{"collections":[{"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":"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"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.CO2_DATA_FROM_SATELLITE_SENSORS_2002_PRESENT","title":"EO.ECMWF.DAT.CO2_DATA_FROM_SATELLITE_SENSORS_2002_PRESENT"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.CO2_DATA_FROM_SATELLITE_SENSORS_2002_PRESENT/items","title":"items"}],"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":"proprietary","keywords":["Atmospheric conditions","Satellite observations","Global","Atmosphere (composition)","Past"],"stac_version":"1.0.0","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/"}],"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":"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"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.DERIVED_GRIDDED_GLACIER_MASS_CHANGE","title":"EO.ECMWF.DAT.DERIVED_GRIDDED_GLACIER_MASS_CHANGE"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.DERIVED_GRIDDED_GLACIER_MASS_CHANGE/items","title":"items"}],"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":"proprietary","keywords":["Product type: Satellite observations","Variable domain: Land (cryosphere)","Spatial coverage: Global","Product type: In-situ observations","Temporal coverage: Past"],"stac_version":"1.0.0","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/"}],"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":"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":"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"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.ERA5_HOURLY_VARIABLES_ON_PRESSURE_LEVELS","title":"EO.ECMWF.DAT.ERA5_HOURLY_VARIABLES_ON_PRESSURE_LEVELS"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.ERA5_HOURLY_VARIABLES_ON_PRESSURE_LEVELS/items","title":"items"}],"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":"proprietary","keywords":["Atmospheric conditions","Copernicus C3S","Global","Atmosphere (surface)","Reanalysis","Atmosphere (upper air)","Past"],"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 (relative)","type":"application/grib"}},"stac_version":"1.0.0","stac_extensions":["https://stac-extensions.github.io/datacube/v2.2.0/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":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Climate Change Service (C3S)","roles":["host"],"url":"https://climate.copernicus.eu/"}],"cube:dimensions":{"time":{"extent":["1940-01-01T00:00:00Z",null],"step":"T1H","type":"temporal"},"x":{"axis":"x","description":"longitude","extent":[-180,180],"step":0.25,"type":"spatial"},"y":{"axis":"y","description":"latitude","extent":[-90,90],"step":0.25,"type":"spatial"},"z":{"axis":"z","description":"pressure","extent":[1,1000],"type":"spatial","unit":"hPa","values":[1,2,3,5,7,10,20,30,50,70,100,125,150,175,200,225,250,300,350,400,450,500,550,600,650,700,750,775,800,825,850,875,900,925,950,975,1000]}},"cube:variables":{"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":["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"}},"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":"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.e2161bac","title":"ERA5-Land hourly data from 1950 to 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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"}},"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":"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"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.ERA5_LAND_MONTHLY","title":"EO.ECMWF.DAT.ERA5_LAND_MONTHLY"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.ERA5_LAND_MONTHLY/items","title":"items"}],"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":"proprietary","keywords":["Land (biosphere)","Land (physics)","Land conditions","Copernicus C3S","Global","Reanalysis","Land (hydrology)","Past"],"stac_version":"1.0.0","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/"}],"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":"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"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.ERA5_MONTHLY_MEANS_VARIABLES_ON_PRESSURE_LEVELS","title":"EO.ECMWF.DAT.ERA5_MONTHLY_MEANS_VARIABLES_ON_PRESSURE_LEVELS"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.ERA5_MONTHLY_MEANS_VARIABLES_ON_PRESSURE_LEVELS/items","title":"items"}],"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":"proprietary","keywords":["Atmospheric conditions","Copernicus C3S","Global","Atmosphere (surface)","Reanalysis","Atmosphere (upper air)","Past"],"stac_version":"1.0.0","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/"}],"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":"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"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.GLACIERS_DISTRIBUTION_DATA_FROM_RANDOLPH_GLACIER_INVENTORY_2000","title":"EO.ECMWF.DAT.GLACIERS_DISTRIBUTION_DATA_FROM_RANDOLPH_GLACIER_INVENTORY_2000"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.GLACIERS_DISTRIBUTION_DATA_FROM_RANDOLPH_GLACIER_INVENTORY_2000/items","title":"items"}],"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":"proprietary","keywords":["Satellite observations","Copernicus C3S","Global","Land (cryosphere)","Land cover","Past"],"stac_version":"1.0.0","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/"}],"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":"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"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.METHANE_DATA_SATELLITE_SENSORS_2002_PRESENT","title":"EO.ECMWF.DAT.METHANE_DATA_SATELLITE_SENSORS_2002_PRESENT"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.METHANE_DATA_SATELLITE_SENSORS_2002_PRESENT/items","title":"items"}],"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":"proprietary","keywords":["Atmospheric conditions","Satellite observations","Global","Atmosphere (composition)","Past"],"stac_version":"1.0.0","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/"}],"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":"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"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.REANALYSIS_ERA5_SINGLE_LEVELS","title":"EO.ECMWF.DAT.REANALYSIS_ERA5_SINGLE_LEVELS"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.REANALYSIS_ERA5_SINGLE_LEVELS/items","title":"items"}],"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":"proprietary","keywords":["Atmospheric conditions","Copernicus C3S","Global","Atmosphere (surface)","Reanalysis","Atmosphere (upper air)","Past"],"stac_version":"1.0.0","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/"}],"dedl:short_description":"This dataset contains hourly ERA5 reanalysed data from 1940-present on various spatial resolutions, combining model outputs with historical observations through data assimilation methods, providing multiple variables including uncertainties and monthly-means."},{"type":"Collection","title":"ERA5 monthly averaged data on single levels from 1940 to present","id":"EO.ECMWF.DAT.REANALYSIS_ERA5_SINGLE_LEVELS_MONTHLY_MEANS","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. 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":"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"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.REANALYSIS_ERA5_SINGLE_LEVELS_MONTHLY_MEANS","title":"EO.ECMWF.DAT.REANALYSIS_ERA5_SINGLE_LEVELS_MONTHLY_MEANS"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.REANALYSIS_ERA5_SINGLE_LEVELS_MONTHLY_MEANS/items","title":"items"}],"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":"proprietary","keywords":["Atmospheric conditions","Copernicus C3S","Global","Atmosphere (surface)","Reanalysis","Atmosphere (upper air)","Past"],"stac_version":"1.0.0","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/"}],"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":"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"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.REANALYSIS_UERRA_EUROPE_SINGLE_LEVELS","title":"EO.ECMWF.DAT.REANALYSIS_UERRA_EUROPE_SINGLE_LEVELS"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.REANALYSIS_UERRA_EUROPE_SINGLE_LEVELS/items","title":"items"}],"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":"proprietary","keywords":["Atmospheric conditions","Copernicus C3S","Europe","Reanalysis","Atmosphere (upper air)","Past"],"stac_version":"1.0.0","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/"}],"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). Climatological field.","dimensions":["lon","lat","time"],"type":"data","unit":"m"},"total_cloud_cover":{"attrs":{"long_name":"Total Cloud Cover","shortName":"tcc"},"description":"","dimensions":["lon","lat","time"],"type":"data","unit":"%"},"total_column_integrated_water_vapour":{"attrs":{"long_name":"Total column integrated water vapour","shortName":"tciwv"},"description":"","dimensions":["lon","lat","time"],"type":"data","unit":"kg m**-2"},"total_precipitation":{"attrs":{"long_name":"Total Precipitation","shortName":"tp"},"description":"","dimensions":["lon","lat","time"],"type":"data","unit":"kg m**-2"}},"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":"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"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.SATELLITE_SEA_ICE_CONCENTRATION","title":"EO.ECMWF.DAT.SATELLITE_SEA_ICE_CONCENTRATION"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.SATELLITE_SEA_ICE_CONCENTRATION/items","title":"items"}],"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":"proprietary","keywords":["Sea ice"],"stac_version":"1.0.0","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/"}],"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":"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"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.SATELLITE_SEA_ICE_EDGE_TYPE","title":"EO.ECMWF.DAT.SATELLITE_SEA_ICE_EDGE_TYPE"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.SATELLITE_SEA_ICE_EDGE_TYPE/items","title":"items"}],"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":"proprietary","keywords":["Sea ice"],"stac_version":"1.0.0","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/"}],"dedl:short_description":"This dataset contains daily gridded data of sea ice edges and types derived from satellite measurements that track changes in this crucial climate variable impacting global weather patterns and ecosystems."},{"type":"Collection","title":"Sea ice thickness","id":"EO.ECMWF.DAT.SATELLITE_SEA_ICE_THICKNESS","description":"This dataset provides monthly gridded data of sea ice thickness for the Arctic region based on satellite radar altimetry observations. 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 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":"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"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.SATELLITE_SEA_ICE_THICKNESS","title":"EO.ECMWF.DAT.SATELLITE_SEA_ICE_THICKNESS"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.SATELLITE_SEA_ICE_THICKNESS/items","title":"items"}],"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":"proprietary","keywords":["Sea ice"],"stac_version":"1.0.0","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/"}],"dedl:short_description":"This dataset contains monthly gridded sea ice thickness measurements from satellite radar altimetry over the Arctic region, crucial for monitoring climate change impacts on global systems."},{"type":"Collection","title":"Seasonal forecast anomalies on pressure levels","id":"EO.ECMWF.DAT.SEASONAL_FORECAST_ANOMALIES_ON_PRESSURE_LEVELS_2017_PRESENT","description":"This entry covers pressure-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. 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.\n\nVariables in the dataset/application are:\nGeopotential anomaly, Specific humidity anomaly, Temperature anomaly, U-component of wind anomaly, V-component of wind anomaly","links":[{"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.7d481b7a","title":"Seasonal forecast anomalies on pressure levels"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.SEASONAL_FORECAST_ANOMALIES_ON_PRESSURE_LEVELS_2017_PRESENT","title":"EO.ECMWF.DAT.SEASONAL_FORECAST_ANOMALIES_ON_PRESSURE_LEVELS_2017_PRESENT"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.SEASONAL_FORECAST_ANOMALIES_ON_PRESSURE_LEVELS_2017_PRESENT/items","title":"items"}],"assets":{"thumbnail":{"href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/resources/seasonal-postprocessed-pressure-levels/overview_15999ae2b613698b2dc2304232059ba4341c57da7d42d90d1ff939f405ed5986.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2017-01-01T00:00:00Z",null]]}},"license":"proprietary","keywords":["Seasonal forecasts","Atmospheric conditions","Copernicus C3S","Global","Atmosphere (surface)","Present","Future","Atmosphere (upper air)","Past"],"stac_version":"1.0.0","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/"}],"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. 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.\n\nVariables in the dataset/application are:\n10m u-component of wind anomaly, 10m v-component of wind anomaly, 10m wind gust anomaly, 10m wind speed anomaly, 2m dewpoint temperature anomaly, 2m temperature anomaly, East-west surface stress anomalous rate of accumulation, Evaporation anomalous rate of accumulation, Maximum 2m temperature in the last 24 hours anomaly, Mean sea level pressure anomaly, Mean sub-surface runoff rate anomaly, Mean surface runoff rate anomaly, Minimum 2m temperature in the last 24 hours anomaly, North-south surface stress anomalous rate of accumulation, Runoff anomalous rate of accumulation, Sea surface temperature anomaly, Sea-ice cover anomaly, Snow density anomaly, Snow depth anomaly, Snowfall anomalous rate of accumulation, Soil temperature anomaly level 1, Solar insolation anomalous rate of accumulation, Surface latent heat flux anomalous rate of accumulation, Surface sensible heat flux anomalous rate of accumulation, Surface solar radiation anomalous rate of accumulation, Surface solar radiation downwards anomalous rate of accumulation, Surface thermal radiation anomalous rate of accumulation, Surface thermal radiation downwards anomalous rate of accumulation, Top solar radiation anomalous rate of accumulation, Top thermal radiation anomalous rate of accumulation, Total cloud cover anomaly, Total precipitation anomalous rate of accumulation","links":[{"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.7e37c951","title":"Seasonal forecast anomalies on single levels"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.SEASONAL_FORECAST_ANOMALIES_ON_SINGLE_LEVELS_2017_PRESENT","title":"EO.ECMWF.DAT.SEASONAL_FORECAST_ANOMALIES_ON_SINGLE_LEVELS_2017_PRESENT"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.SEASONAL_FORECAST_ANOMALIES_ON_SINGLE_LEVELS_2017_PRESENT/items","title":"items"}],"assets":{"thumbnail":{"href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/resources/seasonal-postprocessed-single-levels/overview_15999ae2b613698b2dc2304232059ba4341c57da7d42d90d1ff939f405ed5986.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2017-01-01T00:00:00Z",null]]}},"license":"proprietary","keywords":["Seasonal forecasts","Atmospheric conditions","Copernicus C3S","Global","Atmosphere (surface)","Present","Future","Atmosphere (upper air)","Past"],"stac_version":"1.0.0","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/"}],"dedl:short_description":"This dataset contains seasonally adjusted single-level forecast anomalies covering various meteorological parameters at a monthly time resolution since 2017."},{"type":"Collection","title":"Seasonal forecast subdaily data on pressure levels","id":"EO.ECMWF.DAT.SEASONAL_FORECAST_DAILY_DATA_ON_PRESSURE_LEVELS_2017_PRESENT","description":"This entry covers pressure-level data at the original time resolution (once every 12 hours). \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. 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:\nGeopotential, Specific humidity, Temperature, U-component of wind, V-component of wind","links":[{"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.50ed0a73","title":"Seasonal forecast subdaily data on pressure levels"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.SEASONAL_FORECAST_DAILY_DATA_ON_PRESSURE_LEVELS_2017_PRESENT","title":"EO.ECMWF.DAT.SEASONAL_FORECAST_DAILY_DATA_ON_PRESSURE_LEVELS_2017_PRESENT"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.SEASONAL_FORECAST_DAILY_DATA_ON_PRESSURE_LEVELS_2017_PRESENT/items","title":"items"}],"assets":{"thumbnail":{"href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/resources/seasonal-original-pressure-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":"proprietary","keywords":["Seasonal forecasts","Atmospheric conditions","Copernicus C3S","Global","Atmosphere (surface)","Present","Future","Atmosphere (upper air)","Past"],"stac_version":"1.0.0","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/"}],"dedl:short_description":"This dataset contains seasonally forecasted subdaily pressure-level data covering various parameters including geopotential, specific humidity, temperature, u-wind component, and v-wind component since 1993."},{"type":"Collection","title":"Seasonal forecast daily and subdaily data on single levels","id":"EO.ECMWF.DAT.SEASONAL_FORECAST_DAILY_DATA_ON_SINGLE_LEVELS_2017_PRESENT","description":"This entry covers single-level data at the original time resolution (once a day, or once every 6 hours, depending on the variable).\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.\nThis 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.\nThe data includes forecasts created in real-time (since 2017) and retrospective forecasts (hindcasts) initialised at equivalent intervals during the period 1993-2016.","links":[{"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.181d637e","title":"Seasonal forecast daily and subdaily data on single levels"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.SEASONAL_FORECAST_DAILY_DATA_ON_SINGLE_LEVELS_2017_PRESENT","title":"EO.ECMWF.DAT.SEASONAL_FORECAST_DAILY_DATA_ON_SINGLE_LEVELS_2017_PRESENT"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.SEASONAL_FORECAST_DAILY_DATA_ON_SINGLE_LEVELS_2017_PRESENT/items","title":"items"}],"assets":{"thumbnail":{"href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/resources/seasonal-original-single-levels/overview_15999ae2b613698b2dc2304232059ba4341c57da7d42d90d1ff939f405ed5986.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1981-01-01T00:00:00Z",null]]}},"license":"proprietary","keywords":["Seasonal forecasts","Atmospheric conditions","Copernicus C3S","Global","Atmosphere (surface)","Present","Future","Atmosphere (upper air)","Past"],"stac_version":"1.0.0","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/"}],"dedl:short_description":"Daily to sub-daily seasonal forecast data for various atmospheric variables at single levels with varying resolutions, including hindcasted historical records since 1993 and real-time predictions initiated since 2017."},{"type":"Collection","title":"Seasonal forecast monthly statistics on pressure levels","id":"EO.ECMWF.DAT.SEASONAL_FORECAST_MONTHLY_STATISTICS_ON_PRESSURE_LEVELS_2017_PRESENT","description":"This entry covers pressure-level data aggregated 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. 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 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:\nGeopotential, Specific humidity, Temperature, U-component of wind, V-component of wind","links":[{"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.0b79e7c5","title":"Seasonal forecast monthly statistics on pressure levels"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.SEASONAL_FORECAST_MONTHLY_STATISTICS_ON_PRESSURE_LEVELS_2017_PRESENT","title":"EO.ECMWF.DAT.SEASONAL_FORECAST_MONTHLY_STATISTICS_ON_PRESSURE_LEVELS_2017_PRESENT"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.SEASONAL_FORECAST_MONTHLY_STATISTICS_ON_PRESSURE_LEVELS_2017_PRESENT/items","title":"items"}],"assets":{"thumbnail":{"href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/resources/seasonal-monthly-pressure-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":"proprietary","keywords":["Seasonal forecasts","Atmospheric conditions","Copernicus C3S","Global","Atmosphere (surface)","Present","Future","Atmosphere (upper air)","Past"],"stac_version":"1.0.0","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/"}],"dedl:short_description":"This dataset contains seasonally forecasted monthly statistics on various atmospheric variables including geopotential, specific humidity, temperature, u-wind component, v-wind component at multiple pressure levels since 1993."},{"type":"Collection","title":"Seasonal forecast monthly statistics on single levels","id":"EO.ECMWF.DAT.SEASONAL_FORECAST_MONTHLY_STATISTICS_ON_SINGLE_LEVELS_2017_PRESENT","description":"This entry covers single-level data aggregated 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. 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":"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"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.SEASONAL_FORECAST_MONTHLY_STATISTICS_ON_SINGLE_LEVELS_2017_PRESENT","title":"EO.ECMWF.DAT.SEASONAL_FORECAST_MONTHLY_STATISTICS_ON_SINGLE_LEVELS_2017_PRESENT"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.SEASONAL_FORECAST_MONTHLY_STATISTICS_ON_SINGLE_LEVELS_2017_PRESENT/items","title":"items"}],"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":"proprietary","keywords":["Seasonal forecasts","Atmospheric conditions","Copernicus C3S","Global","Atmosphere (surface)","Present","Future","Atmosphere (upper air)","Past"],"stac_version":"1.0.0","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/"}],"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":"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"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.SEA_LEVEL_DAILY_GRIDDED_DATA_FOR_GLOBAL_OCEAN_1993_PRESENT","title":"EO.ECMWF.DAT.SEA_LEVEL_DAILY_GRIDDED_DATA_FOR_GLOBAL_OCEAN_1993_PRESENT"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.SEA_LEVEL_DAILY_GRIDDED_DATA_FOR_GLOBAL_OCEAN_1993_PRESENT/items","title":"items"}],"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":"proprietary","keywords":["Oceanographic geographical features","Satellite observations","Copernicus C3S","Ocean (physics)","Global","Past"],"stac_version":"1.0.0","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/"}],"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":"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"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.SIS_HYDROLOGY_METEOROLOGY_DERIVED_PROJECTIONS","title":"EO.ECMWF.DAT.SIS_HYDROLOGY_METEOROLOGY_DERIVED_PROJECTIONS"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.SIS_HYDROLOGY_METEOROLOGY_DERIVED_PROJECTIONS/items","title":"items"}],"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":"proprietary","keywords":["Copernicus C3S","Europe","Present","Atmosphere (hydrology)","Meteorological geographical features","Future","Water management","Climate projections","Past"],"stac_version":"1.0.0","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/"}],"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":"Hydrology-related climate impact indicators from 1970 to 2100 derived from bias adjusted European climate projections","id":"EO.ECMWF.DAT.SIS_HYDROLOGY_VARIABLES_DERIVED_PROJECTIONS","description":"This dataset provides water variables and indicators based on hydrological impact modelling, forced by bias adjusted regional climate simulations from the European Coordinated Regional Climate Downscaling Experiment (EURO-CORDEX). The dataset contains Essential Climate Variable (ECV) data in the form of daily mean river discharge and a set of climate impact indicators (CIIs) for both water quantity and quality. \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.\nThe data represent the current state-of-the-art in Europe for regional climate and hydrological modelling and indicator production. Eight bias adjusted model simulations from the EURO-CORDEX EUR-11 were used to force a multi-model setup of the hydrological model E-HYPEcatch at a pan-European domain. A total of 18 water quality and quantity CIIs and 1 water ECV are provided in this dataset at catchment scale and on a 5km x 5km grid. \nThe 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 and 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.\nThe river discharge ECV data meet the technical specification set by the Global Climate Observing System (GCOS), as such they are provided on a daily time step. Note these are model output data, not observation data as is the general case for ECVs.\nThis dataset is produced and quality assured by the Swedish Meteorological and Hydrological Institute on behalf of the Copernicus Climate Change Service.","links":[{"rel":"license","href":"https://object-store.os-api.cci2.ecmwf.int:443/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://cds.climate.copernicus.eu/datasets/sis-hydrology-variables-derived-projections?tab=overview","title":"Hydrology-related climate impact indicators from 1970 to 2100 derived from bias adjusted European climate projections - cds"},{"rel":"describedby","type":"text/html","href":"https://www.wekeo.eu/data?view=dataset\u0026dataset=EO%3AECMWF%3ADAT%3ASIS_HYDROLOGY_VARIABLES_DERIVED_PROJECTIONS","title":"Hydrology-related climate impact indicators from 1970 to 2100 derived from bias adjusted European climate projections - wekeo"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.SIS_HYDROLOGY_VARIABLES_DERIVED_PROJECTIONS","title":"EO.ECMWF.DAT.SIS_HYDROLOGY_VARIABLES_DERIVED_PROJECTIONS"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.SIS_HYDROLOGY_VARIABLES_DERIVED_PROJECTIONS/items","title":"items"}],"assets":{"thumbnail":{"href":"https://object-store.os-api.cci2.ecmwf.int:443/cci2-prod-catalogue/resources/sis-hydrology-variables-derived-projections/overview_145a5da25057cb5278bdf4f1b67828327e893da1ea9cc58251dd68cbb3633c74.png","roles":["thumbnail"],"type":"image/jpg"}},"extent":{"spatial":{"bbox":[[-22,27,45,72]]},"temporal":{"interval":[["1970-01-01T00:00:00Z","2100-12-31T00:00:00Z"]]}},"license":"proprietary","keywords":["Provider: Copernicus C3S","Sector: Water management","Temporal coverage: Present","Temporal coverage: Future","Product type: Climate projections","Spatial coverage: Europe","Temporal coverage: Past","Variable domain: Atmosphere (surface)"],"stac_version":"1.0.0","stac_extensions":["https://stac-extensions.github.io/timestamps/v1.1.0/schema.json","https://stac-extensions.github.io/scientific/v1.0.0/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/"}],"dedl:short_description":"This dataset includes hydrology-related climate impacts from 1970 to 2100, providing daily river discharge and various climate indicators for water quantity and quality across Europe, derived from eight bias-adjusted climate models and a hydrological model."}],"links":[{"rel":"root","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"parent","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"self","type":"application/json","href":"https://hda.data.destination-earth.eu/stac/collections"}]}