{"collections":[{"type":"Collection","title":"In-service Aircraft for a Global Observing System","id":"EO.AERIS.DAT.IAGOS","description":"In-service Aircraft for a Global Observing System (IAGOS) is a European Research Infrastructure for global observations of atmospheric composition from commercial aircraft. IAGOS combines the expertise of scientific institutions with the infrastructure of civil aviation in order to provide essential data on climate change and air quality at a global scale. In order to provide optimal information, two complementary systems have been implemented, (i) IAGOS-CORE providing global coverage on a day-to-day basis of key observables and (ii) IAGOS-CARIBIC providing a more in-depth and complex set of observations with lesser geographical and temporal coverage.\n## How to cite IAGOS data\nUse of the data requires proper reference and citation of the IAGOS data, using the exact citation (including the provided DOI) as provided at the moment of upload from IAGOS, if applicable. For all of the IAGOS data we will provide you with the citation to use through the landing page of the data object; from this page you will always be able to download the data object again. The Digital Object Identifier (DOI) of the data object will always resolve to its landing page.\n ##Aknowledgements\nBy downloading the IAGOS data product you agree to the licencing conditions that apply to the data. Under this license derived products and redistribution are allowed, but you are required to always inform your users of the original source of the data used, refer them to the license text and the original source at IAGOS for possible updates or uploads. We ask you to inform the data providers, traceable through the metadata connected to the provided DOI, when the data is used for publication(s), and to offer them the possibility to comment and/or offer them co-authorship or acknowledgement in the publication when this is justified by the added value of the data for your results. In accordance with the IAGOS data policy, users of IAGOS data products are required to:\n\t 1. include the following acknowledgements in publications: 'MOZAIC/CARIBIC/IAGOS data were created with support from the European Commission, national agencies in Germany (BMBF), France (MESR), and the UK (NERC), and the IAGOS member institutions (http://www.iagos.org/partners). The participating airlines (Lufthansa, Air France, Austrian, China Airlines, Hawaiian Airlines, Air Canada, Iberia, Eurowings Discover, Cathay Pacific, Air Namibia, Sabena) supported IAGOS by carrying the measurement equipment free of charge since 1994. The data are available at http://www.iagos.fr thanks to additional support from AERIS.'\n\t 2. offer co-authorship to the IAGOS Principal Investigators if the IAGOS data play a significant role in the publication\n\t 3. identify themselves and provide contact information (valid email address)\n\t 4. provide a short description of the intended research","links":[{"rel":"describedby","type":"text/html","href":"https://iagos.aeris-data.fr","title":"IAGOS Data Portal"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.AERIS.DAT.IAGOS","title":"EO.AERIS.DAT.IAGOS"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.AERIS.DAT.IAGOS/items","title":"items"}],"assets":{"thumbnail":{"href":"https://wp1.aeris-data.fr/wp-content-aeris/uploads/sites/66/2020/09/iagos_core_cut-1024x574.png","roles":["thumbnail"],"title":"IAGOS Core","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1994-08-01T00:00:00Z",null]]}},"license":"CC-BY-4.0","keywords":["ATMOSPHERIC","AIRCRAFT","L2","AERIS","IAGOS"],"summaries":{"mission":["IAGOS-CORE","IAGOS-MOZAIC","IAGOS-CARIBIC"],"processing:level":["L2"]},"item_assets":{"data":{"roles":["data"],"title":"timeseries","type":"application/netcdf"}},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/processing/v1.2.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"IAGOS-CORE","roles":["producer"],"url":"https://iagos.aeris-data.fr/"},{"name":"FZ Jülich","roles":["producer"],"url":"https://www.fz-juelich.de/portal/EN/Home/home_node.html"},{"name":"IAGOS-AISBL","roles":["licensor","producer","processor"],"url":"https://iagos.aeris-data.fr/"},{"name":"Destination Earth Data Lake (DEDL)","roles":["host"],"url":"https://data.destination-earth.eu/"}],"dedl:short_description":"The In-Service Aircraft for a Global Observing System (IAGOS) provides comprehensive global observations of atmospheric composition from commercial flights combining scientific expertise with civil aviation infrastructure for climate and air quality monitoring."},{"type":"Collection","title":"CORINE Land Cover","id":"EO.CLMS.DAT.CORINE","description":"The CORINE Land Cover (CLC) inventory consists of 44 land cover and land use classes derived from a \nseries of satellite missions since it was first established.\n","links":[{"rel":"license","type":"text/html","href":"https://land.copernicus.eu/en/data-policy","title":"Copernicus Land Data Policy"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.2909/c62bb056-5ac3-4512-b642-7f484175d951","title":"CORINE Land Cover Change 1990-2000 (raster 100 m), Europe, 6-yearly - version 2020_20u1, May 2020"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.2909/17ab2088-6907-470f-90b6-8c1364865803","title":"CORINE Land Cover Change 1990-2000 (vector), Europe, 6-yearly - version 2020_20u1, May 2020"},{"rel":"describedby","type":"text/html","href":"https://land.copernicus.eu/en/products/corine-land-cover","title":"CORINE Land Cover"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.CLMS.DAT.CORINE","title":"EO.CLMS.DAT.CORINE"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.CLMS.DAT.CORINE/items","title":"items"}],"assets":{"thumbnail":{"href":"https://land.copernicus.eu/en/products/corine-land-cover/@@images/image-400-7d8e8dfc63d50c9bf89ff5a7475dcd46.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-31.561261,27.405827,44.820775,71.409109]]},"temporal":{"interval":[["1990-01-01T00:00:00Z","2018-12-31T23:59:59Z"]]}},"license":"other","keywords":["Satellite Image Interpretation","Land Cover Change","landscape alteration","Corine","CHA20012-2018","landscape","EEA39","geospatial data","Copernicus","land use","Land cover","CLC","land cover","European"],"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"European Environment Agency (EEA)","roles":["producer","licensor","host"],"url":"https://www.eea.europa.eu/"},{"name":"European Topic Centre on Urban, Land and Soil Systems (ETC/ULS)","roles":["processor"],"url":"https://www.etc.uma.es/etc-uls/"}],"dedl:short_description":"The CORINE Land Cover inventory comprises 44 land cover and land use classes mapped from various satellite data sources."},{"type":"Collection","title":"Dry Matter Productivity 2014-present (raster 300 m), global, 10-daily - version 1","id":"EO.CLMS.DAT.GLO.DMP300_V1","description":"Dry matter Productivity (DMP) is an indication of the overall growth rate or dry biomass increase of the vegetation and is directly related to ecosystem Net Primary Productivity (NPP), however its units (kilograms of gross dry matter per hectare per day) are customized for agro-statistical purposes. Compared to the Gross DMP (GDMP), or its equivalent Gross Primary Productivity, the main difference lies in the inclusion of the autotrophic respiration. Like the FAPAR products that are used as input for the GDMP estimation, these GDMP products are provided in Near Real Time, with consolidations in the next periods, or as offline product.","links":[{"rel":"license","type":"text/html","href":"https://land.copernicus.eu/en/data-policy","title":"Copernicus Land Data Policy"},{"rel":"describedby","type":"text/html","href":"https://land.copernicus.eu/en/products/vegetation/dry-matter-productivity-v1-0-300m#general_info","title":"General Info"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.CLMS.DAT.GLO.DMP300_V1","title":"EO.CLMS.DAT.GLO.DMP300_V1"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.CLMS.DAT.GLO.DMP300_V1/items","title":"items"}],"assets":{"thumbnail":{"href":"https://wekeo2-prod-data-access-config.s3.waw3-2.cloudferro.com/previews/EO_CLMS_DAT_CGLS_GLOBAL_DMP300_V1_333M.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-60,180,80]]},"temporal":{"interval":[["2014-01-01T00:00:00Z",null]]}},"license":"other","keywords":["crops","GLOBE","dry matter","primary productivity","agricultural production","Dekad","Orthoimagery","10-daily composite"],"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"VITO NV","roles":["producer","processor"],"url":"https://vito.be/en"},{"name":"European Environment Agency (EEA)","roles":["licensor"],"url":"https://www.eea.europa.eu/"},{"name":"WEkEO","roles":["host"],"url":"https://www.wekeo.eu/"}],"dedl:short_description":"The Dry Matter Productivity dataset from 2014 onwards provides global 10-daily raster data at 300m resolution, indicating vegetation growth rates in kilograms of gross dry matter per hectare per day, accounting for autotrophic respiration."},{"type":"Collection","title":"Fraction of Absorbed Photosynthetically Active Radiation 2014-present (raster 300 m), global, 10-daily – version 1","id":"EO.CLMS.DAT.GLO.FAPAR300_V1","description":"The FAPAR quantifies the fraction of the solar radiation absorbed by plants for photosynthesis. It refers only to the green and living elements of the canopy. The FAPAR depends on the canopy structure, vegetation element optical properties, atmospheric conditions and angular configuration. To overcome this latter dependency, a daily integrated FAPAR value is assessed. FAPAR is very useful as input to a number of primary productivity models and is recognized as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS). The product at 333m resolution is provided in Near Real Time and consolidated in the next six periods.","links":[{"rel":"license","type":"text/html","href":"https://land.copernicus.eu/en/data-policy","title":"Copernicus Land Data Policy"},{"rel":"describedby","type":"text/html","href":"https://land.copernicus.eu/en/products/vegetation/fraction-of-absorbed-photosynthetically-active-radiation-v1-0-300m","title":"General Info"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.CLMS.DAT.GLO.FAPAR300_V1","title":"EO.CLMS.DAT.GLO.FAPAR300_V1"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.CLMS.DAT.GLO.FAPAR300_V1/items","title":"items"}],"assets":{"thumbnail":{"href":"https://land.copernicus.eu/en/products/vegetation/fraction-of-absorbed-photosynthetically-active-radiation-v1-0-300m/@@images/image-400-a98d2eea72ced9b2aa158e37618cc607.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-60,180,80]]},"temporal":{"interval":[["2014-01-01T00:00:00Z",null]]}},"license":"other","keywords":["fapar","geophysical environment","GLOBE","biogeophysical","Dekad","Orthoimagery","10-daily composite"],"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"VITO NV","roles":["producer","processor"],"url":"https://vito.be/en"},{"name":"European Environment Agency (EEA)","roles":["licensor"],"url":"https://www.eea.europa.eu/"},{"name":"WEkEO","roles":["host"],"url":"https://www.wekeo.eu/"}],"dedl:short_description":"The Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) dataset from 2014 onwards provides a 10-daily assessment of the fraction of solar radiation absorbed by plant life across the globe at 300-meter resolution."},{"type":"Collection","title":"Fraction of Green Vegetation Cover 2014-present (raster 300 m), global, 10-daily – version 1","id":"EO.CLMS.DAT.GLO.FCOVER300_V1","description":"The Fraction of Vegetation Cover (FCover) corresponds to the fraction of ground covered by green vegetation. \n        Practically, it quantifies the spatial extent of the vegetation. Because it is independent from the \n        illumination direction and it is sensitive to the vegetation amount, FCover is a very good candidate \n        for the replacement of classical vegetation indices for the monitoring of ecosystems.\n        The product at 333m resolution is provided in Near Real Time and consolidated in the next six periods.","links":[{"rel":"license","type":"text/html","href":"https://land.copernicus.eu/en/data-policy","title":"Copernicus Land Data Policy"},{"rel":"describedby","type":"text/html","href":"https://land.copernicus.eu/en/products/vegetation/fraction-of-green-vegetation-cover-v1-0-300m","title":"General Info"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.CLMS.DAT.GLO.FCOVER300_V1","title":"EO.CLMS.DAT.GLO.FCOVER300_V1"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.CLMS.DAT.GLO.FCOVER300_V1/items","title":"items"}],"assets":{"thumbnail":{"href":"https://wekeo2-prod-data-access-config.s3.waw3-2.cloudferro.com/previews/EO_CLMS_DAT_CGLS_GLOBAL_FCOVER300_V1_333M.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-60,180,80]]},"temporal":{"interval":[["2014-01-01T00:00:00Z",null]]}},"license":"other","keywords":["leaf area","geophysical environment","GLOBE","biogeophysical","Dekad","Orthoimagery","10-daily composite"],"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"VITO NV","roles":["producer","processor"],"url":"https://vito.be/en"},{"name":"European Environment Agency (EEA)","roles":["licensor"],"url":"https://www.eea.europa.eu/"},{"name":"WEkEO","roles":["host"],"url":"https://www.wekeo.eu/"}],"dedl:short_description":"The Fraction of Vegetation Cover dataset provides global, 10-day raster data on the percentage of land area covered by green vegetation since 2014, available in near real-time with subsequent consolidation over six days."},{"type":"Collection","title":"Gross Dry Matter Productivity 2014-present (raster 300 m), global, 10-daily – version 1","id":"EO.CLMS.DAT.GLO.GDMP300_V1","description":"Gross dry matter Productivity (GDMP) is an indication of the overall growth rate or dry biomass increase of the vegetation and is directly related to ecosystem Gross Primary Productivity (GPP), that reflects the ecosystem's overall production of organic compounds from atmospheric carbon dioxide, however its units (kilograms of gross dry matter per hectare per day) are customized for agro-statistical purposes. Like the FAPAR products that are used as input for the GDMP estimation, these GDMP products are provided in Near Real Time, with consolidations in the next periods, or as offline product.","links":[{"rel":"license","type":"text/html","href":"https://land.copernicus.eu/en/data-policy","title":"Copernicus Land Data Policy"},{"rel":"describedby","type":"text/html","href":"https://land.copernicus.eu/en/products/vegetation/gross-dry-matter-productivity-v1-0-300m","title":"General Info"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.CLMS.DAT.GLO.GDMP300_V1","title":"EO.CLMS.DAT.GLO.GDMP300_V1"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.CLMS.DAT.GLO.GDMP300_V1/items","title":"items"}],"assets":{"thumbnail":{"href":"https://land.copernicus.eu/en/products/vegetation/gross-dry-matter-productivity-v1-0-300m/@@images/image-400-aa448faf2cb0047474dc82b7aa75cabf.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-60,180,80]]},"temporal":{"interval":[["2014-01-01T00:00:00Z",null]]}},"license":"other","keywords":["GLOBE","agricultural production","primary productivity","crops","Dekad","Orthoimagery","10-daily composite","gross dry matter"],"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"VITO NV","roles":["producer","processor"],"url":"https://vito.be/en"},{"name":"European Environment Agency (EEA)","roles":["licensor"],"url":"https://www.eea.europa.eu/"},{"name":"WEkEO","roles":["host"],"url":"https://www.wekeo.eu/"}],"dedl:short_description":"The Gross Dry Matter Productivity dataset provides global, 10-daily raster data at 300m resolution since 2014, indicating the daily growth rate of vegetation dry biomass in kilograms per hectare."},{"type":"Collection","title":"Leaf Area Index 2014-present (raster 300 m), global, 10-daily – version 1","id":"EO.CLMS.DAT.GLO.LAI300_V1","description":"LAI was defined by CEOS as half the developed area of the convex hull wrapping the green canopy elements per unit horizontal ground. This definition allows accounting for elements which are not flat such as needles or stems. LAI is strongly non linearly related to reflectance. Therefore, its estimation from remote sensing observations will be scale dependant over heterogeneous landscapes. When observing a canopy made of different layers of vegetation, it is therefore mandatory to consider all the green layers. This is particularly important for forest canopies where the understory may represent a very significant contribution to the total canopy LAI. The derived LAI corresponds therefore to the total green LAI, including the contribution of the green elements of the understory. The product at 333m resolution is provided in Near Real Time and consolidated in the next six periods.","links":[{"rel":"license","type":"text/html","href":"https://land.copernicus.eu/en/data-policy","title":"Copernicus Land Data Policy"},{"rel":"describedby","type":"text/html","href":"https://land.copernicus.eu/en/products/vegetation/leaf-area-index-300m-v1.0","title":"General Info"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.CLMS.DAT.GLO.LAI300_V1","title":"EO.CLMS.DAT.GLO.LAI300_V1"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.CLMS.DAT.GLO.LAI300_V1/items","title":"items"}],"assets":{"thumbnail":{"href":"https://land.copernicus.eu/en/products/vegetation/leaf-area-index-300m-v1.0/@@images/image-400-a296946aa1a765380c77a012fed027ff.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-60,180,80]]},"temporal":{"interval":[["2014-01-01T00:00:00Z",null]]}},"license":"other","keywords":["leaf area","geophysical environment","GLOBE","biogeophysical","Dekad","Orthoimagery","10-daily composite"],"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"VITO NV","roles":["producer","processor"],"url":"https://vito.be/en"},{"name":"European Environment Agency (EEA)","roles":["licensor"],"url":"https://www.eea.europa.eu/"},{"name":"WEkEO","roles":["host"],"url":"https://www.wekeo.eu/"}],"dedl:short_description":"The Leaf Area Index dataset provides global, 10-daily raster data from 2014 onwards at 300-m resolution, representing the total green leaf area index, including understory contributions, with each value calculated based on the developed area of the convex hull wrapping green canopy elements per unit horizontal ground."},{"type":"Collection","title":"Normalised Difference Vegetation Index 2014-2020 (raster 300 m), global, 10-daily – version 1","id":"EO.CLMS.DAT.GLO.NDVI300_V1","description":"The Normalized Difference Vegetation Index (NDVI) is a proxy to quantify the vegetation amount. It is defined as NDVI=(NIR-Red)/(NIR+Red) where NIR corresponds to the reflectance in the near infrared band, and Red to the reflectance in the red band. It is closely related to FAPAR and is little scale dependant.\nEvery 10-days estimates are available at global scale in the spatial resolution of about 300m from 2014 to June 2020.","links":[{"rel":"license","type":"text/html","href":"https://land.copernicus.eu/en/data-policy","title":"Copernicus Land Data Policy"},{"rel":"describedby","type":"text/html","href":"https://land.copernicus.eu/en/products/vegetation/normalized-difference-vegetation-index-300m-v1.0","title":"General Info"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.CLMS.DAT.GLO.NDVI300_V1","title":"EO.CLMS.DAT.GLO.NDVI300_V1"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.CLMS.DAT.GLO.NDVI300_V1/items","title":"items"}],"assets":{"thumbnail":{"href":"https://land.copernicus.eu/en/products/vegetation/normalized-difference-vegetation-index-300m-v1.0/@@images/image-400-9533006b9c06e10ada162cf8283f652f.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-60,180,80]]},"temporal":{"interval":[["2014-01-01T00:00:00Z","2020-12-31T23:59:59Z"]]}},"license":"other","keywords":["ndvi","10-day composite","GLOBE","plant resource","Dekad","Orthoimagery","biogeophysical"],"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"VITO NV","roles":["producer","processor"],"url":"https://vito.be/en"},{"name":"European Environment Agency (EEA)","roles":["licensor"],"url":"https://www.eea.europa.eu/"},{"name":"WEkEO","roles":["host"],"url":"https://www.wekeo.eu/"}],"dedl:short_description":"The Normalized Difference Vegetation Index (NDVI) raster data for 2014-2020 provides global, 10-daily estimates with approximately 300m resolution, quantifying vegetation amounts through the ratio of near-infrared to red reflectances."},{"type":"Collection","title":"Normalised Difference Vegetation Index 1998-2020 (raster 1 km), global, 10-daily – version 2","id":"EO.CLMS.DAT.GLO.NDVI_1KM_V2","description":"The Normalized Difference Vegetation Index (NDVI) is a proxy to quantify the vegetation amount. It is defined as NDVI=(NIR-Red)/(NIR+Red) where NIR corresponds to the reflectance in the near infrared band, and Red to the reflectance in the red band. It is closely related to FAPAR and is little scale dependant.\nEvery 10-days estimates are available at global scale in the spatial resolution of about 1km from April 1998 to 2013 based upon SPOT/VEGETATION data and from 2014 to 2020 based upon PROBA-V data","links":[{"rel":"license","type":"text/html","href":"https://land.copernicus.eu/en/data-policy","title":"Copernicus Land Data Policy"},{"rel":"describedby","type":"text/html","href":"https://land.copernicus.eu/en/products/vegetation/normalised-difference-vegetation-index-v2-0-1km","title":"General Info"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.CLMS.DAT.GLO.NDVI_1KM_V2","title":"EO.CLMS.DAT.GLO.NDVI_1KM_V2"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.CLMS.DAT.GLO.NDVI_1KM_V2/items","title":"items"}],"assets":{"thumbnail":{"href":"https://wekeo2-prod-data-access-config.s3.waw3-2.cloudferro.com/previews/EO_CLMS_DAT_CGLS_GLOBAL_NDVI_V2_1KM.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-60,180,80]]},"temporal":{"interval":[["1998-01-01T00:00:00Z","2020-12-31T23:59:59Z"]]}},"license":"other","keywords":["ndvi","10-day composite","GLOBE","plant resource","Dekad","Orthoimagery","biogeophysical"],"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"VITO 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Data were acquired through the TanDEM-X mission between 2011 and 2015 [https://spacedata.copernicus.eu/collections/copernicus-digital-elevation-model].","links":[{"rel":"license","type":"application/pdf","href":"https://dataspace.copernicus.eu/sites/default/files/media/files/2025-06/copernicus_contributing_mission_data_access_v2_cop_dem_licenses.pdf","title":"Licence for the use of the Copernicus WorldDEM-90"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.5270/ESA-c5d3d65","title":"Copernicus DEM - Global and European Digital Elevation Model (COP-DEM)"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.DEM.DAT.COP-DEM_GLO-90-DTED","title":"EO.DEM.DAT.COP-DEM_GLO-90-DTED"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.DEM.DAT.COP-DEM_GLO-90-DTED/items","title":"items"}],"assets":{"thumbnail":{"href":"https://dataspace.copernicus.eu/sites/default/files/styles/full_scaled_desktop/public/media/images/2024-05/02.jpg.webp","roles":["thumbnail"],"title":"overview","type":"image/jpeg"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2010-12-12T00:00:00Z","2023-01-01T23:59:59Z"],["2010-12-12T00:00:00Z","2010-12-31T23:59:59Z"],["2011-01-01T00:00:00Z","2011-12-20T23:59:59Z"],["2012-01-01T00:00:00Z","2012-12-22T23:59:59Z"],["2013-01-31T00:00:00Z","2013-07-28T23:59:59Z"],["2014-04-28T00:00:00Z","2014-06-28T23:59:59Z"],["2019-09-11T00:00:00Z","2019-09-13T23:59:59Z"],["2023-01-01T00:00:00Z","2023-01-01T23:59:59Z"]]}},"license":"other","keywords":["elevation","satellite imagery","Elevation","earth observation","World","MOOD-H2020"],"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"European Space Agency (ESA)","roles":["producer","processor","licensor"],"url":"https://earth.esa.int"},{"name":"Destination Earth Data Lake (DEDL)","roles":["host"],"url":"https://data.destination-earth.eu/"},{"name":"Copernicus Data Space Ecosystem (CDSE)","roles":["host"],"url":"https://dataspace.copernicus.eu/"},{"name":"CREODIAS","roles":["host"],"url":"https://creodias.eu/"}],"dedl:short_description":"The COPERNICUS Digital Elevation Model is a DSM representing the Earth's surface with features like buildings, infrastructure, and vegetation from data collected by the TanDEM-X mission between 2011-2015."},{"type":"Collection","title":"CAMS European air quality forecasts","id":"EO.ECMWF.DAT.CAMS_EUROPE_AIR_QUALITY_FORECASTS","description":"This dataset provides daily air quality analyses and forecasts for Europe.\nCAMS produces specific daily air quality analyses and forecasts for the European domain at significantly higher spatial resolution (0.1 degrees, approx. 10km) than is available from the global analyses and forecasts. The production is based on an ensemble of eleven air quality forecasting systems across Europe. A median ensemble is calculated from individual outputs, since ensemble products yield on average better performance than the individual model products. The spread between the eleven models are used to provide an estimate of the forecast uncertainty. The analysis combines model data with observations provided by the European Environment Agency (EEA) into a complete and consistent dataset using various data assimilation techniques depending upon the air-quality forecasting system used. In parallel, air quality forecasts are produced once a day for the next four days. Both the analysis and the forecast are available at hourly time steps at seven height levels.\nNote that only nitrogen monoxide, nitrogen dioxide, sulphur dioxide, ozone, PM2.5, PM10 and dust are regularly validated against in situ observations, and therefore forecasts of all other variables are unvalidated and should be considered experimental.","links":[{"rel":"license","type":"application/pdf","href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/licences/licence-to-use-copernicus-products/licence-to-use-copernicus-products_b4b9451f54cffa16ecef5c912c9cebd6979925a956e3fa677976e0cf198c2c18.pdf","title":"Licence to Use Copernicus Products"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/CKB/CAMS+Regional%3A+European+air+quality+analysis+and+forecast+data+documentation","title":"CAMS Regional Products Documentation"},{"rel":"describedby","type":"text/html","href":"https://atmosphere.copernicus.eu/regional-services","title":"Evaluation and quality assurance (EQA) reports"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.5194/gmd-8-2777-2015","title":"A regional air quality forecasting system over Europe: the MACC-II daily ensemble production"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.CAMS_EUROPE_AIR_QUALITY_FORECASTS","title":"EO.ECMWF.DAT.CAMS_EUROPE_AIR_QUALITY_FORECASTS"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.CAMS_EUROPE_AIR_QUALITY_FORECASTS/items","title":"items"}],"assets":{"thumbnail":{"href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/resources/cams-europe-air-quality-forecasts/overview_4e21f3b83980b68134ee3b2b792f5f0fdb3132d8c545becbba8cd3d801f66411.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-25,30,45,72]]},"temporal":{"interval":[["2020-08-26T00:00:00Z",null]]}},"license":"other","keywords":["Analysis","Atmosphere (composition)","Future","Europe","Aerosol","Atmospheric conditions","Past","Forecast","Present","airPollution","Reactive gas"],"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Atmosphere Monitoring Service (CAMS)","roles":["host"],"url":"https://atmosphere.copernicus.eu/"}],"dedl:short_description":"The CAMS European air quality dataset offers high-resolution daily analyses and forecasts for Europe, combining model data with EEA observations through ensemble methods and providing estimates of forecast uncertainty."},{"type":"Collection","title":"CAMS European air quality reanalyses","id":"EO.ECMWF.DAT.CAMS_EUROPE_AIR_QUALITY_REANALYSES","description":"This dataset provides annual air quality reanalyses for Europe based on both unvalidated (interim) and validated observations.\nCAMS produces annual air quality (interim) reanalyses for the European domain at significantly higher spatial resolution (0.1 degrees, approx. 10km) than is available from the global reanalyses. The production is currently based on an ensemble of nine air quality data assimilation systems across Europe. A median ensemble is calculated from individual outputs, since ensemble products yield on average better performance than the individual model products. The spread between the nine models can be used to provide an estimate of the analysis uncertainty.\nThe reanalysis combines model data with observations provided by the European Environment Agency (EEA) into a complete and consistent dataset using various data assimilation techniques depending upon the air-quality forecasting system used. Additional sources of observations can complement the in-situ data assimilation, like satellite data.\nAn interim reanalysis is provided each year for the year before based on the unvalidated near-real-time observation data stream that has not undergone full quality control by the data providers yet. Once the fully quality-controlled observations are available from the data provider, typically with an additional delay of about 1 year, a final validated annual reanalysis is provided. Both reanalyses are available at hourly time steps at height levels.","links":[{"rel":"license","type":"application/pdf","href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/licences/licence-to-use-copernicus-products/licence-to-use-copernicus-products_b4b9451f54cffa16ecef5c912c9cebd6979925a956e3fa677976e0cf198c2c18.pdf","title":"Licence to Use Copernicus Products"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/CKB/CAMS+Regional%3A+European+air+quality+reanalyses+data+documentation","title":"CAMS European air quality reanalyses documentation"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.5194/gmd-8-2777-2015","title":"A regional air quality forecasting system over Europe: the MACC-II daily ensemble production"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.CAMS_EUROPE_AIR_QUALITY_REANALYSES","title":"EO.ECMWF.DAT.CAMS_EUROPE_AIR_QUALITY_REANALYSES"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.CAMS_EUROPE_AIR_QUALITY_REANALYSES/items","title":"items"}],"assets":{"thumbnail":{"href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/resources/cams-europe-air-quality-reanalyses/overview_bbfe0b2e1170acdb9c1e489b34a4c7db89080f96fcde03acfdca90f450c8582a.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-25,30,45,72]]},"temporal":{"interval":[["2013-01-01T00:00:00Z","2023-12-31T23:59:59Z"]]}},"license":"other","keywords":["Atmosphere (composition)","Europe","Aerosol","Atmospheric conditions","Past","Reanalysis","airPollution","Reactive gas"],"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Atmosphere Monitoring Service (CAMS)","roles":["host"],"url":"https://atmosphere.copernicus.eu/"}],"dedl:short_description":"This dataset contains high-resolution annual air quality reanalyses for Europe combining model data with EEA observations through multiple ensemble methods, providing both interim and validated results."},{"type":"Collection","title":"CAMS global atmospheric composition forecasts","id":"EO.ECMWF.DAT.CAMS_GLOBAL_ATMOSHERIC_COMPO_FORECAST","description":"CAMS produces global forecasts for atmospheric composition twice a day. The forecasts consist of more than 50 chemical species (e.g. ozone, nitrogen dioxide, carbon monoxide) and seven different types of aerosol (desert dust, sea salt, organic matter, black carbon, sulphate, nitrate and ammonium aerosol). In addition, several meteorological variables are available as well.\nThe initial conditions of each forecast are obtained by combining a previous forecast with current satellite observations through a process called data assimilation. This best estimate of the state of the atmosphere at the initial forecast time step, called the analysis, provides a globally complete and consistent dataset allowing for estimates at locations where observation data coverage is low or for atmospheric pollutants for which no direct observations are available.\nThe forecast itself uses a model of the atmosphere based on the laws of physics and chemistry to determine the evolution of the concentrations of all species over time for the next five days. Apart from the required initial state, it also uses inventory-based or observation-based emission estimates as a boundary condition at the surface.\nThe CAMS global forecasting system is upgraded about once a year resulting in technical and scientific changes. The horizontal or vertical resolution can change, new species can be added, and more generally the accuracy of the forecasts can be improved. Details of these system changes can be found in the documentation. Users looking for a more consistent long-term data set should consider using the CAMS Global Reanalysis instead, which is available through the ADS and spans the period from 2003 onwards.\n Finally, because some meteorological fields in the forecast do not fall within the general CAMS data licence, they are only available with a delay of 5 days.","links":[{"rel":"license","type":"application/pdf","href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/licences/licence-to-use-copernicus-products/licence-to-use-copernicus-products_b4b9451f54cffa16ecef5c912c9cebd6979925a956e3fa677976e0cf198c2c18.pdf","title":"Licence to Use Copernicus Products"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/x/5cqpD","title":"CAMS Global atmospheric composition forecast data documentation"},{"rel":"describedby","type":"text/html","href":"https://atmosphere.copernicus.eu/node/326","title":"Changes in CAMS global production system"},{"rel":"describedby","type":"text/html","href":"https://atmosphere.copernicus.eu/node/325#fd66a9d3-e408-4b51-84c0-c2253a06f727","title":"Evaluation and quality assurance (EQA) reports"},{"rel":"describedby","type":"text/html","href":"https://global-evaluation.atmosphere.copernicus.eu/","title":"Evaluation of global forecasts"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.CAMS_GLOBAL_ATMOSHERIC_COMPO_FORECAST","title":"EO.ECMWF.DAT.CAMS_GLOBAL_ATMOSHERIC_COMPO_FORECAST"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.CAMS_GLOBAL_ATMOSHERIC_COMPO_FORECAST/items","title":"items"}],"assets":{"thumbnail":{"href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/resources/cams-global-atmospheric-composition-forecasts/overview_a484dee4988156df196a92b874b23a6d2686921df6b17c528d1a171cb51d0240.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2015-01-01T00:00:00Z",null]]}},"license":"other","keywords":["Analysis","Atmosphere (composition)","Global","Future","Atmosphere (meteorology)","Aerosol","Atmospheric conditions","Past","Forecast","Present","airPollution","Reactive gas"],"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Atmosphere Monitoring Service (CAMS)","roles":["host"],"url":"https://atmosphere.copernicus.eu/"}],"dedl:short_description":"CAMS generates daily global atmospheric composition forecasts including over 50 chemicals and seven aerosols, utilizing past predictions combined with real-time satellite data and physical/chemical models for up to five-day projections."},{"type":"Collection","title":"CAMS global emission inventories","id":"EO.ECMWF.DAT.CAMS_GLOBAL_EMISSION_INVENTORIES","description":"This data set contains gridded distributions of global anthropogenic and natural emissions.\nNatural and anthropogenic emissions of atmospheric pollutants and greenhouse gases are key drivers of the evolution of the composition of the atmosphere, so an accurate representation of them in forecast models of atmospheric composition is essential. CAMS compiles inventories of emission data that serve as input to its own forecast models, but which can also be used by other atmospheric chemical transport models. These inventories are based on a combination of existing data sets and new information, describing anthropogenic emissions from fossil fuel use on land, shipping, and aviation, and natural emissions from vegetation, soil, the ocean and termites. The anthropogenic emissions on land are further separated in specific activity sectors (e.g., power generation, road traffic, industry). The CAMS emission data sets provide good consistency between the emissions of greenhouse gases, reactive gases, and aerosol particles and their precursors. Because most inventory-based data sets are only available with a delay of several years, the CAMS emission inventories also extend these existing data sets forward in time by using the trends from the most recent available years, producing timely input data for real-time forecast models.\nMost of the data sets are updated once or twice per year adding the most recent year to the data record, while re-processing the original data record for consistency, when needed. This is reflected by the different version numbers.","links":[{"rel":"license","type":"application/pdf","href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/licences/licence-to-use-copernicus-products/licence-to-use-copernicus-products_b4b9451f54cffa16ecef5c912c9cebd6979925a956e3fa677976e0cf198c2c18.pdf","title":"Licence to Use Copernicus Products"},{"rel":"describedby","type":"text/html","href":"https://atmosphere.copernicus.eu/anthropogenic-and-natural-emissions","title":"CAMS anthropogenic and natural emissions documentation"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.CAMS_GLOBAL_EMISSION_INVENTORIES","title":"EO.ECMWF.DAT.CAMS_GLOBAL_EMISSION_INVENTORIES"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.CAMS_GLOBAL_EMISSION_INVENTORIES/items","title":"items"}],"assets":{"thumbnail":{"href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/resources/cams-global-emission-inventories/overview_def42da7bfd33d3f2cc49e07bc5cac39340cc56944bcbbd96d7f8117e5bd3756.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2003-01-01T00:00:00Z","2020-12-31T23:59:59Z"]]}},"license":"other","keywords":["Global","Aerosol","Atmospheric conditions","Past","Emission inventory","Emissions and surface fluxes","airPollution","Reactive gas"],"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Atmosphere Monitoring Service (CAMS)","roles":["host"],"url":"https://atmosphere.copernicus.eu/"}],"dedl:short_description":"The CAMS global emission inventories contain gridded distributions of anthropogenic and natural emissions compiled from various sources including fossil fuels, shipping, aviation, vegetation, oceans, soils, and termites, providing consistent datasets for forecasting atmospheric composition."},{"type":"Collection","title":"CAMS global biomass burning emissions based on fire radiative power (GFAS)","id":"EO.ECMWF.DAT.CAMS_GLOBAL_FIRE_EMISSIONS_GFAS","description":"Emissions of atmospheric pollutants from biomass burning and vegetation fires are key drivers of the evolution of atmospheric composition, with a high degree of spatial and temporal variability, and an accurate representation of them in models is essential.\n\nThe CAMS Global Fire Assimilation System (GFAS) utilises satellite observations of fire radiative power (FRP) to provide near-real-time information on the location, relative intensity and estimated emissions from biomass burning and vegetation fires. Emissions are estimated by (i) conversion of FRP observations to the dry matter (DM) consumed by the fire, and (ii) application of emission factors to DM for different biomes, based on field and laboratory studies in the scientific literature, to estimate the emissions. Emissions estimates for 40 pyrogenic species are available from GFAS, including aerosols, reactive gases and greenhouse gases, on a regular grid with a spatial resolution of 0.1 degrees longitude by 0.1 degrees latitude.\n\nThis version of GFAS (v1.2) provides daily averaged data based on a combination of FRP observations from two Moderate Resolution Imaging Spectroradiometer (MODIS) instruments, one on the NASA EOS-Terra satellite and the other on the NASA EOS-Aqua satellite from 1 January 2003 to present. GFAS also provides daily estimates of smoke plume injection heights derived from FRP observations and meteorological information from the operational weather forecasts from ECMWF.\n\nGFAS data have been used to provide surface boundary conditions for the CAMS global atmospheric composition and European regional air quality forecasts, and the wider atmospheric chemistry modelling community.\n\nMore details about the products are given in the Documentation section.\n\n## How to cite the CAMS GFAS data\n\nPlease acknowledge the use of the CAMS GFAS data as stated in the [Copernicus CAMS License agreement](http://apps.ecmwf.int/datasets/licences/copernicus/):\n\tWhere the Licensee communicates to the public or distributes or publishes CAMS Information, the Licensee shall inform the recipients of the source of that information by using the following or any similar notice:\n'Generated using Copernicus Atmosphere Monitoring Service Information [Year]'.\n\tWhere the Licensee makes or contributes to a publication or distribution containing adapted or modified CAMS Information, the Licensee shall provide the following or any similar notice:\n'Contains modified Copernicus Atmosphere Monitoring Service Information [Year]';\n\nAny such publication or distribution shall state that \"neither the European Commission nor ECMWF is responsible for any use that may be made of the information it contains.\"","links":[{"rel":"license","type":"application/pdf","href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/licences/licence-to-use-copernicus-products/licence-to-use-copernicus-products_b4b9451f54cffa16ecef5c912c9cebd6979925a956e3fa677976e0cf198c2c18.pdf","title":"Licence to Use Copernicus Products"},{"rel":"describedby","type":"application/pdf","href":"https://confluence.ecmwf.int/x/to1CBQ","title":"Documentation on the CAMS global fire assimilation system"},{"rel":"describedby","type":"application/pdf","href":"https://www.ecmwf.int/sites/default/files/elibrary/2016/16906-improving-gfas-and-cams-biomass-burning-estimations-means-global-ecmwf-fire-forecast-system.pdf","title":"Improving CAMS biomass burning estimations by means of the Global ECMWFFire Forecast system (GEFF)"},{"rel":"describedby","type":"application/pdf","href":"https://www.ecmwf.int/sites/default/files/elibrary/2013/7707-assessment-global-fire-assimilation-system-gfasv1.pdf","title":"Assessment of the Global Fire Assimilation System (GFASv1)"},{"rel":"describedby","type":"application/pdf","href":"https://www.ecmwf.int/sites/default/files/elibrary/2010/9842-assessment-real-time-fire-emissions-gfasv0-macc.pdf","title":"Assessment of the Real-Time Fire Emissions (GFASv0) by MACC"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.5194/acp-2017-790"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.5194/acp-17-2921-2017"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.CAMS_GLOBAL_FIRE_EMISSIONS_GFAS","title":"EO.ECMWF.DAT.CAMS_GLOBAL_FIRE_EMISSIONS_GFAS"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.CAMS_GLOBAL_FIRE_EMISSIONS_GFAS/items","title":"items"}],"assets":{"thumbnail":{"href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/resources/cams-global-fire-emissions-gfas/overview_eaf1132e6ada572e7ab38d00431929a48be55ac21bb5ec24bbe5d0c65be670ad.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2003-01-01T00:00:00Z",null]]}},"license":"other","keywords":["Analysis","Global","Aerosol","Atmospheric conditions","Past","Emissions and surface fluxes","Present","airPollution","Reactive gas"],"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Atmosphere Monitoring Service (CAMS)","roles":["host"],"url":"https://atmosphere.copernicus.eu/"}],"dedl:short_description":"The CAMS Global Fire Assimilation System (GFAS v1.2) uses satellite observations of fire radiative power to estimate daily biomass burning emissions of 40 pyrogenic species at 0.1-degree resolution since 2003."},{"type":"Collection","title":"CAMS global greenhouse gas reanalysis (EGG4)","id":"EO.ECMWF.DAT.CAMS_GLOBAL_GREENHOUSE_GAS_REANALYSIS","description":"This dataset is part of the ECMWF Atmospheric Composition Reanalysis focusing on long-lived greenhouse gases: carbon dioxide (CO2) and methane (CH4). The emissions and natural fluxes at the surface are crucial for the evolution of the long-lived greenhouse gases in the atmosphere. In this dataset the CO2 fluxes from terrestrial vegetation are modelled in order to simulate the variability across a wide range of scales from diurnal to inter-annual. The CH4 chemical loss is represented by a climatological loss rate and the emissions at the surface are taken from a range of datasets.\nReanalysis combines model data with observations from across the world into a globally complete and consistent dataset using a model of the atmosphere based on the laws of physics and chemistry. This principle, called data assimilation, is based on the method used by numerical weather prediction centres and air quality forecasting centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way to allow for the provision of a dataset spanning back more than a decade. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product.\nThe assimilation system is able to estimate biases between observations and to sift good-quality data from poor data. The atmosphere model allows for estimates at locations where data coverage is low or for atmospheric pollutants for which no direct observations are available. The provision of estimates at each grid point around the globe for each regular output time, over a long period, always using the same format, makes reanalysis a very convenient and popular dataset to work with.\nThe observing system has changed drastically over time, and although the assimilation system can resolve data holes, the initially much sparser networks will lead to less accurate estimates. For this reason, EAC4 is only available from 2003 onwards.\nThe analysis procedure assimilates data in a window of 12 hours using the 4D-Var assimilation method, which takes account of the exact timing of the observations and model evolution within the assimilation window.\nThese data are available in 3-hourly resolution, worldwide. Monthly means can be accessed at:\nhttps://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-global-ghg-reanalysis-egg4-monthly","links":[{"rel":"license","type":"application/pdf","href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/licences/licence-to-use-copernicus-products/licence-to-use-copernicus-products_b4b9451f54cffa16ecef5c912c9cebd6979925a956e3fa677976e0cf198c2c18.pdf","title":"Licence to Use Copernicus Products"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/x/OIX4B","title":"CAMS: Reanalysis data documentation"},{"rel":"describedby","type":"text/html","href":"https://atmosphere.copernicus.eu/eqa-reports-global-services#fe56bdb4-1bdf-4d47-b46b-261a1ea57243","title":"Evaluation and quality assurance (EQA) reports"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.5194/acp-23-3829-2023","title":"Technical note: The CAMS greenhouse gas reanalysis from 2003 to 2020"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.CAMS_GLOBAL_GREENHOUSE_GAS_REANALYSIS","title":"EO.ECMWF.DAT.CAMS_GLOBAL_GREENHOUSE_GAS_REANALYSIS"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.CAMS_GLOBAL_GREENHOUSE_GAS_REANALYSIS/items","title":"items"}],"assets":{"thumbnail":{"href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/resources/cams-global-ghg-reanalysis-egg4/overview_0e58aaaa6c287649388046e898d0a25cfa1a936903e4e71352c365e59dd5dcef.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2003-01-01T00:00:00Z","2020-01-01T00:00:00Z"]]}},"license":"other","keywords":["Greenhouse gas","Atmosphere (composition)","Global","Atmosphere (meteorology)","Atmospheric conditions","Past","Reanalysis","airPollution"],"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Atmosphere Monitoring Service (CAMS)","roles":["host"],"url":"https://atmosphere.copernicus.eu/"}],"dedl:short_description":"This CAMS global greenhouse gas reanalysis (EGG4) dataset provides a comprehensive, consistently formatted, and high-resolution representation of global CO2 and CH4 concentrations from 2003 onwards, combining model simulations with observational data through a physically-based atmospheric model and advanced data assimilation techniques."},{"type":"Collection","title":"CAMS global greenhouse gas reanalysis (EGG4) monthly averaged fields","id":"EO.ECMWF.DAT.CAMS_GLOBAL_GREENHOUSE_GAS_REANALYSIS_MONTHLY_AV_FIELDS","description":"This dataset is part of the ECMWF Atmospheric Composition Reanalysis focusing on long-lived greenhouse gases: carbon dioxide (CO2) and methane (CH4). The emissions and natural fluxes at the surface are crucial for the evolution of the long-lived greenhouse gases in the atmosphere. In this dataset the CO2 fluxes from terrestrial vegetation are modelled in order to simulate the variability across a wide range of scales from diurnal to inter-annual. The CH4 chemical loss is represented by a climatological loss rate and the emissions at the surface are taken from a range of datasets.\nReanalysis combines model data with observations from across the world into a globally complete and consistent dataset using a model of the atmosphere based on the laws of physics and chemistry. This principle, called data assimilation, is based on the method used by numerical weather prediction centres and air quality forecasting centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way to allow for the provision of a dataset spanning back more than a decade. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product.\nThe assimilation system is able to estimate biases between observations and to sift good-quality data from poor data. The atmosphere model allows for estimates at locations where data coverage is low or for atmospheric pollutants for which no direct observations are available. The provision of estimates at each grid point around the globe for each regular output time, over a long period, always using the same format, makes reanalysis a very convenient and popular dataset to work with.\nThe observing system has changed drastically over time, and although the assimilation system can resolve data holes, the initially much sparser networks will lead to less accurate estimates. For this reason, EAC4 is only available from 2003 onwards.\nThe analysis procedure assimilates data in a window of 12 hours using the 4D-Var assimilation method, which takes account of the exact timing of the observations and model evolution within the assimilation window.\nThis page provides monthly mean values, worldwide. Original 3-hourly outputs can be accessed at:\nhttps://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-global-ghg-reanalysis-egg4","links":[{"rel":"license","type":"application/pdf","href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/licences/licence-to-use-copernicus-products/licence-to-use-copernicus-products_b4b9451f54cffa16ecef5c912c9cebd6979925a956e3fa677976e0cf198c2c18.pdf","title":"Licence to Use Copernicus Products"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/x/OIX4B","title":"CAMS: Reanalysis data documentation"},{"rel":"describedby","type":"text/html","href":"https://atmosphere.copernicus.eu/eqa-reports-global-services#fe56bdb4-1bdf-4d47-b46b-261a1ea57243","title":"Evaluation and quality assurance (EQA) reports"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.5194/acp-23-3829-2023","title":"Technical note: The CAMS greenhouse gas reanalysis from 2003 to 2020"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.CAMS_GLOBAL_GREENHOUSE_GAS_REANALYSIS_MONTHLY_AV_FIELDS","title":"EO.ECMWF.DAT.CAMS_GLOBAL_GREENHOUSE_GAS_REANALYSIS_MONTHLY_AV_FIELDS"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.CAMS_GLOBAL_GREENHOUSE_GAS_REANALYSIS_MONTHLY_AV_FIELDS/items","title":"items"}],"assets":{"thumbnail":{"href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/resources/cams-global-ghg-reanalysis-egg4-monthly/overview_e8cbff72d10984bcb4a7b718a22ebfc227099a10f5b838038c6bfd36bb60c57a.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2003-01-01T00:00:00Z","2020-12-31T23:59:59Z"]]}},"license":"other","keywords":["Greenhouse gas","Atmosphere (composition)","Global","Atmosphere (meteorology)","Aerosol","Atmospheric conditions","Past","Reanalysis","airPollution"],"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Atmosphere Monitoring Service (CAMS)","roles":["host"],"url":"https://atmosphere.copernicus.eu/"}],"dedl:short_description":"The CAMS global greenhouse gas reanalysis (EGG4) dataset contains monthly averaged fields combining model data with observations from across the world, providing a globally complete and consistent dataset of CO2 and CH4 concentrations since 2003."},{"type":"Collection","title":"CAMS global radiative forcings","id":"EO.ECMWF.DAT.CAMS_GLOBAL_RADIATIVE_FORCING","description":"This dataset provides geographical distributions of the radiative forcing (RF) by key atmospheric constituents. The radiative forcing estimates are based on the CAMS reanalysis and additional model simulations and are provided separately for...\n\t- carbon dioxide\n\t- methane\n\t- tropospheric ozone\n\t- stratospheric ozone\n\t- interactions between anthropogenic aerosols and radiation\n\t- interactions between anthropogenic aerosols and clouds\nRadiative forcing measures the imbalance in the Earth's energy budget caused by a perturbation of the climate system, such as changes in atmospheric composition caused by human activities. RF is a useful predictor of globally-averaged temperature change, especially when rapid adjustments of atmospheric temperature and moisture profiles are taken into account. RF has therefore become a quantitative metric to compare the potential climate response to different perturbations. Increases in greenhouse gas concentrations over the industrial era exerted a positive RF, causing a gain of energy in the climate system. In contrast, concurrent changes in atmospheric aerosol concentrations are thought to exert a negative RF leading to a loss of energy.\nProducts are quantified both in “all-sky” conditions, meaning that the radiative effects of clouds are included in the radiative transfer calculations, and in “clear-sky” conditions, which are computed by excluding clouds in the radiative transfer calculations.\nThe upgrade from version 1.5 to 2 consists of an extension of the period by 2017-2018, the addition of an \"effective radiative forcing\" product and new ways to calculate the pre-industrial reference state for aerosols and cloud condensation nuclei. More details are given in the documentation section. New versions may be released in future as scientific methods develop, and existing versions may be extended with later years if data for the period is available from the CAMS reanalysis. Newer versions supercede old versions so it is always recommended to use the latest one.\nCAMS also produces distributions of aerosol optical depths, distinguishing natural from anthropogenic aerosols, which are a separate dataset. See \"Related Data\".","links":[{"rel":"license","type":"application/pdf","href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/licences/licence-to-use-copernicus-products/licence-to-use-copernicus-products_b4b9451f54cffa16ecef5c912c9cebd6979925a956e3fa677976e0cf198c2c18.pdf","title":"Licence to Use Copernicus Products"},{"rel":"describedby","type":"application/pdf","href":"https://atmosphere.copernicus.eu/sites/default/files/2020-07/CAMS74_2019SC4_D2.2.1_202002_Documentation_v1.pdf","title":"Documentation of CAMS Climate Forcing products, version 1.5 "},{"rel":"describedby","type":"application/pdf","href":"https://atmosphere.copernicus.eu/sites/default/files/2020-12/CAMS74_2019SC4_D2.2.2_202008_Documentation_v1.pdf","title":"Documentation of CAMS Climate Forcing products, version 2 "},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.5194/essd-12-1649-2020","title":"Radiative forcing of climate change from the Copernicus reanalysis of atmospheric composition"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.CAMS_GLOBAL_RADIATIVE_FORCING","title":"EO.ECMWF.DAT.CAMS_GLOBAL_RADIATIVE_FORCING"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.CAMS_GLOBAL_RADIATIVE_FORCING/items","title":"items"}],"assets":{"thumbnail":{"href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/resources/cams-global-radiative-forcings/overview_64b7f931c9378814193a3f8879de137f73271a1e1320780d78ec894e974b75e0.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2003-01-01T00:00:00Z","2018-12-31T23:59:59Z"]]}},"license":"other","keywords":["Greenhouse gas","Atmosphere (composition)","Global","Aerosol","Solar radiation","Atmospheric conditions","Past","Reanalysis","airPollution","Radiation","Reactive gas"],"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Atmosphere Monitoring Service (CAMS)","roles":["host"],"url":"https://atmosphere.copernicus.eu/"}],"dedl:short_description":"This dataset contains geographical distributions of radiative forcing by various atmospheric constituents including CO2, CH4, O3, aerosols, and their interactions, calculated under all-sky and clear-sky conditions."},{"type":"Collection","title":"CAMS global radiative forcing - auxilliary variables","id":"EO.ECMWF.DAT.CAMS_GLOBAL_RADIATIVE_FORCING_AUX","description":"This dataset provides aerosol optical depths and aerosol-radiation radiative effects for four different aerosol origins: anthropogenic, mineral dust, marine, and land-based fine-mode natural aerosol. The latter mostly consists of biogenic aerosols.\nThe data are a necessary complement to the \"CAMS global radiative forcings\" dataset (see \"Related Data\"). The calculation of aerosol radiative forcing requires a discrimination between aerosol of anthropogenic and natural origin. However, the CAMS reanalysis, which is used to provide the aerosol concentrations, does not make this distinction. The anthropogenic fraction was therefore derived by a method which uses aerosol size as a proxy for aerosol origin.","links":[{"rel":"license","type":"application/pdf","href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/licences/licence-to-use-copernicus-products/licence-to-use-copernicus-products_b4b9451f54cffa16ecef5c912c9cebd6979925a956e3fa677976e0cf198c2c18.pdf","title":"Licence to Use Copernicus Products"},{"rel":"describedby","type":"application/pdf","href":"https://atmosphere.copernicus.eu/sites/default/files/2020-07/CAMS74_2019SC4_D2.2.1_202002_Documentation_v1.pdf","title":"Documentation of CAMS Climate Forcing products, version 1.5"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.5194/essd-12-1649-2020","title":"Radiative forcing of climate change from the Copernicus reanalysis of atmospheric composition"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.CAMS_GLOBAL_RADIATIVE_FORCING_AUX","title":"EO.ECMWF.DAT.CAMS_GLOBAL_RADIATIVE_FORCING_AUX"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.CAMS_GLOBAL_RADIATIVE_FORCING_AUX/items","title":"items"}],"assets":{"thumbnail":{"href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/resources/cams-global-radiative-forcing-auxilliary-variables/overview_f9fb14508ff797a23b9b8e88df38d174307ef27015c6145805b74544a47a04d3.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2003-01-01T00:00:00Z","2017-12-31T23:59:59Z"]]}},"license":"other","keywords":["Analysis","Global","Solar radiation","Atmospheric conditions","Past","Satellite image area","Radiation","airPollution"],"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Atmosphere Monitoring Service (CAMS)","roles":["host"],"url":"https://atmosphere.copernicus.eu/"}],"dedl:short_description":"This dataset contains aerosol optical depths and radiative effects from four aerosol sources: anthropogenic, mineral dust, marine, and land-based fine-mode natural aerosol primarily consisting of biogenic particles."},{"type":"Collection","title":"CAMS global reanalysis (EAC4)","id":"EO.ECMWF.DAT.CAMS_GLOBAL_REANALYSIS_EAC4","description":"EAC4 (ECMWF Atmospheric Composition Reanalysis 4) is the fourth generation ECMWF global reanalysis of atmospheric composition. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using a model of the atmosphere based on the laws of physics and chemistry. This principle, called data assimilation, is based on the method used by numerical weather prediction centres and air quality forecasting centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way to allow for the provision of a dataset spanning back more than a decade. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product.\nThe assimilation system is able to estimate biases between observations and to sift good-quality data from poor data. The atmosphere model allows for estimates at locations where data coverage is low or for atmospheric pollutants for which no direct observations are available. The provision of estimates at each grid point around the globe for each regular output time, over a long period, always using the same format, makes reanalysis a very convenient and popular dataset to work with.\nThe observing system has changed drastically over time, and although the assimilation system can resolve data holes, the initially much sparser networks will lead to less accurate estimates. For this reason, EAC4 is only available from 2003 onwards.\nAlthough the analysis procedure considers chunks of data in a window of 12 hours in one go, EAC4 provides estimates every 3 hours, worldwide. This is made possible by the 4D-Var assimilation method, which takes account of the exact timing of the observations and model evolution within the assimilation window.","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/CAMS%3A+Reanalysis+data+documentation","title":"CAMS: Reanalysis data documentation"},{"rel":"cite-as","type":"text/html","href":"https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-global-reanalysis-eac4-monthly?tab=overview","title":"CAMS global reanalysis (EAC4) monthly averaged fields"},{"rel":"describedby","type":"text/html","href":"https://atmosphere.copernicus.eu/eqa-reports-global-services#fe56bdb4-1bdf-4d47-b46b-261a1ea57243","title":"Evaluation and quality assurance (EQA) reports"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.5194/acp-19-3515-2019","title":"The CAMS reanalysis of atmospheric composition"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.CAMS_GLOBAL_REANALYSIS_EAC4","title":"EO.ECMWF.DAT.CAMS_GLOBAL_REANALYSIS_EAC4"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.CAMS_GLOBAL_REANALYSIS_EAC4/items","title":"items"}],"assets":{"thumbnail":{"href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/resources/cams-global-reanalysis-eac4/overview_e082c9db427fb72cdd252bf0cde444a48b36e772a19a72f1c749dc72c4a00929.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2003-01-01T00:00:00Z","2022-12-31T23:59:59Z"]]}},"license":"other","keywords":["Atmosphere (composition)","Global","Atmosphere (meteorology)","Aerosol","Atmospheric conditions","Past","Reanalysis","airPollution","Reactive gas"],"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Atmosphere Monitoring Service (CAMS)","roles":["host"],"url":"https://atmosphere.copernicus.eu/"}],"dedl:short_description":"The CAMS global reanalysis (EAC4) dataset combines modelled data with historical observations to create a comprehensive, physically-based representation of the Earth's atmosphere since 2003, providing hourly estimates of atmospheric composition worldwide through its advanced 4D-Var assimilation technique."},{"type":"Collection","title":"CAMS global reanalysis (EAC4) monthly averaged fields","id":"EO.ECMWF.DAT.CAMS_GLOBAL_REANALYSIS_EAC4_MONTHLY_AV_FIELDS","description":"EAC4 (ECMWF Atmospheric Composition Reanalysis 4) is the fourth generation ECMWF global reanalysis of atmospheric composition. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using a model of the atmosphere based on the laws of physics and chemistry. This principle, called data assimilation, is based on the method used by numerical weather prediction centres and air quality forecasting centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way to allow for the provision of a dataset spanning back more than a decade. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product.\nThe assimilation system is able to estimate biases between observations and to sift good-quality data from poor data. The atmosphere model allows for estimates at locations where data coverage is low or for atmospheric pollutants for which no direct observations are available. The provision of estimates at each grid point around the globe for each regular output time, over a long period, always using the same format, makes reanalysis a very convenient and popular dataset to work with.\nThe observing system has changed drastically over time, and although the assimilation system can resolve data holes, the initially much sparser networks will lead to less accurate estimates. For this reason, EAC4 is only available from 2003 onwards.\nAlthough the analysis procedure considers chunks of data in a window of 12 hours in one go, EAC4 provides estimates every 3 hours, worldwide. This is made possible by the 4D-Var assimilation method, which takes account of the exact timing of the observations and model evolution within the assimilation window.","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/CAMS%3A+Reanalysis+data+documentation","title":"CAMS: Reanalysis data documentation"},{"rel":"cite-as","type":"text/html","href":"https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-global-reanalysis-eac4-monthly?tab=overview","title":"CAMS global reanalysis (EAC4) monthly averaged fields"},{"rel":"describedby","type":"text/html","href":"https://atmosphere.copernicus.eu/eqa-reports-global-services#fe56bdb4-1bdf-4d47-b46b-261a1ea57243","title":"Evaluation and quality assurance (EQA) reports"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.5194/acp-19-3515-2019","title":"The CAMS reanalysis of atmospheric composition"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.CAMS_GLOBAL_REANALYSIS_EAC4_MONTHLY_AV_FIELDS","title":"EO.ECMWF.DAT.CAMS_GLOBAL_REANALYSIS_EAC4_MONTHLY_AV_FIELDS"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.CAMS_GLOBAL_REANALYSIS_EAC4_MONTHLY_AV_FIELDS/items","title":"items"}],"assets":{"thumbnail":{"href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/resources/cams-global-reanalysis-eac4-monthly/overview_e26d2a815d3e6499bc8d18623930c253deb87757ca8df91fa99e939137d38a46.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2003-01-01T00:00:00Z","2022-12-31T23:59:59Z"]]}},"license":"other","keywords":["Atmosphere (composition)","Global","Atmosphere (meteorology)","Aerosol","Atmospheric conditions","Past","Reanalysis","airPollution","Reactive gas"],"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Atmosphere Monitoring Service (CAMS)","roles":["host"],"url":"https://atmosphere.copernicus.eu/"}],"dedl:short_description":"The CAMS global reanalysis (EAC4) dataset combines modelled and observed atmospheric data into a comprehensive record since 2003, providing hourly estimates of atmospheric conditions worldwide through its advanced 4D-Var assimilation technique."},{"type":"Collection","title":"CAMS global inversion-optimised greenhouse gas fluxes and concentrations","id":"EO.ECMWF.DAT.CAMS_GREENHOUSE_GAS_FLUXES","description":"This data set contains net fluxes at the surface, atmospheric mixing ratios at model levels, and column-mean atmospheric mixing ratios for carbon dioxide (CO2), methane (CH4) and nitrous oxide (N20).\nNatural and anthropogenic surface fluxes of greenhouse gases are key drivers of the evolution of Earth's climate, so their monitoring is essential. Such information has been used in particular as part of the Assessment Reports of the Intergovernmental Panel on Climate Change (IPCC). Ground-based and satellite remote-sensing observations provide a means to quantifying the net fluxes between the land and ocean on the one hand and the atmosphere on the other hand. This is done through a process called atmospheric inversion, which uses transport models of the atmosphere to link the observed concentrations of CO2, CH4 and N2O to the net fluxes at the Earth's surface. By correctly modelling the winds, vertical diffusion, and convection in the global atmosphere, the observed concentrations of the greenhouse gases are used to infer the surface fluxes for the last few decades. For CH4 and N2O, the flux inversions account also for the chemical loss of these greenhouse gases. The net fluxes include contributions from the natural biosphere (e.g., vegetation, wetlands) as well anthropogenic contributions (e.g., fossil fuel emissions, rice fields).\nThe data sets for the three species are updated once or twice per year adding the most recent year to the data record, while re-processing the original data record for consistency. This is reflected by the different version numbers. In addition, fluxes for methane are available based on surface air samples only or based on a combination of surface air samples and satellite observations (reflected by an 's' in the version number).","links":[{"rel":"license","type":"application/pdf","href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/licences/licence-to-use-copernicus-products/licence-to-use-copernicus-products_b4b9451f54cffa16ecef5c912c9cebd6979925a956e3fa677976e0cf198c2c18.pdf","title":"Licence to Use Copernicus Products"},{"rel":"describedby","type":"text/html","href":"https://atmosphere.copernicus.eu/greenhouse-gases-supplementary-products","title":"CAMS Greenhouse Gas Fluxes Documentation"},{"rel":"describedby","type":"text/html","href":"https://atmosphere.copernicus.eu/supplementary-services#c21ece9c-9469-4605-bcb1-2831778d052b","title":"Evaluation and quality assurance (EQA) reports"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.CAMS_GREENHOUSE_GAS_FLUXES","title":"EO.ECMWF.DAT.CAMS_GREENHOUSE_GAS_FLUXES"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.CAMS_GREENHOUSE_GAS_FLUXES/items","title":"items"}],"assets":{"thumbnail":{"href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/resources/cams-global-greenhouse-gas-inversion/overview_38d8bdbbbdcb2ad03e03cf6fed7c2fa885db8128b8da42dcccc57c90064c73be.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1979-01-01T00:00:00Z","2023-01-01T00:00:00Z"]]}},"license":"other","keywords":["Greenhouse gas","Atmosphere (composition)","Global","Atmospheric conditions","Past","Emissions and surface fluxes","Reanalysis","airPollution"],"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Atmosphere Monitoring Service (CAMS)","roles":["host"],"url":"https://atmosphere.copernicus.eu/"}],"dedl:short_description":"This dataset provides CAMS-inversion optimised greenhouse gas fluxes and concentrations for CO2, CH4, and N2O with updates every 1-2 years, including both natural and anthropogenic sources such as vegetation, wetlands, fossil fuels, and rice fields."},{"type":"Collection","title":"CAMS solar radiation time-series","id":"EO.ECMWF.DAT.CAMS_SOLAR_RADIATION_TIMESERIES","description":"The CAMS solar radiation services provide historical values (2004 to present) of global (GHI), direct (BHI) and diffuse (DHI) solar irradiation, as well as direct normal irradiation (BNI). The aim is to fulfil the needs of European and national policy development and the requirements of both commercial and public downstream services, e.g. for planning, monitoring, efficiency improvements and the integration of solar energy systems into energy supply grids.\nFor clear-sky conditions, an irradiation time series is provided for any location in the world using information on aerosol, ozone and water vapour from the CAMS global forecasting system. Other properties, such as ground albedo and ground elevation, are also taken into account. Similar time series are available for cloudy (or \"all sky\") conditions but, since the high-resolution cloud information is directly inferred from satellite observations, these are currently only available inside the field-of-view of the Meteosat Second Generation (MSG) satellite, which is roughly Europe, Africa, the Atlantic Ocean and the Middle East.\nData is offered in both ASCII and netCDF format. Additionally, an ASCII \"expert mode\" format can be selected which contains in addition to the irradiation, all the input data used in their calculation (aerosol optical properties, water vapour concentration, etc). This additional information is only meaningful in the time frame at which the calculation is performed and so is only available at 1-minute time steps in universal time (UT).","links":[{"rel":"license","type":"application/pdf","href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/licences/licence-to-use-copernicus-products/licence-to-use-copernicus-products_b4b9451f54cffa16ecef5c912c9cebd6979925a956e3fa677976e0cf198c2c18.pdf","title":"Licence to Use Copernicus Products"},{"rel":"cite-as","type":"text/html","href":"https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-solar-radiation-timeseries?tab=overview","title":"CAMS solar radiation time-series"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/x/jOLjDw","title":"CAMS solar radiation time-series: data documentation"},{"rel":"describedby","type":"text/html","href":"https://atmosphere.copernicus.eu/supplementary-services#fa6856b7-a306-4cc4-9137-f3e0cb703093","title":"Evaluation and quality assurance (EQA) reports"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.1127/metz/2022/1132","title":"Surface solar irradiation retrieval from MSG/SEVIRI based on APOLLO Next Generation and HELIOSAT‑4 methods"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.1127/metz/2016/0781","title":"Fast radiative transfer parameterisation for assessing the surface solar irradiance: The Heliosat‑4 method"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.CAMS_SOLAR_RADIATION_TIMESERIES","title":"EO.ECMWF.DAT.CAMS_SOLAR_RADIATION_TIMESERIES"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.CAMS_SOLAR_RADIATION_TIMESERIES/items","title":"items"}],"assets":{"thumbnail":{"href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/resources/cams-solar-radiation-timeseries/overview_51b05dc9c04479bfe717483ad887ef69461fc77410257222a19775cf68790cce.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2004-02-01T00:00:00Z",null]]}},"license":"other","keywords":["Analysis","Global","Solar radiation","Atmospheric conditions","Past","Satellite image area","Radiation","airPollution"],"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Atmosphere Monitoring Service (CAMS)","roles":["host"],"url":"https://atmosphere.copernicus.eu/"}],"dedl:short_description":"The CAMS solar radiation service provides historical global solar irradiance datasets from 2004 onwards, covering various types including GHI, BHI, DHI, and BNI, with options for clear-sky or cloudy conditions worldwide except outside MSG's field-of-view, offering data formats like ASCII and netCDF along with detailed inputs in \"expert mode\"."},{"type":"Collection","title":"Fire danger indices historical data from the Copernicus Emergency Management Service","id":"EO.ECMWF.DAT.CEMS_FIRE_HISTORICAL","description":"This data set provides complete historical reconstruction of meteorological conditions favourable to the start, spread and sustainability of fires. The fire danger metrics provided are part of a vast dataset produced by the Copernicus Emergency Management Service for the\nEuropean Forest Fire Information System (EFFIS). The European Forest Fire Information System incorporates the fire danger indices for three different models developed in Canada, United States and Australia. In this dataset the fire danger indices are calculated using weather forecast from historical simulations provided by ECMWF ERA5 reanalysis.\nERA5 by combining  model data and  a vast set of quality controlled observations provides a  globally complete and consistent data-set and is regarded as a good proxy for observed atmospheric conditions.\nThe selected data records in this data set are regularly extended with time as ERA5 forcing data become available. \nThis dataset is produced by ECMWF in its role of the computational centre for fire danger forecast of the CEMS,  on behalf of the Joint Research Centre which is the managing entity of the service.\n\nVariables in the dataset/application are:\nBuild-up index, Burning index, Danger rating, Drought code, Duff moisture code, Energy release component, Fine fuel moisture code, Fire daily severity index, Fire danger index, Fire weather index, Ignition component, Initial spread index, Keetch-Byram drought index, Spread component","links":[{"rel":"license","type":"application/pdf","href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/licences/licence-to-use-copernicus-products/licence-to-use-copernicus-products_b4b9451f54cffa16ecef5c912c9cebd6979925a956e3fa677976e0cf198c2c18.pdf","title":"Licence to Use Copernicus Products"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.24381/cds.0e89c522","title":"Fire danger indices historical data from the Copernicus Emergency Management Service"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/CEMS/Fire+danger+indices+historical+data+from+the+Copernicus+Emergency+Management+Service","title":"Product User Guide"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.1038/s41597-020-0554-z","title":"ERA5-based global meteorological wildfire danger maps"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.1038/sdata.2019.32","title":"A 1980–2018 global fire danger re-analysis dataset for the Canadian Fire Weather Indices"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.5194/nhess-20-2365-2020","title":"Fire Weather Index: the skill provided by the European Centre for Medium-Range Weather Forecasts ensemble prediction system"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.1175/JAMC-D-15-0297.1","title":"The Potential Predictability of Fire Danger Provided by Numerical Weather Prediction"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.CEMS_FIRE_HISTORICAL","title":"EO.ECMWF.DAT.CEMS_FIRE_HISTORICAL"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.CEMS_FIRE_HISTORICAL/items","title":"items"}],"assets":{"thumbnail":{"href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/resources/cems-fire-historical-v1/overview_7d52aa9bfd619151f13dba1d6a0c625edcb157696f3fbf0db3c24f0993f45e77.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1979-01-01T00:00:00Z","2022-10-29T23:59:59Z"]]}},"license":"other","keywords":["Global","Copernicus CEMS","Past","Land (biosphere)","Reanalysis","Land cover"],"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Emergency Management Service (CEMS)","roles":["host"],"url":"https://emergency.copernicus.eu/"}],"dedl:short_description":"Historical fire danger indices from the Copernicus Emergency Management Service's European Forest Fire Information System include various fire danger metrics based on Canadian, US, and Australian models, derived from ECMWF ERA5 reanalysed weather forecasts."},{"type":"Collection","title":"River discharge and related forecasted data by the European Flood Awareness System","id":"EO.ECMWF.DAT.CEMS_GLOFAS_FORECAST","description":"This dataset provides gridded modelled hydrological time series forced with medium-range meteorological forecasts. The data is a consistent representation of the most important hydrological variables across the European Flood Awareness System (EFAS) domain. The temporal resolution is sub-daily high-resolution and ensemble forecasts of:\n\nRiver discharge\nSoil moisture for three soil layers\nSnow water equivalent\n\nIt also provides static data on soil depth for the three soil layers. Soil moisture and river discharge data are accompanied by ancillary files for interpretation (see related variables and links in the documentation).\nThis data set was produced by forcing the LISFLOOD hydrological model at a 5x5km resolution with meteorological forecasts. The forecasts are initialised twice daily at 00 and 12 UTC with time steps of 6 or 24 hours and lead times between 5 and 15 days depending on the forcing numerical weather prediction model. The forcing meteorological data are high-resolution and ensemble forecasts from the European Centre of Medium-range Weather Forecasts (ECMWF) with 51 ensemble members, high-resolution forecasts from the Deutsches Wetter Dienst (DWD) and the ensemble forecasts from the COSMO Local Ensemble Prediction System (COSMO-LEPS) with 20 ensemble members. The hydrological forecasts are available from 2018-10-10 up until present with a 30-day delay. The real-time data is only available to EFAS partners.\nCompanion datasets, also available through the CDS, are historical simulations which can be used to derive the hydrological climatology and for verification; reforecasts for research, local skill assessment and post-processing; and seasonal forecasts and reforecasts for users looking for longer leadtime forecasts. For users looking for global hydrological data, we refer to the Global Flood Awareness System (GloFAS) forecasts and historical simulations. All these datasets are part of the operational flood forecasting within the Copernicus Emergency Management Service (CEMS).\n\nVariables in the dataset/application are:\nRiver discharge in the last 24 hours, River discharge in the last 6 hours, Snow depth water equivalent, Soil depth, Volumetric soil moisture\n\nVariables in the dataset/application are:\nOrography, Upstream area","links":[{"rel":"license","type":"application/pdf","href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/licences/cems-floods/cems-floods_428a6e1019ec50b3dad9c37a90d630fab139059933a939dd5df620bfcb420cc3.pdf","title":"CEMS-FLOODS datasets licence"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.24381/cds.ff1aef77","title":"River discharge and related forecasted data by the Global Flood Awareness System"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/CEMS/Global+Flood+Awareness+System","title":"Global Flood Awareness System"},{"rel":"describedby","type":"text/html","href":"http://www.globalfloods.eu/","title":"GloFAS web site"},{"rel":"describedby","type":"text/html","href":"https://www.sciencedirect.com/science/article/pii/S0022169418307467","title":"Calibration of the Global Flood Awareness System (GloFAS) using daily streamflow data"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/CEMS/Auxiliary+Data","title":"Auxiliary data"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.CEMS_GLOFAS_FORECAST","title":"EO.ECMWF.DAT.CEMS_GLOFAS_FORECAST"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.CEMS_GLOFAS_FORECAST/items","title":"items"}],"assets":{"thumbnail":{"href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/resources/efas-forecast/overview_ddd9074d456be00a54d03c320485bdbb1d1871507eccaa1039404a9c2c62fe31.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2019-11-05T00:00:00Z",null]]}},"license":"other","keywords":["Europe","Copernicus CEMS","Land (hydrology)","Climatology","Past","Reanalysis"],"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Emergency Management Service (CEMS)","roles":["host"],"url":"https://emergency.copernicus.eu/"}],"dedl:short_description":"The dataset contains gridded modelled hydrological time series including river discharge, snow water equivalent, soil moisture, and soil depth over Europe, driven by high-resolution ensemble meteorological forecasts from multiple sources."},{"type":"Collection","title":"River discharge and related historical data from the Global Flood Awareness System","id":"EO.ECMWF.DAT.CEMS_GLOFAS_HISTORICAL","description":"This dataset contains global modelled daily data of river discharge from the Global Flood Awareness System (GloFAS), which is part of the Copernicus Emergency Management Service (CEMS). River discharge, or river flow as it is also known, is defined as the amount of water that flows through a river section at a given time. \nThis dataset is simulated by forcing a hydrological modelling chain with inputs from a global reanalysis. Data availability for the historical simulation is from 1979-01-01 up to near real time.","links":[{"rel":"license","type":"application/pdf","href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/licences/cems-floods/cems-floods_428a6e1019ec50b3dad9c37a90d630fab139059933a939dd5df620bfcb420cc3.pdf","title":"CEMS-FLOODS datasets licence"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.24381/cds.a4fdd6b9","title":"River discharge and related historical data from the Global Flood Awareness System"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/CEMS/Global+Flood+Awareness+System","title":"Information pages for GloFAS"},{"rel":"describedby","type":"text/html","href":"http://www.globalfloods.eu/","title":"GloFAS web site"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/CEMS/Auxiliary+Data","title":"Auxiliary data"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.CEMS_GLOFAS_HISTORICAL","title":"EO.ECMWF.DAT.CEMS_GLOFAS_HISTORICAL"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.CEMS_GLOFAS_HISTORICAL/items","title":"items"}],"assets":{"thumbnail":{"href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/resources/cems-glofas-historical/overview_779934fbef02d194554ae626a1c5570e24a2e2a2e4b2ae7e8c54b4ca0c7fe9d0.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1979-01-01T00:00:00Z",null]]}},"license":"other","keywords":["Global","Land (hydrology)","Copernicus CEMS","Climatology","Past","Reanalysis"],"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Emergency Management Service (CEMS)","roles":["host"],"url":"https://emergency.copernicus.eu/"}],"dedl:short_description":"The dataset includes globally-modelled daily river discharge data from GloFAS, covering 1979-present, derived from a hydrological modelling chain forced by global reanalyses."},{"type":"Collection","title":"Reforecasts of river discharge and related data by the Global Flood Awareness System","id":"EO.ECMWF.DAT.CEMS_GLOFAS_REFORECAST","description":"This dataset provides a gridded modelled time series of river discharge, forced with medium- to sub-seasonal range meteorological reforecasts. The data is a consistent representation of a key hydrological variable across the global domain, and is a product of the Global Flood Awareness System (GloFAS). It is accompanied by an ancillary file for interpretation that provides the upstream area (see the related variables table and associated link in the documentation).\nThis dataset was produced by forcing a hydrological modelling chain with input from the European Centre for Medium-range Weather Forecasts (ECMWF) 11-member ensemble ECMWF-ENS reforecasts. Reforecasts are forecasts run over past dates, and those presented here are used for providing a suitably long time period against which the skill of the 30-day real-time operational forecast can be assessed. The reforecasts are initialised twice weekly with lead times up to 46 days, at 24-hour steps for 20 years in the recent history. For more specific information on the how the reforecast dataset is produced we refer to the documentation.\nCompanion datasets, also available through the Climate Data Store (CDS), are the operational forecasts, historical simulations that can be used to derive the hydrological climatology, and seasonal forecasts and reforecasts for users looking for long term forecasts. For users looking specifically for European hydrological data, we refer to the European Flood Awareness System (EFAS) forecasts and historical simulations. All these datasets are part of the operational flood forecasting within the Copernicus Emergency Management Service (CEMS).","links":[{"rel":"license","type":"application/pdf","href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/licences/cems-floods/cems-floods_428a6e1019ec50b3dad9c37a90d630fab139059933a939dd5df620bfcb420cc3.pdf","title":"CEMS-FLOODS datasets licence"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/CEMS/Global+Flood+Awareness+System","title":"Information pages for GloFAS"},{"rel":"describedby","type":"text/html","href":"http://www.globalfloods.eu/","title":"GloFAS web site"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/CEMS/Auxiliary+Data","title":"Auxiliary data"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.24381/cds.2d78664e","title":"Reforecasts of river discharge and related data by the Global Flood Awareness System"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.CEMS_GLOFAS_REFORECAST","title":"EO.ECMWF.DAT.CEMS_GLOFAS_REFORECAST"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.CEMS_GLOFAS_REFORECAST/items","title":"items"}],"assets":{"thumbnail":{"href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/resources/cems-glofas-reforecast/overview_36fc7b601512e3619bc5ba70ae0488b911d9d74e203400f9a321f5745768f6a5.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1999-01-03T00:00:00Z","2023-11-25T23:59:59Z"]]}},"license":"other","keywords":["Reforecast","Global","Land (hydrology)","Reforecasts","Copernicus CEMS","Past"],"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Emergency Management Service (CEMS)","roles":["host"],"url":"https://emergency.copernicus.eu/"}],"dedl:short_description":"The GloFAS dataset contains gridded modelled river discharge time series based on medium- to sub-seasonal meteorological reforecasts from the ECMWF-ENS system, covering a 20-year period with bi-weekly initialisations up to 46 days ahead."},{"type":"Collection","title":"Seasonal forecasts of river discharge and related data by the Global Flood Awareness System","id":"EO.ECMWF.DAT.CEMS_GLOFAS_SEASONAL","description":"This dataset provides a gridded modelled time series of river discharge, forced with seasonal range meteorological forecasts. The data is a consistent representation of a key hydrological variable across the global domain, and is a product of the Global Flood Awareness System (GloFAS). It is accompanied by an ancillary file for interpretation that provides the upstream area (see the related variables table and associated link in the documentation).\nThis dataset was produced by forcing the LISFLOOD hydrological model at a 0.1° (~11 km at the equator) resolution with downscaled runoff forecasts from the European Centre for Medium-range Weather Forecasts (ECMWF) 51-member ensemble seasonal forecasting system, SEAS5. The forecasts are initialised on the first of each month with a 24-hourly time step, and cover 123 days.\nCompanion datasets, also available through the Climate Data Store (CDS), are the operational forecasts, historical simulations that can be used to derive the hydrological climatology, and medium-range and seasonal reforecasts. The latter dataset enables research, local skill assessment and post-processing of the seasonal forecasts. In addition, the seasonal reforecasts are also used to derive a specific range dependent climatology for the seasonal system. For users looking specifically for European hydrological data, we refer to the European Flood Awareness System (EFAS) forecasts and historical simulations. All these datasets are part of the operational flood forecasting within the Copernicus Emergency Management Service (CEMS).","links":[{"rel":"license","type":"application/pdf","href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/licences/cems-floods/cems-floods_428a6e1019ec50b3dad9c37a90d630fab139059933a939dd5df620bfcb420cc3.pdf","title":"CEMS-FLOODS datasets licence"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.24381/cds.00b6c4fb","title":"Seasonal forecasts of river discharge and related data by the Global Flood Awareness System"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/CEMS/Global+Flood+Awareness+System","title":"Information pages for GloFAS"},{"rel":"describedby","type":"text/html","href":"http://www.globalfloods.eu","title":"GloFAS web site"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/CEMS/Auxiliary+Data","title":"Auxiliary data"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.CEMS_GLOFAS_SEASONAL","title":"EO.ECMWF.DAT.CEMS_GLOFAS_SEASONAL"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.CEMS_GLOFAS_SEASONAL/items","title":"items"}],"assets":{"thumbnail":{"href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/resources/cems-glofas-seasonal/overview_0885f764fd7ce14e5c511c5751d22b0610d3004d3de56f389562cace8f67e2bc.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2020-12-01T00:00:00Z","2024-12-31T23:59:59Z"]]}},"license":"other","keywords":["Global","Land (hydrology)","Copernicus CEMS","Seasonal forecasts","Forecast","Present"],"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Emergency Management Service (CEMS)","roles":["host"],"url":"https://emergency.copernicus.eu/"}],"dedl:short_description":"The GloFAS dataset contains globally gridded modelled river discharge forecasts based on ECMWF's SEAS5 seasonal forecast system, covering 123 days with a 24-hourly time step at approximately 11km resolution."},{"type":"Collection","title":"Seasonal reforecasts of river discharge and related data from the Global Flood Awareness System","id":"EO.ECMWF.DAT.CEMS_GLOFAS_SEASONAL_REFORECAST","description":"This dataset provides a gridded modelled time series of river discharge forced with seasonal range meteorological reforecasts. The data is a consistent representation of a key hydrological variable across the global domain, and is a product of the Global Flood Awareness System (GloFAS). It is accompanied by an ancillary file for interpretation that provides the upstream area (see the related variables table and associated link in the documentation).\nThis dataset was produced by forcing a hydrological modelling chain with input from the European Centre for Medium-range Weather Forecasts (ECMWF) ensemble seasonal forecasting system, SEAS5. For the period of 1981 to 2016 the number of ensemble members is 25, whilst reforecasts produced for 2017 onwards use a 51-member ensemble. Reforecasts are forecasts run over past dates, with those presented here used for producing the seasonal river discharge thresholds. In addition, they provide a suitably long time period against which the skill of the seasonal forecast can be assessed. The reforecasts are initialised monthly and run for 123 days, with a 24-hourly time step. For more specific information on the how the seasonal reforecast dataset is produced we refer to the documentation.\nCompanion datasets, also available through the Climate Data Store (CDS), include the seasonal forecasts, for which the dataset provided here can be useful for local skill assessment and post-processing. For users looking for shorter term forecasts there are also medium-range forecasts and reforecasts available, as well as historical simulations that can be used to derive the hydrological climatology. For users looking specifically for European hydrological data, we refer to the European Flood Awareness System (EFAS) forecasts and historical simulations. All these datasets are part of the operational flood forecasting within the Copernicus Emergency Management Service (CEMS).","links":[{"rel":"license","type":"application/pdf","href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/licences/cems-floods/cems-floods_428a6e1019ec50b3dad9c37a90d630fab139059933a939dd5df620bfcb420cc3.pdf","title":"CEMS-FLOODS datasets licence"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.24381/cds.0aa9b9dd","title":"Seasonal reforecasts of river discharge and related data from the Global Flood Awareness System"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/CEMS/Global+Flood+Awareness+System","title":"Information pages for GloFAS"},{"rel":"describedby","type":"text/html","href":"http://www.globalfloods.eu/","title":"GloFAS web site"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/CEMS/Auxiliary+Data","title":"Auxiliary data"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.CEMS_GLOFAS_SEASONAL_REFORECAST","title":"EO.ECMWF.DAT.CEMS_GLOFAS_SEASONAL_REFORECAST"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.CEMS_GLOFAS_SEASONAL_REFORECAST/items","title":"items"}],"assets":{"thumbnail":{"href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/resources/cems-glofas-seasonal-reforecast/overview_607aab0c8084d2b2ecdae18f6a6c7023219edd0cadf9f2d9eb44d8a6dcef7dda.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1981-01-01T00:00:00Z",null]]}},"license":"other","keywords":["Reforecast","Seasonal reforecasts","Global","Land (hydrology)","Copernicus CEMS","Past"],"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Emergency Management Service (CEMS)","roles":["host"],"url":"https://emergency.copernicus.eu/"}],"dedl:short_description":"The dataset contains globally gridded modelled river discharge time series based on seasonally forced ECMWF's SEAS5 reforecasts, spanning 1981-2016 with 25 ensemble members and 2017-onwards with 51 members."},{"type":"Collection","title":"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":"other","keywords":["Atmosphere (composition)","Global","Atmospheric conditions","Past","Satellite observations"],"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Climate Change Service (C3S)","roles":["host"],"url":"https://climate.copernicus.eu/"}],"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":"other","keywords":["Product type: Satellite observations","Spatial coverage: Global","Variable domain: Land (cryosphere)","Temporal coverage: Past","Product type: In-situ observations"],"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Climate Change Service (C3S)","roles":["host"],"url":"https://climate.copernicus.eu/"}],"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":"Climate Change Adaptation Digital Twin (Climate Adaptation DT) - Generation-1","id":"EO.ECMWF.DAT.DT_CLIMATE_ADAPTATION","description":"Note: This Generation 1 Collection has been superseded by Generation 2 Simulation-level Collections\n\nThe DestinE Digital Twin for Climate Change Adaptation (Climate DT) supports adaptation activities by providing innovative climate information on multi-decadal timescales, globally, at scales at which many impacts of climate change are observed. It combines cutting-edge global Earth-system models, impact-sector applications and observations into a unified framework to provide global climate projections and impact-sector information on multi-decadal timescales (1990 to ~2050), at very high spatial resolutions (5 to 10 km).\n\nThe Climate DT represents the first ever attempt to operationalise the production of global multi-decadal climate projections, leveraging the world-leading supercomputing facilities of the EuroHPC Joint Undertaking along with some of the leading European climate models. A concise overview of what the Climate DT aims to achieve, and of the different concepts essential for an understanding of the Digital Twin’s characteristics, is included in the [Climate DT factsheet](https://destine.ecmwf.int/wp-content/uploads/2024/06/2024.06.07_Climate-DT-Fact-Sheet_V7-2.pdf)\n\n## Models\n\nThe Climate DT exploits and further evolves a new generation of global storm-resolving and eddy-rich models built through a cooperative model development approach. For more information on models please click [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#models)\n\n## Simulations\n\nThe Climate DT team carries out several types of digital twin simulations on the EuroHPC supercomputers.  Multi-decadal simulations are produced to cover the recent past (from 1990) and possible future evolutions of the climate up to 2050. See [here](https://destine.ecmwf.int/climate-change-adaptation-digital-twin-climate-dt/#simulations) for more information on Simulations\n\n## Parameters\n\nBelow we see the list of parameters extracted from the 'DestinE Climate DT data portfolio', for more information please refer to the page [Climate DT Parameters](https://confluence.ecmwf.int/display/DDCZ/Climate+DT+Phase+1+data+catalogue)","links":[{"rel":"example","type":"application/x-ipynb+json","href":"https://raw.githubusercontent.com/destination-earth/DestinE-DataLake-Lab/refs/heads/main/HDA/DestinE%20Digital%20Twins/DEDL-HDA-EO.ECMWF.DAT.DT_CLIMATE.ipynb","title":"Destination Earth - Climate DT Parameter - Data Access using DEDL HDA"},{"rel":"example","type":"application/x-ipynb+json","href":"https://raw.githubusercontent.com/destination-earth/DestinE-DataLake-Lab/refs/heads/main/HDA/DestinE%20Digital%20Twins/ClimateDT-ExtractLocationValues.ipynb","title":"DT Tutorial: Is it going to rain in the next 3 weekends?"},{"rel":"example","type":"application/x-ipynb+json","href":"https://raw.githubusercontent.com/destination-earth/DestinE-DataLake-Lab/refs/heads/main/HDA/DestinE%20Digital%20Twins/ClimateDT-ParameterPlotter.ipynb","title":"Destination Earth - HDA Climate DT Parameter Plotter Tutorial"},{"rel":"example","type":"application/x-ipynb+json","href":"https://raw.githubusercontent.com/destination-earth/DestinE-DataLake-Lab/refs/heads/main/HDA/DestinE%20Digital%20Twins/DEDL-HDA-EO.ECMWF.DAT.DT_CLIMATE-Series.ipynb","title":"Destination Earth - Climate DT Parameter Series Plot- Data Access using DEDL HDA"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/DestinE+ClimateDT+Parameters","title":"DestinE ClimateDT Parameters"},{"rel":"cite-as","type":"text/html","href":"https://dl.acm.org/doi/abs/10.1109/MCSE.2023.3260519","title":"Destination Earth: High-Performance Computing for Weather and Climate"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.21957/d3f982672e","title":"Destination Earth Digital Twin for Climate Change Adaptation (DestinE Climate DT V1)"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.DT_CLIMATE_ADAPTATION","title":"EO.ECMWF.DAT.DT_CLIMATE_ADAPTATION"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.DT_CLIMATE_ADAPTATION/items","title":"items"}],"assets":{"thumbnail":{"href":"https://s3.central.data.destination-earth.eu/swift/v1/dedl-public/collections/climate-dt-min.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2020-07-01T00:00:00Z","2039-12-31T23:59:59Z"]]}},"license":"CC-BY-4.0","keywords":["Earth","Meteorology","Atmosphere","Climate Change","Decision Making","Europe","Soil","Digital Twins","High Performance Computing","Ocean","Land","Snow","Sea Ice"],"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/datacube/v2.1.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json","https://stac-extensions.github.io/application/v0.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["producer","processor","licensor"],"url":"https://www.ecmwf.int/"},{"name":"Destination Earth Data Lake (DEDL)","roles":["host"],"url":"https://data.destination-earth.eu/"}],"cube:dimensions":{"lat":{"axis":"y","description":"latitude","extent":[-90,90],"type":"spatial"},"lon":{"axis":"x","description":"longitude","extent":[-180,180],"type":"spatial"},"time":{"extent":["2020-07-01T00:00:00Z","2025-07-01T00:00:00Z"],"step":"P0Y0M0DT1H0M0S","type":"temporal"}},"cube:variables":{"100_metre_U_wind_component":{"attrs":{"long_name":"100 metre U wind component","parameter_ID":228246,"product_type":"forecast","shortName":"100u","standard_name":"100_metre_U_wind_component"},"description":"This parameter is the eastward component of the 100 m wind. It is the horizontal speed of air moving towards the east, at a height of 100 metres above the surface of the Earth, in metres per second.","dimensions":["lat","lon","time"],"type":"data","unit":"m s**-1"},"100_metre_V_wind_component":{"attrs":{"long_name":"100 metre V wind component","parameter_ID":228247,"product_type":"forecast","shortName":"100v","standard_name":"100_metre_V_wind_component"},"description":"This parameter is the northward component of the 100 m wind. It is the horizontal speed of air moving towards the east, at a height of 100 metres above the surface of the Earth, in metres per second.","dimensions":["lat","lon","time"],"type":"data","unit":"m s**-1"},"10_metre_U_wind_component":{"attrs":{"long_name":"10 metre U wind component","parameter_ID":165,"product_type":"forecast","shortName":"10u","standard_name":"eastward_wind"},"description":"Eastward component of the near-surface (usually, 10 meters) wind","dimensions":["lat","lon","time"],"type":"data","unit":"m s**-1"},"10_metre_V_wind_component":{"attrs":{"long_name":"10 metre V wind component","parameter_ID":166,"product_type":"forecast","shortName":"10v","standard_name":"northward_wind"},"description":"Northward component of the near surface wind","dimensions":["lat","lon","time"],"type":"data","unit":"m s**-1"},"2_metre_dewpoint_temperature":{"attrs":{"long_name":"2 metre dewpoint temperature","parameter_ID":168,"product_type":"forecast","shortName":"2d","standard_name":"dew_point_temperature"},"description":"This parameter is the temperature to which the air, at 2 metres above the surface of the Earth, would have to be cooled for saturation to occur. It is a measure of the humidity of the air. Combined with temperature and pressure, it can be used to calculate the relative humidity.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"2_metre_temperature":{"attrs":{"long_name":"2 metre temperature","parameter_ID":167,"product_type":"forecast","shortName":"2t","standard_name":"air_temperature"},"description":"near-surface (usually, 2 meter) air temperature","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Boundary_layer_height":{"attrs":{"long_name":"Boundary layer height","parameter_ID":159,"product_type":"forecast","shortName":"blh","standard_name":"Boundary_layer_height"},"description":"This parameter is the depth of air next to the Earth's surface which is most affected by the resistance to the transfer of momentum, heat or moisture across the surface. The boundary layer height can be as low as a few tens of metres, such as in cooling air at night, or as high as several kilometres over the desert in the middle of a hot sunny day. When the boundary layer height is low, higher concentrations of pollutants (emitted from the Earth's surface) can develop.","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Charnock":{"attrs":{"long_name":"Charnock","parameter_ID":148,"product_type":"forecast","shortName":"chnk","standard_name":"Charnock"},"description":"This parameter accounts for increased aerodynamic roughness as wave heights grow due to increasing surface stress. It depends on the wind speed, wave age and other aspects of the sea state and is used to calculate how much the waves slow down the wind.","dimensions":["lat","lon","time"],"type":"data","unit":"Numeric"},"Evaporation":{"attrs":{"long_name":"Evaporation","parameter_ID":182,"product_type":"forecast","shortName":"e","standard_name":"Evaporation"},"description":"This parameter is the accumulated amount of water that has evaporated from the Earth's surface, including a simplified representation of transpiration (from vegetation), into vapour in the air above. This parameter is accumulated over a particular time period which depends on the data extracted.","dimensions":["lat","lon","time"],"type":"data","unit":"m of water equivalent"},"Geopotential":{"attrs":{"long_name":"Geopotential","parameter_ID":129,"product_type":"forecast","shortName":"z","standard_name":"Geopotential"},"description":"This parameter is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level.","dimensions":["lat","lon","time"],"type":"data","unit":"m**2 s**-2"},"High_cloud_cover":{"attrs":{"long_name":"High cloud cover","parameter_ID":188,"product_type":"forecast","shortName":"hcc","standard_name":"High_cloud_cover"},"description":"The proportion of a grid box covered by cloud occurring in the high levels of the troposphere. High cloud is a single level field calculated from cloud occurring on model levels with a pressure less than 0.45 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), high cloud would be calculated using levels with a pressure of less than 450 hPa (approximately 6km and above ( assuming a `standard atmosphere`)).","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Land_sea_mask":{"attrs":{"long_name":"Land-sea mask","parameter_ID":172,"product_type":"forecast","shortName":"lsm","standard_name":"Land_sea_mask"},"description":"This parameter is the proportion of land, as opposed to ocean or inland waters (lakes, reservoirs, rivers and coastal waters), in a grid box. This parameter has values ranging between zero and one and is dimensionless.","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Low_cloud_cover":{"attrs":{"long_name":"Low cloud cover","parameter_ID":186,"product_type":"forecast","shortName":"lcc","standard_name":"Low_cloud_cover"},"description":"This parameter is the proportion of a grid box covered by cloud occurring in the lower levels of the troposphere. Low cloud is a single level field calculated from cloud occurring on model levels with a pressure greater than 0.8 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), low cloud would be calculated using levels with a pressure greater than 800 hPa (below approximately 2km (assuming a 'standard atmosphere')).","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Mean_sea_level_pressure":{"attrs":{"long_name":"Mean sea level pressure","parameter_ID":151,"product_type":"forecast","shortName":"msl","standard_name":"Mean_sea_level_pressure"},"description":"This parameter is the pressure (force per unit area) of the atmosphere adjusted to the height of mean sea level.","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Medium_cloud_cover":{"attrs":{"long_name":"Medium cloud cover","parameter_ID":187,"product_type":"forecast","shortName":"mcc","standard_name":"Medium_cloud_cover"},"description":"This parameter is the proportion of a grid box covered by cloud occurring in the middle levels of the troposphere. Medium cloud is a single level field calculated from cloud occurring on model levels with a pressure between 0.45 and 0.8 times the surface pressure. So, if the surface pressure is 1000 hPa (hectopascal), medium cloud would be calculated using levels with a pressure of less than or equal to 800 hPa and greater than or equal to 450 hPa (between approximately 2km and 6km (assuming a 'standard atmosphere')).","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Potential_vorticity":{"attrs":{"long_name":"Potential vorticity","parameter_ID":60,"product_type":"forecast","shortName":"pv","standard_name":"Potential_vorticity"},"description":"Potential vorticity is a measure of the capacity for air to rotate in the atmosphere. If we ignore the effects of heating and friction, potential vorticity is conserved following an air parcel. It is used to look for places where large wind storms are likely to originate and develop. Potential vorticity increases strongly above the tropopause and therefore, it can also be used in studies related to the stratosphere and stratosphere-troposphere exchanges.","dimensions":["lat","lon","time"],"type":"data","unit":"K m**2 kg**-1 s**-1"},"Relative_humidity":{"attrs":{"long_name":"Relative humidity","parameter_ID":157,"product_type":"forecast","shortName":"r","standard_name":"Relative_humidity"},"description":"This parameter is the water vapour pressure as a percentage of the value at which the air becomes saturated (the point at which water vapour begins to condense into liquid water or deposition into ice).","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Skin_temperature":{"attrs":{"long_name":"Skin temperature","parameter_ID":235,"product_type":"forecast","shortName":"skt","standard_name":"Skin_temperature"},"description":"This parameter is the temperature of the surface of the Earth. The skin temperature is the theoretical temperature that is required to satisfy the surface energy balance. It represents the temperature of the uppermost surface layer, which has no heat capacity and so can respond instantaneously to changes in surface fluxes. Skin temperature is calculated differently over land and sea.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Snow_depth":{"attrs":{"long_name":"Snow depth","parameter_ID":141,"product_type":"forecast","shortName":"sd","standard_name":"Snow_depth"},"description":"This parameter is the depth of snow from the snow-covered area of a grid box.","dimensions":["lat","lon","time"],"type":"data","unit":"m of water equivalent"},"Snow_depth_water_equivalent":{"attrs":{"long_name":"Snow depth water equivalent","parameter_ID":228141,"product_type":"forecast","shortName":"sd","standard_name":"Snow_depth_water_equivalent"},"description":"Snow depth water equivalent in kg m**-2 (mm) water equivalent. Please note that the encodings listed here for s2s \u0026 uerra (which includes carra/cerra) include entries for Time-mean snow depth water equivalent. The specific encoding for Time-mean snow depth water equivalent can be found in 235078.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m**-2"},"Snowfall":{"attrs":{"long_name":"Snowfall","parameter_ID":144,"product_type":"forecast","shortName":"sf","standard_name":"Snowfall"},"description":"This parameter is the accumulated snow that falls to the Earth's surface. It is the sum of large-scale snowfall and convective snowfall. Large-scale snowfall is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of the grid box or larger.","dimensions":["lat","lon","time"],"type":"data","unit":"m of water equivalent"},"Specific_cloud_liquid_water_content":{"attrs":{"long_name":"Specific cloud liquid water content","parameter_ID":246,"product_type":"forecast","shortName":"clwc","standard_name":"Specific_cloud_liquid_water_content"},"description":"This parameter is the mass of cloud liquid water droplets per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg**-1"},"Specific_humidity":{"attrs":{"long_name":"Specific humidity","parameter_ID":133,"product_type":"forecast","shortName":"q","standard_name":"Specific_humidity"},"description":"This parameter is the mass of water vapour per kilogram of moist air. The total mass of moist air is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg**-1"},"Sub_surface_runoff":{"attrs":{"long_name":"Sub-surface runoff","parameter_ID":9,"product_type":"forecast","shortName":"ssro","standard_name":"Sub_surface_runoff"},"description":"Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is simply called 'runoff'. This parameter is the total amount of water accumulated over a particular time period which depends on the data extracted.The units of runoff are depth in metres. This is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid square area. Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here.","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Surface_latent_heat_flux":{"attrs":{"long_name":"Surface latent heat flux","parameter_ID":147,"product_type":"forecast","shortName":"slhf","standard_name":"Surface_latent_heat_flux"},"description":"This parameter is the transfer of latent heat (resulting from water phase changes, such as evaporation or condensation) between the Earth's surface and the atmosphere through the effects of turbulent air motion. Evaporation from the Earth's surface represents a transfer of energy from the surface to the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"J m**-2"},"Surface_long_wave_(thermal)_radiation_downwards":{"attrs":{"long_name":"Surface short-wave (solar) radiation downwards","parameter_ID":175,"product_type":"forecast","shortName":"strd","standard_name":"Surface_long_wave_(thermal)_radiation_downwards"},"description":"This parameter is the amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere and clouds that reaches a horizontal plane at the surface of the Earth. The surface of the Earth emits thermal radiation, some of which is absorbed by the atmosphere and clouds. The atmosphere and clouds likewise emit thermal radiation in all directions, some of which reaches the surface (represented by this parameter).","dimensions":["lat","lon","time"],"type":"data","unit":"J m**-2"},"Surface_net_long_wave_(thermal)_radiation":{"attrs":{"long_name":"Surface net long-wave (thermal) radiation","parameter_ID":177,"product_type":"forecast","shortName":"str","standard_name":"Surface_net_long_wave_(thermal)_radiation"},"description":"Thermal radiation (also known as longwave or terrestrial radiation) refers to radiation emitted by the atmosphere, clouds and the surface of the Earth. This parameter is the difference between downward and upward thermal radiation at the surface of the Earth. It the amount passing through a horizontal plane. The atmosphere and clouds emit thermal radiation in all directions, some of which reaches the surface as downward thermal radiation. The upward thermal radiation at the surface consists of thermal radiation emitted by the surface plus the fraction of downwards thermal radiation reflected upward by the surface.","dimensions":["lat","lon","time"],"type":"data","unit":"J m**-2"},"Surface_net_short_wave_(solar)_radiation":{"attrs":{"long_name":"Surface net short-wave (solar) radiation","parameter_ID":176,"product_type":"forecast","shortName":"ssr","standard_name":"Surface_net_short_wave_(solar)_radiation"},"description":"This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo). Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The remainder is incident on the Earth's surface, where some of it is reflected.","dimensions":["lat","lon","time"],"type":"data","unit":"J m**-2"},"Surface_sensible_heat_flux":{"attrs":{"long_name":"Surface sensible heat flux","parameter_ID":146,"product_type":"forecast","shortName":"sshf","standard_name":"Surface_sensible_heat_flux"},"description":"This parameter is the transfer of heat between the Earth's surface and the atmosphere through the effects of turbulent air motion (but excluding any heat transfer resulting from condensation or evaporation). The magnitude of the sensible heat flux is governed by the difference in temperature between the surface and the overlying atmosphere, wind speed and the surface roughness. For example, cold air overlying a warm surface would produce a sensible heat flux from the land (or ocean) into the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"J m**-2"},"Surface_short_wave_(solar)_radiation_downwards":{"attrs":{"long_name":"Surface short-wave (solar) radiation downwards","parameter_ID":169,"product_type":"forecast","shortName":"ssrd","standard_name":"Surface_short_wave_(solar)_radiation_downwards"},"description":"This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth. This parameter comprises both direct and diffuse solar radiation. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface (represented by this parameter).","dimensions":["lat","lon","time"],"type":"data","unit":"J m**-2"},"TOA_incident_short_wave_(solar)_radiation":{"attrs":{"long_name":"TOA incident short-wave (solar) radiation","parameter_ID":212,"product_type":"forecast","shortName":"tisr","standard_name":"TOA_incident_short_wave_(solar)_radiation"},"description":"Accumulated field","dimensions":["lat","lon","time"],"type":"data","unit":"J m**-2"},"Temperature":{"attrs":{"long_name":"Temperature","parameter_ID":130,"product_type":"forecast","shortName":"t","standard_name":"Temperature"},"description":"This parameter is the temperature in the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time-integrated eastward turbulent surface stress due to surface roughness":{"attrs":{"long_name":"Time-integrated eastward turbulent surface stress due to surface roughness","parameter_ID":260654,"product_type":"forecast","shortName":"etsssr","standard_name":"Time-integrated eastward turbulent surface stress due to surface roughness"},"description":" ","dimensions":["lat","lon","time"],"type":"data","unit":"N m**-2 s"},"Time-integrated northward turbulent surface stress due to surface roughness":{"attrs":{"long_name":"Time-integrated northward turbulent surface stress due to surface roughness","parameter_ID":260655,"product_type":"forecast","shortName":"ntsssr","standard_name":"Time-integrated northward turbulent surface stress due to surface roughness"},"description":" ","dimensions":["lat","lon","time"],"type":"data","unit":"N m**-2 s"},"Time-mean_snow_thickness_over_sea_ice":{"attrs":{"long_name":"Time-mean snow thickness over sea ice","parameter_ID":263002,"product_type":"forecast","shortName":"avg_sisnthick","standard_name":"Time-mean_snow_thickness_over_sea_ice"},"description":"","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Time_integrated_eastward_turbulent_surface_stress":{"attrs":{"long_name":"Time-integrated eastward turbulent surface stress","parameter_ID":180,"product_type":"forecast","shortName":"ewss","standard_name":"Time_integrated_eastward_turbulent_surface_stress"},"description":"Air flowing over a surface exerts a stress that transfers momentum to the surface and slows the wind. This parameter is the accumulated stress on the Earth's surface in the eastward direction due to both the turbulent interactions between the atmosphere and the surface, and to turbulent orographic form drag. The turbulent interactions between the atmosphere and the surface are due to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km being derived from land surface data at about 1 km resolution.","dimensions":["lat","lon","time"],"type":"data","unit":"N m**-2 s"},"Time_integrated_northward_turbulent_surface_stress":{"attrs":{"long_name":"Time_integrated_northward_turbulent_surface_stress","parameter_ID":181,"product_type":"forecast","shortName":"nsss","standard_name":"Time_integrated_eastward_turbulent_surface_stress"},"description":"Air flowing over a surface exerts a stress that transfers momentum to the surface and slows the wind. This parameter is the accumulated stress on the Earth's surface in the northward direction due to both the turbulent interactions between the atmosphere and the surface, and to turbulent orographic form drag. The turbulent interactions between the atmosphere and the surface are due to the roughness of the surface.","dimensions":["lat","lon","time"],"type":"data","unit":"N m**-2 s"},"Time_mean_X_component_of_sea_ice_velocity":{"attrs":{"long_name":"Time_mean_X_component_of_sea_ice_velocity","parameter_ID":263021,"product_type":"forecast","shortName":"avg_six","standard_name":"Time_mean_X_component_of_sea_ice_velocity"},"description":" ","dimensions":["lat","lon","time"],"type":"data","unit":"m s**-1"},"Time_mean_Y_component_of_sea_ice_velocity":{"attrs":{"long_name":"Time_mean_Y_component_of_sea_ice_velocity","parameter_ID":263022,"product_type":"forecast","shortName":"avg_siy","standard_name":"Time_mean_Y_component_of_sea_ice_velocity"},"description":" ","dimensions":["lat","lon","time"],"type":"data","unit":"m s**-1"},"Time_mean_eastward_sea_ice_velocity":{"attrs":{"long_name":"Time-mean eastward sea ice velocity","parameter_ID":263003,"product_type":"forecast","shortName":"avg_siue","standard_name":"Time_mean_eastward_sea_ice_velocity"},"description":" ","dimensions":["lat","lon","time"],"type":"data","unit":"m s**-1"},"Time_mean_eastward_sea_water_velocity":{"attrs":{"long_name":"Time-mean eastward sea water velocity","parameter_ID":263506,"product_type":"forecast","shortName":"avg_uoe","standard_name":"Time_mean_eastward_sea_water_velocity"},"description":" ","dimensions":["lat","lon","time"],"type":"data","unit":"m s**-1"},"Time_mean_northward_sea_ice_velocity":{"attrs":{"long_name":"Time-mean northward sea ice velocity","parameter_ID":263004,"product_type":"forecast","shortName":"avg_sivn","standard_name":"Time_mean_northward_sea_ice_velocity"},"description":" ","dimensions":["lat","lon","time"],"type":"data","unit":"m s**-1"},"Time_mean_northward_sea_water_velocity":{"attrs":{"long_name":"Time-mean northward sea water velocity","parameter_ID":263505,"product_type":"forecast","shortName":"avg_von","standard_name":"Time_mean_northward_sea_water_velocity"},"description":" ","dimensions":["lat","lon","time"],"type":"data","unit":"m s**-1"},"Time_mean_ocean_mixed_layer_depth_defined_by_sigma_theta_0.03_kg_m-3":{"attrs":{"long_name":"Time-mean ocean mixed layer depth defined by sigma theta 0.03 kg m-3","parameter_ID":263114,"product_type":"forecast","shortName":"avg_mlotst030","standard_name":"Time_mean_ocean_mixed_layer_depth_defined_by_sigma_theta_0.03_kg_m-3"},"description":" ","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Time_mean_sea_ice_area_fraction":{"attrs":{"long_name":"Time-mean sea ice area fraction","parameter_ID":263001,"product_type":"forecast","shortName":"avg_siconc","standard_name":"Time_mean_sea_ice_area_fraction"},"description":" ","dimensions":["lat","lon","time"],"type":"data","unit":"Fraction"},"Time_mean_sea_ice_thickness":{"attrs":{"long_name":"Time-mean sea ice thickness","parameter_ID":263000,"product_type":"forecast","shortName":"avg_sithick","standard_name":"Time_mean_sea_ice_thickness"},"description":" ","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Time_mean_sea_ice_volume_per_unit_area":{"attrs":{"long_name":"Time-mean sea ice volume per unit area","parameter_ID":263008,"product_type":"forecast","shortName":"avg_sivol","standard_name":"Time_mean_sea_ice_volume_per_unit_area"},"description":" ","dimensions":["lat","lon","time"],"type":"data","unit":"m**3 m**-2"},"Time_mean_sea_surface_height":{"attrs":{"long_name":"Time-mean sea surface height","parameter_ID":263124,"product_type":"forecast","shortName":"avg_zos","standard_name":"Time_mean_sea_surface_height"},"description":" ","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Time_mean_sea_surface_practical_salinity":{"attrs":{"long_name":"Time-mean sea surface practical salinity","parameter_ID":263100,"product_type":"forecast","shortName":"avg_sos","standard_name":"Time_mean_sea_surface_practical_salinity"},"description":" ","dimensions":["lat","lon","time"],"type":"data","unit":"g kg**-1"},"Time_mean_sea_surface_temperature":{"attrs":{"long_name":"Time_mean_sea_surface_temperature","parameter_ID":263101,"product_type":"forecast","shortName":"avg_tos","standard_name":"Time_mean_sea_surface_temperature"},"description":" ","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time_mean_sea_water_potential_temperature":{"attrs":{"long_name":"Time-mean sea water potential temperature","parameter_ID":263501,"product_type":"forecast","shortName":"avg_thetao","standard_name":"Time_mean_sea_water_potential_temperature"},"description":" ","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time_mean_sea_water_practical_salinity":{"attrs":{"long_name":"Time-mean sea water practical salinity","parameter_ID":263500,"product_type":"forecast","shortName":"avg_so","standard_name":"Time_mean_sea_water_practical_salinity"},"description":" ","dimensions":["lat","lon","time"],"type":"data","unit":"g kg**-1"},"Time_mean_snow_volume_over_sea_ice_per_unit_area":{"attrs":{"long_name":"Time-mean snow volume over sea ice per unit area","parameter_ID":263009,"product_type":"forecast","shortName":"avg_snvol","standard_name":"Time_mean_snow_volume_over_sea_ice_per_unit_area"},"description":" ","dimensions":["lat","lon","time"],"type":"data","unit":"m**3 m**-2"},"Time_mean_upward_sea_water_velocity":{"attrs":{"long_name":"Time-mean upward sea water velocity","parameter_ID":263507,"product_type":"forecast","shortName":"avg_wo","standard_name":"Time_mean_upward_sea_water_velocity"},"description":" ","dimensions":["lat","lon","time"],"type":"data","unit":"m s**-1"},"Time_mean_vertically_integrated_heat_content_in_the_upper_300_m":{"attrs":{"long_name":"Time-mean vertically-integrated heat content in the upper 300 m","parameter_ID":263121,"product_type":"forecast","shortName":"avg_hc300m","standard_name":"Time_mean_vertically_integrated_heat_content_in_the_upper_300_m"},"description":" ","dimensions":["lat","lon","time"],"type":"data","unit":"J m**-2"},"Time_mean_vertically_integrated_heat_content_in_the_upper_700_m":{"attrs":{"long_name":"Time-mean vertically-integrated heat content in the upper 700 m","parameter_ID":263122,"product_type":"forecast","shortName":"avg_hc700m","standard_name":"Time_mean_vertically_integrated_heat_content_in_the_upper_700_m"},"description":" ","dimensions":["lat","lon","time"],"type":"data","unit":"J m**-2"},"Top_net_long_wave_(thermal)_radiation":{"attrs":{"long_name":"Top net long-wave (thermal) radiation","parameter_ID":179,"product_type":"forecast","shortName":"ttr","standard_name":"Top_net_long_wave_(thermal)_radiation"},"description":"The thermal (also known as terrestrial or longwave) radiation emitted to space at the top of the atmosphere is commonly known as the Outgoing Longwave Radiation (OLR). The top net thermal radiation (this parameter) is equal to the negative of OLR.","dimensions":["lat","lon","time"],"type":"data","unit":"J m**-2"},"Top_net_short_wave_(solar)_radiation":{"attrs":{"long_name":"Top net short-wave (solar) radiation","parameter_ID":178,"product_type":"forecast","shortName":"tsr","standard_name":"Top_net_short_wave_(solar)_radiation"},"description":"This parameter is the incoming solar radiation (also known as shortwave radiation) minus the outgoing solar radiation at the top of the atmosphere. It is the amount of radiation passing through a horizontal plane. The incoming solar radiation is the amount received from the Sun. The outgoing solar radiation is the amount reflected and scattered by the Earth's atmosphere and surface.","dimensions":["lat","lon","time"],"type":"data","unit":"J m**-2"},"Total_cloud_cover":{"attrs":{"long_name":"Total cloud cover","parameter_ID":164,"product_type":"forecast","shortName":"tcc","standard_name":"Total_cloud_cover"},"description":"This parameter is the proportion of a grid box covered by cloud. Total cloud cover is a single level field calculated from the cloud occurring at different model levels through the atmosphere. Assumptions are made about the degree of overlap/randomness between clouds at different heights.","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Total_cloud_cover_":{"attrs":{"long_name":"Total cloud cover","parameter_ID":228164,"product_type":"forecast","shortName":"tcc_","standard_name":"Total_cloud_cover_"},"description":"[NOTE: See 164 for the equivalent parameter in 0-1]","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Total_column_cloud_ice_water":{"attrs":{"long_name":"Total column cloud ice water","parameter_ID":79,"product_type":"forecast","shortName":"tciw","standard_name":"Total_column_cloud_ice_water"},"description":"the amount of ice contained within clouds in a column extending from the surface of the Earth to the top of the atmosphere. Snow (aggregated ice crystals) is not included in this parameter.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m**-2"},"Total_column_cloud_liquid_water":{"attrs":{"long_name":"Total column cloud liquid water","parameter_ID":78,"product_type":"forecast","shortName":"tclw","standard_name":"Total_column_cloud_liquid_water"},"description":"the amount of liquid water contained within clouds in a column extending from the surface of the Earth to the top of the atmosphere. Snow (aggregated ice crystals) is not included in this parameter.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m**-2"},"Total_column_vertically_integrated_water_vapour":{"attrs":{"long_name":"Total column vertically-integrated water vapour","parameter_ID":137,"product_type":"forecast","shortName":"tcwv","standard_name":"Total_column_vertically_integrated_water_vapour"},"description":"This parameter is the total amount of water vapour in a column extending from the surface of the Earth to the top of the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m**-2"},"Total_precipitation_rate":{"attrs":{"long_name":"Total precipitation rate","parameter_ID":260048,"product_type":"forecast","shortName":"tprate","standard_name":"Total_precipitation_rate"},"description":"This parameter is the rate of total precipitation, at the specified time. In the ECMWF Integrated Forecasting System (IFS), total precipitation is rain and snow that falls to the Earth's surface. It is the sum of large-scale precipitation and convective precipitation. Large-scale precipitation is generated by the cloud scheme in the IFS. The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of a grid box or larger. Convective precipitation is generated by the convection scheme in the IFS. The convection scheme represents convection at spatial scales smaller than the grid box.See further information. Precipitation parameters do not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m**-2 s**-1"},"U_component_of_wind":{"attrs":{"long_name":"U component of wind","parameter_ID":131,"product_type":"forecast","shortName":"u","standard_name":"U_component_of_wind"},"description":"This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east, in metres per second. A negative sign thus indicates air movement towards the west.","dimensions":["lat","lon","time"],"type":"data","unit":"m s**-1"},"V_component_of_wind":{"attrs":{"long_name":"V component of wind","parameter_ID":132,"product_type":"forecast","shortName":"v","standard_name":"V_component_of_wind"},"description":"This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the east, in metres per second. A negative sign thus indicates air movement towards the west.","dimensions":["lat","lon","time"],"type":"data","unit":"m s**-1"},"Vertical_velocity":{"attrs":{"long_name":"Vertical velocity","parameter_ID":135,"product_type":"forecast","shortName":"w","standard_name":"Vertical_velocity"},"description":"This parameter is the speed of air motion in the upward or downward direction. The ECMWF Integrated Forecasting System (IFS) uses a pressure based vertical co-ordinate system and pressure decreases with height, therefore negative values of vertical velocity indicate upward motion.","dimensions":["lat","lon","time"],"type":"data","unit":"Pa s**-1"},"surface_pressure":{"attrs":{"long_name":"Surface pressure","parameter_ID":134,"product_type":"forecast","shortName":"sp","standard_name":"surface_air_pressure"},"description":"Surface pressure (not mean sea-level pressure), 2-D field to calculate the 3-D pressure field from hybrid coordinates","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"surface_runoff":{"attrs":{"long_name":"Surface runoff","parameter_ID":8,"product_type":"forecast","shortName":"sro","standard_name":"surface_runoff_amount"},"description":"Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. Otherwise, the water drains away, either over the surface (surface runoff), or under the ground (sub-surface runoff) and the sum of these two is simply called 'runoff'. This parameter is the total amount of water accumulated over a particular time period which depends on the data extracted.The units of runoff are depth in metres. This is the depth the water would have if it were spread evenly over the grid box. Care should be taken when comparing model parameters with observations, because observations are often local to a particular point rather than averaged over a grid square area. Observations are also often taken in different units, such as mm/day, rather than the accumulated metres produced here.","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"total_precipitation":{"attrs":{"long_name":"Total precipitation","parameter_ID":228,"product_type":"forecast","shortName":"tp","standard_name":"lwe_thickness_of_precipitation_amount"},"description":"The construction lwe_thickness_of_X_amount or _content means the vertical extent of a layer of liquid water having the same mass per unit area. 'Precipitation' in the Earth's atmosphere means precipitation of water in all phases. The abbreviation 'lwe' means liquid water equivalent.","dimensions":["lat","lon","time"],"type":"data","unit":"m"}},"dedl:short_description":"The Climate Change Adaptation Digital Twin provides global climate projections and sector-specific information over multiple decades at high resolution via a unified framework combining advanced Earth system models, impact assessments, and observations. This Collection exposes Generation-1 simulations."},{"type":"Collection","title":"Weather-Induced Extremes Digital Twin (Extremes DT)","id":"EO.ECMWF.DAT.DT_EXTREMES","description":"The DestinE Digital Twin for Weather-Induced Extremes (Extremes DT) supports responding and adapting to extreme events in a changing world by providing a capability to produce tailored simulations and address what-if scenarios related to extreme events in a past, present and future climate, complementing existing capabilities at national and European level.\n\nThe Extremes DT combines cutting-edge Earth-system models, impact-sector models and observations. It uses a global and a regional component to provide information on extreme events on a timescale of a few days ahead, at very high spatial resolution (4.4 km globally, 500 – 750 m over Europe).\n\nThe Extremes DT sets up a unified, flexible, framework to simulate extreme weather events and their associated impacts at km-scale resolutions, using the world-leading supercomputing facilities of the EuroHPC Joint Undertaking. A concise overview of what the Extremes DT aims to achieve, and the different concepts essential for an understanding of the Digital Twin’s characteristics is included in the [Extremes DT factsheet](https://destine.ecmwf.int/wp-content/uploads/2024/06/Extreme-Events-DT_General.pdf)\n\n## Models\n\nThe global component of the Extremes DT builds on ECMWF’s Integrated Forecasting System. It is based on the configuration used operationally at ECMWF, by further increasing the resolution of several components of the Earth System Model (atmosphere, land and waves) to a resolution of 4.4 km. The ocean component remains unchanged and is NEMO on the ORCA025 grid. For more information on models please click [here](https://destine.ecmwf.int/weather-induced-extremes-digital-twin-1/#models-extremes)\n\n## Simulations\n\nThe global Extremes DT simulations are carried out by ECMWF on EuroHPC supercomputers.\n\nThe global component of the Extremes DT provides four-day global simulations with a resolution of 4.4 km, initialized daily from the 00UTC ECMWF operational analysis at a resolution of 9 km (depending on EuroHPC availability, and queuing times). See [here](https://destine.ecmwf.int/weather-induced-extremes-digital-twin-1/#simulations-extremes) for more information on Simulations\n\n## Parameters\n\nBelow we see the list of parameters extracted from the 'DestinE Extremes DT data portfolio', for more information please refer to the page [Extremes DT Parameters](https://confluence.ecmwf.int/display/DDCZ/Extremes+DT+data+catalogue)","links":[{"rel":"example","type":"application/x-ipynb+json","href":"https://raw.githubusercontent.com/destination-earth/DestinE-DataLake-Lab/refs/heads/main/HDA/DestinE%20Digital%20Twins/DEDL-HDA-EO.ECMWF.DAT.DT_EXTREMES-Series.ipynb","title":"Destination Earth - Weather-Induced Extremes Digital Twin Series - Data Access using DEDL HDA"},{"rel":"example","type":"application/x-ipynb+json","href":"https://raw.githubusercontent.com/destination-earth/DestinE-DataLake-Lab/refs/heads/main/HDA/DestinE%20Digital%20Twins/DEDL-HDA-EO.ECMWF.DAT.DT_EXTREMES.ipynb","title":"Destination Earth - Weather-Induced Extremes Digital Twin - Data Access using DEDL HDA"},{"rel":"example","type":"application/x-ipynb+json","href":"https://raw.githubusercontent.com/destination-earth/DestinE-DataLake-Lab/refs/heads/main/HDA/DestinE%20Digital%20Twins/ExtremeDT-ParameterPlotter.ipynb","title":"Destination Earth - Weather-Induced Extremes DT Parameter Plotter Tutorial"},{"rel":"example","type":"application/x-ipynb+json","href":"https://raw.githubusercontent.com/destination-earth/DestinE-DataLake-Lab/refs/heads/main/HDA/DestinE%20Digital%20Twins/ExtremeDT-dataAvailability.ipynb","title":"Destination Earth - Aviso notification for DT data availability"},{"rel":"describedby","type":"text/html","href":"https://confluence.ecmwf.int/display/DDCZ/DestinE+ExtremesDT+Parameters","title":"DestinE ExtremesDT Parameters"},{"rel":"cite-as","type":"text/html","href":"https://dl.acm.org/doi/abs/10.1109/MCSE.2023.3260519","title":"Destination Earth: High-Performance Computing for Weather and Climate"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.DT_EXTREMES","title":"EO.ECMWF.DAT.DT_EXTREMES"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.DT_EXTREMES/items","title":"items"}],"assets":{"thumbnail":{"href":"https://s3.central.data.destination-earth.eu/swift/v1/dedl-public/collections/extremes-dt-min.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2026-06-17T00:00:00Z","2026-07-12T00:00:00Z"]]}},"license":"CC-BY-4.0","keywords":["Earth","Meteorology","Atmosphere","Decision Making","Europe","Soil","Digital Twins","Weather","Weather Forecasting","High Performance Computing","Ocean","Land","Snow","Sea Ice"],"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/datacube/v2.1.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json","https://stac-extensions.github.io/application/v0.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["producer","processor","licensor"],"url":"https://www.ecmwf.int/"},{"name":"Destination Earth Data Lake (DEDL)","roles":["host"],"url":"https://data.destination-earth.eu/"}],"cube:dimensions":{"lat":{"axis":"y","description":"latitude","extent":[-90,90],"type":"spatial"},"lon":{"axis":"x","description":"longitude","extent":[-180,180],"type":"spatial"},"time":{"extent":[null,null],"step":"P0Y0M0DT1H0M0S","type":"temporal"}},"cube:variables":{"10_metre_U_component_of_wind":{"attrs":{"long_name":"10 metre U wind component","parameter_ID":165,"product_type":"forecast","shortName":"10u","standard_name":"eastward_wind"},"description":"Eastward component of the near-surface (usually, 10 meters) wind","dimensions":["lat","lon","time"],"type":"data","unit":"m s**-1"},"10_metre_V_component_of_wind":{"attrs":{"long_name":"10 metre V wind component","parameter_ID":166,"product_type":"forecast","shortName":"10v","standard_name":"northward_wind"},"description":"Northward component of the near surface wind","dimensions":["lat","lon","time"],"type":"data","unit":"m s**-1"},"2_metre_dewpoint_temperature":{"attrs":{"long_name":"2 metre dewpoint temperature","parameter_ID":168,"product_type":"forecast","shortName":"2d","standard_name":"dew_point_temperature"},"description":"","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"2_metre_temperature":{"attrs":{"long_name":"2 metre temperature","parameter_ID":167,"product_type":"forecast","shortName":"2t","standard_name":"air_temperature"},"description":"near-surface (usually, 2 meter) air temperature","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Accumulated_freezing_rain":{"attrs":{"long_name":"Accumulated freezing rain","parameter_ID":228216,"product_type":"forecast","shortName":"fzra","standard_name":"Accumulated_freezing_rain"},"description":"This parameter is the total amount of precipitation falling as freezing rain, accumulated over a particular time period which depends on the data extracted.","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Averaged_total_lightning_flash_density_in_the_last_6_hours":{"attrs":{"long_name":"Averaged total lightning flash density in the last 6 hours","parameter_ID":228058,"product_type":"forecast","shortName":"litota6","standard_name":"Averaged_total_lightning_flash_density_in_the_last_6_hours"},"description":"This parameter gives the total lightning flash rate averaged over the last 6 hours. Note that this parameter has units of flashes per square kilometre per day. Conversion of this parameter to units of flashes per 100 square kilometres per hour can give values that are easier to interpret.","dimensions":["lat","lon","time"],"type":"data","unit":"km**-2 day**-1"},"Convective_rain_rate":{"attrs":{"long_name":"Convective rain rate","parameter_ID":228218,"product_type":"forecast","shortName":"crr","standard_name":"Convective_rain_rate"},"description":"This parameter is the rate of rainfall (rainfall intensity), at the specified time ","dimensions":["lat","lon","time"],"type":"data","unit":"kg m**-2 s**-1"},"Divergence":{"attrs":{"long_name":"Divergence","parameter_ID":155,"product_type":"forecast","shortName":"d","standard_name":"Divergence"},"description":"This parameter is the horizontal divergence of velocity. It is the rate at which air is spreading out horizontally from a point, per square metre. This parameter is positive for air that is spreading out, or diverging, and negative for the opposite, for air that is concentrating, or converging (convergence).","dimensions":["lat","lon","time"],"type":"data","unit":"s**-1"},"Geopotential":{"attrs":{"long_name":"Geopotential","parameter_ID":129,"product_type":"forecast","shortName":"z","standard_name":"Geopotential"},"description":"This parameter is the gravitational potential energy of a unit mass, at a particular location, relative to mean sea level. It is also the amount of work that would have to be done, against the force of gravity, to lift a unit mass to that location from mean sea level.","dimensions":["lat","lon","time"],"type":"data","unit":"m**2 s**-2"},"Instantaneous_10_metre_wind_gust":{"attrs":{"long_name":"Instantaneous 10 metre wind gust","parameter_ID":228029,"product_type":"forecast","shortName":"i10fg","standard_name":"Instantaneous_10_metre_wind_gust"},"description":"This parameter is the maximum wind gust at the specified time, at a height of ten metres above the surface of the Earth.","dimensions":["lat","lon","time"],"type":"data","unit":"m s**-1"},"Instantaneous_total_lightning_flash_density":{"attrs":{"long_name":"Instantaneous total lightning flash density","parameter_ID":228050,"product_type":"forecast","shortName":"litoti","standard_name":"Instantaneous_total_lightning_flash_density"},"description":"This parameter gives the total lightning flash rate at the specified time.","dimensions":["lat","lon","time"],"type":"data","unit":"km**-2 day**-1"},"Large_scale_precipitation":{"attrs":{"long_name":"Large-scale precipitation","parameter_ID":142,"product_type":"forecast","shortName":"lsp","standard_name":"Large_scale_precipitation"},"description":"This parameter is the accumulated liquid and frozen water, comprising rain and snow, that falls to the Earth's surface and which is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of the grid box or larger.","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Large_scale_rain_rate":{"attrs":{"long_name":"Large scale rain rate","parameter_ID":228219,"product_type":"forecast","shortName":"lsrr","standard_name":"Large_scale_rain_rate"},"description":"This parameter is the rate of rainfall (rainfall intensity), at the specified time, generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of a grid box or larger.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m**-2 s**-1"},"Large_scale_snowfall_rate_water_equivalent":{"attrs":{"long_name":"Large scale snowfall rate water equivalent","parameter_ID":228221,"product_type":"forecast","shortName":"lssfr","standard_name":"Large_scale_snowfall_rate_water_equivalent"},"description":"This parameter is the rate of snowfall (snowfall intensity), at the specified time, generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale snowfall due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of a grid box or larger.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m**-2 s**-1"},"Mean_sea_level_pressure":{"attrs":{"long_name":"Mean sea level pressure","parameter_ID":151,"product_type":"forecast","shortName":"msl","standard_name":"Mean_sea_level_pressure"},"description":"This parameter is the pressure (force per unit area) of the atmosphere adjusted to the height of mean sea level.","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"Mean_wave_direction":{"attrs":{"long_name":"Mean wave direction","parameter_ID":140230,"product_type":"forecast","shortName":"mwd","standard_name":"Mean_wave_direction"},"description":"This parameter is the mean direction of ocean/sea surface waves. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is a mean over all frequencies and directions of the two-dimensional wave spectrum.","dimensions":["lat","lon","time"],"type":"data","unit":"degrees"},"Mean_wave_period":{"attrs":{"long_name":"Mean wave period","parameter_ID":140232,"product_type":"forecast","shortName":"mwp","standard_name":"Mean_wave_period"},"description":"This parameter is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point. The ocean/sea surface wave field consists of a combination of waves with different heights, lengths and directions (known as the two-dimensional wave spectrum). This parameter is a mean over all frequencies and directions of the two-dimensional wave spectrum.","dimensions":["lat","lon","time"],"type":"data","unit":"s"},"Mean_zero_crossing_wave_period":{"attrs":{"long_name":"Mean zero-crossing wave period","parameter_ID":140221,"product_type":"forecast","shortName":"mp2","standard_name":"Mean_zero_crossing_wave_period"},"description":"This parameter represents the mean length of time between occasions where the sea/ocean surface crosses mean sea level. In combination with wave height information, it could be used to assess the length of time that a coastal structure might be under water, for example.","dimensions":["lat","lon","time"],"type":"data","unit":"s"},"Most_unstable_CAPE":{"attrs":{"long_name":"Most-unstable CAPE","parameter_ID":228235,"product_type":"forecast","shortName":"mucape","standard_name":"Most_unstable_CAPE"},"description":"Convective Available Potential Energy (CAPE) is a measure of the amount of energy available for convection. It is related to the maximum potential vertical velocity in the updraught.","dimensions":["lat","lon","time"],"type":"data","unit":"J kg**-1"},"Peak_wave_period":{"attrs":{"long_name":"Peak wave period","parameter_ID":140231,"product_type":"forecast","shortName":"pp1d","standard_name":"Peak_wave_period"},"description":"This parameter represents the period of the most energetic ocean waves generated by local winds and associated with swell. The wave period is the average time it takes for two consecutive wave crests, on the surface of the ocean/sea, to pass through a fixed point.","dimensions":["lat","lon","time"],"type":"data","unit":"s"},"Precipitation_type":{"attrs":{"long_name":"Precipitation type","parameter_ID":260015,"product_type":"forecast","shortName":"ptype","standard_name":"Precipitation_type"},"description":"This parameter describes the type of precipitation at the surface, at the specified time. Values of precipitation type defined in the IFS: 0 = No precipitation 1 = Rain 3 = Freezing rain (i.e. supercooled raindrops which freeze on contact with the ground and other surfaces) 5 = Snow 6 = Wet snow (i.e. snow particles which are starting to melt) 7 = Mixture of rain and snow 8 = Ice pellets 12 = Freezing drizzle (i.e. supercooled drizzle which freezes on contact with the ground and other surfaces). These precipitation types are consistent with WMO Code Table 4.201. 2 (thunderstorm), 4 (mixed ice) and 9 (graupel), 10 (hail) and 11 (drizzle) are not diagnosed in the IFS.","dimensions":["lat","lon","time"],"type":"data","unit":"code table (4.201)"},"Relative_humidity":{"attrs":{"long_name":"Relative humidity","parameter_ID":157,"product_type":"forecast","shortName":"r","standard_name":"Relative_humidity"},"description":"This parameter is the water vapour pressure as a percentage of the value at which the air becomes saturated (the point at which water vapour begins to condense into liquid water or deposition into ice).","dimensions":["lat","lon","time"],"type":"data","unit":"%"},"Sea_ice_area_fraction":{"attrs":{"long_name":"Sea ice area fraction","parameter_ID":31,"product_type":"forecast","shortName":"ci","standard_name":"Sea_ice_area_fraction"},"description":"This parameter is the fraction of a grid box which is covered by sea ice. Sea ice can only occur in a grid box which includes ocean or inland water according to the land sea mask and lake cover, at the resolution being used. This parameter can be known as sea-ice (area) fraction, sea-ice concentration and more generally as sea-ice cover.","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Sea_surface_temperature":{"attrs":{"long_name":"Sea surface temperature","parameter_ID":34,"product_type":"forecast","shortName":"sst","standard_name":"Sea_surface_temperature"},"description":"This parameter is the temperature of sea water near the surface.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Significant_height_of_combined_wind_waves_and_swell":{"attrs":{"long_name":"Significant height of combined wind waves and swell","parameter_ID":140229,"product_type":"forecast","shortName":"swh","standard_name":"Significant_height_of_combined_wind_waves_and_swell"},"description":"This parameter represents the average height of the highest third of surface ocean/sea waves generated by wind and swell. It represents the vertical distance between the wave crest and the wave trough.","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Snowfall":{"attrs":{"long_name":"Snowfall","parameter_ID":144,"product_type":"forecast","shortName":"sf","standard_name":"Snowfall"},"description":"This parameter is the accumulated snow that falls to the Earth's surface. It is the sum of large-scale snowfall and convective snowfall. Large-scale snowfall is generated by the cloud scheme in the ECMWF Integrated Forecasting System (IFS). The cloud scheme represents the formation and dissipation of clouds and large-scale precipitation due to changes in atmospheric quantities (such as pressure, temperature and moisture) predicted directly by the IFS at spatial scales of the grid box or larger.","dimensions":["lat","lon","time"],"type":"data","unit":"m of water equivalent"},"Specific_humidity":{"attrs":{"long_name":"Specific humidity","parameter_ID":133,"product_type":"forecast","shortName":"q","standard_name":"Specific_humidity"},"description":"This parameter is the mass of water vapour per kilogram of moist air. The total mass of moist air is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow.","dimensions":["lat","lon","time"],"type":"data","unit":"kg kg**-1"},"Surface_long_wave_(thermal)_radiation_downwards":{"attrs":{"long_name":"Surface short-wave (solar) radiation downwards","parameter_ID":175,"product_type":"forecast","shortName":"strd","standard_name":"Surface_long_wave_(thermal)_radiation_downwards"},"description":"This parameter is the amount of thermal (also known as longwave or terrestrial) radiation emitted by the atmosphere and clouds that reaches a horizontal plane at the surface of the Earth. The surface of the Earth emits thermal radiation, some of which is absorbed by the atmosphere and clouds. The atmosphere and clouds likewise emit thermal radiation in all directions, some of which reaches the surface (represented by this parameter).","dimensions":["lat","lon","time"],"type":"data","unit":"J m**-2"},"Surface_net_long_wave_(thermal)_radiation":{"attrs":{"long_name":"Surface net long-wave (thermal) radiation","parameter_ID":177,"product_type":"forecast","shortName":"str","standard_name":"Surface_net_long_wave_(thermal)_radiation"},"description":"Thermal radiation (also known as longwave or terrestrial radiation) refers to radiation emitted by the atmosphere, clouds and the surface of the Earth. This parameter is the difference between downward and upward thermal radiation at the surface of the Earth. It the amount passing through a horizontal plane. The atmosphere and clouds emit thermal radiation in all directions, some of which reaches the surface as downward thermal radiation. The upward thermal radiation at the surface consists of thermal radiation emitted by the surface plus the fraction of downwards thermal radiation reflected upward by the surface.","dimensions":["lat","lon","time"],"type":"data","unit":"J m**-2"},"Surface_net_short_wave_(solar)_radiation":{"attrs":{"long_name":"Surface net short-wave (solar) radiation","parameter_ID":176,"product_type":"forecast","shortName":"ssr","standard_name":"Surface_net_short_wave_(solar)_radiation"},"description":"This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth (both direct and diffuse) minus the amount reflected by the Earth's surface (which is governed by the albedo). Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The remainder is incident on the Earth's surface, where some of it is reflected.","dimensions":["lat","lon","time"],"type":"data","unit":"J m**-2"},"Surface_short_wave_(solar)_radiation_downwards":{"attrs":{"long_name":"Surface short-wave (solar) radiation downwards","parameter_ID":169,"product_type":"forecast","shortName":"ssrd","standard_name":"Surface_short_wave_(solar)_radiation_downwards"},"description":"This parameter is the amount of solar radiation (also known as shortwave radiation) that reaches a horizontal plane at the surface of the Earth. This parameter comprises both direct and diffuse solar radiation. Radiation from the Sun (solar, or shortwave, radiation) is partly reflected back to space by clouds and particles in the atmosphere (aerosols) and some of it is absorbed. The rest is incident on the Earth's surface (represented by this parameter).","dimensions":["lat","lon","time"],"type":"data","unit":"J m**-2"},"Temperature":{"attrs":{"long_name":"Temperature","parameter_ID":130,"product_type":"forecast","shortName":"t","standard_name":"Temperature"},"description":"This parameter is the temperature in the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"K"},"Time_integrated_eastward_turbulent_surface_stress":{"attrs":{"long_name":"Time-integrated eastward turbulent surface stress","parameter_ID":180,"product_type":"forecast","shortName":"ewss","standard_name":"Time_integrated_eastward_turbulent_surface_stress"},"description":"Air flowing over a surface exerts a stress that transfers momentum to the surface and slows the wind. This parameter is the accumulated stress on the Earth's surface in the eastward direction due to both the turbulent interactions between the atmosphere and the surface, and to turbulent orographic form drag. The turbulent interactions between the atmosphere and the surface are due to the roughness of the surface. The turbulent orographic form drag is the stress due to the valleys, hills and mountains on horizontal scales below 5km being derived from land surface data at about 1 km resolution.","dimensions":["lat","lon","time"],"type":"data","unit":"N m**-2 s"},"Time_integrated_northward_turbulent_surface_stress":{"attrs":{"long_name":"Time_integrated_northward_turbulent_surface_stress","parameter_ID":181,"product_type":"forecast","shortName":"nsss","standard_name":"Time_integrated_eastward_turbulent_surface_stress"},"description":"Air flowing over a surface exerts a stress that transfers momentum to the surface and slows the wind. This parameter is the accumulated stress on the Earth's surface in the northward direction due to both the turbulent interactions between the atmosphere and the surface, and to turbulent orographic form drag. The turbulent interactions between the atmosphere and the surface are due to the roughness of the surface.","dimensions":["lat","lon","time"],"type":"data","unit":"N m**-2 s"},"Top_net_long_wave_(thermal)_radiation":{"attrs":{"long_name":"Top net long-wave (thermal) radiation","parameter_ID":179,"product_type":"forecast","shortName":"ttr","standard_name":"Top_net_long_wave_(thermal)_radiation"},"description":"The thermal (also known as terrestrial or longwave) radiation emitted to space at the top of the atmosphere is commonly known as the Outgoing Longwave Radiation (OLR). The top net thermal radiation (this parameter) is equal to the negative of OLR.","dimensions":["lat","lon","time"],"type":"data","unit":"J m**-2"},"Top_net_short_wave_(solar)_radiation":{"attrs":{"long_name":"Top net short-wave (solar) radiation","parameter_ID":178,"product_type":"forecast","shortName":"tsr","standard_name":"Top_net_short_wave_(solar)_radiation"},"description":"This parameter is the incoming solar radiation (also known as shortwave radiation) minus the outgoing solar radiation at the top of the atmosphere. It is the amount of radiation passing through a horizontal plane. The incoming solar radiation is the amount received from the Sun. The outgoing solar radiation is the amount reflected and scattered by the Earth's atmosphere and surface.","dimensions":["lat","lon","time"],"type":"data","unit":"J m**-2"},"Total_cloud_cover":{"attrs":{"long_name":"Total cloud cover","parameter_ID":164,"product_type":"forecast","shortName":"tcc","standard_name":"Total_cloud_cover"},"description":"This parameter is the proportion of a grid box covered by cloud. Total cloud cover is a single level field calculated from the cloud occurring at different model levels through the atmosphere. Assumptions are made about the degree of overlap/randomness between clouds at different heights.","dimensions":["lat","lon","time"],"type":"data","unit":"(0 - 1)"},"Total_column_cloud_liquid_water":{"attrs":{"long_name":"Total column cloud liquid water","parameter_ID":78,"product_type":"forecast","shortName":"tclw","standard_name":"Total_column_cloud_liquid_water"},"description":"the amount of liquid water contained within clouds in a column extending from the surface of the Earth to the top of the atmosphere. Snow (aggregated ice crystals) is not included in this parameter.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m**-2"},"Total_column_vertically_integrated_water_vapour":{"attrs":{"long_name":"Total column vertically-integrated water vapour","parameter_ID":137,"product_type":"forecast","shortName":"tcwv","standard_name":"Total_column_vertically_integrated_water_vapour"},"description":"This parameter is the total amount of water vapour in a column extending from the surface of the Earth to the top of the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m**-2"},"Total_column_water":{"attrs":{"long_name":"Total column water","parameter_ID":136,"product_type":"forecast","shortName":"tcw","standard_name":"Total_column_water"},"description":"This parameter is the sum of water vapour, liquid water, cloud ice, rain and snow in a column extending from the surface of the Earth to the top of the atmosphere.","dimensions":["lat","lon","time"],"type":"data","unit":"kg m**-2"},"U_component_of_wind":{"attrs":{"long_name":"U component of wind","parameter_ID":131,"product_type":"forecast","shortName":"u","standard_name":"U_component_of_wind"},"description":"This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east, in metres per second. A negative sign thus indicates air movement towards the west.","dimensions":["lat","lon","time"],"type":"data","unit":"m s**-1"},"V_component_of_wind":{"attrs":{"long_name":"V component of wind","parameter_ID":132,"product_type":"forecast","shortName":"v","standard_name":"V_component_of_wind"},"description":"This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the east, in metres per second. A negative sign thus indicates air movement towards the west.","dimensions":["lat","lon","time"],"type":"data","unit":"m s**-1"},"Visibility":{"attrs":{"long_name":"Visibility","parameter_ID":3020,"product_type":"forecast","shortName":"vis","standard_name":"Visibility"},"description":"A visibility parameter was introduced in the ECMWF Integrated Forecasting System (IFS) from 12 May 2015. It uses model projections of water vapour, cloud, rain and snow, and climatological aerosol fields to estimate the visibility that would be recorded by weather observers.","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"Vorticity_(relative)":{"attrs":{"long_name":"Vorticity (relative)","parameter_ID":138,"product_type":"forecast","shortName":"vo","standard_name":"Vorticity_(relative)"},"description":"This parameter is a measure of the rotation of air in the horizontal, around a vertical axis, relative to a fixed point on the surface of the Earth.","dimensions":["lat","lon","time"],"type":"data","unit":"s**-1"},"runoff":{"attrs":{"long_name":"Runoff","parameter_ID":205,"product_type":"forecast","shortName":"ro","standard_name":"runoff_amount"},"description":"The total run-off (including drainage through the base of the soil model) per unit area leaving the land portion of the grid cell. 'Amount' means mass per unit area. Runoff is the liquid water which drains from land. If not specified, 'runoff' refers to the sum of surface runoff and subsurface drainage.","dimensions":["lat","lon","time"],"type":"data","unit":"m"},"surface_pressure":{"attrs":{"long_name":"Surface pressure","parameter_ID":134,"product_type":"forecast","shortName":"sp","standard_name":"surface_air_pressure"},"description":"Surface pressure (not mean sea-level pressure), 2-D field to calculate the 3-D pressure field from hybrid coordinates","dimensions":["lat","lon","time"],"type":"data","unit":"Pa"},"total_precipitation":{"attrs":{"long_name":"Total precipitation","parameter_ID":228,"product_type":"forecast","shortName":"tp","standard_name":"lwe_thickness_of_precipitation_amount"},"description":"The construction lwe_thickness_of_X_amount or _content means the vertical extent of a layer of liquid water having the same mass per unit area. 'Precipitation' in the Earth's atmosphere means precipitation of water in all phases. The abbreviation 'lwe' means liquid water equivalent.","dimensions":["lat","lon","time"],"type":"data","unit":"m"}},"dedl:short_description":"The Weather-Induced Extremes Digital Twin (Extremes DT) is a digital twin that simulates extreme weather events and their impacts at km-scale resolutions using combined Earth-system models, impact-sector models, and observations across a global and regional scale."},{"type":"Collection","title":"River discharge and related forecasted data by the European Flood Awareness System","id":"EO.ECMWF.DAT.EFAS_FORECAST","description":"This dataset provides gridded modelled hydrological time series forced with medium-range meteorological forecasts.\nThe data represents most important hydrological variables across the European Flood Awareness System (EFAS) domain.\nThe temporal resolution is sub-daily high-resolution and ensemble forecasts of: River discharge, Soil moisture for three soil layers, Snow water equivalent.\nAlso provided are auxiliary (time invariant) data to aid interpretation of river discharge and soil moisture data.","links":[{"rel":"license","type":"application/pdf","href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/licences/cems-floods/cems-floods_428a6e1019ec50b3dad9c37a90d630fab139059933a939dd5df620bfcb420cc3.pdf","title":"CEMS-FLOODS datasets licence"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.24381/cds.9f696a7a","title":"River discharge and related forecasted data by the European Flood Awareness System"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.EFAS_FORECAST","title":"EO.ECMWF.DAT.EFAS_FORECAST"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.EFAS_FORECAST/items","title":"items"}],"assets":{"thumbnail":{"href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/resources/efas-forecast/overview_ddd9074d456be00a54d03c320485bdbb1d1871507eccaa1039404a9c2c62fe31.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2018-10-11T00:00:00Z","2023-09-19T00:00:00Z"]]}},"license":"other","keywords":["Reforecast","Europe","Copernicus CEMS","Land (hydrology)","Forecasts","Present"],"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Emergency Management Service (CEMS)","roles":["host"],"url":"https://emergency.copernicus.eu/"}],"dedl:short_description":"This dataset contains gridded, sub-daily, high-resolution hydrological time series including river discharge, soil moisture, snow water equivalent, and auxiliary data over Europe based on medium-range meteorological forecasts."},{"type":"Collection","title":"River discharge and related historical data from the European Flood Awareness System","id":"EO.ECMWF.DAT.EFAS_HISTORICAL","description":"This dataset provides gridded modelled daily hydrological time series forced with meteorological observations. The data set is a consistent representation of the most important hydrological variables across the European Flood Awareness System (EFAS) domain. The temporal resolution is up to 30 years modelled time series of:\n\nRiver discharge\nSoil moisture for three soil layers\nSnow water equivalent\n\nIt also provides static data on soil depth for the three soil layers. Soil moisture and river discharge data are accompanied by ancillary files for interpretation (see related variables and links in the documentation).\nThis dataset was produced by forcing the LISFLOOD hydrological model with gridded observational data of precipitation and temperature at a 5x5 km resolution across the EFAS domain. The most recent version\nuses a 6-hourly time step, whereas older versions uses a 24-hour time step. It is available from 1991-01-01 up until near-real time, with a delay of 6 days. The real-time data is only available to EFAS partners.\nCompanion datasets, also available through the CDS, are forecasts for users who are looking medium-range forecasts, reforecasts for research, local skill assessment and post-processing, and seasonal forecasts and reforecasts for users looking for long-term forecasts. For users looking for global hydrological data, we refer to the Global Flood Awareness System (GloFAS) forecasts and historical simulations. All these datasets are part of the operational flood forecasting within the Copernicus Emergency Management Service (CEMS).\n\nVariables in the dataset/application are:\nRiver discharge in the last 24 hours, River discharge in the last 6 hours, Snow depth water equivalent, Soil depth, Volumetric soil moisture\n\nVariables in the dataset/application are:\nOrography, Upstream area","links":[{"rel":"license","type":"application/pdf","href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/licences/cems-floods/cems-floods_428a6e1019ec50b3dad9c37a90d630fab139059933a939dd5df620bfcb420cc3.pdf","title":"CEMS-FLOODS datasets licence"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.24381/cds.e3458969","title":"River discharge and related historical data from the European Flood Awareness System"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.EFAS_HISTORICAL","title":"EO.ECMWF.DAT.EFAS_HISTORICAL"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.EFAS_HISTORICAL/items","title":"items"}],"assets":{"thumbnail":{"href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/resources/efas-historical/overview_ddd9074d456be00a54d03c320485bdbb1d1871507eccaa1039404a9c2c62fe31.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1991-01-01T06:00:00Z",null]]}},"license":"other","keywords":["Europe","Land (hydrology)","Copernicus CEMS","Climatology","Past","Reanalysis"],"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Emergency Management Service (CEMS)","roles":["host"],"url":"https://emergency.copernicus.eu/"}],"dedl:short_description":"The dataset contains gridded modelled daily hydrological time series covering up to 30 years of river discharge, snow water equivalent, soil moisture, and soil depth over Europe, generated by forcing the LISFLOOD model with observed precipitation and temperature data."},{"type":"Collection","title":"Reforecasts of river discharge and related data by the European Flood Awareness System","id":"EO.ECMWF.DAT.EFAS_REFORECAST","description":"This dataset provides gridded modelled hydrological time series forced with medium- to sub-seasonal range meteorological reforecasts. The data is a consistent representation of the most important hydrological variables across the European Flood Awareness System (EFAS) domain. The temporal resolution is 20 years of sub-daily reforecasts initialised twice weekly (Mondays and Thursdays) of:\n\nRiver discharge\nSoil moisture for three soil layers\nSnow water equivalent\n\nIt also provides static data on soil depth for the three soil layers. Soil moisture and river discharge data are accompanied by ancillary files for interpretation (see related variables and links in the documentation).\nThis dataset was produced by forcing the LISFLOOD hydrological model at a 5x5km resolution with ensemble meteorological reforecasts from the European Centre of Medium-range Weather Forecasts (ECMWF). Reforecasts are forecasts run over past dates and are typically used to assess the skill of a forecast system or to develop tools for statistical error correction of the forecasts. The reforecasts are initialised twice weekly with lead times up to 46 days, at 6-hourly time steps for 20 years. For more specific information on the how the reforecast dataset is produced we refer to the documentation.\nCompanion datasets, also available through the Climate Data Store (CDS), are the operational forecasts, historical simulations which can be used to derive the hydrological climatology, and seasonal forecasts and reforecasts for users looking for long term forecasts. For users looking for global hydrological data, we refer to the Global Flood Awareness System (GloFAS) forecasts and historical simulations. All these datasets are part of the operational flood forecasting within the Copernicus Emergency Management Service (CEMS).\n\nVariables in the dataset/application are:\nRiver discharge, Snow depth water equivalent, Soil depth, Volumetric soil moisture\n\nVariables in the dataset/application are:\nOrography, Upstream area","links":[{"rel":"license","type":"application/pdf","href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/licences/cems-floods/cems-floods_428a6e1019ec50b3dad9c37a90d630fab139059933a939dd5df620bfcb420cc3.pdf","title":"CEMS-FLOODS datasets licence"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.24381/cds.c83f560f","title":"Reforecasts of river discharge and related data by the European Flood Awareness System"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.EFAS_REFORECAST","title":"EO.ECMWF.DAT.EFAS_REFORECAST"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.EFAS_REFORECAST/items","title":"items"}],"assets":{"thumbnail":{"href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/resources/efas-reforecast/overview_0e118ef0dc64e1b78c2a7bba3af6e2c0221e25b1b23e2ad16f46dd70c7005faa.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1999-01-03T00:00:00Z","2023-11-21T23:59:59Z"]]}},"license":"other","keywords":["Reforecast","Europe","Reforecasts","Copernicus CEMS","Land (hydrology)","Present"],"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Emergency Management Service (CEMS)","roles":["host"],"url":"https://emergency.copernicus.eu/"}],"dedl:short_description":"The dataset contains 20-year sub-daily reforecasts of river discharge, snow water equivalent, and soil moisture initiated every Monday and Thursday with ECMWF's 5x5 km resolution LISFLOOD model."},{"type":"Collection","title":"Seasonal forecasts of river discharge and related data by the European Flood Awareness System","id":"EO.ECMWF.DAT.EFAS_SEASONAL","description":"This dataset provides gridded modelled daily hydrological time series forced with seasonal meteorological forecasts. The dataset is a consistent representation of the most important hydrological variables across the European Flood Awareness (EFAS) domain. The temporal resolution is daily forecasts initialised once a month consisting of:\n\nRiver discharge\nSoil moisture for three soil layers\nSnow water equivalent\n\nIt also provides static data on soil depth for the three soil layers. Soil moisture and river discharge data are accompanied by ancillary files for interpretation (see related variables and links in the documentation).\nThis dataset was produced by forcing the LISFLOOD hydrological model at a 5x5km resolution with seasonal meteorological ensemble forecasts. The forecasts are initialised on the first of each month with a lead time of 215 days at 24-hour time steps. The meteorological data are seasonal forecasts (SEAS5) from the European Centre of Medium-range Weather Forecasts (ECMWF) with 51 ensemble members. The forecasts are available from November 2020.\nCompanion datasets, also available through the Climate Data Store (CDS), are seasonal reforecasts for research, local skill assessment and post-processing of the seasonal forecasts. There are also medium-range forecasts for users who want to look at shorter time ranges. These are accompanied by historical simulations which can be used to derive the hydrological climatology, and medium-range reforecasts. For users looking for global hydrological data, we refer to the Global Flood Awareness System (GloFAS) forecasts and historical simulations. All these datasets are part of the operational flood forecasting within the Copernicus Emergency Management Service (CEMS).\n\nVariables in the dataset/application are:\nRiver discharge in the last 24 hours, Snow depth water equivalent, Soil depth, Volumetric soil moisture\n\nVariables in the dataset/application are:\nOrography, Upstream area","links":[{"rel":"license","type":"application/pdf","href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/licences/cems-floods/cems-floods_428a6e1019ec50b3dad9c37a90d630fab139059933a939dd5df620bfcb420cc3.pdf","title":"CEMS-FLOODS datasets licence"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.24381/cds.eb224b0e","title":"Seasonal forecasts of river discharge and related data by the European Flood Awareness System"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.EFAS_SEASONAL","title":"EO.ECMWF.DAT.EFAS_SEASONAL"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.EFAS_SEASONAL/items","title":"items"}],"assets":{"thumbnail":{"href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/resources/efas-seasonal/overview_027e11ba4b15ebcb3217e766cb5c74cfd08665cf129d439a9ef52724a1961fa0.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2020-11-01T00:00:00Z","2023-09-30T23:59:59Z"]]}},"license":"other","keywords":["Europe","Copernicus CEMS","Seasonal forecasts","Land (hydrology)","Forecast","Present"],"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Emergency Management Service (CEMS)","roles":["host"],"url":"https://emergency.copernicus.eu/"}],"dedl:short_description":"The dataset contains gridded modelled daily hydrological time series over Europe, including river discharge, snow water equivalent, soil moisture, and soil depth, driven by ECMWF's SEAS5 seasonal meteorological forecasts."},{"type":"Collection","title":"Seasonal reforecasts of river discharge and related data by the European Flood Awareness System","id":"EO.ECMWF.DAT.EFAS_SEASONAL_REFORECAST","description":"This dataset provides modelled daily hydrological time series forced with seasonal meteorological reforecasts. The dataset is a consistent representation of the most important hydrological variables across the European Flood Awareness (EFAS) domain. The temporal resolution is daily forecasts initialised once a month over the reforecast period 1991-2020 of:\n\nRiver discharge\nSoil moisture for three soil layers\nSnow water equivalent\n\nIt also provides static data on soil depth for the three soil layers. Soil moisture and river discharge data are accompanied by ancillary files for interpretation (see related variables and links in the documentation).\nThis dataset was produced by forcing the LISFLOOD hydrological model at a 5x5km gridded resolution with seasonal meteorological ensemble reforecasts. Reforecasts are forecasts run over past dates and are typically used to assess the skill of a forecast system or to develop tools for statistical error correction of the forecasts. The reforecasts are initialised on the first of each month with a lead time of 215 days at 24-hour time steps. The forcing meteorological data are seasonal reforecasts from the European Centre of Medium-range Weather Forecasts (ECMWF), consisting of 25 ensemble members up until December 2016, and after that 51 members. Hydrometeorological reforecasts are available from 1991-01-01 up until 2020-10-01.\nCompanion datasets, also available through the Climate Data Store (CDS), are seasonal forecasts, for which the seasonal reforecasts can be useful for local skill assessment and post-processing of the seasonal forecasts. For users looking for shorter time ranges there are medium-range forecasts and reforecasts, as well as historical simulations which can be used to derive the hydrological climatology. For users looking for global hydrological data, we refer to the Global Flood Awareness System (GloFAS) forecasts and historical simulations. All these datasets are part of the operational flood forecasting within the Copernicus Emergency Management Service (CEMS).\n\nVariables in the dataset/application are:\nRiver discharge in the last 24 hours, Snow depth water equivalent, Soil depth, Volumetric soil moisture\n\nVariables in the dataset/application are:\nOrography, Upstream area","links":[{"rel":"license","type":"application/pdf","href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/licences/cems-floods/cems-floods_428a6e1019ec50b3dad9c37a90d630fab139059933a939dd5df620bfcb420cc3.pdf","title":"CEMS-FLOODS datasets licence"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.24381/cds.768eefc2","title":"Seasonal reforecasts of river discharge and related data by the European Flood Awareness System"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.EFAS_SEASONAL_REFORECAST","title":"EO.ECMWF.DAT.EFAS_SEASONAL_REFORECAST"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ECMWF.DAT.EFAS_SEASONAL_REFORECAST/items","title":"items"}],"assets":{"thumbnail":{"href":"https://object-store.os-api.cci2.ecmwf.int/cci2-prod-catalogue/resources/efas-seasonal-reforecast/overview_ccf1de467bf1208cba89aebab94f143aa32772f4f3306b8159358d3655173f92.png","roles":["thumbnail"],"title":"overview","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1999-01-01T00:00:00Z",null]]}},"license":"other","keywords":["Reforecast","Seasonal reforecasts","Europe","Copernicus CEMS","Land (hydrology)","Present"],"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Emergency Management Service (CEMS)","roles":["host"],"url":"https://emergency.copernicus.eu/"}],"dedl:short_description":"The dataset contains daily hydrological time-series predictions covering Europe's EFAS region from 1991-2020, including river discharge, snow water equivalent, soil moisture, and soil depth, based on monthly-initialized seasonal meteorological reforecasts."},{"type":"Collection","title":"ERA5 hourly data on pressure levels from 1940 to present","id":"EO.ECMWF.DAT.ERA5_HOURLY_VARIABLES_ON_PRESSURE_LEVELS","description":"ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades.\nData is available from 1940 onwards.\nERA5 replaces the ERA-Interim reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product. ERA5 provides hourly estimates for a large number of atmospheric, ocean-wave and land-surface quantities.\nAn uncertainty estimate is sampled by an underlying 10-member ensemble\nat three-hourly intervals. Ensemble mean and spread have been pre-computed for convenience.\nSuch uncertainty estimates are closely related to the information content of the available observing system which\nhas evolved considerably over time. They also indicate flow-dependent sensitive areas.\nTo facilitate many climate applications, monthly-mean averages have been pre-calculated too,\nthough monthly means are not available for the ensemble mean and spread. ERA5 is updated daily with a latency of about 5 days. In case that serious flaws are detected in this early release (called ERA5T), this data could be different from the final release 2 to 3 months later. In case that this occurs users are notified. The data set presented here is a regridded subset of the full ERA5 data set on native resolution.\nIt is online on spinning disk, which should ensure fast and easy access.\nIt should satisfy the requirements for most common applications. An overview of all ERA5 datasets can be found in this article .\nInformation on access to ERA5 data on native resolution is provided in these guidelines . Data has been regridded to a regular lat-lon grid of 0.25 degrees for the reanalysis and 0.5 degrees for\nthe uncertainty estimate (0.5 and 1 degree respectively for ocean waves).\nThere are four main sub sets: hourly and monthly products, both on pressure levels (upper air fields) and single levels (atmospheric, ocean-wave and land surface quantities). The present entry is \"ERA5 hourly data on pressure levels from 1940 to present\".\n\n## How to acknowledge, cite and refer to ERA5\n\nAll users of data uploaded on the Climate Data Store (CDS) must:\n\nProvide clear and visible attribution to the Copernicus programme by referencing the web catalogue entry\n\nAcknowledge according to the [licence to use Copernicus Products](https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf).\n\nCite each product used.\n\nPlease refer to [How to acknowledge, cite and reference data published on the Climate Data Store](https://confluence.ecmwf.int/x/srnICw) for complete details.","links":[{"rel":"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":"other","keywords":["Atmosphere (surface)","Global","Copernicus C3S","Atmosphere (upper air)","Atmospheric conditions","Past","Reanalysis"],"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":"1.1.0","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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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":"other","keywords":["Land (physics)","Land conditions","Global","Copernicus C3S","Land (hydrology)","Past","Land (biosphere)","Reanalysis"],"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Climate Change Service (C3S)","roles":["host"],"url":"https://climate.copernicus.eu/"}],"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":"other","keywords":["Atmosphere (surface)","Global","Copernicus C3S","Atmosphere (upper air)","Atmospheric conditions","Past","Reanalysis"],"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Climate Change Service (C3S)","roles":["host"],"url":"https://climate.copernicus.eu/"}],"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":"other","keywords":["Copernicus C3S","Global","Past","Land (cryosphere)","Satellite observations","Land cover"],"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Climate Change Service (C3S)","roles":["host"],"url":"https://climate.copernicus.eu/"}],"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":"other","keywords":["Atmosphere (composition)","Global","Atmospheric conditions","Past","Satellite observations"],"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Climate Change Service (C3S)","roles":["host"],"url":"https://climate.copernicus.eu/"}],"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":"other","keywords":["Atmosphere (surface)","Global","Copernicus C3S","Atmosphere (upper air)","Atmospheric conditions","Past","Reanalysis"],"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json","https://stac-extensions.github.io/application/v0.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Climate Change Service (C3S)","roles":["host"],"url":"https://climate.copernicus.eu/"}],"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":"other","keywords":["Atmosphere (surface)","Global","Copernicus C3S","Atmosphere (upper air)","Atmospheric conditions","Past","Reanalysis"],"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Climate Change Service (C3S)","roles":["host"],"url":"https://climate.copernicus.eu/"}],"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":"other","keywords":["Copernicus C3S","Atmosphere (upper air)","Europe","Atmospheric conditions","Past","Reanalysis"],"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/datacube/v2.0.0/schema.json","https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["producer","processor","licensor"],"url":"https://www.ecmwf.int"},{"name":"Copernicus Climate Change Service (C3S)","roles":["host"],"url":"https://climate.copernicus.eu/"}],"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":"other","keywords":["Sea ice"],"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Climate Change Service (C3S)","roles":["host"],"url":"https://climate.copernicus.eu/"}],"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":"other","keywords":["Sea ice"],"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Climate Change Service (C3S)","roles":["host"],"url":"https://climate.copernicus.eu/"}],"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":"other","keywords":["Sea ice"],"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Climate Change Service (C3S)","roles":["host"],"url":"https://climate.copernicus.eu/"}],"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":"other","keywords":["Atmosphere (surface)","Global","Future","Atmosphere (upper air)","Copernicus C3S","Seasonal forecasts","Atmospheric conditions","Past","Present"],"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Climate Change Service (C3S)","roles":["host"],"url":"https://climate.copernicus.eu/"}],"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":"other","keywords":["Atmosphere (surface)","Global","Future","Atmosphere (upper air)","Copernicus C3S","Seasonal forecasts","Atmospheric conditions","Past","Present"],"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Climate Change Service (C3S)","roles":["host"],"url":"https://climate.copernicus.eu/"}],"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":"other","keywords":["Atmosphere (surface)","Global","Future","Atmosphere (upper air)","Copernicus C3S","Seasonal forecasts","Atmospheric conditions","Past","Present"],"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Climate Change Service (C3S)","roles":["host"],"url":"https://climate.copernicus.eu/"}],"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":"other","keywords":["Atmosphere (surface)","Global","Future","Atmosphere (upper air)","Copernicus C3S","Seasonal forecasts","Atmospheric conditions","Past","Present"],"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Climate Change Service (C3S)","roles":["host"],"url":"https://climate.copernicus.eu/"}],"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":"other","keywords":["Atmosphere (surface)","Global","Future","Atmosphere (upper air)","Copernicus C3S","Seasonal forecasts","Atmospheric conditions","Past","Present"],"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Climate Change Service (C3S)","roles":["host"],"url":"https://climate.copernicus.eu/"}],"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":"other","keywords":["Atmosphere (surface)","Global","Future","Atmosphere (upper air)","Copernicus C3S","Seasonal forecasts","Atmospheric conditions","Past","Present"],"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Climate Change Service (C3S)","roles":["host"],"url":"https://climate.copernicus.eu/"}],"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":"other","keywords":["Copernicus C3S","Global","Past","Satellite observations","Ocean (physics)","Oceanographic geographical features"],"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Climate Change Service (C3S)","roles":["host"],"url":"https://climate.copernicus.eu/"}],"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":"other","keywords":["Meteorological geographical features","Copernicus C3S","Future","Climate projections","Europe","Past","Atmosphere (hydrology)","Water management","Present"],"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"ECMWF","roles":["licensor","producer","processor"],"url":"https://www.ecmwf.int/"},{"name":"Copernicus Climate Change Service (C3S)","roles":["host"],"url":"https://climate.copernicus.eu/"}],"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":"other","keywords":["Provider: Copernicus C3S","Spatial coverage: Europe","Temporal coverage: Present","Temporal coverage: Future","Temporal coverage: Past","Variable domain: Atmosphere (surface)","Sector: Water management","Product type: Climate projections"],"stac_version":"1.1.0","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."},{"type":"Collection","title":"SENTINEL-1 Level 1 Ground Range Detected (GRD)","id":"EO.ESA.DAT.SENTINEL-1.L1_GRD","description":"The [Sentinel-1](https://sentinel.esa.int/web/sentinel/missions/sentinel-1) mission is a constellation of two polar-orbiting satellites, operating day and night performing C-band synthetic aperture radar imaging. Level-1 Ground Range Detected (GRD) products consist of focused SAR data that has been detected, multi-looked and projected to ground range using an Earth ellipsoid model. The ellipsoid projection of the GRD products is corrected using the terrain height specified in the product general annotation. The terrain height used varies in azimuth but is constant in range.\n\nGround range coordinates are the slant range coordinates projected onto the ellipsoid of the Earth. Pixel values represent detected magnitude. Phase information is lost. The resulting product has approximately square spatial resolution and square pixel spacing with reduced speckle due to the multi-look processing.\n\nThe noise vector annotation data set, within the product annotations, contains thermal noise vectors so that users can apply a thermal noise correction by subtracting the noise from the power detected image. The thermal noise correction is, for example, supported by the [Sentinel-1 Toolbox](https://sentinels.copernicus.eu/web/sentinel/toolboxes/sentinel-1) (S1TBX).\n\nFor the IW and EW GRD products, multi-looking is performed on each burst individually. All bursts in all sub-swaths are then seamlessly merged to form a single, contiguous, ground range detected image per polarisation channel.\n\nGRD products are available in three resolutions, characterised by the acquisition mode and the level of multi-looking applied: Full Resolution (FR), High Resolution (HR), Medium Resolution (MR).","links":[{"rel":"license","type":"application/pdf","href":"https://sentinels.copernicus.eu/documents/247904/690755/Sentinel_Data_Legal_Notice","title":"Copernicus Sentinel data terms"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ESA.DAT.SENTINEL-1.L1_GRD","title":"EO.ESA.DAT.SENTINEL-1.L1_GRD"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ESA.DAT.SENTINEL-1.L1_GRD/items","title":"items"}],"assets":{"thumbnail":{"href":"https://collections.eurodatacube.com/sentinel-1-grd/sentinel-1-grd.png","roles":["thumbnail"],"title":"Sentinel 1 GRD","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2014-10-04T00:58:44Z",null]]}},"license":"other","keywords":["GRD","C-SAR","C-Band","SAR","L1","Sentinel-1A,Sentinel-1B","Sentinel-1","Copernicus","ESA","Sentinel"],"summaries":{"constellation":["Sentinel-1"],"instruments":["C-SAR"],"intruments":["C-SAR"],"platform":["Sentinel-1A,Sentinel-1B"],"processing:level":["L1"],"s1:orbit_source":["DOWNLINK","POEORB","PREORB","RESORB"],"s1:processing_level":[1],"s1:product_timeliness":["NRT-10m","NRT-1h","NRT-3h","Fast-24h","Off-line","Reprocessing"],"s1:resolution":["full","high","medium"],"sar:center_frequency":[5.405],"sar:frequency_band":["C"],"sar:instrument_mode":["IW","EW","SM"],"sar:looks_azimuth":[1,6,2],"sar:looks_equivalent_number":[4.4,29.7,2.7,10.7,3.7],"sar:looks_range":[5,6,3,2],"sar:observation_direction":["right"],"sar:pixel_spacing_azimuth":[10,25,40,3.5],"sar:pixel_spacing_range":[10,25,40,3.5],"sar:polarizations":[["VV","VH"],["HH","HV"],["VV"],["VH"],["HH"],["HV"]],"sar:product_type":["GRD"],"sar:resolution_azimuth":[22,23,50,87,9],"sar:resolution_range":[20,23,50,93,9],"sat:orbit_state":["ascending","descending"],"sat:platform_international_designator":["2014-016A","2016-025A","0000-000A"]},"item_assets":{"hh":{"description":"Amplitude of signal transmitted with horizontal polarization and received with horizontal polarization with radiometric terrain correction applied.","roles":["data"],"title":"HH: horizontal transmit, horizontal receive","type":"image/tiff; application=geotiff; profile=cloud-optimized"},"hv":{"description":"Amplitude of signal transmitted with horizontal polarization and received with vertical polarization with radiometric terrain correction applied.","roles":["data"],"title":"HV: horizontal transmit, vertical receive","type":"image/tiff; application=geotiff; profile=cloud-optimized"},"safe-manifest":{"description":"General product metadata in XML format. 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Single polarisation products are represented with a grey scale image. Dual polarisation products are represented by a single composite colour image in RGB with the red channel (R) representing the  co-polarisation VV or HH), the green channel (G) represents the cross-polarisation (VH or HV) and the blue channel (B) represents the ratio of the cross an co-polarisations.","roles":["thumbnail"],"title":"Preview Image","type":"image/png"},"vh":{"description":"Amplitude of signal transmitted with vertical polarization and received with horizontal polarization with radiometric terrain correction applied.","roles":["data"],"title":"VH: vertical transmit, horizontal receive","type":"image/tiff; application=geotiff; profile=cloud-optimized"},"vv":{"description":"Amplitude of signal transmitted with vertical polarization and received with vertical polarization with radiometric terrain correction applied.","roles":["data"],"title":"VV: vertical transmit, vertical receive","type":"image/tiff; application=geotiff; profile=cloud-optimized"}},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/sar/v1.0.0/schema.json","https://stac-extensions.github.io/sat/v1.0.0/schema.json","https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json","https://stac-extensions.github.io/processing/v1.2.0/schema.json"],"providers":[{"name":"European Space Agency (ESA)","roles":["producer","processor","licensor"],"url":"https://earth.esa.int"},{"name":"Destination Earth Data Lake (DEDL)","roles":["host"],"url":"https://data.destination-earth.eu/"},{"name":"Copernicus Data Space Ecosystem (CDSE)","roles":["host"],"url":"https://dataspace.copernicus.eu/"},{"name":"CREODIAS","roles":["host"],"url":"https://creodias.eu/"}],"dedl:short_description":"The Sentinel-1 Level 1 Ground Range Detected (GRD) dataset consists of focused SAR data processed into approximately square images with reduced speckle at various resolutions based on acquisition modes and multi-looking levels."},{"type":"Collection","title":"SENTINEL-1 Level 1 Single Look Complex (SLC) - EODC store","id":"EO.ESA.DAT.SENTINEL-1.L1_SLC","description":"Sentinel-1 is a polar-orbiting, all-weather, day-and-night radar imaging mission for land and ocean services. \t\t\t\tThe first Sentinel-1 satellite was launched on a Soyuz rocket from Europe's Spaceport in French Guiana on 3 April 2014.    \t\t\t\t\t\t\t\t\t\t\t\tLevel-1 Single Look Complex (SLC) products consist of focused SAR data geo-referenced using orbit and attitude data from the satellite and provided in zero-Doppler slant-range geometry. The products include a single look in each dimension using the full TX signal bandwidth and consist of complex samples preserving the phase information.","links":[{"rel":"license","type":"application/pdf","href":"https://sentinels.copernicus.eu/documents/247904/690755/Sentinel_Data_Legal_Notice","title":"Copernicus Sentinel data terms"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ESA.DAT.SENTINEL-1.L1_SLC","title":"EO.ESA.DAT.SENTINEL-1.L1_SLC"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ESA.DAT.SENTINEL-1.L1_SLC/items","title":"items"}],"assets":{"thumbnail":{"href":"https://wekeo2-prod-data-access-config.s3.waw3-2.cloudferro.com/previews/EO_ESA_DAT_EODC-SENTINEL-1_L1_SLC.jpg","roles":["thumbnail"],"title":"Sentinel 1 SLC","type":"image/jpeg"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2014-04-03T00:00:00Z",null]]}},"license":"other","keywords":["Sea Ice","Atmosphere","Science","SLC","Sentinel-1A,Sentinel-1B","Copernicus","Sea","Absorption","Sentinel","C-SAR","Natural Disaster","Ocean Optics","SAR","ESA","Albedo","Atmospheric Radiation","C-Band","Glacier","Land","Level 1 Data","Oceans","Earth Science","L1","Ice","Sentinel-1","Land cover","Iceberg"],"summaries":{"constellation":["Sentinel-1"],"instruments":["C-SAR"],"intruments":["C-SAR"],"platform":["Sentinel-1A,Sentinel-1B"],"processing:level":["L1"],"s1:processing_level":[1],"s1:product_timeliness":["NRT-10m","NRT-1h","NRT-3h","Fast-24h","Off-line","Reprocessing"],"sar:center_frequency":[5.405],"sar:frequency_band":["C"],"sar:instrument_mode":["IW","EW","SM"],"sar:polarizations":[["VV","VH"],["HH","HV"],["VV"],["VH"],["HH"],["HV"]],"sar:product_type":["SLC"],"sat:orbit_state":["ascending","descending"],"sat:platform_international_designator":["2014-016A","2016-025A","0000-000A"]},"item_assets":{"hh":{"description":"Amplitude of signal transmitted with horizontal polarization and received with horizontal polarization with radiometric terrain correction applied.","roles":["data"],"title":"HH: horizontal transmit, horizontal receive","type":"image/tiff; application=geotiff; profile=cloud-optimized"},"hv":{"description":"Amplitude of signal transmitted with horizontal polarization and received with vertical polarization with radiometric terrain correction applied.","roles":["data"],"title":"HV: horizontal transmit, vertical receive","type":"image/tiff; application=geotiff; profile=cloud-optimized"},"thumbnail":{"description":"An averaged, decimated preview image in PNG format. 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Partially covered tiles correspond to those at the edge of the swath.","links":[{"rel":"license","type":"application/pdf","href":"https://sentinels.copernicus.eu/documents/247904/690755/Sentinel_Data_Legal_Notice","title":"Copernicus Sentinel data terms"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ESA.DAT.SENTINEL-2.MSI.L1C","title":"EO.ESA.DAT.SENTINEL-2.MSI.L1C"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ESA.DAT.SENTINEL-2.MSI.L1C/items","title":"items"}],"assets":{"thumbnail":{"href":"https://wekeo2-prod-data-access-config.s3.waw3-2.cloudferro.com/previews/EO_ESA_DAT_EODC-SENTINEL-2_MSI1C.jpg","roles":["thumbnail"],"title":"Sentinel 2 MSI L1C","type":"image/jpeg"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2015-07-04T00:00:00Z",null]]}},"license":"other","keywords":["Land Cover","Flood Mapping","Burned Area Monitoring","Crop Monitoring","Copernicus","Sentinel","Snow Cover Monitoring","Sentinel-2A,Sentinel-2B","ESA","Chlorophyll","Glacier Monitoring","Ice Extent Mapping","MSI","Land","Level 1 Data","Inland Water Monitoring","Lava Flow Mapping","L1C","Leaf Area Index","Sentinel-2","Coastal Zone Monitoring"],"summaries":{"bands":[{"description":"Coastal Aerosol","eo:center_wavelength":"442.7 (S2B:442.2)","eo:common_name":"Coastal","name":"B01"},{"description":"Visible Blue","eo:center_wavelength":"492.4 (S2B:492.1)","eo:common_name":"Blue","name":"B02"},{"description":"Visible Green","eo:center_wavelength":"559.8 (S2B:559.0)","eo:common_name":"Green","name":"B03"},{"description":"Visible Red","eo:center_wavelength":664.6,"eo:common_name":"Red","name":"B04"},{"description":"Vegetation Red Edge","eo:center_wavelength":"704.1 (S2B:703.8)","eo:common_name":"Red Edge","name":"B05"},{"description":"Vegetation Red Edge","eo:center_wavelength":"740.5 (S2B:739.1)","eo:common_name":"Red Edge","name":"B06"},{"description":"Vegetation Red Edge","eo:center_wavelength":"782.8 (S2B:779.7)","eo:common_name":"Red Edge","name":"B07"},{"description":"Near Infrared","eo:center_wavelength":"832.8 (S2B:832.9)","eo:common_name":"NIR","name":"B08"},{"description":"Narrow Near Infrared","eo:center_wavelength":"864.7 (S2B:864.0)","eo:common_name":"NIR08","name":"B8A"},{"description":"Water Vapour","eo:center_wavelength":"945.1 (S2B:943.2)","eo:common_name":"WV","name":"B09"},{"description":"Short-Wave Infrared Cirrus","eo:center_wavelength":"1373.5 (S2B:1376.9)","eo:common_name":"Cirrus","name":"B10"},{"description":"short-wave infrared","eo:center_wavelength":"1613.7 (S2B:1610.4)","eo:common_name":"SWIR16","name":"B11"},{"description":"Short-Wave Infrared","eo:center_wavelength":"2202.4 (S2B: 2185.7)","eo:common_name":"SWIR22","name":"B12"}],"constellation":["Sentinel-2"],"instruments":["MSI"],"intruments":["MSI"],"platform":["Sentinel-2A,Sentinel-2B"],"processing:level":["L1C"],"s1:product_timeliness":["NRT-100m to 3h","Off-line"]},"item_assets":{"thumbnail":{"description":"An averaged, decimated preview image in PNG format. 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Level-2A: Surface reflectances in cartographic geometry. This product is considered as the mission Analysis Ready Data (ARD), the product that can be used directly in downstream applications without the need for further processing. The Level-2A product contains: Bottom-Of-Atmosphere (BOA) reflectance orthoimage, Aerosol Optical Thickness (AOT) map, Water Vapour (WV) map, Scene Classification map and Quality Indicators data.","links":[{"rel":"license","type":"application/pdf","href":"https://sentinels.copernicus.eu/documents/247904/690755/Sentinel_Data_Legal_Notice","title":"Copernicus Sentinel data terms"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ESA.DAT.SENTINEL-2.MSI.L2A","title":"EO.ESA.DAT.SENTINEL-2.MSI.L2A"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ESA.DAT.SENTINEL-2.MSI.L2A/items","title":"items"}],"assets":{"thumbnail":{"href":"https://sentinels.copernicus.eu/documents/247904/3681412/Sentinel-2-Level-1C-Level-2A-TOA-full.png","roles":["thumbnail"],"title":"TOA Level-1C image data (left) and associated Level-2A surface reflectance image data (right) generated using Sen2Cor processor","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2015-07-04T00:00:00Z",null]]}},"license":"other","keywords":["lava flow mapping","Burned area monitoring","Copernicus","land cover","Sentinel","land","Sentinel-2A,Sentinel-2B","L2A","leaf area index","ice extent mapping","ESA","Level 2 Data","Glacier monitoring","Inland water monitoring","chlorophyll","MSI","Crop monitoring","Coastal zone monitoring","snow cover monitoring","Sentinel-2","Flood mapping"],"summaries":{"bands":[{"description":"Coastal aerosol","eo:center_wavelength":"442.7 (S2B:442.2)","eo:common_name":"coastal","name":"B01"},{"description":"visible blue","eo:center_wavelength":"492.4 (S2B:492.1)","eo:common_name":"blue","name":"B02"},{"description":"visible green","eo:center_wavelength":"559.8 (S2B:559.0)","eo:common_name":"green","name":"B03"},{"description":"visible red","eo:center_wavelength":664.6,"eo:common_name":"red","name":"B04"},{"description":"Vegetation red edge","eo:center_wavelength":"704.1 (S2B:703.8)","eo:common_name":"rededge","name":"B05"},{"description":"Vegetation red edge","eo:center_wavelength":"740.5 (S2B:739.1)","eo:common_name":"rededge","name":"B06"},{"description":"Vegetation red edge","eo:center_wavelength":"782.8 (S2B:779.7)","eo:common_name":"rededge","name":"B07"},{"description":"near infrared","eo:center_wavelength":"832.8 (S2B:832.9)","eo:common_name":"nir","name":"B08"},{"description":"narrow nir infrared","eo:center_wavelength":"864.7 (S2B:864.0)","eo:common_name":"nir08","name":"B8A"},{"description":"water vapour","eo:center_wavelength":"945.1 (S2B:943.2)","name":"B09"},{"description":"short-wave infrared cirrus","eo:center_wavelength":"1373.5 (S2B:1376.9)","eo:common_name":"cirrus","name":"B10"},{"description":"short-wave infrared","eo:center_wavelength":"1613.7 (S2B:1610.4)","eo:common_name":"swir16","name":"B11"},{"description":"short-wave infrared","eo:center_wavelength":"2202.4 (S2B: 2185.7)","eo:common_name":"swir22","name":"B12"}],"constellation":["Sentinel-2"],"instruments":["MSI"],"intruments":["MSI"],"platform":["Sentinel-2A,Sentinel-2B"],"processing:level":["L2A"],"s1:product_timeliness":["NRT-8h","Off-line"]},"item_assets":{"thumbnail":{"description":"An averaged, decimated preview image in PNG format. 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Sentinel-3 is part of a series of Sentinel satellites, under the umbrella of the EU Copernicus programme.","links":[{"rel":"license","type":"application/pdf","href":"https://sentinels.copernicus.eu/documents/247904/690755/Sentinel_Data_Legal_Notice","title":"Copernicus Sentinel data terms"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ESA.DAT.SENTINEL-3.OL_2_LFR___","title":"EO.ESA.DAT.SENTINEL-3.OL_2_LFR___"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ESA.DAT.SENTINEL-3.OL_2_LFR___/items","title":"items"}],"assets":{"thumbnail":{"href":"https://wekeo2-prod-data-access-config.s3.waw3-2.cloudferro.com/previews/EO_ESA_DAT_SENTINEL-3_OL_2_LFR___.jpg","roles":["thumbnail"],"title":"Sentinel-3A OLCI land","type":"image/jpeg"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2016-04-06T00:00:00Z",null]]}},"license":"other","keywords":["Sentinel-3","Land","OLCI","Sentinel-3A,Sentinel-3B","Level 2 Data","Land Colour","L1B"],"summaries":{"bands":[{"description":"Yellow Substance and Detrital Pigments","eo:center_wavelength":"413 nm","name":"O1"},{"description":"Chl Absorption Max. /Vegetation","eo:center_wavelength":"443 nm","name":"O2"},{"description":"Chl, Other Pigments","eo:center_wavelength":"490 nm","name":"O3"},{"description":"Chl, Sediment, Turbidity, Red tide","eo:center_wavelength":"510 nm","name":"O4"},{"description":"Chlorophyll Reference","eo:center_wavelength":"560 nm","name":"O5"},{"description":"Sediment Loading","eo:center_wavelength":"620 nm","name":"O6"},{"description":"Chl, Sediment, Yellow Substance / Vegetation","eo:center_wavelength":"665 nm","name":"O7"},{"description":"Chl Fluorescence Peak, Red Edge","eo:center_wavelength":"681 nm","name":"O8"},{"description":"Chl Fluorescence Baseline","eo:center_wavelength":"709 nm","name":"O9"},{"description":"O2 Absorption /Cloud/ Ocean Colour","eo:center_wavelength":"754 nm","name":"O10"},{"description":"O2 Absorption Band/Aerosol Corr.","eo:center_wavelength":"761 nm","name":"O11"},{"description":"Atmos. / Aerosol Corr.","eo:center_wavelength":"779 nm","name":"O12"},{"description":"Aerosols, Clouds, Pixel Co-registration","eo:center_wavelength":"865 nm","name":"O13"},{"description":"Water Vap. 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Sentinel-3 is part of a series of Sentinel satellites, under the umbrella of the EU Copernicus programme.","links":[{"rel":"license","type":"application/pdf","href":"https://sentinels.copernicus.eu/documents/247904/690755/Sentinel_Data_Legal_Notice","title":"Copernicus Sentinel data terms"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ESA.DAT.SENTINEL-3.OL_2_LRR___","title":"EO.ESA.DAT.SENTINEL-3.OL_2_LRR___"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.ESA.DAT.SENTINEL-3.OL_2_LRR___/items","title":"items"}],"assets":{"thumbnail":{"href":"https://wekeo2-prod-data-access-config.s3.waw3-2.cloudferro.com/previews/EO_ESA_DAT_SENTINEL-3_OL_2_LRR___.jpg","roles":["thumbnail"],"title":"Sentinel-3A OLCI land","type":"image/jpeg"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2016-04-06T00:00:00Z",null]]}},"license":"other","keywords":["Sentinel-3","Land","OLCI","Sentinel-3A,Sentinel-3B","Level 2 Data","Land Colour","L1B"],"summaries":{"bands":[{"description":"Yellow Substance and Detrital Pigments","eo:center_wavelength":"413 nm","name":"O1"},{"description":"Chl Absorption Max. /Vegetation","eo:center_wavelength":"443 nm","name":"O2"},{"description":"Chl, Other Pigments","eo:center_wavelength":"490 nm","name":"O3"},{"description":"Chl, Sediment, Turbidity, Red tide","eo:center_wavelength":"510 nm","name":"O4"},{"description":"Chlorophyll Reference","eo:center_wavelength":"560 nm","name":"O5"},{"description":"Sediment Loading","eo:center_wavelength":"620 nm","name":"O6"},{"description":"Chl, Sediment, Yellow Substance / Vegetation","eo:center_wavelength":"665 nm","name":"O7"},{"description":"Chl Fluorescence Peak, Red Edge","eo:center_wavelength":"681 nm","name":"O8"},{"description":"Chl Fluorescence Baseline","eo:center_wavelength":"709 nm","name":"O9"},{"description":"O2 Absorption /Cloud/ Ocean Colour","eo:center_wavelength":"754 nm","name":"O10"},{"description":"O2 Absorption Band/Aerosol Corr.","eo:center_wavelength":"761 nm","name":"O11"},{"description":"Atmos. / Aerosol Corr.","eo:center_wavelength":"779 nm","name":"O12"},{"description":"Aerosols, Clouds, Pixel Co-registration","eo:center_wavelength":"865 nm","name":"O13"},{"description":"Water Vap. 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This is a Thematic Climate Data Record (TCDR).","links":[{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.15770/EUM_SEC_CLM_0020","title":"Digital Object Identifier (DOI): 10.15770/EUM_SEC_CLM_0020"},{"rel":"describedby","type":"application/pdf","href":"https://user.eumetsat.int/s3/eup-strapi-media/pdf_msg_met_prod_atbd_15e4917e25.pdf","title":"MSG Meteorological Products ATBD"},{"rel":"describedby","type":"application/pdf","href":"https://user.eumetsat.int/s3/eup-strapi-media/Atmospheric_Motion_Vectors_Release_2_Product_Users_Guide_e8bbb2c9d7.pdf","title":"Atmospheric Motion Vectors Release 2 Product Users Guide"},{"rel":"describedby","type":"application/pdf","href":"https://user.eumetsat.int/s3/eup-strapi-media/Atmospheric_Motion_Vectors_Release_2_Validation_Report_a73858d4a7.pdf","title":"Atmospheric Motion Vectors Release 2 Validation Report"},{"rel":"describedby","type":"text/html","href":"https://data.eumetsat.int/product/EO:EUM:DAT:0405","title":"https://data.eumetsat.int/product/EO:EUM:DAT:0405"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.AMVR02","title":"EO.EUM.DAT.AMVR02"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.AMVR02/items","title":"items"}],"assets":{"thumbnail":{"href":"https://user.eumetsat.int/s3/eup-strapi-media/AMV_Compare_20050726113000_Z_MFG_20050726114500_Z_MSG_2edb40b5b3.png","roles":["thumbnail"],"title":"Atmospheric Motion Vectors Climate Data Record Release 2 - MFG and MSG - 0 degree","type":"image/png"}},"extent":{"spatial":{"bbox":[[-65,-65,65,65]]},"temporal":{"interval":[["1981-09-03T00:00:00Z","2019-08-31T00:00:00Z"]]}},"license":"CC-BY-4.0","keywords":["MFG,MSG","Climate","Atmosphere","L2","Thematic Climate Data Record","MVIRI,SEVIRI","Observation","METEOSAT-2,METEOSAT-3,METEOSAT-4,METEOSAT-5,METEOSAT-6,METEOSAT-7,METEOSAT-8,METEOSAT-9,METEOSAT-10,METEOSAT-11,MSG","Wind","Level 2 Data"],"summaries":{"constellation":["MFG,MSG"],"instruments":["MVIRI","SEVIRI"],"intruments":["MVIRI,SEVIRI"],"platform":["METEOSAT-2,METEOSAT-3,METEOSAT-4,METEOSAT-5,METEOSAT-6,METEOSAT-7,METEOSAT-8,METEOSAT-9,METEOSAT-10,METEOSAT-11,MSG"],"processing:level":["L2"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/processing/v1.2.0/schema.json","https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)","roles":["producer","processor","licensor","host"],"url":"https://www.eumetsat.int"}],"dedl:short_description":"The Atmosphere Motion Vector Climate Data Record Release 2 combines reprocessed data from Meteosat First and Second Generations into a single climate record spanning 38 years with hourly updates."},{"type":"Collection","title":"GSA Level 2 Climate Data Record Release 2 - MFG and MSG - 0 degree","id":"EO.EUM.DAT.GSAL2R02","description":"Release 2 of the Thematic Climate Data Record (TCDR) of the Meteosat First Generation (MFG) and Meteosat Second Generation (MSG) Level 2 land surface albedo. The variables estimated are black-sky albedo (BSA) and white-sky albedo (WSA) with the corresponding uncertainties as explained in the Product User Guide (PUM). The data record validation and limitations are provided in the Validation Report (VR). The products are available in netCDF4 format. This release contains products generated with Meteosat-2 to Meteosat-10.","links":[{"rel":"describedby","type":"application/pdf","href":"https://user.eumetsat.int/s3/eup-strapi-media/pdf_msa_atbd_9a0528c3db.pdf","title":"Meteosat Surface Albedo Retrieval: Algorithm Theoretical Basis Document"},{"rel":"describedby","type":"application/pdf","href":"https://user.eumetsat.int/s3/eup-strapi-media/pdf_gsa_r2_vr_b67f4b9ca8.pdf","title":"Geostationary Surface Albedo (GSA) Release 2 Validation Report"},{"rel":"describedby","type":"application/pdf","href":"https://user.eumetsat.int/s3/eup-strapi-media/pdf_gsa_r2_pum_4e2e625a27.pdf","title":"Geostationary Surface Albedo Release 2 Product Users Manual"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.15770/EUM_SEC_CLM_0023","title":"Digital Object Identifier (DOI): 10.15770/EUM_SEC_CLM_0023"},{"rel":"describedby","type":"text/html","href":"https://data.eumetsat.int/product/EO:EUM:DAT:0300","title":"https://data.eumetsat.int/product/EO:EUM:DAT:0300"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.GSAL2R02","title":"EO.EUM.DAT.GSAL2R02"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.GSAL2R02/items","title":"items"}],"assets":{"thumbnail":{"href":"https://user.eumetsat.int/s3/eup-strapi-media/GSA_V2_MS_09_0_0_DHR_30_2007_221_230_ae3a5b8328.png","roles":["thumbnail"],"title":"GSA Level 2 Climate Data Record Release 2 - MFG and MSG - 0 degree","type":"image/png"}},"extent":{"spatial":{"bbox":[[-70,-70,70,70]]},"temporal":{"interval":[["1982-02-10T00:00:00Z","2017-12-31T00:00:00Z"]]}},"license":"CC-BY-4.0","keywords":["MFG,MSG","Climate","METEOSAT-2,METEOSAT-3,METEOSAT-4,METEOSAT-5,METEOSAT-6,METEOSAT-7,METEOSAT-8,METEOSAT-9,METEOSAT-10,MSG","L2","MVIRI,SEVIRI","Thematic Climate Data Record","Level 2 Data"],"summaries":{"constellation":["MFG,MSG"],"instruments":["MVIRI","SEVIRI"],"intruments":["MVIRI,SEVIRI"],"platform":["METEOSAT-2,METEOSAT-3,METEOSAT-4,METEOSAT-5,METEOSAT-6,METEOSAT-7,METEOSAT-8,METEOSAT-9,METEOSAT-10,MSG"],"processing:level":["L2"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/processing/v1.2.0/schema.json","https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)","roles":["producer","processor","licensor","host"],"url":"https://www.eumetsat.int"}],"dedl:short_description":"This is Release 2 of the TCDR containing GSA level 2 climate data records for MFG and MSG at 0 degrees latitude, including BSA and WSA estimates along with their uncertainties from Meteosat-2 to Meteosat-10 satellites."},{"type":"Collection","title":"AMSU-A Level 1B - Metop - Global","id":"EO.EUM.DAT.METOP.AMSUL1","description":"The Advanced Microwave Sounding Unit-A (AMSU-A) is a 15-channel microwave radiometer that is used for measuring global atmospheric temperature profiles and will provide information on atmospheric water in all of its phases (with the exception of small ice particles, which are transparent at microwave frequencies). AMSU-A will provide information even in cloudy conditions. AMSU-A measures Earth radiance at frequencies (in GHz) as listed under the instrument channel information.","links":[{"rel":"license","type":"application/pdf","href":"https://www.eumetsat.int/data-policy/eumetsat-data-policy.pdf","title":"EUMETSAT Data Policy"},{"rel":"describedby","type":"text/html","href":"https://data.eumetsat.int/product/EO:EUM:DAT:METOP:AMSUL1","title":"AMSU-A Level 1B - Metop - Global"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.METOP.AMSUL1","title":"EO.EUM.DAT.METOP.AMSUL1"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.METOP.AMSUL1/items","title":"items"}],"assets":{"thumbnail":{"href":"https://user.eumetsat.int/s3/eup-strapi-media/amsu_a_395bd20778.jpg","roles":["thumbnail"],"title":"AMSU-A Level 1B - Metop - Global","type":"image/jpeg"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2008-03-01T00:00:00Z",null]]}},"license":"other","keywords":["METOP-A,METOP-B,METOP-C","AMSU-A","Atmosphere","METOP","L1","Level 1 Data"],"summaries":{"constellation":["METOP"],"instruments":["AMSU-A"],"intruments":["AMSU-A"],"platform":["METOP-A,METOP-B,METOP-C"],"processing:level":["L1"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/timestamps/v1.1.0/schema.json","https://stac-extensions.github.io/processing/v1.2.0/schema.json"],"providers":[{"name":"European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)","roles":["producer","processor","licensor","host"],"url":"https://www.eumetsat.int/"}],"dedl:short_description":"The AMSU-A provides global atmospheric temperature profile data along with information about atmospheric water content through measurements from 15 channels across various microwave frequencies despite cloud cover."},{"type":"Collection","title":"ASCAT Level 1 Sigma0 Full Resolution - Metop - Global","id":"EO.EUM.DAT.METOP.ASCSZF1B","description":"The prime objective of the Advanced SCATterometer (ASCAT) is to measure wind speed and direction over the oceans, and the main operational application is the assimilation of ocean winds in NWP models. Other operational applications, based on the use of measurements of the backscattering coefficient, are sea ice edge detection and monitoring, monitoring sea ice, snow cover, soil moisture and surface parameters. This product consists of geo-located radar backscatter values along the six ASCAT beams. The different beam measurements are not collocated into a regular swath grid and the individual measurements are not spatially averaged. The resolution of each of the 255 backscatter values per each beam varies slightly along the beam, but it is approximately 10km (in the along beam direction) x 25 km (across the beam). This product is usually referred to as 'ASCAT Level 1B Full resolution product'. Note that some of the data are reprocessed. Please refer to the associated product validation reports or product release notes for further information.","links":[{"rel":"license","type":"application/pdf","href":"https://www.eumetsat.int/data-policy/eumetsat-data-policy.pdf","title":"EUMETSAT Data Policy"},{"rel":"describedby","type":"text/html","href":"https://data.eumetsat.int/product/EO:EUM:DAT:METOP:ASCSZF1B","title":"ASCAT Level 1 Sigma0 Full Resolution - Metop - Global"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.METOP.ASCSZF1B","title":"EO.EUM.DAT.METOP.ASCSZF1B"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.METOP.ASCSZF1B/items","title":"items"}],"assets":{"thumbnail":{"href":"https://user.eumetsat.int/s3/eup-strapi-media/metop_Ascat_thumbnail_UP_e18ca9c42a.jpg","roles":["thumbnail"],"title":"ASCAT Level 1 Sigma0 Full Resolution - Metop - Global","type":"image/jpeg"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2007-05-31T00:00:00Z",null]]}},"license":"other","keywords":["METOP-A,METOP-B,METOP-C","ASCAT","METOP","L1","Radar Backscatter NRCS","Ocean","Land","Level 1 Data"],"summaries":{"constellation":["METOP"],"instruments":["ASCAT"],"intruments":["ASCAT"],"platform":["METOP-A,METOP-B,METOP-C"],"processing:level":["L1"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/timestamps/v1.1.0/schema.json","https://stac-extensions.github.io/processing/v1.2.0/schema.json"],"providers":[{"name":"European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)","roles":["producer","processor","licensor","host"],"url":"https://www.eumetsat.int/"}],"dedl:short_description":"The ASCAT Level 1 Sigma0 Full Resolution dataset contains global, geolocated radar backscatter values from six ASCAT beams with varying resolutions around 10x25 km, used primarily for measuring oceanic wind speeds and directions."},{"type":"Collection","title":"ASCAT Level 1 Sigma0 resampled at 25 km Swath Grid - Metop - Global","id":"EO.EUM.DAT.METOP.ASCSZO1B","description":"The prime objective of the Advanced SCATterometer (ASCAT) is to measure wind speed and direction over the oceans, and the main operational application is the assimilation of ocean winds in NWP models. Other operational applications, based on the use of measurements of the backscattering coefficient, are sea ice edge detection and monitoring, monitoring sea ice, snow cover, soil moisture and surface parameters. The product is available from the archive in 2 different spatial resolutions; 25 km and 12.5 km. Note that some of the data are reprocessed. Please refer to the associated product validation reports or product release notes for further information. Near real-time distribution discontinued on 29/09/2015 but the product contents are now available in the corresponding Level 2 product 'ASCAT Soil Moisture at 25 km Swath Grid'.","links":[{"rel":"license","type":"application/pdf","href":"https://www.eumetsat.int/data-policy/eumetsat-data-policy.pdf","title":"EUMETSAT Data Policy"},{"rel":"describedby","type":"text/html","href":"https://data.eumetsat.int/product/EO:EUM:DAT:METOP:ASCSZO1B","title":"ASCAT Level 1 Sigma0 resampled at 25 km Swath Grid - Metop - Global"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.METOP.ASCSZO1B","title":"EO.EUM.DAT.METOP.ASCSZO1B"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.METOP.ASCSZO1B/items","title":"items"}],"assets":{"thumbnail":{"href":"https://user.eumetsat.int/s3/eup-strapi-media/metop_Ascat_thumbnail_UP_e18ca9c42a.jpg","roles":["thumbnail"],"title":"ASCAT Level 1 Sigma0 resampled at 25 km Swath Grid - Metop - Global","type":"image/jpeg"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2007-03-01T00:00:00Z",null]]}},"license":"other","keywords":["METOP-A,METOP-B,METOP-C","ASCAT","METOP","L1","Radar Backscatter NRCS","Ocean","Land","Level 1 Data"],"summaries":{"constellation":["METOP"],"instruments":["ASCAT"],"intruments":["ASCAT"],"platform":["METOP-A,METOP-B,METOP-C"],"processing:level":["L1"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/timestamps/v1.1.0/schema.json","https://stac-extensions.github.io/processing/v1.2.0/schema.json"],"providers":[{"name":"European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)","roles":["producer","processor","licensor","host"],"url":"https://www.eumetsat.int/"}],"dedl:short_description":"The ASCAT Level 1 Sigma0 dataset contains global oceanic wind speed and direction measurements with additional applications including sea ice detection and land parameter analysis at 25km resolution."},{"type":"Collection","title":"ASCAT Level 1 Sigma0 resampled at 12.5 km Swath Grid - Metop - Global","id":"EO.EUM.DAT.METOP.ASCSZR1B","description":"The prime objective of the Advanced SCATterometer (ASCAT) is to measure wind speed and direction over the oceans, and the main operational application is the assimilation of ocean winds in NWP models. 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Near real-time distribution discontinued on 29/09/2015 but the product contents are now available in the corresponding Level 2 product 'ASCAT Soil Moisture at 12.5 km Swath Grid'.","links":[{"rel":"license","type":"application/pdf","href":"https://www.eumetsat.int/data-policy/eumetsat-data-policy.pdf","title":"EUMETSAT Data Policy"},{"rel":"describedby","type":"text/html","href":"https://data.eumetsat.int/product/EO:EUM:DAT:METOP:ASCSZR1B","title":"ASCAT Level 1 Sigma0 resampled at 12.5 km Swath Grid - Metop - Global"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.METOP.ASCSZR1B","title":"EO.EUM.DAT.METOP.ASCSZR1B"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.METOP.ASCSZR1B/items","title":"items"}],"assets":{"thumbnail":{"href":"https://user.eumetsat.int/s3/eup-strapi-media/metop_Ascat_thumbnail_UP_e18ca9c42a.jpg","roles":["thumbnail"],"title":"ASCAT Level 1 Sigma0 resampled at 12.5 km Swath Grid - Metop - Global","type":"image/jpeg"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2007-03-01T00:00:00Z",null]]}},"license":"other","keywords":["METOP-A,METOP-B,METOP-C","ASCAT","METOP","L1","Radar Backscatter NRCS","Ocean","Land","Level 1 Data"],"summaries":{"constellation":["METOP"],"instruments":["ASCAT"],"intruments":["ASCAT"],"platform":["METOP-A,METOP-B,METOP-C"],"processing:level":["L1"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/timestamps/v1.1.0/schema.json","https://stac-extensions.github.io/processing/v1.2.0/schema.json"],"providers":[{"name":"European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)","roles":["producer","processor","licensor","host"],"url":"https://www.eumetsat.int/"}],"dedl:short_description":"The ASCAT Level 1 Sigma0 dataset contains global oceanic wind speed and direction measurements with additional applications including sea ice detection and land parameter analysis at 12.5 km resolution."},{"type":"Collection","title":"AVHRR GAC Atmospheric Motion Vectors Climate Data Record Release 2 - Multimission - Polar","id":"EO.EUM.DAT.METOP.AVHRRGACR02","description":"This is the second release of the reprocessed polar Atmospheric Motion Vectors (AMV) Thematic Climate Data Record (TCDR) from the Advanced Very High Resolution Radiometer (AVHRR) in Global Area Coverage (GAC), from TIROS-N, NOAA-06, 07, 08, 09, 10, 11, 12, 14, 15, 16, 17, 18 and 19 and Metop-A and -B. It contains AMVs at all heights below the tropopause, derived from images in the Infrared channel at 10.8 microns. Vectors are retrieved by tracking the motion of clouds in two consecutive images. The height assignment of the AMVs is calculated using the Cross-Correlation Contribution (CCC) function to determine the height using the pixels that contribute the most to the vectors. A quality indicator is derived for each vector to assess the reliability of the retrieval. Products are stored in netCDF4 format and cover the period from January 1979 to September 2019. 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In combination with AMSU-A information it can also be used to process precipitation rates and related cloud properties, as well as to detect sea ice and snow coverage.","links":[{"rel":"license","type":"application/pdf","href":"https://www.eumetsat.int/data-policy/eumetsat-data-policy.pdf","title":"EUMETSAT Data Policy"},{"rel":"describedby","type":"text/html","href":"https://data.eumetsat.int/product/EO:EUM:DAT:METOP:MHSL1","title":"MHS Level 1B - Metop - Global"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.METOP.MHSL1","title":"EO.EUM.DAT.METOP.MHSL1"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.METOP.MHSL1/items","title":"items"}],"assets":{"thumbnail":{"href":"https://user.eumetsat.int/s3/eup-strapi-media/metopmhsl1_0929e6488e.jpg","roles":["thumbnail"],"title":"MHS Level 1B - Metop - Global","type":"image/jpeg"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2009-03-23T00:00:00Z",null]]}},"license":"other","keywords":["METOP-A,METOP-B,METOP-C","Atmosphere","METOP","L1","MHS","Level 1 Data"],"summaries":{"constellation":["METOP"],"instruments":["MHS"],"intruments":["MHS"],"platform":["METOP-A,METOP-B,METOP-C"],"processing:level":["L1"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/timestamps/v1.1.0/schema.json","https://stac-extensions.github.io/processing/v1.2.0/schema.json"],"providers":[{"name":"European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)","roles":["producer","processor","licensor","host"],"url":"https://www.eumetsat.int/"}],"dedl:short_description":"The MHS provides data for retrieving surface temperatures, emissivities, atmospheric humidity, precipitation rates, cloud properties, sea ice, and snow coverage globally from its five channels combined with AMSU-A information."},{"type":"Collection","title":"ASCAT Coastal Winds at 12.5 km Swath Grid - Metop","id":"EO.EUM.DAT.METOP.OSI-104","description":"Equivalent neutral 10m winds over the global oceans, with specific sampling to provide as many observations as possible near the coasts. Better than using this archived NRT product, please use the reprocessed ASCAT winds data records (METOP_OSI_150A, METOP_OSI_150B). For Metop-A, t is recommended that the reprocessed ASCAT winds data records (10.15770/EUM_SAF_OSI_0007) are used instead of this archived NRT product for the period before 1 April 2014.","links":[{"rel":"license","type":"application/pdf","href":"https://www.eumetsat.int/data-policy/eumetsat-data-policy.pdf","title":"EUMETSAT Data Policy"},{"rel":"describedby","type":"text/html","href":"https://data.eumetsat.int/product/EO:EUM:DAT:METOP:OSI-104","title":"ASCAT Coastal Winds at 12.5 km Swath Grid - Metop"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.METOP.OSI-104","title":"EO.EUM.DAT.METOP.OSI-104"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.METOP.OSI-104/items","title":"items"}],"assets":{"thumbnail":{"href":"https://user.eumetsat.int/s3/eup-strapi-media/ASCAT_Winds_279e12e3ac.png","roles":["thumbnail"],"title":"ASCAT Coastal Winds at 12.5 km Swath Grid - Metop","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2013-04-16T00:00:00Z",null]]}},"license":"other","keywords":["METOP-A,METOP-B,METOP-C","ASCAT","METOP","Ocean Surface Wind","L2","Weather","Radar Backscatter NRCS","Ocean","Level 2 Data"],"summaries":{"constellation":["METOP"],"instruments":["ASCAT"],"intruments":["ASCAT"],"platform":["METOP-A,METOP-B,METOP-C"],"processing:level":["L2"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/timestamps/v1.1.0/schema.json","https://stac-extensions.github.io/processing/v1.2.0/schema.json"],"providers":[{"name":"European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)","roles":["producer","processor","licensor","host"],"url":"https://www.eumetsat.int/"}],"dedl:short_description":"The dataset consists of reprocessed ASCAT coastal wind measurements from Metop satellites, providing high-resolution oceanic wind data globally and specifically around coastlines since 2006."},{"type":"Collection","title":"ASCAT L2 25 km Winds Data Record Release 1 - Metop","id":"EO.EUM.DAT.METOP.OSI-150-A","description":"The ASCAT Wind Product contains stress equivalent 10m winds (speed and direction) over the global oceans. The winds are obtained through the processing of reprocessed scatterometer backscatter data originating from the ASCAT instrument on EUMETSAT's Metop satellite.","links":[{"rel":"license","type":"application/pdf","href":"https://www.eumetsat.int/data-policy/eumetsat-data-policy.pdf","title":"EUMETSAT Data Policy"},{"rel":"describedby","type":"text/html","href":"https://data.eumetsat.int/product/EO:EUM:DAT:METOP:OSI-150-A","title":"ASCAT L2 25 km Winds Data Record Release 1 - Metop"},{"rel":"cite-as","type":"text/html","href":"http://doi.org/10.15770/EUM_SAF_OSI_0006","title":"ASCAT L2 25 km Winds Data Record Release 1 - Metop"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.METOP.OSI-150-A","title":"EO.EUM.DAT.METOP.OSI-150-A"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.METOP.OSI-150-A/items","title":"items"}],"assets":{"thumbnail":{"href":"https://user.eumetsat.int/s3/eup-strapi-media/ASCAT_Winds_279e12e3ac.png","roles":["thumbnail"],"title":"ASCAT L2 25 km Winds Data Record Release 1 - Metop","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2007-01-01T00:00:00Z","2014-04-01T00:00:00Z"]]}},"license":"other","keywords":["METOP-A,METOP-B,METOP-C","ASCAT","METOP","Ocean Surface Wind","L2","Radar Backscatter NRCS","Ocean","Level 2 Data"],"summaries":{"constellation":["METOP"],"instruments":["ASCAT"],"intruments":["ASCAT"],"platform":["METOP-A,METOP-B,METOP-C"],"processing:level":["L2"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json","https://stac-extensions.github.io/processing/v1.2.0/schema.json"],"providers":[{"name":"European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)","roles":["producer","processor","licensor","host"],"url":"https://www.eumetsat.int/"}],"dedl:short_description":"The ASCAT L2 25km wind product provides global oceanic surface wind speed and direction at 10 meters height derived from reprocessed scatterometer backscattering measurements by the Metop satellite's ASCAT instrument."},{"type":"Collection","title":"ASCAT L2 12.5 km Winds Data Record Release 1 - Metop","id":"EO.EUM.DAT.METOP.OSI-150-B","description":"The ASCAT Wind Product contains stress equivalent 10m winds (speed and direction) over the global oceans. The winds are obtained through the processing of reprocessed scatterometer backscatter data originating from the ASCAT instrument on EUMETSAT's Metop satellite.","links":[{"rel":"license","type":"application/pdf","href":"https://www.eumetsat.int/data-policy/eumetsat-data-policy.pdf","title":"EUMETSAT Data Policy"},{"rel":"describedby","type":"text/html","href":"https://data.eumetsat.int/product/EO:EUM:DAT:METOP:OSI-150-B","title":"ASCAT L2 12.5 km Winds Data Record Release 1 - Metop"},{"rel":"cite-as","type":"text/html","href":"http://dx.doi.org/10.15770/EUM_SAF_OSI_0007","title":"ASCAT L2 12.5 km Winds Data Record Release 1 - Metop"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.METOP.OSI-150-B","title":"EO.EUM.DAT.METOP.OSI-150-B"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.METOP.OSI-150-B/items","title":"items"}],"assets":{"thumbnail":{"href":"https://user.eumetsat.int/s3/eup-strapi-media/ASCAT_Winds_279e12e3ac.png","roles":["thumbnail"],"title":"ASCAT L2 12.5 km Winds Data Record Release 1 - Metop","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2007-01-01T00:00:00Z","2014-04-01T00:00:00Z"]]}},"license":"other","keywords":["METOP-A,METOP-B,METOP-C","ASCAT","METOP","Ocean Surface Wind","L2","Radar Backscatter NRCS","Ocean","Level 2 Data"],"summaries":{"constellation":["METOP"],"instruments":["ASCAT"],"intruments":["ASCAT"],"platform":["METOP-A,METOP-B,METOP-C"],"processing:level":["L2"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json","https://stac-extensions.github.io/processing/v1.2.0/schema.json"],"providers":[{"name":"European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)","roles":["producer","processor","licensor","host"],"url":"https://www.eumetsat.int/"}],"dedl:short_description":"The ASCAT L2 12.5 km Winds Data Record Release 1 provides global ocean wind speed and direction at 10 meters above sea level derived from processed scatterometer backscatter data from the Metop satellite."},{"type":"Collection","title":"ASCAT Soil Moisture at 12.5 km Swath Grid in NRT - Metop","id":"EO.EUM.DAT.METOP.SOMO12","description":"The Soil Moisture (SM) product is derived from the Advanced SCATterometer (ASCAT) backscatter observations and given in swath orbit geometry (12.5 km sampling). This SM product provides an estimate of the water content of the 0-5 cm topsoil layer, expressed in degree of saturation between 0 and 100 [%]. The algorithm used to derive this parameter is based on a linear relationship of SM and scatterometer backscatter and uses change detection techniques to eliminate the contributions of vegetation, land cover and surface topography, considered invariant from year to year. Seasonal vegetation effects are modelled by exploiting the multi-angle viewing capabilities of ASCAT. The SM processor has been developed by Vienna University of Technology (TU Wien). Note that some of the data are reprocessed. Please refer to the associated product validation reports or product release notes for further information.","links":[{"rel":"license","type":"application/pdf","href":"https://www.eumetsat.int/data-policy/eumetsat-data-policy.pdf","title":"EUMETSAT Data Policy"},{"rel":"describedby","type":"text/html","href":"https://data.eumetsat.int/product/EO:EUM:DAT:METOP:SOMO12","title":"ASCAT Soil Moisture at 12.5 km Swath Grid in NRT - Metop"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.METOP.SOMO12","title":"EO.EUM.DAT.METOP.SOMO12"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.METOP.SOMO12/items","title":"items"}],"assets":{"thumbnail":{"href":"https://user.eumetsat.int/s3/eup-strapi-media/ASCAT_Soil_Moisture_f1e01310e4.png","roles":["thumbnail"],"title":"ASCAT Soil Moisture at 12.5 km Swath Grid in NRT - Metop","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2007-06-01T00:00:00Z",null]]}},"license":"other","keywords":["METOP-A,METOP-B,METOP-C","Land","Soil Moisture","ASCAT","METOP","L2","Level 2 Data"],"summaries":{"constellation":["METOP"],"instruments":["ASCAT"],"intruments":["ASCAT"],"platform":["METOP-A,METOP-B,METOP-C"],"processing:level":["L2"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/timestamps/v1.1.0/schema.json","https://stac-extensions.github.io/processing/v1.2.0/schema.json"],"providers":[{"name":"European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)","roles":["producer","processor","licensor","host"],"url":"https://www.eumetsat.int/"}],"dedl:short_description":"The ASCAT Soil Moisture product estimates soil moisture as a percentage of saturation within the top 0-5cm soil layer with a resolution of 12.5km, accounting for seasonal vegetation variations through multi-angle views."},{"type":"Collection","title":"ASCAT Soil Moisture at 25 km Swath Grid in NRT - Metop","id":"EO.EUM.DAT.METOP.SOMO25","description":"The Soil Moisture (SM) product is derived from the Advanced SCATterometer (ASCAT) backscatter observations and given in swath orbit geometry (25 km sampling). 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Please refer to the associated product validation reports or product release notes for further information.","links":[{"rel":"license","type":"application/pdf","href":"https://www.eumetsat.int/data-policy/eumetsat-data-policy.pdf","title":"EUMETSAT Data Policy"},{"rel":"describedby","type":"text/html","href":"https://data.eumetsat.int/product/EO:EUM:DAT:METOP:SOMO25","title":"ASCAT Soil Moisture at 25 km Swath Grid in NRT - Metop"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.METOP.SOMO25","title":"EO.EUM.DAT.METOP.SOMO25"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.METOP.SOMO25/items","title":"items"}],"assets":{"thumbnail":{"href":"https://user.eumetsat.int/s3/eup-strapi-media/ASCAT_Soil_Moisture_f1e01310e4.png","roles":["thumbnail"],"title":"ASCAT Soil Moisture at 25 km Swath Grid in NRT - Metop","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2007-06-01T00:00:00Z",null]]}},"license":"other","keywords":["METOP-A,METOP-B,METOP-C","Land","Soil Moisture","ASCAT","METOP","L2","Level 2 Data"],"summaries":{"constellation":["METOP"],"instruments":["ASCAT"],"intruments":["ASCAT"],"platform":["METOP-A,METOP-B,METOP-C"],"processing:level":["L2"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/timestamps/v1.1.0/schema.json","https://stac-extensions.github.io/processing/v1.2.0/schema.json"],"providers":[{"name":"European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)","roles":["producer","processor","licensor","host"],"url":"https://www.eumetsat.int/"}],"dedl:short_description":"The ASCAT Soil Moisture product estimates soil moisture as a percentage of saturation in the top 0-5cm layer with a 25km resolution, accounting for seasonal vegetation variations through multi-angle views and correcting for non-soil influences like land cover and topography."},{"type":"Collection","title":"GSA Level 2 Climate Data Record Release 2 - MFG - 57 degree","id":"EO.EUM.DAT.MFG.GSA-57","description":"Release 2 of the Thematic Climate Data Record (TCDR) of the Meteosat First Generation (MFG) Level 2 land surface albedo. The variables estimated are black-sky albedo (BSA) and white-sky albedo (WSA) with the corresponding uncertainties as explained in the Product User Guide (PUM). The data record validation and limitations are provided in the Validation Report (VR). The products are available in netCDF4 format. 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This Data Record was obtained with the best version of its equivalent NRT product (MLST) which can also complement the time series from 2016 onwards.","links":[{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.15770/EUM_SAF_LSA_0001","title":"Digital Object Identifier (DOI): 10.15770/EUM_SAF_LSA_0001"},{"rel":"describedby","type":"text/html","href":"https://lsa-saf.eumetsat.int/en/data/products/land-surface-temperature-and-emissivity/","title":"Product information"},{"rel":"describedby","type":"text/html","href":"https://www.eumetsat.int/lsa-saf","title":"EUMETSAT LSA SAF page"},{"rel":"describedby","type":"text/html","href":"https://lsa-saf.eumetsat.int/","title":"LSA SAF"},{"rel":"describedby","type":"text/html","href":"https://data.eumetsat.int/product/EO:EUM:DAT:0088","title":"https://data.eumetsat.int/product/EO:EUM:DAT:0088"},{"rel":"license","type":"application/pdf","href":"https://www.eumetsat.int/data-policy/eumetsat-data-policy.pdf","title":"EUMETSAT Data Policy"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.MSG.LSA-LST-CDR","title":"EO.EUM.DAT.MSG.LSA-LST-CDR"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.MSG.LSA-LST-CDR/items","title":"items"}],"assets":{"thumbnail":{"href":"https://user.eumetsat.int/s3/eup-strapi-media/HDF_5_LSASAF_MSG_MLST_R_LSA_050_05d85861ff.png","roles":["thumbnail"],"title":"Land Surface Temperature Climate Data Record - MSG","type":"image/png"}},"extent":{"spatial":{"bbox":[[-79,-81,79,81]]},"temporal":{"interval":[["2004-01-21T00:00:00Z","2015-12-31T23:59:59Z"]]}},"license":"other","keywords":["MSG","Level 3 Data","SEVIRI","L3","Vegetation","Land"],"summaries":{"constellation":["MSG"],"instruments":["SEVIRI"],"intruments":["SEVIRI"],"platform":["MSG"],"processing:level":["L3"],"sat:orbit_state":["geostationary"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/processing/v1.2.0/schema.json","https://stac-extensions.github.io/sat/v1.0.0/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":"European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)","roles":["producer","processor","licensor","host"],"url":"http://www.eumetsat.int"}],"dedl:short_description":"The MSG/SEVIRI land surface temperature climate data record is a 12-year archive of consistently processed 15-minute LST data spanning 2004-2015."},{"type":"Collection","title":"Land Surface Temperature with Directional Effects - MSG","id":"EO.EUM.DAT.MSG.LSA-LSTDE","description":"Land Surface Temperature (LST) is the radiative skin temperature over land. LST plays an important role in the physics of land surface as it is involved in the processes of energy and water exchange with the atmosphere. LST is useful for the scientific community, namely for those dealing with meteorological and climate models. Accurate values of LST are also of special interest in a wide range of areas related to land surface processes, including meteorology, hydrology, agrometeorology, climatology and environmental studies. Land Surface Emissivity (EM), a crucial parameter for LST retrieval from space, is independently estimated as a function of (satellite derived) Fraction of Vegetation Cover (FVC) and land cover classification.\nIn the most recent version of the dataset, information on the expected deviation of LST estimates from SEVIRI/MSG with respect to a reference view – here considered to be nadir view – has been added to the original product (LSA-001) as an extra data layer (LSA-004).","links":[{"rel":"describedby","type":"text/html","href":"https://data.eumetsat.int/product/EO:EUM:DAT:0394","title":"https://data.eumetsat.int/product/EO:EUM:DAT:0394"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.MSG.LSA-LSTDE","title":"EO.EUM.DAT.MSG.LSA-LSTDE"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.MSG.LSA-LSTDE/items","title":"items"}],"assets":{"thumbnail":{"href":"https://user.eumetsat.int/s3/eup-strapi-media/HDF_5_LSASAF_MSG_MLST_DE_LSA_004_a1bc5973d4.png","roles":["thumbnail"],"title":"Land Surface Temperature with Directional Effects - MSG","type":"image/png"}},"extent":{"spatial":{"bbox":[[-79,-81,79,81]]},"temporal":{"interval":[["2005-01-16T00:00:00Z",null]]}},"license":"CC-BY-4.0","keywords":["Land","L2","MSG","SEVIRI","Vegetation","Level 2 Data"],"summaries":{"constellation":["MSG"],"instruments":["SEVIRI"],"intruments":["SEVIRI"],"platform":["MSG"],"processing:level":["L2"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/processing/v1.2.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)","roles":["producer","processor","licensor","host"],"url":"http://www.eumetsat.int"}],"dedl:short_description":"The dataset contains Land Surface Temperatures retrieved from MSG satellite imagery with directional effects taken into account, along with additional layers estimating deviations from nadir views."},{"type":"Collection","title":"Rapid Scan High Rate SEVIRI Level 1.5 Image Data - MSG","id":"EO.EUM.DAT.MSG.MSG15-RSS","description":"Rectified (level 1.5) Meteosat SEVIRI Rapid Scan image data. The baseline scan region is a reduced area of the top 1/3 of a nominal repeat cycle, covering a latitude range from approximately 15 degrees to 70 degrees. The service generates repeat cycles at 5-minute intervals (the same as currently used for weather radars). The dissemination of RSS data is similar to the normal dissemination, with image segments based on 464 lines and compatible with the full disk level 1.5 data scans. Epilogue and prologue (L1.5 Header and L1.5 Trailer) have the same structure. Calibration is as in Full Earth Scan. Image rectification is to 9.5 degreesE. The scans start at 00:00, 00:05, 00:10, 00:15 ... etc. (5 min scan). The differences from the nominal Full Earth scan are that for channels 1 - 11, only segments 6 - 8 are disseminated and for the High Resolution Visible Channel only segments 16 - 24 are disseminated.","links":[{"rel":"describedby","type":"text/html","href":"https://user.eumetsat.int/resources/service-statuses/rss-schedule","title":"Rapid Scanning Service"},{"rel":"describedby","type":"application/pdf","href":"https://user.eumetsat.int/s3/eup-strapi-media/pdf_ten_05105_msg_img_data_e7c8b315e6.pdf","title":"MSG Level 1.5 Image Data Format Description"},{"rel":"describedby","type":"text/html","href":"https://data.eumetsat.int/product/EO:EUM:DAT:MSG:MSG15-RSS","title":"https://data.eumetsat.int/product/EO:EUM:DAT:MSG:MSG15-RSS"},{"rel":"license","type":"text/html","href":"https://user.eumetsat.int/resources/user-guides/data-registration-and-licensing#ID-Data-Licensing","title":"EUMETSAT Meteosat \u003c1hr latency"},{"rel":"license","type":"text/html","href":"https://user.eumetsat.int/resources/user-guides/data-registration-and-licensing#ID-Data-Licensing","title":"EUMETSAT Meteosat \u003e1hr latency \u0026 Metop"},{"rel":"license","type":"application/pdf","href":"https://www.eumetsat.int/data-policy/eumetsat-data-policy.pdf","title":"EUMETSAT Data Policy - PDF"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.MSG.MSG15-RSS","title":"EO.EUM.DAT.MSG.MSG15-RSS"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.MSG.MSG15-RSS/items","title":"items"}],"assets":{"thumbnail":{"href":"https://user.eumetsat.int/s3/eup-strapi-media/upload/img_msg_rss_onethird_scan_ae3f15cf21.jpg","roles":["thumbnail"],"title":"Rapid Scan High Rate SEVIRI Level 1.5 Image Data - MSG","type":"image/jpeg"}},"extent":{"spatial":{"bbox":[[-65,16,84,70]]},"temporal":{"interval":[["2008-05-13T00:00:00Z",null]]}},"license":"various","keywords":["Atmosphere","L1","MSG","SEVIRI","Ocean","Land","Level 1 Data"],"summaries":{"constellation":["MSG"],"instruments":["SEVIRI"],"intruments":["SEVIRI"],"platform":["MSG"],"processing:level":["L1"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/processing/v1.2.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)","roles":["producer","processor","licensor","host"],"url":"https://www.eumetsat.int"}],"dedl:short_description":"The dataset consists of rectified Meteosat SEVIRI rapid scan images, covering latitudes 15-70°N, generated every 5 minutes within a reduced area of each nominal repeat cycle."},{"type":"Collection","title":"Optimal Cloud Analysis Climate Data Record Release 1 - MSG - 0 degree","id":"EO.EUM.DAT.MSG.OCA-CDR","description":"The OCA Release 1 Climate Data Record (CDR) covers the MSG observation period from 2004 up to 2019, providing a homogenous cloud properties time series. It is generated at full Meteosat repeat cycle (15 minutes) fequency. Cloud properties retrieved by OCA are cloud top pressure, cloud optical thickness, and cloud effective radius, together with uncertainties. The OCA algorithm has been slightly adapted for climate data record processing. The adaptation mainly consists in the usage of different inputs, because the one used for Near Real Time (NRT) were not available for the reprocessing (cloud mask, clear sky reflectance map) and also not homogenous (reanalysis) over the complete time period. it extends the NRT data record more than 9 years back in time. This is a Thematic Climate Data Record (TCDR).","links":[{"rel":"describedby","type":"text/html","href":"https://www.eumetsat.int/eumetview","title":"Real-Time Imagery"},{"rel":"describedby","type":"text/html","href":"https://data.eumetsat.int/product/EO:EUM:DAT:0617","title":"Product Information"},{"rel":"describedby","type":"application/pdf","href":"https://user.eumetsat.int/s3/eup-strapi-media/Optimal_Cloud_Analysis_OCA_Release_1_Product_Users_Guide_5120a10382.pdf","title":"Optimal Cloud Analysis (OCA) Release 1 Validation Report"},{"rel":"describedby","type":"application/pdf","href":"https://user.eumetsat.int/s3/eup-strapi-media/Optimal_Cloud_Analysis_OCA_Release_1_Validation_Report_838e398fac.pdf","title":"Optimal Cloud Analysis (OCA) Release 1 Product Users Guide"},{"rel":"describedby","type":"application/pdf","href":"https://user.eumetsat.int/s3/eup-strapi-media/pdf_msg_met_prod_atbd_15e4917e25.pdf","title":"MSG Meteorological Products Extraction Facility Algorithm Specification Document"},{"rel":"describedby","type":"application/pdf","href":"https://user.eumetsat.int/s3/eup-strapi-media/OCA_R1_list_common_outliers_bf3ad5df65.pdf","title":"MSG OCA R1 common outliers list"},{"rel":"license","type":"application/pdf","href":"https://www.eumetsat.int/data-policy/eumetsat-data-policy.pdf","title":"EUMETSAT Data Policy"},{"rel":"cite-as","type":"text/html","href":"http://doi.org/10.15770/EUM_SEC_CLM_0049","title":"Optimal Cloud Analysis Climate Data Record Release 1 - MSG - 0 degree"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.MSG.OCA-CDR","title":"EO.EUM.DAT.MSG.OCA-CDR"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.MSG.OCA-CDR/items","title":"items"}],"assets":{"thumbnail":{"href":"https://user.eumetsat.int/s3/eup-strapi-media/OCA_R1_5e4fb95d4a.png","roles":["thumbnail"],"title":"Optimal Cloud Analysis Climate Data Record Release 1 - MSG - 0 degree","type":"image/png"}},"extent":{"spatial":{"bbox":[[-67.5,-67.5,67.5,67.5]]},"temporal":{"interval":[["2004-01-19T00:00:00Z","2019-08-31T23:59:59Z"]]}},"license":"other","keywords":["Cloud","OCA","Atmosphere","L2","CDR","MSG","SEVIRI","O degree"],"summaries":{"constellation":["MSG"],"instruments":["SEVIRI"],"intruments":["SEVIRI"],"platform":["MSG"],"processing:level":["L2"],"sat:orbit_state":["geostationary"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/processing/v1.2.0/schema.json","https://stac-extensions.github.io/sat/v1.0.0/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":"European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)","roles":["producer","processor","licensor","host"],"url":"https://www.eumetsat.int"}],"dedl:short_description":"The Optimal Cloud Analysis Release 1 Climate Data Record provides a homogeneous cloud property time series from 2004-2019 at 15-minute intervals, including cloud top pressure, optical thickness, and effective radius with associated uncertainties."},{"type":"Collection","title":"Rapid Scan Cloud Mask - MSG","id":"EO.EUM.DAT.MSG.RSS-CLM","description":"The Rapid Scanning Services (RSS) Cloud Mask product describes the scene type (either 'clear' or 'cloudy') on a pixel level. 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Applications \u0026 Uses: The main use is in support of Nowcasting applications, where it frequently serves as a basis for other cloud products, and the remote sensing of continental and ocean surfaces.","links":[{"rel":"describedby","type":"application/pdf","href":"https://user.eumetsat.int/s3/eup-strapi-media/pdf_msg_met_prod_atbd_15e4917e25.pdf","title":"MSG Meteorological Products ATBD"},{"rel":"describedby","type":"text/html","href":"https://user.eumetsat.int/resources/service-statuses/rss-schedule","title":"Rapid Scanning Service"},{"rel":"describedby","type":"text/html","href":"https://data.eumetsat.int/product/EO:EUM:DAT:MSG:RSS-CLM","title":"https://data.eumetsat.int/product/EO:EUM:DAT:MSG:RSS-CLM"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.MSG.RSS-CLM","title":"EO.EUM.DAT.MSG.RSS-CLM"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.MSG.RSS-CLM/items","title":"items"}],"assets":{"thumbnail":{"href":"https://user.eumetsat.int/s3/eup-strapi-media/rss_color_cloud_mask_15361ad2de.png","roles":["thumbnail"],"title":"Rapid Scan Cloud Mask - MSG","type":"image/png"}},"extent":{"spatial":{"bbox":[[-45,33,65,65]]},"temporal":{"interval":[["2013-02-28T00:00:00Z",null]]}},"license":"CC-BY-4.0","keywords":["Clouds","Atmosphere","L2","MSG","SEVIRI","Level 2 Data"],"summaries":{"constellation":["MSG"],"instruments":["SEVIRI"],"intruments":["SEVIRI"],"platform":["MSG"],"processing:level":["L2"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/processing/v1.2.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)","roles":["producer","processor","licensor","host"],"url":"https://www.eumetsat.int"}],"dedl:short_description":"The Rapid Scanning Services (RSS) Cloud Mask product classifies each pixel into clear sky over water/land, cloudy, or off-Earth conditions based on scene type."},{"type":"Collection","title":"SEVIRI Rapid Scan Atmospheric Motion Vectors Climate Data Record Release 1 - MSG","id":"EO.EUM.DAT.MSG.SEVIRI-RSS_AMV-CDR-V1","description":"Release 1 of the Climate Data Record (CDR) of MSG imager SEVIRI (Spinning Enhanced Visible Infra-Red Imager) High-Resolution Wind (HRW) from the Rapid Scan Service (RSS). This release covers the MSG SEVIRI RSS record from three different satellite platforms Meteosat-8 to -10. The CDR covers 12 years’ worth of data from 2008-05-06 until 2020-04-03 and there is a product every five minutes in NetCDF format. It provides information on Atmospheric motion vectors (AMV) speed (ms-1), direction (degree), and height (given in pressure units Pa) of vectors which are derived by tracking cloud or water vapour features between two images in time. AMV are wind vectors retrieved at all heights below the tropopause, derived from High Resolution Visible (HRV), Visible 0.6 microns Water Vapour (6.2 microns and 7.3 microns) and 10.8 microns. Infrared channels. In all channels, winds are retrieved by tracking cloud features. For the two WV channels, water vapour features are also tracked (clear sky winds).\nThe data record has been generated using the NWC SAF software for geostationary satellites (NWC/GEO).","links":[{"rel":"describedby","type":"application/pdf","href":"https://user.eumetsat.int/s3/ope-eup-strapi-media/SEVIRI_Rapid_Scanning_Atmospheric_Motion_Vectors_Climate_Data_Record_Release_1_Quality_Evaluation_Report_6b360ef6b0.pdf","title":"SEVIRI RSS AMV CDR Release 1 Quality Evaluation Report"},{"rel":"describedby","type":"application/pdf","href":"https://user.eumetsat.int/s3/ope-eup-strapi-media/SEVIRI_Rapid_Scanning_Atmospheric_Motion_Vectors_Climate_Data_Record_Release_1_Product_User_Guide_53d42b747f.pdf","title":"SEVIRI Rapid Scanning AMV CDR Release 1 – Product User Guide"},{"rel":"describedby","type":"text/html","href":"https://data.eumetsat.int/product/EO:EUM:DAT:1083","title":"SEVIRI Rapid Scan Atmospheric Motion Vectors Climate Data Record Release 1 - MSG"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.MSG.SEVIRI-RSS_AMV-CDR-V1","title":"EO.EUM.DAT.MSG.SEVIRI-RSS_AMV-CDR-V1"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.MSG.SEVIRI-RSS_AMV-CDR-V1/items","title":"items"}],"assets":{"thumbnail":{"href":"https://user.eumetsat.int/s3/eup-strapi-media/1083_01_5ea6506dc0.jpg","roles":["thumbnail"],"title":"SEVIRI Rapid Scan Atmospheric Motion Vectors Climate Data Record Release 1 - MSG","type":"image/jpeg"}},"extent":{"spatial":{"bbox":[[-64,16,70,70]]},"temporal":{"interval":[["2008-05-06T00:00:00Z","2020-04-03T00:00:00Z"]]}},"license":"CC-BY-4.0","keywords":["L2","MSG","SEVIRI","Wind","Level 2 Data"],"summaries":{"instruments":["SEVIRI"],"intruments":["SEVIRI"],"platform":["MSG"],"processing:level":["L2"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/processing/v1.2.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)","roles":["producer","processor","licensor","host"],"url":"https://www.eumetsat.int"}],"dedl:short_description":"MSG SEVIRI HRW Release 1: 12-year high-resolution atmospheric wind records (2008-2020) from Meteosat-8 to -10, providing 5-minute updates in NetCDF format, containing vector speeds, directions, and pressures."},{"type":"Collection","title":"SEVIRI Rapid Scan High Rate Level 1.5 Image Data Climate Data Record Release 1 - MSG","id":"EO.EUM.DAT.MSG.SEVIRI-RSS_HR_IMG-L1_5-V1","description":"Release 1 of the Fundamental Climate Data Record (FCDR) of MSG imager SEVIRI (Spinning Enhanced Visible Infra-Red Imager) level 1.5 counts and recalibration coefficients (only for the infrared channels) from the Rapid Scan Service (RSS). This release covers the MSG SEVIRI RSS record from three different satellite platforms Meteosat-8 to -10. Both operational and recalibrated calibration coefficients are provided. The FCDR covers 13 years’ worth of data from 2008-05-06 until 2021-09-17, but recalibrated parameters are only available until 2020-04. To improve usability, the data are reformatted to NetCDF and additionally contain variable grouping, geolocation information, and sun/satellite angles.","links":[{"rel":"cite-as","href":"http://doi.org/10.15770/EUM_SEC_CLM_0098","title":"Digital Object Identifier (DOI): 10.15770/EUM_SEC_CLM_0098"},{"rel":"describedby","type":"application/pdf","href":"https://user.eumetsat.int/s3/eup-strapi-media/C3_S2_D310_4_1_1_202306_SEVIRI_RSS_FCDR_Release1_Product_User_Guide_3_75b0ff3f18.pdf","title":"SEVIRI Rapid-Scan Service  Fundamental Climate Data Record  Release 1 – Product User Guide (D4.1)"},{"rel":"describedby","type":"application/pdf","href":"https://user.eumetsat.int/s3/eup-strapi-media/C3_S2_D310_4_1_1_202306_SEVIRI_RSS_FCDR_Release1_Quality_Evaluation_Report_5_b41364937f.pdf","title":"SEVIRI Rapid-Scan Service  Fundamental Climate Data Record  Release 1 – Quality Evaluation Report (D4.1)"},{"rel":"describedby","type":"text/html","href":"https://data.eumetsat.int/product/EO:EUM:DAT:0962","title":"SEVIRI Rapid Scan High Rate Level 1.5 Image Data Climate Data Record Release 1 - MSG"},{"rel":"license","type":"text/html","href":"https://user.eumetsat.int/resources/user-guides/data-registration-and-licensing#ID-Data-Licensing","title":"EUMETSAT Meteosat \u003e1hr latency \u0026 Metop"},{"rel":"license","type":"application/pdf","href":"https://www.eumetsat.int/data-policy/eumetsat-data-policy.pdf","title":"EUMETSAT Data Policy - PDF"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.MSG.SEVIRI-RSS_HR_IMG-L1_5-V1","title":"EO.EUM.DAT.MSG.SEVIRI-RSS_HR_IMG-L1_5-V1"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.MSG.SEVIRI-RSS_HR_IMG-L1_5-V1/items","title":"items"}],"extent":{"spatial":{"bbox":[[-71.22,12.78,90.22,81.26]]},"temporal":{"interval":[["2008-05-06T00:00:00Z","2021-09-17T00:00:00Z"]]}},"license":"other","keywords":["Fundamental Climate Data Record","L1","MSG","SEVIRI","Calibration","Level 1 Data","Radiation"],"summaries":{"instruments":["SEVIRI"],"intruments":["SEVIRI"],"platform":["MSG"],"processing:level":["L1"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/processing/v1.2.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json","https://stac-extensions.github.io/scientific/v1.0.0/schema.json"],"providers":[{"name":"European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)","roles":["producer","processor","licensor","host"],"url":"https://www.eumetsat.int"}],"dedl:short_description":"MSG SEVIRI Level 1.5 count data with recalibration coefficients for infrared channels from Meteosat-8 to -10's Rapid Scan Service, covering 13 years (2008-2019), reformat into NetCDF format with added metadata and variables."},{"type":"Collection","title":"Surface Radiation Data Set - Heliosat (SARAH) - Edition 3","id":"EO.EUM.DAT.MSG.SEVIRI-SARAH-CDR-V003","description":"The third edition of the Surface Solar Radiation Data Set - Heliosat (SARAH-3) is a satellite-based climate data record of the solar surface irradiance (SIS), the surface direct irradiance ((direct horizontal and direct normalized, SID and DNI), the sunshine duration (SDU), the photosynthetically active radiation (PAR), daylight (DAL), and the effective cloud albedo derived (CAL) from satellite-observations of the visible channels of the MVIRI and the SEVIRI instruments onboard the geostationary Meteosat satellites. SARAH-3 covers the time period 1 Jan 1983 - 31 Dec 2020 as climate data record (CDR) and is operationally extended as Interim Climate Data Record (ICDR) to the present with a latency of 5 days; the data cover the region ±65° longitude and ±65° latitude. The products are available as monthly (P1M) and daily (P1D) means, and as 30-min instantaneous data (PT30M) (sunshine duration is available as monthly and daily sum) on a regular latitude/longitude grid with a spatial resolution of 0.05° x 0.05° degrees. The data record is complemented with a comprehensive documentation of the algorithms used and the generation of the data record. Validation report and user guidance are also available.\n\nAll product are available via the CM SAF Web User Interface. The daily and monthly mean surface incoming shortwave radiation (SIS) and the daily and monthly sums of sunshine duration (SDU) are also provided via EUMETCast.\n\nNote: the CDR coverage ends on the 31/12/2020. Dates after 31/12/2020 are covered by the ICDR.","links":[{"rel":"cite-as","href":"https://doi.org/10.5676/EUM_SAF_CM/SARAH/V003","title":"Digital Object Identifier (DOI): 10.5676/EUM_SAF_CM/SARAH/V003"},{"rel":"cite-as","href":"https://doi.org/10.5676/EUM_SAF_CM/SARAH/V002_01","title":"Previous version: SARAH-2.1"},{"rel":"cite-as","href":"https://doi.org/10.5676/EUM_SAF_CM/SARAH/V002","title":"Previous version: SARAH-2"},{"rel":"cite-as","href":"https://doi.org/10.5676/EUM_SAF_CM/SARAH/V001","title":"Previous version: SARAH-1"},{"rel":"cite-as","href":"https://doi.org/10.5676/EUM_SAF_CM/SARAH/V003","title":"Product User Manual, ATBD and Validation Report"},{"rel":"describedby","type":"text/html","href":"https://data.eumetsat.int/product/EO:EUM:DAT:0863","title":"Surface Radiation Data Set - Heliosat (SARAH) - Edition 3"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.MSG.SEVIRI-SARAH-CDR-V003","title":"EO.EUM.DAT.MSG.SEVIRI-SARAH-CDR-V003"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.MSG.SEVIRI-SARAH-CDR-V003/items","title":"items"}],"assets":{"thumbnail":{"href":"https://user.eumetsat.int/s3/eup-strapi-media/SIS_Climatology_CMSAF_SARAH_3_Full_Disc_6dcb1c33ee.png","roles":["thumbnail"],"title":"Surface Radiation Data Set - Heliosat (SARAH) - Edition 3","type":"image/png"}},"extent":{"spatial":{"bbox":[[-65,-65,65,65]]},"temporal":{"interval":[["1983-01-01T00:00:00Z",null]]}},"license":"CC-BY-4.0","keywords":["MFG,MSG","Climate","Interim Climate Data Record","Thematic Climate Data Record","Radiation"],"summaries":{"platform":["MFG,MSG"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/processing/v1.2.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json","https://stac-extensions.github.io/scientific/v1.0.0/schema.json"],"providers":[{"name":"European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)","roles":["producer","processor","licensor","host"],"url":"https://www.eumetsat.int"}],"dedl:short_description":"Surface Solar Radiation Data Set - Heliosat version 3 (SARAH-3): a 38-year satellite-based climate record (1983-2020) of various solar irradiances and related metrics from Meteosat observations, now operatively extended up to 5-day latency beyond 2020, available at 0.05°x0.05° resolution."},{"type":"Collection","title":"Active Fire Monitoring (netCDF) - MTG - 0 degree","id":"EO.EUM.DAT.MTG.FCI-ACTIVE_FIRE-L2-V1","description":"The Active Fire Monitoring product indicates the presence of fire within a pixel. The key input to the algorithm is the FCI channel IR-3.8, which is very sensitive to hot spots caused by fire.","links":[{"rel":"describedby","type":"text/html","href":"https://data.eumetsat.int/product/EO:EUM:DAT:0682","title":"Active Fire Monitoring (netCDF) - MTG - 0 degree"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.MTG.FCI-ACTIVE_FIRE-L2-V1","title":"EO.EUM.DAT.MTG.FCI-ACTIVE_FIRE-L2-V1"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.MTG.FCI-ACTIVE_FIRE-L2-V1/items","title":"items"}],"assets":{"thumbnail":{"href":"https://user.eumetsat.int/s3/eup-strapi-media/mtg_fci_fir_20250612_131121_1715573019.png","roles":["thumbnail"],"title":"Active Fire Monitoring (netCDF) - MTG - 0 degree","type":"image/png"}},"extent":{"spatial":{"bbox":[[-67.5,-67.5,67.5,67.5]]},"temporal":{"interval":[["2025-07-03T00:00:00Z",null]]}},"license":"CC-BY-4.0","keywords":["FCI","MTG","L2","Fire","Level 2 Data"],"summaries":{"instruments":["FCI"],"intruments":["FCI"],"platform":["MTG"],"processing:level":["L2"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/processing/v1.2.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)","roles":["producer","processor","licensor","host"],"url":"https://www.eumetsat.int"}],"dedl:short_description":"Active Fire Monitoring product using Meteosat FCI channel IR-3.8 detects fires through its sensitivity to heat signatures."},{"type":"Collection","title":"Atmospheric Motion Vectors (BUFR) - MTG - 0 degree","id":"EO.EUM.DAT.MTG.FCI-AMV-BUFR","description":"The Atmospheric Motion Vector (AMV) product is realised by tracking clouds or water vapour features in consecutive FCI satellite images based on feature tracking between each pair of consecutive repeat cycles, leading to two intermediate AMV products for an image triplet. The final product is then derived from these two intermediate products, and includes information on wind speed, direction, height, and quality. AMVs are extracted from the FCI VIS 0.8, IR 3.8 (night only), IR 10.5, WV 6.3 and WV 7.3 channels. The AMV product is available in BUFR and netCDF format, every 30 minutes.","links":[{"rel":"describedby","type":"text/html","href":"https://data.eumetsat.int/product/EO:EUM:DAT:0998","title":"Product Description"},{"rel":"license","type":"text/html","href":"https://user.eumetsat.int/resources/user-guides/data-registration-and-licensing#ID-Data-Licensing","title":"EUMETSAT Data Policy"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.MTG.FCI-AMV-BUFR","title":"EO.EUM.DAT.MTG.FCI-AMV-BUFR"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.MTG.FCI-AMV-BUFR/items","title":"items"}],"assets":{"thumbnail":{"href":"https://user.eumetsat.int/s3/eup-strapi-media/MTG_AMV_0248b7514f.png","roles":["thumbnail"],"title":"Atmospheric Motion Vectors (BUFR) - MTG - 0 degree","type":"image/png"}},"extent":{"spatial":{"bbox":[[0,0,0,0]]},"temporal":{"interval":[["2025-01-22T00:00:00Z",null]]}},"license":"other","keywords":["BUFR","FCI","AMV","Clouds","MTG","L2","Level 2 Data"],"summaries":{"constellation":["MTG"],"instruments":["FCI"],"intruments":["FCI"],"platform":["MTG"],"processing:level":["L2"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/processing/v1.2.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)","roles":["producer","processor","licensor","host"],"url":"https://www.eumetsat.int"}],"dedl:short_description":"The Atmospheric Motion Vector product combines cloud and water vapor feature tracking across consecutive satellite images to derive wind speed, direction, height, and quality data at 30-minute intervals."},{"type":"Collection","title":"Atmospheric Motion Vectors (netCDF) - MTG - 0 degree","id":"EO.EUM.DAT.MTG.FCI-AMV-NETCDF","description":"The Atmospheric Motion Vector (AMV) product is realised by tracking clouds or water vapour features in consecutive FCI satellite images based on feature tracking between each pair of consecutive repeat cycles, leading to two intermediate AMV products for an image triplet. 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The statistics are computed on the L1C radiances (for all FCI channels), brightness temperatures (for the eight IR channels) and reflectances (for the eight visible and near-infrared channels) and include the mean value, standard deviation, minimum and maximum values within the FoR. 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The statistics are computed on the L1C radiances (for all FCI channels), brightness temperatures (for the eight IR channels) and reflectances (for the eight visible and near-infrared channels) and include the mean value, standard deviation, minimum and maximum values within the FoR. The ASR product is available in BUFR and netCDF format, every 10 minutes, at a spatial resolution of 16x16 pixels (IR) and 32x32 pixels (VIS).","links":[{"rel":"describedby","type":"text/html","href":"https://data.eumetsat.int/product/EO:EUM:DAT:0677","title":"Product Description"},{"rel":"license","type":"text/html","href":"https://user.eumetsat.int/resources/user-guides/data-registration-and-licensing#ID-Data-Licensing","title":"EUMETSAT Data Policy"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.MTG.FCI-ASR-NETCDF","title":"EO.EUM.DAT.MTG.FCI-ASR-NETCDF"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.MTG.FCI-ASR-NETCDF/items","title":"items"}],"assets":{"thumbnail":{"href":"https://user.eumetsat.int/s3/eup-strapi-media/MTG_ASR_7c11f1ca7f.png","roles":["thumbnail"],"title":"All Sky Radiance (netCDF) - MTG - 0 degree","type":"image/png"}},"extent":{"spatial":{"bbox":[[0,0,0,0]]},"temporal":{"interval":[["2025-01-22T00:00:00Z",null]]}},"license":"other","keywords":["netCDF","ASR","FCI","Radiance","MTG","L2","Level 2 Data"],"summaries":{"constellation":["MTG"],"instruments":["FCI"],"intruments":["FCI"],"platform":["MTG"],"processing:level":["L2"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/processing/v1.2.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)","roles":["producer","processor","licensor","host"],"url":"https://www.eumetsat.int"}],"dedl:short_description":"The All-Sky Radiance product contains segment-level statistics for FCI Level 1C data including means, deviations, minima, maxima across various channels within each 16/32 pixel field-of-regard area."},{"type":"Collection","title":"Cloud Mask - MTG - 0 degree","id":"EO.EUM.DAT.MTG.FCI-CLM","description":"The central aim of the cloud mask (CLM) product is to identify cloudy and cloud free FCI Level 1c pixels with high confidence. The product also provides information on the presence of snow/sea ice, volcanic ash and dust. This information is crucial both for spatiotemporal analyses of the cloud coverage and for the subsequent retrieval of other meteorological products that are only valid for cloudy (e.g. cloud properties) or clear pixels (e.g. clear sky reflectance maps or global instability indices). The algorithm is based on multispectral threshold techniques applied to each pixel of the image. CLM is available in netCDF and GRIB format, every 10 minutes, at a spatial resolution of 2 km at nadir.","links":[{"rel":"describedby","type":"text/html","href":"https://data.eumetsat.int/product/EO:EUM:DAT:0678","title":"Product Description"},{"rel":"license","type":"text/html","href":"https://user.eumetsat.int/resources/user-guides/data-registration-and-licensing#ID-Data-Licensing","title":"EUMETSAT Data Policy"},{"rel":"example","type":"application/x-ipynb+json","href":"https://raw.githubusercontent.com/destination-earth/DestinE-DataLake-Lab/refs/heads/main/HDA/EUM_data/DEDL-HDA-EO.EUM.DAT.MTG.ipynb","title":"MTG FCI CLM through HDA Examples"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.MTG.FCI-CLM","title":"EO.EUM.DAT.MTG.FCI-CLM"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.MTG.FCI-CLM/items","title":"items"}],"assets":{"thumbnail":{"href":"https://user.eumetsat.int/s3/eup-strapi-media/MTG_CLM_78821c04d7.png","roles":["thumbnail"],"title":"Cloud Mask - MTG - 0 degree","type":"image/png"}},"extent":{"spatial":{"bbox":[[0,0,0,0]]},"temporal":{"interval":[["2025-01-22T00:00:00Z",null]]}},"license":"other","keywords":["CLM","FCI","Clouds","MTG","L2","Level 2 Data"],"summaries":{"constellation":["MTG"],"instruments":["FCI"],"intruments":["FCI"],"platform":["MTG"],"processing:level":["L2"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/processing/v1.2.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json","https://stac-extensions.github.io/application/v0.1.0/schema.json"],"providers":[{"name":"European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)","roles":["producer","processor","licensor","host"],"url":"https://www.eumetsat.int"}],"dedl:short_description":"The Cloud Mask (CLM) product identifies cloudy/cloud-free pixels with high confidence from MTG data at 0 degrees, providing additional information on snow/ice, volcanic ash, and dust, suitable for various applications including cloud analysis and meteorological retrievals."},{"type":"Collection","title":"Global Instability Indices - MTG - 0 degree","id":"EO.EUM.DAT.MTG.FCI-GII","description":"The Global Instability Index (GII) product provides information about instability of the atmosphere and thus can identify regions of convective potential. GII is a segmented product that uses an optimal estimation scheme to fit clear-sky vertical profiles of temperature and humidity, constrained by NWP forecast products, to FCI observations in the seven channels WV6.3, WV7.3, IR8.7, IR9.7, IR10.5, IR12.3, and IR13.3. The retrieved profiles are then used to compute atmospheric instability indices: Lifted Index, K Index, Layer Precipitable Water, Total Precipitable Water. The GII product is available in netCDF format, every 10 minutes, in 3x3 pixels (IR channels), leading to a spatial resolution of 6 km at nadir.","links":[{"rel":"describedby","type":"text/html","href":"https://data.eumetsat.int/product/EO:EUM:DAT:0683","title":"Product Description"},{"rel":"license","type":"text/html","href":"https://user.eumetsat.int/resources/user-guides/data-registration-and-licensing#ID-Data-Licensing","title":"EUMETSAT Data Policy"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.MTG.FCI-GII","title":"EO.EUM.DAT.MTG.FCI-GII"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.MTG.FCI-GII/items","title":"items"}],"assets":{"thumbnail":{"href":"https://user.eumetsat.int/s3/eup-strapi-media/MTG_GII_dd8734e27e.png","roles":["thumbnail"],"title":"Global Instability Indices - MTG - 0 degree","type":"image/png"}},"extent":{"spatial":{"bbox":[[0,0,0,0]]},"temporal":{"interval":[["2025-01-22T00:00:00Z",null]]}},"license":"other","keywords":["FCI","MTG","L2","GII","atmosphere","Level 2 Data"],"summaries":{"constellation":["MTG"],"instruments":["FCI"],"intruments":["FCI"],"platform":["MTG"],"processing:level":["L2"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/processing/v1.2.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)","roles":["producer","processor","licensor","host"],"url":"https://www.eumetsat.int"}],"dedl:short_description":"The Global Instability Index product retrieves atmospheric instability from satellite data with a 6km resolution, providing indices like Lifted Index, K Index, and precipitable water content through fitting clear-sky profiles to NWP forecasts and FCI observations."},{"type":"Collection","title":"Optimal Cloud Analysis - MTG - 0 degree","id":"EO.EUM.DAT.MTG.FCI-OCA","description":"The Optimal Cloud Analysis (OCA) product uses an optimal estimation retrieval scheme to retrieve cloud properties (phase, height and microphysical properties) from visible, near-infrared and thermal infrared FCI channels. The optimal estimation framework aims to ensure that measurements and any prior information may be given appropriate weight in the solution depending on error characteristics whether instrumental or from modelling sources. The product can also contain information on dust and volcanic ash clouds if these are flagged in the corresponding Cloud Analysis Product. The OCA product is available in netCDF format, every 10 minutes, at 2km spatial resolution at nadir.","links":[{"rel":"describedby","type":"text/html","href":"https://data.eumetsat.int/product/EO:EUM:DAT:0684","title":"Product Description"},{"rel":"license","type":"text/html","href":"https://user.eumetsat.int/resources/user-guides/data-registration-and-licensing#ID-Data-Licensing","title":"EUMETSAT Data Policy"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.MTG.FCI-OCA","title":"EO.EUM.DAT.MTG.FCI-OCA"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.MTG.FCI-OCA/items","title":"items"}],"assets":{"thumbnail":{"href":"https://user.eumetsat.int/s3/eup-strapi-media/MTG_OCA_4fb9843841.png","roles":["thumbnail"],"title":"Optimal Cloud Analysis - MTG - 0 degree","type":"image/png"}},"extent":{"spatial":{"bbox":[[0,0,0,0]]},"temporal":{"interval":[["2025-01-22T00:00:00Z",null]]}},"license":"other","keywords":["Cloud","OCA","FCI","MTG","L2","Level 2 Data"],"summaries":{"constellation":["MTG"],"instruments":["FCI"],"intruments":["FCI"],"platform":["MTG"],"processing:level":["L2"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/processing/v1.2.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)","roles":["producer","processor","licensor","host"],"url":"https://www.eumetsat.int"}],"dedl:short_description":"The Optimal Cloud Analysis product retrieves cloud properties with high accuracy by combining satellite data and prior knowledge through an optimal estimation retrieval scheme for various wavelengths."},{"type":"Collection","title":"Outgoing LW radiation at TOA - MTG - 0 degree","id":"EO.EUM.DAT.MTG.FCI-OLR","description":"The Outgoing Longwave Radiation (OLR) product is important for Earth radiation budget studies as well as for weather and climate model validation purposes, since variations in OLR reflect the response of the Earth-atmosphere system to solar diurnal forcing. The product is based on a statistical relationship linking the radiance measured in each FCI infrared channel to the top-of-atmosphere outgoing longwave flux integrated over the full infrared spectrum. The computation is done for each pixel considering the cloud cover characteristics (clear sky, semi-transparent and opaque cloud cover). The OLR product is available in netCDF format, every 10 minutes, at 2 km spatial resolution at nadir.","links":[{"rel":"describedby","type":"text/html","href":"https://data.eumetsat.int/product/EO:EUM:DAT:0685","title":"Product Description"},{"rel":"license","type":"text/html","href":"https://user.eumetsat.int/resources/user-guides/data-registration-and-licensing#ID-Data-Licensing","title":"EUMETSAT Data Policy"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.MTG.FCI-OLR","title":"EO.EUM.DAT.MTG.FCI-OLR"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.MTG.FCI-OLR/items","title":"items"}],"assets":{"thumbnail":{"href":"https://user.eumetsat.int/s3/eup-strapi-media/MTG_OLR_258a8ffb35.png","roles":["thumbnail"],"title":"Outgoing LW radiation at TOA - MTG - 0 degree","type":"image/png"}},"extent":{"spatial":{"bbox":[[0,0,0,0]]},"temporal":{"interval":[["2025-01-22T00:00:00Z",null]]}},"license":"other","keywords":["FCI","MTG","L2","LW","Radiation","Level 2 Data","OLR"],"summaries":{"constellation":["MTG"],"instruments":["FCI"],"intruments":["FCI"],"platform":["MTG"],"processing:level":["L2"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/processing/v1.2.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)","roles":["producer","processor","licensor","host"],"url":"https://www.eumetsat.int"}],"dedl:short_description":"The Outgoing Longwave Radiation (OLR) product measures TOA outgoing longwave flux from satellite data with 2km resolution, accounting for clear and cloudy conditions."},{"type":"Collection","title":"LI Accumulated Flashes - MTG - 0 degree","id":"EO.EUM.DAT.MTG.LI-AF","description":"LI Level 2 Accumulated Flashes (AF) complements the LI Level 2 Accumulated Flash Area (AFA) by providing one with the variation of the number of events within those regions reported to have lightning flashes in the Accumulated Flash Area (AFA). Accumulated Flashes provide users with data about the mapping of the number of LI events/detections rather than the mapping of flashes. One should keep in mind that the absolute value within each pixel of the Accumulated Flashes has no real physical meaning; it is rather a proxy for the pixel-by-pixel variation of the number of events. It is worth noting that one can derive the flash rate over a region encompassing a complete lightning feature (not within an FCI grid pixel) in Accumulated Flashes; this stems from the definition in Accumulated (gridded) data.","links":[{"rel":"describedby","type":"text/html","href":"https://data.eumetsat.int/product/EO:EUM:DAT:0686","title":"Product information"},{"rel":"describedby","type":"text/html","href":"https://user.eumetsat.int/resources/user-guides/mtg-li-level-2-data-guide","title":"MTG LI level 2 data guide"},{"rel":"describedby","type":"application/pdf","href":"https://user.eumetsat.int/s3/eup-strapi-media/MTG_Products_Distribution_Baseline_MTGDIS_v4_B_8e06009946.pdf","title":"MTG Products Distribution Baseline [MTGDIS]"},{"rel":"describedby","type":"text/html","href":"https://user.eumetsat.int/resources/user-guides/mtg-data-access-guide","title":"Data Store MTG data access guide"},{"rel":"describedby","type":"text/html","href":"https://gitlab.eumetsat.int/eumetlab/data-services/eumdac_data_store/-/blob/master/1_6_MTG_LI_data_access.ipynb","title":"Accessing MTG LI Level 2 products"},{"rel":"describedby","type":"text/html","href":"https://gitlab.eumetsat.int/eumetlab/weather/weather-labs/weather-labs-1/-/blob/master/Lab11_LI_data_display/LI_Data_explore.ipynb","title":"Exploring MTG LI data"},{"rel":"describedby","type":"text/html","href":"https://user.eumetsat.int/resources/user-guides/mtg-in-operations","title":"MTG in operations"},{"rel":"describedby","type":"text/html","href":"https://user.eumetsat.int/resources/user-guides/mtg-africa-data-service-guide","title":"MTG Africa data service guide"},{"rel":"describedby","type":"application/pdf","href":"https://user.eumetsat.int/s3/eup-strapi-media/MTG_LI_Level_2_data_guide_Appendices_5967c5a472.pdf","title":"MTG LI Level 2 data guide - 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It is important to keep in mind that each flash is treated as a flat (uniform) optical emission in this data. Accumulated Flash Area allows one to monitor the regions within a cloud top from which lightning-related optical emissions over 30 sec are emerging and accumulating and to know the number of flashes that were observed within the FCI grid pixels composing those regions. For example, from the Accumulated Flash Area, one can derive the flash rate for each pixel of the FCI 2km grid. 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These are closely related to other similar space-born instruments. The definition of a group is shared by LI, GLM and ISS-LIS groups: collections of pixel-based lightning events that are acquired within the same acquisition frame and are spatially clustered. LI groups provide users with information about the time-slicing imaging (over the LI acquisition time, ie one millisecond) of lightning optical emissions. When comparing LI groups with either GLM or ISS-LIS groups, users must consider the differences in design between instruments, such as integration time and spatial sampling/resolution. Both GLM and ISS-LIS acquire over two milliseconds. When observing the same storm, this difference in design can potentially create considerable differences in the total number of groups, as well as differences between the acquisition times of the groups. In addition, differences will be found also for the geolocation of groups. In general, the discrepancies mentioned above are expected to be of the order of a few milliseconds for the group time and of the order of a few kilometres for the group geolocation.","links":[{"rel":"describedby","type":"text/html","href":"https://data.eumetsat.int/product/EO:EUM:DAT:0782","title":"Product Description"},{"rel":"describedby","type":"text/html","href":"https://user.eumetsat.int/resources/user-guides/mtg-li-level-2-data-guide","title":"MTG LI level 2 data guide"},{"rel":"describedby","type":"application/pdf","href":"https://user.eumetsat.int/s3/eup-strapi-media/MTG_LI_Level_2_Format_Specification_12c067c926.pdf","title":"MTG LI Level 2 Format Specification"},{"rel":"describedby","type":"text/html","href":"https://user.eumetsat.int/resources/user-guides/mtg-data-access-guide","title":"Data Store MTG data access guide"},{"rel":"describedby","type":"text/html","href":"https://gitlab.eumetsat.int/eumetlab/data-services/eumdac_data_store/-/blob/master/1_6_MTG_LI_data_access.ipynb","title":"Accessing MTG LI Level 2 products"},{"rel":"describedby","type":"text/html","href":"https://user.eumetsat.int/resources/user-guides/mtg-in-operations","title":"MTG in operations"},{"rel":"describedby","type":"text/html","href":"https://user.eumetsat.int/resources/user-guides/mtg-africa-data-service-guide","title":"MTG Africa data service guide"},{"rel":"describedby","type":"application/pdf","href":"https://user.eumetsat.int/s3/eup-strapi-media/MTG_LI_Level_2_data_guide_Appendices_5967c5a472.pdf","title":"MTG LI Level 2 data guide - 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Its main purpose is to provide input for the vertical temperature and humidity profile retrievals. 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The data record covers more than 40 years from 29 October 1978 to 31 December 2022. Release 2 provides recalibrated Level 1c brightness temperatures based on the V4.0 calibration method developed by Cao et al. (2007). This method was implemented into the NWP-SAF software ATOVS and AVHRR processing Package (AAPP). This software was consistently used to recalibrate and reprocess data from all HIRS instruments on board TIROS-N, NOAA-06 to NOAA-19, Metop-A, and Metop-B. Input HIRS data were collected from NOAA/CLASS and ECMWF archives and merged to produce a longer time series of some of the satellites.","links":[{"rel":"describedby","type":"application/pdf","href":"https://user.eumetsat.int/s3/eup-strapi-media/C3_S2_D310_1_1_1_202306_HIRS_FDR_Release2_Product_User_Guide_1afc0a6771.pdf","title":"HIRS Fundamental Data Record Release 2 - Product User Guide (D1.1)"},{"rel":"describedby","type":"text/html","href":"https://user.eumetsat.int/catalogue/EO:EUM:DAT:0961","title":"Product information"},{"rel":"describedby","type":"text/html","href":"https://data.eumetsat.int/product/EO:EUM:DAT:0961","title":"https://data.eumetsat.int/product/EO:EUM:DAT:0961"},{"rel":"license","type":"application/pdf","href":"https://www.eumetsat.int/data-policy/eumetsat-data-policy.pdf","title":"EUMETSAT Data Policy"},{"rel":"cite-as","type":"text/html","href":"https://dx.doi.org/10.15770/EUM_SEC_CLM_0036","title":"HIRS Level 1C Fundamental Data Record Release 2 - Multimission - Global"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.MULT.HIRSL1C-FDR","title":"EO.EUM.DAT.MULT.HIRSL1C-FDR"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.MULT.HIRSL1C-FDR/items","title":"items"}],"assets":{"thumbnail":{"href":"https://user.eumetsat.int/s3/eup-strapi-media/HI_Rxxx1_C02000961_60b23cd056.jpg","roles":["thumbnail"],"title":"HIRS Level 1C Fundamental Data Record Release 2 - Multimission - Global","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["1978-10-29T00:00:00Z","2022-12-31T23:59:59Z"]]}},"license":"other","keywords":["METOP,NOAA,TIROS","FDR","Atmosphere","HIRS","L1C","MULTIMISSION","METOP-A,METOP-B,TIROS-N,NOAA-6,NOAA-7,NOAA-8,NOAA-9,NOAA-10,NOAA-11,NOAA-12,NOAA-13,NOAA-14,NOAA-15,NOAA-16,NOAA-17,NOAA-18,NOAA-19","Level 1 Data"],"summaries":{"constellation":["METOP,NOAA,TIROS"],"instruments":["HIRS"],"intruments":["HIRS"],"platform":["METOP-A,METOP-B,TIROS-N,NOAA-6,NOAA-7,NOAA-8,NOAA-9,NOAA-10,NOAA-11,NOAA-12,NOAA-13,NOAA-14,NOAA-15,NOAA-16,NOAA-17,NOAA-18,NOAA-19"],"processing:level":["L1C"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/processing/v1.2.0/schema.json","https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)","roles":["producer","processor","licensor","host"],"url":"https://www.eumetsat.int"}],"dedl:short_description":"This dataset contains over 40 years of globally calibrated HIRS level 1c brightness temperature data from various satellites spanning from 1978 to 2020."},{"type":"Collection","title":"GIRAFE v1: CM SAF Global Interpolated RAinFall Estimation version 1","id":"EO.EUM.DAT.MULT.PMW_IR-GIRAFE_PRECIP-CDR-V001","description":"The GIRAFE v1 climate data record (CDR) provides precipitation estimates derived from a combination of passive microwave (PMW) observations onboard polar orbiting satellites and infrared (IR) observations onboard geostationary satellites. GIRAFE v1 covers the time period 2002/01/01 until 2022/12/31. The PMW input to GIRAFE v1 is from various microwave imager and sounder instruments. Precipitation rate estimates are retrieved from the observed PMW brightness temperatures by precipitation retrieval algorithms HOAPS, PNPR-CLIM*, and PRPS. The resulting archives of instantaneous precipitation rate estimates are homogenized using quantile mapping. The IR input to GIRAFE comes from the five geostationary positions forming the Geo-Ring, providing observations along all geographical longitudes. The spatially and temporally highly resolved IR input is trained to detect the occurrence of precipitation using the PMW-based instantaneous precipitation rate estimates. Conditional precipitation rates are computed based on PMW observations only. At latitudes higher than 55°N/S where Geo-Ring IR pixels are extremely distorted, GIRAFE v1 relies only on the PMW input. GIRAFE v1 is a gridded product which is available globally at a spatial resolution of 1° x 1°  and at a temporal resolution of 24h as accumulated precipitation computed from the (IR-based) fraction of precipitation and the conditional precipitation rate. Additionally, 1° x 1° monthly mean values of the daily accumulated precipitation are provided. The daily accumulated precipitation features a dedicated sampling uncertainty at the same 1° x 1° x 24 h resolution which is based on the analysis of decorrelation scales in space and time in the IR-based  precipitation fields. This is a Thematic Climate Data Record (TCDR).\n*The PNPR-CLIM algorithm has been developed by CNR-ISAC in the C3S_312b_Lot1 Copernicus project.","links":[{"rel":"cite-as","href":"https://doi.org/10.5676/EUM_SAF_CM/GIRAFE/V001","title":"CM SAF landing page for GIRAFE v1: CM SAF Global Interpolated RAinFall Estimation version 1"},{"rel":"describedby","type":"text/html","href":"https://data.eumetsat.int/product/EO:EUM:DAT:0921","title":"GIRAFE v1: CM SAF Global Interpolated RAinFall Estimation version 1"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.MULT.PMW_IR-GIRAFE_PRECIP-CDR-V001","title":"EO.EUM.DAT.MULT.PMW_IR-GIRAFE_PRECIP-CDR-V001"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.MULT.PMW_IR-GIRAFE_PRECIP-CDR-V001/items","title":"items"}],"assets":{"thumbnail":{"href":"https://user.eumetsat.int/s3/eup-strapi-media/girafe_bd1b6a610b.png","roles":["thumbnail"],"title":"GIRAFE v1: CM SAF Global Interpolated RAinFall Estimation version 1","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2002-01-01T00:00:00Z","2022-12-31T00:00:00Z"]]}},"license":"CC-BY-4.0","keywords":["GIRAFE","GOES,Himawari,MFG,MSG","Precipitation","Atmosphere","Climate Data Record","CDR","MSG","MULTIMISSION","Level 3 Data","MFG","L3"],"summaries":{"platform":["GOES,Himawari,MFG,MSG"],"processing:level":["L3"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/processing/v1.2.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json","https://stac-extensions.github.io/scientific/v1.0.0/schema.json"],"providers":[{"name":"European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)","roles":["producer","processor","licensor","host"],"url":"https://www.eumetsat.int"}],"dedl:short_description":"GIRAFE v1 climate data record combines PMW and IR satellite observations to estimate global precipitation rates from 2002-2022, offering 1°x1° resolution and 24-hour accumulation, including sampling uncertainties."},{"type":"Collection","title":"SLSTR Level 2 Aerosol Optical Depth - Sentinel-3","id":"EO.EUM.DAT.SENTINEL-3.AOD","description":"The Copernicus Sentinel-3 (S3) NRT AOD product quantifies the abundance of aerosol particles, and monitors their global distribution \u0026 long-range transport, at the scale of 9.5 x 9.5 km2. 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The NRT S3 FRP product will become operational after a higher level of quality and maturity is reached, including the full processing of granules during daytime, a comprehensive global validation, and positive feedback by experts and users.","links":[{"rel":"license","type":"application/pdf","href":"https://www.eumetsat.int/data-policy/eumetsat-data-policy.pdf","title":"EUMETSAT Data Policy"},{"rel":"describedby","type":"text/html","href":"https://data.eumetsat.int/product/EO:EUM:DAT:0417","title":"SLSTR Level 2 Fire Radiative Power - Sentinel-3"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.SENTINEL-3.FRP","title":"EO.EUM.DAT.SENTINEL-3.FRP"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.SENTINEL-3.FRP/items","title":"items"}],"assets":{"thumbnail":{"href":"https://user.eumetsat.int/s3/eup-strapi-media/s3afrpmwirnight_1e5f31ae6b.png","roles":["thumbnail"],"title":"SLSTR Level 2 Fire Radiative Power - Sentinel 3","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2020-08-08T00:00:00Z",null]]}},"license":"other","keywords":["Wildfires","SLSTR","Sentinel-3","Fire Radiative Power","L2","Sentinel-3A,Sentinel-3B","FRP","Fire","Level 2 Data"],"summaries":{"constellation":["Sentinel-3"],"instruments":["SLSTR"],"intruments":["SLSTR"],"platform":["Sentinel-3A,Sentinel-3B"],"processing:level":["L2"],"s1:product_timeliness":["NRT-3h"]},"item_assets":{"thumbnail":{"description":"An averaged, decimated preview image in PNG format. 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Level 1 products are calibrated Top Of Atmosphere radiance values at OLCI 21 spectral bands. Radiances are computed from the instrument digital counts by applying geo-referencing, radiometric processing (non-linearity correction, smear correction, dark offset correction, absolute gain calibration adjusted for gain evolution with time), and stray-light correction for straylight effects in OLCI camera's spectrometer and ground imager. Additionally, spatial resampling of OLCI pixels to the 'ideal' instrument grid, initial pixel classification, and annotation at tie points with auxiliary meteorological data and acquisition geometry are provided. The radiance products are accompanied by error estimate products, however the error values are currently not available.\n\n- All Sentinel-3 NRT products are available at pick-up point in less than 3h.\n- All Sentinel-3 Non Time Critical (NTC) products are available at pick-up point in less than 30 days.\nSentinel-3 is part of a series of Sentinel satellites, under the umbrella of the EU Copernicus programme.","links":[{"rel":"license","type":"application/pdf","href":"https://www.eumetsat.int/data-policy/eumetsat-data-policy.pdf","title":"EUMETSAT Data Policy"},{"rel":"describedby","type":"text/html","href":"https://data.eumetsat.int/product/EO:EUM:DAT:0409","title":"OLCI Level 1B Full Resolution - Sentinel-3"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.SENTINEL-3.OL_1_EFR___","title":"EO.EUM.DAT.SENTINEL-3.OL_1_EFR___"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.SENTINEL-3.OL_1_EFR___/items","title":"items"}],"assets":{"thumbnail":{"href":"https://user.eumetsat.int/s3/eup-strapi-media/s3_olci_9eea044f43.png","roles":["thumbnail"],"title":"Sentinel-3A Sea Level Anomaly (cm)","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2016-04-25T00:00:00Z",null]]}},"license":"other","keywords":["Sentinel-3","Water quality","OLCI","Chlorophyll-a concentration","Sentinel-3A,Sentinel-3B","Ocean Colour","Ocean","Level 1 Data","L1B"],"summaries":{"bands":[{"description":"Yellow substance and detrital pigments","eo:center_wavelength":"413 nm","name":"O1"},{"description":"Chl absorption max. /Vegetation","eo:center_wavelength":"443 nm","name":"O2"},{"description":"Chl, Other pigments","eo:center_wavelength":"490 nm","name":"O3"},{"description":"Chl, Sediment, Turbidity, Red tide","eo:center_wavelength":"510 nm","name":"O4"},{"description":"Chlorophyll reference","eo:center_wavelength":"560 nm","name":"O5"},{"description":"Sediment Loading","eo:center_wavelength":"620 nm","name":"O6"},{"description":"Chl, Sediment, Yellow Substance / Vegetation","eo:center_wavelength":"665 nm","name":"O7"},{"description":"Chl fluorescence peak, red edge","eo:center_wavelength":"681 nm","name":"O8"},{"description":"Chl fluorescence baseline","eo:center_wavelength":"709 nm","name":"O9"},{"description":"O2 absorption /Cloud/ Ocean colour","eo:center_wavelength":"754 nm","name":"O10"},{"description":"O2 absorption band/Aerosol corr.","eo:center_wavelength":"761 nm","name":"O11"},{"description":"Atmos. / Aerosol corr.","eo:center_wavelength":"779 nm","name":"O12"},{"description":"Aerosols, Clouds, Pixel co-registration","eo:center_wavelength":"865 nm","name":"O13"},{"description":"Water vap. absorption ref.","eo:center_wavelength":"885 nm","name":"O14"},{"description":"Water vap. 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All Sentinel-3 NRT products are available at pick-up point in less than 3h. Level 1 products are calibrated Top Of Atmosphere radiance values at OLCI 21 spectral bands. Radiances are computed from the instrument digital counts by applying geo-referencing, radiometric processing (non-linearity correction, smear correction, dark offset correction, absolute gain calibration adjusted for gain evolution with time), and stray-light correction for straylight effects in OLCI camera's spectrometer and ground imager. Additionally, spatial resampling of OLCI pixels to the 'ideal' instrument grid, initial pixel classification, and annotation at tie points with auxiliary meteorological data and acquisition geometry are provided. The radiance products are accompanied by error estimate products, however the error values are currently not available.\n\n- All Sentinel-3 NRT products are available at pick-up point in less than 3h\n- All Sentinel-3 Non Time Critical (NTC) products are available at pick-up point in less than 30 days\nSentinel-3 is part of a series of Sentinel satellites, under the umbrella of the EU Copernicus programme.","links":[{"rel":"license","type":"application/pdf","href":"https://www.eumetsat.int/data-policy/eumetsat-data-policy.pdf","title":"EUMETSAT Data Policy"},{"rel":"example","type":"application/x-ipynb+json","href":"https://raw.githubusercontent.com/destination-earth/DestinE-DataLake-Lab/refs/heads/main/HDA/EUM_data/DEDL-HDA-EO.EUM.DAT.SENTINEL-3.OL_1_ERR___.ipynb","title":"Destination Earth - OLCI Level 1B Reduced Resolution - Sentinel-3 - Data Access using DEDL HDA"},{"rel":"describedby","type":"text/html","href":"https://data.eumetsat.int/product/EO:EUM:DAT:0410","title":"OLCI Level 1B Reduced Resolution - Sentinel-3"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.SENTINEL-3.OL_1_ERR___","title":"EO.EUM.DAT.SENTINEL-3.OL_1_ERR___"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.SENTINEL-3.OL_1_ERR___/items","title":"items"}],"assets":{"thumbnail":{"href":"https://user.eumetsat.int/s3/eup-strapi-media/s3_olci_9eea044f43.png","roles":["thumbnail"],"title":"Sentinel-3A Sea Level Anomaly (cm)","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2016-04-25T00:00:00Z",null]]}},"license":"other","keywords":["Sentinel-3","Water quality","OLCI","Chlorophyll-a concentration","Sentinel-3A,Sentinel-3B","Ocean Colour","Ocean","Level 1 Data","L1B"],"summaries":{"bands":[{"description":"Yellow substance and detrital pigments","eo:center_wavelength":"413 nm","name":"O1"},{"description":"Chl absorption max. /Vegetation","eo:center_wavelength":"443 nm","name":"O2"},{"description":"Chl, Other pigments","eo:center_wavelength":"490 nm","name":"O3"},{"description":"Chl, Sediment, Turbidity, Red tide","eo:center_wavelength":"510 nm","name":"O4"},{"description":"Chlorophyll reference","eo:center_wavelength":"560 nm","name":"O5"},{"description":"Sediment Loading","eo:center_wavelength":"620 nm","name":"O6"},{"description":"Chl, Sediment, Yellow Substance / Vegetation","eo:center_wavelength":"665 nm","name":"O7"},{"description":"Chl fluorescence peak, red edge","eo:center_wavelength":"681 nm","name":"O8"},{"description":"Chl fluorescence baseline","eo:center_wavelength":"709 nm","name":"O9"},{"description":"O2 absorption /Cloud/ Ocean colour","eo:center_wavelength":"754 nm","name":"O10"},{"description":"O2 absorption band/Aerosol corr.","eo:center_wavelength":"761 nm","name":"O11"},{"description":"Atmos. / Aerosol corr.","eo:center_wavelength":"779 nm","name":"O12"},{"description":"Aerosols, Clouds, Pixel co-registration","eo:center_wavelength":"865 nm","name":"O13"},{"description":"Water vap. absorption ref.","eo:center_wavelength":"885 nm","name":"O14"},{"description":"Water vap. 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These radiometric products are used to estimate geophysical parameters e.g. estimates of phytoplankton biomass through determining the Chlorophyll-a (Chl) concentration.  In coastal areas, they also allow monitoring of the sediment load via the Total Suspended Matter (TSM) product.  Full resolution products are at a nominal 300m resolution.\n\nThis collection contains reprocessed data from baseline collection 003. Operational data can be found in the corresponding collection.","links":[{"rel":"cite-as","href":"https://doi.org/10.15770/EUM_SEC_CLM_0061","title":"Digital Object Identifier (DOI): 10.15770/EUM_SEC_CLM_0061"},{"rel":"describedby","type":"application/pdf","href":"https://user.eumetsat.int/s3/eup-strapi-media/Sentinel_3_OLCI_L2_report_for_baseline_collection_OL_L2_M_003_2_B_c8bbc6d986.pdf","title":"Sentinel-3 OLCI L2 report for baseline collection OL_L2M_003"},{"rel":"describedby","href":"https://metis.eumetsat.int/oc/","title":"METIS Ocean Colour"},{"rel":"describedby","type":"text/html","href":"https://data.eumetsat.int/product/EO:EUM:DAT:0556","title":"https://data.eumetsat.int/product/EO:EUM:DAT:0556"},{"rel":"license","type":"application/pdf","href":"https://user.eumetsat.int/s3/eup-strapi-media/Copernicus_Data_Usage_Terms_and_Condition_05ee8142ce.pdf","title":"Copernicus Data Usage Terms and Conditions"},{"rel":"license","type":"application/pdf","href":"https://www.eumetsat.int/data-policy/eumetsat-data-policy.pdf","title":"EUMETSAT Data Policy - PDF"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.SENTINEL-3.OL_2_WFRBC003","title":"EO.EUM.DAT.SENTINEL-3.OL_2_WFRBC003"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.SENTINEL-3.OL_2_WFRBC003/items","title":"items"}],"assets":{"thumbnail":{"href":"https://user.eumetsat.int/s3/eup-strapi-media/WFR_d2cd1af2f6.jpg","roles":["thumbnail"],"title":"OLCI Level 2 Ocean Colour Full Resolution (version BC003) - Sentinel-3 - Reprocessed","type":"image/jpeg"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2016-04-25T00:00:00Z","2021-04-28T00:00:00Z"]]}},"license":"other","keywords":["Sentinel-3","OLCI","L2","Sentinel-3A,Sentinel-3B","Ocean Colour","Radiation","Ocean","Level 2 Data"],"summaries":{"constellation":["Sentinel-3"],"instruments":["OLCI"],"intruments":["OLCI"],"platform":["Sentinel-3A,Sentinel-3B"],"processing:level":["L2"],"s1:product_timeliness":["NTC"]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/processing/v1.2.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json","https://stac-extensions.github.io/scientific/v1.0.0/schema.json"],"providers":[{"name":"European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)","roles":["producer","processor","licensor","host"],"url":"https://www.eumetsat.int/"}],"dedl:short_description":"OLCI Level 2 Marine products provide spectral information on the colour of the oceans (water reflectances). These radiometric products are used to estimate geophysical parameters e.g. estimates of phytoplankton biomass through determining the Chlorophyll-a (Chl) concentration. \n\nThis collection contains reprocessed data from baseline collection 003."},{"type":"Collection","title":"OLCI Level 2 Ocean Colour Full Resolution - Sentinel-3","id":"EO.EUM.DAT.SENTINEL-3.OL_2_WFR___","description":"OLCI (Ocean and Land Colour Instrument) Ocean Colour Geophysical Products. Full Resolution: 300m at nadir. All Sentinel-3 NRT products are available at pick-up point in less than 3h. Level 2 marine products include the following: * water-leaving reflectances in 16 bands, Oa**_reflectance (Baseline Atmospheric Correction (BAC) algorithm, bands: 400, 412, 442, 490, 510, 560, 620, 665, 674, 681, 709, 754, 779, 865, 885, 1024 nanometer (nm)); *algal pigment concentration in clear waters, chl_oc4me (BAC and maximum band ratio algorithm, log10 scaled); *algal pigment concentration in turbid waters, chl_nn (neural net algorithm, log10 scaled); *total suspended matter concentration, tsm_nn (neural net algorithm, log10 scaled); *diffuse attenuation coefficient Kd of downward irradiance at 490 nm, trsp (BAC and M07 algorithm, log10 scaled); *absorption coefficient at 443 nm of coloured detrital and dissolved organic matter, iop_nn (neural net algorithm, log10 scaled); *instantaneous photosynthetically active radiation, PAR (BAC and clear-sky ocean algorithm); *aerosol optical thickness T865 and aerosol Angstrom exponent A865, w_aer (BAC algorithm, A for bands 779 and 865 nm) *integrated water vapour column, iwv (1D-Var algorithm). The geophysical products are accompanied by error estimate products. Pixel classification, quality and science flags, as well as meteorological, geometry and geolocation data at tie points are provided.\n\n- All Sentinel-3 NRT products are available at pick-up point in less than 3h\n- All Sentinel-3 Non Time Critical (NTC) products are available at pick-up point in less than 30 days\nSentinel-3 is part of a series of Sentinel satellites, under the umbrella of the EU Copernicus programme.","links":[{"rel":"license","type":"application/pdf","href":"https://www.eumetsat.int/data-policy/eumetsat-data-policy.pdf","title":"EUMETSAT Data Policy"},{"rel":"describedby","type":"text/html","href":"https://data.eumetsat.int/product/EO:EUM:DAT:0407","title":"OLCI Level 2 Ocean Colour Full Resolution - Sentinel-3"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.SENTINEL-3.OL_2_WFR___","title":"EO.EUM.DAT.SENTINEL-3.OL_2_WFR___"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.SENTINEL-3.OL_2_WFR___/items","title":"items"}],"assets":{"thumbnail":{"href":"https://user.eumetsat.int/s3/eup-strapi-media/chl_OC_4_ME_20_June2017_e70d742681.png","roles":["thumbnail"],"title":"Sentinel-3A OLCI algal pigment concentration","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2017-11-01T00:00:00Z",null]]}},"license":"other","keywords":["Sentinel-3","OLCI","Chlorophyll-a Concentration","L2","Water Quality","Sentinel-3A,Sentinel-3B","Ocean Colour","Ocean","Level 2 Data"],"summaries":{"bands":[{"description":"Yellow Substance and Detrital Pigments","eo:center_wavelength":"413 nm","name":"O1"},{"description":"Chl Absorption Max. /Vegetation","eo:center_wavelength":"443 nm","name":"O2"},{"description":"Chl, Other Pigments","eo:center_wavelength":"490 nm","name":"O3"},{"description":"Chl, Sediment, Turbidity, Red tide","eo:center_wavelength":"510 nm","name":"O4"},{"description":"Chlorophyll Reference","eo:center_wavelength":"560 nm","name":"O5"},{"description":"Sediment Loading","eo:center_wavelength":"620 nm","name":"O6"},{"description":"Chl, Sediment, Yellow Substance / Vegetation","eo:center_wavelength":"665 nm","name":"O7"},{"description":"Chl Fluorescence Peak, Red Edge","eo:center_wavelength":"681 nm","name":"O8"},{"description":"Chl Fluorescence Baseline","eo:center_wavelength":"709 nm","name":"O9"},{"description":"O2 Absorption /Cloud/ Ocean Colour","eo:center_wavelength":"754 nm","name":"O10"},{"description":"O2 Absorption Band/Aerosol Corr.","eo:center_wavelength":"761 nm","name":"O11"},{"description":"Atmos. / Aerosol Corr.","eo:center_wavelength":"779 nm","name":"O12"},{"description":"Aerosols, Clouds, Pixel Co-registration","eo:center_wavelength":"865 nm","name":"O13"},{"description":"Water Vap. 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Reduced Resolution: 1200m at nadir. 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The geophysical products are accompanied by error estimate products. Pixel classification, quality and science flags, as well as meteorological, geometry and geolocation data at tie points are provided. \n\n- All Sentinel-3 NRT products are available at pick-up point in less than 3h\n- All Sentinel-3 Non Time Critical (NTC) products are available at pick-up point in less than 30 days.\nSentinel-3 is part of a series of Sentinel satellites, under the umbrella of the EU Copernicus programme.","links":[{"rel":"license","type":"application/pdf","href":"https://www.eumetsat.int/data-policy/eumetsat-data-policy.pdf","title":"EUMETSAT Data Policy"},{"rel":"describedby","type":"text/html","href":"https://data.eumetsat.int/product/EO:EUM:DAT:0408","title":"OLCI Level 2 Ocean Colour Reduced Resolution - Sentinel-3"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.SENTINEL-3.OL_2_WRR___","title":"EO.EUM.DAT.SENTINEL-3.OL_2_WRR___"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.SENTINEL-3.OL_2_WRR___/items","title":"items"}],"assets":{"thumbnail":{"href":"https://user.eumetsat.int/s3/eup-strapi-media/chl_OC_4_ME_20_June2017_e70d742681.png","roles":["thumbnail"],"title":"Sentinel-3A OLCI algal pigment concentration","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2017-11-01T00:00:00Z",null]]}},"license":"other","keywords":["Sentinel-3","OLCI","Chlorophyll-a Concentration","L2","Water Quality","Sentinel-3A,Sentinel-3B","Ocean Colour","Ocean","Level 2 Data"],"summaries":{"bands":[{"description":"Yellow Substance and Detrital Pigments","eo:center_wavelength":"413 nm","name":"O1"},{"description":"Chl Absorption Max. /Vegetation","eo:center_wavelength":"443 nm","name":"O2"},{"description":"Chl, Other Pigments","eo:center_wavelength":"490 nm","name":"O3"},{"description":"Chl, Sediment, Turbidity, Red tide","eo:center_wavelength":"510 nm","name":"O4"},{"description":"Chlorophyll Reference","eo:center_wavelength":"560 nm","name":"O5"},{"description":"Sediment Loading","eo:center_wavelength":"620 nm","name":"O6"},{"description":"Chl, Sediment, Yellow Substance / Vegetation","eo:center_wavelength":"665 nm","name":"O7"},{"description":"Chl Fluorescence Peak, Red Edge","eo:center_wavelength":"681 nm","name":"O8"},{"description":"Chl Fluorescence Baseline","eo:center_wavelength":"709 nm","name":"O9"},{"description":"O2 Absorption /Cloud/ Ocean Colour","eo:center_wavelength":"754 nm","name":"O10"},{"description":"O2 Absorption Band/Aerosol Corr.","eo:center_wavelength":"761 nm","name":"O11"},{"description":"Atmos. / Aerosol Corr.","eo:center_wavelength":"779 nm","name":"O12"},{"description":"Aerosols, Clouds, Pixel Co-registration","eo:center_wavelength":"865 nm","name":"O13"},{"description":"Water Vap. Absorption Ref.","eo:center_wavelength":"885 nm","name":"O14"},{"description":"Water Vap. Absorption / Vegetation","eo:center_wavelength":"900 nm","name":"O15"},{"description":"Atmos. / Aerosol Corr.","eo:center_wavelength":"1020 nm","name":"O16"}],"constellation":["Sentinel-3"],"instruments":["OLCI"],"intruments":["OLCI"],"platform":["Sentinel-3A,Sentinel-3B"],"processing:level":["L2"],"s1:product_timeliness":["NRT-3h","STC-48h","NTC-1m"]},"item_assets":{"thumbnail":{"description":"An averaged, decimated preview image in PNG format. Single polarisation products are represented with a grey scale image. Dual polarisation products are represented by a single composite colour image in RGB with the red channel (R) representing the  co-polarisation VV or HH), the green channel (G) represents the cross-polarisation (VH or HV) and the blue channel (B) represents the ratio of the cross an co-polarisations.","roles":["thumbnail"],"title":"Preview Image","type":"image/png"}},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/eo/v1.1.0/schema.json","https://stac-extensions.github.io/processing/v1.2.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)","roles":["producer","processor","licensor","host"],"url":"https://www.eumetsat.int/"}],"dedl:short_description":"The OLCC L2 product suite from Sentinel-3 provides reduced-resolution ocean colour data with various geophysical parameters including algal pigments, total suspended matter, diffuse attenuation coefficients, and atmospheric properties, along with accompanying error estimates and metadata."},{"type":"Collection","title":"SLSTR Level 1B Radiances and Brightness Temperatures - Sentinel-3","id":"EO.EUM.DAT.SENTINEL-3.SL_1_RBT___","description":"The SLSTR level 1 products contain: the radiances of the 6 visible (VIS), Near Infra-Red (NIR) and Short Wave Infra-Red (SWIR) bands (on the A and B stripe grids); the Brightness Temperature (BT) for the 3 Thermal Infra-Red (TIR) bands; the BT for the 2 Fire (FIR) bands. Resolution: 1km at nadir (TIR), 500m (VIS). All are provided for both the oblique and nadir view. These measurements are accompanied with grid and time information, quality flags, error estimates and meteorological auxiliary data.\n\n- All Sentinel-3 NRT products are available at pick-up point in less than 3h\n- All Sentinel-3 Non Time Critical (NTC) products are available at pick-up point in less than 30 days.\nSentinel-3 is part of a series of Sentinel satellites, under the umbrella of the EU Copernicus programme.","links":[{"rel":"license","type":"application/pdf","href":"https://www.eumetsat.int/data-policy/eumetsat-data-policy.pdf","title":"EUMETSAT Data Policy"},{"rel":"describedby","type":"text/html","href":"https://data.eumetsat.int/product/EO:EUM:DAT:0411","title":"SLSTR Level 1B Radiances and Brightness Temperatures - Sentinel-3"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.SENTINEL-3.SL_1_RBT___","title":"EO.EUM.DAT.SENTINEL-3.SL_1_RBT___"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.SENTINEL-3.SL_1_RBT___/items","title":"items"}],"assets":{"thumbnail":{"href":"https://user.eumetsat.int/s3/eup-strapi-media/s3_slstr_1_98197fa184.png","roles":["thumbnail"],"title":"SLSTR Level 1B","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2016-04-19T00:00:00Z",null]]}},"license":"other","keywords":["Sentinel-3","SLSTR","Sentinel-3A,Sentinel-3B","Ocean","Level 1 Data","L1B","Sea Surface Temperature"],"summaries":{"bands":[{"description":"\u003eCloud screening, vegetation monitoring, aerosol - VNIR - Solar Reflectance Bands - resolution: 500m","eo:center_wavelength":"554.27 nm","name":"S1"},{"description":"NDVI, vegetation monitoring, aerosol - VNIR - Solar Reflectance Bands - resolution: 500m","eo:center_wavelength":"659.47 nm","name":"S2"},{"description":"NDVI, cloud flagging,Pixel co-registration - VNIR - Solar Reflectance Bands - resolution: 500m","eo:center_wavelength":"868.00 nm","name":"S3"},{"description":"Cirrus detection over land - SWIR - Solar Reflectance Bands - resolution: 500m","eo:center_wavelength":"1374.80 nm","name":"S4"},{"description":"loud clearing, ice, snow,vegetation monitoring - SWIR - Solar Reflectance Bands - resolution: 500m","eo:center_wavelength":"1613.40 nm","name":"S5"},{"description":"Vegetation state and cloud clearing - SWIR - Solar Reflectance Bands - resolution: 500m","eo:center_wavelength":"2255.70 nm","name":"S6"},{"description":"SST, LST, Active fire - Thermal IR Ambient bands (200 K -320 K) - resolution: 1000m","eo:center_wavelength":"3742.00 nm","name":"S7"},{"description":"SST, LST, Active fire - Thermal IR Ambient bands (200 K -320 K) - resolution: 1000m","eo:center_wavelength":"10854.00 nm","name":"S8"},{"description":"SST, LST - Thermal IR Ambient bands (200 K -320 K) - resolution: 1000m","eo:center_wavelength":"12022.50 nm","name":"S9"},{"description":"Active fire - Thermal IR fire emission bands - resolution: 1000m","eo:center_wavelength":"3742.00 nm","name":"F1"},{"description":"Active fire - Thermal IR fire emission bands - resolution: 1000m","eo:center_wavelength":"10854.00 nm","name":"F2"}],"constellation":["Sentinel-3"],"instruments":["SLSTR"],"intruments":["SLSTR"],"platform":["Sentinel-3A,Sentinel-3B"],"processing:level":["L1B"],"s1:product_timeliness":["NRT-3h","STC-48h","NTC-1m"]},"item_assets":{"thumbnail":{"description":"An averaged, decimated preview image in PNG format. Single polarisation products are represented with a grey scale image. Dual polarisation products are represented by a single composite colour image in RGB with the red channel (R) representing the  co-polarisation VV or HH), the green channel (G) represents the cross-polarisation (VH or HV) and the blue channel (B) represents the ratio of the cross an co-polarisations.","roles":["thumbnail"],"title":"Preview Image","type":"image/png"}},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/eo/v1.1.0/schema.json","https://stac-extensions.github.io/processing/v1.2.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)","roles":["producer","processor","licensor","host"],"url":"https://www.eumetsat.int/"}],"dedl:short_description":"The SLSTR Level 1B product contains radiance values from six optical bands and brightness temperatures from three thermal infrared and two fire radiation bands across various resolutions and viewing angles along with accompanying metadata."},{"type":"Collection","title":"SLSTR Level 2 Sea Surface Temperature (SST) - Sentinel-3","id":"EO.EUM.DAT.SENTINEL-3.SL_2_WST___","description":"SLSTR SST has a spatial resolution of 1km at nadir. Skin Sea Surface Temperature following the GHRSST L2P GDS2 format specification, see https://www.ghrsst.org/ .\n\n- All Sentinel-3 NRT products are available at pick-up point in less than 3h\n- All Sentinel-3 Non Time Critical (NTC) products are available at pick-up point in less than 30 days.\nSentinel-3 is part of a series of Sentinel satellites, under the umbrella of the EU Copernicus programme.","links":[{"rel":"license","type":"application/pdf","href":"https://www.eumetsat.int/data-policy/eumetsat-data-policy.pdf","title":"EUMETSAT Data Policy"},{"rel":"describedby","type":"text/html","href":"https://data.eumetsat.int/product/EO:EUM:DAT:0412","title":"SLSTR Level 2 Sea Surface Temperature (SST) - Sentinel-3"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.SENTINEL-3.SL_2_WST___","title":"EO.EUM.DAT.SENTINEL-3.SL_2_WST___"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.SENTINEL-3.SL_2_WST___/items","title":"items"}],"assets":{"thumbnail":{"href":"https://user.eumetsat.int/s3/eup-strapi-media/20170617_sst_slstra_mar_l2p_v1_0_daytime_nrt_ref_5c85939491.png","roles":["thumbnail"],"title":"Sea Surface Skin Temperature 27 Jun 2017 - Sentinel-3A/SLSTR WST nr [REF] - day time - no cut-off'","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2017-10-31T00:00:00Z",null]]}},"license":"other","keywords":["Sentinel-3","SLSTR","L2P","Sentinel-3A,Sentinel-3B","Ocean","Level 2 Data","Sea Surface Temperature"],"summaries":{"bands":[{"description":"\u003eCloud screening, vegetation monitoring, aerosol - VNIR - Solar Reflectance Bands - resolution: 500m","eo:center_wavelength":"554.27 nm","name":"S1"},{"description":"NDVI, vegetation monitoring, aerosol - VNIR - Solar Reflectance Bands - resolution: 500m","eo:center_wavelength":"659.47 nm","name":"S2"},{"description":"NDVI, cloud flagging,Pixel co-registration - VNIR - Solar Reflectance Bands - resolution: 500m","eo:center_wavelength":"868.00 nm","name":"S3"},{"description":"Cirrus detection over land - SWIR - Solar Reflectance Bands - resolution: 500m","eo:center_wavelength":"1374.80 nm","name":"S4"},{"description":"loud clearing, ice, snow,vegetation monitoring - SWIR - Solar Reflectance Bands - resolution: 500m","eo:center_wavelength":"1613.40 nm","name":"S5"},{"description":"Vegetation state and cloud clearing - SWIR - Solar Reflectance Bands - resolution: 500m","eo:center_wavelength":"2255.70 nm","name":"S6"},{"description":"SST, LST, Active fire - Thermal IR Ambient bands (200 K -320 K) - resolution: 1000m","eo:center_wavelength":"3742.00 nm","name":"S7"},{"description":"SST, LST, Active fire - Thermal IR Ambient bands (200 K -320 K) - resolution: 1000m","eo:center_wavelength":"10854.00 nm","name":"S8"},{"description":"SST, LST - Thermal IR Ambient bands (200 K -320 K) - resolution: 1000m","eo:center_wavelength":"12022.50 nm","name":"S9"},{"description":"Active fire - Thermal IR fire emission bands - resolution: 1000m","eo:center_wavelength":"3742.00 nm","name":"F1"},{"description":"Active fire - Thermal IR fire emission bands - resolution: 1000m","eo:center_wavelength":"10854.00 nm","name":"F2"}],"constellation":["Sentinel-3"],"instruments":["SLSTR"],"intruments":["SLSTR"],"platform":["Sentinel-3A,Sentinel-3B"],"processing:level":["L2P"],"s1:product_timeliness":["NRT-3h","STC-48h","NTC-1m"]},"item_assets":{"thumbnail":{"description":"An averaged, decimated preview image in PNG format. 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Sentinel-3 is part of a series of Sentinel satellites, under the umbrella of the EU Copernicus programme. - All Sentinel-3 Non Time Critical (NTC) products are available at pick-up point in less than 30 days. - All Sentinel-3 Short Time Critical (STC) products are available at pick-up point in less than 48 hours Sentinel-3 is part of a series of Sentinel satellites, under the umbrella of the EU Copernicus programme.","links":[{"rel":"license","type":"application/pdf","href":"https://www.eumetsat.int/data-policy/eumetsat-data-policy.pdf","title":"EUMETSAT Data Policy"},{"rel":"describedby","type":"text/html","href":"https://data.eumetsat.int/product/EO:EUM:DAT:0413","title":"SRAL Level 1A Unpacked L0 Complex echos - Sentinel-3"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.SENTINEL-3.SR_1_SRA_A_","title":"EO.EUM.DAT.SENTINEL-3.SR_1_SRA_A_"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.EUM.DAT.SENTINEL-3.SR_1_SRA_A_/items","title":"items"}],"assets":{"thumbnail":{"href":"https://user.eumetsat.int/s3/eup-strapi-media/s3_sral_a9bfdbb823.png","roles":["thumbnail"],"title":"Sentinel-3A Sea Level Anomaly (cm)","type":"image/png"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2016-03-01T00:00:00Z",null]]}},"license":"other","keywords":["Sentinel-3","Surface Height","sea wind speed","SRAL","Sentinel-3A,Sentinel-3B","Ocean","Level 1 Data","Sea Surface Height","L1B"],"summaries":{"constellation":["Sentinel-3"],"instruments":["SRAL"],"intruments":["SRAL"],"platform":["Sentinel-3A,Sentinel-3B"],"processing:level":["L1B"],"s1:product_timeliness":["NRT-3h","STC-48h","NTC-1m"],"sar:center_frequency":[5.41,13.575],"sar:frequency_band":["C","Ku"]},"item_assets":{"thumbnail":{"description":"An averaged, decimated preview image in PNG format. 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This product also contains forcing data: net primary production, euphotic depth, depth of each pelagic layers zooplankton and micronekton inhabit, average temperature and currents over pelagic layers.\n\n**Forcings sources:**\n* Ocean currents and temperature (CMEMS multiyear product)\n* Net Primary Production computed from chlorophyll a, Sea Surface Temperature and Photosynthetically Active Radiation observations (chlorophyll from CMEMS multiyear product, SST from NOAA NCEI AVHRR-only Reynolds, PAR from INTERIM) and relaxed by model outputs at high latitudes (CMEMS biogeochemistry multiyear product)\n\n**Vertical coverage:**\n* Epipelagic layer \n* Upper mesopelagic layer\n* Lower mesopelagic layer (max. 1000m)\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00020\n\n**References:**\n\n* Lehodey P., Murtugudde R., Senina I. (2010). Bridging the gap from ocean models to population dynamics of large marine predators: a model of mid-trophic functional groups. Progress in Oceanography, 84, p. 69-84.\n* Lehodey, P., Conchon, A., Senina, I., Domokos, R., Calmettes, B., Jouanno, J., Hernandez, O., Kloser, R. (2015) Optimization of a micronekton model with acoustic data. ICES Journal of Marine Science, 72(5), p. 1399-1412.\n* Conchon A. (2016). Modélisation du zooplancton et du micronecton marins. Thèse de Doctorat, Université de La Rochelle, 136 p.\n","links":[{"rel":"license","type":"text/html","href":"https://marine.copernicus.eu/user-corner/service-commitments-and-licence","title":"Copernicus Marine Service Commitments and Licence"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.48670/moi-00020","title":"10.48670/moi-00020"},{"rel":"alternative","type":"text/html","href":"https://data.marine.copernicus.eu/product/GLOBAL_MULTIYEAR_BGC_001_033","title":"Product page"},{"rel":"describedby","type":"application/pdf","href":"https://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-GLO-QUID-001-033.pdf","title":"Quality Information Document"},{"rel":"describedby","type":"application/pdf","href":"https://catalogue.marine.copernicus.eu/documents/PUM/CMEMS-GLO-PUM-001-033.pdf","title":"Product User Manual"},{"rel":"describedby","type":"application/pdf","href":"https://catalogue.marine.copernicus.eu/documents/SQO/CMEMS-GLO-SQO-001-033.pdf","title":"Synthesis Quality 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The reprocessed product is provided at 0.25°x0.25° horizontal resolution, over 36 levels from the surface to 1000 m depth. \nA neural network method estimates both the vertical distribution of Chla concentration and of particulate backscattering coefficient (bbp), a bio-optical proxy for POC, from merged surface ocean color satellite measurements with hydrological properties and additional relevant drivers. \n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00046\n\n**Product Citation:** \nPlease refer to our Technical FAQ for citing products: http://marine.copernicus.eu/faq/cite-cmems-products-cmems-credit/?idpage=169.\n\n**References:**\n\n* Sauzede R., H. Claustre, J. Uitz, C. Jamet, G. Dall’Olmo, F. D’Ortenzio, B. Gentili, A. Poteau, and C. Schmechtig, 2016: A neural network-based method for merging ocean color and Argo data to extend surface bio-optical properties to depth: Retrieval of the particulate backscattering coefficient, J. Geophys. Res. 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satellite observations since 1997 with variables including chlorophyll-a, phytoplankton functional types, suspended matter, and more."},{"type":"Collection","title":"Global Ocean Colour Plankton and Reflectances MY L3 daily observations","id":"EO.MO.DAT.OCEANCOLOUR_GLO_BGC_L3_MY_009_107","description":"For the **Global** Ocean **Satellite Observations**, Brockmann Consult (BC) is providing **Bio-Geo_Chemical (BGC)** products based on the ESA-CCI inputs.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP, OLCI-S3A \u0026 OLCI-S3B for the **\"\"multi\"\"** products.\n* Variables: Chlorophyll-a (**CHL**), Phytoplankton Functional types and sizes (**PFT**) and  Reflectance (**RRS**).\n\n* Temporal resolutions: **daily**, **monthly**.\n* Spatial resolutions: **4 km** (multi).\n* Recent products are organized in datasets called Near Real Time (**NRT**) and long time-series (from 1997) in datasets called Multi-Years (**MY**).\n\nTo find these products in the catalogue, use the search keyword **\"\"ESA-CCI\"\"**.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00282","links":[{"rel":"license","type":"text/html","href":"https://marine.copernicus.eu/user-corner/service-commitments-and-licence","title":"Copernicus Marine Service Commitments and Licence"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.48670/moi-00282","title":"10.48670/moi-00282"},{"rel":"alternative","type":"text/html","href":"https://data.marine.copernicus.eu/product/OCEANCOLOUR_GLO_BGC_L3_MY_009_107","title":"Product page"},{"rel":"describedby","type":"application/pdf","href":"https://catalogue.marine.copernicus.eu/documents/PUM/CMEMS-OC-PUM.pdf","title":"Product User Manual"},{"rel":"describedby","type":"application/pdf","href":"https://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-OC-QUID-009-107to108.pdf","title":"Quality Information 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oceans, including chlorophyll-a concentrations, phytoplankton functional types and reflectance, available as near real-time or historical records since 1997."},{"type":"Collection","title":"Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L3 (daily) from Satellite Observations (Near Real Time)","id":"EO.MO.DAT.OCEANCOLOUR_GLO_BGC_L3_NRT_009_101","description":"For the **Global** Ocean **Satellite Observations**, ACRI-ST company (Sophia Antipolis, France) is providing **Bio-Geo-Chemical (BGC)** products based on the **Copernicus-GlobColour** processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP \u0026 JPSS1, OLCI-S3A \u0026 S3B for the **\"multi\"** products, and S3A \u0026 S3B only for the **\"olci\"** products.\n* Variables: Chlorophyll-a (**CHL**), Phytoplankton Functional types and sizes (**PFT**), Suspended Matter (**SPM**), Secchi Transparency Depth (**ZSD**), Diffuse Attenuation (**KD490**), Particulate Backscattering (**BBP**), Absorption Coef. (**CDM**) and  Reflectance (**RRS**).\n\n* Temporal resolutions: **daily**\n* Spatial resolutions: **4 km** and a finer resolution based on olci **300 meters** inputs.\n* Recent products are organized in datasets called Near Real Time (**NRT**) and long time-series (from 1997) in datasets called Multi-Years (**MY**).\n\nTo find the **Copernicus-GlobColour** products in the catalogue, use the search keyword **\"GlobColour\"**.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00278","links":[{"rel":"license","type":"text/html","href":"https://marine.copernicus.eu/user-corner/service-commitments-and-licence","title":"Copernicus Marine Service Commitments and Licence"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.48670/moi-00278","title":"10.48670/moi-00278"},{"rel":"alternative","type":"text/html","href":"https://data.marine.copernicus.eu/product/OCEANCOLOUR_GLO_BGC_L3_NRT_009_101","title":"Product 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resolutions including 4km and 0.3km derived from multiple upstream sensors like SeaWiFS, MODIS, and others since 1997 or near real-time."},{"type":"Collection","title":"Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (1997-ongoing)","id":"EO.MO.DAT.OCEANCOLOUR_GLO_BGC_L4_MY_009_104","description":"For the **Global** Ocean **Satellite Observations**, ACRI-ST company (Sophia Antipolis, France) is providing **Bio-Geo-Chemical (BGC)** products based on the **Copernicus-GlobColour** processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP \u0026 JPSS1, OLCI-S3A \u0026 S3B for the **\"\"multi\"\"** products, and S3A \u0026 S3B only for the **\"\"olci\"\"** products.\n* Variables: Chlorophyll-a (**CHL**), Phytoplankton Functional types and sizes (**PFT**), Primary Production (**PP**), Suspended Matter (**SPM**), Secchi Transparency Depth (**ZSD**), Diffuse Attenuation (**KD490**), Particulate Backscattering (**BBP**), Absorption Coef. 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interpolated values at various spatial resolutions including 4km and 0.3km derived from multiple upstream satellites like SeaWiFS, MODIS, and others."},{"type":"Collection","title":"Global Ocean - Arctic and Antarctic - Sea Ice Concentration, Edge, Type and Drift (OSI-SAF)","id":"EO.MO.DAT.SEAICE_GLO_SEAICE_L4_NRT_OBSERVATIONS_011_001","description":"For the Global - Arctic and Antarctic - Ocean. 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OSTIA uses satellite data provided by the GHRSST project together with in-situ observations to determine the sea surface temperature.\nA high resolution (1/20° - approx. 6 km) daily analysis of sea surface temperature (SST) is produced for the global ocean and some lakes.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00165\n\n**References:**\n\n* Good, S.; Fiedler, E.; Mao, C.; Martin, M.J.; Maycock, A.; Reid, R.; Roberts-Jones, J.; Searle, T.; Waters, J.; While, J.; Worsfold, M. The Current Configuration of the OSTIA System for Operational Production of Foundation Sea Surface Temperature and Ice Concentration Analyses. Remote Sens. 2020, 12, 720. doi: 10.3390/rs12040720\n* Donlon, C.J., Martin, M., Stark, J., Roberts-Jones, J., Fiedler, E., and Wimmer, W., 2012, The Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) system. Remote Sensing of the Environment. doi: 10.1016/j.rse.2010.10.017 2011.\n* John D. Stark, Craig J. Donlon, Matthew J. Martin and Michael E. McCulloch, 2007, OSTIA : An operational, high resolution, real time, global sea surface temperature analysis system., Oceans 07 IEEE Aberdeen, conference proceedings. Marine challenges: coastline to deep sea. 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This product provides the foundation Sea Surface Temperature, which is the temperature free of diurnal variability.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00168\n\n**References:**\n\n* Good, S.; Fiedler, E.; Mao, C.; Martin, M.J.; Maycock, A.; Reid, R.; Roberts-Jones, J.; Searle, T.; Waters, J.; While, J.; Worsfold, M. The Current Configuration of the OSTIA System for Operational Production of Foundation Sea Surface Temperature and Ice Concentration Analyses. 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The ESA SST CCI and C3S level 4 analyses were produced by running the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) system (Good et al., 2020) to provide a high resolution (1/20deg. - approx. 5km grid resolution) daily analysis of the daily average sea surface temperature (SST) at 20 cm depth for the global ocean. Only (A)ATSR, SLSTR and AVHRR satellite data processed by the ESA SST CCI and C3S projects were used, giving a stable product. It also uses reprocessed sea-ice concentration data from the EUMETSAT OSI-SAF (OSI-450 and OSI-430; Lavergne et al., 2019).\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00169\n\n**References:**\n\n* Good, S., Fiedler, E., Mao, C., Martin, M.J., Maycock, A., Reid, R., Roberts-Jones, J., Searle, T., Waters, J., While, J., Worsfold, M. The Current Configuration of the OSTIA System for Operational Production of Foundation Sea Surface Temperature and Ice Concentration Analyses. Remote Sens. 2020, 12, 720, doi:10.3390/rs12040720.\n* Lavergne, T., Sørensen, A. M., Kern, S., Tonboe, R., Notz, D., Aaboe, S., Bell, L., Dybkjær, G., Eastwood, S., Gabarro, C., Heygster, G., Killie, M. A., Brandt Kreiner, M., Lavelle, J., Saldo, R., Sandven, S., and Pedersen, L. T.: Version 2 of the EUMETSAT OSI SAF and ESA CCI sea-ice concentration climate data records, The Cryosphere, 13, 49-78, doi:10.5194/tc-13-49-2019, 2019.\n* Merchant, C.J., Embury, O., Bulgin, C.E. et al. Satellite-based time-series of sea-surface temperature since 1981 for climate applications. Sci Data 6, 223 (2019) doi:10.1038/s41597-019-0236-x.\n","links":[{"rel":"license","type":"text/html","href":"https://marine.copernicus.eu/user-corner/service-commitments-and-licence","title":"Copernicus Marine Service Commitments and Licence"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.48670/moi-00169","title":"10.48670/moi-00169"},{"rel":"alternative","type":"text/html","href":"https://data.marine.copernicus.eu/product/SST_GLO_SST_L4_REP_OBSERVATIONS_010_024","title":"Product page"},{"rel":"describedby","type":"application/pdf","href":"https://catalogue.marine.copernicus.eu/documents/PUM/CMEMS-SST-PUM-010-024.pdf","title":"Product User Manual"},{"rel":"describedby","type":"application/pdf","href":"https://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-SST-QUID-010-024.pdf","title":"Quality Information Document"},{"rel":"describedby","type":"application/pdf","href":"https://catalogue.marine.copernicus.eu/documents/SQO/CMEMS-SST-SQO-010-024.pdf","title":"Synthesis Quality Overview"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.MO.DAT.SST_GLO_SST_L4_REP_OBSERVATIONS_010_024","title":"EO.MO.DAT.SST_GLO_SST_L4_REP_OBSERVATIONS_010_024"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.MO.DAT.SST_GLO_SST_L4_REP_OBSERVATIONS_010_024/items","title":"items"}],"assets":{"thumbnail":{"href":"https://mdl-metadata.s3.waw3-1.cloudferro.com/metadata/thumbnails/SST_GLO_SST_L4_REP_OBSERVATIONS_010_024.jpg","roles":["thumbnail"],"title":"ESA SST CCI and C3S reprocessed sea surface temperature analyses thumbnail","type":"image/jpeg"}},"extent":{"spatial":{"bbox":[[-179.97500610351562,-89.9749984741211,179.97500610351562,89.9749984741211]]},"temporal":{"interval":[["1981-09-01T00:00:00Z","2022-10-31T00:00:00Z"]]}},"license":"other","keywords":["oceanographic-geographical-features","target-application#seaiceclimate","L4","multi-year","weather-climate-and-seasonal-forecasting","global-ocean","marine-resources","coastal-marine-environment","satellite-observation","level-4","marine-safety","sea-water-temperature","sea-ice-area-fraction"],"summaries":{"allVariables":["Sea water temperature (T)","Sea ice area fraction"],"areas":["Global Ocean"],"colors":["White Ocean","Blue Ocean"],"communities":["Polar environment monitoring","Climate \u0026 adaptation","Policy \u0026 governance","Science \u0026 innovation","Extremes, hazards \u0026 safety","Coastal services"],"directives":["Marine Strategy Framework Directive (MSFD)","Water Framework Directive (WFD)"],"featureTypes":["Grid"],"formats":["NetCDF-4"],"geoResolution":[{"column":{"magnitude":0.05,"units":"degree"},"row":{"magnitude":0.05,"units":"degree"}}],"isStaging":["false"],"mainVariables":["Temperature","Sea ice"],"processing:level":["L4"],"projection":["WGS 84 (EPSG:4326)"],"rank":[12051],"sources":["Satellite observations"],"tempResolutions":["Daily"],"times":["Past"],"updateFrequencies":[{"notapplicable":null}],"vertExtentMax":[0],"vertExtentMin":[0]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/contacts/v0.1.1/schema.json","https://stac-extensions.github.io/projection/v2.0.0/schema.json","https://stac-extensions.github.io/processing/v1.2.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"Mercator Ocean International (MOi)","roles":["producer","processor"]},{"name":"Copernicus Marine Environment Monitoring Service (CMEMS)","roles":["host"],"url":"https://marine.copernicus.eu"},{"name":"European Union (EU)","roles":["licensor"],"url":"https://european-union.europa.eu/"}],"dedl:short_description":"The ESA SST CCI and C3S reprocessed sea surface temperature analyses provide gap-free daily average SST maps at 20cm depth with 0.05°x0.05° resolution, derived from ATSR, SLSTR, and AVHRR satellite data combined with EUMETSAT OSI-SAF ice concentration data."},{"type":"Collection","title":"GLOBAL OCEAN L3 SPECTRAL PARAMETERS FROM NRT SATELLITE MEASUREMENTS","id":"EO.MO.DAT.WAVE_GLO_PHY_SPC_FWK_L3_NRT_014_002","description":"Near-Real-Time mono-mission satellite-based integral parameters derived from the directional wave spectra. Using linear propagation wave model, only wave observations that can be back-propagated to wave converging regions are considered. The dataset parameters includes partition significant wave height, partition peak period and partition peak or principal direction given along swell propagation path in space and time at a 3-hour timestep, from source to land. Validity flags are also included for each parameter and indicates the valid time steps along propagation (eg. no propagation for significant wave height close to the storm source or any integral parameter when reaching the land). The integral parameters at observation point are also available together with a quality flag based on the consistency between each propagated observation and the overall swell field.This product is processed by the WAVE-TAC multi-mission SAR data processing system. It serves in near-real time the main operational oceanography and climate forecasting centers in Europe and worldwide. It processes near-real-time data from the following SAR missions: Sentinel-1A and Sentinel-1B.One file is produced for each mission and is available in two formats: one gathering in one netcdf file all observations related to the same swell field, and for another all observations available in a 3-hour time range, and for both formats, propagated information from source to land.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00178","links":[{"rel":"license","type":"text/html","href":"https://marine.copernicus.eu/user-corner/service-commitments-and-licence","title":"Copernicus Marine Service Commitments and Licence"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.48670/moi-00178","title":"10.48670/moi-00178"},{"rel":"alternative","type":"text/html","href":"https://data.marine.copernicus.eu/product/WAVE_GLO_PHY_SPC-FWK_L3_NRT_014_002","title":"Product page"},{"rel":"describedby","type":"application/pdf","href":"https://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-WAV-QUID-014-002.pdf","title":"Quality Information Document"},{"rel":"describedby","type":"application/pdf","href":"http://marine.copernicus.eu/documents/PUM/CMEMS-WAV-PUM-014-001-002-003-004.pdf","title":"Product User Manual"},{"rel":"describedby","type":"application/pdf","href":"https://catalogue.marine.copernicus.eu/documents/SQO/CMEMS-WAV-SQO-014-002.pdf","title":"Synthesis Quality Overview"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.MO.DAT.WAVE_GLO_PHY_SPC_FWK_L3_NRT_014_002","title":"EO.MO.DAT.WAVE_GLO_PHY_SPC_FWK_L3_NRT_014_002"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.MO.DAT.WAVE_GLO_PHY_SPC_FWK_L3_NRT_014_002/items","title":"items"}],"assets":{"thumbnail":{"href":"https://mdl-metadata.s3.waw3-1.cloudferro.com/metadata/thumbnails/WAVE_GLO_PHY_SPC-FWK_L3_NRT_014_002.jpg","roles":["thumbnail"],"title":"GLOBAL OCEAN L3 SPECTRAL PARAMETERS FROM NRT SATELLITE MEASUREMENTS thumbnail","type":"image/jpeg"}},"extent":{"spatial":{"bbox":[[-180,-90,180,90]]},"temporal":{"interval":[["2018-04-22T00:00:00.000000Z",null]]}},"license":"other","keywords":["coastal-marine-environment","sea-surface-wave-period-at-variance-spectral-density-maximum","sea-surface-wave-significant-height","sea-surface-wave-from-direction-at-variance-spectral-density-maximum","iberian-biscay-irish-seas","black-sea","global-ocean","weather-climate-and-seasonal-forecasting","north-west-shelf-seas","arctic-ocean","near-real-time","oceanographic-geographical-features","level-3","mediterranean-sea","L3","marine-safety","marine-resources","baltic-sea","satellite-observation"],"summaries":{"allVariables":["Sea surface wave significant height (SWH)","Sea surface wave period at variance spectral density maximum (MWT)","Sea surface wave from direction at variance spectral density maximum (VMDR)"],"areas":["Global Ocean","Arctic Ocean","Baltic Sea","Black Sea","Mediterranean Sea","Atlantic: Iberia-Biscay-Ireland","Atlantic: North","Atlantic: NW European Shelf"],"colors":["Blue Ocean"],"communities":["Climate \u0026 adaptation","Policy \u0026 governance","Science \u0026 innovation","Extremes, hazards \u0026 safety","Coastal services","Natural resources \u0026 energy","Trade \u0026 marine navigation"],"featureTypes":["Swath","Trajectory"],"formats":["NetCDF-4"],"isStaging":["false"],"mainVariables":["Wave"],"processing:level":["L3"],"projection":["WGS84 / Simple Mercator (EPSG:41001)"],"rank":[15015],"sources":["Satellite observations"],"tempResolutions":["Hourly","Instantaneous"],"times":["Present","Past"],"updateFrequencies":[{"daily":"12:00","irregular":null}],"vertExtentMax":[0],"vertExtentMin":[0]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/contacts/v0.1.1/schema.json","https://stac-extensions.github.io/projection/v2.0.0/schema.json","https://stac-extensions.github.io/processing/v1.2.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"Mercator Ocean International (MOi)","roles":["producer","processor"]},{"name":"Copernicus Marine Environment Monitoring Service (CMEMS)","roles":["host"],"url":"https://marine.copernicus.eu"},{"name":"European Union (EU)","roles":["licensor"],"url":"https://european-union.europa.eu/"}],"dedl:short_description":"The GLOBAL OCEAN L3 SPECTRAL PARAMETERS dataset contains near-real-time spectral wave measurements from Sentinel-1A/B satellites, providing integrated parameters like significant wave height, peak period, and direction of waves propagating towards land over a 3-hour interval."},{"type":"Collection","title":"GLOBAL OCEAN L3 SIGNIFICANT WAVE HEIGHT FROM NRT SATELLITE MEASUREMENTS","id":"EO.MO.DAT.WAVE_GLO_PHY_SWH_L3_NRT_014_001","description":"Near-Real-Time mono-mission satellite-based along-track significant wave height. Only valid data are included, based on a rigorous editing combining various criteria such as quality flags (surface flag, presence of ice) and thresholds on parameter values. Such thresholds are applied on parameters linked to significant wave height determination from retracking (e.g. SWH, sigma0, range, off nadir angle…). All the missions are homogenized with respect to a reference mission (Jason-3 until April 2022, Sentinel-6A afterwards) and calibrated on in-situ buoy measurements. Finally, an along-track filter is applied to reduce the measurement noise.\n\nAs a support of information to the significant wave height, wind speed measured by the altimeters is also processed and included in the files. Wind speed values are provided by upstream products (L2) for each mission and are based on different algorithms. Only valid data are included and all the missions are homogenized with respect to the reference mission.\n\nThis product is processed by the WAVE-TAC multi-mission altimeter data processing system. It serves in near-real time the main operational oceanography and climate forecasting centers in Europe and worldwide. It processes operational data (OGDR and NRT, produced in near-real-time) from the following altimeter missions: Sentinel-6A, Jason-3, Sentinel-3A, Sentinel-3B, Cryosat-2, SARAL/AltiKa, CFOSAT ; and interim data (IGDR, 1 to 2 days delay) from Hai Yang-2B mission.\n\nOne file containing valid SWH is produced for each mission and for a 3-hour time window. It contains the filtered SWH (VAVH), the unfiltered SWH (VAVH_UNFILTERED) and the wind speed (wind_speed).\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00179","links":[{"rel":"license","type":"text/html","href":"https://marine.copernicus.eu/user-corner/service-commitments-and-licence","title":"Copernicus Marine Service Commitments and Licence"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.48670/moi-00179","title":"10.48670/moi-00179"},{"rel":"alternative","type":"text/html","href":"https://data.marine.copernicus.eu/product/WAVE_GLO_PHY_SWH_L3_NRT_014_001","title":"Product page"},{"rel":"describedby","type":"application/pdf","href":"https://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-WAV-QUID-014-001.pdf","title":"Quality Information Document"},{"rel":"describedby","type":"application/pdf","href":"http://marine.copernicus.eu/documents/PUM/CMEMS-WAV-PUM-014-001-002-003-004.pdf","title":"Product User Manual"},{"rel":"describedby","type":"application/pdf","href":"https://catalogue.marine.copernicus.eu/documents/SQO/CMEMS-WAV-SQO-014-001.pdf","title":"Synthesis Quality Overview"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.MO.DAT.WAVE_GLO_PHY_SWH_L3_NRT_014_001","title":"EO.MO.DAT.WAVE_GLO_PHY_SWH_L3_NRT_014_001"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.MO.DAT.WAVE_GLO_PHY_SWH_L3_NRT_014_001/items","title":"items"}],"assets":{"thumbnail":{"href":"https://mdl-metadata.s3.waw3-1.cloudferro.com/metadata/thumbnails/WAVE_GLO_PHY_SWH_L3_NRT_014_001.jpg","roles":["thumbnail"],"title":"GLOBAL OCEAN L3 SIGNIFICANT WAVE HEIGHT FROM NRT SATELLITE MEASUREMENTS thumbnail","type":"image/jpeg"}},"extent":{"spatial":{"bbox":[[-180,-82.649,179.999999,87.987402]]},"temporal":{"interval":[["2021-01-01T00:00:00Z","2025-04-01T10:48:28Z"]]}},"license":"other","keywords":["coastal-marine-environment","sea-surface-wave-significant-height","iberian-biscay-irish-seas","black-sea","global-ocean","weather-climate-and-seasonal-forecasting","north-west-shelf-seas","arctic-ocean","near-real-time","oceanographic-geographical-features","level-3","mediterranean-sea","L3","marine-safety","wind-speed","marine-resources","baltic-sea","satellite-observation"],"summaries":{"allVariables":["Wind speed (WIND)","Sea surface wave significant height (SWH)"],"areas":["Global Ocean","Arctic Ocean","Baltic Sea","Black Sea","Mediterranean Sea","Atlantic: Iberia-Biscay-Ireland","Atlantic: North","Atlantic: NW European Shelf"],"colors":["Blue Ocean"],"communities":["Policy \u0026 governance","Science \u0026 innovation","Extremes, hazards \u0026 safety","Coastal services","Natural resources \u0026 energy","Trade \u0026 marine navigation"],"featureTypes":["Swath"],"formats":["NetCDF-4"],"geoResolution":[{"column":{"magnitude":7,"units":"km"},"row":{"magnitude":7,"units":"km"}}],"isStaging":["false"],"mainVariables":["Wind","Wave"],"processing:level":["L3"],"projection":["WGS84 / Simple Mercator (EPSG:41001)"],"rank":[15010],"sources":["Satellite observations"],"tempResolutions":["Instantaneous"],"times":["Present","Past"],"updateFrequencies":[{"continual":null}],"vertExtentMax":[0],"vertExtentMin":[0]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/contacts/v0.1.1/schema.json","https://stac-extensions.github.io/projection/v2.0.0/schema.json","https://stac-extensions.github.io/processing/v1.2.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"Mercator Ocean International (MOi)","roles":["producer","processor"]},{"name":"Copernicus Marine Environment Monitoring Service (CMEMS)","roles":["host"],"url":"https://marine.copernicus.eu"},{"name":"European Union (EU)","roles":["licensor"],"url":"https://european-union.europa.eu/"}],"dedl:short_description":"The GLOBAL OCEAN L3 Significant Wave Height dataset provides near-real-time, rigorously edited and homogenized significant wave heights and accompanying wind speeds derived from multiple satellite altimetry missions over global oceans."},{"type":"Collection","title":"GLOBAL OCEAN L4 SIGNIFICANT WAVE HEIGHT FROM NRT SATELLITE MEASUREMENTS","id":"EO.MO.DAT.WAVE_GLO_PHY_SWH_L4_NRT_014_003","description":"Near-Real-Time gridded multi-mission merged satellite significant wave height. Only valid data are included. This product is processed in Near-Real-Time by the WAVE-TAC multi-mission altimeter data processing system and is based on CMEMS level-3 SWH datasets (see the product WAVE_GLO_WAV_L3_SWH_NRT_OBSERVATIONS_014_001).\nIt merges along-track SWH data from the following missions: Jason-3, Sentinel-3A, Sentinel-3B, SARAL/AltiKa, Cryosat-2, CFOSAT and HaiYang-2B. The resulting gridded product has a 2° horizontal resolution and is produced daily. Different SWH fields are produced: VAVH_DAILY fields are daily statistics computed from all available level 3 along-track measurements from 00 UTC until 23:59 UTC ; VAVH_INST field provides an estimate of the instantaneous wave field at 12:00UTC (noon), using all available Level 3 along-track measurements and accounting for their spatial and temporal proximity.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00180","links":[{"rel":"license","type":"text/html","href":"https://marine.copernicus.eu/user-corner/service-commitments-and-licence","title":"Copernicus Marine Service Commitments and Licence"},{"rel":"cite-as","type":"text/html","href":"https://doi.org/10.48670/moi-00180","title":"10.48670/moi-00180"},{"rel":"alternative","type":"text/html","href":"https://data.marine.copernicus.eu/product/WAVE_GLO_PHY_SWH_L4_NRT_014_003","title":"Product page"},{"rel":"describedby","type":"application/pdf","href":"https://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-WAV-QUID-014-003.pdf","title":"Quality Information Document"},{"rel":"describedby","type":"application/pdf","href":"http://marine.copernicus.eu/documents/PUM/CMEMS-WAV-PUM-014-001-002-003-004.pdf","title":"Product User Manual"},{"rel":"describedby","type":"application/pdf","href":"https://catalogue.marine.copernicus.eu/documents/SQO/CMEMS-WAV-SQO-014-003.pdf","title":"Synthesis Quality Overview"},{"rel":"self","href":"https://hda.data.destination-earth.eu/stac/collections/EO.MO.DAT.WAVE_GLO_PHY_SWH_L4_NRT_014_003","title":"EO.MO.DAT.WAVE_GLO_PHY_SWH_L4_NRT_014_003"},{"rel":"root","href":"https://hda.data.destination-earth.eu/stac/"},{"rel":"items","href":"https://hda.data.destination-earth.eu/stac/collections/EO.MO.DAT.WAVE_GLO_PHY_SWH_L4_NRT_014_003/items","title":"items"}],"assets":{"thumbnail":{"href":"https://mdl-metadata.s3.waw3-1.cloudferro.com/metadata/thumbnails/WAVE_GLO_PHY_SWH_L4_NRT_014_003.jpg","roles":["thumbnail"],"title":"GLOBAL OCEAN L4 SIGNIFICANT WAVE HEIGHT FROM NRT SATELLITE MEASUREMENTS thumbnail","type":"image/jpeg"}},"extent":{"spatial":{"bbox":[[-179,-89,179,89]]},"temporal":{"interval":[["2021-01-01T00:00:00Z","2025-03-31T00:00:00Z"]]}},"license":"other","keywords":["iberian-biscay-irish-seas","near-real-time","black-sea","oceanographic-geographical-features","arctic-ocean","global-ocean","weather-climate-and-seasonal-forecasting","north-west-shelf-seas","marine-resources","coastal-marine-environment","mediterranean-sea","L4","level-4","marine-safety","baltic-sea","sea-surface-wave-significant-height","satellite-observation"],"summaries":{"allVariables":["Sea surface wave significant height (SWH)"],"areas":["Global Ocean","Arctic Ocean","Baltic Sea","Black Sea","Mediterranean Sea","Atlantic: Iberia-Biscay-Ireland","Atlantic: North","Atlantic: NW European Shelf"],"colors":["Blue Ocean"],"communities":["Policy \u0026 governance","Science \u0026 innovation","Extremes, hazards \u0026 safety","Coastal services","Natural resources \u0026 energy","Trade \u0026 marine navigation"],"featureTypes":["Grid"],"formats":["NetCDF-4"],"geoResolution":[{"column":{"magnitude":2,"units":"degree"},"row":{"magnitude":2,"units":"degree"}}],"isStaging":["false"],"mainVariables":["Wave"],"processing:level":["L4"],"rank":[15001],"sources":["Satellite observations"],"tempResolutions":["Daily"],"times":["Present","Past"],"updateFrequencies":[{"daily":"12:00"}],"vertExtentMax":[0],"vertExtentMin":[0]},"stac_version":"1.1.0","stac_extensions":["https://stac-extensions.github.io/scientific/v1.0.0/schema.json","https://stac-extensions.github.io/contacts/v0.1.1/schema.json","https://stac-extensions.github.io/processing/v1.2.0/schema.json","https://stac-extensions.github.io/timestamps/v1.1.0/schema.json"],"providers":[{"name":"Mercator Ocean International (MOi)","roles":["producer","processor"]},{"name":"Copernicus Marine Environment Monitoring Service (CMEMS)","roles":["host"],"url":"https://marine.copernicus.eu"},{"name":"European Union (EU)","roles":["licensor"],"url":"https://european-union.europa.eu/"}],"dedl:short_description":"This dataset contains near-real-time global ocean significant wave heights derived from merged satellite measurements with a 2° grid resolution, combining multiple mission data including Jason-3, Sentinel-3A/B, SARAL/AltiKa, CryoSat-2, CFOSAT, and Haiyang-2B."},{"type":"Collection","title":"Global Ocean Monthly Mean Sea Surface Wind and Stress from Scatterometer and Model","id":"EO.MO.DAT.WIND_GLO_PHY_CLIMATE_L4_MY_012_003","description":"For the Global Ocean - The product contains monthly Level-4 sea surface wind and stress fields at 0.25 degrees horizontal spatial resolution. The monthly averaged wind and stress fields are based on monthly average ECMWF ERA5 reanalysis fields, corrected for persistent biases using all available Level-3 scatterometer observations from the Metop-A, Metop-B and Metop-C ASCAT, QuikSCAT SeaWinds, ERS-1 and ERS-2 SCAT satellite instruments.  The product provides monthly mean stress-equivalent wind and stress variables as well as their standard deviation. 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Earth Data Lake (DEDL)","roles":["host","processor"],"url":"https://data.destination-earth.eu/"}],"dedl:short_description":"This dataset provides annual demographic data on demographic balance and crude rates for regions across 37 European countries at NUTS 3 level through the Unidemo project."},{"type":"Collection","title":"Population density by NUTS 3 region","id":"STAT.EUSTAT.DAT.POP_DENSITY_NUTS3","description":"Eurostat’s annual data collections on population. Member States send population data to Eurostat data as on of 31 December for the reference year under Regulation 1260/2013 on European demographic statistics. The data are conventionally published by Eurostat as population on 1 January of the following year (reference year + 1). \nThe aim is to collect annual mandatory and voluntary demographic data from the national statistical institutes. Mandatory data are those defined by the legislation listed under ‘6.1. Institutional mandate — legal acts and other agreements’. \nThe completeness of the demographic data collected on a voluntary basis depends on the availability and completeness of information provided by the national statistical institutes.\nFor more information on mandatory/voluntary data collection, see 6.1. Institutional mandate — legal acts and other agreements. \nThe following statistics are available. \nPopulation on 1 January by sex and by:\n- single age and educational attainment / marital status / broad group of citizenship / broad group of country of birth;\n  - five-year age group and citizenship / country of birth;\n  - citizenship and broad group of country of birth / country of birth and broad group of citizenship;\n  - broad age group and NUTS 3 (under regional data population folder);\n  - single age and NUTS 2 (under regional data population folder);\n  - five-year age group and NUTS 2 / NUTS 3 (under regional data population folder).\nPopulation structure statistics: median age of population, proportion of population by various age groups, old age dependency ratio.","links":[{"rel":"license","type":"text/html","href":"http://ec.europa.eu/eurostat/statistics-explained/index.php/Copyright/licence_policy","title":"Eurostat - Copyright notice and free re-use of 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The indicator comprises nationally designated protected areas and Natura 2000 sites. A nationally designated area is an area protected by national legislation. 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