-07-01T12:38:54Z","published":"2026-04-30T18:31:54Z","sci:doi":"10.5676/EUM_SAF_CM/UTH/V002","sci:citation":"Digital Object Identifier (DOI): 10.5676/EUM_SAF_CM/UTH/V002","dedl:short_description":"CM SAF Upper Tropospheric Humidity (UTH), Edition 2, provides near-global 1°x1° gridded upper-troposphere humidity data every hour/day from 12 passive MW sounders over 24 years (July 1994 - Dec 2018)."},{"type":"Collection","title":"SLSTR Level 2 Aerosol Optical Depth - Sentinel-3","id":"EO.EUM.DAT.SENTINEL-3.AOD","description":"The Copernicus Sentinel-3 (S3) NRT AOD product quantifies the abundance of aerosol particles, and monitors their global distribution \u0026 long-range transport, at the scale of 9.5 x 9.5 km2. All observations are made available in less than 3 hours from the SLSTR observation sensing time. It is only applicable during daytime.\nThe current S3 NRT AOD product is the first release as Baseline Collection 1.\nUsers are advised to bear in mind the different maturity statuses between ocean and land surfaces where:\n• AOD Ocean is pre-operational\n• AOD Land is currently demonstrational\nFurther improvements to the S3 NRT AOD product are planned, including the optimization of the spectral constraints over land continents in case of unfavourable dual-view geometry, a comprehensive global validation, and additional updates following feedback from selected Sentinel-3 NRT atmosphere experts.","links":[{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.EUM.DAT.SENTINEL-3.AOD/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.EUM.DAT.SENTINEL-3.AOD/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.EUM.DAT.SENTINEL-3.AOD","title":"SLSTR Level 2 Aerosol Optical Depth - Sentinel-3"},{"rel":"license","href":"https://www.eumetsat.int/data-policy/eumetsat-data-policy.pdf","title":"EUMETSAT Data Policy"},{"rel":"describedby","type":"text/html","href":"https://data.eumetsat.int/product/EO:EUM:DAT:0416","title":"SLSTR Level 2 Aerosol Optical Depth - Sentinel-3"}],"assets":{"thumbnail":{"href":"https://user.eumetsat.int/s3/eup-strapi-media/S3_NRT_AOD_0da7853494.png","roles":["thumbnail"],"title":"Sentinel-3 AOD NRT","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2021-02-14T00:00:00Z",null]]}},"license":"other","keywords":["Aerosol","Level 2 Data","AOD","Aerosol Optical Depth","Atmosphere","Air Pollution"],"summaries":{"constellation":["Sentinel-3"],"federation:backends":["eumetsat","external_fdp","creodias"],"instruments":["SLSTR"],"platform":["Sentinel-3A","Sentinel-3B"],"processing:level":["L2"]},"item_assets":{"thumbnail":{"description":"An averaged, decimated preview image in PNG format. Single polarisation products are represented with a grey scale image. Dual polarisation products are represented by a single composite colour image in RGB with the red channel (R) representing the  co-polarisation VV or HH), the green channel (G) represents the cross-polarisation (VH or HV) and the blue channel (B) represents the ratio of the cross an co-polarisations.","roles":["thumbnail"],"title":"Preview Image","type":"image/png"}},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/processing/v1.2.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)","roles":["producer","processor","licensor","host"],"url":"https://www.eumetsat.int/"}],"created":"2023-08-11T18:04:28Z","updated":"2026-05-06T10:04:22Z","published":"2023-08-11T18:04:28Z","dedl:short_description":"This dataset provides near-real-time Sentinel-3 data on aerosol optical depth with a resolution of approximately 10km², covering both daytimes globally but has varying levels of maturity for oceanic versus terrestrial applications."},{"type":"Collection","title":"SLSTR Level 2 Fire Radiative Power - Sentinel 3","id":"EO.EUM.DAT.SENTINEL-3.FRP","description":"The Copernicus NRT S3 FRP product identifies the location, and quantifies the radiative power, of any hotspot present on land and ocean Earth surfaces, that radiates a heating signal within a pixel size of 1 km2\nAll threatening hotspots are identified and characterised within three hours from SLSTR observation sensing time.\nThe current version of the NRT S3 FRP processor is mainly applicable during the night while only a few daytime granules, with non-saturated background (i.e. no fires) radiance, are processed at this stage. The NRT S3 FRP product will become operational after a higher level of quality and maturity is reached, including the full processing of granules during daytime, a comprehensive global validation, and positive feedback by experts and users.","links":[{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.EUM.DAT.SENTINEL-3.FRP/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.EUM.DAT.SENTINEL-3.FRP/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.EUM.DAT.SENTINEL-3.FRP","title":"SLSTR Level 2 Fire Radiative Power - Sentinel 3"},{"rel":"license","type":"application/pdf","href":"https://www.eumetsat.int/data-policy/eumetsat-data-policy.pdf","title":"EUMETSAT Data Policy"},{"rel":"describedby","type":"text/html","href":"https://data.eumetsat.int/product/EO:EUM:DAT:0417","title":"SLSTR Level 2 Fire Radiative Power - Sentinel-3"}],"assets":{"thumbnail":{"href":"https://user.eumetsat.int/s3/eup-strapi-media/s3afrpmwirnight_1e5f31ae6b.png","roles":["thumbnail"],"title":"SLSTR Level 2 Fire Radiative Power - Sentinel 3","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2020-08-08T00:00:00Z",null]]}},"license":"other","keywords":["Fire","Level 2 Data","FRP","Fire Radiative Power","Wildfires"],"summaries":{"constellation":["Sentinel-3"],"federation:backends":["eumetsat","external_fdp","creodias"],"instruments":["SLSTR"],"platform":["Sentinel-3A","Sentinel-3B"],"processing:level":["L2"]},"item_assets":{"thumbnail":{"description":"An averaged, decimated preview image in PNG format. 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Level 1 products are calibrated Top Of Atmosphere radiance values at OLCI 21 spectral bands. Radiances are computed from the instrument digital counts by applying geo-referencing, spectral calibration adjusted for wavelength evolution with time, radiometric processing (non-linearity correction, smear correction, dark offset correction, absolute calibration adjusted for radiometric evolution with time), and the correction for straylight effects in OLCI cameras’ spectrometer and ground imager. Per-pixel uncertainties are included in the reprocessed timeseries.  Additionally, spatial resampling of OLCI pixels to the 'ideal' cross-track grid, initial pixel classification, and annotation at tie points with auxiliary meteorological data and acquisition geometry are provided. The radiance parameters are accompanied by error estimate parameters. 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Available in two formats - Standard (with 1Hz \u0026 20Hz measurements) and Reduced (only 1Hz measurements) - these products support analysis of sea states and creation of multi-altimeter added-value products."},{"type":"Collection","title":"Poseidon-4 Level 3 Altimetry Low Resolution (baseline DT2024) - Sentinel-6 - Reprocessed","id":"EO.EUM.DAT.SENTINEL-6.P4_3__LR","description":"This is a reprocessed Level-3 (L3) dataset at DT-2024 baseline, which covers from 29 December 2021 to 31 December 2023.\nThe L3 LR products contain global valid 1Hz subsampled along-track sea level anomaly multi-mission intercalibrated (orbit error and long wave-length error) over water, based on LR Level 2P products.\nThe reference mission used for the altimeter inter-calibration processing is Topex/Poseidon between 01-01-1993 and 23-04-2002, Jason-1 between 24-04-2002 and 18-10-2008, OSTM/Jason-2 between 19-10-2008 and 25-06-2016, Jason-3 between 25-06-2016 and 09-02-2022, and Sentinel-6A from 10-02-2022.\n\nThese products are suitable for users seeking information on downstream sea level added value products.\nSentinel-6 is part of a series of Sentinel satellites, under the umbrella of the EU Copernicus programme. 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Assessing wildfire danger therefore requires indicators that combine the effects of multiple meteorological variables into a single measure of fire-conducive conditions. The Fire Weather Index (FWI, Van Wagner 1987; Wotton 2009) application fulfils this need by providing information on wildfire danger globally based on DestinE Climate Digital Twin (Climate DT) model data. The application enables users to assess the temporal evolution, spatial distribution and long-term changes of fire weather conditions and associated wildfire risk. More information can be found in the [User Guide](https://platform.destine.eu/docs/climate-dt-user-guide/doc/index.html).\n\nThe FWI application is based on the Canadian Forest Fire Weather Index System, one of the most widely used operational wildfire danger rating systems worldwide. The system combines temperature, relative humidity, wind speed and precipitation to estimate the moisture content of different fuel layers and the potential behaviour of wildfires. The output includes the Fire Weather Index (FWI), which is calculated using the Fine Fuel Moisture Code (FFMC), Duff Moisture Code (DMC), Drought Code (DC), Initial Spread Index (ISI), and Buildup Index (BUI). Together, these components describe the likelihood of ignition, fuel availability, and potential fire intensity.\n\nBy applying the Fire Weather Index System consistently to Climate DT simulations, the application provides a harmonized assessment of current and future fire weather conditions, enabling the analysis of extreme fire seasons, climate-driven trends in wildfire danger and regional differences in fire-prone environments across Europe.\n\nVan Wagner, C. E. (1987). Development and structure of the Canadian Forest Fire Weather Index System. Forestry Technical Report 35, Canadian Forestry Service.\n\nWotton, B. M. (2009). Interpreting and using outputs from the Canadian Forest Fire Danger Rating System in research applications. 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(2018). EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification [Data set]. In EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification (Vol. 12, Number 7, pp. 2217–2226). Zenodo. https://doi.org/10.5281/zenodo.7711096","sci:publications":[{"doi":"10.1109/JSTARS.2019.2918242","citation":"P. Helber, B. Bischke, A. Dengel and D. Borth, \"EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification,\" in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 12, no. 7, pp. 2217-2226, July 2019, doi: 10.1109/JSTARS.2019.2918242."},{"doi":"10.1109/IGARSS.2018.8519248","citation":"P. Helber, B. Bischke, A. Dengel and D. 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The dataset is based on Sentinel-2 satellite imagery covering 13 spectral bands and consists of 10 LULC classes with a total of 27,000 labeled and geo-referenced images."},{"type":"Collection","title":"Available beds in hospitals by NUTS 2 region","id":"STAT.EUSTAT.DAT.AVAILABLE_BEDS_HOSPITALS_NUTS2","description":"Non-expenditure healthcare data provide information on institutions providing healthcare in countries, on resources used and on output produced in the framework of healthcare provision. \nData on healthcare form a major element of public health information as they describe the capacities available for different types of healthcare provision as well as potential 'bottlenecks' observed. The quantity and quality of healthcare services provided and the work sharing established between the different institutions are a subject of ongoing debate in all countries. Sustainability - continuously providing the necessary monetary and personal resources needed - and meeting the challenges of ageing societies are the primary perspectives used when analysing and using the data. \nThe resource-related data refer to both human and technical resources, i.e. they relate to: \n- Health care staff: 'manpower' active in the health care sector (doctors, dentists, nurses, etc.);\n- Heath workforce migration: migration movements of doctors and nurses;\n- Healthcare facilities: technical capacity dimensions (hospital beds, beds in nursing and residential care facilities, etc.).\nThe output-related data ('activities') refer to contacts between patients and the healthcare system, and to the treatment thereby received. Data are available for hospital discharges of in-patients and day cases, average length of stay of in-patients, consultations with medical professionals, and medical procedures performed in hospitals.\nAnnual national and regional data are provided in absolute numbers, percentages, and in population-standardised rates (per 100 000 inhabitants).\nWherever applicable, the definitions and classifications of the System of Health Accounts (SHA) are followed, e.g. International Classification for Health Accounts - Providers of health care (ICHA-HP). For hospital discharges, the International Shortlist for Hospital Morbidity Tabulation (ISHMT) is used. Surgical procedures are classified according to a shortlist mapped to ICD-9-CM.\nThese healthcare data are largely based on administrative data sources in the countries. Therefore, they reflect the country-specific way of organising healthcare and may not always be completely comparable.","links":[{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/STAT.EUSTAT.DAT.AVAILABLE_BEDS_HOSPITALS_NUTS2/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/STAT.EUSTAT.DAT.AVAILABLE_BEDS_HOSPITALS_NUTS2/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/STAT.EUSTAT.DAT.AVAILABLE_BEDS_HOSPITALS_NUTS2","title":"Available beds in hospitals by NUTS 2 region"},{"rel":"license","type":"text/html","href":"http://ec.europa.eu/eurostat/statistics-explained/index.php/Copyright/licence_policy","title":"Eurostat - Copyright notice and free re-use of data"}],"assets":{"data":{"href":"https://ec.europa.eu/eurostat/api/dissemination/sdmx/2.1/data/hlth_rs_bdsrg2?format=tsv","roles":["data"],"title":"Eurostat hlth_rs_bdsrg2","type":"text/tab-separated-values"}},"extent":{"spatial":{"bbox":[[-69.103165,-26.018616,61.78629,80.77476]]},"temporal":{"interval":[["2013-01-01T00:00:00Z","2022-12-31T23:59:59Z"]]}},"license":"other","keywords":["Eurostat","Health care","Hospital","Bed","Health"],"summaries":{"federation:backends":["internal_fdp"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"Eurostat","roles":["producer","licensor"],"url":"https://ec.europa.eu/eurostat"},{"name":"Destination Earth Data Lake (DEDL)","roles":["host","processor"],"url":"https://data.destination-earth.eu/"}],"created":"2025-02-10T15:42:09Z","updated":"2026-04-24T10:24:59Z","published":"2025-02-10T15:42:09Z","dedl:short_description":"This dataset contains annual non-expenditure healthcare data describing hospital bed availability across European regions, along with related healthcare personnel, facility capacity, patient interactions, treatments, and outcomes per capita."},{"type":"Collection","title":"Bathing sites with excellent water quality by location","id":"STAT.EUSTAT.DAT.BATHING_SITES_WATER_QUALITY","description":"The indicator measures the number and proportion of coastal and inland bathing sites with excellent water quality. The indicator assessment is based on microbiological parameters (intestinal enterococci and Escherichia coli). The new Bathing Water Directive requires Member States to identify and assess the quality of all inland and marine bathing waters and to classify these waters as ‘poor’, ‘sufficient’, ‘good’ or ‘excellent’.","links":[{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/STAT.EUSTAT.DAT.BATHING_SITES_WATER_QUALITY/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/STAT.EUSTAT.DAT.BATHING_SITES_WATER_QUALITY/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/STAT.EUSTAT.DAT.BATHING_SITES_WATER_QUALITY","title":"Bathing sites with excellent water quality by location"},{"rel":"license","type":"text/html","href":"http://ec.europa.eu/eurostat/statistics-explained/index.php/Copyright/licence_policy","title":"Eurostat - Copyright notice and free re-use of data"}],"assets":{"data":{"href":"https://ec.europa.eu/eurostat/api/dissemination/sdmx/2.1/data/sdg_14_40?format=tsv","roles":["data"],"title":"Eurostat sdg_14_40","type":"text/tab-separated-values"}},"extent":{"spatial":{"bbox":[[-69.103165,-26.018616,61.78629,80.77476]]},"temporal":{"interval":[["2011-01-01T00:00:00Z","2023-12-31T23:59:59Z"]]}},"license":"other","keywords":["Bath","Water","Water quality"],"summaries":{"federation:backends":["internal_fdp"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"Eurostat","roles":["producer","licensor"],"url":"https://ec.europa.eu/eurostat"},{"name":"Destination Earth Data Lake (DEDL)","roles":["host","processor"],"url":"https://data.destination-earth.eu/"}],"created":"2025-02-10T15:42:09Z","updated":"2026-04-24T10:24:59Z","published":"2025-02-10T15:42:09Z","dedl:short_description":"The indicator tracks the percentage of European Union's coastal and inland bathing sites meeting excellent water quality standards for intestinal enterococci and E.coli bacteria under the revised Bathing Water Directive."},{"type":"Collection","title":"Eurostat - Greenhouse gas emissions from agriculture","id":"STAT.EUSTAT.DAT.GREENHOUSE_GAS_EMISSION_AGRICULTURE","description":"This indicator tracks trends in greenhouse gas (GHG) emissions by agriculture, estimated and reported under the United Nations Framework Convention on Climate Change (UNFCCC), the Kyoto Protocol and the Decision 525/2013/EC. The annual data collection covers in principle all Member States of the European Union as well as some other European countries","links":[{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/STAT.EUSTAT.DAT.GREENHOUSE_GAS_EMISSION_AGRICULTURE/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/STAT.EUSTAT.DAT.GREENHOUSE_GAS_EMISSION_AGRICULTURE/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/STAT.EUSTAT.DAT.GREENHOUSE_GAS_EMISSION_AGRICULTURE","title":"Eurostat - Greenhouse gas emissions from agriculture"},{"rel":"license","type":"text/html","href":"http://ec.europa.eu/eurostat/statistics-explained/index.php/Copyright/licence_policy","title":"Eurostat - Copyright notice and free re-use of data"}],"assets":{"data":{"href":"https://ec.europa.eu/eurostat/api/dissemination/sdmx/2.1/data/tai08?format=tsv","roles":["data"],"title":"Eurostat tai08","type":"text/tab-separated-values"},"thumbnail":{"href":"https://www.eea.europa.eu/themes/agriculture/theme_image/image_panoramic","roles":["thumbnail"],"title":"Eurostat Greenhouse","type":"image/jpeg"}},"extent":{"spatial":{"bbox":[[-69.103165,-26.018616,61.78629,80.77476]]},"temporal":{"interval":[["2009-01-01T00:00:00Z","2020-12-31T23:59:59Z"]]}},"license":"other","keywords":["Eurostat","Agriculture","Greenhouse gas","CO2","Emmission","Air pollutants"],"summaries":{"federation:backends":["internal_fdp"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"Eurostat","roles":["producer","licensor"],"url":"https://ec.europa.eu/eurostat"},{"name":"Destination Earth Data Lake (DEDL)","roles":["host","processor"],"url":"https://data.destination-earth.eu/"}],"created":"2023-08-11T18:04:28Z","updated":"2026-04-24T10:24:59Z","published":"2023-08-11T18:04:28Z","dedl:short_description":"The Eurostat dataset tracks EU member states' and selected non-member country's agricultural GHG emissions annually based on UNFCCC reporting requirements."},{"type":"Collection","title":"Population on 1 January by age, sex and NUTS 3 region","id":"STAT.EUSTAT.DAT.POP_AGE_GROUP_SEX_NUTS3","description":"Each year Eurostat collects demographic data at regional level from 37 countries as part of the Unified Demography (Unidemo) project. UNIDEMO is Eurostat’s main annual demographic data collection and aims to gather information on demography and migration. This dataset contains information about the population by sex, age and region of residence (NUTS 3 level).","links":[{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/STAT.EUSTAT.DAT.POP_AGE_GROUP_SEX_NUTS3/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/STAT.EUSTAT.DAT.POP_AGE_GROUP_SEX_NUTS3/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/STAT.EUSTAT.DAT.POP_AGE_GROUP_SEX_NUTS3","title":"Population on 1 January by age, sex and NUTS 3 region"},{"rel":"license","type":"text/html","href":"http://ec.europa.eu/eurostat/statistics-explained/index.php/Copyright/licence_policy","title":"Eurostat - Copyright notice and free re-use of data"}],"assets":{"data":{"href":"https://ec.europa.eu/eurostat/api/dissemination/sdmx/2.1/data/demo_r_pjangrp3?format=tsv","roles":["data"],"title":"Eurostat demo_r_pjangrp3","type":"text/tab-separated-values"},"thumbnail":{"href":"https://www.publicdomainpictures.net/pictures/140000/velka/group-of-young-people.jpg","roles":["thumbnail"],"title":"Eurostat NUTS3","type":"image/jpeg"}},"extent":{"spatial":{"bbox":[[-69.103165,-26.018616,61.78629,80.77476]]},"temporal":{"interval":[["2014-01-01T00:00:00Z","2021-12-31T23:59:59Z"]]}},"license":"other","keywords":["Eurostat","Population","Age","Sex","NUTS 3","Unidemo","Demographic"],"summaries":{"federation:backends":["internal_fdp"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"Eurostat","roles":["producer","licensor"],"url":"https://ec.europa.eu/eurostat"},{"name":"Destination Earth Data Lake (DEDL)","roles":["host","processor"],"url":"https://data.destination-earth.eu/"}],"created":"2023-08-11T18:04:28Z","updated":"2026-04-24T10:24:59Z","published":"2023-08-11T18:04:28Z","dedl:short_description":"Eurostat's Unidemo project annually gathers demographic data for 37 countries across Europe, providing detailed population statistics by age, sex, and geographic region down to the NUTS 3 level."},{"type":"Collection","title":"Population on 1 January by age, sex and NUTS 2 region","id":"STAT.EUSTAT.DAT.POP_AGE_SEX_NUTS2","description":"Each year Eurostat collects demographic data at regional level from 37 countries as part of the Unified Demography (Unidemo) project. UNIDEMO is Eurostat’s main annual demographic data collection and aims to gather information on demography and migration. This dataset contains information about the population by sex, age and region of residence (NUTS 2 level).","links":[{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/STAT.EUSTAT.DAT.POP_AGE_SEX_NUTS2/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/STAT.EUSTAT.DAT.POP_AGE_SEX_NUTS2/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/STAT.EUSTAT.DAT.POP_AGE_SEX_NUTS2","title":"Population on 1 January by age, sex and NUTS 2 region"},{"rel":"license","type":"text/html","href":"http://ec.europa.eu/eurostat/statistics-explained/index.php/Copyright/licence_policy","title":"Eurostat - Copyright notice and free re-use of data"}],"assets":{"data":{"href":"https://ec.europa.eu/eurostat/api/dissemination/sdmx/2.1/data/demo_r_d2jan?format=tsv","roles":["data"],"title":"Eurostat demo_r_d2jan","type":"text/tab-separated-values"},"thumbnail":{"href":"https://www.publicdomainpictures.net/pictures/140000/velka/group-of-young-people.jpg","roles":["thumbnail"],"title":"Eurostat NUTS2","type":"image/jpeg"}},"extent":{"spatial":{"bbox":[[-69.103165,-26.018616,61.78629,80.77476]]},"temporal":{"interval":[["1990-01-01T00:00:00Z","2021-12-31T23:59:59Z"]]}},"license":"other","keywords":["Eurostat","Population","Age","Sex","NUTS 2","Unidemo","Demographic"],"summaries":{"federation:backends":["internal_fdp"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"Eurostat","roles":["producer","licensor"],"url":"https://ec.europa.eu/eurostat"},{"name":"Destination Earth Data Lake (DEDL)","roles":["host","processor"],"url":"https://data.destination-earth.eu/"}],"created":"2023-08-11T18:04:28Z","updated":"2026-04-24T10:24:59Z","published":"2023-08-11T18:04:28Z","dedl:short_description":"This dataset provides yearly demographic data for 37 European countries collected through the Unidemo project, detailing population numbers by age, sex, and geographic region down to the NUTS 2 level."},{"type":"Collection","title":"Eurostat - Population change - Demographic balance and crude rates at regional level (NUTS 3) ","id":"STAT.EUSTAT.DAT.POP_CHANGE_DEMO_BALANCE_CRUDE_RATES_NUTS3","description":"Each year Eurostat collects demographic data at regional level from 37 countries as part of the Unified Demography (Unidemo) project. This dataset contains information about demographic balance and crude rates of a population at regional level (NUTS 3 level).","links":[{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/STAT.EUSTAT.DAT.POP_CHANGE_DEMO_BALANCE_CRUDE_RATES_NUTS3/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/STAT.EUSTAT.DAT.POP_CHANGE_DEMO_BALANCE_CRUDE_RATES_NUTS3/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/STAT.EUSTAT.DAT.POP_CHANGE_DEMO_BALANCE_CRUDE_RATES_NUTS3","title":"Eurostat - Population change - Demographic balance and crude rates at regional level (NUTS 3) "},{"rel":"license","type":"text/html","href":"http://ec.europa.eu/eurostat/statistics-explained/index.php/Copyright/licence_policy","title":"Eurostat - Copyright notice and free re-use of data"}],"assets":{"data":{"href":"https://ec.europa.eu/eurostat/api/dissemination/sdmx/2.1/data/demo_r_gind3?format=tsv","roles":["data"],"title":"Eurostat demo_r_gind3","type":"text/tab-separated-values"},"thumbnail":{"href":"https://th.bing.com/th/id/OIP.hWFLXRGub-BhrHIu7dvx4gHaFj?pid=ImgDet\u0026rs=1","roles":["thumbnail"],"title":"Eurostat Population","type":"image/jpeg"}},"extent":{"spatial":{"bbox":[[-69.103165,-26.018616,61.78629,80.77476]]},"temporal":{"interval":[["2000-01-01T00:00:00Z","2021-12-31T23:59:59Z"]]}},"license":"other","keywords":["Eurostat","Population","Demographic","Births","Deaths","Migration","Unidemo","NUTS 3"],"summaries":{"federation:backends":["internal_fdp"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"Eurostat","roles":["producer","licensor"],"url":"https://ec.europa.eu/eurostat"},{"name":"Destination Earth Data Lake (DEDL)","roles":["host","processor"],"url":"https://data.destination-earth.eu/"}],"created":"2023-08-11T18:04:28Z","updated":"2026-04-24T10:24:59Z","published":"2023-08-11T18:04:28Z","dedl:short_description":"This dataset provides annual demographic data on demographic balance and crude rates for regions across 37 European countries at NUTS 3 level through the Unidemo project."},{"type":"Collection","title":"Population density by NUTS 3 region","id":"STAT.EUSTAT.DAT.POP_DENSITY_NUTS3","description":"Eurostat’s annual data collections on population. Member States send population data to Eurostat data as on of 31 December for the reference year under Regulation 1260/2013 on European demographic statistics. The data are conventionally published by Eurostat as population on 1 January of the following year (reference year + 1). \nThe aim is to collect annual mandatory and voluntary demographic data from the national statistical institutes. Mandatory data are those defined by the legislation listed under ‘6.1. Institutional mandate — legal acts and other agreements’. \nThe completeness of the demographic data collected on a voluntary basis depends on the availability and completeness of information provided by the national statistical institutes.\nFor more information on mandatory/voluntary data collection, see 6.1. Institutional mandate — legal acts and other agreements. \nThe following statistics are available. \nPopulation on 1 January by sex and by:\n- single age and educational attainment / marital status / broad group of citizenship / broad group of country of birth;\n  - five-year age group and citizenship / country of birth;\n  - citizenship and broad group of country of birth / country of birth and broad group of citizenship;\n  - broad age group and NUTS 3 (under regional data population folder);\n  - single age and NUTS 2 (under regional data population folder);\n  - five-year age group and NUTS 2 / NUTS 3 (under regional data population folder).\nPopulation structure statistics: median age of population, proportion of population by various age groups, old age dependency ratio.","links":[{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/STAT.EUSTAT.DAT.POP_DENSITY_NUTS3/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/STAT.EUSTAT.DAT.POP_DENSITY_NUTS3/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/STAT.EUSTAT.DAT.POP_DENSITY_NUTS3","title":"Population density by NUTS 3 region"},{"rel":"license","type":"text/html","href":"http://ec.europa.eu/eurostat/statistics-explained/index.php/Copyright/licence_policy","title":"Eurostat - Copyright notice and free re-use of data"}],"assets":{"data":{"href":"https://ec.europa.eu/eurostat/api/dissemination/sdmx/2.1/data/demo_r_d3dens?format=tsv","roles":["data"],"title":"Eurostat demo_r_d3dens","type":"text/tab-separated-values"}},"extent":{"spatial":{"bbox":[[-69.103165,-26.018616,61.78629,80.77476]]},"temporal":{"interval":[["1990-01-01T00:00:00Z","2021-12-31T23:59:59Z"]]}},"license":"other","keywords":["Eurostat","Population","Density","NUTS 3"],"summaries":{"federation:backends":["internal_fdp"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"Eurostat","roles":["producer","licensor"],"url":"https://ec.europa.eu/eurostat"},{"name":"Destination Earth Data Lake (DEDL)","roles":["host","processor"],"url":"https://data.destination-earth.eu/"}],"created":"2025-02-10T15:42:09Z","updated":"2026-04-24T10:24:59Z","published":"2025-02-10T15:42:09Z","dedl:short_description":"Eurostat collects annual population data from member states based on Regulation 1260/2013, providing statistics such as population density by NUTS 3 regions along with additional demographics like age, education level, marital status, nationality, and place of birth."},{"type":"Collection","title":"Eurostat - Share of energy from renewable sources","id":"STAT.EUSTAT.DAT.SHARE_ENERGY_FROM_RENEWABLE","description":"This dataset covers the indicator for monitoring progress towards renewable energy targets of the Europe 2020 strategy implemented by Directive 2009/28/EC on the promotion of the use of energy from renewable sources. The annual data collection covers in principle all Member States of the European Union. Time series starts in the year 2004.The calculation is based on data collected in the framework of Regulation (EC) No 1099/2008 on energy statistics and complemented by specific supplementary data transmitted by national administrations to Eurostat.In some countries the statistical systems are not yet fully developed to meet all requirements of Directive 2009/28/EC, in particular with respect to ambient heat captured from the environment by heat pumps.This is indicator is a Sustainable Development Goal (SDG). It has been chosen for the assessment of the progress towards the objectives and targets of the EU Sustainable Development Strategy. The data collection covers the full spectrum of the Member States of the European Union.The share of energy from renewable sources is calculated for four indicators: Transport (RES-T), Heating and Cooling (RES-H\u0026C), Electricity (RES-E),Overall RES share (RES)","links":[{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/STAT.EUSTAT.DAT.SHARE_ENERGY_FROM_RENEWABLE/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/STAT.EUSTAT.DAT.SHARE_ENERGY_FROM_RENEWABLE/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/STAT.EUSTAT.DAT.SHARE_ENERGY_FROM_RENEWABLE","title":"Eurostat - Share of energy from renewable sources"},{"rel":"license","type":"text/html","href":"http://ec.europa.eu/eurostat/statistics-explained/index.php/Copyright/licence_policy","title":"Eurostat - Copyright notice and free re-use of data"}],"assets":{"data":{"href":"https://ec.europa.eu/eurostat/api/dissemination/sdmx/2.1/data/nrg_ind_ren?format=tsv","roles":["data"],"title":"Eurostat nrg_ind_ren","type":"text/tab-separated-values"},"thumbnail":{"href":"https://www.eea.europa.eu/themes/energy/theme_image/image_panoramic","roles":["thumbnail"],"title":"Eurostat Energy","type":"image/jpeg"}},"extent":{"spatial":{"bbox":[[-69.103165,-26.018616,61.78629,80.77476]]},"temporal":{"interval":[["2004-01-01T00:00:00Z","2021-12-31T23:59:59Z"]]}},"license":"other","keywords":["Eurostat","Energy","Renewable","Transport","Heating","Cooling","Electricity"],"summaries":{"federation:backends":["internal_fdp"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"Eurostat","roles":["producer","licensor"],"url":"https://ec.europa.eu/eurostat"},{"name":"Destination Earth Data Lake (DEDL)","roles":["host","processor"],"url":"https://data.destination-earth.eu/"}],"created":"2023-08-11T18:04:28Z","updated":"2026-04-24T10:24:59Z","published":"2023-08-11T18:04:28Z","dedl:short_description":"The Eurostat dataset tracks the annual share of energy sourced from renewables across various sectors within the European Union since 2004, covering transport, heating and cooling, electricity generation, and overall renewable energy usage."},{"type":"Collection","title":"Soil sealing index","id":"STAT.EUSTAT.DAT.SOIL_SEALING_INDEX","description":"The indicator estimates the increase in sealed soil surfaces with impervious materials due to urban development and construction (e.g. buildings, constructions and laying of completely or partially impermeable artificial material, such as asphalt, metal, glass, plastic or concrete). 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All bursts in all sub-swaths are then seamlessly merged to form a single, contiguous, ground range detected image per polarisation channel.\n\nGRD products are available in three resolutions, characterised by the acquisition mode and the level of multi-looking applied: Full Resolution (FR), High Resolution (HR), Medium Resolution (MR).","links":[{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ESA.DAT.SENTINEL-1.L1_GRD/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ESA.DAT.SENTINEL-1.L1_GRD/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ESA.DAT.SENTINEL-1.L1_GRD","title":"SENTINEL-1 Level 1 Ground Range Detected (GRD)"},{"rel":"license","type":"application/pdf","href":"https://sentinels.copernicus.eu/documents/247904/690755/Sentinel_Data_Legal_Notice","title":"Copernicus Sentinel data terms"}],"assets":{"thumbnail":{"href":"https://collections.eurodatacube.com/sentinel-1-grd/sentinel-1-grd.png","roles":["thumbnail"],"title":"Sentinel 1 GRD","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2014-10-04T00:58:44Z",null]]}},"license":"other","keywords":["ESA","Copernicus","Sentinel","C-Band","SAR","GRD"],"summaries":{"constellation":["Sentinel-1"],"federation:backends":["external_fdp","creodias","planetary_computer","wekeo_main"],"instruments":["C-SAR"],"platform":["Sentinel-1A","Sentinel-1B"],"processing:level":["L1"]},"item_assets":{"hh":{"description":"Amplitude of signal transmitted with horizontal polarization and received with horizontal polarization with radiometric terrain correction applied.","roles":["data"],"title":"HH: horizontal transmit, horizontal receive","type":"image/tiff; application=geotiff; profile=cloud-optimized"},"hv":{"description":"Amplitude of signal transmitted with horizontal polarization and received with vertical polarization with radiometric terrain correction applied.","roles":["data"],"title":"HV: horizontal transmit, vertical receive","type":"image/tiff; application=geotiff; profile=cloud-optimized"},"safe-manifest":{"description":"General product metadata in XML format. Contains a high-level textual description of the product and references to all of product's components, the product metadata, including the product identification and the resource references, and references to the physical location of each component file contained in the product.","roles":["metadata"],"title":"Manifest File","type":"application/xml"},"schema-calibration-hh":{"description":"Calibration metadata including calibration information and the beta nought, sigma nought, gamma and digital number look-up tables that can be used for absolute product calibration.","roles":["metadata"],"title":"Calibration Schema","type":"application/xml"},"schema-calibration-hv":{"description":"Calibration metadata including calibration information and the beta nought, sigma nought, gamma and digital number look-up tables that can be used for absolute product calibration.","roles":["metadata"],"title":"Calibration Schema","type":"application/xml"},"schema-calibration-vh":{"description":"Calibration metadata including calibration information and the beta nought, sigma nought, gamma and digital number look-up tables that can be used for absolute product calibration.","roles":["metadata"],"title":"Calibration Schema","type":"application/xml"},"schema-calibration-vv":{"description":"Calibration metadata including calibration information and the beta nought, sigma nought, gamma and digital number look-up tables that can be used for absolute product calibration.","roles":["metadata"],"title":"Calibration Schema","type":"application/xml"},"schema-noise-hh":{"description":"Estimated thermal noise look-up tables","roles":["metadata"],"title":"Noise Schema","type":"application/xml"},"schema-noise-hv":{"description":"Estimated thermal noise look-up tables","roles":["metadata"],"title":"Noise Schema","type":"application/xml"},"schema-noise-vh":{"description":"Estimated thermal noise look-up tables","roles":["metadata"],"title":"Noise Schema","type":"application/xml"},"schema-noise-vv":{"description":"Estimated thermal noise look-up tables","roles":["metadata"],"title":"Noise Schema","type":"application/xml"},"schema-product-hh":{"description":"Describes the main characteristics corresponding to the band: state of the platform during acquisition, image properties, Doppler information, geographic location, etc.","roles":["metadata"],"title":"Product Schema","type":"application/xml"},"schema-product-hv":{"description":"Describes the main characteristics corresponding to the band: state of the platform during acquisition, image properties, Doppler information, geographic location, etc.","roles":["metadata"],"title":"Product Schema","type":"application/xml"},"schema-product-vh":{"description":"Describes the main characteristics corresponding to the band: state of the platform during acquisition, image properties, Doppler information, geographic location, etc.","roles":["metadata"],"title":"Product Schema","type":"application/xml"},"schema-product-vv":{"description":"Describes the main characteristics corresponding to the band: state of the platform during acquisition, image properties, Doppler information, geographic location, etc.","roles":["metadata"],"title":"Product Schema","type":"application/xml"},"thumbnail":{"description":"An averaged, decimated preview image in PNG format. 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The products include a single look in each dimension using the full TX signal bandwidth and consist of complex samples preserving the phase information.","links":[{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ESA.DAT.SENTINEL-1.L1_SLC/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ESA.DAT.SENTINEL-1.L1_SLC/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ESA.DAT.SENTINEL-1.L1_SLC","title":"SENTINEL-1 Level 1 Single Look Complex (SLC) - EODC store"},{"rel":"license","type":"application/pdf","href":"https://sentinels.copernicus.eu/documents/247904/690755/Sentinel_Data_Legal_Notice","title":"Copernicus Sentinel data terms"}],"assets":{"thumbnail":{"href":"https://wekeo2-prod-data-access-config.s3.waw3-2.cloudferro.com/previews/EO_ESA_DAT_EODC-SENTINEL-1_L1_SLC.jpg","roles":["thumbnail"],"title":"Sentinel 1 SLC","type":"image/jpeg"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2014-04-03T00:00:00Z",null]]}},"license":"other","keywords":["Land","Land cover","Sea","Ice","Sea Ice","Glacier","Iceberg","Natural Disaster","Science","Earth Science","Oceans","Ocean Optics","Absorption","Atmosphere","Atmospheric Radiation","Albedo","Level 1 Data","ESA","Copernicus","Sentinel","C-Band","SAR","SLC"],"summaries":{"constellation":["Sentinel-1"],"federation:backends":["external_fdp","creodias","wekeo_main"],"instruments":["C-SAR"],"platform":["Sentinel-1A","Sentinel-1B"],"processing:level":["L1"]},"item_assets":{"hh":{"description":"Amplitude of signal transmitted with horizontal polarization and received with horizontal polarization with radiometric terrain correction applied.","roles":["data"],"title":"HH: horizontal transmit, horizontal receive","type":"image/tiff; application=geotiff; profile=cloud-optimized"},"hv":{"description":"Amplitude of signal transmitted with horizontal polarization and received with vertical polarization with radiometric terrain correction applied.","roles":["data"],"title":"HV: horizontal transmit, vertical receive","type":"image/tiff; application=geotiff; profile=cloud-optimized"},"thumbnail":{"description":"An averaged, decimated preview image in PNG format. 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Partially covered tiles correspond to those at the edge of the swath.","links":[{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ESA.DAT.SENTINEL-2.MSI.L1C/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ESA.DAT.SENTINEL-2.MSI.L1C/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ESA.DAT.SENTINEL-2.MSI.L1C","title":"Sentinel 2 MSI Level 1C"},{"rel":"license","type":"application/pdf","href":"https://sentinels.copernicus.eu/documents/247904/690755/Sentinel_Data_Legal_Notice","title":"Copernicus Sentinel data terms"}],"assets":{"thumbnail":{"href":"https://wekeo2-prod-data-access-config.s3.waw3-2.cloudferro.com/previews/EO_ESA_DAT_EODC-SENTINEL-2_MSI1C.jpg","roles":["thumbnail"],"title":"Sentinel 2 MSI L1C","type":"image/jpeg"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2015-07-04T00:00:00Z",null]]}},"license":"other","keywords":["Land","Land Cover","Crop Monitoring","Leaf Area Index","Chlorophyll","Coastal Zone Monitoring","Inland Water Monitoring","Glacier Monitoring","Ice Extent Mapping","Snow Cover Monitoring","Flood Mapping","Lava Flow Mapping","Burned Area Monitoring","Level 1 Data","ESA","Copernicus","Sentinel","MSI"],"summaries":{"constellation":["Sentinel-2"],"federation:backends":["external_fdp","creodias","wekeo_main"],"instruments":["MSI"],"platform":["Sentinel-2A","Sentinel-2B"],"processing:level":["L1C"]},"item_assets":{"thumbnail":{"description":"An averaged, decimated preview image in PNG format. 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Level-2A: Surface reflectances in cartographic geometry. This product is considered as the mission Analysis Ready Data (ARD), the product that can be used directly in downstream applications without the need for further processing. The Level-2A product contains: Bottom-Of-Atmosphere (BOA) reflectance orthoimage, Aerosol Optical Thickness (AOT) map, Water Vapour (WV) map, Scene Classification map and Quality Indicators data.","links":[{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ESA.DAT.SENTINEL-2.MSI.L2A/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ESA.DAT.SENTINEL-2.MSI.L2A/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ESA.DAT.SENTINEL-2.MSI.L2A","title":"Sentinel 2 MSI Level 2-A"},{"rel":"license","type":"application/pdf","href":"https://sentinels.copernicus.eu/documents/247904/690755/Sentinel_Data_Legal_Notice","title":"Copernicus Sentinel data terms"}],"assets":{"thumbnail":{"href":"https://sentinels.copernicus.eu/documents/247904/3681412/Sentinel-2-Level-1C-Level-2A-TOA-full.png","roles":["thumbnail"],"title":"TOA Level-1C image data (left) and associated Level-2A surface reflectance image data (right) generated using Sen2Cor processor","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2015-07-04T00:00:00Z",null]]}},"license":"other","keywords":["land","land cover","Crop monitoring","leaf area index","chlorophyll","Coastal zone monitoring","Inland water monitoring","Glacier monitoring","ice extent mapping","snow cover monitoring","Flood mapping","lava flow mapping","Burned area monitoring","Level 2 Data","ESA","Copernicus","Sentinel","MSI"],"summaries":{"constellation":["Sentinel-2"],"federation:backends":["external_fdp","creodias","planetary_computer","wekeo_main"],"instruments":["MSI"],"platform":["Sentinel-2A","Sentinel-2B"],"processing:level":["L2A"]},"item_assets":{"thumbnail":{"description":"An averaged, decimated preview image in PNG format. 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Compared to the Gross DMP (GDMP), or its equivalent Gross Primary Productivity, the main difference lies in the inclusion of the autotrophic respiration. 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It refers only to the green and living elements of the canopy. The FAPAR depends on the canopy structure, vegetation element optical properties, atmospheric conditions and angular configuration. To overcome this latter dependency, a daily integrated FAPAR value is assessed. FAPAR is very useful as input to a number of primary productivity models and is recognized as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS). The product at 333m resolution is provided in Near Real Time and consolidated in the next six periods.","links":[{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.CLMS.DAT.GLO.FAPAR300_V1/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.CLMS.DAT.GLO.FAPAR300_V1/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.CLMS.DAT.GLO.FAPAR300_V1","title":"Fraction of Absorbed Photosynthetically Active Radiation 2014-present (raster 300 m), global, 10-daily – version 1"},{"rel":"license","type":"text/html","href":"https://land.copernicus.eu/en/data-policy","title":"Copernicus Land Data Policy"},{"rel":"describedby","type":"text/html","href":"https://land.copernicus.eu/en/products/vegetation/fraction-of-absorbed-photosynthetically-active-radiation-v1-0-300m","title":"General Info"}],"assets":{"thumbnail":{"href":"https://land.copernicus.eu/en/products/vegetation/fraction-of-absorbed-photosynthetically-active-radiation-v1-0-300m/@@images/image-400-a98d2eea72ced9b2aa158e37618cc607.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-60,180,80]]},"temporal":{"interval":[["2014-01-01T00:00:00Z",null]]}},"license":"other","keywords":["Dekad","biogeophysical","GLOBE","10-daily composite","Orthoimagery","fapar","geophysical environment"],"summaries":{"federation:backends":["wekeo_main"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"VITO NV","roles":["producer","processor"],"url":"https://vito.be/en"},{"name":"European Environment Agency (EEA)","roles":["licensor"],"url":"https://www.eea.europa.eu/"},{"name":"WEkEO","roles":["host"],"url":"https://www.wekeo.eu/"}],"created":"2023-08-11T19:50:59Z","updated":"2026-04-24T10:24:59Z","published":"2023-08-11T19:50:59Z","dedl:short_description":"The Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) dataset from 2014 onwards provides a 10-daily assessment of the fraction of solar radiation absorbed by plant life across the globe at 300-meter resolution."},{"type":"Collection","title":"Fraction of Green Vegetation Cover 2014-present (raster 300 m), global, 10-daily – version 1","id":"EO.CLMS.DAT.GLO.FCOVER300_V1","description":"The Fraction of Vegetation Cover (FCover) corresponds to the fraction of ground covered by green vegetation. \n        Practically, it quantifies the spatial extent of the vegetation. 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Like the FAPAR products that are used as input for the GDMP estimation, these GDMP products are provided in Near Real Time, with consolidations in the next periods, or as offline product.","links":[{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.CLMS.DAT.GLO.GDMP300_V1/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.CLMS.DAT.GLO.GDMP300_V1/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.CLMS.DAT.GLO.GDMP300_V1","title":"Gross Dry Matter Productivity 2014-present (raster 300 m), global, 10-daily – version 1"},{"rel":"license","type":"text/html","href":"https://land.copernicus.eu/en/data-policy","title":"Copernicus Land Data Policy"},{"rel":"describedby","type":"text/html","href":"https://land.copernicus.eu/en/products/vegetation/gross-dry-matter-productivity-v1-0-300m","title":"General Info"}],"assets":{"thumbnail":{"href":"https://land.copernicus.eu/en/products/vegetation/gross-dry-matter-productivity-v1-0-300m/@@images/image-400-aa448faf2cb0047474dc82b7aa75cabf.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-60,180,80]]},"temporal":{"interval":[["2014-01-01T00:00:00Z",null]]}},"license":"other","keywords":["Dekad","GLOBE","10-daily composite","agricultural production","crops","Orthoimagery","primary productivity","gross dry matter"],"summaries":{"federation:backends":["wekeo_main"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"VITO NV","roles":["producer","processor"],"url":"https://vito.be/en"},{"name":"European Environment Agency (EEA)","roles":["licensor"],"url":"https://www.eea.europa.eu/"},{"name":"WEkEO","roles":["host"],"url":"https://www.wekeo.eu/"}],"created":"2023-08-11T19:50:59Z","updated":"2026-04-24T10:24:59Z","published":"2023-08-11T19:50:59Z","dedl:short_description":"The Gross Dry Matter Productivity dataset provides global, 10-daily raster data at 300m resolution since 2014, indicating the daily growth rate of vegetation dry biomass in kilograms per hectare."},{"type":"Collection","title":"Leaf Area Index 2014-present (raster 300 m), global, 10-daily – version 1","id":"EO.CLMS.DAT.GLO.LAI300_V1","description":"LAI was defined by CEOS as half the developed area of the convex hull wrapping the green canopy elements per unit horizontal ground. 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The product at 333m resolution is provided in Near Real Time and consolidated in the next six periods.","links":[{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.CLMS.DAT.GLO.LAI300_V1/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.CLMS.DAT.GLO.LAI300_V1/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.CLMS.DAT.GLO.LAI300_V1","title":"Leaf Area Index 2014-present (raster 300 m), global, 10-daily – version 1"},{"rel":"license","type":"text/html","href":"https://land.copernicus.eu/en/data-policy","title":"Copernicus Land Data Policy"},{"rel":"describedby","type":"text/html","href":"https://land.copernicus.eu/en/products/vegetation/leaf-area-index-300m-v1.0","title":"General Info"}],"assets":{"thumbnail":{"href":"https://land.copernicus.eu/en/products/vegetation/leaf-area-index-300m-v1.0/@@images/image-400-a296946aa1a765380c77a012fed027ff.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-60,180,80]]},"temporal":{"interval":[["2014-01-01T00:00:00Z",null]]}},"license":"other","keywords":["Dekad","biogeophysical","GLOBE","10-daily composite","Orthoimagery","geophysical environment","leaf area"],"summaries":{"federation:backends":["wekeo_main"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"VITO NV","roles":["producer","processor"],"url":"https://vito.be/en"},{"name":"European Environment Agency (EEA)","roles":["licensor"],"url":"https://www.eea.europa.eu/"},{"name":"WEkEO","roles":["host"],"url":"https://www.wekeo.eu/"}],"created":"2023-08-11T19:50:59Z","updated":"2026-04-24T10:24:59Z","published":"2023-08-11T19:50:59Z","dedl:short_description":"The Leaf Area Index dataset provides global, 10-daily raster data from 2014 onwards at 300-m resolution, representing the total green leaf area index, including understory contributions, with each value calculated based on the developed area of the convex hull wrapping green canopy elements per unit horizontal ground."},{"type":"Collection","title":"Normalised Difference Vegetation Index 2014-2020 (raster 300 m), global, 10-daily – version 1","id":"EO.CLMS.DAT.GLO.NDVI300_V1","description":"The Normalized Difference Vegetation Index (NDVI) is a proxy to quantify the vegetation amount. 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The analysis combines model data with observations provided by the European Environment Agency (EEA) into a complete and consistent dataset using various data assimilation techniques depending upon the air-quality forecasting system used. In parallel, air quality forecasts are produced once a day for the next four days. Both the analysis and the forecast are available at hourly time steps at seven height levels.\nNote that only nitrogen monoxide, nitrogen dioxide, sulphur dioxide, ozone, PM2.5, PM10 and dust are regularly validated against in situ observations, and therefore forecasts of all other variables are unvalidated and should be considered experimental.","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CAMS_EUROPE_AIR_QUALITY_FORECASTS/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CAMS_EUROPE_AIR_QUALITY_FORECASTS/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CAMS_EUROPE_AIR_QUALITY_FORECASTS/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CAMS_EUROPE_AIR_QUALITY_FORECASTS","title":"CAMS European air quality forecasts"},{"rel":"license","type":"application/pdf","href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/licences/licence-to-use-copernicus-products/licence-to-use-copernicus-products_b4b9451f54cffa16ecef5c912c9cebd6979925a956e3fa677976e0cf198c2c18.pdf","title":"Licence to Use Copernicus Products"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/CKB/CAMS+Regional%3A+European+air+quality+analysis+and+forecast+data+documentation","title":"CAMS Regional Products Documentation"},{"rel":"describedby","type":"text/html","href":"https://atmosphere.copernicus.eu/regional-services","title":"Evaluation and quality assurance (EQA) reports"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.5194/gmd-8-2777-2015","title":"A regional air quality forecasting system over Europe: the MACC-II daily ensemble production"}],"assets":{"thumbnail":{"href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/resources/cams-europe-air-quality-forecasts/overview_4e21f3b83980b68134ee3b2b792f5f0fdb3132d8c545becbba8cd3d801f66411.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-25,30,45,72]]},"temporal":{"interval":[["2020-08-26T00:00:00Z",null]]}},"license":"other","keywords":["Future","Aerosol","Reactive gas","Europe","Atmospheric conditions","Past","airPollution","Atmosphere (composition)","Present","Analysis","Forecast"],"summaries":{"federation:backends":["cop_ads","wekeo_ecmwf"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Atmosphere Monitoring Service (CAMS)","roles":["host"],"url":"https://atmosphere.copernicus.eu/"}],"created":"2023-12-12T15:24:18Z","updated":"2026-04-24T10:24:59Z","published":"2023-12-12T15:24:18Z","sci:doi":"10.5194/gmd-8-2777-2015","sci:citation":"Marécal, V., Peuch, V.-H., Andersson, C., Andersson, S., Arteta, J., Beekmann, M., Benedictow, A., Bergström, R., Bessagnet, B., Cansado, A., Chéroux, F., Colette, A., Coman, A., Curier, R. L., Denier van der Gon, H. A. C., Drouin, A., Elbern, H., Emili, E., Engelen, R. J., Eskes, H. J., Foret, G., Friese, E., Gauss, M., Giannaros, C., Guth, J., Joly, M., Jaumouillé, E., Josse, B., Kadygrov, N., Kaiser, J. W., Krajsek, K., Kuenen, J., Kumar, U., Liora, N., Lopez, E., Malherbe, L., Martinez, I., Melas, D., Meleux, F., Menut, L., Moinat, P., Morales, T., Parmentier, J., Piacentini, A., Plu, M., Poupkou, A., Queguiner, S., Robertson, L., Rouïl, L., Schaap, M., Segers, A., Sofiev, M., Tarasson, L., Thomas, M., Timmermans, R., Valdebenito, Á., van Velthoven, P., van Versendaal, R., Vira, J., and Ung, A.: A regional air quality forecasting system over Europe: the MACC-II daily ensemble production, Geosci. Model Dev., 8, 2777–2813, https://doi.org/10.5194/gmd-8-2777-2015, 2015.","dedl:short_description":"The CAMS European air quality dataset offers high-resolution daily analyses and forecasts for Europe, combining model data with EEA observations through ensemble methods and providing estimates of forecast uncertainty."},{"type":"Collection","title":"CAMS European air quality reanalyses","id":"EO.ECMWF.DAT.CAMS_EUROPE_AIR_QUALITY_REANALYSES","description":"This dataset provides annual air quality reanalyses for Europe based on both unvalidated (interim) and validated observations.\nCAMS produces annual air quality (interim) reanalyses for the European domain at significantly higher spatial resolution (0.1 degrees, approx. 10km) than is available from the global reanalyses. The production is currently based on an ensemble of nine air quality data assimilation systems across Europe. A median ensemble is calculated from individual outputs, since ensemble products yield on average better performance than the individual model products. The spread between the nine models can be used to provide an estimate of the analysis uncertainty.\nThe reanalysis combines model data with observations provided by the European Environment Agency (EEA) into a complete and consistent dataset using various data assimilation techniques depending upon the air-quality forecasting system used. Additional sources of observations can complement the in-situ data assimilation, like satellite data.\nAn interim reanalysis is provided each year for the year before based on the unvalidated near-real-time observation data stream that has not undergone full quality control by the data providers yet. Once the fully quality-controlled observations are available from the data provider, typically with an additional delay of about 1 year, a final validated annual reanalysis is provided. Both reanalyses are available at hourly time steps at height levels.","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CAMS_EUROPE_AIR_QUALITY_REANALYSES/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CAMS_EUROPE_AIR_QUALITY_REANALYSES/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CAMS_EUROPE_AIR_QUALITY_REANALYSES/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CAMS_EUROPE_AIR_QUALITY_REANALYSES","title":"CAMS European air quality reanalyses"},{"rel":"license","type":"application/pdf","href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/licences/licence-to-use-copernicus-products/licence-to-use-copernicus-products_b4b9451f54cffa16ecef5c912c9cebd6979925a956e3fa677976e0cf198c2c18.pdf","title":"Licence to Use Copernicus Products"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/CKB/CAMS+Regional%3A+European+air+quality+reanalyses+data+documentation","title":"CAMS European air quality reanalyses documentation"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.5194/gmd-8-2777-2015","title":"A regional air quality forecasting system over Europe: the MACC-II daily ensemble production"}],"assets":{"thumbnail":{"href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/resources/cams-europe-air-quality-reanalyses/overview_bbfe0b2e1170acdb9c1e489b34a4c7db89080f96fcde03acfdca90f450c8582a.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-25,30,45,72]]},"temporal":{"interval":[["2013-01-01T00:00:00Z","2023-12-31T23:59:59Z"]]}},"license":"other","keywords":["Aerosol","Reactive gas","Europe","Atmospheric conditions","Reanalysis","Past","airPollution","Atmosphere (composition)"],"summaries":{"federation:backends":["cop_ads","wekeo_ecmwf"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Atmosphere Monitoring Service (CAMS)","roles":["host"],"url":"https://atmosphere.copernicus.eu/"}],"created":"2024-04-11T22:18:54Z","updated":"2026-04-24T10:24:59Z","published":"2024-04-11T22:18:54Z","sci:doi":"10.5194/gmd-8-2777-2015","sci:citation":"Marécal, V., Peuch, V.-H., Andersson, C., Andersson, S., Arteta, J., Beekmann, M., Benedictow, A., Bergström, R., Bessagnet, B., Cansado, A., Chéroux, F., Colette, A., Coman, A., Curier, R. L., Denier van der Gon, H. A. C., Drouin, A., Elbern, H., Emili, E., Engelen, R. J., Eskes, H. J., Foret, G., Friese, E., Gauss, M., Giannaros, C., Guth, J., Joly, M., Jaumouillé, E., Josse, B., Kadygrov, N., Kaiser, J. W., Krajsek, K., Kuenen, J., Kumar, U., Liora, N., Lopez, E., Malherbe, L., Martinez, I., Melas, D., Meleux, F., Menut, L., Moinat, P., Morales, T., Parmentier, J., Piacentini, A., Plu, M., Poupkou, A., Queguiner, S., Robertson, L., Rouïl, L., Schaap, M., Segers, A., Sofiev, M., Tarasson, L., Thomas, M., Timmermans, R., Valdebenito, Á., van Velthoven, P., van Versendaal, R., Vira, J., and Ung, A.: A regional air quality forecasting system over Europe: the MACC-II daily ensemble production, Geosci. Model Dev., 8, 2777–2813, https://doi.org/10.5194/gmd-8-2777-2015, 2015.","dedl:short_description":"This dataset contains high-resolution annual air quality reanalyses for Europe combining model data with EEA observations through multiple ensemble methods, providing both interim and validated results."},{"type":"Collection","title":"CAMS global atmospheric composition forecasts","id":"EO.ECMWF.DAT.CAMS_GLOBAL_ATMOSHERIC_COMPO_FORECAST","description":"CAMS produces global forecasts for atmospheric composition twice a day. The forecasts consist of more than 50 chemical species (e.g. ozone, nitrogen dioxide, carbon monoxide) and seven different types of aerosol (desert dust, sea salt, organic matter, black carbon, sulphate, nitrate and ammonium aerosol). In addition, several meteorological variables are available as well.\nThe initial conditions of each forecast are obtained by combining a previous forecast with current satellite observations through a process called data assimilation. This best estimate of the state of the atmosphere at the initial forecast time step, called the analysis, provides a globally complete and consistent dataset allowing for estimates at locations where observation data coverage is low or for atmospheric pollutants for which no direct observations are available.\nThe forecast itself uses a model of the atmosphere based on the laws of physics and chemistry to determine the evolution of the concentrations of all species over time for the next five days. Apart from the required initial state, it also uses inventory-based or observation-based emission estimates as a boundary condition at the surface.\nThe CAMS global forecasting system is upgraded about once a year resulting in technical and scientific changes. The horizontal or vertical resolution can change, new species can be added, and more generally the accuracy of the forecasts can be improved. Details of these system changes can be found in the documentation. Users looking for a more consistent long-term data set should consider using the CAMS Global Reanalysis instead, which is available through the ADS and spans the period from 2003 onwards.\n Finally, because some meteorological fields in the forecast do not fall within the general CAMS data licence, they are only available with a delay of 5 days.","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CAMS_GLOBAL_ATMOSHERIC_COMPO_FORECAST/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CAMS_GLOBAL_ATMOSHERIC_COMPO_FORECAST/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CAMS_GLOBAL_ATMOSHERIC_COMPO_FORECAST/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CAMS_GLOBAL_ATMOSHERIC_COMPO_FORECAST","title":"CAMS global atmospheric composition forecasts"},{"rel":"license","type":"application/pdf","href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/licences/licence-to-use-copernicus-products/licence-to-use-copernicus-products_b4b9451f54cffa16ecef5c912c9cebd6979925a956e3fa677976e0cf198c2c18.pdf","title":"Licence to Use Copernicus Products"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/x/5cqpD","title":"CAMS Global atmospheric composition forecast data documentation"},{"rel":"describedby","type":"text/html","href":"https://atmosphere.copernicus.eu/node/326","title":"Changes in CAMS global production system"},{"rel":"describedby","type":"text/html","href":"https://atmosphere.copernicus.eu/node/325#fd66a9d3-e408-4b51-84c0-c2253a06f727","title":"Evaluation and quality assurance (EQA) reports"},{"rel":"describedby","type":"text/html","href":"https://global-evaluation.atmosphere.copernicus.eu/","title":"Evaluation of global forecasts"}],"assets":{"thumbnail":{"href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/resources/cams-global-atmospheric-composition-forecasts/overview_a484dee4988156df196a92b874b23a6d2686921df6b17c528d1a171cb51d0240.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2015-01-01T00:00:00Z",null]]}},"license":"other","keywords":["Future","Aerosol","Reactive gas","Atmospheric conditions","Atmosphere (meteorology)","Global","Past","airPollution","Atmosphere (composition)","Present","Analysis","Forecast"],"summaries":{"federation:backends":["cop_ads","wekeo_ecmwf"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Atmosphere Monitoring Service (CAMS)","roles":["host"],"url":"https://atmosphere.copernicus.eu/"}],"created":"2023-12-12T15:24:18Z","updated":"2026-04-24T10:24:59Z","published":"2023-12-12T15:24:18Z","dedl:short_description":"CAMS generates daily global atmospheric composition forecasts including over 50 chemicals and seven aerosols, utilizing past predictions combined with real-time satellite data and physical/chemical models for up to five-day projections."},{"type":"Collection","title":"CAMS global emission inventories","id":"EO.ECMWF.DAT.CAMS_GLOBAL_EMISSION_INVENTORIES","description":"This data set contains gridded distributions of global anthropogenic and natural emissions.\nNatural and anthropogenic emissions of atmospheric pollutants and greenhouse gases are key drivers of the evolution of the composition of the atmosphere, so an accurate representation of them in forecast models of atmospheric composition is essential. CAMS compiles inventories of emission data that serve as input to its own forecast models, but which can also be used by other atmospheric chemical transport models. These inventories are based on a combination of existing data sets and new information, describing anthropogenic emissions from fossil fuel use on land, shipping, and aviation, and natural emissions from vegetation, soil, the ocean and termites. The anthropogenic emissions on land are further separated in specific activity sectors (e.g., power generation, road traffic, industry). The CAMS emission data sets provide good consistency between the emissions of greenhouse gases, reactive gases, and aerosol particles and their precursors. Because most inventory-based data sets are only available with a delay of several years, the CAMS emission inventories also extend these existing data sets forward in time by using the trends from the most recent available years, producing timely input data for real-time forecast models.\nMost of the data sets are updated once or twice per year adding the most recent year to the data record, while re-processing the original data record for consistency, when needed. This is reflected by the different version numbers.","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CAMS_GLOBAL_EMISSION_INVENTORIES/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CAMS_GLOBAL_EMISSION_INVENTORIES/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CAMS_GLOBAL_EMISSION_INVENTORIES/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CAMS_GLOBAL_EMISSION_INVENTORIES","title":"CAMS global emission inventories"},{"rel":"license","type":"application/pdf","href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/licences/licence-to-use-copernicus-products/licence-to-use-copernicus-products_b4b9451f54cffa16ecef5c912c9cebd6979925a956e3fa677976e0cf198c2c18.pdf","title":"Licence to Use Copernicus Products"},{"rel":"describedby","type":"text/html","href":"https://atmosphere.copernicus.eu/anthropogenic-and-natural-emissions","title":"CAMS anthropogenic and natural emissions documentation"}],"assets":{"thumbnail":{"href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/resources/cams-global-emission-inventories/overview_def42da7bfd33d3f2cc49e07bc5cac39340cc56944bcbbd96d7f8117e5bd3756.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2003-01-01T00:00:00Z","2020-12-31T23:59:59Z"]]}},"license":"other","keywords":["Emissions and surface fluxes","Aerosol","Reactive gas","Atmospheric conditions","Emission inventory","Global","Past","airPollution"],"summaries":{"federation:backends":["cop_ads","wekeo_ecmwf"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Atmosphere Monitoring Service (CAMS)","roles":["host"],"url":"https://atmosphere.copernicus.eu/"}],"created":"2023-12-12T15:24:18Z","updated":"2026-04-24T10:24:59Z","published":"2023-12-12T15:24:18Z","dedl:short_description":"The CAMS global emission inventories contain gridded distributions of anthropogenic and natural emissions compiled from various sources including fossil fuels, shipping, aviation, vegetation, oceans, soils, and termites, providing consistent datasets for forecasting atmospheric composition."},{"type":"Collection","title":"CAMS global biomass burning emissions based on fire radiative power (GFAS)","id":"EO.ECMWF.DAT.CAMS_GLOBAL_FIRE_EMISSIONS_GFAS","description":"Emissions of atmospheric pollutants from biomass burning and vegetation fires are key drivers of the evolution of atmospheric composition, with a high degree of spatial and temporal variability, and an accurate representation of them in models is essential.\n\nThe CAMS Global Fire Assimilation System (GFAS) utilises satellite observations of fire radiative power (FRP) to provide near-real-time information on the location, relative intensity and estimated emissions from biomass burning and vegetation fires. Emissions are estimated by (i) conversion of FRP observations to the dry matter (DM) consumed by the fire, and (ii) application of emission factors to DM for different biomes, based on field and laboratory studies in the scientific literature, to estimate the emissions. Emissions estimates for 40 pyrogenic species are available from GFAS, including aerosols, reactive gases and greenhouse gases, on a regular grid with a spatial resolution of 0.1 degrees longitude by 0.1 degrees latitude.\n\nThis version of GFAS (v1.2) provides daily averaged data based on a combination of FRP observations from two Moderate Resolution Imaging Spectroradiometer (MODIS) instruments, one on the NASA EOS-Terra satellite and the other on the NASA EOS-Aqua satellite from 1 January 2003 to present. GFAS also provides daily estimates of smoke plume injection heights derived from FRP observations and meteorological information from the operational weather forecasts from ECMWF.\n\nGFAS data have been used to provide surface boundary conditions for the CAMS global atmospheric composition and European regional air quality forecasts, and the wider atmospheric chemistry modelling community.\n\nMore details about the products are given in the Documentation section.\n\n## How to cite the CAMS GFAS data\n\nPlease acknowledge the use of the CAMS GFAS data as stated in the [Copernicus CAMS License agreement](http://apps.ecmwf.int/datasets/licences/copernicus/):\n\tWhere the Licensee communicates to the public or distributes or publishes CAMS Information, the Licensee shall inform the recipients of the source of that information by using the following or any similar notice:\n'Generated using Copernicus Atmosphere Monitoring Service Information [Year]'.\n\tWhere the Licensee makes or contributes to a publication or distribution containing adapted or modified CAMS Information, the Licensee shall provide the following or any similar notice:\n'Contains modified Copernicus Atmosphere Monitoring Service Information [Year]';\n\nAny such publication or distribution shall state that \"neither the European Commission nor ECMWF is responsible for any use that may be made of the information it contains.\"","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CAMS_GLOBAL_FIRE_EMISSIONS_GFAS/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CAMS_GLOBAL_FIRE_EMISSIONS_GFAS/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CAMS_GLOBAL_FIRE_EMISSIONS_GFAS/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CAMS_GLOBAL_FIRE_EMISSIONS_GFAS","title":"CAMS global biomass burning emissions based on fire radiative power (GFAS)"},{"rel":"license","type":"application/pdf","href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/licences/licence-to-use-copernicus-products/licence-to-use-copernicus-products_b4b9451f54cffa16ecef5c912c9cebd6979925a956e3fa677976e0cf198c2c18.pdf","title":"Licence to Use Copernicus Products"},{"rel":"describedby","type":"application/pdf","href":"https://confluence.ecmwf.int/x/to1CBQ","title":"Documentation on the CAMS global fire assimilation system"},{"rel":"describedby","type":"application/pdf","href":"https://www.ecmwf.int/sites/default/files/elibrary/2016/16906-improving-gfas-and-cams-biomass-burning-estimations-means-global-ecmwf-fire-forecast-system.pdf","title":"Improving CAMS biomass burning estimations by means of the Global ECMWFFire Forecast system (GEFF)"},{"rel":"describedby","type":"application/pdf","href":"https://www.ecmwf.int/sites/default/files/elibrary/2013/7707-assessment-global-fire-assimilation-system-gfasv1.pdf","title":"Assessment of the Global Fire Assimilation System (GFASv1)"},{"rel":"describedby","type":"application/pdf","href":"https://www.ecmwf.int/sites/default/files/elibrary/2010/9842-assessment-real-time-fire-emissions-gfasv0-macc.pdf","title":"Assessment of the Real-Time Fire Emissions (GFASv0) by MACC"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.5194/acp-2017-790"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.5194/acp-17-2921-2017"}],"assets":{"thumbnail":{"href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/resources/cams-global-fire-emissions-gfas/overview_eaf1132e6ada572e7ab38d00431929a48be55ac21bb5ec24bbe5d0c65be670ad.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2003-01-01T00:00:00Z",null]]}},"license":"other","keywords":["Emissions and surface fluxes","Aerosol","Reactive gas","Atmospheric conditions","Global","Past","airPollution","Present","Analysis"],"summaries":{"federation:backends":["cop_ads","wekeo_ecmwf"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Atmosphere Monitoring Service (CAMS)","roles":["host"],"url":"https://atmosphere.copernicus.eu/"}],"created":"2024-05-31T09:50:00Z","updated":"2026-04-24T10:24:59Z","published":"2024-05-31T09:50:00Z","sci:publications":[{"citation":"Kaiser, J. W., Heil, A., Andreae, M. O., Benedetti, A., Chubarova, N., Jones, L., Morcrette, J.-J., Razinger, M., Schultz, M. G., Suttie, M., and van der Werf, G. R. (2012). Biomass burning emissions estimated with a global fire assimilation system based on observed fire radiative power. BG, 9:527-554."},{"citation":"Di Giuseppe, F, Remy, S, Pappenberger, F, Wetterhall, F (2016): Improving GFAS and CAMS biomass burning estimations by means of the Global ECMWF Fire Forecast system (GEFF), ECMWF Tech. Memo No. 790"},{"doi":"10.5194/acp-2017-790","citation":"Di Giuseppe, F, Remy, S, Pappenberger, F, Wetterhall, F, 2018: Combining fire radiative power observations with the fire weather index improves the estimation of fire emissions, Atmos. Chem. Phys. Discuss., 18, 5359–5370, https://doi.org/10.5194/acp-2017-790"},{"citation":"Heil et al. (2010) Assessment of the Real-Time Fire Emissions (GFASv0) by MACC, ECMWF Tech. Memo No. 628"},{"citation":"N. Andela (VUA), J.W. Kaiser (ECMWF, KCL), A. Heil (FZ Jülich), T.T. van Leeuwen (VUA), G.R. van der Werf (VUA), M.J. Wooster (KCL), S. Remy (ECMWF) and M.G. Schultz (FZ Jülich), Assessment of the Global Fire Assimilation System (GFASv1)."},{"doi":"10.5194/acp-17-2921-2017","citation":"Rémy, S., A. Veira, R. Paugam, M. Sofiev, J. W. Kaiser, F. Marenco, S. P. Burton, A. Benedetti, R. J. Engelen, R. Ferrare, and J. W. Hair, 2017: Two global data sets of daily fire emission injection heights since 2003, Atmos. Chem. Phys., 17, 2921-2942, https://doi.org/10.5194/acp-17-2921-2017"}],"dedl:short_description":"The CAMS Global Fire Assimilation System (GFAS v1.2) uses satellite observations of fire radiative power to estimate daily biomass burning emissions of 40 pyrogenic species at 0.1-degree resolution since 2003."},{"type":"Collection","title":"CAMS global greenhouse gas reanalysis (EGG4)","id":"EO.ECMWF.DAT.CAMS_GLOBAL_GREENHOUSE_GAS_REANALYSIS","description":"This dataset is part of the ECMWF Atmospheric Composition Reanalysis focusing on long-lived greenhouse gases: carbon dioxide (CO2) and methane (CH4). The emissions and natural fluxes at the surface are crucial for the evolution of the long-lived greenhouse gases in the atmosphere. In this dataset the CO2 fluxes from terrestrial vegetation are modelled in order to simulate the variability across a wide range of scales from diurnal to inter-annual. The CH4 chemical loss is represented by a climatological loss rate and the emissions at the surface are taken from a range of datasets.\nReanalysis combines model data with observations from across the world into a globally complete and consistent dataset using a model of the atmosphere based on the laws of physics and chemistry. This principle, called data assimilation, is based on the method used by numerical weather prediction centres and air quality forecasting centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way to allow for the provision of a dataset spanning back more than a decade. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product.\nThe assimilation system is able to estimate biases between observations and to sift good-quality data from poor data. The atmosphere model allows for estimates at locations where data coverage is low or for atmospheric pollutants for which no direct observations are available. The provision of estimates at each grid point around the globe for each regular output time, over a long period, always using the same format, makes reanalysis a very convenient and popular dataset to work with.\nThe observing system has changed drastically over time, and although the assimilation system can resolve data holes, the initially much sparser networks will lead to less accurate estimates. For this reason, EAC4 is only available from 2003 onwards.\nThe analysis procedure assimilates data in a window of 12 hours using the 4D-Var assimilation method, which takes account of the exact timing of the observations and model evolution within the assimilation window.\nThese data are available in 3-hourly resolution, worldwide. Monthly means can be accessed at:\nhttps://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-global-ghg-reanalysis-egg4-monthly","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CAMS_GLOBAL_GREENHOUSE_GAS_REANALYSIS/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CAMS_GLOBAL_GREENHOUSE_GAS_REANALYSIS/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CAMS_GLOBAL_GREENHOUSE_GAS_REANALYSIS/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CAMS_GLOBAL_GREENHOUSE_GAS_REANALYSIS","title":"CAMS global greenhouse gas reanalysis (EGG4)"},{"rel":"license","type":"application/pdf","href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/licences/licence-to-use-copernicus-products/licence-to-use-copernicus-products_b4b9451f54cffa16ecef5c912c9cebd6979925a956e3fa677976e0cf198c2c18.pdf","title":"Licence to Use Copernicus Products"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/x/OIX4B","title":"CAMS: Reanalysis data documentation"},{"rel":"describedby","type":"text/html","href":"https://atmosphere.copernicus.eu/eqa-reports-global-services#fe56bdb4-1bdf-4d47-b46b-261a1ea57243","title":"Evaluation and quality assurance (EQA) reports"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.5194/acp-23-3829-2023","title":"Technical note: The CAMS greenhouse gas reanalysis from 2003 to 2020"}],"assets":{"thumbnail":{"href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/resources/cams-global-ghg-reanalysis-egg4/overview_0e58aaaa6c287649388046e898d0a25cfa1a936903e4e71352c365e59dd5dcef.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2003-01-01T00:00:00Z","2020-01-01T00:00:00Z"]]}},"license":"other","keywords":["Atmospheric conditions","Atmosphere (meteorology)","Reanalysis","Global","Greenhouse gas","Past","airPollution","Atmosphere (composition)"],"summaries":{"federation:backends":["cop_ads","wekeo_ecmwf"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Atmosphere Monitoring Service (CAMS)","roles":["host"],"url":"https://atmosphere.copernicus.eu/"}],"created":"2024-05-27T12:12:11Z","updated":"2026-04-24T10:24:59Z","published":"2024-05-27T12:12:11Z","sci:publications":[{"doi":"10.5194/acp-23-3829-2023","citation":"Agustí-Panareda, A., and Coauthors, 2023: Technical note: The CAMS greenhouse gas reanalysis from 2003 to 2020, Atmos. Chem. Phys., 23, 3829–3859, https://doi.org/10.5194/acp-23-3829-2023"}],"dedl:short_description":"This CAMS global greenhouse gas reanalysis (EGG4) dataset provides a comprehensive, consistently formatted, and high-resolution representation of global CO2 and CH4 concentrations from 2003 onwards, combining model simulations with observational data through a physically-based atmospheric model and advanced data assimilation techniques."},{"type":"Collection","title":"CAMS global greenhouse gas reanalysis (EGG4) monthly averaged fields","id":"EO.ECMWF.DAT.CAMS_GLOBAL_GREENHOUSE_GAS_REANALYSIS_MONTHLY_AV_FIELDS","description":"This dataset is part of the ECMWF Atmospheric Composition Reanalysis focusing on long-lived greenhouse gases: carbon dioxide (CO2) and methane (CH4). The emissions and natural fluxes at the surface are crucial for the evolution of the long-lived greenhouse gases in the atmosphere. In this dataset the CO2 fluxes from terrestrial vegetation are modelled in order to simulate the variability across a wide range of scales from diurnal to inter-annual. The CH4 chemical loss is represented by a climatological loss rate and the emissions at the surface are taken from a range of datasets.\nReanalysis combines model data with observations from across the world into a globally complete and consistent dataset using a model of the atmosphere based on the laws of physics and chemistry. This principle, called data assimilation, is based on the method used by numerical weather prediction centres and air quality forecasting centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way to allow for the provision of a dataset spanning back more than a decade. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product.\nThe assimilation system is able to estimate biases between observations and to sift good-quality data from poor data. The atmosphere model allows for estimates at locations where data coverage is low or for atmospheric pollutants for which no direct observations are available. The provision of estimates at each grid point around the globe for each regular output time, over a long period, always using the same format, makes reanalysis a very convenient and popular dataset to work with.\nThe observing system has changed drastically over time, and although the assimilation system can resolve data holes, the initially much sparser networks will lead to less accurate estimates. For this reason, EAC4 is only available from 2003 onwards.\nThe analysis procedure assimilates data in a window of 12 hours using the 4D-Var assimilation method, which takes account of the exact timing of the observations and model evolution within the assimilation window.\nThis page provides monthly mean values, worldwide. Original 3-hourly outputs can be accessed at:\nhttps://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-global-ghg-reanalysis-egg4","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CAMS_GLOBAL_GREENHOUSE_GAS_REANALYSIS_MONTHLY_AV_FIELDS/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CAMS_GLOBAL_GREENHOUSE_GAS_REANALYSIS_MONTHLY_AV_FIELDS/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CAMS_GLOBAL_GREENHOUSE_GAS_REANALYSIS_MONTHLY_AV_FIELDS/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CAMS_GLOBAL_GREENHOUSE_GAS_REANALYSIS_MONTHLY_AV_FIELDS","title":"CAMS global greenhouse gas reanalysis (EGG4) monthly averaged fields"},{"rel":"license","type":"application/pdf","href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/licences/licence-to-use-copernicus-products/licence-to-use-copernicus-products_b4b9451f54cffa16ecef5c912c9cebd6979925a956e3fa677976e0cf198c2c18.pdf","title":"Licence to Use Copernicus Products"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/x/OIX4B","title":"CAMS: Reanalysis data documentation"},{"rel":"describedby","type":"text/html","href":"https://atmosphere.copernicus.eu/eqa-reports-global-services#fe56bdb4-1bdf-4d47-b46b-261a1ea57243","title":"Evaluation and quality assurance (EQA) reports"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.5194/acp-23-3829-2023","title":"Technical note: The CAMS greenhouse gas reanalysis from 2003 to 2020"}],"assets":{"thumbnail":{"href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/resources/cams-global-ghg-reanalysis-egg4-monthly/overview_e8cbff72d10984bcb4a7b718a22ebfc227099a10f5b838038c6bfd36bb60c57a.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2003-01-01T00:00:00Z","2020-12-31T23:59:59Z"]]}},"license":"other","keywords":["Aerosol","Atmospheric conditions","Atmosphere (meteorology)","Reanalysis","Global","Greenhouse gas","Past","airPollution","Atmosphere (composition)"],"summaries":{"federation:backends":["cop_ads","wekeo_ecmwf"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Atmosphere Monitoring Service (CAMS)","roles":["host"],"url":"https://atmosphere.copernicus.eu/"}],"created":"2024-05-27T12:12:11Z","updated":"2026-04-24T10:24:59Z","published":"2024-05-27T12:12:11Z","sci:publications":[{"doi":"10.5194/acp-23-3829-2023","citation":"Agustí-Panareda, A., and Coauthors, 2023: Technical note: The CAMS greenhouse gas reanalysis from 2003 to 2020, Atmos. Chem. Phys., 23, 3829–3859, https://doi.org/10.5194/acp-23-3829-2023"}],"dedl:short_description":"The CAMS global greenhouse gas reanalysis (EGG4) dataset contains monthly averaged fields combining model data with observations from across the world, providing a globally complete and consistent dataset of CO2 and CH4 concentrations since 2003."},{"type":"Collection","title":"CAMS global radiative forcings","id":"EO.ECMWF.DAT.CAMS_GLOBAL_RADIATIVE_FORCING","description":"This dataset provides geographical distributions of the radiative forcing (RF) by key atmospheric constituents. The radiative forcing estimates are based on the CAMS reanalysis and additional model simulations and are provided separately for...\n\t- carbon dioxide\n\t- methane\n\t- tropospheric ozone\n\t- stratospheric ozone\n\t- interactions between anthropogenic aerosols and radiation\n\t- interactions between anthropogenic aerosols and clouds\nRadiative forcing measures the imbalance in the Earth's energy budget caused by a perturbation of the climate system, such as changes in atmospheric composition caused by human activities. RF is a useful predictor of globally-averaged temperature change, especially when rapid adjustments of atmospheric temperature and moisture profiles are taken into account. RF has therefore become a quantitative metric to compare the potential climate response to different perturbations. Increases in greenhouse gas concentrations over the industrial era exerted a positive RF, causing a gain of energy in the climate system. In contrast, concurrent changes in atmospheric aerosol concentrations are thought to exert a negative RF leading to a loss of energy.\nProducts are quantified both in “all-sky” conditions, meaning that the radiative effects of clouds are included in the radiative transfer calculations, and in “clear-sky” conditions, which are computed by excluding clouds in the radiative transfer calculations.\nThe upgrade from version 1.5 to 2 consists of an extension of the period by 2017-2018, the addition of an \"effective radiative forcing\" product and new ways to calculate the pre-industrial reference state for aerosols and cloud condensation nuclei. More details are given in the documentation section. New versions may be released in future as scientific methods develop, and existing versions may be extended with later years if data for the period is available from the CAMS reanalysis. Newer versions supercede old versions so it is always recommended to use the latest one.\nCAMS also produces distributions of aerosol optical depths, distinguishing natural from anthropogenic aerosols, which are a separate dataset. See \"Related Data\".","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CAMS_GLOBAL_RADIATIVE_FORCING/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CAMS_GLOBAL_RADIATIVE_FORCING/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CAMS_GLOBAL_RADIATIVE_FORCING/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CAMS_GLOBAL_RADIATIVE_FORCING","title":"CAMS global radiative forcings"},{"rel":"license","type":"application/pdf","href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/licences/licence-to-use-copernicus-products/licence-to-use-copernicus-products_b4b9451f54cffa16ecef5c912c9cebd6979925a956e3fa677976e0cf198c2c18.pdf","title":"Licence to Use Copernicus Products"},{"rel":"describedby","type":"application/pdf","href":"https://atmosphere.copernicus.eu/sites/default/files/2020-07/CAMS74_2019SC4_D2.2.1_202002_Documentation_v1.pdf","title":"Documentation of CAMS Climate Forcing products, version 1.5 "},{"rel":"describedby","type":"application/pdf","href":"https://atmosphere.copernicus.eu/sites/default/files/2020-12/CAMS74_2019SC4_D2.2.2_202008_Documentation_v1.pdf","title":"Documentation of CAMS Climate Forcing products, version 2 "},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.5194/essd-12-1649-2020","title":"Radiative forcing of climate change from the Copernicus reanalysis of atmospheric composition"}],"assets":{"thumbnail":{"href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/resources/cams-global-radiative-forcings/overview_64b7f931c9378814193a3f8879de137f73271a1e1320780d78ec894e974b75e0.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2003-01-01T00:00:00Z","2018-12-31T23:59:59Z"]]}},"license":"other","keywords":["Solar radiation","Aerosol","Reactive gas","Atmospheric conditions","Reanalysis","Global","Greenhouse gas","Radiation","Past","airPollution","Atmosphere (composition)"],"summaries":{"federation:backends":["cop_ads","wekeo_ecmwf"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Atmosphere Monitoring Service (CAMS)","roles":["host"],"url":"https://atmosphere.copernicus.eu/"}],"created":"2023-12-12T15:24:18Z","updated":"2026-04-24T10:24:59Z","published":"2023-12-12T15:24:18Z","sci:publications":[{"doi":"10.5194/essd-12-1649-2020","citation":"Bellouin, N., Davies, W., Shine, K. P., Quaas, J., Mülmenstädt, J., Forster, P. M., Smith, C., Lee, L., Regayre, L., Brasseur, G., Sudarchikova, N., Bouarar, I., Boucher, O., and Myhre, G.: Radiative forcing of climate change from the Copernicus reanalysis of atmospheric composition, Earth Syst. Sci. Data, 12, 1649–1677, https://doi.org/10.5194/essd-12-1649-2020, 2020."}],"dedl:short_description":"This dataset contains geographical distributions of radiative forcing by various atmospheric constituents including CO2, CH4, O3, aerosols, and their interactions, calculated under all-sky and clear-sky conditions."},{"type":"Collection","title":"CAMS global radiative forcing - auxilliary variables","id":"EO.ECMWF.DAT.CAMS_GLOBAL_RADIATIVE_FORCING_AUX","description":"This dataset provides aerosol optical depths and aerosol-radiation radiative effects for four different aerosol origins: anthropogenic, mineral dust, marine, and land-based fine-mode natural aerosol. The latter mostly consists of biogenic aerosols.\nThe data are a necessary complement to the \"CAMS global radiative forcings\" dataset (see \"Related Data\"). The calculation of aerosol radiative forcing requires a discrimination between aerosol of anthropogenic and natural origin. However, the CAMS reanalysis, which is used to provide the aerosol concentrations, does not make this distinction. The anthropogenic fraction was therefore derived by a method which uses aerosol size as a proxy for aerosol origin.","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CAMS_GLOBAL_RADIATIVE_FORCING_AUX/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CAMS_GLOBAL_RADIATIVE_FORCING_AUX/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CAMS_GLOBAL_RADIATIVE_FORCING_AUX/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CAMS_GLOBAL_RADIATIVE_FORCING_AUX","title":"CAMS global radiative forcing - auxilliary variables"},{"rel":"license","type":"application/pdf","href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/licences/licence-to-use-copernicus-products/licence-to-use-copernicus-products_b4b9451f54cffa16ecef5c912c9cebd6979925a956e3fa677976e0cf198c2c18.pdf","title":"Licence to Use Copernicus Products"},{"rel":"describedby","type":"application/pdf","href":"https://atmosphere.copernicus.eu/sites/default/files/2020-07/CAMS74_2019SC4_D2.2.1_202002_Documentation_v1.pdf","title":"Documentation of CAMS Climate Forcing products, version 1.5"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.5194/essd-12-1649-2020","title":"Radiative forcing of climate change from the Copernicus reanalysis of atmospheric composition"}],"assets":{"thumbnail":{"href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/resources/cams-global-radiative-forcing-auxilliary-variables/overview_f9fb14508ff797a23b9b8e88df38d174307ef27015c6145805b74544a47a04d3.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2003-01-01T00:00:00Z","2017-12-31T23:59:59Z"]]}},"license":"other","keywords":["Solar radiation","Atmospheric conditions","Global","Radiation","Past","airPollution","Analysis","Satellite image area"],"summaries":{"federation:backends":["cop_ads","wekeo_ecmwf"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Atmosphere Monitoring Service (CAMS)","roles":["host"],"url":"https://atmosphere.copernicus.eu/"}],"created":"2023-12-12T15:24:18Z","updated":"2026-04-24T10:24:59Z","published":"2023-12-12T15:24:18Z","sci:publications":[{"doi":"10.5194/essd-12-1649-2020","citation":"Bellouin, N., Davies, W., Shine, K. P., Quaas, J., Mülmenstädt, J., Forster, P. M., Smith, C., Lee, L., Regayre, L., Brasseur, G., Sudarchikova, N., Bouarar, I., Boucher, O., and Myhre, G.: Radiative forcing of climate change from the Copernicus reanalysis of atmospheric composition, Earth Syst. Sci. Data, 12, 1649–1677, https://doi.org/10.5194/essd-12-1649-2020, 2020."}],"dedl:short_description":"This dataset contains aerosol optical depths and radiative effects from four aerosol sources: anthropogenic, mineral dust, marine, and land-based fine-mode natural aerosol primarily consisting of biogenic particles."},{"type":"Collection","title":"CAMS global reanalysis (EAC4)","id":"EO.ECMWF.DAT.CAMS_GLOBAL_REANALYSIS_EAC4","description":"EAC4 (ECMWF Atmospheric Composition Reanalysis 4) is the fourth generation ECMWF global reanalysis of atmospheric composition. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using a model of the atmosphere based on the laws of physics and chemistry. This principle, called data assimilation, is based on the method used by numerical weather prediction centres and air quality forecasting centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way to allow for the provision of a dataset spanning back more than a decade. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product.\nThe assimilation system is able to estimate biases between observations and to sift good-quality data from poor data. The atmosphere model allows for estimates at locations where data coverage is low or for atmospheric pollutants for which no direct observations are available. The provision of estimates at each grid point around the globe for each regular output time, over a long period, always using the same format, makes reanalysis a very convenient and popular dataset to work with.\nThe observing system has changed drastically over time, and although the assimilation system can resolve data holes, the initially much sparser networks will lead to less accurate estimates. For this reason, EAC4 is only available from 2003 onwards.\nAlthough the analysis procedure considers chunks of data in a window of 12 hours in one go, EAC4 provides estimates every 3 hours, worldwide. 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Copernicus Atmosphere Monitoring Service (CAMS) Atmosphere Data Store (ADS). (Accessed on \u003cDD-MMM-YYYY\u003e),https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-global-reanalysis-eac4-monthly?tab=overview","sci:publications":[{"doi":"10.5194/acp-19-3515-2019","citation":"Inness, A., Ades, M., Agustí-Panareda, A., Barré, J., Benedictow, A., Blechschmidt, A.-M., Dominguez, J. J., Engelen, R., Eskes, H., Flemming, J., Huijnen, V., Jones, L., Kipling, Z., Massart, S., Parrington, M., Peuch, V.-H., Razinger, M., Remy, S., Schulz, M., and Suttie, M.: The CAMS reanalysis of atmospheric composition, Atmos. Chem. 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This principle, called data assimilation, is based on the method used by numerical weather prediction centres and air quality forecasting centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way to allow for the provision of a dataset spanning back more than a decade. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product.\nThe assimilation system is able to estimate biases between observations and to sift good-quality data from poor data. 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Copernicus Atmosphere Monitoring Service (CAMS) Atmosphere Data Store (ADS). (Accessed on \u003cDD-MMM-YYYY\u003e),https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-global-reanalysis-eac4-monthly?tab=overview","sci:publications":[{"doi":"10.5194/acp-19-3515-2019","citation":"Inness, A., Ades, M., Agustí-Panareda, A., Barré, J., Benedictow, A., Blechschmidt, A.-M., Dominguez, J. J., Engelen, R., Eskes, H., Flemming, J., Huijnen, V., Jones, L., Kipling, Z., Massart, S., Parrington, M., Peuch, V.-H., Razinger, M., Remy, S., Schulz, M., and Suttie, M.: The CAMS reanalysis of atmospheric composition, Atmos. Chem. 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Ground-based and satellite remote-sensing observations provide a means to quantifying the net fluxes between the land and ocean on the one hand and the atmosphere on the other hand. This is done through a process called atmospheric inversion, which uses transport models of the atmosphere to link the observed concentrations of CO2, CH4 and N2O to the net fluxes at the Earth's surface. By correctly modelling the winds, vertical diffusion, and convection in the global atmosphere, the observed concentrations of the greenhouse gases are used to infer the surface fluxes for the last few decades. For CH4 and N2O, the flux inversions account also for the chemical loss of these greenhouse gases. The net fluxes include contributions from the natural biosphere (e.g., vegetation, wetlands) as well anthropogenic contributions (e.g., fossil fuel emissions, rice fields).\nThe data sets for the three species are updated once or twice per year adding the most recent year to the data record, while re-processing the original data record for consistency. This is reflected by the different version numbers. In addition, fluxes for methane are available based on surface air samples only or based on a combination of surface air samples and satellite observations (reflected by an 's' in the version number).","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CAMS_GREENHOUSE_GAS_FLUXES/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CAMS_GREENHOUSE_GAS_FLUXES/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CAMS_GREENHOUSE_GAS_FLUXES/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CAMS_GREENHOUSE_GAS_FLUXES","title":"CAMS global inversion-optimised greenhouse gas fluxes and concentrations"},{"rel":"license","type":"application/pdf","href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/licences/licence-to-use-copernicus-products/licence-to-use-copernicus-products_b4b9451f54cffa16ecef5c912c9cebd6979925a956e3fa677976e0cf198c2c18.pdf","title":"Licence to Use Copernicus Products"},{"rel":"describedby","type":"text/html","href":"https://atmosphere.copernicus.eu/greenhouse-gases-supplementary-products","title":"CAMS Greenhouse Gas Fluxes Documentation"},{"rel":"describedby","type":"text/html","href":"https://atmosphere.copernicus.eu/supplementary-services#c21ece9c-9469-4605-bcb1-2831778d052b","title":"Evaluation and quality assurance (EQA) reports"}],"assets":{"thumbnail":{"href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/resources/cams-global-greenhouse-gas-inversion/overview_38d8bdbbbdcb2ad03e03cf6fed7c2fa885db8128b8da42dcccc57c90064c73be.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1979-01-01T00:00:00Z","2023-01-01T00:00:00Z"]]}},"license":"other","keywords":["Emissions and surface fluxes","Atmospheric conditions","Reanalysis","Global","Greenhouse gas","Past","airPollution","Atmosphere (composition)"],"summaries":{"federation:backends":["cop_ads","wekeo_ecmwf"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Atmosphere Monitoring Service (CAMS)","roles":["host"],"url":"https://atmosphere.copernicus.eu/"}],"created":"2023-12-12T15:24:18Z","updated":"2026-04-24T10:24:59Z","published":"2023-12-12T15:24:18Z","dedl:short_description":"This dataset provides CAMS-inversion optimised greenhouse gas fluxes and concentrations for CO2, CH4, and N2O with updates every 1-2 years, including both natural and anthropogenic sources such as vegetation, wetlands, fossil fuels, and rice fields."},{"type":"Collection","title":"CAMS solar radiation time-series","id":"EO.ECMWF.DAT.CAMS_SOLAR_RADIATION_TIMESERIES","description":"The CAMS solar radiation services provide historical values (2004 to present) of global (GHI), direct (BHI) and diffuse (DHI) solar irradiation, as well as direct normal irradiation (BNI). The aim is to fulfil the needs of European and national policy development and the requirements of both commercial and public downstream services, e.g. for planning, monitoring, efficiency improvements and the integration of solar energy systems into energy supply grids.\nFor clear-sky conditions, an irradiation time series is provided for any location in the world using information on aerosol, ozone and water vapour from the CAMS global forecasting system. Other properties, such as ground albedo and ground elevation, are also taken into account. Similar time series are available for cloudy (or \"all sky\") conditions but, since the high-resolution cloud information is directly inferred from satellite observations, these are currently only available inside the field-of-view of the Meteosat Second Generation (MSG) satellite, which is roughly Europe, Africa, the Atlantic Ocean and the Middle East.\nData is offered in both ASCII and netCDF format. Additionally, an ASCII \"expert mode\" format can be selected which contains in addition to the irradiation, all the input data used in their calculation (aerosol optical properties, water vapour concentration, etc). This additional information is only meaningful in the time frame at which the calculation is performed and so is only available at 1-minute time steps in universal time (UT).","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CAMS_SOLAR_RADIATION_TIMESERIES/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CAMS_SOLAR_RADIATION_TIMESERIES/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CAMS_SOLAR_RADIATION_TIMESERIES/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CAMS_SOLAR_RADIATION_TIMESERIES","title":"CAMS solar radiation time-series"},{"rel":"license","type":"application/pdf","href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/licences/licence-to-use-copernicus-products/licence-to-use-copernicus-products_b4b9451f54cffa16ecef5c912c9cebd6979925a956e3fa677976e0cf198c2c18.pdf","title":"Licence to Use Copernicus Products"},{"rel":"cite-as","type":"text/html","href":"https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-solar-radiation-timeseries?tab=overview","title":"CAMS solar radiation time-series"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/x/jOLjDw","title":"CAMS solar radiation time-series: data documentation"},{"rel":"describedby","type":"text/html","href":"https://atmosphere.copernicus.eu/supplementary-services#fa6856b7-a306-4cc4-9137-f3e0cb703093","title":"Evaluation and quality assurance (EQA) reports"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.1127/metz/2022/1132","title":"Surface solar irradiation retrieval from MSG/SEVIRI based on APOLLO Next Generation and HELIOSAT‑4 methods"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.1127/metz/2016/0781","title":"Fast radiative transfer parameterisation for assessing the surface solar irradiance: The Heliosat‑4 method"}],"assets":{"thumbnail":{"href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/resources/cams-solar-radiation-timeseries/overview_51b05dc9c04479bfe717483ad887ef69461fc77410257222a19775cf68790cce.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2004-02-01T00:00:00Z",null]]}},"license":"other","keywords":["Solar radiation","Atmospheric conditions","Global","Radiation","Past","airPollution","Analysis","Satellite image area"],"summaries":{"federation:backends":["cop_ads","wekeo_ecmwf"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Atmosphere Monitoring Service (CAMS)","roles":["host"],"url":"https://atmosphere.copernicus.eu/"}],"created":"2023-12-12T15:24:18Z","updated":"2026-04-24T10:24:59Z","published":"2023-12-12T15:24:18Z","sci:citation":"CAMS solar radiation time-series. Copernicus Atmosphere Monitoring Service (CAMS) Atmosphere Data Store (ADS). (Accessed on DD-MMM-YYY), https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-solar-radiation-timeseries?tab=overview","sci:publications":[{"doi":"10.1127/metz/2022/1132","citation":"Schroedter-Homscheidt, M., Azam, F., Betcke, J., Hanrieder, N., Lefèvre, M., Saboret, L., Saint-Drenan, Y.-M.: Surface solar irradiation retrieval from MSG/SEVIRI based on APOLLO Next Generation and HELIOSAT-4 methods, Contrib. Atm. Sci./Meteorol. Z.,2022, doi:10.1127/metz/2022/1132."},{"doi":"10.1127/metz/2016/0781","citation":"Qu, Z., Oumbe, A., Blanc, P., Espinar, B., Gesell, G., Gschwind, B., Klüser, L., Lefèvre, M., Saboret, L., Schroedter-Homscheidt, M., and Wald L.: Fast radiative transfer parameterisation for assessing the surface solar irradiance: The Heliosat-4 method, Meteorol. Z., 26, 33-57, doi: 10.1127/metz/2016/0781,2017. Available for download at https://www.schweizerbart.de/papers/metz/detail/26/87036/Fast_radiative_transfer_parameterisation_for_asses?af=crossref."}],"dedl:short_description":"The CAMS solar radiation service provides historical global solar irradiance datasets from 2004 onwards, covering various types including GHI, BHI, DHI, and BNI, with options for clear-sky or cloudy conditions worldwide except outside MSG's field-of-view, offering data formats like ASCII and netCDF along with detailed inputs in \"expert mode\"."},{"type":"Collection","title":"Fire danger indices historical data from the Copernicus Emergency Management Service","id":"EO.ECMWF.DAT.CEMS_FIRE_HISTORICAL","description":"This data set provides complete historical reconstruction of meteorological conditions favourable to the start, spread and sustainability of fires. The fire danger metrics provided are part of a vast dataset produced by the Copernicus Emergency Management Service for the\nEuropean Forest Fire Information System (EFFIS). The European Forest Fire Information System incorporates the fire danger indices for three different models developed in Canada, United States and Australia. In this dataset the fire danger indices are calculated using weather forecast from historical simulations provided by ECMWF ERA5 reanalysis.\nERA5 by combining  model data and  a vast set of quality controlled observations provides a  globally complete and consistent data-set and is regarded as a good proxy for observed atmospheric conditions.\nThe selected data records in this data set are regularly extended with time as ERA5 forcing data become available. \nThis dataset is produced by ECMWF in its role of the computational centre for fire danger forecast of the CEMS,  on behalf of the Joint Research Centre which is the managing entity of the service.\n\nVariables in the dataset/application are:\nBuild-up index, Burning index, Danger rating, Drought code, Duff moisture code, Energy release component, Fine fuel moisture code, Fire daily severity index, Fire danger index, Fire weather index, Ignition component, Initial spread index, Keetch-Byram drought index, Spread component","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CEMS_FIRE_HISTORICAL/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CEMS_FIRE_HISTORICAL/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CEMS_FIRE_HISTORICAL/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CEMS_FIRE_HISTORICAL","title":"Fire danger indices historical data from the Copernicus Emergency Management Service"},{"rel":"license","type":"application/pdf","href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/licences/licence-to-use-copernicus-products/licence-to-use-copernicus-products_b4b9451f54cffa16ecef5c912c9cebd6979925a956e3fa677976e0cf198c2c18.pdf","title":"Licence to Use Copernicus Products"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.24381/cds.0e89c522","title":"Fire danger indices historical data from the Copernicus Emergency Management Service"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/CEMS/Fire+danger+indices+historical+data+from+the+Copernicus+Emergency+Management+Service","title":"Product User Guide"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.1038/s41597-020-0554-z","title":"ERA5-based global meteorological wildfire danger maps"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.1038/sdata.2019.32","title":"A 1980–2018 global fire danger re-analysis dataset for the Canadian Fire Weather Indices"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.5194/nhess-20-2365-2020","title":"Fire Weather Index: the skill provided by the European Centre for Medium-Range Weather Forecasts ensemble prediction system"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.1175/JAMC-D-15-0297.1","title":"The Potential Predictability of Fire Danger Provided by Numerical Weather Prediction"}],"assets":{"thumbnail":{"href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/resources/cems-fire-historical-v1/overview_7d52aa9bfd619151f13dba1d6a0c625edcb157696f3fbf0db3c24f0993f45e77.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1979-01-01T00:00:00Z","2022-10-29T23:59:59Z"]]}},"license":"other","keywords":["Land cover","Reanalysis","Global","Land (biosphere)","Past","Copernicus CEMS"],"summaries":{"federation:backends":["cop_ewds","wekeo_ecmwf"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Emergency Management Service (CEMS)","roles":["host"],"url":"https://emergency.copernicus.eu/"}],"created":"2023-08-28T15:11:18Z","updated":"2026-04-24T10:24:59Z","published":"2023-08-28T15:11:18Z","sci:doi":"10.24381/cds.0e89c522","sci:citation":"Copernicus Climate Change Service, Climate Data Store, (2019): Fire danger indices historical data from the Copernicus Emergency Management Service. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: [10.24381/cds.0e89c522](https://doi.org/10.24381/cds.0e89c522) (Accessed on DD-MMM-YYYY)","sci:publications":[{"doi":"10.1038/s41597-020-0554-z","citation":"Vitolo, C., Di Giuseppe, F., Barnard, C. et al. ERA5-based global meteorological wildfire danger maps. Sci Data 7, 216 (2020). https://doi.org/10.1038/s41597-020-0554-z"},{"doi":"10.1038/sdata.2019.32","citation":"Vitolo, C., Di Giuseppe, F., Krzeminski, B. et al. A 1980–2018 global fire danger re-analysis dataset for the Canadian Fire Weather Indices. Sci Data 6, 190032 (2019). https://doi.org/10.1038/sdata.2019.32"},{"doi":"10.5194/nhess-20-2365-2020","citation":"Di Giuseppe, F., Vitolo, C., Krzeminski, B., Barnard, C., Maciel, P., and San-Miguel, J.: Fire Weather Index: the skill provided by the European Centre for Medium-Range Weather Forecasts ensemble prediction system, Nat. Hazards Earth Syst. Sci., 20, 2365–2378, https://doi.org/10.5194/nhess-20-2365-2020, 2020."},{"doi":"10.1175/JAMC-D-15-0297.1","citation":"Di Giuseppe, F., Pappenberger, F., Wetterhall, F., Krzeminski, B., Camia, A., Libertá, G., and San Miguel, J. (2016). The Potential Predictability of Fire Danger Provided by Numerical Weather Prediction. Journal of Applied Meteorology and Climatology 55, 11, 2469-2491, available from:  https://doi.org/10.1175/JAMC-D-15-0297.1"}],"dedl:short_description":"Historical fire danger indices from the Copernicus Emergency Management Service's European Forest Fire Information System include various fire danger metrics based on Canadian, US, and Australian models, derived from ECMWF ERA5 reanalysed weather forecasts."},{"type":"Collection","title":"River discharge and related forecasted data by the European Flood Awareness System","id":"EO.ECMWF.DAT.CEMS_GLOFAS_FORECAST","description":"This dataset provides gridded modelled hydrological time series forced with medium-range meteorological forecasts. The data is a consistent representation of the most important hydrological variables across the European Flood Awareness System (EFAS) domain. The temporal resolution is sub-daily high-resolution and ensemble forecasts of:\n\nRiver discharge\nSoil moisture for three soil layers\nSnow water equivalent\n\nIt also provides static data on soil depth for the three soil layers. Soil moisture and river discharge data are accompanied by ancillary files for interpretation (see related variables and links in the documentation).\nThis data set was produced by forcing the LISFLOOD hydrological model at a 5x5km resolution with meteorological forecasts. The forecasts are initialised twice daily at 00 and 12 UTC with time steps of 6 or 24 hours and lead times between 5 and 15 days depending on the forcing numerical weather prediction model. The forcing meteorological data are high-resolution and ensemble forecasts from the European Centre of Medium-range Weather Forecasts (ECMWF) with 51 ensemble members, high-resolution forecasts from the Deutsches Wetter Dienst (DWD) and the ensemble forecasts from the COSMO Local Ensemble Prediction System (COSMO-LEPS) with 20 ensemble members. The hydrological forecasts are available from 2018-10-10 up until present with a 30-day delay. The real-time data is only available to EFAS partners.\nCompanion datasets, also available through the CDS, are historical simulations which can be used to derive the hydrological climatology and for verification; reforecasts for research, local skill assessment and post-processing; and seasonal forecasts and reforecasts for users looking for longer leadtime forecasts. For users looking for global hydrological data, we refer to the Global Flood Awareness System (GloFAS) forecasts and historical simulations. All these datasets are part of the operational flood forecasting within the Copernicus Emergency Management Service (CEMS).\n\nVariables in the dataset/application are:\nRiver discharge in the last 24 hours, River discharge in the last 6 hours, Snow depth water equivalent, Soil depth, Volumetric soil moisture\n\nVariables in the dataset/application are:\nOrography, Upstream area","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CEMS_GLOFAS_FORECAST/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CEMS_GLOFAS_FORECAST/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CEMS_GLOFAS_FORECAST/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CEMS_GLOFAS_FORECAST","title":"River discharge and related forecasted data by the European Flood Awareness System"},{"rel":"license","type":"application/pdf","href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/licences/cems-floods/cems-floods_428a6e1019ec50b3dad9c37a90d630fab139059933a939dd5df620bfcb420cc3.pdf","title":"CEMS-FLOODS datasets licence"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.24381/cds.ff1aef77","title":"River discharge and related forecasted data by the Global Flood Awareness System"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/CEMS/Global+Flood+Awareness+System","title":"Global Flood Awareness System"},{"rel":"describedby","type":"text/html","href":"http://www.globalfloods.eu/","title":"GloFAS web site"},{"rel":"describedby","type":"text/html","href":"https://www.sciencedirect.com/science/article/pii/S0022169418307467","title":"Calibration of the Global Flood Awareness System (GloFAS) using daily streamflow data"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/CEMS/Auxiliary+Data","title":"Auxiliary data"}],"assets":{"thumbnail":{"href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/resources/efas-forecast/overview_ddd9074d456be00a54d03c320485bdbb1d1871507eccaa1039404a9c2c62fe31.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2019-11-05T00:00:00Z",null]]}},"license":"other","keywords":["Climatology","Europe","Reanalysis","Past","Copernicus CEMS","Land (hydrology)"],"summaries":{"federation:backends":["cop_ewds"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Emergency Management Service (CEMS)","roles":["host"],"url":"https://emergency.copernicus.eu/"}],"created":"2023-08-28T15:11:18Z","updated":"2026-04-24T10:24:59Z","published":"2023-08-28T15:11:18Z","sci:doi":"10.24381/cds.ff1aef77","sci:citation":"## For forecast data issued from 2019-05-19, v2.1:\nZsoter, E., Harrigan, S., Barnard, C., Wetterhall, F., Salamon, P., Prudhomme, C. (2019): River discharge and related forecasted data from the Global Flood Awareness System. v2.1. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). URL: https://cds.climate.copernicus.eu/cdsapp#!/dataset/cems-glofas-forecast (Accessed on DD-MMM-YYYY)\n## For forecast data issued from 2021-05-26, v3.1:\nZsoter, E., Harrigan, S., Barnard, C., Wetterhall, F., Ferrario, I., Mazzetti, C., Alfieri, L., Salamon, P., Prudhomme, C. (2021): River discharge and related forecasted data from the Global Flood Awareness System. v3.1. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). URL: https://cds.climate.copernicus.eu/cdsapp#!/dataset/cems-glofas-forecast (Accessed on DD-MMM-YYYY)\n## For forecast data issued from 2023-07-26, v4.0: Grimaldi, S., Salamon, P., Disperati, J., Zsoter, E., Russo, C., Ramos, A., Carton De Wiart, C., Barnard, C., Hansford, E., Gomes, G., Prudhomme, C. (2023): River discharge and related forecasted data from the Global Flood Awareness System. v4.0. European Commission, Joint Research Centre (JRC). URL: https://cds.climate.copernicus.eu/cdsapp#!/dataset/cems-glofas-forecast (Accessed on DD-MMM-YYYY)\n## Citing the web catalogue entry:\nCopernicus Climate Change Service (C3S) (2020): River discharge and related forecasted data from the Global Flood Awareness System. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.ff1aef77 (Accessed on DD-MMM-YYYY)","dedl:short_description":"The dataset contains gridded modelled hydrological time series including river discharge, snow water equivalent, soil moisture, and soil depth over Europe, driven by high-resolution ensemble meteorological forecasts from multiple sources."},{"type":"Collection","title":"River discharge and related historical data from the Global Flood Awareness System","id":"EO.ECMWF.DAT.CEMS_GLOFAS_HISTORICAL","description":"This dataset contains global modelled daily data of river discharge from the Global Flood Awareness System (GloFAS), which is part of the Copernicus Emergency Management Service (CEMS). River discharge, or river flow as it is also known, is defined as the amount of water that flows through a river section at a given time. \nThis dataset is simulated by forcing a hydrological modelling chain with inputs from a global reanalysis. Data availability for the historical simulation is from 1979-01-01 up to near real time.","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CEMS_GLOFAS_HISTORICAL/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CEMS_GLOFAS_HISTORICAL/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CEMS_GLOFAS_HISTORICAL/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CEMS_GLOFAS_HISTORICAL","title":"River discharge and related historical data from the Global Flood Awareness System"},{"rel":"license","type":"application/pdf","href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/licences/cems-floods/cems-floods_428a6e1019ec50b3dad9c37a90d630fab139059933a939dd5df620bfcb420cc3.pdf","title":"CEMS-FLOODS datasets licence"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.24381/cds.a4fdd6b9","title":"River discharge and related historical data from the Global Flood Awareness System"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/CEMS/Global+Flood+Awareness+System","title":"Information pages for GloFAS"},{"rel":"describedby","type":"text/html","href":"http://www.globalfloods.eu/","title":"GloFAS web site"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/CEMS/Auxiliary+Data","title":"Auxiliary data"}],"assets":{"thumbnail":{"href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/resources/cems-glofas-historical/overview_779934fbef02d194554ae626a1c5570e24a2e2a2e4b2ae7e8c54b4ca0c7fe9d0.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1979-01-01T00:00:00Z",null]]}},"license":"other","keywords":["Climatology","Reanalysis","Global","Land (hydrology)","Past","Copernicus CEMS"],"summaries":{"federation:backends":["cop_ewds"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Emergency Management Service (CEMS)","roles":["host"],"url":"https://emergency.copernicus.eu/"}],"created":"2023-08-28T15:11:18Z","updated":"2026-04-24T10:24:59Z","published":"2023-08-28T15:11:18Z","sci:doi":"10.24381/cds.a4fdd6b9","sci:citation":"## For v2.1:\nHarrigan, S., Zsoter, E., Barnard, C., Wetterhall, F., Ferrario, I., Mazzetti, C., Alfieri, L., Salamon, P., Prudhomme, C. (2021): River discharge and related historical data from the Global Flood Awareness System. v2.1. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). URL: https://cds.climate.copernicus.eu/cdsapp#!/dataset/cems-glofas-historical (Accessed on DD-MMM-YYYY)\n## For v3.1:\nZsoter, E., Harrigan, S., Barnard, C., Wetterhall, F., Ferrario, I., Mazzetti, C., Alfieri, L., Salamon, P., Prudhomme, C. (2021): River discharge and related historical data from the Global Flood Awareness System. v3.1. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). URL: https://cds.climate.copernicus.eu/cdsapp#!/dataset/cems-glofas-historical (Accessed on DD-MMM-YYYY)\n## For v4.0:\nGrimaldi, S., Salamon, P., Disperati, J., Zsoter, E., Russo, C., Ramos, A., Carton De Wiart, C., Barnard, C., Hansford, E., Gomes, G., Prudhomme, C. (2022): River discharge and related historical data from the Global Flood Awareness System. v4.0. European Commission, Joint Research Centre (JRC). URL: https://cds.climate.copernicus.eu/cdsapp#!/dataset/cems-glofas-historical (Accessed on DD-MMM-YYYY)\n## Citing the web catalogue entry:\nCopernicus Climate Change Service (C3S) (2019): River discharge and related historical data from the Global Flood Awareness System. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.a4fdd6b9 (Accessed on DD-MMM-YYYY)","dedl:short_description":"The dataset includes globally-modelled daily river discharge data from GloFAS, covering 1979-present, derived from a hydrological modelling chain forced by global reanalyses."},{"type":"Collection","title":"Reforecasts of river discharge and related data by the Global Flood Awareness System","id":"EO.ECMWF.DAT.CEMS_GLOFAS_REFORECAST","description":"This dataset provides a gridded modelled time series of river discharge, forced with medium- to sub-seasonal range meteorological reforecasts. The data is a consistent representation of a key hydrological variable across the global domain, and is a product of the Global Flood Awareness System (GloFAS). It is accompanied by an ancillary file for interpretation that provides the upstream area (see the related variables table and associated link in the documentation).\nThis dataset was produced by forcing a hydrological modelling chain with input from the European Centre for Medium-range Weather Forecasts (ECMWF) 11-member ensemble ECMWF-ENS reforecasts. Reforecasts are forecasts run over past dates, and those presented here are used for providing a suitably long time period against which the skill of the 30-day real-time operational forecast can be assessed. The reforecasts are initialised twice weekly with lead times up to 46 days, at 24-hour steps for 20 years in the recent history. For more specific information on the how the reforecast dataset is produced we refer to the documentation.\nCompanion datasets, also available through the Climate Data Store (CDS), are the operational forecasts, historical simulations that can be used to derive the hydrological climatology, and seasonal forecasts and reforecasts for users looking for long term forecasts. For users looking specifically for European hydrological data, we refer to the European Flood Awareness System (EFAS) forecasts and historical simulations. All these datasets are part of the operational flood forecasting within the Copernicus Emergency Management Service (CEMS).","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CEMS_GLOFAS_REFORECAST/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CEMS_GLOFAS_REFORECAST/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CEMS_GLOFAS_REFORECAST/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CEMS_GLOFAS_REFORECAST","title":"Reforecasts of river discharge and related data by the Global Flood Awareness System"},{"rel":"license","type":"application/pdf","href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/licences/cems-floods/cems-floods_428a6e1019ec50b3dad9c37a90d630fab139059933a939dd5df620bfcb420cc3.pdf","title":"CEMS-FLOODS datasets licence"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/CEMS/Global+Flood+Awareness+System","title":"Information pages for GloFAS"},{"rel":"describedby","type":"text/html","href":"http://www.globalfloods.eu/","title":"GloFAS web site"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/CEMS/Auxiliary+Data","title":"Auxiliary data"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.24381/cds.2d78664e","title":"Reforecasts of river discharge and related data by the Global Flood Awareness System"}],"assets":{"thumbnail":{"href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/resources/cems-glofas-reforecast/overview_36fc7b601512e3619bc5ba70ae0488b911d9d74e203400f9a321f5745768f6a5.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1999-01-03T00:00:00Z","2023-11-25T23:59:59Z"]]}},"license":"other","keywords":["Reforecast","Reforecasts","Global","Land (hydrology)","Past","Copernicus CEMS"],"summaries":{"federation:backends":["cop_ewds"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Emergency Management Service (CEMS)","roles":["host"],"url":"https://emergency.copernicus.eu/"}],"created":"2023-08-28T15:11:18Z","updated":"2026-04-24T10:24:59Z","published":"2023-08-28T15:11:18Z","sci:doi":"10.24381/cds.2d78664e","sci:citation":"## For v2.2:\nZsoter, E., Harrigan, S., Barnard, C., Blick, M., Ferrario, I., Wetterhall, F., Prudhomme, C. (2020): Reforecasts of river discharge and related data by the Global Flood Awareness System. v2.2. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). URL: https://cds.climate.copernicus.eu/cdsapp#!/dataset/cems-glofas-reforecast (Accessed on DD-MMM-YYYY)\n## For v3.1:\nZsoter, E., Harrigan, S., Barnard, C., Blick, M., Ferrario, I., Wetterhall, F., Mazzetti, C., Alfieri, L., Salamon, P., Prudhomme, C. (2021): Reforecasts of river discharge and related data by the Global Flood Awareness System. v3.1. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). URL: https://cds.climate.copernicus.eu/cdsapp#!/dataset/cems-glofas-reforecast (Accessed on DD-MMM-YYYY)\n## For v4.0:\nGrimaldi, S., Salamon, P., Disperati, J., Zsoter, E., Russo, C., Ramos, A., Carton De Wiart, C., Barnard, C., Hansford, E., Gomes, G., Prudhomme, C. (2023): Reforecasts of river discharge and related data by the Global Flood Awareness System. v4.0. European Commission, Joint Research Centre (JRC). URL: https://cds.climate.copernicus.eu/cdsapp#!/dataset/cems-glofas-reforecast (Accessed on DD-MMM-YYYY)\n## Citing the web catalogue entry:\nCopernicus Climate Change Service (C3S) (2020): Reforecasts of river discharge and related data by the Global Flood Awareness System. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.2d78664e (Accessed on DD-MMM-YYYY)","dedl:short_description":"The GloFAS dataset contains gridded modelled river discharge time series based on medium- to sub-seasonal meteorological reforecasts from the ECMWF-ENS system, covering a 20-year period with bi-weekly initialisations up to 46 days ahead."},{"type":"Collection","title":"Seasonal forecasts of river discharge and related data by the Global Flood Awareness System","id":"EO.ECMWF.DAT.CEMS_GLOFAS_SEASONAL","description":"This dataset provides a gridded modelled time series of river discharge, forced with seasonal range meteorological forecasts. The data is a consistent representation of a key hydrological variable across the global domain, and is a product of the Global Flood Awareness System (GloFAS). It is accompanied by an ancillary file for interpretation that provides the upstream area (see the related variables table and associated link in the documentation).\nThis dataset was produced by forcing the LISFLOOD hydrological model at a 0.1° (~11 km at the equator) resolution with downscaled runoff forecasts from the European Centre for Medium-range Weather Forecasts (ECMWF) 51-member ensemble seasonal forecasting system, SEAS5. The forecasts are initialised on the first of each month with a 24-hourly time step, and cover 123 days.\nCompanion datasets, also available through the Climate Data Store (CDS), are the operational forecasts, historical simulations that can be used to derive the hydrological climatology, and medium-range and seasonal reforecasts. The latter dataset enables research, local skill assessment and post-processing of the seasonal forecasts. In addition, the seasonal reforecasts are also used to derive a specific range dependent climatology for the seasonal system. For users looking specifically for European hydrological data, we refer to the European Flood Awareness System (EFAS) forecasts and historical simulations. All these datasets are part of the operational flood forecasting within the Copernicus Emergency Management Service (CEMS).","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CEMS_GLOFAS_SEASONAL/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CEMS_GLOFAS_SEASONAL/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CEMS_GLOFAS_SEASONAL/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CEMS_GLOFAS_SEASONAL","title":"Seasonal forecasts of river discharge and related data by the Global Flood Awareness System"},{"rel":"license","type":"application/pdf","href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/licences/cems-floods/cems-floods_428a6e1019ec50b3dad9c37a90d630fab139059933a939dd5df620bfcb420cc3.pdf","title":"CEMS-FLOODS datasets licence"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.24381/cds.00b6c4fb","title":"Seasonal forecasts of river discharge and related data by the Global Flood Awareness System"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/CEMS/Global+Flood+Awareness+System","title":"Information pages for GloFAS"},{"rel":"describedby","type":"text/html","href":"http://www.globalfloods.eu","title":"GloFAS web site"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/CEMS/Auxiliary+Data","title":"Auxiliary data"}],"assets":{"thumbnail":{"href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/resources/cems-glofas-seasonal/overview_0885f764fd7ce14e5c511c5751d22b0610d3004d3de56f389562cace8f67e2bc.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2020-12-01T00:00:00Z","2024-12-31T23:59:59Z"]]}},"license":"other","keywords":["Forecast","Seasonal forecasts","Global","Land (hydrology)","Present","Copernicus CEMS"],"summaries":{"federation:backends":["cop_ewds"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Emergency Management Service (CEMS)","roles":["host"],"url":"https://emergency.copernicus.eu/"}],"created":"2023-08-28T15:11:18Z","updated":"2026-04-24T10:24:59Z","published":"2023-08-28T15:11:18Z","sci:doi":"10.24381/cds.00b6c4fb","sci:citation":"## For forecasts issued from 2020-11-01, v2.2:\nBarnard, C., Zsoter, E., Blick, M., Ferrario, I., Wetterhall, F., Prudhomme, C. (2020): Seasonal forecasts of river discharge and related data by the Global Flood Awareness System. v2.2. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). URL: https://cds.climate.copernicus.eu/cdsapp#!/dataset/cems-glofas-seasonal (Accessed on DD-MMM-YYYY)\n## For forecasts issued from 2021-05-26, v3.1:\nBarnard, C., Zsoter, E., Blick, M., Ferrario, I., Wetterhall, F., Mazzetti, C., Alfieri, L., Salamon, P., Prudhomme, C. (2021): Seasonal forecasts of river discharge and related data by the Global Flood Awareness System. v3.1. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). URL: https://cds.climate.copernicus.eu/cdsapp#!/dataset/cems-glofas-seasonal (Accessed on DD-MMM-YYYY)\n## For forecasts issued from 2023-07-26, v4.0:\nGrimaldi, S., Salamon, P., Disperati, J., Zsoter, E., Russo, C., Ramos, A., Carton De Wiart, C., Barnard, C., Hansford, E., Gomes, G., Prudhomme, C. (2023): Seasonal forecasts of river discharge and related data by the Global Flood Awareness System. v4.0. European Commission, Joint Research Centre (JRC). URL: https://cds.climate.copernicus.eu/cdsapp#!/dataset/cems-glofas-seasonal (Accessed on DD-MMM-YYYY)\n## Citing the web catalogue entry:\nCopernicus Climate Change Service (C3S) (2020): Seasonal forecasts of river discharge and related data by the Global Flood Awareness System. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.00b6c4fb (Accessed on DD-MMM-YYYY)","dedl:short_description":"The GloFAS dataset contains globally gridded modelled river discharge forecasts based on ECMWF's SEAS5 seasonal forecast system, covering 123 days with a 24-hourly time step at approximately 11km resolution."},{"type":"Collection","title":"Seasonal reforecasts of river discharge and related data from the Global Flood Awareness System","id":"EO.ECMWF.DAT.CEMS_GLOFAS_SEASONAL_REFORECAST","description":"This dataset provides a gridded modelled time series of river discharge forced with seasonal range meteorological reforecasts. The data is a consistent representation of a key hydrological variable across the global domain, and is a product of the Global Flood Awareness System (GloFAS). It is accompanied by an ancillary file for interpretation that provides the upstream area (see the related variables table and associated link in the documentation).\nThis dataset was produced by forcing a hydrological modelling chain with input from the European Centre for Medium-range Weather Forecasts (ECMWF) ensemble seasonal forecasting system, SEAS5. For the period of 1981 to 2016 the number of ensemble members is 25, whilst reforecasts produced for 2017 onwards use a 51-member ensemble. Reforecasts are forecasts run over past dates, with those presented here used for producing the seasonal river discharge thresholds. In addition, they provide a suitably long time period against which the skill of the seasonal forecast can be assessed. The reforecasts are initialised monthly and run for 123 days, with a 24-hourly time step. For more specific information on the how the seasonal reforecast dataset is produced we refer to the documentation.\nCompanion datasets, also available through the Climate Data Store (CDS), include the seasonal forecasts, for which the dataset provided here can be useful for local skill assessment and post-processing. For users looking for shorter term forecasts there are also medium-range forecasts and reforecasts available, as well as historical simulations that can be used to derive the hydrological climatology. For users looking specifically for European hydrological data, we refer to the European Flood Awareness System (EFAS) forecasts and historical simulations. All these datasets are part of the operational flood forecasting within the Copernicus Emergency Management Service (CEMS).","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CEMS_GLOFAS_SEASONAL_REFORECAST/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CEMS_GLOFAS_SEASONAL_REFORECAST/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CEMS_GLOFAS_SEASONAL_REFORECAST/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CEMS_GLOFAS_SEASONAL_REFORECAST","title":"Seasonal reforecasts of river discharge and related data from the Global Flood Awareness System"},{"rel":"license","type":"application/pdf","href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/licences/cems-floods/cems-floods_428a6e1019ec50b3dad9c37a90d630fab139059933a939dd5df620bfcb420cc3.pdf","title":"CEMS-FLOODS datasets licence"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.24381/cds.0aa9b9dd","title":"Seasonal reforecasts of river discharge and related data from the Global Flood Awareness System"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/CEMS/Global+Flood+Awareness+System","title":"Information pages for GloFAS"},{"rel":"describedby","type":"text/html","href":"http://www.globalfloods.eu/","title":"GloFAS web site"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/CEMS/Auxiliary+Data","title":"Auxiliary data"}],"assets":{"thumbnail":{"href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/resources/cems-glofas-seasonal-reforecast/overview_607aab0c8084d2b2ecdae18f6a6c7023219edd0cadf9f2d9eb44d8a6dcef7dda.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1981-01-01T00:00:00Z",null]]}},"license":"other","keywords":["Reforecast","Seasonal reforecasts","Global","Land (hydrology)","Past","Copernicus CEMS"],"summaries":{"federation:backends":["cop_ewds"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Emergency Management Service (CEMS)","roles":["host"],"url":"https://emergency.copernicus.eu/"}],"created":"2023-08-28T15:11:18Z","updated":"2026-04-24T10:24:59Z","published":"2023-08-28T15:11:18Z","sci:doi":"10.24381/cds.0aa9b9dd","sci:citation":"## For v2.2:\nBarnard, C., Zsoter, E., Blick, M., Ferrario, I., Wetterhall, F., Prudhomme, C. (2020): Seasonal reforecasts of river discharge and related data by the Global Flood Awareness System. v2.2. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). URL: https://cds.climate.copernicus.eu/cdsapp#!/dataset/cems-glofas-seasonal-reforecast (Accessed on DD-MMM-YYYY)\n## For v3.1:\nBarnard, C., Zsoter, E., Blick, M., Ferrario, I., Wetterhall, F., Mazzetti, C., Alfieri, L., Salamon, P., Prudhomme, C. (2021): Seasonal reforecasts of river discharge and related data by the Global Flood Awareness System. v3.1. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). URL: https://cds.climate.copernicus.eu/cdsapp#!/dataset/cems-glofas-seasonal-reforecast (Accessed on DD-MMM-YYYY)\n## For v4.0:\nGrimaldi, S., Salamon, P., Disperati, J., Zsoter, E., Russo, C., Ramos, A., Carton De Wiart, C., Barnard, C., Hansford, E., Gomes, G., Prudhomme, C. (2023): Seasonal reforecasts of river discharge and related data by the Global Flood Awareness System. v4.0. European Commission, Joint Research Centre (JRC). URL: https://cds.climate.copernicus.eu/cdsapp#!/dataset/cems-glofas-seasonal-reforecast (Accessed on DD-MMM-YYYY)\n## Citing the web catalogue entry:\nCopernicus Climate Change Service (C3S) (2021): Seasonal reforecasts of river discharge and related data by the Global Flood Awareness System. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.0aa9b9dd (Accessed on DD-MMM-YYYY)","dedl:short_description":"The dataset contains globally gridded modelled river discharge time series based on seasonally forced ECMWF's SEAS5 reforecasts, spanning 1981-2016 with 25 ensemble members and 2017-onwards with 51 members."},{"type":"Collection","title":"CMIP6 climate projections","id":"EO.ECMWF.DAT.CMIP6_CLIMATE_PROJECTIONS","description":"This catalogue entry provides daily and monthly global climate projections data from a large number of experiments, models and time periods computed in the framework of the sixth phase of the Coupled Model Intercomparison Project (CMIP6).\nCMIP6 data underpins the Intergovernmental Panel on Climate Change 6th Assessment Report. The use of these data is mostly aimed at:\n\naddressing outstanding scientific questions that arose as part of the IPCC reporting process;\nimproving the understanding of the climate system;\nproviding estimates of future climate change and related uncertainties;\nproviding input data for the adaptation to the climate change;\nexamining climate predictability and exploring the ability of models to predict climate on decadal time scales;\nevaluating how realistic the different models are in simulating the recent past.\n\nThe term \"experiments\" refers to the three main categories of CMIP6 simulations:\n\nHistorical experiments which cover the period where modern climate observations exist. These experiments show how the GCMs performs for the past climate and can be used as a reference period for comparison with scenario runs for the future. The period covered is typically 1850-2014.\nClimate projection experiments following the combined pathways of Shared Socioeconomic Pathway (SSP) and Representative Concentration Pathway (RCP). The SSP scenarios provide different pathways of the future climate forcing. The period covered is typically 2015-2100.\n\nThis catalogue entry provides both two- and three-dimensional data, along with an option to apply spatial and/or temporal subsetting to data requests. This is a new feature of the global climate projection dataset, which relies on compute processes run simultaneously in the ESGF nodes, where the data are originally located.\nThe data are produced by the participating institutes of the CMIP6 project.","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CMIP6_CLIMATE_PROJECTIONS/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CMIP6_CLIMATE_PROJECTIONS/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CMIP6_CLIMATE_PROJECTIONS/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CMIP6_CLIMATE_PROJECTIONS","title":"CMIP6 climate projections"},{"rel":"license","href":"https://object-store.os-api.cci2.ecmwf.int:443/cci2-prod-catalogue/licences/cmip6-wps/cmip6-wps_23f724282307e697d793a31124a30efac989841c65936f5b2b3f738b7c861bf7.pdf","title":"CMIP6 - Data Access - Terms of Use"},{"rel":"related","href":"https://cds.climate.copernicus.eu/api/catalogue/v1/collections/projections-cmip5-daily-pressure-levels","title":"CMIP5 daily data on pressure levels"},{"rel":"related","href":"https://cds.climate.copernicus.eu/api/catalogue/v1/collections/projections-cmip5-monthly-pressure-levels","title":"CMIP5 monthly data on pressure levels"},{"rel":"related","href":"https://cds.climate.copernicus.eu/api/catalogue/v1/collections/projections-climate-atlas","title":"Gridded monthly climate projection dataset underpinning the IPCC AR6 Interactive Atlas"},{"rel":"related","href":"https://cds.climate.copernicus.eu/api/catalogue/v1/collections/projections-cmip5-daily-single-levels","title":"CMIP5 daily data on single levels"},{"rel":"related","href":"https://cds.climate.copernicus.eu/api/catalogue/v1/collections/multi-origin-c3s-atlas","title":"Gridded dataset underpinning the Copernicus Interactive Climate Atlas"},{"rel":"related","href":"https://cds.climate.copernicus.eu/api/catalogue/v1/collections/projections-cmip5-monthly-single-levels","title":"CMIP5 monthly data on single levels"},{"rel":"related","href":"https://cds.climate.copernicus.eu/api/catalogue/v1/collections/projections-cmip6-decadal-prototype","title":"CMIP6 predictions underpinning the C3S decadal prediction prototypes"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.24381/cds.c866074c","title":"CMIP6 climate projections"}],"assets":{"thumbnail":{"href":"https://object-store.os-api.cci2.ecmwf.int:443/cci2-prod-catalogue/resources/projections-cmip6/overview_452b3df21b144e5084354f2e6053b7712dd83d6592126c445d3f9bc046f98f84.png","roles":["thumbnail"],"title":null,"type":"image/jpg"}},"extent":{"spatial":{"bbox":[[0,-89,360,89]]},"temporal":{"interval":[["1860-01-01T00:00:00Z","2300-12-31T00:00:00Z"]]}},"license":"other","keywords":["Past","Present","Future","Global","Atmosphere (surface)","Atmosphere (upper air)","Climate projections"],"summaries":{"federation:backends":["cop_cds"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Climate Change Service (C3S)","roles":["host"],"url":"https://climate.copernicus.eu/"}],"created":"2025-09-15T14:58:05Z","updated":"2026-04-24T10:24:59Z","published":"2021-03-23T00:00:00Z","sci:doi":"10.24381/cds.c866074c","sci:citation":"Copernicus Climate Change Service, Climate Data Store, (2021): CMIP6 climate projections. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.c866074c (Accessed on DD-MMM-YYYY)","dedl:short_description":"This dataset provides daily and monthly global climate projections data from a large number of experiments, models and time periods computed in the framework of the sixth phase of the Coupled Model Intercomparison Project (CMIP6)."},{"type":"Collection","title":"Carbon dioxide data from 2002 to present derived from satellite observations","id":"EO.ECMWF.DAT.CO2_DATA_FROM_SATELLITE_SENSORS_2002_PRESENT","description":"This dataset provides observations of atmospheric carbon dioxide (CO2)\namounts obtained from observations collected by several current and historical \nsatellite instruments. Carbon dioxide is a naturally occurring Greenhouse Gas (GHG), but one whose abundance has been increased substantially above its pre-industrial value of some 280 ppm by human activities, primarily because of emissions from combustion of fossil fuels, deforestation and other land-use change. The annual cycle (especially in the northern hemisphere) is primarily due to seasonal uptake and release of atmospheric CO2 by terrestrial vegetation.\nAtmospheric carbon dioxide abundance is indirectly observed by various satellite instruments. These instruments measure spectrally resolved near-infrared and/or infrared radiation reflected or emitted by the Earth and its atmosphere. In the measured signal, molecular absorption signatures from carbon dioxide and other constituent gasses can be identified. It is through analysis of those absorption lines in these radiance observations that the averaged carbon dioxide abundance in the sampled atmospheric column can be determined.\nThe software used to analyse the absorption lines and determine the carbon dioxide concentration in the sampled atmospheric column is referred to as the retrieval algorithm. For this dataset, carbon dioxide abundances have been determined by applying several algorithms to different satellite \ninstruments. Typically, different algorithms have different strengths and weaknesses and therefore, which product to use for a given application typically depends on the application.\nThe data set consists of 2 types of products: (i) column-averaged mixing ratios of CO2, denoted XCO2 and (ii) mid-tropospheric CO2 columns.  The XCO2 products have been retrieved from SCIAMACHY/ENVISAT, TANSO-FTS/GOSAT and OCO-2. The mid-tropospheric CO2 product has been retrieved from the IASI instruments on-board the Metop satellite series and from AIRS. \nThe XCO2 products are available as Level 2 (L2) products (satellite orbit tracks) and as Level 3 (L3) product (gridded). The L2 products are available as individual sensor products (SCIAMACHY: BESD and WFMD algorithms; GOSAT: OCFP and SRFP algorithms) and as a multi-sensor merged product (EMMA algorithm). The L3 XCO2 product is provided in OBS4MIPS format. \nThe IASI and AIRS products are available as L2 products generated with the NLIS algorithm.\nThis data set is updated on a yearly basis, with each update cycle adding (if required) a new data version for the entire period, up to one year behind real time.\nThis dataset is produced on behalf of C3S with the exception of the SCIAMACHY and AIRS L2 products that were generated in the framework of the GHG-CCI project of the European Space Agency (ESA) Climate Change Initiative (CCI).\n\nVariables in the dataset/application are:\nColumn-average dry-air mole fraction of atmospheric carbon dioxide (XCO2), Mid-tropospheric columns of atmospheric carbon dioxide (CO2)","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CO2_DATA_FROM_SATELLITE_SENSORS_2002_PRESENT/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CO2_DATA_FROM_SATELLITE_SENSORS_2002_PRESENT/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CO2_DATA_FROM_SATELLITE_SENSORS_2002_PRESENT/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.CO2_DATA_FROM_SATELLITE_SENSORS_2002_PRESENT","title":"Carbon dioxide data from 2002 to present derived from satellite observations"},{"rel":"license","type":"application/pdf","href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/licences/ghg-cci/ghg-cci_0911d58e24365e15589377902e562c6e9231290f75b14ddc3c7cb5fd09a265af.pdf","title":"GHG-CCI Licence"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.24381/cds.f74805c8","title":"Carbon dioxide data from 2002 to present derived from satellite observations"}],"assets":{"thumbnail":{"href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/resources/satellite-carbon-dioxide/overview_c7da6512e3b4771cca9e37bd5c22213bc650818c85cbe05e034672d32c07aa6b.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-10-01T00:00:00Z","2022-12-31T00:00:00Z"]]}},"license":"other","keywords":["Satellite observations","Atmospheric conditions","Global","Past","Atmosphere (composition)"],"summaries":{"federation:backends":["cop_cds","wekeo_ecmwf"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Climate Change Service (C3S)","roles":["host"],"url":"https://climate.copernicus.eu/"}],"created":"2023-08-28T15:11:18Z","updated":"2026-04-24T10:24:59Z","published":"2023-08-28T15:11:18Z","sci:doi":"10.24381/cds.f74805c8","sci:citation":"Copernicus Climate Change Service, Climate Data Store, (2018): \"Carbon dioxide data from 2002 to present derived from satellite observations\". Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.f74805c8 (Accessed on DD-MMM-YYYY)","dedl:short_description":"This dataset contains satellite-derived carbon dioxide measurements from multiple instruments between 2002-present, providing information on global CO2 concentrations via two main products: column-averaged mixing ratios (XCO2) and mid-tropospheric CO2 columns."},{"type":"Collection","title":"Glacier mass change gridded data from 1976 to present derived from the Fluctuations of Glaciers Database","id":"EO.ECMWF.DAT.DERIVED_GRIDDED_GLACIER_MASS_CHANGE","description":"The dataset provides global annual glacier mass changes distributed on a global regular grid at 0.5° resolution (latitude, longitude) based on the Fluctuations of Glaciers (FoG) database of the World Glacier Monitoring Service (WGMS). Glaciers play a fundamental role in the Earth’s water cycles. They are one of the most important freshwater resources for societies and ecosystems and the recent increase in ice melt contributes directly to the rise of ocean levels. Due to this they have been declared as an Essential Climate Variable (ECV) by GCOS, the Global Climate Observing System. Within the Copernicus Services, the global gridded annual glacier mass change dataset provides information on changing glacier resources by combining glacier change observations from the Fluctuations of Glaciers (FoG) database that is brokered from World Glacier Monitoring Service (WGMS).\nInspired by previous methodological frameworks, a new approach was developed to combine the glacier mass balance and elevation change observations, providing a new and unique product of annual glacier mass change and related uncertainties for every hydrological year since 1975/76 distributed on a 0.5° global regular grid. The present dataset bridges the gap regarding the spatio-temporal coverage of glacier change observations, providing for the first time in the Copernicus Climate Change Service (C3S) Climate Data Store (CDS) an annually resolved glacier mass change product using the glacier elevation change sample as calibration. This goal has become feasible at the global scale thanks to a new globally near-complete (96% of the world glaciers) dataset of glacier elevation change observations ingested by the FoG database.\nTo develop the distributed glacier change product, the use of glacier outlines from the C3S Glacier Area product version 2 are used. A glacier is considered to belong to a grid-point when its geometric centroid lies within the grid point. The centroid is obtained from the glacier outlines from the C3S Glacier Area product version 2. The glacier changes in Gt correspond to the total mass of water lost/gained over the glacier surface during a given year. Note that to propagate to mm/cm/m of water column on the grid cell, the grid cell area needs to be considered. Note that hydrological year vary on the Southern Hemisphere (October to September next year) and Northern Hemispheres (April to March next year). The annual distributed glacier change dataset cannot resolve for this seasonal difference and is important for the user to account for them when using the datasets. This issue can only be resolved with a monthly distributed glacier change product.\nThis dataset has been produced by researchers at the WGMS on behalf of Copernicus Climate Change Service.","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.DERIVED_GRIDDED_GLACIER_MASS_CHANGE/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.DERIVED_GRIDDED_GLACIER_MASS_CHANGE/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.DERIVED_GRIDDED_GLACIER_MASS_CHANGE/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.DERIVED_GRIDDED_GLACIER_MASS_CHANGE","title":"Glacier mass change gridded data from 1976 to present derived from the Fluctuations of Glaciers Database"},{"rel":"license","type":"application/pdf","href":"https://object-store.os-api.cci2.ecmwf.int:443/cci2-prod-catalogue/licences/licence-to-use-insitu-glaciers-elevation-mass/licence-to-use-insitu-glaciers-elevation-mass_8646d9ec87f54c700db06589e04244db6141a2b29390e76e954f44e87071a1b3.pdf","title":"UZH Glaciers Elevation and Mass Change licence"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.24381/cds.ba597449","title":"Glacier mass change gridded data from 1976 to present derived from the Fluctuations of Glaciers Database"}],"assets":{"thumbnail":{"href":"https://object-store.os-api.cci2.ecmwf.int:443/cci2-prod-catalogue/resources/derived-gridded-glacier-mass-change/overview_e83c869dab0a0835ec4b89fb679ac5fc028c1d4d72ff8d501b4bc810b31b403d.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-179.75,-89.75,179.75,89.75]]},"temporal":{"interval":[["1975-01-01T00:00:00Z",null]]}},"license":"other","keywords":["Temporal coverage: Past","Spatial coverage: Global","Variable domain: Land (cryosphere)","Product type: Satellite observations","Product type: In-situ observations"],"summaries":{"federation:backends":["cop_cds","wekeo_ecmwf"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Climate Change Service (C3S)","roles":["host"],"url":"https://climate.copernicus.eu/"}],"created":"2024-10-11T13:04:06Z","updated":"2026-04-24T10:24:59Z","published":"2024-10-11T13:04:06Z","sci:doi":"10.24381/cds.ba597449","sci:citation":"Dussaillant, I., Bannwart, J., Paul, F., Zemp, M. (2024): Glacier mass change global gridded data from 1976 to present derived from the Fluctuations of Glaciers Database. World Glacier Monitoring Service. DOI: 10.24381/cds.ba597449 (Accessed on DD-MMM-YYYY)","dedl:short_description":"The dataset contains global annual glacier mass changes from 1976 onwards, provided on a 0.5° grid, derived from the Fluctuations of Glaciers Database and bridging the gap in spatiotemporal glacier change observations."},{"type":"Collection","title":"River discharge and related forecasted data by the European Flood Awareness System","id":"EO.ECMWF.DAT.EFAS_FORECAST","description":"This dataset provides gridded modelled hydrological time series forced with medium-range meteorological forecasts.\nThe data represents most important hydrological variables across the European Flood Awareness System (EFAS) domain.\nThe temporal resolution is sub-daily high-resolution and ensemble forecasts of: River discharge, Soil moisture for three soil layers, Snow water equivalent.\nAlso provided are auxiliary (time invariant) data to aid interpretation of river discharge and soil moisture data.","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.EFAS_FORECAST/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.EFAS_FORECAST/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.EFAS_FORECAST/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.EFAS_FORECAST","title":"River discharge and related forecasted data by the European Flood Awareness System"},{"rel":"license","type":"application/pdf","href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/licences/cems-floods/cems-floods_428a6e1019ec50b3dad9c37a90d630fab139059933a939dd5df620bfcb420cc3.pdf","title":"CEMS-FLOODS datasets licence"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.24381/cds.9f696a7a","title":"River discharge and related forecasted data by the European Flood Awareness System"}],"assets":{"thumbnail":{"href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/resources/efas-forecast/overview_ddd9074d456be00a54d03c320485bdbb1d1871507eccaa1039404a9c2c62fe31.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2018-10-11T00:00:00Z","2023-09-19T00:00:00Z"]]}},"license":"other","keywords":["Forecasts","Europe","Reforecast","Present","Copernicus CEMS","Land (hydrology)"],"summaries":{"federation:backends":["cop_ewds"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Emergency Management Service (CEMS)","roles":["host"],"url":"https://emergency.copernicus.eu/"}],"created":"2023-08-11T18:04:28Z","updated":"2026-04-24T10:24:59Z","published":"2023-08-11T18:04:28Z","sci:doi":"10.24381/cds.9f696a7a","sci:citation":"## for v2.0:\nKrzeminski, B., Barnard, C., Thiemig, V., Mazzetti, C., Wetterhall F., Pappenberger, F., Smith, P., Arnal, L., Baugh, C., Latini, M., Salamon, P., Prudhomme, C. (2019), River discharge and related forecasted data from the European Flood Awareness System, v2.0. European Commission, Joint Research Center (JRC). DOI: 10.24381/cds.9f696a7a (Accessed on DD-MMM-YYYY)\n## For v3.0:\nKrzeminski, B., Barnard, C., Mazzetti, C., Latini, M., Wetterhall F., Prudhomme, C. (2019), River discharge and related forecasted data from the European Flood Awareness System, v3.0. European Commission, Joint Research Center (JRC). DOI: 10.24381/cds.9f696a7a (Accessed on DD-MMM-YYYY)\n## For v3.5:\nBarnard, C., Krzeminski, B., Mazzetti, C., Decremer, D., Wetterhall F., Prudhomme, C. (2020), River discharge and related forecasted data from the European Flood Awareness System, v3.5. European Commission, Joint Research Center (JRC). DOI: 10.24381/cds.9f696a7a (Accessed on DD-MMM-YYYY)\n## For v4.0:\nBarnard, C., Blick, M., Wetterhall F., Mazzetti, C., Decremer, D., Jurlina, T., Baugh, C., Harrigan, S., Battino, P., Prudhomme, C. (2020), River discharge and related forecasted data from the European Flood Awareness System, v4.0. European Commission, Joint Research Center (JRC). DOI: 10.24381/cds.9f696a7a (Accessed on DD-MMM-YYYY)\n## For v5.0:\nMazzetti, C., Carton de Wiart, C., Gomes, G., Russo, C., Decremer, D., Ramos, A., Grimaldi, S., Disperati, J., Ziese, M., Schweim, C., Sanchez Garcia, R., Jacobson, T., Salamon, P., Prudhomme, C. (2023): River discharge and related forecasted data from the European Flood Awareness System, v5.0. European Commission, Joint Research Center (JRC). DOI: 10.24381/cds.9f696a7a (Accessed on DD-MMM-YYYY)","dedl:short_description":"This dataset contains gridded, sub-daily, high-resolution hydrological time series including river discharge, soil moisture, snow water equivalent, and auxiliary data over Europe based on medium-range meteorological forecasts."},{"type":"Collection","title":"River discharge and related historical data from the European Flood Awareness System","id":"EO.ECMWF.DAT.EFAS_HISTORICAL","description":"This dataset provides gridded modelled daily hydrological time series forced with meteorological observations. The data set is a consistent representation of the most important hydrological variables across the European Flood Awareness System (EFAS) domain. The temporal resolution is up to 30 years modelled time series of:\n\nRiver discharge\nSoil moisture for three soil layers\nSnow water equivalent\n\nIt also provides static data on soil depth for the three soil layers. Soil moisture and river discharge data are accompanied by ancillary files for interpretation (see related variables and links in the documentation).\nThis dataset was produced by forcing the LISFLOOD hydrological model with gridded observational data of precipitation and temperature at a 5x5 km resolution across the EFAS domain. The most recent version\nuses a 6-hourly time step, whereas older versions uses a 24-hour time step. It is available from 1991-01-01 up until near-real time, with a delay of 6 days. The real-time data is only available to EFAS partners.\nCompanion datasets, also available through the CDS, are forecasts for users who are looking medium-range forecasts, reforecasts for research, local skill assessment and post-processing, and seasonal forecasts and reforecasts for users looking for long-term forecasts. For users looking for global hydrological data, we refer to the Global Flood Awareness System (GloFAS) forecasts and historical simulations. All these datasets are part of the operational flood forecasting within the Copernicus Emergency Management Service (CEMS).\n\nVariables in the dataset/application are:\nRiver discharge in the last 24 hours, River discharge in the last 6 hours, Snow depth water equivalent, Soil depth, Volumetric soil moisture\n\nVariables in the dataset/application are:\nOrography, Upstream area","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.EFAS_HISTORICAL/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.EFAS_HISTORICAL/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.EFAS_HISTORICAL/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.EFAS_HISTORICAL","title":"River discharge and related historical data from the European Flood Awareness System"},{"rel":"license","type":"application/pdf","href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/licences/cems-floods/cems-floods_428a6e1019ec50b3dad9c37a90d630fab139059933a939dd5df620bfcb420cc3.pdf","title":"CEMS-FLOODS datasets licence"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.24381/cds.e3458969","title":"River discharge and related historical data from the European Flood Awareness System"}],"assets":{"thumbnail":{"href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/resources/efas-historical/overview_ddd9074d456be00a54d03c320485bdbb1d1871507eccaa1039404a9c2c62fe31.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1991-01-01T06:00:00Z",null]]}},"license":"other","keywords":["Climatology","Reanalysis","Europe","Land (hydrology)","Past","Copernicus CEMS"],"summaries":{"federation:backends":["cop_ewds"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Emergency Management Service (CEMS)","roles":["host"],"url":"https://emergency.copernicus.eu/"}],"created":"2023-08-28T15:11:18Z","updated":"2026-04-24T10:24:59Z","published":"2023-08-28T15:11:18Z","sci:doi":"10.24381/cds.e3458969","sci:citation":"## for v2.0:\nMazzetti, C., Barnard, C., Krzeminski, B., Thiemig, V., Wetterhall F., Pappenberger, F., Smith, P., Arnal, L., Baugh, C., Salamon, P., Prudhomme, C.. (2019), River discharge and related historical data from the European Flood Awareness System, v2.0. European Commission, Joint Research Center (JRC). DOI: 10.24381/cds.e3458969 (Accessed on DD-MMM-YYYY)\n## For v3.0:\nMazzetti, C., Krzeminski, B., Barnard, C., Wetterhall F., Prudhomme, C. (2019), River discharge and related historical data from the European Flood Awareness System, v3.0. European Commission, Joint Research Center (JRC). DOI: 10.24381/cds.e3458969 (Accessed on DD-MMM-YYYY)\n## For v3.5:\nMazzetti, C., Decremer, D., Krzeminski, B., Barnard, C., Wetterhall F., Prudhomme, C. (2019), River discharge and related historical data from the European Flood Awareness System, v3.5. European Commission, Joint Research Center (JRC). DOI: 10.24381/cds.e3458969 (Accessed on DD-MMM-YYYY)\n## For v4.0:\nMazzetti, C., Decremer, D., Barnard, C., Blick, M., Carton de Wiart, C., Wetterhall F., Prudhomme, C. (2020), River discharge and related historical data from the European Flood Awareness System, v4.0. Joint Research Center (JRC). DOI:10.24381/cds.e3458969 (Accessed on DD-MMM-YYYY)\n## For v5.0:\nMazzetti, C., Carton de Wiart, C., Gomes, G., Russo, C., Decremer, D., Ramos, A., Grimaldi, S., Disperati, J., Ziese, M., Schweim, C., Sanchez Garcia, R., Jacobson, T., Salamon, P., Prudhomme, C. (2023): River discharge and related historical data from the European Flood Awareness System, v5.0. European Commission, Joint Research Centre (JRC). DOI:10.24381/cds.e3458969 (Accessed on DD-MMM-YYYY)","dedl:short_description":"The dataset contains gridded modelled daily hydrological time series covering up to 30 years of river discharge, snow water equivalent, soil moisture, and soil depth over Europe, generated by forcing the LISFLOOD model with observed precipitation and temperature data."},{"type":"Collection","title":"Reforecasts of river discharge and related data by the European Flood Awareness System","id":"EO.ECMWF.DAT.EFAS_REFORECAST","description":"This dataset provides gridded modelled hydrological time series forced with medium- to sub-seasonal range meteorological reforecasts. The data is a consistent representation of the most important hydrological variables across the European Flood Awareness System (EFAS) domain. The temporal resolution is 20 years of sub-daily reforecasts initialised twice weekly (Mondays and Thursdays) of:\n\nRiver discharge\nSoil moisture for three soil layers\nSnow water equivalent\n\nIt also provides static data on soil depth for the three soil layers. Soil moisture and river discharge data are accompanied by ancillary files for interpretation (see related variables and links in the documentation).\nThis dataset was produced by forcing the LISFLOOD hydrological model at a 5x5km resolution with ensemble meteorological reforecasts from the European Centre of Medium-range Weather Forecasts (ECMWF). Reforecasts are forecasts run over past dates and are typically used to assess the skill of a forecast system or to develop tools for statistical error correction of the forecasts. The reforecasts are initialised twice weekly with lead times up to 46 days, at 6-hourly time steps for 20 years. For more specific information on the how the reforecast dataset is produced we refer to the documentation.\nCompanion datasets, also available through the Climate Data Store (CDS), are the operational forecasts, historical simulations which can be used to derive the hydrological climatology, and seasonal forecasts and reforecasts for users looking for long term forecasts. For users looking for global hydrological data, we refer to the Global Flood Awareness System (GloFAS) forecasts and historical simulations. All these datasets are part of the operational flood forecasting within the Copernicus Emergency Management Service (CEMS).\n\nVariables in the dataset/application are:\nRiver discharge, Snow depth water equivalent, Soil depth, Volumetric soil moisture\n\nVariables in the dataset/application are:\nOrography, Upstream area","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.EFAS_REFORECAST/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.EFAS_REFORECAST/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.EFAS_REFORECAST/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.EFAS_REFORECAST","title":"Reforecasts of river discharge and related data by the European Flood Awareness System"},{"rel":"license","type":"application/pdf","href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/licences/cems-floods/cems-floods_428a6e1019ec50b3dad9c37a90d630fab139059933a939dd5df620bfcb420cc3.pdf","title":"CEMS-FLOODS datasets licence"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.24381/cds.c83f560f","title":"Reforecasts of river discharge and related data by the European Flood Awareness System"}],"assets":{"thumbnail":{"href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/resources/efas-reforecast/overview_0e118ef0dc64e1b78c2a7bba3af6e2c0221e25b1b23e2ad16f46dd70c7005faa.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1999-01-03T00:00:00Z","2023-11-21T23:59:59Z"]]}},"license":"other","keywords":["Reforecasts","Europe","Reforecast","Present","Copernicus CEMS","Land (hydrology)"],"summaries":{"federation:backends":["cop_ewds"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Emergency Management Service (CEMS)","roles":["host"],"url":"https://emergency.copernicus.eu/"}],"created":"2023-08-28T15:11:18Z","updated":"2026-04-24T10:24:59Z","published":"2023-08-28T15:11:18Z","sci:doi":"10.24381/cds.c83f560f","sci:citation":"## for v4.0:\nBarnard, C., Krzeminski, B., Mazzetti, C., Decremer, D., Carton de Wiart, C., Harrigan, S., Blick, M., Ferrario, I., Wetterhall, F., Thiemig, V., Salamon, P., Prudhomme, C. (2020). Reforecasts of river discharge and related data by the European Flood Awareness System, v4.0. European Commission, Joint Research Center (JRC). DOI: 10.24381/cds.c83f560f (Accessed on DD-MMM-YYYY)\n## For v5.0:\nMazzetti, C., Carton de Wiart, C., Gomes, G., Russo, C., Decremer, D., Ramos, A., Grimaldi, S., Disperati, J., Ziese, M., Schweim, C., Sanchez Garcia, R., Jacobson, T., Salamon, P., Prudhomme, C. (2023): Reforecasts of river discharge and related data by the European Flood Awareness System, v5.0, European Commission, Joint Research Centre (JRC). DOI: 10.24381/cds.c83f560f (Accessed on DD-MMM-YYYY)","dedl:short_description":"The dataset contains 20-year sub-daily reforecasts of river discharge, snow water equivalent, and soil moisture initiated every Monday and Thursday with ECMWF's 5x5 km resolution LISFLOOD model."},{"type":"Collection","title":"Seasonal forecasts of river discharge and related data by the European Flood Awareness System","id":"EO.ECMWF.DAT.EFAS_SEASONAL","description":"This dataset provides gridded modelled daily hydrological time series forced with seasonal meteorological forecasts. The dataset is a consistent representation of the most important hydrological variables across the European Flood Awareness (EFAS) domain. The temporal resolution is daily forecasts initialised once a month consisting of:\n\nRiver discharge\nSoil moisture for three soil layers\nSnow water equivalent\n\nIt also provides static data on soil depth for the three soil layers. Soil moisture and river discharge data are accompanied by ancillary files for interpretation (see related variables and links in the documentation).\nThis dataset was produced by forcing the LISFLOOD hydrological model at a 5x5km resolution with seasonal meteorological ensemble forecasts. The forecasts are initialised on the first of each month with a lead time of 215 days at 24-hour time steps. The meteorological data are seasonal forecasts (SEAS5) from the European Centre of Medium-range Weather Forecasts (ECMWF) with 51 ensemble members. The forecasts are available from November 2020.\nCompanion datasets, also available through the Climate Data Store (CDS), are seasonal reforecasts for research, local skill assessment and post-processing of the seasonal forecasts. There are also medium-range forecasts for users who want to look at shorter time ranges. These are accompanied by historical simulations which can be used to derive the hydrological climatology, and medium-range reforecasts. For users looking for global hydrological data, we refer to the Global Flood Awareness System (GloFAS) forecasts and historical simulations. All these datasets are part of the operational flood forecasting within the Copernicus Emergency Management Service (CEMS).\n\nVariables in the dataset/application are:\nRiver discharge in the last 24 hours, Snow depth water equivalent, Soil depth, Volumetric soil moisture\n\nVariables in the dataset/application are:\nOrography, Upstream area","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.EFAS_SEASONAL/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.EFAS_SEASONAL/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.EFAS_SEASONAL/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.EFAS_SEASONAL","title":"Seasonal forecasts of river discharge and related data by the European Flood Awareness System"},{"rel":"license","type":"application/pdf","href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/licences/cems-floods/cems-floods_428a6e1019ec50b3dad9c37a90d630fab139059933a939dd5df620bfcb420cc3.pdf","title":"CEMS-FLOODS datasets licence"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.24381/cds.eb224b0e","title":"Seasonal forecasts of river discharge and related data by the European Flood Awareness System"}],"assets":{"thumbnail":{"href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/resources/efas-seasonal/overview_027e11ba4b15ebcb3217e766cb5c74cfd08665cf129d439a9ef52724a1961fa0.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2020-11-01T00:00:00Z","2023-09-30T23:59:59Z"]]}},"license":"other","keywords":["Europe","Forecast","Seasonal forecasts","Present","Copernicus CEMS","Land (hydrology)"],"summaries":{"federation:backends":["cop_ewds"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Emergency Management Service (CEMS)","roles":["host"],"url":"https://emergency.copernicus.eu/"}],"created":"2023-08-28T15:11:18Z","updated":"2026-04-24T10:24:59Z","published":"2023-08-28T15:11:18Z","sci:doi":"10.24381/cds.eb224b0e","sci:citation":"## for v4.0:\nWetterhall F., Arnal., L., Barnard, C., Krzeminski, B., Ferrario, I., Mazzetti, C., Prudhomme, C. (2020). Seasonal forecasts of river discharge and related data by the European Flood Awareness System, v4.0. European Commission, Joint Research Center (JRC). DOI: 10.24381/cds.eb224b0e\n## For v5.0:\nMazzetti, C., Carton de Wiart, C., Gomes, G., Russo, C., Decremer, D., Ramos, A., Grimaldi, S., Disperati, J., Ziese, M., Schweim, C., Sanchez Garcia, R., Jacobson, T., Salamon, P., Prudhomme, C. (2023): Seasonal forecasts of river discharge and related data by the European Flood Awareness System, v5.0. European Commission, Joint Research Centre (JRC). DOI: 10.24381/cds.eb224b0e","dedl:short_description":"The dataset contains gridded modelled daily hydrological time series over Europe, including river discharge, snow water equivalent, soil moisture, and soil depth, driven by ECMWF's SEAS5 seasonal meteorological forecasts."},{"type":"Collection","title":"Seasonal reforecasts of river discharge and related data by the European Flood Awareness System","id":"EO.ECMWF.DAT.EFAS_SEASONAL_REFORECAST","description":"This dataset provides modelled daily hydrological time series forced with seasonal meteorological reforecasts. The dataset is a consistent representation of the most important hydrological variables across the European Flood Awareness (EFAS) domain. The temporal resolution is daily forecasts initialised once a month over the reforecast period 1991-2020 of:\n\nRiver discharge\nSoil moisture for three soil layers\nSnow water equivalent\n\nIt also provides static data on soil depth for the three soil layers. Soil moisture and river discharge data are accompanied by ancillary files for interpretation (see related variables and links in the documentation).\nThis dataset was produced by forcing the LISFLOOD hydrological model at a 5x5km gridded resolution with seasonal meteorological ensemble reforecasts. Reforecasts are forecasts run over past dates and are typically used to assess the skill of a forecast system or to develop tools for statistical error correction of the forecasts. The reforecasts are initialised on the first of each month with a lead time of 215 days at 24-hour time steps. The forcing meteorological data are seasonal reforecasts from the European Centre of Medium-range Weather Forecasts (ECMWF), consisting of 25 ensemble members up until December 2016, and after that 51 members. Hydrometeorological reforecasts are available from 1991-01-01 up until 2020-10-01.\nCompanion datasets, also available through the Climate Data Store (CDS), are seasonal forecasts, for which the seasonal reforecasts can be useful for local skill assessment and post-processing of the seasonal forecasts. For users looking for shorter time ranges there are medium-range forecasts and reforecasts, as well as historical simulations which can be used to derive the hydrological climatology. For users looking for global hydrological data, we refer to the Global Flood Awareness System (GloFAS) forecasts and historical simulations. All these datasets are part of the operational flood forecasting within the Copernicus Emergency Management Service (CEMS).\n\nVariables in the dataset/application are:\nRiver discharge in the last 24 hours, Snow depth water equivalent, Soil depth, Volumetric soil moisture\n\nVariables in the dataset/application are:\nOrography, Upstream area","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.EFAS_SEASONAL_REFORECAST/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.EFAS_SEASONAL_REFORECAST/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.EFAS_SEASONAL_REFORECAST/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.EFAS_SEASONAL_REFORECAST","title":"Seasonal reforecasts of river discharge and related data by the European Flood Awareness System"},{"rel":"license","type":"application/pdf","href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/licences/cems-floods/cems-floods_428a6e1019ec50b3dad9c37a90d630fab139059933a939dd5df620bfcb420cc3.pdf","title":"CEMS-FLOODS datasets licence"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.24381/cds.768eefc2","title":"Seasonal reforecasts of river discharge and related data by the European Flood Awareness System"}],"assets":{"thumbnail":{"href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/resources/efas-seasonal-reforecast/overview_ccf1de467bf1208cba89aebab94f143aa32772f4f3306b8159358d3655173f92.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1999-01-01T00:00:00Z",null]]}},"license":"other","keywords":["Seasonal reforecasts","Europe","Reforecast","Present","Copernicus CEMS","Land (hydrology)"],"summaries":{"federation:backends":["cop_ewds"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Emergency Management Service (CEMS)","roles":["host"],"url":"https://emergency.copernicus.eu/"}],"created":"2023-08-28T15:11:18Z","updated":"2026-04-24T10:24:59Z","published":"2023-08-28T15:11:18Z","sci:doi":"10.24381/cds.768eefc2","sci:citation":"## for v4.0:\nWetterhall, F., Arnal., L., Barnard, C., Krzeminski, B., Ferrario, I., Mazzetti, C., Thiemig, V., Salamon, P., Prudhomme, C. (2020). Seasonal reforecasts of river discharge and related data by the European Flood Awareness System, v4.0. European Commission, Joint Research Center (JRC). DOI: 10.24381/cds.768eefc2 (Accessed on DD-MMM-YYYY)\n## For v5.0:\nMazzetti, C., Carton de Wiart, C., Gomes, G., Russo, C., Decremer, D., Ramos, A., Grimaldi, S., Disperati, J., Ziese, M., Schweim, C., Sanchez Garcia, R., Jacobson, T., Salamon, P., Prudhomme, C. (2023): Seasonal reforecasts of river discharge and related data by the European Flood Awareness System, v5.0. European Commission, Joint Research Centre (JRC). DOI: 10.24381/cds.768eefc2 (Accessed on DD-MMM-YYYY)","dedl:short_description":"The dataset contains daily hydrological time-series predictions covering Europe's EFAS region from 1991-2020, including river discharge, snow water equivalent, soil moisture, and soil depth, based on monthly-initialized seasonal meteorological reforecasts."},{"type":"Collection","title":"ERA5 hourly data on pressure levels from 1940 to present","id":"EO.ECMWF.DAT.ERA5_HOURLY_VARIABLES_ON_PRESSURE_LEVELS","description":"ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades.\nData is available from 1940 onwards.\nERA5 replaces the ERA-Interim reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product. ERA5 provides hourly estimates for a large number of atmospheric, ocean-wave and land-surface quantities.\nAn uncertainty estimate is sampled by an underlying 10-member ensemble\nat three-hourly intervals. Ensemble mean and spread have been pre-computed for convenience.\nSuch uncertainty estimates are closely related to the information content of the available observing system which\nhas evolved considerably over time. They also indicate flow-dependent sensitive areas.\nTo facilitate many climate applications, monthly-mean averages have been pre-calculated too,\nthough monthly means are not available for the ensemble mean and spread. ERA5 is updated daily with a latency of about 5 days. In case that serious flaws are detected in this early release (called ERA5T), this data could be different from the final release 2 to 3 months later. In case that this occurs users are notified. The data set presented here is a regridded subset of the full ERA5 data set on native resolution.\nIt is online on spinning disk, which should ensure fast and easy access.\nIt should satisfy the requirements for most common applications. An overview of all ERA5 datasets can be found in this article .\nInformation on access to ERA5 data on native resolution is provided in these guidelines . Data has been regridded to a regular lat-lon grid of 0.25 degrees for the reanalysis and 0.5 degrees for\nthe uncertainty estimate (0.5 and 1 degree respectively for ocean waves).\nThere are four main sub sets: hourly and monthly products, both on pressure levels (upper air fields) and single levels (atmospheric, ocean-wave and land surface quantities). The present entry is \"ERA5 hourly data on pressure levels from 1940 to present\".\n\n## How to acknowledge, cite and refer to ERA5\n\nAll users of data uploaded on the Climate Data Store (CDS) must:\n\nProvide clear and visible attribution to the Copernicus programme by referencing the web catalogue entry\n\nAcknowledge according to the [licence to use Copernicus Products](https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf).\n\nCite each product used.\n\nPlease refer to [How to acknowledge, cite and reference data published on the Climate Data Store](https://confluence.ecmwf.int/x/srnICw) for complete details.","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.ERA5_HOURLY_VARIABLES_ON_PRESSURE_LEVELS/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.ERA5_HOURLY_VARIABLES_ON_PRESSURE_LEVELS/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.ERA5_HOURLY_VARIABLES_ON_PRESSURE_LEVELS/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.ERA5_HOURLY_VARIABLES_ON_PRESSURE_LEVELS","title":"ERA5 hourly data on pressure levels from 1940 to present"},{"rel":"license","type":"application/pdf","href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/licences/licence-to-use-copernicus-products/licence-to-use-copernicus-products_b4b9451f54cffa16ecef5c912c9cebd6979925a956e3fa677976e0cf198c2c18.pdf","title":"Licence to Use Copernicus Products"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/CKB/ERA5%3A+data+documentation","title":"ERA5: data documentation"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.24381/cds.bd0915c6","title":"ERA5 hourly data on pressure levels from 1940 to present"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.1002/qj.3803","title":"The ERA5 global reanalysis"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.1002/qj.4174","title":"The ERA5 global reanalysis: Preliminary extension to 1950"}],"assets":{"thumbnail":{"href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/resources/reanalysis-era5-pressure-levels/overview_652fd83a7b2ed724ce541e563beff9c4484c3482bc08334a638a4bc47ae4cf0f.png","roles":["thumbnail"],"title":"ERA5","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1940-01-01T00:00:00Z","2023-05-13T00:00:00Z"]]}},"license":"other","keywords":["Atmospheric conditions","Atmosphere (surface)","Atmosphere (upper air)","Past","Global","Reanalysis","Copernicus C3S"],"summaries":{"federation:backends":["cop_cds","wekeo_ecmwf"]},"item_assets":{"divergence":{"title":"Divergence","type":"application/grib"},"fraction_of_cloud_cover":{"title":"Fraction of Cloud Cover","type":"application/grib"},"geopotential":{"title":"Geopotential","type":"application/grib"},"ozone_mass_mixing_ratio":{"title":"Ozone Mass Mixing Ratio","type":"application/grib"},"potential_vorticity":{"title":"Potential Vorticity","type":"application/grib"},"relative_humidity":{"title":"Relative humidity","type":"application/grib"},"specific_cloud_ice_water_content":{"title":"Specific Cloud Ice Water Content","type":"application/grib"},"specific_cloud_liquid_water_content":{"title":"Specific Cloud Liquid Water Content","type":"application/grib"},"specific_humidity":{"title":"Specific humidity","type":"application/grib"},"specific_rain_water_content":{"title":"Specific Rain Water Content","type":"application/grib"},"specific_snow_water_content":{"title":"Specific Snow Water Content","type":"application/grib"},"temperature":{"title":"Temperature","type":"application/grib"},"u_component_of_wind":{"title":"U-component of Wind","type":"application/grib"},"v_component_of_wind":{"title":"V-component of Wind","type":"application/grib"},"vertical_velocity":{"title":"Vertical Velocity","type":"application/grib"},"vorticity":{"title":"Vorticity 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It is the rate at which air is spreading out horizontally from a point, per square metre. This parameter is positive for air that is spreading out, or diverging, and negative for the opposite, for air that is concentrating, or converging (convergence).","dimensions":["x","y","z","time"],"shortNameECMWF":"d","type":"data","units":"s^-1"},"fraction_of_cloud_cover":{"description":"This parameter is the proportion of a grid box covered by cloud (liquid or ice) and varies between zero and one. This parameter is available on multiple levels through the atmosphere.","dimensions":["x","y","z","time"],"shortNameECMWF":"cc","type":"data","units":"(0 - 1)"},"geopotential":{"description":"This parameter is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level. The geopotential height can be calculated by dividing the geopotential by the Earth's gravitational acceleration, g (=9.80665 m s-2). The geopotential height plays an important role in synoptic meteorology (analysis of weather patterns). Charts of geopotential height plotted at constant pressure levels (e.g., 300, 500 or 850 hPa) can be used to identify weather systems such as cyclones, anticyclones, troughs and ridges. At the surface of the Earth, this parameter shows the variations in geopotential (height) of the surface, and is often referred to as the orography.","dimensions":["x","y","z","time"],"shortNameECMWF":"z","type":"data","units":"m^2 s^-2"},"ozone_mass_mixing_ratio":{"description":"This parameter is the mass of ozone per kilogram of air. In the ECMWF Integrated Forecasting System (IFS), there is a simplified representation of ozone chemistry (including representation of the chemistry which has caused the ozone hole). Ozone is also transported around in the atmosphere through the motion of air. Naturally occurring ozone in the stratosphere helps protect organisms at the surface of the Earth from the harmful effects of ultraviolet (UV) radiation from the Sun. Ozone near the surface, often produced because of pollution, is harmful to organisms. Most of the IFS chemical species are archived as mass mixing ratios [kg kg-1].","dimensions":["x","y","z","time"],"shortNameECMWF":"o3","type":"data","units":"kg kg^-1"},"potential_vorticity":{"description":"Potential vorticity is a measure of the capacity for air to rotate in the atmosphere. If we ignore the effects of heating and friction, potential vorticity is conserved following an air parcel. It is used to look for places where large wind storms are likely to originate and develop. Potential vorticity increases strongly above the tropopause and therefore, it can also be used in studies related to the stratosphere and stratosphere-troposphere exchanges. Large wind storms develop when a column of air in the atmosphere starts to rotate. Potential vorticity is calculated from the wind, temperature and pressure across a column of air in the atmosphere.","dimensions":["x","y","z","time"],"shortNameECMWF":"pv","type":"data","units":"K m^2 kg^-1 s^-1"},"relative_humidity":{"description":"This parameter is the water vapour pressure as a percentage of the value at which the air becomes saturated (the point at which water vapour begins to condense into liquid water or deposition into ice). For temperatures over 0°C (273.15 K) it is calculated for saturation over water. At temperatures below -23°C it is calculated for saturation over ice. Between -23°C and 0°C this parameter is calculated by interpolating between the ice and water values using a quadratic function.","dimensions":["x","y","z","time"],"shortNameECMWF":"r","type":"data","units":"%"},"specific_cloud_ice_water_content":{"description":"This parameter is the mass of cloud ice particles per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Water within clouds can be liquid or ice, or a combination of the two. Note that 'cloud frozen water' is the same as 'cloud ice water'.","dimensions":["x","y","z","time"],"shortNameECMWF":"ciwc","type":"data","units":"kg kg^-1"},"specific_cloud_liquid_water_content":{"description":"This parameter is the mass of cloud liquid water droplets per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Water within clouds can be liquid or ice, or a combination of the two.","dimensions":["x","y","z","time"],"shortNameECMWF":"clwc","type":"data","units":"kg kg^-1"},"specific_humidity":{"description":"This parameter is the mass of water vapour per kilogram of moist air. The total mass of moist air is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow.","dimensions":["x","y","z","time"],"shortNameECMWF":"q","type":"data","units":"kg kg^-1"},"specific_rain_water_content":{"description":"The mass of water produced from large-scale clouds that is of raindrop size and so can fall to the surface as precipitation. Large-scale clouds are generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of a grid box or larger. The quantity is expressed in kilograms per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The IFS cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["x","y","z","time"],"shortNameECMWF":"crwc","type":"data","units":"kg kg^-1"},"specific_snow_water_content":{"description":"The mass of snow (aggregated ice crystals) produced from large-scale clouds that can fall to the surface as precipitation. Large-scale clouds are generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of a grid box or larger. The mass is expressed in kilograms per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The IFS cloud scheme simplifies this to represent a number of discrete cloud droplets/particles including cloud water droplets, raindrops, ice crystals and snow (aggregated ice crystals). The processes of droplet formation, phase transition and aggregation are also highly simplified in the IFS.","dimensions":["x","y","z","time"],"shortNameECMWF":"cswc","type":"data","units":"kg kg^-1"},"temperature":{"description":"This parameter is the temperature in the atmosphere. It has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15. This parameter is available on multiple levels through the atmosphere.","dimensions":["x","y","z","time"],"shortNameECMWF":"t","type":"data","units":"K"},"u_component_of_wind":{"description":"This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east. A negative sign indicates air moving towards the west. This parameter can be combined with the V component of wind to give the speed and direction of the horizontal wind.","dimensions":["x","y","z","time"],"shortNameECMWF":"u","type":"data","units":"m s^-1"},"v_component_of_wind":{"description":"This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north. A negative sign indicates air moving towards the south. This parameter can be combined with the U component of wind to give the speed and direction of the horizontal wind.","dimensions":["x","y","z","time"],"shortNameECMWF":"v","type":"data","units":"m s^-1"},"vertical_velocity":{"description":"This parameter is the speed of air motion in the upward or downward direction. The ECMWF Integrated Forecasting System (IFS) uses a pressure based vertical co-ordinate system and pressure decreases with height, therefore negative values of vertical velocity indicate upward motion. Vertical velocity can be useful to understand the large-scale dynamics of the atmosphere, including areas of upward motion/ascent (negative values) and downward motion/subsidence (positive values).","dimensions":["x","y","z","time"],"shortNameECMWF":"w","type":"data","units":"Pa s^-1"},"vorticity":{"description":"This parameter is a measure of the rotation of air in the horizontal, around a vertical axis, relative to a fixed point on the surface of the Earth. On the scale of weather systems, troughs (weather features that can include rain) are associated with anticlockwise rotation (in the northern hemisphere), and ridges (weather features that bring light or still winds) are associated with clockwise rotation. Adding the effect of rotation of the Earth, the Coriolis parameter, to the relative vorticity produces the absolute vorticity.","dimensions":["x","y","z","time"],"shortNameECMWF":"vo","type":"data","units":"s^-1"}},"sci:doi":"10.24381/cds.bd0915c6","sci:citation":"Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J-N. (2023): ERA5 hourly data on pressure levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: [10.24381/cds.bd0915c6](https://doi.org/10.24381/cds.bd0915c6) (Accessed on DD-MMM-YYYY)","sci:publications":[{"doi":"10.1002/qj.3803","citation":"Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 Global Reanalysis, Q. J. Roy. Meteorol. Soc., 146, 1999-2049, https://doi.org/10.1002/qj.3803, 2020."},{"doi":"10.1002/qj.4174","citation":"Bell, B., Hersbach, H., Simmons, A., Berrisford, P., Dahlgren, P., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Radu, R., Schepers, D., Soci, C., Villaume, Sebastien., Bidlot, J.-R., Haimberger, L., Woollen, J., Buontempo, C. and Thépaut, J.-N.: The ERA5 global reanalysis: Preliminary extension to 1950, Q. J. Roy. Meteorol. Soc., 147, 4186-4227, https://doi.org/10.1002/qj.4174, 2021."}],"dedl:short_description":"The ERA5 dataset contains hourly global climate and weather data from 1940 to present, combining model data with worldwide observations through physical laws-based data assimilation, providing various atmospheric, oceanic, and terrestrial variables with associated uncertainties."},{"type":"Collection","title":"ERA5-Land hourly data from 1950 to present","id":"EO.ECMWF.DAT.ERA5_LAND_HOURLY","description":"ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past. ERA5-Land uses as input to control the simulated land fields ERA5 atmospheric variables, such as air temperature and air humidity. This is called the atmospheric forcing. Without the constraint of the atmospheric forcing, the model-based estimates can rapidly deviate from reality. Therefore, while observations are not directly used in the production of ERA5-Land, they have an indirect influence through the atmospheric forcing used to run the simulation. In addition, the input air temperature, air humidity and pressure used to run ERA5-Land are corrected to account for the altitude difference between the grid of the forcing and the higher resolution grid of ERA5-Land. This correction is called 'lapse rate correction'. The ERA5-Land dataset, as any other simulation, provides estimates which have some degree of uncertainty. Numerical models can only provide a more or less accurate representation of the real physical processes governing different components of the Earth System. In general, the uncertainty of model estimates grows as we go back in time, because the number of observations available to create a good quality atmospheric forcing is lower. ERA5-land parameter fields can currently be used in combination with the uncertainty of the equivalent ERA5 fields. The temporal and spatial resolutions of ERA5-Land makes this dataset very useful for all kind of land surface applications such as flood or drought forecasting. The temporal and spatial resolution of this dataset, the period covered in time, as well as the fixed grid used for the data distribution at any period enables decisions makers, businesses and individuals to access and use more accurate information on land states.\n\nMain Variables:[['Name'\t'Full_Name'\t'ShortName'\t'Units'\t'Description'\t'url']\n ['10m u-component of wind'\t'10m U wind over land'\t'~'\t'm.s⁻¹'\n  'Eastward component of the 10m wind. It is the horizontal speed of air moving towards the east, at a height of ten metres above the surface of the Earth, in metres per second. Care should be taken when comparing this variable with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System. This variable can be combined with the V component of 10m wind to give the speed and direction of the horizontal 10m wind.'\n  'https://codes.ecmwf.int/grib/param-db/?id=174085']\n ['10m v-component of wind'\t'10m V wind over land'\t'~'\t'm.s⁻¹'\n  'Northward component of the 10m wind. It is the horizontal speed of air moving towards the north, at a height of ten metres above the surface of the Earth, in metres per second. Care should be taken when comparing this variable with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System. This variable can be combined with the U component of 10m wind to give the speed and direction of the horizontal 10m wind.'\n  'https://codes.ecmwf.int/grib/param-db/?id=174086']\n ['2m dewpoint temperature'\t'2m Dew Point Temperature'\t'td_2m'\t'K'\n  \"Temperature to which the air, at 2 metres above the surface of the Earth, would have to be cooled for saturation to occur.It is a measure of the humidity of the air. Combined with temperature and pressure, it can be used to calculate the relative humidity. 2m dew point temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions. Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.\"\n  'https://codes.ecmwf.int/grib/param-db/?id=500017']\n ['2m temperature'\t'2m Temperature'\t't_2m'\t'K'\n  \"Temperature of air at 2m above the surface of land, sea or in-land waters. 2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions. Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.\"\n  'https://codes.ecmwf.int/grib/param-db/?id=500011']\n ['Evaporation from bare soil'\t'Evaporation from bare soil'\t'evabs'\n  'm of water equivalent'\n  'The amount of evaporation from bare soil at the top of the land surface. This variable is accumulated from the beginning of the forecast time to the end of the forecast step.'\n  'https://codes.ecmwf.int/grib/param-db/?id=228101']\n ['Evaporation from open water surfaces excluding oceans'\n  'Evaporation from open water surfaces excluding oceans'\t'evaow'\n  'm of water equivalent'\n  'Amount of evaporation from surface water storage like lakes and inundated areas but excluding oceans. This variable is accumulated from the beginning of the forecast time to the end of the forecast step.'\n  'https://codes.ecmwf.int/grib/param-db/?id=228102']\n ['Evaporation from the top of canopy'\n  'Evaporation from the top of canopy'\t'evatc'\t'm of water equivalent'\n  'The amount of evaporation from the canopy interception reservoir at the top of the canopy. This variable is accumulated from the beginning of the forecast time to the end of the forecast step.'\n  'https://codes.ecmwf.int/grib/param-db/?id=228100']\n ['Evaporation from vegetation transpiration'\n  'Evaporation from vegetation transpiration'\t'evavt'\n  'm of water equivalent'\n  'Amount of evaporation from vegetation transpiration. This has the same meaning as root extraction i.e. the amount of water extracted from the different soil layers. This variable is accumulated from the beginning of the forecast time to the end of the forecast step.'\n  'https://codes.ecmwf.int/grib/param-db/?id=228103']\n ['Forecast albedo'\t'Forecast albedo'\t'fal'\t'(0 - 1)'\n  \"Is a measure of the reflectivity of the Earth's surface. It is the fraction of solar (shortwave) radiation reflected by Earth's surface, across the solar spectrum, for both direct and diffuse radiation. Values are between 0 and 1. Typically, snow and ice have high reflectivity with albedo values of 0.8 and above, land has intermediate values between about 0.1 and 0.4 and the ocean has low values of 0.1 or less. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface, where some of it is reflected. The portion that is reflected by the Earth's surface depends on the albedo. In the ECMWF Integrated Forecasting System (IFS), a climatological background albedo (observed values averaged over a period of several years) is used, modified by the model over water, ice and snow. Albedo is often shown as a percentage (%).\"\n  'https://codes.ecmwf.int/grib/param-db/?id=243']\n ['Lake bottom temperature'\t'Lake bottom temperature'\t'lblt'\t'K'\n  'Temperature of water at the bottom of inland water bodies (lakes, reservoirs, rivers) and coastal waters. ECMWF implemented a lake model in May 2015 to represent the water temperature and lake ice of all the world’s major inland water bodies in the Integrated Forecasting System. The model keeps lake depth and surface area (or fractional cover) constant in time.'\n  'https://codes.ecmwf.int/grib/param-db/?id=228010']\n ['Lake ice depth'\t'Lake ice total depth'\t'licd'\t'm'\n  'The thickness of ice on inland water bodies (lakes, reservoirs and rivers) and coastal waters. The ECMWF Integrated Forecasting System (IFS) represents the formation and melting of ice on inland water bodies (lakes, reservoirs and rivers) and coastal water. A single ice layer is represented. This parameter is the thickness of that ice layer.'\n  'https://codes.ecmwf.int/grib/param-db/?id=228014']\n ['Lake ice temperature'\t'Lake ice surface temperature'\t'lict'\t'K'\n  'The temperature of the uppermost surface of ice on inland water bodies (lakes, reservoirs, rivers) and coastal waters. The ECMWF Integrated Forecasting System represents the formation and melting of ice on lakes. A single ice layer is represented. The temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.'\n  'https://codes.ecmwf.int/grib/param-db/?id=228013']\n ['Lake mix-layer depth'\tnan\tnan\tnan\n  'The thickness of the upper most layer of an inland water body (lake, reservoirs, and rivers) or coastal waters that is well mixed and has a near constant temperature with depth (uniform distribution of temperature). The ECMWF Integrated Forecasting System represents inland water bodies with two layers in the vertical, the mixed layer above and the thermocline below. Thermoclines upper boundary is located at the mixed layer bottom, and the lower boundary at the lake bottom. Mixing within the mixed layer can occur when the density of the surface (and near-surface) water is greater than that of the water below. Mixing can also occur through the action of wind on the surface of the lake.'\n  nan]\n ['Lake mix-layer temperature'\tnan\tnan\tnan\n  'The temperature of the upper most layer of inland water bodies (lakes, reservoirs and rivers) or coastal waters) that is well mixed. The ECMWF Integrated Forecasting System represents inland water bodies with two layers in the vertical, the mixed layer above and the thermocline below. Thermoclines upper boundary is located at the mixed layer bottom, and the lower boundary at the lake bottom. Mixing within the mixed layer can occur when the density of the surface (and near-surface) water is greater than that of the water below. Mixing can also occur through the action of wind on the surface of the lake. Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.'\n  nan]\n ['Lake shape factor'\t'Lake shape factor'\t'lshf'\t'dimensionless'\n  'This parameter describes the way that temperature changes with depth in the thermocline layer of inland water bodies (lakes, reservoirs and rivers) and coastal waters. It is used to calculate the lake bottom temperature and other lake-related parameters. The ECMWF Integrated Forecasting System represents inland and coastal water bodies with two layers in the vertical, the mixed layer above and the thermocline below where temperature changes with depth.'\n  'https://codes.ecmwf.int/grib/param-db/?id=228012']\n ['Lake total layer temperature'\t'Lake total layer temperature'\t'ltlt'\n  'K'\n  'The mean temperature of total water column in inland water bodies (lakes, reservoirs and rivers) and coastal waters. The ECMWF Integrated Forecasting System represents inland water bodies with two layers in the vertical, the mixed layer above and the thermocline below where temperature changes with depth. This parameter is the mean over the two layers. Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.'\n  'https://codes.ecmwf.int/grib/param-db/?id=228011']\n ['Leaf area index, high vegetation'\t'Leaf area index high vegetation'\n  'lai_hv'\t'm².m⁻²'\n  'One-half of the total green leaf area per unit horizontal ground surface area for high vegetation type.'\n  'https://codes.ecmwf.int/grib/param-db/?id=67']\n ['Leaf area index, low vegetation'\t'Leaf area index low vegetation'\n  'lai_lv'\t'm².m⁻²'\n  'One-half of the total green leaf area per unit horizontal ground surface area for low vegetation type.'\n  'https://codes.ecmwf.int/grib/param-db/?id=66']\n ['Potential evaporation'\t'Potential evaporation'\t'pev'\t'm'\n  'Potential evaporation (pev) in the current ECMWF model is computed, by making a second call to the surface energy balance routine with the vegetation variables set to \"crops/mixed farming\" and assuming no stress from soil moisture. In other words, evaporation is computed for agricultural land as if it is well watered and assuming that the atmosphere is not affected by this artificial surface condition. The latter may not always be realistic. Although pev is meant to provide an estimate of irrigation requirements, the method can give unrealistic results in arid conditions due to too strong evaporation forced by dry air. Note that in ERA5-Land pev is computed as an open water evaporation (Pan evaporation) and assuming that the atmosphere is not affected by this artificial surface condition. The latter is different  from the way pev is computed in ERA5. This variable is accumulated from the beginning of the forecast time to the end of the forecast step.'\n  'https://codes.ecmwf.int/grib/param-db/?id=228251']\n ['Runoff'\t'Runoff'\t'ro'\t'm'\n  \"Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is simply called 'runoff'. This variable is the total amount of water accumulated from the beginning of the forecast time to the end of the forecast step. The units of runoff are depth in metres. This is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model variables with observations, because observations are often local to a particular point rather than averaged over a grid square area.  Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. More information about how runoff is calculated is given in the IFS Physical Processes documentation.\"\n  'https://codes.ecmwf.int/grib/param-db/?id=205']\n ['Skin reservoir content'\t'Skin reservoir content'\t'src'\n  'm of water equivalent'\n  \"Amount of water in the vegetation canopy and/or in a thin layer on the soil. It represents the amount of rain intercepted by foliage, and water from dew. The maximum amount of 'skin reservoir content' a grid box can hold depends on the type of vegetation, and may be zero.  Water leaves the 'skin reservoir' by evaporation.\"\n  'https://codes.ecmwf.int/grib/param-db/?id=198']\n ['Skin temperature'\t'Skin temperature'\t'skt'\t'K'\n  'Temperature of the surface of the Earth. The skin temperature is the theoretical temperature that is required to satisfy the surface energy balance. It represents the temperature of the uppermost surface layer, which has no heat capacity and so can respond instantaneously to changes in surface fluxes. Skin temperature is calculated differently over land and sea. Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.'\n  'https://codes.ecmwf.int/grib/param-db/?id=235']\n ['Snow albedo'\t'Snow albedo'\t'asn'\t'(0 - 1)'\n  'It is defined as the fraction of solar (shortwave) radiation reflected by the snow, across the solar spectrum, for both direct and diffuse radiation. It is a measure of the reflectivity of the snow covered grid cells. Values vary between 0 and 1. Typically, snow and ice have high reflectivity with albedo values of 0.8 and above.'\n  'https://codes.ecmwf.int/grib/param-db/?id=32']\n ['Snow cover'\t'Snow cover'\t'snowc'\t'%'\n  'It represents the fraction (0-1) of the cell / grid-box occupied by snow (similar to the cloud cover fields of ERA5).'\n  'https://codes.ecmwf.int/grib/param-db/?id=260038']\n ['Snow density'\t'Snow density'\t'rsn'\t'kg.m⁻³'\n  'Mass of snow per cubic metre in the snow layer. The ECMWF Integrated Forecast System (IFS) model represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box.'\n  'https://codes.ecmwf.int/grib/param-db/?id=33']\n ['Snow depth'\t'Snow depth'\t'sd'\t'm of water equivalent'\n  'Instantaneous grib-box average of the snow thickness on the ground (excluding snow on canopy).'\n  'https://codes.ecmwf.int/grib/param-db/?id=141']\n ['Snow depth water equivalent'\t'Snow depth water equivalent'\t'sd'\n  'kg.m⁻²'\n  'Depth of snow from the snow-covered area of a grid box. Its units are metres of water equivalent, so it is the depth the water would have if the snow melted and was spread evenly over the whole grid box. The ECMWF Integrated Forecast System represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box.'\n  'https://codes.ecmwf.int/grib/param-db/?id=228141']\n ['Snow evaporation'\t'Snow evaporation'\t'es'\t'm of water equivalent'\n  'Evaporation from snow averaged over the grid box (to find flux over snow, divide by snow fraction). This variable is accumulated from the beginning of the forecast time to the end of the forecast step.'\n  'https://codes.ecmwf.int/grib/param-db/?id=44']\n ['Snowfall'\t'Snowfall'\t'sf'\t'm of water equivalent'\n  \"Accumulated total snow that has fallen to the Earth's surface. It consists of snow due to the large-scale atmospheric flow (horizontal scales greater than around a few hundred metres) and convection where smaller scale areas (around 5km to a few hundred kilometres) of warm air rise. If snow has melted during the period over which this variable was accumulated, then it will be higher than the snow depth. This variable is the total amount of water accumulated from the beginning of the forecast time to the end of the forecast step. The units given measure the depth the water would have if the snow melted and was spread evenly over the grid box. Care should be taken when comparing model variables with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box and model time step.\"\n  'https://codes.ecmwf.int/grib/param-db/?id=144']\n ['Snowmelt'\t'Snowmelt'\t'smlt'\t'm of water equivalent'\n  'Melting of snow averaged over the grid box (to find melt over snow, divide by snow fraction). This variable is accumulated from the beginning of the forecast time to the end of the forecast step.'\n  'https://codes.ecmwf.int/grib/param-db/?id=45']\n ['Soil temperature level 1'\t'Soil temperature level 1'\t'stl1'\t'K'\n  'Temperature of the soil in layer 1 (0 - 7 cm) of the ECMWF Integrated Forecasting System. The surface is at 0 cm. Soil temperature is set at the middle of each layer, and heat transfer is calculated at the interfaces between them. It is assumed that there is no heat transfer out of the bottom of the lowest layer. Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.'\n  'https://codes.ecmwf.int/grib/param-db/?id=139']\n ['Soil temperature level 2'\t'Soil temperature level 2'\t'stl2'\t'K'\n  'Temperature of the soil in layer 2 (7 -28cm) of the ECMWF Integrated Forecasting System.'\n  'https://codes.ecmwf.int/grib/param-db/?id=170']\n ['Soil temperature level 3'\t'Soil temperature level 3'\t'stl3'\t'K'\n  'Temperature of the soil in layer 3 (28-100cm) of the ECMWF Integrated Forecasting System.'\n  'https://codes.ecmwf.int/grib/param-db/?id=183']\n ['Soil temperature level 4'\t'Soil temperature level 4'\t'stl4'\t'K'\n  'Temperature of the soil in layer 4 (100-289 cm) of the ECMWF Integrated Forecasting System.'\n  'https://codes.ecmwf.int/grib/param-db/?id=236']\n ['Sub-surface runoff'\tnan\tnan\tnan\n  \"Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is simply called 'runoff'. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. The units of runoff are depth in metres. This is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model variables with observations, because observations are often local to a particular point rather than averaged over a grid square area.  Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. More information about how runoff is calculated is given in the IFS Physical Processes documentation.\"\n  nan]\n ['Surface latent heat flux'\t'Surface latent heat flux'\t'slhf'\t'J.m⁻²'\n  'Exchange of latent heat with the surface through turbulent diffusion. This variables is accumulated from the beginning of the forecast time to the end of the forecast step. By model convention, downward fluxes are positive.'\n  'https://codes.ecmwf.int/grib/param-db/?id=147']\n ['Surface net solar radiation'\t'Surface net solar radiation'\t'ssr'\n  'J.m⁻²'\n  \"Amount of solar radiation (also known as shortwave radiation) reaching the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo).Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface, where some of it is reflected. The difference between downward and reflected solar radiation is the surface net solar radiation. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. The units are joules per square metre (J m -2). To convert to watts per square metre (W m -2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards.\"\n  'https://codes.ecmwf.int/grib/param-db/?id=180176']\n ['Surface net thermal radiation'\n  'Surface net long-wave (thermal) radiation'\t'str'\t'J.m⁻²'\n  'Net thermal radiation at the surface. Accumulated field from the beginning of the forecast time to the end of the forecast step. By model convention downward fluxes are positive.'\n  'https://codes.ecmwf.int/grib/param-db/?id=177']\n ['Surface net thermal radiation'\t'Surface net thermal radiation'\t'str'\n  'J.m⁻²'\n  'Net thermal radiation at the surface. Accumulated field from the beginning of the forecast time to the end of the forecast step. By model convention downward fluxes are positive.'\n  'https://codes.ecmwf.int/grib/param-db/?id=180177']\n ['Surface pressure'\t'Surface pressure'\t'sp'\t'Pa'\n  \"Pressure (force per unit area) of the atmosphere on the surface of land, sea and in-land water. It is a measure of the weight of all the air in a column vertically above the area of the Earth's surface represented at a fixed point. Surface pressure is often used in combination with temperature to calculate air density. The strong variation of pressure with altitude makes it difficult to see the low and high pressure systems over mountainous areas, so mean sea level pressure, rather than surface pressure, is normally used for this purpose. The units of this variable are Pascals (Pa). Surface pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb = 100 Pa).\"\n  'https://codes.ecmwf.int/grib/param-db/?id=134']\n ['Surface runoff'\t'Surface runoff'\t'sro'\t'm'\n  \"Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is simply called 'runoff'. This variable is the total amount of water accumulated from the beginning of the forecast time to the end of the forecast step. The units of runoff are depth in metres. This is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model variables with observations, because observations are often local to a particular point rather than averaged over a grid square area. Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here. Runoff is a measure of the availability of water in the soil, and can, for example, be used as an indicator of drought or flood. More information about how runoff is calculated is given in the IFS Physical Processes documentation.\"\n  'https://codes.ecmwf.int/grib/param-db/?id=8']\n ['Surface sensible heat flux'\t'Surface sensible heat flux'\t'sshf'\n  'J.m⁻²'\n  \"Transfer of heat between the Earth's surface and the atmosphere through the effects of turbulent air motion (but excluding any heat transfer resulting from condensation or evaporation). The magnitude of the sensible heat flux is governed by the difference in temperature between the surface and the overlying atmosphere, wind speed and the surface roughness. For example, cold air overlying a warm surface would produce a sensible heat flux from the land (or ocean) into the atmosphere. This is a single level variable and it is accumulated from the beginning of the forecast time to the end of the forecast step. The units are joules per square metre (J m -2). To convert to watts per square metre (W m -2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards.\"\n  'https://codes.ecmwf.int/grib/param-db/?id=146']\n ['Surface solar radiation downwards'\n  'Surface short-wave (solar) radiation downwards'\t'ssrd'\t'J.m⁻²'\n  \"Amount of solar radiation (also known as shortwave radiation) reaching the surface of the Earth. This variable comprises both direct and diffuse solar radiation. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed.  The rest is incident on the Earth's surface (represented by this variable). To a reasonably good approximation, this variable is the model equivalent of what would be measured by a pyranometer (an instrument used for measuring solar radiation) at the surface. However, care should be taken when comparing model variables with observations, because observations are often local to a particular point in space and time, rather than representing averages over a  model grid box and model time step. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. The units are joules per square metre (J m -2). To convert to watts per square metre (W m -2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards.\"\n  'https://codes.ecmwf.int/grib/param-db/?id=169']\n ['Surface thermal radiation downwards'\tnan\tnan\tnan\n  \"Amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere and clouds that reaches the Earth's surface. The surface of the Earth emits thermal radiation, some of which is absorbed by the atmosphere and clouds. The atmosphere and clouds likewise emit thermal radiation in all directions, some of which reaches the surface (represented by this variable). This variable is accumulated from the beginning of the forecast time to the end of the forecast step. The units are joules per square metre (J m -2). To convert to watts per square metre (W m -2), the accumulated values should be divided by the accumulation period expressed in seconds. The ECMWF convention for vertical fluxes is positive downwards.\"\n  nan]\n ['Temperature of snow layer'\t'Temperature of snow layer'\t'tsn'\t'K'\n  'This variable gives the temperature of the snow layer from the ground to the snow-air interface. The ECMWF Integrated Forecast System (IFS) model represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the  grid box. Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.'\n  'https://codes.ecmwf.int/grib/param-db/?id=238']\n ['Total evaporation'\t'Evaporation'\t'e'\t'm of water equivalent'\n  \"Accumulated amount of water that has evaporated from the Earth's surface, including a simplified representation of transpiration (from vegetation), into vapour in the air above. This variable is accumulated from the beginning of the forecast to the end of the forecast step. The ECMWF Integrated Forecasting System convention is that downward fluxes are positive. Therefore, negative values indicate evaporation and positive values indicate condensation.\"\n  'https://codes.ecmwf.int/grib/param-db/?id=182']\n ['Total precipitation'\t'Total precipitation'\t'tp'\t'm'\n  \"Accumulated liquid and frozen water, including rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation (that precipitation which is generated by large-scale weather patterns, such as troughs and cold fronts) and convective precipitation (generated by convection which occurs when air at lower levels in the atmosphere is warmer and less dense than the air above, so it rises). Precipitation variables do not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth. This variable is accumulated from the beginning of the forecast time to the end of the forecast step. The units of precipitation are depth in metres. It is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model variables with observations, because observations are often local to a particular point in space and time, rather than representing averages over a model grid box and  model time step.\"\n  'https://codes.ecmwf.int/grib/param-db/?id=228']\n ['Volumetric soil water layer 1'\t'Volumetric soil water layer 1'\t'swvl1'\n  'm³.m⁻³'\n  'Volume of water in soil layer 1 (0 - 7 cm) of the ECMWF Integrated Forecasting System. The surface is at 0 cm. The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level.'\n  'https://codes.ecmwf.int/grib/param-db/?id=39']\n ['Volumetric soil water layer 2'\t'Volumetric soil water layer 2'\t'swvl2'\n  'm³.m⁻³'\n  'Volume of water in soil layer 2 (7 -28 cm) of the ECMWF Integrated Forecasting System.'\n  'https://codes.ecmwf.int/grib/param-db/?id=40']\n ['Volumetric soil water layer 3'\t'Volumetric soil water layer 3'\t'swvl3'\n  'm³.m⁻³'\n  'Volume of water in soil layer 3 (28-100 cm) of the ECMWF Integrated Forecasting System.'\n  'https://codes.ecmwf.int/grib/param-db/?id=41']\n ['Volumetric soil water layer 4'\t'Volumetric soil water layer 4'\t'swvl4'\n  'm³.m⁻³'\n  'Volume of water in soil layer 4 (100-289 cm) of the ECMWF Integrated Forecasting System.'\n  'https://codes.ecmwf.int/grib/param-db/?id=42']]\n\nData type: Gridded\nProjection: Regular latitude-longitude grid\nHorizontal coverage: Global\nHorizontal resolution: 0.1° x 0.1°; Native resolution is 9 km.\nVertical coverage: From 2 m above the surface level, to a soil depth of 289 cm.\nVertical resolution: 4 levels of the ECMWF surface model: Layer 1: 0 -7cm, Layer 2: 7 -28cm, Layer 3: 28-100cm, Layer 4: 100-289cm\nSome parameters are defined at 2 m over the surface.\nJanuary 1950 to present\nTemporal resolution: Hourly\nFile format: GRIB\nUpdate frequency: Monthly with a delay of about three months relatively to actual date.","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.ERA5_LAND_HOURLY/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.ERA5_LAND_HOURLY/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.ERA5_LAND_HOURLY/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.ERA5_LAND_HOURLY","title":"ERA5-Land hourly data from 1950 to 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(The conversion of solid alone into vapor is called 'sublimation'.)","dimensions":["lat","lon","time"],"type":"data","unit":"m of water equivalent"},"evaporation_from_the_top_of_canopy":{"attrs":{"long_name":"Evaporation from the top of canopy","product_type":"forecast","shortName":"evatc","standard_name":"lwe_thickness_of_water_evaporation_amount"},"description":"'lwe' means liquid water equivalent. 'Amount' means mass per unit area. The construction lwe_thickness_of_X_amount or _content means the vertical extent of a layer of liquid water having the same mass per unit area. 'Water' means water in all phases. Evaporation is the conversion of liquid or solid into vapor. (The conversion of solid alone into vapor is called 'sublimation'.)","dimensions":["lat","lon","time"],"type":"data","unit":"m of water equivalent"},"evaporation_from_vegetation_transpiration":{"attrs":{"long_name":"Evaporation from vegetation transpiration","product_type":"forecast","shortName":"evavt","standard_name":"lwe_thickness_of_water_evaporation_amount"},"description":"'lwe' means liquid water equivalent. 'Amount' means mass per unit area. The construction lwe_thickness_of_X_amount or _content means the vertical extent of a layer of liquid water having the same mass per unit area. 'Water' means water in all phases. Evaporation is the conversion of liquid or solid into vapor. 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Albedo is the ratio of outgoing to incoming shortwave irradiance, where 'shortwave irradiance' means that both the incoming and outgoing radiation are integrated across the solar spectrum. A phrase assuming_condition indicates that the named quantity is the value which would obtain if all aspects of the system were unaltered except for the assumption of the circumstances specified by the condition.","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"snow_cover":{"attrs":{"long_name":"Snow cover","product_type":"forecast","shortName":"snowc","standard_name":"surface_snow_area_fraction"},"description":"Percentage of each grid cell that is occupied by snow that rests on land portion of cell","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"snow_density":{"attrs":{"long_name":"Snow density","product_type":"analysis","shortName":"rsn","standard_name":"snow_density"},"description":"Where land over land, this is computed as the mean thickness of snow in the land portion of the grid cell (averaging over the entire land portion, including the snow-free fraction). Reported as 0.0 where the land fraction is 0.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m**-3"},"snow_depth":{"attrs":{"long_name":"Snow depth","product_type":"forecast","shortName":"sde","standard_name":"surface_snow_thickness"},"description":"Where land over land, this is computed as the mean thickness of snow in the land portion of the grid cell (averaging over the entire land portion, including the snow-free fraction). Reported as 0.0 where the land fraction is 0.","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"snow_depth_water_equivalent":{"attrs":{"long_name":"Snow depth","product_type":"analysis","shortName":"sd","standard_name":"lwe_thickness_of_surface_snow_amount"},"description":"The surface called 'surface' means the lower boundary of the atmosphere. 'lwe' means liquid water equivalent. 'Amount' means mass per unit area. The construction lwe_thickness_of_X_amount or _content means the vertical extent of a layer of liquid water having the same mass per unit area. Surface amount refers to the amount on the ground, excluding that on the plant or vegetation canopy.","dimensions":["lat","lon","time"],"type":"data","unit":"m of water equivalent"},"snowfall":{"attrs":{"long_name":"Snowfall","product_type":"forecast","shortName":"sf","standard_name":"lwe_thickness_of_snowfall_amount"},"description":"The construction lwe_thickness_of_X_amount or _content means the vertical extent of a layer of liquid water having the same mass per unit area. The abbreviation 'lwe' means liquid water equivalent.","dimensions":["lat","lon","time"],"type":"data","unit":"m of water equivalent"},"snowmelt":{"attrs":{"long_name":"Snowmelt","product_type":"forecast","shortName":"smlt","standard_name":""},"description":"","dimensions":["lat","lon","time"],"type":"data","unit":"m of water equivalent"},"soil_temperature_level_1":{"attrs":{"long_name":"Soil temperature level 1","product_type":"analysis","shortName":"stl1","standard_name":"soil_temperature"},"description":"Top soil layer: 1-7 cm. Soil temperature (ST) before 19930804","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"soil_temperature_level_2":{"attrs":{"long_name":"Soil temperature level 2","product_type":"analysis","shortName":"stl2","standard_name":"soil_temperature"},"description":"Soil layer 2: 7-28 cm. Deep soil temperature (DST) before 19930804","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"soil_temperature_level_3":{"attrs":{"long_name":"Soil temperature level 3","product_type":"analysis","shortName":"stl3","standard_name":"soil_temperature"},"description":"Soil layer 3: 28-100 cm. Climatological deep soil temperature (CDST) before 19930804","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"soil_temperature_level_4":{"attrs":{"long_name":"Soil temperature level 4","product_type":"analysis","shortName":"stl4","standard_name":"soil_temperature"},"description":"Soil layer 4: 100-289 cm.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"sub_surface_runoff":{"attrs":{"long_name":"Sub-surface runoff","product_type":"forecast","shortName":"ssro","standard_name":"subsurface_runoff_amount"},"description":"The subsurface run-off (including drainage through the base of the soil model) per unit area leaving the land portion of the grid cell. 'Amount' means mass per unit area. Runoff is the liquid water which drains from land.","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"surface_latent_heat_flux":{"attrs":{"long_name":"Surface latent heat flux","product_type":"forecast","shortName":"slhf","standard_name":"integral_wrt_time_of_surface_upward_latent_heat_flux"},"description":"The surface called 'surface' means the lower boundary of the atmosphere. 'Upward' indicates a vector component which is positive when directed upward (negative downward). The surface latent heat flux is the exchange of heat between the surface and the air on account of evaporation (including sublimation). In accordance with common usage in geophysical disciplines, 'flux' implies per unit area, called 'flux density' in physics. Accumulated means the value represents the sum of the quantity during the accumulation period.","dimensions":["lat","lon","time"],"type":"data","unit":"J m**-2"},"surface_net_solar_radiation":{"attrs":{"long_name":"Surface net solar radiation","product_type":"forecast","shortName":"ssr","standard_name":"integral_wrt_time_of_surface_net_downward_shortwave_flux"},"description":"Accumulated net downward shortwave radiation at the surface","dimensions":["lat","lon","time"],"type":"data","unit":"J m**-2"},"surface_net_thermal_radiation":{"attrs":{"long_name":"Surface net thermal radiation","product_type":"forecast","shortName":"str","standard_name":"integral_wrt_time_of_surface_net_downward_longwave_flux"},"description":"Accumulated net longwave surface radiation","dimensions":["lat","lon","time"],"type":"data","unit":"J m**-2"},"surface_pressure":{"attrs":{"long_name":"Surface pressure","product_type":"forecast","shortName":"sp","standard_name":"surface_air_pressure"},"description":"Surface pressure (not mean sea-level pressure), 2-D field to calculate the 3-D pressure field from hybrid coordinates","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"surface_runoff":{"attrs":{"long_name":"Surface runoff","product_type":"forecast","shortName":"sro","standard_name":"surface_runoff_amount"},"description":"The surface run-off per unit area leaving the land portion of the grid cell. 'Amount' means mass per unit area. Runoff is the liquid water which drains from land.","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"surface_sensible_heat_flux":{"attrs":{"long_name":"Surface sensible heat flux","product_type":"forecast","shortName":"sshf","standard_name":"integral_wrt_time_of_surface_upward_sensible_heat_flux"},"description":"The surface sensible heat flux, also called turbulent heat flux, is the exchange of heat between the surface and the air by motion of air. Accumulated means the value represents the sum of the quantity during the accumulation period.","dimensions":["lat","lon","time"],"type":"data","unit":"J m**-2"},"surface_solar_radiation_downwards":{"attrs":{"long_name":"Surface solar radiation downwards","product_type":"forecast","shortName":"ssrd","standard_name":"integral_wrt_time_of_surface_downwelling_shortwave_flux_in_air"},"description":"Surface solar irradiance for UV calculations. 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Accumulated means the value represents the sum of the quantity during the accumulation period.","dimensions":["lat","lon","time"],"type":"data","unit":"J m**-2"},"temperature_of_snow_layer":{"attrs":{"long_name":"Temperature of snow layer","product_type":"analysis","shortName":"tsn","standard_name":"temperature_in_surface_snow"},"description":"This temperature is averaged over all the snow in the grid cell that rests on land or land ice.  When computing the time-mean here, the time samples, weighted by the mass of snow on the land portion of the grid cell, are accumulated and then divided by the sum of the weights.   Reported as missing in regions free of snow on land.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"total_evaporation":{"attrs":{"long_name":"Evaporation","product_type":"forecast","shortName":"e","standard_name":"lwe_thickness_of_water_evaporation_amount"},"description":"'lwe' means liquid water equivalent. 'Amount' means mass per unit area. The construction lwe_thickness_of_X_amount or _content means the vertical extent of a layer of liquid water having the same mass per unit area. 'Water' means water in all phases. Evaporation is the conversion of liquid or solid into vapor. (The conversion of solid alone into vapor is called 'sublimation'.)","dimensions":["lat","lon","time"],"type":"data","unit":"m of water equivalent"},"total_precipitation":{"attrs":{"long_name":"Total precipitation","product_type":"forecast","shortName":"tp","standard_name":"lwe_thickness_of_precipitation_amount"},"description":"The construction lwe_thickness_of_X_amount or _content means the vertical extent of a layer of liquid water having the same mass per unit area. 'Precipitation' in the Earth's atmosphere means precipitation of water in all phases. The abbreviation 'lwe' means liquid water equivalent.","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"volumetric_soil_water_layer_1":{"attrs":{"long_name":"Volumetric soil water layer 1","product_type":"analysis","shortName":"swvl1","standard_name":"volumetric_soil_water"},"description":"","dimensions":["lat","lon","time"],"type":"data","unit":"m**3 m**-3"},"volumetric_soil_water_layer_2":{"attrs":{"long_name":"Volumetric soil water layer 2","product_type":"analysis","shortName":"swvl2","standard_name":"volumetric_soil_water"},"description":"","dimensions":["lat","lon","time"],"type":"data","unit":"m**3 m**-3"},"volumetric_soil_water_layer_3":{"attrs":{"long_name":"Volumetric soil water layer 3","product_type":"analysis","shortName":"swvl3","standard_name":"volumetric_soil_water"},"description":"","dimensions":["lat","lon","time"],"type":"data","unit":"m**3 m**-3"},"volumetric_soil_water_layer_4":{"attrs":{"long_name":"Volumetric soil water layer 4","product_type":"analysis","shortName":"swvl4","standard_name":"volumetric_soil_water"},"description":"","dimensions":["lat","lon","time"],"type":"data","unit":"m**3 m**-3"}},"sci:doi":"10.24381/cds.e2161bac","sci:citation":"Muñoz Sabater, J. (2019): ERA5-Land hourly data from 1950 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac) (Accessed on DD-MMM-YYYY)","sci:publications":[{"doi":"10.5194/essd-13-4349-2021","citation":"J. Muñoz-Sabater, Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C., and Thépaut, J.-N.: ERA5-Land: A state-of-the-art global reanalysis dataset for land applications, Earth Syst. Sci. Data,13, 4349–4383, 2021. https://doi.org/10.5194/essd-13-4349-2021."}],"dedl:short_description":"The ERA5-Land dataset contains gridded global land surface variables at a 0.1°x0.1° resolution, covering January 1950 to present, updated monthly with a lag of approximately three months, offering insights into various aspects of the Earth system, including meteorology, hydrology, and ecology."},{"type":"Collection","title":"ERA5-Land monthly averaged data from 1950 to present","id":"EO.ECMWF.DAT.ERA5_LAND_MONTHLY","description":"ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past.\n\nERA5-Land provides a consistent view of the water and energy cycles at surface level during several decades. It contains a detailed record from 1950 onwards, with a temporal resolution of 1 hour. The native spatial resolution of the ERA5-Land reanalysis dataset is 9km on a reduced Gaussian grid (TCo1279). The data in the CDS has been regridded to a regular lat-lon grid of 0.1x0.1 degrees.\n\nThe data presented here is a post-processed subset of the full ERA5-Land dataset. Monthly-mean averages have been pre-calculated to facilitate many applications requiring easy and fast access to the data, when sub-monthly fields are not required.\n\nHourly fields can be found in the dataset \"ERA5-Land hourly data from 1950 to present\"","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.ERA5_LAND_MONTHLY/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.ERA5_LAND_MONTHLY/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.ERA5_LAND_MONTHLY/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.ERA5_LAND_MONTHLY","title":"ERA5-Land monthly averaged data from 1950 to present"},{"rel":"license","type":"application/pdf","href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/licences/licence-to-use-copernicus-products/licence-to-use-copernicus-products_b4b9451f54cffa16ecef5c912c9cebd6979925a956e3fa677976e0cf198c2c18.pdf","title":"Licence to Use Copernicus Products"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.24381/cds.68d2bb30","title":"ERA5-Land monthly averaged data from 1950 to present"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.5194/essd-13-4349-2021","title":"ERA5-Land: a state-of-the-art global reanalysis dataset for land applications"}],"assets":{"thumbnail":{"href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/resources/reanalysis-era5-land-monthly-means/overview_b50879b09a1fdb1f128c7784f2ce62378d4c68e156ca0c4ebdc0fe4f26375cf0.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1950-01-01T00:00:00Z",null]]}},"license":"other","keywords":["Land conditions","Reanalysis","Global","Past","Land (hydrology)","Land (physics)","Land (biosphere)","Copernicus C3S"],"summaries":{"federation:backends":["cop_cds","wekeo_ecmwf"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Climate Change Service (C3S)","roles":["host"],"url":"https://climate.copernicus.eu/"}],"created":"2023-08-28T15:11:18Z","updated":"2026-04-24T10:24:59Z","published":"2023-08-28T15:11:18Z","sci:doi":"10.24381/cds.68d2bb30","sci:citation":"Muñoz Sabater, J. (2019): ERA5-Land monthly averaged data from 1950 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.68d2bb30 (Accessed on DD-MMM-YYYY)","sci:publications":[{"doi":"10.5194/essd-13-4349-2021","citation":"J. Muñoz-Sabater, Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C., and Thépaut, J.-N.: ERA5-Land: A state-of-the-art global reanalysis dataset for land applications, Earth Syst. Sci. Data,13, 4349–4383, 2021. https://doi.org/10.5194/essd-13-4349-2021."}],"dedl:short_description":"This dataset consists of ERA5-Land's monthly averaged data from 1950 to present, offering a high-resolution, decade-spanning view of global land variables through combining modelled and observed data according to physical laws."},{"type":"Collection","title":"ERA5 monthly averaged data on pressure levels from 1940 to present","id":"EO.ECMWF.DAT.ERA5_MONTHLY_MEANS_VARIABLES_ON_PRESSURE_LEVELS","description":"ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades. Data is available from 1940 onwards. ERA5 replaces the ERA-Interim reanalysis.\n\nReanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product.\n\nERA5 provides hourly estimates for a large number of atmospheric, ocean-wave and land-surface quantities. An uncertainty estimate is sampled by an underlying 10-member ensemble at three-hourly intervals. Ensemble mean and spread have been pre-computed for convenience. Such uncertainty estimates are closely related to the information content of the available observing system which has evolved considerably over time. They also indicate flow-dependent sensitive areas. To facilitate many climate applications, monthly-mean averages have been pre-calculated too, though monthly means are not available for the ensemble mean and spread.\n\nERA5 is updated daily with a latency of about 5 days (monthly means are available around the 6th of each month). In case that serious flaws are detected in this early release (called ERA5T), this data could be different from the final release 2 to 3 months later. So far this has only been the case for the month September 2021, while it will also be the case for October, November and December 2021. For months prior to September 2021 the final release has always been equal to ERA5T, and the goal is to align the two again after December 2021.\n\nERA5 is updated daily with a latency of about 5 days (monthly means are available around the 6th of each month). In case that serious flaws are detected in this early release (called ERA5T), this data could be different from the final release 2 to 3 months later. In case that this occurs users are notified.\n\nThe data set presented here is a regridded subset of the full ERA5 data set on native resolution. It is online on spinning disk, which should ensure fast and easy access. It should satisfy the requirements for most common applications.\n\nData has been regridded to a regular lat-lon grid of 0.25 degrees for the reanalysis and 0.5 degrees for the uncertainty estimate (0.5 and 1 degree respectively for ocean waves). There are four main sub sets: hourly and monthly products, both on pressure levels (upper air fields) and single levels (atmospheric, ocean-wave and land surface quantities).","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.ERA5_MONTHLY_MEANS_VARIABLES_ON_PRESSURE_LEVELS/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.ERA5_MONTHLY_MEANS_VARIABLES_ON_PRESSURE_LEVELS/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.ERA5_MONTHLY_MEANS_VARIABLES_ON_PRESSURE_LEVELS/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.ERA5_MONTHLY_MEANS_VARIABLES_ON_PRESSURE_LEVELS","title":"ERA5 monthly averaged data on pressure levels from 1940 to present"},{"rel":"license","type":"application/pdf","href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/licences/licence-to-use-copernicus-products/licence-to-use-copernicus-products_b4b9451f54cffa16ecef5c912c9cebd6979925a956e3fa677976e0cf198c2c18.pdf","title":"Licence to Use Copernicus Products"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.24381/cds.6860a573","title":"ERA5 monthly averaged data on pressure levels from 1940 to present"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.1002/qj.3803","title":"The ERA5 global reanalysis"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.1002/qj.4174","title":"The ERA5 global reanalysis: Preliminary extension to 1950"}],"assets":{"thumbnail":{"href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/resources/reanalysis-era5-pressure-levels-monthly-means/overview_f71dc114a2f6dd433f4ddecbf6b358a107864c4844c826c2da37c7044986e7fe.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1940-01-01T00:00:00Z",null]]}},"license":"other","keywords":["Atmospheric conditions","Atmosphere (surface)","Atmosphere (upper air)","Past","Global","Reanalysis","Copernicus C3S"],"summaries":{"federation:backends":["cop_cds","wekeo_ecmwf"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Climate Change Service (C3S)","roles":["host"],"url":"https://climate.copernicus.eu/"}],"created":"2023-08-28T15:11:18Z","updated":"2026-04-24T10:24:59Z","published":"2023-08-28T15:11:18Z","sci:doi":"10.24381/cds.6860a573","sci:citation":"Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J-N. (2023): ERA5 monthly averaged data on pressure levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.6860a573 (Accessed on DD-MMM-YYYY)","sci:publications":[{"doi":"10.1002/qj.3803","citation":"Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 Global Reanalysis, Q. J. Roy. Meteorol. Soc., 146, 1999-2049, https://doi.org/10.1002/qj.3803, 2020."},{"doi":"10.1002/qj.4174","citation":"Bell, B., Hersbach, H., Simmons, A., Berrisford, P., Dahlgren, P., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Radu, R., Schepers, D., Soci, C., Villaume, Sebastien., Bidlot, J.-R., Haimberger, L., Woollen, J., Buontempo, C. and Thépaut, J.-N.: The ERA5 global reanalysis: Preliminary extension to 1950, Q. J. Roy. Meteorol. Soc., 147, 4186-4227, https://doi.org/10.1002/qj.4174, 2021."}],"dedl:short_description":"This dataset contains ERA5's fifth-generation ECMWF reanalysis data on various parameters such as pressure levels, temperature, wind speed, etc., covering the period from 1940 to present, provided at multiple resolutions including 0.25-degree latitude-longitude grids."},{"type":"Collection","title":"Glaciers distribution data from the Randolph Glacier Inventory for year 2000","id":"EO.ECMWF.DAT.GLACIERS_DISTRIBUTION_DATA_FROM_RANDOLPH_GLACIER_INVENTORY_2000","description":"A glacier is defined as a perennial mass of ice, and possibly firn and snow, originating on the land surface from the recrystallization of snow or other forms of solid precipitation and showing evidence of past or present flow. There are several types of glaciers such as glacierets, mountain glaciers, valley glaciers and ice fields, as well as ice caps. Some glacier tongues reach into lakes or the sea, and can develop floating ice tongues or ice shelves. Glacier changes are recognized as independent and high-confidence natural indicators of climate change. Past, current and future glacier changes affect global sea level, the regional water cycle and local hazards.\nThis dataset is a snapshot of global glacier outlines compiled from\nmaps, aerial photographs and satellite images mostly acquired in the period 2000-2010.","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.GLACIERS_DISTRIBUTION_DATA_FROM_RANDOLPH_GLACIER_INVENTORY_2000/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.GLACIERS_DISTRIBUTION_DATA_FROM_RANDOLPH_GLACIER_INVENTORY_2000/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.GLACIERS_DISTRIBUTION_DATA_FROM_RANDOLPH_GLACIER_INVENTORY_2000/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.GLACIERS_DISTRIBUTION_DATA_FROM_RANDOLPH_GLACIER_INVENTORY_2000","title":"Glaciers distribution data from the Randolph Glacier Inventory for year 2000"},{"rel":"license","type":"application/pdf","href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/licences/licence-to-use-insitu-glaciers-extent/licence-to-use-insitu-glaciers-extent_d69ddaeac01d0b556cc932144abe3c5a7f5433e31e5188c111591e455fc25497.pdf","title":"UZH Glaciers Extent licence"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.24381/cds.553f1387","title":"Glaciers distribution data from the Randolph Glacier Inventory for year 2000"}],"assets":{"thumbnail":{"href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/resources/insitu-glaciers-extent/overview_a947fbd2fbb24a95d90de559bb4f3b726bc6f819f147434a162b23c9232d91b3.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2000-01-01T00:00:00Z","2000-12-31T23:59:00Z"]]}},"license":"other","keywords":["Satellite observations","Global","Past","Land (cryosphere)","Copernicus C3S","Land cover"],"summaries":{"federation:backends":["cop_cds","wekeo_ecmwf"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Climate Change Service (C3S)","roles":["host"],"url":"https://climate.copernicus.eu/"}],"created":"2023-08-28T15:11:18Z","updated":"2026-04-24T10:24:59Z","published":"2023-08-28T15:11:18Z","sci:doi":"10.24381/cds.553f1387","sci:citation":"## for v5.0:\nRGI Consortium (2015). Randolph Glacier Inventory – A Dataset of Global Glacier Outlines: Version 5.0: Technical Report, Global Land Ice Measurements from Space, Colorado, USA. Digital Media. DOI: 10.7265/N5-RGI-50 (Accessed on DD-MMM-YYYY)\n## for v6.0:\nRGI Consortium (2017). Randolph Glacier Inventory – A Dataset of Global Glacier Outlines: Version 6.0: Technical Report, Global Land Ice Measurements from Space, Colorado, USA. Digital Media. https://doi.org/10.7265/N5-RGI-60 (Accessed on DD-MMM-YYYY)","dedl:short_description":"The dataset contains a 2000s-era snapshot of global glacier distributions mapped from various sources including maps, aerial photos, and satellite imagery."},{"type":"Collection","title":"Methane data from 2002 to present derived from satellite observations","id":"EO.ECMWF.DAT.METHANE_DATA_SATELLITE_SENSORS_2002_PRESENT","description":"This dataset provides observations of atmospheric methane (CH4)\namounts obtained from observations collected by several current and historical \nsatellite instruments.  Methane is a naturally occurring Greenhouse Gas (GHG), but one whose abundance has been increased substantially above its pre-industrial value of some 720 ppb by human activities, primarily because of agricultural emissions (e.g., rice production, ruminants) and fossil fuel production and use. A clear annual cycle is largely due to seasonal wetland emissions.\nAtmospheric methane abundance is indirectly observed by various satellite instruments. These instruments measure spectrally resolved near-infrared and infrared radiation reflected or emitted by the Earth and its atmosphere. In the measured signal, molecular absorption signatures from methane and constituent gasses can be identified. It is through analysis of those absorption lines in these radiance observations that the averaged methane abundance in the sampled atmospheric column can be determined.\nThe software used to analyse the absorption lines and determine the methane concentration in the sampled atmospheric column is referred to as the retrieval algorithm. For this dataset, methane abundances have been determined by applying several algorithms to different satellite instruments.\nThe data set consists of 2 types of products: (i) column-averaged mixing ratios of CH4, denoted XCH4 and (ii) mid-tropospheric CH4 columns. \nThe XCH4 products have been retrieved from SCIAMACHY/ENVISAT and TANSO-FTS/GOSAT. The mid-tropospheric CH4 product has been retrieved from the IASI instruments onboard the Metop satellite series. The XCH4 products are available as Level 2 (L2) products (satellite orbit tracks) and as Level 3 (L3) product (gridded). The L2 products are available as individual sensor products (SCIAMACHY: WFMD and IMAP algorithms; GOSAT: OCFP, OCPR, SRFP and SRPR algorithms) and as a multi-sensor merged product (EMMA algorithm). The L3 XCH4 product is provided in OBS4MIPS format. The IASI products are available as L2 products generated with the NLIS algorithm.\nThis data set is updated on a yearly basis, with each update cycle adding (if required) a new data version for the entire period, up to one year behind real time.\nThis dataset is produced on behalf of C3S with the exception of the SCIAMACHY L2 products that were generated in the framework of the GHG-CCI project of the European Space Agency (ESA) Climate Change Initiative (CCI).\n\nVariables in the dataset/application are:\nColumn-average dry-air mole fraction of atmospheric methane (XCH4), Mid-tropospheric columns of atmospheric methane (CH4)","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.METHANE_DATA_SATELLITE_SENSORS_2002_PRESENT/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.METHANE_DATA_SATELLITE_SENSORS_2002_PRESENT/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.METHANE_DATA_SATELLITE_SENSORS_2002_PRESENT/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.METHANE_DATA_SATELLITE_SENSORS_2002_PRESENT","title":"Methane data from 2002 to present derived from satellite observations"},{"rel":"license","type":"application/pdf","href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/licences/ghg-cci/ghg-cci_0911d58e24365e15589377902e562c6e9231290f75b14ddc3c7cb5fd09a265af.pdf","title":"GHG-CCI Licence"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.24381/cds.b25419f8","title":"Methane data from 2002 to present derived from satellite observations"}],"assets":{"thumbnail":{"href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/resources/satellite-methane/overview_a8d840c9ea39792c2691b0414da4eb40f8bf9241aa4abf76da96f316fca7c729.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-10-01T00:00:00Z","2018-12-31T00:00:00Z"]]}},"license":"other","keywords":["Satellite observations","Atmospheric conditions","Global","Past","Atmosphere (composition)"],"summaries":{"federation:backends":["cop_cds","wekeo_ecmwf"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Climate Change Service (C3S)","roles":["host"],"url":"https://climate.copernicus.eu/"}],"created":"2023-08-28T15:11:18Z","updated":"2026-04-24T10:24:59Z","published":"2023-08-28T15:11:18Z","sci:doi":"10.24381/cds.b25419f8","sci:citation":"Copernicus Climate Change Service, Climate Data Store, (2018): Methane data from 2002 to present derived from satellite observations. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.b25419f8 (Accessed on DD-MMM-YYYY)","dedl:short_description":"Satellite-derived methane data from 2002-present provide observations of atmospheric methane amounts via spectral analysis of near-infrared and infrared radiation, offering insights into natural and anthropogenic sources contributing to elevated levels beyond pre-industrial values around 720 parts per billion."},{"type":"Collection","title":"ERA5 hourly data on single levels from 1940 to present","id":"EO.ECMWF.DAT.REANALYSIS_ERA5_SINGLE_LEVELS","description":"ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades. Data is available from 1940 onwards. ERA5 replaces the ERA-Interim reanalysis.\n\nReanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product.\n\nERA5 provides hourly estimates for a large number of atmospheric, ocean-wave and land-surface quantities. An uncertainty estimate is sampled by an underlying 10-member ensemble at three-hourly intervals. Ensemble mean and spread have been pre-computed for convenience. Such uncertainty estimates are closely related to the information content of the available observing system which has evolved considerably over time. They also indicate flow-dependent sensitive areas. To facilitate many climate applications, monthly-mean averages have been pre-calculated too, though monthly means are not available for the ensemble mean and spread.\n\nERA5 is updated daily with a latency of about 5 days. In case that serious flaws are detected in this early release (called ERA5T), this data could be different from the final release 2 to 3 months later. In case that this occurs users are notified.\n\nThe data set presented here is a regridded subset of the full ERA5 data set on native resolution. It is online on spinning disk, which should ensure fast and easy access. It should satisfy the requirements for most common applications.\n\nData has been regridded to a regular lat-lon grid of 0.25 degrees for the reanalysis and 0.5 degrees for the uncertainty estimate (0.5 and 1 degree respectively for ocean waves). There are four main sub sets: hourly and monthly products, both on pressure levels (upper air fields) and single levels (atmospheric, ocean-wave and land surface quantities).","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.REANALYSIS_ERA5_SINGLE_LEVELS/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.REANALYSIS_ERA5_SINGLE_LEVELS/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.REANALYSIS_ERA5_SINGLE_LEVELS/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.REANALYSIS_ERA5_SINGLE_LEVELS","title":"ERA5 hourly data on single levels from 1940 to present"},{"rel":"license","type":"application/pdf","href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/licences/licence-to-use-copernicus-products/licence-to-use-copernicus-products_b4b9451f54cffa16ecef5c912c9cebd6979925a956e3fa677976e0cf198c2c18.pdf","title":"Licence to Use Copernicus Products"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.24381/cds.adbb2d47","title":"ERA5 hourly data on single levels from 1940 to present"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.1002/qj.3803","title":"The ERA5 global reanalysis"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.1002/qj.4174","title":"The ERA5 global reanalysis: Preliminary extension to 1950"},{"rel":"example","type":"application/x-ipynb+json","href":"https://raw.githubusercontent.com/destination-earth/DestinE-DataLake-Lab/refs/heads/main/HDA/CDS_data/DEDL-HDA-EO.ECMWF.DAT.REANALYSIS_ERA5_SINGLE_LEVELS.ipynb","title":"Destination Earth - ERA5 hourly data on single levels from 1940 to present - Data Access using DEDL HDA","application:type":"jupyter-notebook","application:embedded":true,"application:language":"Python"}],"assets":{"thumbnail":{"href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/resources/reanalysis-era5-single-levels/overview_c37c9fd3b18a36a2c656bb4541d37c3bb8a08d2d9ef6708227b87cb47e90a873.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1940-01-01T00:00:00Z",null]]}},"license":"other","keywords":["Atmospheric conditions","Atmosphere (surface)","Atmosphere (upper air)","Past","Global","Reanalysis","Copernicus C3S"],"summaries":{"federation:backends":["cop_cds","wekeo_ecmwf"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json","https://stac-extensions.github.io/application/v0.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Climate Change Service (C3S)","roles":["host"],"url":"https://climate.copernicus.eu/"}],"created":"2023-08-28T15:11:18Z","updated":"2026-04-24T10:24:59Z","published":"2023-08-28T15:11:18Z","sci:doi":"10.24381/cds.adbb2d47","sci:citation":"Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J-N. 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Soc., 146, 1999-2049, https://doi.org/10.1002/qj.3803, 2020."},{"doi":"10.1002/qj.4174","citation":"Bell, B., Hersbach, H., Simmons, A., Berrisford, P., Dahlgren, P., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Radu, R., Schepers, D., Soci, C., Villaume, Sebastien., Bidlot, J.-R., Haimberger, L., Woollen, J., Buontempo, C. and Thépaut, J.-N.: The ERA5 global reanalysis: Preliminary extension to 1950, Q. J. Roy. Meteorol. 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This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product.\n\nERA5 provides hourly estimates for a large number of atmospheric, ocean-wave and land-surface quantities. An uncertainty estimate is sampled by an underlying 10-member ensemble at three-hourly intervals. Ensemble mean and spread have been pre-computed for convenience. Such uncertainty estimates are closely related to the information content of the available observing system which has evolved considerably over time. They also indicate flow-dependent sensitive areas. To facilitate many climate applications, monthly-mean averages have been pre-calculated too, though monthly means are not available for the ensemble mean and spread.\n\nERA5 is updated daily with a latency of about 5 days (monthly means are available around the 6th of each month). In case that serious flaws are detected in this early release (called ERA5T), this data could be different from the final release 2 to 3 months later. In case that this occurs users are notified.\n\nThe data set presented here is a regridded subset of the full ERA5 data set on native resolution. It is online on spinning disk, which should ensure fast and easy access. It should satisfy the requirements for most common applications.\n\nData has been regridded to a regular lat-lon grid of 0.25 degrees for the reanalysis and 0.5 degrees for the uncertainty estimate (0.5 and 1 degree respectively for ocean waves). There are four main sub sets: hourly and monthly products, both on pressure levels (upper air fields) and single levels (atmospheric, ocean-wave and land surface quantities).","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.REANALYSIS_ERA5_SINGLE_LEVELS_MONTHLY_MEANS/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.REANALYSIS_ERA5_SINGLE_LEVELS_MONTHLY_MEANS/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.REANALYSIS_ERA5_SINGLE_LEVELS_MONTHLY_MEANS/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.REANALYSIS_ERA5_SINGLE_LEVELS_MONTHLY_MEANS","title":"ERA5 monthly averaged data on single levels from 1940 to present"},{"rel":"license","type":"application/pdf","href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/licences/licence-to-use-copernicus-products/licence-to-use-copernicus-products_b4b9451f54cffa16ecef5c912c9cebd6979925a956e3fa677976e0cf198c2c18.pdf","title":"Licence to Use Copernicus Products"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.24381/cds.f17050d7","title":"ERA5 monthly averaged data on single levels from 1940 to present"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.1002/qj.3803","title":"The ERA5 global reanalysis"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.1002/qj.4174","title":"The ERA5 global reanalysis: Preliminary extension to 1950"}],"assets":{"thumbnail":{"href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/resources/reanalysis-era5-single-levels-monthly-means/overview_0f8d6ac4a7d46c1b234a9e26d17f21bbad9f173c2a1ca4b645df6c4048fc35f2.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-89,180,89]]},"temporal":{"interval":[["1940-01-01T00:00:00Z",null]]}},"license":"other","keywords":["Atmospheric conditions","Atmosphere (surface)","Atmosphere (upper air)","Past","Global","Reanalysis","Copernicus C3S"],"summaries":{"federation:backends":["cop_cds","wekeo_ecmwf"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Climate Change Service (C3S)","roles":["host"],"url":"https://climate.copernicus.eu/"}],"created":"2023-08-28T15:11:18Z","updated":"2026-04-24T10:24:59Z","published":"2023-08-28T15:11:18Z","sci:doi":"10.24381/cds.f17050d7","sci:citation":"Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J-N. 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Soc., 146, 1999-2049, https://doi.org/10.1002/qj.3803, 2020."},{"doi":"10.1002/qj.4174","citation":"Bell, B., Hersbach, H., Simmons, A., Berrisford, P., Dahlgren, P., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Radu, R., Schepers, D., Soci, C., Villaume, Sebastien., Bidlot, J.-R., Haimberger, L., Woollen, J., Buontempo, C. and Thépaut, J.-N.: The ERA5 global reanalysis: Preliminary extension to 1950, Q. J. Roy. Meteorol. Soc., 147, 4186-4227, https://doi.org/10.1002/qj.4174, 2021."}],"dedl:short_description":"This dataset contains ERA5's fifth-generation ECMWF reanalysis data from 1940-present, combining modelled and observed data through physical laws, providing various atmospheric, oceanic, and terrestrial variables with associated uncertainties on a regularly gridded scale."},{"type":"Collection","title":"UERRA regional reanalysis for Europe on single levels from 1961 to 2019","id":"EO.ECMWF.DAT.REANALYSIS_UERRA_EUROPE_SINGLE_LEVELS","description":"This UERRA dataset contains analyses of surface and near-surface essential climate variables from\nUERRA-HARMONIE and MESCAN-SURFEX systems. Forecasts up to 30 hours initialised\nfrom the analyses at 00 and 12 UTC are available only through the CDS-API (see Documentation). UERRA-HARMONIE is a 3-dimensional variational data assimilation system,\nwhile MESCAN-SURFEX is a complementary surface analysis system.\nUsing the Optimal Interpolation method, MESCAN provides the best estimate of daily accumulated precipitation\nand six-hourly air temperature and relative humidity at 2 meters above the model topography. The land surface platform SURFEX is forced with downscaled forecast fields from UERRA-HARMONIE as well as MESCAN analyses.\nIt is run offline, i.e. without feedback to the atmospheric analysis performed in MESCAN or the UERRA-HARMONIE data\nassimilation cycles. Using SURFEX offline allows to take full benefit of precipitation analysis and to use the more\nadvanced physics options to better represent surface variables such as surface temperature and\nsurface fluxes, and soil processes related to water and heat transfer in the soil and snow. In general, the assimilation systems are able to estimate biases between observations and to sift good-quality\ndata from poor data. The laws of physics allow for estimates at locations where data coverage is low. The provision of\nestimates at each grid point in Europe for each regular output time, over a long period, always using the same format,\nmakes reanalysis a very convenient and popular dataset to work with.\nThe observing system has changed drastically over time, and although the assimilation system\ncan resolve data holes, the much sparser observational networks, e.g. in 1960s,\nwill have an impact on the quality of analyses leading to less accurate estimates.\nThe improvement over global reanalysis products comes with the higher horizontal resolution\nthat allows incorporating more regional details (e.g. topography). Moreover, it enables\nthe system even to use more observations at places with dense observation networks.\n\nMain Variables:[['Name'\t'Full_Name'\t'ShortName'\t'Units'\t'Description'\t'url']\n ['10m wind direction'\t'10 metre wind direction'\t'dwi'\t'degrees'\n  'Wind direction valid for a grid cell at the  height of 10m above the surface. Values are in the interval [0,360). A value of  0° means a northerly wind and 90° indicates an easterly wind.'\n  'https://codes.ecmwf.int/grib/param-db/?id=140249']\n ['10m wind speed'\t'10 metre wind speed'\t'10si'\t'm.s⁻¹'\n  'Wind speed valid for a grid cell at the height of 10m above the surface.  It is computed from both the zonal (u) and the meridional (v) wind components by sqrt(u 2 + v 2 ).'\n  'https://codes.ecmwf.int/grib/param-db/?id=207']\n ['2m relative humidity'\t'2m Relative Humidity'\t'relhum_2m'\t'%'\n  'Relation between actual humidity and saturation humidity. Values are in the interval [0,100]. 0%means that the air in the grid cell  is totally dry whereas 100% indicates that the air in the cell is saturated with water vapour. The saturation is defined with respect to saturation of the mixed phase, i.e. with respect to saturation over ice below -23°C and with respect to saturation over water above 0°C. In the regime in between a quadratic interpolation is applied.'\n  'https://codes.ecmwf.int/grib/param-db/?id=500036']\n ['2m temperature'\t'2m Temperature'\t't_2m'\t'K'\n  'Air temperature valid for a grid cell at the  height of 2m above the surface.'\n  'https://codes.ecmwf.int/grib/param-db/?id=500011']\n ['Albedo'\t'Albedo'\t'al'\t'(0 - 1)'\n  'Amount of radiation reflected  by a grid cell,  both for ground and water surfaces, relatively to the incoming radiation.  Small values mean that large amounts of the radiation are  absorbed whereas large values mean that more radiation is reflected.'\n  'https://codes.ecmwf.int/grib/param-db/?id=174']\n ['High cloud cover'\t'High cloud cover'\t'hcc'\t'(0 - 1)'\n  'Percentage of the grid cell for which  the sky is covered with clouds at  high altitude.'\n  'https://codes.ecmwf.int/grib/param-db/?id=188']\n ['Land sea mask'\t'Land sea mask'\t'lsmk'\t'(0 - 1)'\n  'The values are between 0 (sea) and 1 (land) and are constant over time.'\n  'https://codes.ecmwf.int/grib/param-db/?id=300081']\n ['Low cloud cover'\t'Low cloud cover'\t'lcc'\t'(0 - 1)'\n  'Percentage of the grid cell for which  the  sky is covered with clouds at low altitude.'\n  'https://codes.ecmwf.int/grib/param-db/?id=186']\n ['Mean sea level pressure'\t'Mean sea level pressure'\t'msl'\t'Pa'\n  'Air pressure in the grid cell reduced to mean sea level.'\n  'https://codes.ecmwf.int/grib/param-db/?id=151']\n ['Medium cloud cover'\t'Medium cloud cover'\t'mcc'\t'(0 - 1)'\n  'Percentage of the grid cell for which  the  sky is covered with clouds at medium altitude.'\n  'https://codes.ecmwf.int/grib/param-db/?id=187']\n ['Orography'\t'Orography'\t'orog'\t'm'\n  'Average height of the surface grid cell with respect to the model defined globe.'\n  'https://codes.ecmwf.int/grib/param-db/?id=228002']\n ['Skin temperature'\t'Skin temperature'\t'skt'\t'K'\n  'Boundary temperature in grid cells  between the ground and water surfaces and the atmosphere above.'\n  'https://codes.ecmwf.int/grib/param-db/?id=235']\n ['Snow density'\t'Snow density'\t'rsn'\t'kg.m⁻³'\n  'Average density of snow over a grid cell.'\n  'https://codes.ecmwf.int/grib/param-db/?id=33']\n ['Snow depth water equivalent'\t'Snow depth water equivalent'\t'sd'\n  'kg.m⁻²'\n  'Amount of snow in  kg over a square meter in average on a grid cell.'\n  'https://codes.ecmwf.int/grib/param-db/?id=228141']\n ['Surface pressure'\t'Surface pressure'\t'sp'\t'Pa'\n  'Air pressure in the grid cell at the land and water surface.'\n  'https://codes.ecmwf.int/grib/param-db/?id=134']\n ['Surface roughness'\t'Surface roughness'\t'sr'\t'm'\n  'Mean value over a grid cell of the aerodynamic roughness length. Only values over land are available.'\n  'https://codes.ecmwf.int/grib/param-db/?id=173']\n ['Total cloud cover'\t'Total cloud cover'\t'tcc'\t'(0 - 1)'\n  'Percentage of the grid cell for which the sky is covered with clouds. Clouds at any height above the surface are considered.'\n  'https://codes.ecmwf.int/grib/param-db/?id=164']\n ['Total column integrated water vapour'\n  'Total column integrated water vapour'\t'tciwv'\t'kg.m⁻²'\n  'Total amount of water vapour from surface to the top of the atmosphere for each grid cell.'\n  'https://codes.ecmwf.int/grib/param-db/?id=260057']\n ['Total precipitation'\t'Total precipitation'\t'tp'\t'm'\n  'Amount of water falling onto the ground/water surface. It includes  all kind of precipitation forms as convective precipitation, large scale precipitation, liquid and solid. It is an accumulated parameter  over the 24 hours from 06:00 to 06:00 of the next day. Values are valid for a grid cell.'\n  'https://codes.ecmwf.int/grib/param-db/?id=228']]\n\nData type: Gridded\nProjection: Lambert conformal conic grid with 565 x 565 grid points for the UERRA-HARMONIE system. Lambert conformal conic grid with 1069 x 1069 grid points for the  MESCAN-SURFEX system.\nHorizontal coverage: Europe: The domain spans from northern Africa beyond the northern tip of Scandinavia. In the west it ranges far into the Atlantic ocean and in the east it reaches to the Ural.\nHorizontal resolution: 11km x 11km for the UERRA-HARMONIE system. 5.5km x 5.5km for the MESCAN-SURFEX system.\nVertical coverage: Near surface.\nVertical resolution: Single level.\nJanuary 1961 to July 2019.\nTemporal resolution: Analysis are availabe each day at 00, 06, 12 and 18 UTC.\nFile format: GRIB2\nUpdate frequency: No expected updates.","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.REANALYSIS_UERRA_EUROPE_SINGLE_LEVELS/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.REANALYSIS_UERRA_EUROPE_SINGLE_LEVELS/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.REANALYSIS_UERRA_EUROPE_SINGLE_LEVELS/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.REANALYSIS_UERRA_EUROPE_SINGLE_LEVELS","title":"UERRA regional reanalysis for Europe on single levels from 1961 to 2019"},{"rel":"license","type":"application/pdf","href":"https://cds.climate.copernicus.eu/api/v2/terms/static/20180314_Copernicus_License_V1.1.pdf","title":"Licence to Use Copernicus Products"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.24381/cds.32b04ec5","title":"UERRA regional reanalysis for Europe on single levels from 1961 to 2019"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/UER/Issues+with+data","title":"Known issues in UERRA"}],"assets":{"thumbnail":{"href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/resources/reanalysis-uerra-europe-single-levels/overview_7986f8aa007997482adbbbb0f1fc2ef61153960022bfabd50bc3e11e61dacd06.png","roles":["thumbnail"],"title":"Preview image","type":"image/png"}},"extent":{"spatial":{"bbox":[[-69.103165,-26.018616,61.78629,80.77476]]},"temporal":{"interval":[["1961-01-01T00:00:00Z","2019-08-01T00:00:00Z"]]}},"license":"other","keywords":["Atmospheric conditions","Reanalysis","Europe","Atmosphere (upper air)","Past","Copernicus C3S"],"summaries":{"federation:backends":["cop_cds","wekeo_ecmwf"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/datacube/v2.0.0/schema.json","https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["producer","processor","licensor"],"url":"https://www.ecmwf.int"},{"name":"Copernicus Climate Change Service (C3S)","roles":["host"],"url":"https://climate.copernicus.eu/"}],"created":"2023-08-11T18:04:28Z","updated":"2026-04-24T10:24:59Z","published":"2023-08-11T18:04:28Z","cube:dimensions":{"lat":{"axis":"y","description":"latitude","extent":[-26.018616,80.77476],"reference_system":"epsg:4326","step":-0.25,"type":"spatial"},"lon":{"axis":"x","description":"longitude","extent":[-69.103165,61.78629],"reference_system":"epsg:4326","step":0.25,"type":"spatial"},"time":{"extent":["1961-01-01T00:00:00Z","2019-08-01T00:00:00Z"],"type":"temporal"}},"cube:variables":{"10m_wind_direction":{"attrs":{"long_name":"10 metre wind direction","shortName":"10wdir"},"description":"Wind direction at a height of 10m.","dimensions":["lon","lat","time"],"type":"data","unit":"degrees"},"10m_wind_speed":{"attrs":{"long_name":"10 metre wind speed","shortName":"10si"},"description":"This parameter is the horizontal speed of the wind, or movement of air, at a height of ten metres above the surface of the Earth. The units of this parameter are metres per second.\nCare should be taken when comparing this parameter with observations, because wind observations vary on small space and time scales and are affected by the local terrain, vegetation and buildings that are represented only on average in the ECMWF Integrated Forecasting System.\nThe eastward and northward components of the horizontal wind at 10m are also available as parameters.","dimensions":["lon","lat","time"],"type":"data","unit":"m s**-1"},"2m_relative_humidity":{"attrs":{"long_name":"2 metre relative humidity","shortName":"2r"},"description":"The ratio of the partial pressure of water vapour to the equilibrium vapour pressure of water at the same temperature near the surface.\nNote that the specific height level above ground might vary from one centre to another.","dimensions":["lon","lat","time"],"type":"data","unit":"%"},"2m_temperature":{"attrs":{"long_name":"2 metre temperature","shortName":"2t"},"description":"This parameter is the temperature of air at 2m above the surface of land, sea or in-land waters.\n2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.\nThis parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.","dimensions":["lon","lat","time"],"type":"data","unit":"K"},"albedo":{"attrs":{"long_name":"Albedo","shortName":"al"},"description":"","dimensions":["lon","lat","time"],"type":"data","unit":"(0 - 1)"},"high_cloud_cover":{"attrs":{"long_name":"High cloud cover","shortName":"hcc"},"description":"Percentage of the sky hidden by high cloud","dimensions":["lon","lat","time"],"type":"data","unit":"%"},"land_sea_mask":{"attrs":{"long_name":"Land-sea mask","shortName":"lsm"},"description":"This parameter is the proportion of land, as opposed to ocean or inland waters (lakes, reservoirs, rivers and coastal waters), in a grid box.\nThis parameter has values ranging between zero and one and is dimensionless.\nIn cycles of the ECMWF Integrated Forecasting System (IFS) from CY41R1 (introduced in May 2015) onwards, grid boxes where this parameter has a value above 0.5 can be comprised of a mixture of land and inland water but not ocean. Grid boxes with a value of 0.5 and below can only be comprised of a water surface. In the latter case, the lake cover is used to determine how much of the water surface is ocean or inland water.\nIn cycles of the IFS before CY41R1, grid boxes where this parameter has a value above 0.5 can only be comprised of land and those grid boxes with a value of 0.5 and below can only be comprised of ocean. In these older model cycles, there is no differentiation between ocean and inland water.","dimensions":["lon","lat","time"],"type":"data","unit":"(0 - 1)"},"low_cloud_cover":{"attrs":{"long_name":"Low cloud cover","shortName":"lcc"},"description":"","dimensions":["lon","lat","time"],"type":"data","unit":"%"},"mean_sea_level_pressure":{"attrs":{"long_name":"Mean sea level pressure","shortName":"msl"},"description":"This parameter is the pressure (force per unit area) of the atmosphere adjusted to the height of mean sea level.\nIt is a measure of the weight that all the air in a column vertically above the area of Earth's surface would have at that point, if the point were located at the mean sea level. It is calculated over all surfaces - land, sea and in-land water.\nMaps of mean sea level pressure are used to identify the locations of low and high pressure systems, often referred to as cyclones and anticyclones. Contours of mean sea level pressure also indicate the strength of the wind. Tightly packed contours show stronger winds.\nThe units of this parameter are pascals (Pa). Mean sea level pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb = 100 Pa).","dimensions":["lon","lat","time"],"type":"data","unit":"Pa"},"medium_cloud_cover":{"attrs":{"long_name":"Medium cloud cover","shortName":"mcc"},"description":"","dimensions":["lon","lat","time"],"type":"data","unit":"%"},"orography":{"attrs":{"long_name":"Orography","shortName":"orog"},"description":"","dimensions":["lon","lat","time"],"type":"data","unit":"gpm"},"skin_temperature":{"attrs":{"long_name":"Skin temperature","shortName":"skt"},"description":"This parameter is the temperature of the surface of the Earth.\nThe skin temperature is the theoretical temperature that is required to satisfy the surface energy balance. It represents the temperature of the uppermost surface layer, which has no heat capacity and so can respond instantaneously to changes in surface fluxes. Skin temperature is calculated differently over land and sea.\nThis parameter has units of kelvin (K). Temperature measured in kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.\nSee further information about the skin temperature over land and over sea.","dimensions":["lon","lat","time"],"type":"data","unit":"K"},"snow_density":{"attrs":{"long_name":"Snow density","shortName":"rsn"},"description":"This parameter is the mass of snow per cubic metre in the snow layer.\nThe ECMWF Integrated Forecast System (IFS) model represents snow as a single additional layer over the uppermost soil level. The snow may cover all or part of the grid box.","dimensions":["lon","lat","time"],"type":"data","unit":"kg m**-3"},"snow_depth_water_equivalent":{"attrs":{"long_name":"Snow depth water equivalent","shortName":"sd"},"description":"Snow depth water equivalent in kg m**-2 (mm) water equivalent","dimensions":["lon","lat","time"],"type":"data","unit":"kg m**-2"},"surface_pressure":{"attrs":{"long_name":"Surface pressure","shortName":"sp"},"description":"This parameter is the pressure (force per unit area) of the atmosphere on the surface of land, sea and in-land water.\nIt is a measure of the weight of all the air in a column vertically above the area of the Earth's surface represented at a fixed point.\nSurface pressure is often used in combination with temperature to calculate air density.\nThe strong variation of pressure with altitude makes it difficult to see the low and high pressure systems over mountainous areas, so mean sea level pressure, rather than surface pressure, is normally used for this purpose.\nThe units of this parameter are Pascals (Pa). Surface pressure is often measured in hPa and sometimes is presented in the old units of millibars, mb (1 hPa = 1 mb= 100 Pa).","dimensions":["lon","lat","time"],"type":"data","unit":"Pa"},"surface_roughness":{"attrs":{"long_name":"Surface roughness","shortName":"sr"},"description":"Aerodynamic roughness length (over land). 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DOI: [10.24381/cds.32b04ec5](https://doi.org/10.24381/cds.32b04ec5) (Accessed on DD-MMM-YYYY)","dedl:short_description":"This dataset consists of gridded European climate variable data from 1961-2019, provided by UERRA-HARMONIE and MESCAN-SURFEX systems at various spatial resolutions, including surface temperatures, winds, precipitations, and others."},{"type":"Collection","title":"Sea ice concentration","id":"EO.ECMWF.DAT.SATELLITE_SEA_ICE_CONCENTRATION","description":"This dataset provides daily gridded data of sea ice concentration for both hemispheres derived from satellite passive microwave brightness temperatures. Sea ice is an important component of our climate system and a sensitive indicator of climate change. Its presence or its retreat has a strong impact on air-sea interactions, the Earth’s energy budget as well as marine ecosystems. It is listed as an Essential Climate Variable by the Global Climate Observing System. Sea ice concentration is defined as the fraction of the ocean surface in a pixel or grid cell that is covered with sea ice. It is one of the parameters commonly used to characterise the sea-ice cover. Other sea ice parameters include sea ice thickness, sea ice edge, and sea ice type, also available in the Climate Data Store.","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.SATELLITE_SEA_ICE_CONCENTRATION/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.SATELLITE_SEA_ICE_CONCENTRATION/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.SATELLITE_SEA_ICE_CONCENTRATION/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.SATELLITE_SEA_ICE_CONCENTRATION","title":"Sea ice concentration"},{"rel":"license","type":"application/pdf","href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/licences/licence-to-use-copernicus-products/licence-to-use-copernicus-products_b4b9451f54cffa16ecef5c912c9cebd6979925a956e3fa677976e0cf198c2c18.pdf","title":"Licence to Use Copernicus Products"},{"rel":"license","type":"application/pdf","href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/licences/eumetsat-osi-saf-sic/eumetsat-osi-saf-sic_a42ac878deec1c647030bed88b93a1e0cc7091168f47192ea38fa603233fa364.pdf","title":"EUMETSAT OSI SAF sea ice concentration licence"},{"rel":"license","type":"application/pdf","href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/licences/ESA-CCI-sea-ice-concentration/ESA-CCI-sea-ice-concentration_8af13faa41f373e5ac56ec224eb0b2102a961cd68adedfdccd5c76e05e553c70.pdf","title":"ESA-CCI sea ice concentration product licence"},{"rel":"describedby","type":"application/pdf","href":"https://confluence.ecmwf.int/x/jDffFw","title":"Sea Ice Concentration v3 OSI SAF: Product User's Manual (PUM)"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.24381/cds.3cd8b812","title":"Sea ice concentration daily gridded data from 1978 to present derived from satellite observations"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.5285/f17f146a31b14dfd960cde0874236ee5","title":"ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Sea Ice Concentration Climate Data Record from the AMSR-E and AMSR-2 instruments at 25km grid spacing, version 2.1"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.15770/EUM_SAF_OSI_0013","title":"EUMETSAT Ocean and Sea Ice Satellite Application Facility, Global sea ice concentration climate data record 1978-2020 (v3.0, 2022), OSI-450-a"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.15770/EUM_SAF_OSI_2014","title":"EUMETSAT Ocean and Sea Ice Satellite Application Facility, Global sea ice concentration interim climate data record 2021-onwards (v3.0, 2022), OSI-430-a"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.15770/EUM_SAF_OSI_2015","title":"EUMETSAT Ocean and Sea Ice Satellite Application Facility, Global sea ice concentration climate data record (AMSR) 2002-2020 (v3.0, 2022), OSI-458"}],"assets":{"thumbnail":{"href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/resources/satellite-sea-ice-concentration/overview_28363b274694d1b6a0a126e7b99f596bb43edf03eeca84729d42b30be06ab6f5.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-06-01T00:00:00Z","2020-12-31T00:00:00Z"]]}},"license":"other","keywords":["Sea ice"],"summaries":{"federation:backends":["cop_cds","wekeo_ecmwf"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Climate Change Service (C3S)","roles":["host"],"url":"https://climate.copernicus.eu/"}],"created":"2024-05-30T07:56:04Z","updated":"2026-04-24T10:24:59Z","published":"2024-05-30T07:56:04Z","sci:doi":"10.24381/cds.3cd8b812","sci:citation":"Copernicus Climate Change Service (C3S) (2020): Sea ice concentration daily gridded data from 1978 to present derived from satellite observations. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.3cd8b812 (Accessed on DD-MMM-YYYY)","sci:publications":[{"doi":"10.5285/f17f146a31b14dfd960cde0874236ee5","citation":"Toudal Pedersen, L.; Dybkjær, G.; Eastwood, S.; Heygster, G.; Ivanova, N.; Kern, S.; Lavergne, T.; Saldo, R.; Sandven, S.; Sørensen, A.; Tonboe, R. (2017): ESA Sea Ice Climate Change Initiative (SeaIcecci): Sea Ice Concentration Climate Data Record from the AMSR-E and AMSR-2 instruments at 25km grid spacing, version 2.1. Centre for Environmental Data Analysis, 05 October 2017. doi:10.5285/f17f146a31b14dfd960cde0874236ee5. http://dx.doi.org/10.5285/f17f146a31b14dfd960cde0874236ee5"},{"doi":"10.15770/EUM_SAF_OSI_0013","citation":"EUMETSAT Ocean and Sea Ice Satellite Application Facility, Global sea ice concentration climate data record 1978-2020 (v3.0, 2022), OSI-450-a, doi: 10.15770/EUM_SAF_OSI_0013, data (for [extracted period], [extracted domain],) extracted on [download date] from the Copernicus Climate Change Service Climate Data Store, https://cds.climate.copernicus.eu/datasets/satellite-sea-ice-concentration"},{"doi":"10.15770/EUM_SAF_OSI_2014","citation":"EUMETSAT Ocean and Sea Ice Satellite Application Facility, Global sea ice concentration interim climate data record 2021-onwards (v3.0, 2022), OSI-430-a, doi: 10.15770/EUM_SAF_OSI_2014, data (for [extracted period], [extracted domain],) extracted on [download date] from the Copernicus Climate Change Service Climate Data Store, https://cds.climate.copernicus.eu/datasets/satellite-sea-ice-concentration"},{"doi":"10.15770/EUM_SAF_OSI_0015","citation":"EUMETSAT Ocean and Sea Ice Satellite Application Facility, Global sea ice concentration climate data record (AMSR) 2002-2020 (v3.0, 2022), OSI-458, doi: 10.15770/EUM_SAF_OSI_0015, data (for [extracted period], [extracted domain],) extracted on [download date] from the Copernicus Climate Change Service Climate Data Store, https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-sea-ice-concentration"}],"dedl:short_description":"This dataset contains daily global sea ice concentrations derived from satellite observations, representing the percentage of each grid cell's ocean area covered by sea ice."},{"type":"Collection","title":"Sea ice edge and type","id":"EO.ECMWF.DAT.SATELLITE_SEA_ICE_EDGE_TYPE","description":"This dataset provides daily gridded data of sea ice edge and sea ice type derived from brightness temperatures measured by satellite passive microwave radiometers. Sea ice is an important component of our climate system and a sensitive indicator of climate change. Its presence or its retreat has a strong impact on air-sea interactions, the Earth’s energy budget as well as marine ecosystems. It is recognized by the Global Climate Observing System as an Essential Climate Variable. Sea ice edge and type are some of the parameters used to characterise sea ice. Other parameters include sea ice concentration and sea ice thickness, also available in the Climate Data Store.","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.SATELLITE_SEA_ICE_EDGE_TYPE/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.SATELLITE_SEA_ICE_EDGE_TYPE/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.SATELLITE_SEA_ICE_EDGE_TYPE/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.SATELLITE_SEA_ICE_EDGE_TYPE","title":"Sea ice edge and type"},{"rel":"license","type":"application/pdf","href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/licences/licence-to-use-copernicus-products/licence-to-use-copernicus-products_b4b9451f54cffa16ecef5c912c9cebd6979925a956e3fa677976e0cf198c2c18.pdf","title":"Licence to Use Copernicus Products"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.24381/cds.29c46d83","title":"Sea ice edge and type daily gridded data from 1978 to present derived from satellite observations"}],"assets":{"thumbnail":{"href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/resources/satellite-sea-ice-edge-type/overview_4934af8d6960da3f56fb7e5cdf3c13dd5cc58cc70c6563e948778ad286a2bc89.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1978-10-25T00:00:00Z",null]]}},"license":"other","keywords":["Sea ice"],"summaries":{"federation:backends":["cop_cds","wekeo_ecmwf"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Climate Change Service (C3S)","roles":["host"],"url":"https://climate.copernicus.eu/"}],"created":"2024-05-30T07:56:04Z","updated":"2026-04-24T10:24:59Z","published":"2024-05-30T07:56:04Z","sci:doi":"10.24381/cds.29c46d83","sci:citation":"## For sea-ice type v3.0:\nAaboe, S., Down, E.J., Sørensen, A., Lavergne, T., Eastwood, S. (2023): Sea-ice type climate data record 1978-present, v3.0. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.29c46d83 (Accessed on DD-MMM-YYYY).\n##For sea-ice edge v3.0:\nAaboe, S., Down, E.J., Sørensen, A., Lavergne, T., Eastwood, S. (2023): Sea-ice edge climate data record 1978-present, v3.0. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.29c46d83 (Accessed on DD-MMM-YYYY).\n##For sea-ice type v2.0:\nAaboe, S., Down, E.J., Sørensen, A., Lavergne, T., Eastwood, S. (2021): Sea-ice type climate data record Oct1978-Aug2023, v2.0. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.29c46d83 (Accessed on DD-MMM-YYYY).\n##For sea-ice edge v2.0:Aaboe, S., Down, E.J., Sørensen, A., Lavergne, T., Eastwood, S. (2021): Sea-ice edge climate data record Oct1978-Aug2023, v2.0. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.29c46d83 (Accessed on DD-MMM-YYYY).\n##For sea-ice type v1.0:\nAaboe, S., Down, E.J., Sørensen, A., Lavergne, T., Eastwood, S. (2018): Sea-ice type climate data record Jan1979-Sep2021, v1.0. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.29c46d83 (Accessed on DD-MMM-YYYY).\n##For sea-ice edge v1.0:\nAaboe, S., Down, E.J., Sørensen, A., Lavergne, T., Eastwood, S. (2018): Sea-ice edge climate data record Jan1979-Sep2021, v1.0. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). 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Sea ice thickness is one of the parameters commonly used to characterise sea ice, alongside sea ice concentration, sea ice edge, and sea ice type, also available in the Climate Data Store.","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.SATELLITE_SEA_ICE_THICKNESS/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.SATELLITE_SEA_ICE_THICKNESS/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.SATELLITE_SEA_ICE_THICKNESS/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.SATELLITE_SEA_ICE_THICKNESS","title":"Sea ice thickness"},{"rel":"license","type":"application/pdf","href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/licences/licence-to-use-copernicus-products/licence-to-use-copernicus-products_b4b9451f54cffa16ecef5c912c9cebd6979925a956e3fa677976e0cf198c2c18.pdf","title":"Licence to Use Copernicus Products"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.24381/cds.6679a99a","title":"Sea ice thickness monthly gridded data for the Arctic from 2002 to present derived from satellite observations"}],"assets":{"thumbnail":{"href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/resources/satellite-sea-ice-thickness/overview_e65139269cab3aa583cfaa665c47fbef633ab8fbff34e26b2a07df1144af3e50.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-10-01T00:00:00Z",null]]}},"license":"other","keywords":["Sea ice"],"summaries":{"federation:backends":["cop_cds","wekeo_ecmwf"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Climate Change Service (C3S)","roles":["host"],"url":"https://climate.copernicus.eu/"}],"created":"2024-05-30T07:56:04Z","updated":"2026-04-24T10:24:59Z","published":"2024-05-30T07:56:04Z","sci:doi":"10.24381/cds.6679a99a","sci:citation":"Copernicus Climate Change Service, Climate Data Store, (2020): Sea ice thickness monthly gridded data for the Arctic from 2002 to present derived from satellite observations. 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For example, ocean temperatures typically vary slowly, on timescales of weeks or months; as the ocean has an impact on the overlaying atmosphere, the variability of its properties (e.g. temperature) can modify both local and remote atmospheric conditions. Such modifications of the 'usual' atmospheric conditions are the essence of all long-range (e.g. seasonal) forecasts. This is different from a weather forecast, which gives a lot more precise detail - both in time and space - of the evolution of the state of the atmosphere over a few days into the future. Beyond a few days, the chaotic nature of the atmosphere limits the possibility to predict precise changes at local scales. This is one of the reasons long-range forecasts of atmospheric conditions have large uncertainties. To quantify such uncertainties, long-range forecasts use ensembles, and meaningful forecast products reflect a distributions of outcomes.\nGiven the complex, non-linear interactions between the individual components of the Earth system, the best tools for long-range forecasting are climate models which include as many of the key components of the system and possible; typically, such models include representations of the atmosphere, ocean and land surface. These models are initialised with data describing the state of the system at the starting point of the forecast, and used to predict the evolution of this state in time.\nWhile uncertainties coming from imperfect knowledge of the initial conditions of the components of the Earth system can be described with the use of ensembles, uncertainty arising from approximations made in the models are very much dependent on the choice of model. A convenient way to quantify the effect of these approximations is to combine outputs from several models, independently developed, initialised and operated.\nTo this effect, the C3S provides a multi-system seasonal forecast service, where data produced by state-of-the-art seasonal forecast systems developed, implemented and operated at forecast centres in several European countries is collected, processed and combined to enable user-relevant applications. The composition of the C3S seasonal multi-system and the full content of the database underpinning the service are described in the documentation. The data is grouped in several catalogue entries (CDS datasets), currently defined by the type of variable (single-level or multi-level, on pressure surfaces) and the level of post-processing applied (data at original time resolution, processing on temporal aggregation and post-processing related to bias adjustment).\nThe variables available in this data set are listed in the table below. 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Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.7d481b7a (Accessed on DD-MMM-YYYY)","dedl:short_description":"This dataset contains seasonally forecasted anomalies of various atmospheric parameters including geopotential height, specific humidity, temperature, u-wind component, and v-wind component at multiple pressure levels, covering a period since 2017 after applying bias adjustments."},{"type":"Collection","title":"Seasonal forecast anomalies on single levels","id":"EO.ECMWF.DAT.SEASONAL_FORECAST_ANOMALIES_ON_SINGLE_LEVELS_2017_PRESENT","description":"This entry covers single-level data post-processed for bias adjustment on a monthly time resolution. \nSeasonal forecasts provide a long-range outlook of changes in the Earth system over periods of a few weeks or months, as a result of predictable changes in some of the slow-varying components of the system. For example, ocean temperatures typically vary slowly, on timescales of weeks or months; as the ocean has an impact on the overlaying atmosphere, the variability of its properties (e.g. temperature) can modify both local and remote atmospheric conditions. Such modifications of the 'usual' atmospheric conditions are the essence of all long-range (e.g. seasonal) forecasts. This is different from a weather forecast, which gives a lot more precise detail - both in time and space - of the evolution of the state of the atmosphere over a few days into the future. Beyond a few days, the chaotic nature of the atmosphere limits the possibility to predict precise changes at local scales. This is one of the reasons long-range forecasts of atmospheric conditions have large uncertainties. 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A convenient way to quantify the effect of these approximations is to combine outputs from several models, independently developed, initialised and operated.\nTo this effect, the C3S provides a multi-system seasonal forecast service, where data produced by state-of-the-art seasonal forecast systems developed, implemented and operated at forecast centres in several European countries is collected, processed and combined to enable user-relevant applications. The composition of the C3S seasonal multi-system and the full content of the database underpinning the service are described in the documentation. The data is grouped in several catalogue entries (CDS datasets), currently defined by the type of variable (single-level or multi-level, on pressure surfaces) and the level of post-processing applied (data at original time resolution, processing on temporal aggregation and post-processing related to bias adjustment).\nThe variables available in this data set are listed in the table below. 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For example, ocean temperatures typically vary slowly, on timescales of weeks or months; as the ocean has an impact on the overlaying atmosphere, the variability of its properties (e.g. temperature) can modify both local and remote atmospheric conditions. Such modifications of the 'usual' atmospheric conditions are the essence of all long-range (e.g. seasonal) forecasts. This is different from a weather forecast, which gives a lot more precise detail - both in time and space - of the evolution of the state of the atmosphere over a few days into the future. Beyond a few days, the chaotic nature of the atmosphere limits the possibility to predict precise changes at local scales. This is one of the reasons long-range forecasts of atmospheric conditions have large uncertainties. 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A convenient way to quantify the effect of these approximations is to combine outputs from several models, independently developed, initialised and operated.\nTo this effect, the C3S provides a multi-system seasonal forecast service, where data produced by state-of-the-art seasonal forecast systems developed, implemented and operated at forecast centres in several European countries is collected, processed and combined to enable user-relevant applications. The composition of the C3S seasonal multi-system and the full content of the database underpinning the service are described in the documentation. The data is grouped in several catalogue entries (CDS datasets), currently defined by the type of variable (single-level or multi-level, on pressure surfaces) and the level of post-processing applied (data at original time resolution, processing on temporal aggregation and post-processing related to bias adjustment).\nThe variables available in this data set are listed in the table below. 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For example, ocean temperatures typically vary slowly, on timescales of weeks or months; as the ocean has an impact on the overlaying atmosphere, the variability of its properties (e.g. temperature) can modify both local and remote atmospheric conditions. Such modifications of the 'usual' atmospheric conditions are the essence of all long-range (e.g. seasonal) forecasts. This is different from a weather forecast, which gives a lot more precise detail - both in time and space - of the evolution of the state of the atmosphere over a few days into the future. Beyond a few days, the chaotic nature of the atmosphere limits the possibility to predict precise changes at local scales. This is one of the reasons long-range forecasts of atmospheric conditions have large uncertainties. To quantify such uncertainties, long-range forecasts use ensembles, and meaningful forecast products reflect a distributions of outcomes.\nGiven the complex, non-linear interactions between the individual components of the Earth system, the best tools for long-range forecasting are climate models which include as many of the key components of the system and possible; typically, such models include representations of the atmosphere, ocean and land surface. These models are initialised with data describing the state of the system at the starting point of the forecast, and used to predict the evolution of this state in time.\nWhile uncertainties coming from imperfect knowledge of the initial conditions of the components of the Earth system can be described with the use of ensembles, uncertainty arising from approximations made in the models are very much dependent on the choice of model. A convenient way to quantify the effect of these approximations is to combine outputs from several models, independently developed, initialised and operated.\nTo this effect, the C3S provides a multi-system seasonal forecast service, where data produced by state-of-the-art seasonal forecast systems developed, implemented and operated at forecast centres in several European countries is collected, processed and combined to enable user-relevant applications. The composition of the C3S seasonal multi-system and the full content of the database underpinning the service are described in the documentation. 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To quantify such uncertainties, long-range forecasts use ensembles, and meaningful forecast products reflect a distributions of outcomes.\nGiven the complex, non-linear interactions between the individual components of the Earth system, the best tools for long-range forecasting are climate models which include as many of the key components of the system and possible; typically, such models include representations of the atmosphere, ocean and land surface. These models are initialised with data describing the state of the system at the starting point of the forecast, and used to predict the evolution of this state in time.\nWhile uncertainties coming from imperfect knowledge of the initial conditions of the components of the Earth system can be described with the use of ensembles, uncertainty arising from approximations made in the models are very much dependent on the choice of model. A convenient way to quantify the effect of these approximations is to combine outputs from several models, independently developed, initialised and operated.\nTo this effect, the C3S provides a multi-system seasonal forecast service, where data produced by state-of-the-art seasonal forecast systems developed, implemented and operated at forecast centres in several European countries is collected, processed and combined to enable user-relevant applications. The composition of the C3S seasonal multi-system and the full content of the database underpinning the service are described in the documentation. The data is grouped in several catalogue entries (CDS datasets), currently defined by the type of variable (single-level or multi-level, on pressure surfaces) and the level of post-processing applied (data at original time resolution, processing on temporal aggregation and post-processing related to bias adjustment).\nThe variables available in this data set are listed in the table below. The data includes forecasts created in real-time (since 2017) and retrospective forecasts (hindcasts) initialised at equivalent intervals during the period 1993-2016.\n\nVariables in the dataset/application are:\n10m u-component of wind, 10m v-component of wind, 10m wind gust since previous post-processing, 10m wind speed, 2m dewpoint temperature, 2m temperature, East-west surface stress rate of accumulation, Evaporation, Maximum 2m temperature in the last 24 hours, Mean sea level pressure, Mean sub-surface runoff rate, Mean surface runoff rate, Minimum 2m temperature in the last 24 hours, North-south surface stress rate of accumulation, Runoff, Sea surface temperature, Sea-ice cover, Snow density, Snow depth, Snowfall, Soil temperature level 1, Solar insolation rate of accumulation, Surface latent heat flux, Surface sensible heat flux, Surface solar radiation, Surface solar radiation downwards, Surface thermal radiation, Surface thermal radiation downwards, Top solar radiation, Top thermal radiation, Total cloud cover, Total precipitation","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.SEASONAL_FORECAST_MONTHLY_STATISTICS_ON_SINGLE_LEVELS_2017_PRESENT/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.SEASONAL_FORECAST_MONTHLY_STATISTICS_ON_SINGLE_LEVELS_2017_PRESENT/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.SEASONAL_FORECAST_MONTHLY_STATISTICS_ON_SINGLE_LEVELS_2017_PRESENT/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.SEASONAL_FORECAST_MONTHLY_STATISTICS_ON_SINGLE_LEVELS_2017_PRESENT","title":"Seasonal forecast monthly statistics on single levels"},{"rel":"license","type":"application/pdf","href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/licences/licence-to-use-copernicus-products/licence-to-use-copernicus-products_b4b9451f54cffa16ecef5c912c9cebd6979925a956e3fa677976e0cf198c2c18.pdf","title":"Licence to Use Copernicus Products"},{"rel":"license","type":"application/pdf","href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/licences/Additional-licence-to-use-non-European-contributions/Additional-licence-to-use-non-European-contributions_7f60a470cb29d48993fa5d9d788b33374a9ff7aae3dd4e7ba8429cc95c53f592.pdf","title":"Additional licence to use non European contributions"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.24381/cds.68dd14c3","title":"Seasonal forecast monthly statistics on single levels"}],"assets":{"thumbnail":{"href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/resources/seasonal-monthly-single-levels/overview_15999ae2b613698b2dc2304232059ba4341c57da7d42d90d1ff939f405ed5986.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1993-01-01T00:00:00Z",null]]}},"license":"other","keywords":["Atmosphere (surface)","Future","Atmospheric conditions","Global","Past","Atmosphere (upper air)","Copernicus C3S","Present","Seasonal forecasts"],"summaries":{"federation:backends":["cop_cds","wekeo_ecmwf"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Climate Change Service (C3S)","roles":["host"],"url":"https://climate.copernicus.eu/"}],"created":"2023-08-28T15:11:18Z","updated":"2026-04-24T10:24:59Z","published":"2023-08-28T15:11:18Z","sci:doi":"10.24381/cds.68dd14c3","sci:citation":"Copernicus Climate Change Service, Climate Data Store, (2018): Seasonal forecast monthly statistics on single levels. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.68dd14c3 (Accessed on DD-MMM-YYYY)","dedl:short_description":"This dataset contains seasonally forecasted monthly statistics on various atmospheric and terrestrial parameters, including winds, temperatures, pressures, moisture, snow, soil, and energy exchanges, covering Europe from 1993 onwards."},{"type":"Collection","title":"Sea level gridded data from satellite observations for the global ocean from 1993 to present","id":"EO.ECMWF.DAT.SEA_LEVEL_DAILY_GRIDDED_DATA_FOR_GLOBAL_OCEAN_1993_PRESENT","description":"This dataset provides gridded daily and monthly mean global estimates of sea level anomaly based on satellite altimetry measurements. The rise in global mean sea level in recent decades has been one of the most important and well-known consequences of climate warming, putting a large fraction of the world population and economic infrastructure at greater risk of flooding. However, changes in the global average sea level mask regional variations that can be one order of magnitude larger. Therefore, it is essential to measure changes in sea level over the world's oceans as accurately as possible.\nSea level anomaly is defined as the height of water over the mean sea surface in a given time and region. In this dataset sea level anomalies are computed with respect to a twenty-year mean reference period (1993-2012) using up-to-date altimeter standards.\nIn the past, the altimeter sea level datasets were distributed on the CNES AVISO altimetry portal until their production was taken over by the Copernicus Marine Environment Monitoring Service (CMEMS) and the Copernicus Climate Change Service (C3S) in 2015 and 2016 respectively.\nThe sea level dataset provided here by C3S is climate-oriented, that is, dedicated to the monitoring of the long-term evolution of sea level and the analysis of the ocean/climate indicators, both requiring a homogeneous and stable sea level record. To achieve this, a steady two-satellite merged constellation is used at all time steps in the production system: one satellite serves as reference and ensures the long-term stability of the data record; the other satellite (which varies across the record) is used to improve accuracy, sample mesoscale processes and provide coverage at high latitudes. The C3S sea level dataset is used to produce Ocean Monitoring Indicators (e.g. global and regional mean sea level evolution), available in the CMEMS catalogue.\nThe CMEMS sea level dataset has a more operational focus as it is dedicated to the retrieval of mesoscale signals in the context of ocean modeling and analysis of the ocean circulation on a global or regional scale. Such applications require the most accurate sea level estimates at each time step with the best spatial sampling of the ocean with all satellites available, with less emphasis on long-term stability and homogeneity.\nThis dataset is updated three times a year with a delay of about 5 months relative to present time. This delay is mainly due to the timeliness of the input data, the centred processing temporal window and the validation process. However, these processing and validation steps are essential to enhance the stability and accuracy of the sea level products and make them suitable for climate applications.\nThis dataset includes estimates of sea level anomaly and absolute dynamic topography together with the corresponding geostrophic velocities, which provide an approximation of the ocean surface currents. More details about these variables, the sea level retrieval algorithms, additional filters, optimisation procedures, and the error estimation can be found in the documentation.","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.SEA_LEVEL_DAILY_GRIDDED_DATA_FOR_GLOBAL_OCEAN_1993_PRESENT/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.SEA_LEVEL_DAILY_GRIDDED_DATA_FOR_GLOBAL_OCEAN_1993_PRESENT/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.SEA_LEVEL_DAILY_GRIDDED_DATA_FOR_GLOBAL_OCEAN_1993_PRESENT/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.SEA_LEVEL_DAILY_GRIDDED_DATA_FOR_GLOBAL_OCEAN_1993_PRESENT","title":"Sea level gridded data from satellite observations for the global ocean from 1993 to present"},{"rel":"license","type":"application/pdf","href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/licences/licence-to-use-copernicus-products/licence-to-use-copernicus-products_b4b9451f54cffa16ecef5c912c9cebd6979925a956e3fa677976e0cf198c2c18.pdf","title":"Licence to Use Copernicus Products"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.24381/cds.4c328c78","title":"Sea level gridded data from satellite observations for the global ocean from 1993 to present"}],"assets":{"thumbnail":{"href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/resources/satellite-sea-level-global/overview_30447f3d2125dd2cb7a6bd3f1926c93f3295639d576f965d8adb50f3d7ef9330.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1993-01-01T00:00:00Z",null]]}},"license":"other","keywords":["Oceanographic geographical features","Past","Global","Satellite observations","Ocean (physics)","Copernicus C3S"],"summaries":{"federation:backends":["cop_cds","wekeo_ecmwf"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Climate Change Service (C3S)","roles":["host"],"url":"https://climate.copernicus.eu/"}],"created":"2023-12-12T15:24:18Z","updated":"2026-04-24T10:24:59Z","published":"2023-12-12T15:24:18Z","sci:doi":"10.24381/cds.4c328c78","sci:citation":"Copernicus Climate Change Service, Climate Data Store, (2018): Sea level daily gridded data from satellite observations for the global ocean from 1993 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.4c328c78 (Accessed on DD-MMM-YYYY)","dedl:short_description":"Global sea level anomaly data from satellite altimetry measurements between 1993-present, providing daily/monthly means with varying levels of precision depending on whether focused on long-term trends or short-term variability."},{"type":"Collection","title":"Temperature and precipitation climate impact indicators from 1970 to 2100 derived from European climate projections","id":"EO.ECMWF.DAT.SIS_HYDROLOGY_METEOROLOGY_DERIVED_PROJECTIONS","description":"This dataset provides precipitation and near surface air temperature for Europe as Essential Climate Variables (ECVs) and as a set of Climate Impact Indicators (CIIs) based on the ECVs. \nECV datasets provide the empirical evidence needed to understand the current climate and predict future changes. \nCIIs contain condensed climate information which facilitate relatively quick and efficient subsequent analysis. Therefore, CIIs make climate information accessible to application focussed users within a sector.\nThe ECVs and CIIs provided here were derived within the water management sectoral information service to address questions specific to the water sector. However, the products are provided in a generic form and are relevant for a range of sectors, for example agriculture and energy. The data represent the current state-of-the-art in Europe for regional climate modelling and indicator production. Data from eight model simulations included in the Coordinated Regional Climate Downscaling Experiment (CORDEX) were used to calculate a total of two ECVs and five CIIs at a spatial resolution of 0.11° x 0.11° and 5km x 5km. The ECV data meet the technical specification set by the Global Climate Observing System (GCOS), as such they are provided on a daily time step. They are bias adjusted using the EFAS gridded observations as a reference dataset. Note these are model output data, not observation data as is the general case for ECVs. The CIIs are provided as mean values over a 30-year time period. For the reference period (1971-2000) data is provided as absolute values, for the future periods the data is provided as absolute values and as the relative or absolute change from the reference period. The future periods cover 3 fixed time periods (2011-2040, 2041-2070 and 2071-2100) and 3 \"degree scenario\" periods defined by when global warming exceeds a given threshold (1.5 °C, 2.0 °C or 3.0 °C). The global warming is calculated from the global climate model (GCM) used, therefore the actual time period of the degree scenarios will be different for each GCM. This dataset is produced and quality assured by the Swedish Meteorological and Hydrological Institute on behalf of the Copernicus Climate Change Service.","links":[{"rel":"retrieve","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.SIS_HYDROLOGY_METEOROLOGY_DERIVED_PROJECTIONS/order","title":"Retrieve","method":"POST"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.SIS_HYDROLOGY_METEOROLOGY_DERIVED_PROJECTIONS/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.SIS_HYDROLOGY_METEOROLOGY_DERIVED_PROJECTIONS/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.ECMWF.DAT.SIS_HYDROLOGY_METEOROLOGY_DERIVED_PROJECTIONS","title":"Temperature and precipitation climate impact indicators from 1970 to 2100 derived from European climate projections"},{"rel":"license","type":"application/pdf","href":"https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf","title":"Licence to Use Copernicus Products"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.24381/cds.9eed87d5","title":"Temperature and precipitation climate impact indicators from 1970 to 2100 derived from European climate projections"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/x/gqDmE","title":"Product User Guide, Specification and Workflow"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/x/MaHmE","title":"Bias adjustment of Euro-CORDEX data"}],"assets":{"thumbnail":{"href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/resources/sis-hydrology-meteorology-derived-projections/overview_72f393f9e80fd90903d8939f892afe274688171891d02412472034e802f340da.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1970-01-01T00:00:00Z",null]]}},"license":"other","keywords":["Meteorological geographical features","Past","Present","Future","Europe","Climate projections","Atmosphere (hydrology)","Copernicus C3S","Water management"],"summaries":{"federation:backends":["cop_cds"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Climate Change Service (C3S)","roles":["host"],"url":"https://climate.copernicus.eu/"}],"created":"2023-08-11T18:04:28Z","updated":"2026-04-24T10:24:59Z","published":"2023-08-11T18:04:28Z","sci:doi":"10.24381/cds.9eed87d5","sci:citation":"Berg, P., Photiadou, C., Simonsson, L., Sjokvist, E., Thuresson, J., and Mook, R., (2021): Temperature and precipitation climate impact indicators from 1970 to 2100 derived from European climate projections. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: [10.24381/cds.9eed87d5](https://doi.org/10.24381/cds.9eed87d5) (Accessed on DD-MMM-YYYY)","dedl:short_description":"This dataset contains projected European climate variables and indicators from 1970 to 2100, including temperature and precipitation data with varying resolutions and formats suitable for multiple sectors like water management, agriculture, and energy."},{"type":"Collection","title":"Global Biodiversity Information Facility (GBIF)","id":"EO.GBIF.DAT.OCCURRENCE","description":"The [Global Biodiversity Information Facility](https://www.gbif.org) (GBIF) is an international network and data infrastructure funded by the world's governments, providing global data that document the occurrence of species. GBIF currently integrates datasets documenting over 1.6 billion species occurrences.\n\nThe GBIF occurrence dataset combines data from a wide array of sources, including specimen-related data from natural history museums, observations from citizen science networks, and automated environmental surveys. While these data are constantly changing at [GBIF.org](https://www.gbif.org), periodic snapshots are taken and made available here. \n\nData are stored in [Parquet](https://parquet.apache.org/) format; the Parquet file schema is described below.  Most field names correspond to [terms from the Darwin Core standard](https://dwc.tdwg.org/terms/), and have been interpreted by GBIF's systems to align taxonomy, location, dates, etc.  Additional information may be retrieved using the [GBIF API](https://www.gbif.org/developer/summary).\n\nPlease refer to the GBIF [citation guidelines](https://www.gbif.org/citation-guidelines) for information about how to cite GBIF data in publications.. For analyses using the whole dataset, please use the following citation:\n\n\u003e GBIF.org ([Date]) GBIF Occurrence Data [DOI of dataset]\n\nFor analyses where data are significantly filtered, please track the datasetKeys used and use a \"[derived dataset](https://www.gbif.org/citation-guidelines#derivedDatasets)\" record for citing the data.\n\nThe [GBIF data blog](https://data-blog.gbif.org/categories/gbif/) contains a number of articles that can help you analyze GBIF data.\n","links":[{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.GBIF.DAT.OCCURRENCE/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.GBIF.DAT.OCCURRENCE/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.GBIF.DAT.OCCURRENCE","title":"Global Biodiversity Information Facility (GBIF)"},{"rel":"license","type":"text/html","href":"https://www.gbif.org/terms","title":"GBIF Terms of Use"},{"rel":"describedby","type":"text/html","href":"https://planetarycomputer.microsoft.com/dataset/gbif","title":"Human readable dataset overview and reference"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.15468/dl.9z6p8m","title":"GBIF.org (01 April 2026) GBIF Occurrence Data"}],"assets":{"geoparquet-items":{"description":"Snapshot of the collection's STAC items exported to GeoParquet format.","href":"abfs://items/gbif.parquet","msft:partition_info":{"is_partitioned":false},"roles":["stac-items"],"table:storage_options":{"account_name":"pcstacitems"},"title":"GeoParquet STAC items","type":"application/x-parquet"},"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/gbif.png","roles":null,"title":"Forest Inventory and Analysis","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2021-04-13T00:00:00Z",null]]}},"license":"CC_BY_4_0","keywords":["GBIF","Biodiversity","Species"],"summaries":{"federation:backends":["planetary_computer"]},"item_assets":{"data":{"roles":["data"],"table:storage_options":{"account_name":"ai4edataeuwest"},"title":"Dataset root","type":"application/x-parquet"}},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/table/v1.2.0/schema.json","https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"Global Biodiversity Information Facility","roles":["producer","licensor","processor"],"url":"https://www.gbif.org/"},{"name":"Microsoft","roles":["host"],"url":"https://planetarycomputer.microsoft.com"}],"created":"2026-04-13T09:58:17Z","updated":"2026-04-13T09:58:17Z","published":"2026-04-13T09:58:17Z","sci:doi":"10.15468/dl.9z6p8m","sci:citation":"GBIF.org (01 April 2026) GBIF Occurrence Data https://doi.org/10.15468/dl.9z6p8m","dedl:short_description":"Global biodiversity observation records, documenting over 1.6 billion species occurrences"},{"type":"Collection","title":"GHS-BUILT-C R2023A - GHS Settlement Characteristics, derived from Sentinel2 composite (2018) and other GHS R2023A data","id":"EO.GHSL.DAT.BUILT-C","description":"The spatial raster dataset delineates the boundaries of the human settlements at 10m resolution, and describes their inner characteristics in terms of the morphology of the built environment and the functional use. The Morphological Settlement Zone (MSZ) delineates the spatial domain of all the human settlements at the neighboring scale of approx. 100m, based on the spatial generalization of the built-up surface fraction (BUFRAC) function. The objective is to fill the open spaces that are surrounded by large patches of built space. MSZ, open spaces, and built spaces basic class abstractions are derived by mathematical morphology spatial filtering (opening, closing, regional maxima) from the BUFRAC function. 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This product includes 3-hourly instantaneous fields of integrated wave parameters from the total spectrum (significant height, period, direction, Stokes drift,...etc), as well as the following partitions: the wind wave, the primary and secondary swell waves.\n \nThe global wave system of Météo-France is based on the wave model MFWAM which is a third generation wave model. MFWAM uses the computing code ECWAM-IFS-38R2 with a dissipation terms developed by Ardhuin et al. (2010). The model MFWAM was upgraded on november 2014 thanks to improvements obtained from the european research project « my wave » (Janssen et al. 2014). The model mean bathymetry is generated by using 2-minute gridded global topography data ETOPO2/NOAA. Native model grid is irregular with decreasing distance in the latitudinal direction close to the poles. At the equator the distance in the latitudinal direction is more or less fixed with grid size 1/10°. The operational model MFWAM is driven by 6-hourly analysis and 3-hourly forecasted winds from the IFS-ECMWF atmospheric system. The wave spectrum is discretized in 24 directions and 30 frequencies starting from 0.035 Hz to 0.58 Hz. The model MFWAM uses the assimilation of altimeters with a time step of 6 hours. The global wave system provides analysis 4 times a day, and a forecast of 10 days at 0:00 UTC. The wave model MFWAM uses the partitioning to split the swell spectrum in primary and secondary swells.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00017\n\n**References:**\n\n* F. Ardhuin, R. Magne, J-F. Filipot, A. Van der Westhyusen, A. Roland, P. Quefeulou, J. M. Lefèvre, L. Aouf, A. Babanin and F. Collard : Semi empirical dissipation source functions for wind-wave models : Part I, definition and calibration and validation at global scales. Journal of Physical Oceanography, March 2010.\n* P. Janssen, L. Aouf, A. Behrens, G. Korres, L. Cavalieri, K. Christiensen, O. Breivik : Final report of work-package I in my wave project. December 2014.\n","links":[{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.MO.DAT.GLOBAL_ANALYSISFORECAST_WAV_001_027/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.MO.DAT.GLOBAL_ANALYSISFORECAST_WAV_001_027/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.MO.DAT.GLOBAL_ANALYSISFORECAST_WAV_001_027","title":"Global Ocean Waves Analysis and Forecast"},{"rel":"license","type":"text/html","href":"https://marine.copernicus.eu/user-corner/service-commitments-and-licence","title":"Copernicus Marine Service Commitments and Licence"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.48670/moi-00017","title":"10.48670/moi-00017"},{"rel":"alternative","type":"text/html","href":"https://data.marine.copernicus.eu/product/GLOBAL_ANALYSISFORECAST_WAV_001_027","title":"Product page"},{"rel":"describedby","type":"application/pdf","href":"https://catalogue.marine.copernicus.eu/documents/PUM/CMEMS-GLO-PUM-001-027.pdf","title":"Product User Manual"},{"rel":"describedby","type":"application/pdf","href":"https://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-GLO-QUID-001-027.pdf","title":"Quality Information Document"},{"rel":"describedby","type":"application/pdf","href":"https://catalogue.marine.copernicus.eu/documents/SQO/CMEMS-GLO-SQO-001-027.pdf","title":"Synthesis Quality Overview"}],"assets":{"thumbnail":{"href":"https://mdl-metadata.s3.waw3-1.cloudferro.com/metadata/thumbnails/GLOBAL_ANALYSISFORECAST_WAV_001_027.jpg","roles":["thumbnail"],"title":"Global Ocean Waves Analysis and Forecast thumbnail","type":"image/jpeg"}},"extent":{"spatial":{"bbox":[[-180,-80,179.91683959960938,90]]},"temporal":{"interval":[["2020-11-01T12:00:00Z","2025-04-06T23:00:00Z"]]}},"license":"other","keywords":["oceanographic-geographical-features","numerical-model","sea-surface-wave-stokes-drift-y-velocity","sea-surface-wave-stokes-drift-x-velocity","sea-floor-depth-below-geoid","sea-ice-speed","sea-water-pressure-at-sea-floor","eastward-sea-ice-velocity","sea-ice-surface-temperature","sea-surface-height-above-geoid","ocean-mixed-layer-thickness-defined-by-sigma-theta","eastward-sea-water-velocity","surface-snow-thickness","model-level-number-at-sea-floor","upward-sea-water-velocity","northward-sea-water-velocity","sea-water-salinity","sea-water-potential-temperature","cell-thickness","northward-sea-ice-velocity","sea-ice-thickness","sea-ice-albedo","sea-ice-area-fraction","sea-water-potential-temperature-at-sea-floor","age-of-sea-ice","forecast","near-real-time","weather-climate-and-seasonal-forecasting","marine-safety","coastal-marine-environment","marine-resources","global-ocean","level-4"],"summaries":{"federation:backends":["cop_marine"],"processing:level":["L4"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/contacts/v0.1.1/schema.json","https://stac-extensions.github.io/processing/v1.2.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"Mercator Ocean International (MOi)","roles":["producer","processor"]},{"name":"Copernicus Marine Environment Monitoring Service (CMEMS)","roles":["host"],"url":"https://marine.copernicus.eu"},{"name":"European Union (EU)","roles":["licensor"],"url":"https://european-union.europa.eu/"}],"created":"2024-06-12T13:48:59Z","updated":"2026-04-24T10:24:59Z","published":"2024-06-12T13:48:59Z","sci:doi":"10.48670/moi-00017","sci:citation":"Global Ocean Waves Analysis and Forecast. E.U. Copernicus Marine Service Information (CMEMS). Marine Data Store (MDS). DOI: 10.48670/moi-00017 (Accessed on DD-MMM-YYYY)","dedl:short_description":"This dataset contains daily global ocean sea surface wave analyses and 10-day forecasts provided by Météo-France's operational global ocean analysis and forecast system with a 1/12-degree resolution."},{"type":"Collection","title":"Global ocean low and mid trophic levels biomass content hindcast","id":"EO.MO.DAT.GLOBAL_MULTIYEAR_BGC_001_033","description":"The Low and Mid-Trophic Levels (LMTL) reanalysis for global ocean is produced at [CLS](https://www.cls.fr) on behalf of Global Ocean Marine Forecasting Center. It provides 2D fields of biomass content of zooplankton and six functional groups of micronekton. It uses the LMTL component of SEAPODYM dynamical population model (http://www.seapodym.eu). No data assimilation has been done. This product also contains forcing data: net primary production, euphotic depth, depth of each pelagic layers zooplankton and micronekton inhabit, average temperature and currents over pelagic layers.\n\n**Forcings sources:**\n* Ocean currents and temperature (CMEMS multiyear product)\n* Net Primary Production computed from chlorophyll a, Sea Surface Temperature and Photosynthetically Active Radiation observations (chlorophyll from CMEMS multiyear product, SST from NOAA NCEI AVHRR-only Reynolds, PAR from INTERIM) and relaxed by model outputs at high latitudes (CMEMS biogeochemistry multiyear product)\n\n**Vertical coverage:**\n* Epipelagic layer \n* Upper mesopelagic layer\n* Lower mesopelagic layer (max. 1000m)\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00020\n\n**References:**\n\n* Lehodey P., Murtugudde R., Senina I. (2010). Bridging the gap from ocean models to population dynamics of large marine predators: a model of mid-trophic functional groups. Progress in Oceanography, 84, p. 69-84.\n* Lehodey, P., Conchon, A., Senina, I., Domokos, R., Calmettes, B., Jouanno, J., Hernandez, O., Kloser, R. (2015) Optimization of a micronekton model with acoustic data. ICES Journal of Marine Science, 72(5), p. 1399-1412.\n* Conchon A. (2016). Modélisation du zooplancton et du micronecton marins. Thèse de Doctorat, Université de La Rochelle, 136 p.\n","links":[{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.MO.DAT.GLOBAL_MULTIYEAR_BGC_001_033/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.MO.DAT.GLOBAL_MULTIYEAR_BGC_001_033/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.MO.DAT.GLOBAL_MULTIYEAR_BGC_001_033","title":"Global ocean low and mid trophic levels biomass content hindcast"},{"rel":"license","type":"text/html","href":"https://marine.copernicus.eu/user-corner/service-commitments-and-licence","title":"Copernicus Marine Service Commitments and Licence"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.48670/moi-00020","title":"10.48670/moi-00020"},{"rel":"alternative","type":"text/html","href":"https://data.marine.copernicus.eu/product/GLOBAL_MULTIYEAR_BGC_001_033","title":"Product page"},{"rel":"describedby","type":"application/pdf","href":"https://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-GLO-QUID-001-033.pdf","title":"Quality Information Document"},{"rel":"describedby","type":"application/pdf","href":"https://catalogue.marine.copernicus.eu/documents/PUM/CMEMS-GLO-PUM-001-033.pdf","title":"Product User Manual"},{"rel":"describedby","type":"application/pdf","href":"https://catalogue.marine.copernicus.eu/documents/SQO/CMEMS-GLO-SQO-001-033.pdf","title":"Synthesis Quality Overview"}],"assets":{"thumbnail":{"href":"https://mdl-metadata.s3.waw3-1.cloudferro.com/metadata/thumbnails/GLOBAL_MULTIYEAR_BGC_001_033.jpg","roles":["thumbnail"],"title":"Global ocean low and mid trophic levels biomass content hindcast thumbnail","type":"image/jpeg"}},"extent":{"spatial":{"bbox":[[-180,-80,179.9166717529297,89.91666412353516]]},"temporal":{"interval":[["1998-01-01T00:00:00Z","2023-12-31T23:00:00Z"]]}},"license":"other","keywords":["oceanographic-geographical-features","numerical-model","euphotic-zone-depth","mass-content-of-zooplankton-expressed-as-carbon-in-sea-water","mass-content-of-epipelagic-micronekton-expressed-as-wet-weight-in-sea-water","mass-content-of-upper-mesopelagic-micronekton-expressed-as-wet-weight-in-sea-water","mass-content-of-migrant-upper-mesopelagic-micronekton-expressed-as-wet-weight-in-sea-water","mass-content-of-lower-mesopelagic-micronekton-expressed-as-wet-weight-in-sea-water","mass-content-of-migrant-lower-mesopelagic-micronekton-expressed-as-wet-weight-in-sea-water","mass-content-of-highly-migrant-lower-mesopelagic-micronekton-expressed-as-wet-weight-in-sea-water","net-primary-productivity-of-biomass-expressed-as-carbon-in-sea-water","sea-water-pelagic-layer-bottom-depth","eastward-sea-water-velocity-vertical-mean-over-pelagic-layer","northward-sea-water-velocity-vertical-mean-over-pelagic-layer","sea-water-potential-temperature-vertical-mean-over-pelagic-layer","invariant","multi-year","marine-resources","coastal-marine-environment","weather-climate-and-seasonal-forecasting","marine-safety","global-ocean","level-4"],"summaries":{"federation:backends":["cop_marine"],"processing:level":["L4"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/contacts/v0.1.1/schema.json","https://stac-extensions.github.io/processing/v1.2.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"Mercator Ocean International (MOi)","roles":["producer","processor"]},{"name":"Copernicus Marine Environment Monitoring Service (CMEMS)","roles":["host"],"url":"https://marine.copernicus.eu"},{"name":"European Union (EU)","roles":["licensor"],"url":"https://european-union.europa.eu/"}],"created":"2024-06-12T13:48:59Z","updated":"2026-04-24T10:24:59Z","published":"2024-06-12T13:48:59Z","sci:doi":"10.48670/moi-00020","sci:citation":"Global ocean low and mid trophic levels biomass content hindcast. 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These reanalyses are built to be as close as possible to the observations (i.e. realistic) and in agreement with the model physics The multi-model ensemble approach allows uncertainties or error bars in the ocean state to be estimated.\n\nThe ensemble mean may even provide for certain regions and/or periods a more reliable estimate than any individual reanalysis product.\n\nThe four reanalyses, used to create the ensemble, covering “altimetric era” period (starting from 1st of January 1993) during which altimeter altimetry data observations are available:\n * GLORYS2V4 from Mercator Ocean (Fr);\n * ORAS5 from ECMWF;\n * GloSea5 from Met Office (UK);\n * and C-GLORSv7 from CMCC (It);\n \nThese four products provided four different time series of global ocean simulations 3D monthly estimates. All numerical products available for users are monthly or daily mean averages describing the ocean.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00024","links":[{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.MO.DAT.GLOBAL_MULTIYEAR_PHY_ENS_001_031/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.MO.DAT.GLOBAL_MULTIYEAR_PHY_ENS_001_031/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.MO.DAT.GLOBAL_MULTIYEAR_PHY_ENS_001_031","title":"Global Ocean Ensemble Physics Reanalysis"},{"rel":"license","type":"text/html","href":"https://marine.copernicus.eu/user-corner/service-commitments-and-licence","title":"Copernicus Marine Service Commitments and Licence"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.48670/moi-00024","title":"10.48670/moi-00024"},{"rel":"alternative","type":"text/html","href":"https://data.marine.copernicus.eu/product/GLOBAL_MULTIYEAR_PHY_ENS_001_031","title":"Product page"},{"rel":"describedby","type":"application/pdf","href":"https://catalogue.marine.copernicus.eu/documents/PUM/CMEMS-GLO-PUM-001-031.pdf","title":"Product User Manual"},{"rel":"describedby","type":"application/pdf","href":"https://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-GLO-QUID-001-031.pdf","title":"Quality Information Document"},{"rel":"describedby","type":"application/pdf","href":"https://catalogue.marine.copernicus.eu/documents/SQO/CMEMS-GLO-SQO-001-031.pdf","title":"Synthesis Quality Overview"}],"assets":{"thumbnail":{"href":"https://mdl-metadata.s3.waw3-1.cloudferro.com/metadata/thumbnails/GLOBAL_MULTIYEAR_PHY_ENS_001_031.jpg","roles":["thumbnail"],"title":"Global Ocean Ensemble Physics Reanalysis thumbnail","type":"image/jpeg"}},"extent":{"spatial":{"bbox":[[-180,-80,179.75,90]]},"temporal":{"interval":[["1993-01-01T00:00:00Z","2023-12-31T00:00:00Z"]]}},"license":"other","keywords":["oceanographic-geographical-features","numerical-model","ocean-mixed-layer-thickness-defined-by-sigma-theta","eastward-sea-water-velocity","sea-surface-height","northward-sea-water-velocity","sea-water-salinity","sea-water-potential-temperature","sea-ice-thickness","multi-year","marine-resources","weather-climate-and-seasonal-forecasting","marine-safety","coastal-marine-environment","global-ocean","level-4","sea-ice-concentration-and/or-thickness","sea-level","in-situ-ts-profiles","sst"],"summaries":{"federation:backends":["cop_marine"],"processing:level":["L4"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/contacts/v0.1.1/schema.json","https://stac-extensions.github.io/processing/v1.2.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"Mercator Ocean International (MOi)","roles":["producer","processor"]},{"name":"Copernicus Marine Environment Monitoring Service (CMEMS)","roles":["host"],"url":"https://marine.copernicus.eu"},{"name":"European Union (EU)","roles":["licensor"],"url":"https://european-union.europa.eu/"}],"created":"2024-06-12T13:48:59Z","updated":"2026-04-24T10:24:59Z","published":"2024-06-12T13:48:59Z","sci:doi":"10.48670/moi-00024","sci:citation":"Global Ocean Ensemble Physics Reanalysis. E.U. Copernicus Marine Service Information (CMEMS). Marine Data Store (MDS). DOI: 10.48670/moi-00024 (Accessed on DD-MMM-YYYY)","dedl:short_description":"This is the CMEMS Global Ocean Ensemble Reanalysis product providing 25-year-long, high-resolution, 3D monthly-mean temperature, salinity, currents, and ice variable datasets from 1993 onwards based on four merged models."},{"type":"Collection","title":"Global Ocean Waves Reanalysis","id":"EO.MO.DAT.GLOBAL_MULTIYEAR_WAV_001_032","description":"GLOBAL_REANALYSIS_WAV_001_032 for the global wave reanalysis describing past sea states since years 1993. This product also bears the name of WAVERYS within the GLO-HR MFC. for correspondence to other global multi-year products like GLORYS. BIORYS. etc. The core of WAVERYS is based on the MFWAM model. a third generation wave model that calculates the wave spectrum. i.e. the distribution of sea state energy in frequency and direction on a 1/5° irregular grid. Average wave quantities derived from this wave spectrum. such as the SWH (significant wave height) or the average wave period. are delivered on a regular 1/5° grid with a 3h time step. The wave spectrum is discretized into 30 frequencies obtained from a geometric sequence of first member 0.035 Hz and a reason 7.5. 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The product is obtained from an ensemble-based forward feed neural network approach mapping situ data for surface ocean fugacity (SOCAT data base, Bakker et al.  2016, https://www.socat.info/) and sea surface salinity, temperature, sea surface height, chlorophyll a, mixed layer depth and atmospheric CO2 mole fraction. Sea-air flux fields are computed from the air-sea gradient of pCO2 and the dependence on wind speed of Wanninkhof (2014). Surface ocean pH on total scale, dissolved inorganic carbon, and saturation states are then computed from surface ocean pCO2 and reconstructed surface ocean alkalinity using the CO2sys speciation software.\n\n**Product Citation**: Please refer to our Technical FAQ for citing products: http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169.\n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00047\n\n**References:**\n\n* Chau, T. T. 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The total velocity fields are obtained by combining CMEMS  satellite Geostrophic surface currents and modelled Ekman currents at the surface and 15m depth (using ERA5 wind stress in REP and ERA5* in NRT). 1 hourly product, daily and monthly means are available. This product has been initiated in the frame of CNES/CLS projects. Then it has been consolidated during the Globcurrent project (funded by the ESA User Element Program).\n\n**Product Citation:**\nPlease refer to our Technical FAQ for citing products: http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169.\n\n**DOI (product):** \nhttps://doi.org/10.48670/mds-00327\n\n**References:**\n\n* Rio, M.-H., S. Mulet, and N. Picot: Beyond GOCE for the ocean circulation estimate: Synergetic use of altimetry, gravimetry, and in situ data provides new insight into geostrophic and Ekman currents, Geophys. Res. 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The product was developed by the Consiglio Nazionale delle Ricerche (CNR) and includes 4 datasets:\n* cmems_obs-mob_glo_phy-sss_nrt_multi_P1D, which provides near-real-time (NRT) daily data\n* cmems_obs-mob_glo_phy-sss_nrt_multi_P1M, which provides near-real-time (NRT) monthly data\n* cmems_obs-mob_glo_phy-sss_my_multi_P1D, which provides multi-year reprocessed (REP) daily data \n* cmems_obs-mob_glo_phy-sss_my_multi_P1M, which provides multi-year reprocessed (REP) monthly data  \n\n**Product citation**: \nPlease refer to our Technical FAQ for citing products: http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169.\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00051\n\n**References:**\n\n* Droghei, R., B. Buongiorno Nardelli, and R. Santoleri, 2016: Combining in-situ and satellite observations to retrieve salinity and density at the ocean surface. J. Atmos. Oceanic Technol. doi:10.1175/JTECH-D-15-0194.1.\n* Buongiorno Nardelli, B., R. Droghei, and R. Santoleri, 2016: Multi-dimensional interpolation of SMOS sea surface salinity with surface temperature and in situ salinity data. Rem. Sens. Environ., doi:10.1016/j.rse.2015.12.052.\n* Droghei, R., B. Buongiorno Nardelli, and R. Santoleri, 2018: A New Global Sea Surface Salinity and Density Dataset From Multivariate Observations (1993–2016), Front. Mar. Sci., 5(March), 1–13, doi:10.3389/fmars.2018.00084.\n* Sammartino, Michela, Salvatore Aronica, Rosalia Santoleri, and Bruno Buongiorno Nardelli. (2022). 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The product includes 4 datasets: \n* dataset-armor-3d-nrt-weekly, which delivers near-real-time (NRT) weekly data\n* dataset-armor-3d-nrt-monthly, which delivers near-real-time (NRT) monthly data\n* dataset-armor-3d-rep-weekly, which delivers multi-year reprocessed (REP) weekly data \n* dataset-armor-3d-rep-monthly, which delivers multi-year reprocessed (REP) monthly data\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00052\n\n\n**Product Citation**: \nPlease refer to our Technical FAQ for citing products: http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169.\n\n**References:**\n\n* Guinehut S., A.-L. Dhomps, G. Larnicol and P.-Y. Le Traon, 2012: High resolution 3D temperature and salinity fields derived from in situ and satellite observations. Ocean Sci., 8(5):845–857.\n* Mulet, S., M.-H. Rio, A. Mignot, S. Guinehut and R. Morrow, 2012: A new estimate of the global 3D geostrophic ocean circulation based on satellite data and in-situ measurements. 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OSTIA uses satellite data provided by the GHRSST project together with in-situ observations to determine the sea surface temperature.\nA high resolution (1/20° - approx. 6 km) daily analysis of sea surface temperature (SST) is produced for the global ocean and some lakes.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00165\n\n**References:**\n\n* Good, S.; Fiedler, E.; Mao, C.; Martin, M.J.; Maycock, A.; Reid, R.; Roberts-Jones, J.; Searle, T.; Waters, J.; While, J.; Worsfold, M. The Current Configuration of the OSTIA System for Operational Production of Foundation Sea Surface Temperature and Ice Concentration Analyses. Remote Sens. 2020, 12, 720. doi: 10.3390/rs12040720\n* Donlon, C.J., Martin, M., Stark, J., Roberts-Jones, J., Fiedler, E., and Wimmer, W., 2012, The Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) system. Remote Sensing of the Environment. doi: 10.1016/j.rse.2010.10.017 2011.\n* John D. Stark, Craig J. Donlon, Matthew J. Martin and Michael E. 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This product provides the foundation Sea Surface Temperature, which is the temperature free of diurnal variability.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00168\n\n**References:**\n\n* Good, S.; Fiedler, E.; Mao, C.; Martin, M.J.; Maycock, A.; Reid, R.; Roberts-Jones, J.; Searle, T.; Waters, J.; While, J.; Worsfold, M. The Current Configuration of the OSTIA System for Operational Production of Foundation Sea Surface Temperature and Ice Concentration Analyses. 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The ESA SST CCI and C3S level 4 analyses were produced by running the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) system (Good et al., 2020) to provide a high resolution (1/20deg. - approx. 5km grid resolution) daily analysis of the daily average sea surface temperature (SST) at 20 cm depth for the global ocean. Only (A)ATSR, SLSTR and AVHRR satellite data processed by the ESA SST CCI and C3S projects were used, giving a stable product. It also uses reprocessed sea-ice concentration data from the EUMETSAT OSI-SAF (OSI-450 and OSI-430; Lavergne et al., 2019).\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00169\n\n**References:**\n\n* Good, S., Fiedler, E., Mao, C., Martin, M.J., Maycock, A., Reid, R., Roberts-Jones, J., Searle, T., Waters, J., While, J., Worsfold, M. The Current Configuration of the OSTIA System for Operational Production of Foundation Sea Surface Temperature and Ice Concentration Analyses. Remote Sens. 2020, 12, 720, doi:10.3390/rs12040720.\n* Lavergne, T., Sørensen, A. M., Kern, S., Tonboe, R., Notz, D., Aaboe, S., Bell, L., Dybkjær, G., Eastwood, S., Gabarro, C., Heygster, G., Killie, M. A., Brandt Kreiner, M., Lavelle, J., Saldo, R., Sandven, S., and Pedersen, L. T.: Version 2 of the EUMETSAT OSI SAF and ESA CCI sea-ice concentration climate data records, The Cryosphere, 13, 49-78, doi:10.5194/tc-13-49-2019, 2019.\n* Merchant, C.J., Embury, O., Bulgin, C.E. et al. Satellite-based time-series of sea-surface temperature since 1981 for climate applications. Sci Data 6, 223 (2019) doi:10.1038/s41597-019-0236-x.\n","links":[{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.MO.DAT.SST_GLO_SST_L4_REP_OBSERVATIONS_010_024/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.MO.DAT.SST_GLO_SST_L4_REP_OBSERVATIONS_010_024/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.MO.DAT.SST_GLO_SST_L4_REP_OBSERVATIONS_010_024","title":"ESA SST CCI and C3S reprocessed sea surface temperature analyses"},{"rel":"license","type":"text/html","href":"https://marine.copernicus.eu/user-corner/service-commitments-and-licence","title":"Copernicus Marine Service Commitments and Licence"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.48670/moi-00169","title":"10.48670/moi-00169"},{"rel":"alternative","type":"text/html","href":"https://data.marine.copernicus.eu/product/SST_GLO_SST_L4_REP_OBSERVATIONS_010_024","title":"Product page"},{"rel":"describedby","type":"application/pdf","href":"https://catalogue.marine.copernicus.eu/documents/PUM/CMEMS-SST-PUM-010-024.pdf","title":"Product User Manual"},{"rel":"describedby","type":"application/pdf","href":"https://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-SST-QUID-010-024.pdf","title":"Quality Information Document"},{"rel":"describedby","type":"application/pdf","href":"https://catalogue.marine.copernicus.eu/documents/SQO/CMEMS-SST-SQO-010-024.pdf","title":"Synthesis Quality Overview"}],"assets":{"thumbnail":{"href":"https://mdl-metadata.s3.waw3-1.cloudferro.com/metadata/thumbnails/SST_GLO_SST_L4_REP_OBSERVATIONS_010_024.jpg","roles":["thumbnail"],"title":"ESA SST CCI and C3S reprocessed sea surface temperature analyses thumbnail","type":"image/jpeg"}},"extent":{"spatial":{"bbox":[[-179.97500610351562,-89.9749984741211,179.97500610351562,89.9749984741211]]},"temporal":{"interval":[["1981-09-01T00:00:00Z","2022-10-31T00:00:00Z"]]}},"license":"other","keywords":["target-application#seaiceclimate","oceanographic-geographical-features","satellite-observation","sea-ice-area-fraction","sea-water-temperature","multi-year","marine-resources","coastal-marine-environment","weather-climate-and-seasonal-forecasting","marine-safety","global-ocean","level-4"],"summaries":{"federation:backends":["cop_marine"],"processing:level":["L4"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/contacts/v0.1.1/schema.json","https://stac-extensions.github.io/projection/v2.0.0/schema.json","https://stac-extensions.github.io/processing/v1.2.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"Mercator Ocean International (MOi)","roles":["producer","processor"]},{"name":"Copernicus Marine Environment Monitoring Service (CMEMS)","roles":["host"],"url":"https://marine.copernicus.eu"},{"name":"European Union (EU)","roles":["licensor"],"url":"https://european-union.europa.eu/"}],"created":"2024-06-12T13:48:59Z","updated":"2026-04-24T10:24:59Z","published":"2024-06-12T13:48:59Z","sci:doi":"10.48670/moi-00169","sci:citation":"ESA SST CCI and C3S reprocessed sea surface temperature analyses. 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T.: Version 2 of the EUMETSAT OSI SAF and ESA CCI sea-ice concentration climate data records, The Cryosphere, 13, 49-78, doi:10.5194/tc-13-49-2019, 2019."},{"doi":"10.1038/s41597-019-0236-x","citation":"Merchant, C.J., Embury, O., Bulgin, C.E. et al. Satellite-based time-series of sea-surface temperature since 1981 for climate applications. Sci Data 6, 223 (2019) doi:10.1038/s41597-019-0236-x."}],"dedl:short_description":"The ESA SST CCI and C3S reprocessed sea surface temperature analyses provide gap-free daily average SST maps at 20cm depth with 0.05°x0.05° resolution, derived from ATSR, SLSTR, and AVHRR satellite data combined with EUMETSAT OSI-SAF ice concentration data."},{"type":"Collection","title":"GLOBAL OCEAN L3 SPECTRAL PARAMETERS FROM NRT SATELLITE MEASUREMENTS","id":"EO.MO.DAT.WAVE_GLO_PHY_SPC_FWK_L3_NRT_014_002","description":"Near-Real-Time mono-mission satellite-based integral parameters derived from the directional wave spectra. Using linear propagation wave model, only wave observations that can be back-propagated to wave converging regions are considered. The dataset parameters includes partition significant wave height, partition peak period and partition peak or principal direction given along swell propagation path in space and time at a 3-hour timestep, from source to land. Validity flags are also included for each parameter and indicates the valid time steps along propagation (eg. no propagation for significant wave height close to the storm source or any integral parameter when reaching the land). The integral parameters at observation point are also available together with a quality flag based on the consistency between each propagated observation and the overall swell field.This product is processed by the WAVE-TAC multi-mission SAR data processing system. It serves in near-real time the main operational oceanography and climate forecasting centers in Europe and worldwide. It processes near-real-time data from the following SAR missions: Sentinel-1A and Sentinel-1B.One file is produced for each mission and is available in two formats: one gathering in one netcdf file all observations related to the same swell field, and for another all observations available in a 3-hour time range, and for both formats, propagated information from source to land.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00178","links":[{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.MO.DAT.WAVE_GLO_PHY_SPC_FWK_L3_NRT_014_002/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.MO.DAT.WAVE_GLO_PHY_SPC_FWK_L3_NRT_014_002/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.MO.DAT.WAVE_GLO_PHY_SPC_FWK_L3_NRT_014_002","title":"GLOBAL OCEAN L3 SPECTRAL PARAMETERS FROM NRT SATELLITE MEASUREMENTS"},{"rel":"license","type":"text/html","href":"https://marine.copernicus.eu/user-corner/service-commitments-and-licence","title":"Copernicus Marine Service Commitments and Licence"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.48670/moi-00178","title":"10.48670/moi-00178"},{"rel":"alternative","type":"text/html","href":"https://data.marine.copernicus.eu/product/WAVE_GLO_PHY_SPC-FWK_L3_NRT_014_002","title":"Product page"},{"rel":"describedby","type":"application/pdf","href":"https://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-WAV-QUID-014-002.pdf","title":"Quality Information Document"},{"rel":"describedby","type":"application/pdf","href":"http://marine.copernicus.eu/documents/PUM/CMEMS-WAV-PUM-014-001-002-003-004.pdf","title":"Product User Manual"},{"rel":"describedby","type":"application/pdf","href":"https://catalogue.marine.copernicus.eu/documents/SQO/CMEMS-WAV-SQO-014-002.pdf","title":"Synthesis Quality Overview"}],"assets":{"thumbnail":{"href":"https://mdl-metadata.s3.waw3-1.cloudferro.com/metadata/thumbnails/WAVE_GLO_PHY_SPC-FWK_L3_NRT_014_002.jpg","roles":["thumbnail"],"title":"GLOBAL OCEAN L3 SPECTRAL PARAMETERS FROM NRT SATELLITE MEASUREMENTS thumbnail","type":"image/jpeg"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2018-04-22T00:00:00.000000Z",null]]}},"license":"other","keywords":["oceanographic-geographical-features","satellite-observation","sea-surface-wave-significant-height","sea-surface-wave-period-at-variance-spectral-density-maximum","sea-surface-wave-from-direction-at-variance-spectral-density-maximum","near-real-time","coastal-marine-environment","weather-climate-and-seasonal-forecasting","marine-resources","marine-safety","north-west-shelf-seas","arctic-ocean","mediterranean-sea","baltic-sea","global-ocean","iberian-biscay-irish-seas","black-sea","level-3"],"summaries":{"federation:backends":["cop_marine"],"processing:level":["L3"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/contacts/v0.1.1/schema.json","https://stac-extensions.github.io/projection/v2.0.0/schema.json","https://stac-extensions.github.io/processing/v1.2.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"Mercator Ocean International (MOi)","roles":["producer","processor"]},{"name":"Copernicus Marine Environment Monitoring Service (CMEMS)","roles":["host"],"url":"https://marine.copernicus.eu"},{"name":"European Union (EU)","roles":["licensor"],"url":"https://european-union.europa.eu/"}],"created":"2025-02-07T16:34:12Z","updated":"2026-04-24T10:24:59Z","published":"2025-02-07T16:34:12Z","sci:doi":"10.48670/moi-00178","sci:citation":"Global Ocean L 3 Spectral Parameters From Nrt Satellite Measurements. 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Such thresholds are applied on parameters linked to significant wave height determination from retracking (e.g. SWH, sigma0, range, off nadir angle…). All the missions are homogenized with respect to a reference mission (Jason-3 until April 2022, Sentinel-6A afterwards) and calibrated on in-situ buoy measurements. Finally, an along-track filter is applied to reduce the measurement noise.\n\nAs a support of information to the significant wave height, wind speed measured by the altimeters is also processed and included in the files. Wind speed values are provided by upstream products (L2) for each mission and are based on different algorithms. Only valid data are included and all the missions are homogenized with respect to the reference mission.\n\nThis product is processed by the WAVE-TAC multi-mission altimeter data processing system. It serves in near-real time the main operational oceanography and climate forecasting centers in Europe and worldwide. It processes operational data (OGDR and NRT, produced in near-real-time) from the following altimeter missions: Sentinel-6A, Jason-3, Sentinel-3A, Sentinel-3B, Cryosat-2, SARAL/AltiKa, CFOSAT ; and interim data (IGDR, 1 to 2 days delay) from Hai Yang-2B mission.\n\nOne file containing valid SWH is produced for each mission and for a 3-hour time window. It contains the filtered SWH (VAVH), the unfiltered SWH (VAVH_UNFILTERED) and the wind speed (wind_speed).\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00179","links":[{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.MO.DAT.WAVE_GLO_PHY_SWH_L3_NRT_014_001/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.MO.DAT.WAVE_GLO_PHY_SWH_L3_NRT_014_001/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.MO.DAT.WAVE_GLO_PHY_SWH_L3_NRT_014_001","title":"GLOBAL OCEAN L3 SIGNIFICANT WAVE HEIGHT FROM NRT SATELLITE MEASUREMENTS"},{"rel":"license","type":"text/html","href":"https://marine.copernicus.eu/user-corner/service-commitments-and-licence","title":"Copernicus Marine Service Commitments and Licence"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.48670/moi-00179","title":"10.48670/moi-00179"},{"rel":"alternative","type":"text/html","href":"https://data.marine.copernicus.eu/product/WAVE_GLO_PHY_SWH_L3_NRT_014_001","title":"Product page"},{"rel":"describedby","type":"application/pdf","href":"https://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-WAV-QUID-014-001.pdf","title":"Quality Information Document"},{"rel":"describedby","type":"application/pdf","href":"http://marine.copernicus.eu/documents/PUM/CMEMS-WAV-PUM-014-001-002-003-004.pdf","title":"Product User Manual"},{"rel":"describedby","type":"application/pdf","href":"https://catalogue.marine.copernicus.eu/documents/SQO/CMEMS-WAV-SQO-014-001.pdf","title":"Synthesis Quality Overview"}],"assets":{"thumbnail":{"href":"https://mdl-metadata.s3.waw3-1.cloudferro.com/metadata/thumbnails/WAVE_GLO_PHY_SWH_L3_NRT_014_001.jpg","roles":["thumbnail"],"title":"GLOBAL OCEAN L3 SIGNIFICANT WAVE HEIGHT FROM NRT SATELLITE MEASUREMENTS thumbnail","type":"image/jpeg"}},"extent":{"spatial":{"bbox":[[-180,-82.649,179.999999,87.987402]]},"temporal":{"interval":[["2021-01-01T00:00:00Z","2025-04-01T10:48:28Z"]]}},"license":"other","keywords":["oceanographic-geographical-features","satellite-observation","sea-surface-wave-significant-height","wind-speed","near-real-time","weather-climate-and-seasonal-forecasting","marine-resources","coastal-marine-environment","marine-safety","north-west-shelf-seas","arctic-ocean","black-sea","iberian-biscay-irish-seas","mediterranean-sea","baltic-sea","global-ocean","level-3"],"summaries":{"federation:backends":["cop_marine"],"processing:level":["L3"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/contacts/v0.1.1/schema.json","https://stac-extensions.github.io/projection/v2.0.0/schema.json","https://stac-extensions.github.io/processing/v1.2.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"Mercator Ocean International (MOi)","roles":["producer","processor"]},{"name":"Copernicus Marine Environment Monitoring Service (CMEMS)","roles":["host"],"url":"https://marine.copernicus.eu"},{"name":"European Union (EU)","roles":["licensor"],"url":"https://european-union.europa.eu/"}],"created":"2024-06-12T13:48:59Z","updated":"2026-04-24T10:24:59Z","published":"2024-06-12T13:48:59Z","sci:doi":"10.48670/moi-00179","sci:citation":"Global Ocean L 3 Significant Wave Height From Nrt Satellite Measurements. E.U. Copernicus Marine Service Information (CMEMS). Marine Data Store (MDS). DOI: 10.48670/moi-00179 (Accessed on DD-MMM-YYYY)","dedl:short_description":"The GLOBAL OCEAN L3 Significant Wave Height dataset provides near-real-time, rigorously edited and homogenized significant wave heights and accompanying wind speeds derived from multiple satellite altimetry missions over global oceans."},{"type":"Collection","title":"GLOBAL OCEAN L4 SIGNIFICANT WAVE HEIGHT FROM NRT SATELLITE MEASUREMENTS","id":"EO.MO.DAT.WAVE_GLO_PHY_SWH_L4_NRT_014_003","description":"Near-Real-Time gridded multi-mission merged satellite significant wave height. Only valid data are included. This product is processed in Near-Real-Time by the WAVE-TAC multi-mission altimeter data processing system and is based on CMEMS level-3 SWH datasets (see the product WAVE_GLO_WAV_L3_SWH_NRT_OBSERVATIONS_014_001).\nIt merges along-track SWH data from the following missions: Jason-3, Sentinel-3A, Sentinel-3B, SARAL/AltiKa, Cryosat-2, CFOSAT and HaiYang-2B. The resulting gridded product has a 2° horizontal resolution and is produced daily. Different SWH fields are produced: VAVH_DAILY fields are daily statistics computed from all available level 3 along-track measurements from 00 UTC until 23:59 UTC ; VAVH_INST field provides an estimate of the instantaneous wave field at 12:00UTC (noon), using all available Level 3 along-track measurements and accounting for their spatial and temporal proximity.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00180","links":[{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.MO.DAT.WAVE_GLO_PHY_SWH_L4_NRT_014_003/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.MO.DAT.WAVE_GLO_PHY_SWH_L4_NRT_014_003/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.MO.DAT.WAVE_GLO_PHY_SWH_L4_NRT_014_003","title":"GLOBAL OCEAN L4 SIGNIFICANT WAVE HEIGHT FROM NRT SATELLITE MEASUREMENTS"},{"rel":"license","type":"text/html","href":"https://marine.copernicus.eu/user-corner/service-commitments-and-licence","title":"Copernicus Marine Service Commitments and Licence"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.48670/moi-00180","title":"10.48670/moi-00180"},{"rel":"alternative","type":"text/html","href":"https://data.marine.copernicus.eu/product/WAVE_GLO_PHY_SWH_L4_NRT_014_003","title":"Product page"},{"rel":"describedby","type":"application/pdf","href":"https://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-WAV-QUID-014-003.pdf","title":"Quality Information Document"},{"rel":"describedby","type":"application/pdf","href":"http://marine.copernicus.eu/documents/PUM/CMEMS-WAV-PUM-014-001-002-003-004.pdf","title":"Product User Manual"},{"rel":"describedby","type":"application/pdf","href":"https://catalogue.marine.copernicus.eu/documents/SQO/CMEMS-WAV-SQO-014-003.pdf","title":"Synthesis Quality Overview"}],"assets":{"thumbnail":{"href":"https://mdl-metadata.s3.waw3-1.cloudferro.com/metadata/thumbnails/WAVE_GLO_PHY_SWH_L4_NRT_014_003.jpg","roles":["thumbnail"],"title":"GLOBAL OCEAN L4 SIGNIFICANT WAVE HEIGHT FROM NRT SATELLITE MEASUREMENTS thumbnail","type":"image/jpeg"}},"extent":{"spatial":{"bbox":[[-179,-89,179,89]]},"temporal":{"interval":[["2021-01-01T00:00:00Z","2025-03-31T00:00:00Z"]]}},"license":"other","keywords":["oceanographic-geographical-features","satellite-observation","sea-surface-wave-significant-height","near-real-time","marine-safety","marine-resources","weather-climate-and-seasonal-forecasting","coastal-marine-environment","mediterranean-sea","iberian-biscay-irish-seas","global-ocean","black-sea","baltic-sea","north-west-shelf-seas","arctic-ocean","level-4"],"summaries":{"federation:backends":["cop_marine"],"processing:level":["L4"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/contacts/v0.1.1/schema.json","https://stac-extensions.github.io/processing/v1.2.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"Mercator Ocean International (MOi)","roles":["producer","processor"]},{"name":"Copernicus Marine Environment Monitoring Service (CMEMS)","roles":["host"],"url":"https://marine.copernicus.eu"},{"name":"European Union (EU)","roles":["licensor"],"url":"https://european-union.europa.eu/"}],"created":"2024-06-12T13:48:59Z","updated":"2026-04-24T10:24:59Z","published":"2024-06-12T13:48:59Z","sci:doi":"10.48670/moi-00180","sci:citation":"Global Ocean L 4 Significant Wave Height From Nrt Satellite Measurements. E.U. Copernicus Marine Service Information (CMEMS). Marine Data Store (MDS). DOI: 10.48670/moi-00180 (Accessed on DD-MMM-YYYY)","dedl:short_description":"This dataset contains near-real-time global ocean significant wave heights derived from merged satellite measurements with a 2° grid resolution, combining multiple mission data including Jason-3, Sentinel-3A/B, SARAL/AltiKa, CryoSat-2, CFOSAT, and Haiyang-2B."},{"type":"Collection","title":"Global Ocean Monthly Mean Sea Surface Wind and Stress from Scatterometer and Model","id":"EO.MO.DAT.WIND_GLO_PHY_CLIMATE_L4_MY_012_003","description":"For the Global Ocean - The product contains monthly Level-4 sea surface wind and stress fields at 0.25 degrees horizontal spatial resolution. The monthly averaged wind and stress fields are based on monthly average ECMWF ERA5 reanalysis fields, corrected for persistent biases using all available Level-3 scatterometer observations from the Metop-A, Metop-B and Metop-C ASCAT, QuikSCAT SeaWinds, ERS-1 and ERS-2 SCAT satellite instruments.  The product provides monthly mean stress-equivalent wind and stress variables as well as their standard deviation. The number of observations used to calculate the monthly averages are included in the product.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00181","links":[{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.MO.DAT.WIND_GLO_PHY_CLIMATE_L4_MY_012_003/queryables","title":"Queryables"},{"rel":"items","type":"application/geo+json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.MO.DAT.WIND_GLO_PHY_CLIMATE_L4_MY_012_003/items","title":"Items"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/v2/collections/EO.MO.DAT.WIND_GLO_PHY_CLIMATE_L4_MY_012_003","title":"Global Ocean Monthly Mean Sea Surface Wind and Stress from Scatterometer and Model"},{"rel":"license","type":"text/html","href":"https://marine.copernicus.eu/user-corner/service-commitments-and-licence","title":"Copernicus Marine Service Commitments and Licence"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.48670/moi-00181","title":"10.48670/moi-00181"},{"rel":"alternative","type":"text/html","href":"https://data.marine.copernicus.eu/product/WIND_GLO_PHY_CLIMATE_L4_MY_012_003","title":"Product page"},{"rel":"describedby","type":"application/pdf","href":"https://catalogue.marine.copernicus.eu/documents/PUM/CMEMS-WIND-PUM-012-003.pdf","title":"Product User Manual"},{"rel":"describedby","type":"application/pdf","href":"https://catalogue.marine.copernicus.eu/documents/SQO/CMEMS-WIND-SQO-012-003.pdf","title":"Synthesis Quality Overview"},{"rel":"describedby","type":"application/pdf","href":"https://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-WIND-QUID-012-003.pdf","title":"Quality Information Document"}],"assets":{"thumbnail":{"href":"https://mdl-metadata.s3.waw3-1.cloudferro.com/metadata/thumbnails/WIND_GLO_PHY_CLIMATE_L4_MY_012_003.jpg","roles":["thumbnail"],"title":"Global Ocean Monthly Mean Sea Surface Wind and Stress from Scatterometer and Model thumbnail","type":"image/jpeg"}},"extent":{"spatial":{"bbox":[[-179.875,-89.875,179.875,89.875]]},"temporal":{"interval":[["1994-07-01T00:00:00Z","2024-11-01T00:00:00Z"]]}},"license":"other","keywords":["oceanographic-geographical-features","satellite-observation","surface-downward-northward-stress","eastward-wind","northward-wind","wind-speed","surface-downward-eastward-stress","multi-year","weather-climate-and-seasonal-forecasting","coastal-marine-environment","marine-safety","marine-resources","mediterranean-sea","global-ocean","north-west-shelf-seas","baltic-sea","iberian-biscay-irish-seas","arctic-ocean","black-sea","level-4"],"summaries":{"federation:backends":["cop_marine"],"processing:level":["L4"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/contacts/v0.1.1/schema.json","https://stac-extensions.github.io/projection/v2.0.0/schema.json","https://stac-extensions.github.io/processing/v1.2.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"Mercator Ocean International (MOi)","roles":["producer","processor"]},{"name":"Copernicus Marine Environment Monitoring Service (CMEMS)","roles":["host"],"url":"https://marine.copernicus.eu"},{"name":"European Union (EU)","roles":["licensor"],"url":"https://european-union.europa.eu/"}],"created":"2024-06-12T13:48:59Z","updated":"2026-04-24T10:24:59Z","published":"2024-06-12T13:48:59Z","sci:doi":"10.48670/moi-00181","sci:citation":"Global Ocean Monthly Mean Sea Surface Wind and Stress from Scatterometer and Model. 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