Observation List
Get a list of Observation objects.
GET /api/v3/observations/?format=api&offset=10400
{ "count": 10437, "next": null, "previous": "https://catalogue.ceda.ac.uk/api/v3/observations/?format=api&limit=100&offset=10300", "results": [ { "ob_id": 46268, "uuid": "afbe3e15db494a72bde24f71e80f5f8c", "title": "NCAS Long Term Observations: NOx and NOxy concentrations from the maqs-42iy-1-NOy deployed at Manchester Air Quality Supersite (MAQS), v3.4.1", "abstract": "NOx and NOxy concentrations measurements from the maqs-42iy-1-NOy deployed at Manchester Air Quality Supersite (MAQS). These observations were taken as part of the National Centre for Atmospheric Science (NCAS) long term observations.\n\nData products from this deployment include: nox-noxy-concentration\n\nFor further details of this deployment and the associated dataset please see the internal file metadata.\n\nThese data conform to the NCAS data standards and are available under the UK Government Open Licence agreement. Acknowledgement of NCAS as the data provider is required whenever and wherever these data are used.\n ", "creationDate": "2026-09-09T13:51:24.852283", "lastUpdatedDate": "2026-09-09T13:51:24.852288", "latestDataUpdateTime": "2026-09-09T13:51:24.852290", "updateFrequency": "daily", "dataLineage": "Data were processed and prepared by the project team before delivery to the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "NCAS, observation measurements", "publicationState": "preview", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": null, "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 5201, "bboxName": "Maqs, Supersite, Firs, Fallowfield", "eastBoundLongitude": -2.214473, "westBoundLongitude": -2.214473, "southBoundLatitude": 53.44421, "northBoundLatitude": 53.44421 }, "verticalExtent": null, "result_field": { "ob_id": 46269, "dataPath": "/badc/ncas-maqs/data/maqs-42iy-1/20231001_longterm/v3.4.1", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 50260164, "numberOfFiles": 2, "fileFormat": "Data are netCDF formatted." }, "timePeriod": { "ob_id": 13349, "startTime": "2023-10-01T00:00:00", "endTime": "2025-10-31T00:00:00" }, "resultQuality": { "ob_id": 4568, "explanation": "These data have been produced in accordance to standard NCAS observational practices. This includes instrument calibration and compliance checking of data against the NCAS Data Standards.", "passesTest": true, "resultTitle": "NCAS Standard Data Quality Statement", "date": "2024-06-07" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 46270, "uuid": "5466e9ca95ef443da3b534ff2e057380", "short_code": "acq", "title": "NCAS Long Term Measurements: MAQS: Thermo Model 42i-Y NOY Analyzer instrument deployed at Manchester Air Quality Supersite (MAQS)", "abstract": "NCAS Long Term Measurements: MAQS: Thermo Model 42i-Y NOY Analyzer instrument deployed at Manchester Air Quality Supersite (MAQS)." }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 223 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" }, { "ob_id": 11, "classification": "attribution required" } ] } } ], "projects": [ { "ob_id": 42342, "uuid": "d49ce24b247e4e619dd6da14a1d79375", "short_code": "proj", "title": "NCAS Long Term Measurements", "abstract": "The Natural Environment Research Council's (NERC) National Centre for Atmospheric Science (NCAS) undertake a number of long term measurements by a suite of instruments to support ongoing atmospheric research at a variety of locations. These include a long-term observation mode of instruments from the NCAS Atmospheric Measurement and Observation Facility (AMOF) when not deployed on specific field campaign duties for other projects." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 53936, 53937, 53938, 53939, 53940, 53941, 53942, 53943, 53944, 74110, 103261, 103262, 103263 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 221046, 221047, 221048, 221049, 221050, 221051, 221052, 221045 ], "onlineresource_set": [] }, { "ob_id": 46271, "uuid": "94c49f9920ad499aa20bab8e5dde6b0c", "title": "Monthly gridded (L3C) atmospheric and surface properties from S-NPP/NOAA-20 derived using the RAL extended Infrared Microwave Sounder (IMS) retrieval scheme, version ?", "abstract": "This dataset provides monthly gridded (Level 3C) data from the Rutherford Appleton Laboratory (RAL) extended Infrared Microwave Sounder (IMS) retrieval scheme. \r\n\r\nThis scheme retrieves vertical profiles of temperature, water vapour (H2O), ozone (O3), carbon monoxide (CO), together with cloud optical depth and effective radius and column amounts of minor gases, dust and sulfuric acid aerosol optical depth. The scheme also provides surface temperature and surface spectral emissivity spanning infrared and microwave. Column amounts of the following minor gases are also retrieved: Nitric acid (HNO3), ammonia (NH3), sulfur dioxide (SO2), methanol (CH3OH), formic acid (HCOOH) and (for Suomi-NPP only) isoprene (C5H8).\r\n\r\nIn this dataset, the retrieval scheme has been applied to the infra-red and microwave sounders on the Suomi-NPP and NOAA-20 satellites.\r\n\r\nDevelopment of the core IMS scheme was funded by the UK’s National Centre for Earth Observation (NCEO) under the Natural Environment Research Council (NERC), with additional funding from EUMETSAT contract EUM/CO/13/4600001252/THH. Development of the extended IMS scheme was funded by NCEO. This version of the data was produced as part of the EOCIS project. \r\nData were produced by the Remote Sensing Group (RSG) at the Rutherford Appleton Laboratory (RAL).”", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": null, "updateFrequency": "", "dataLineage": "Development of the IMS scheme and data production were funded by the UK’s National Centre for Earth Observation (NCEO) under the Natural Environment Research Council (NERC), with additional funding from EUMETSAT contract EUM/CO/13/4600001252/THH. This version of the data was produced as part of the Earth Observation Climate Information Service (EOCIS) project.", "removedDataReason": "", "keywords": "RAL, IMS, atmospheric and surface properties, METOP-A", "publicationState": "preview", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": null, "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": null, "timePeriod": { "ob_id": 9178, "startTime": "2018-04-01T00:00:00", "endTime": "2018-12-31T23:59:59" }, "resultQuality": { "ob_id": 3813, "explanation": "Data are as given by the data provider, no quality control has been performed by the Centre for Environmental Data Analysis (CEDA)", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2021-12-10" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 43471, "uuid": "0c2e0c54eab246deb6ffbd43bba6f2b4", "short_code": "cmppr", "title": "The RAL extended IMS retrieval scheme applied to the IASI, AMSU and MHS instruments on METOP-A", "abstract": "The Rutherford Appleton Laboratory (RAL) extended Infrared Microwave Sounder (IMS) data set contains vertical profiles of temperature, water vapour (H2O), ozone (O3), carbon monoxide (CO), together with estimated total columns of other minor gases, cloud optical depth and effective radius, dust and sulfuric acid aerosol optical depth. The scheme also provides surface temperature and surface spectral emissivity spanning infrared and microwave. Data are retrieved from the infra-red and microwave sounders on platforms Metop (IASI, AMSU and MHS) and Suomi-NPP (CrIS and ATMS).\r\n\r\nColumn amounts of the following minor gases are retrieved: Nitric acid (HNO3), ammonia (NH3), sulfur dioxide (SO2), methanol (CH3OH), formic acid (HCOOH) and (for Suomi-NPP only) isoprene (C5H8).\r\n\r\nIn this dataset, the scheme has been applied to the IASI, AMSU and MHS instruments on the METOP-A satellite." }, "imageDetails": [ 233 ], "discoveryKeywords": [], "permissions": [], "projects": [ { "ob_id": 43216, "uuid": "8bffaba46c4a4b8c82e4be2c91c637b9", "short_code": "proj", "title": "Earth Observation Climate Information Service (EOCIS)", "abstract": "The UK Earth Observation Climate Information Service exploits the observations available from environmental sensors orbiting in space to create climate data records and climate information. EOCIS was announced by the government in November 2022, and formally launched in March 2023. It is funded currently until March 2025. \r\n\r\nEOCIS is a collaboration led by the National Centre for Earth Observation, and involving over a dozen research organisations. EOCIS addresses 12 categories of global and regional essential climate variables, which are the following:\r\n- Sea surface temperature\r\n- Ocean reflectance\r\n- Fire occurrence and emissions\r\n- Aerosol and particulate\r\n- Cloud-aerosol-radiation\r\n- Methane\r\n- Land surface temperature\r\n- Water vapour, ozone\r\n- Arctic: ice sheet mass and sea ice\r\n- Eurasia: surface methane\r\n- Africa: soil water balance\r\n- Antarctic: ice sheet mass and ice velocity\r\n\r\nEOCIS is also creating new climate data at high resolution for the UK specifically. This includes both rapid-response information for climate-linked events (fire early warning and urban flood mapping) and longer term climate data linked to human and ecosystem health and landscape greenhouse gas emissions." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 221055, 221056, 221057, 221058, 221059, 221060, 221061, 221062, 221063, 221064, 221065, 221066 ], "onlineresource_set": [ 96010 ] }, { "ob_id": 46272, "uuid": "49009cbae601405b8dcc0d9ca34f9d80", "title": "Monthly gridded (L3C) atmospheric and surface properties from ? derived using the RAL extended Infrared Microwave Sounder (IMS) retrieval scheme, version ?", "abstract": "This dataset provides monthly gridded (Level 3C) data from the Rutherford Appleton Laboratory (RAL) extended Infrared Microwave Sounder (IMS) retrieval scheme. \r\n\r\nThis scheme retrieves vertical profiles of temperature, water vapour (H2O), ozone (O3), carbon monoxide (CO), together with cloud optical depth and effective radius and column amounts of minor gases, dust and sulfuric acid aerosol optical depth. The scheme also provides surface temperature and surface spectral emissivity spanning infrared and microwave. Column amounts of the following minor gases are also retrieved: Nitric acid (HNO3), ammonia (NH3), sulfur dioxide (SO2), methanol (CH3OH), formic acid (HCOOH) and (for Suomi-NPP only) isoprene (C5H8).\r\n\r\nIn this dataset, the retrieval scheme has been applied to the infra-red and microwave sounders on the Suomi-NPP and NOAA-20 satellites.\r\n\r\nDevelopment of the core IMS scheme was funded by the UK’s National Centre for Earth Observation (NCEO) under the Natural Environment Research Council (NERC), with additional funding from EUMETSAT contract EUM/CO/13/4600001252/THH. Development of the extended IMS scheme was funded by NCEO. This version of the data was produced as part of the EOCIS project. \r\nData were produced by the Remote Sensing Group (RSG) at the Rutherford Appleton Laboratory (RAL).”", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": null, "updateFrequency": "", "dataLineage": "Development of the IMS scheme and data production were funded by the UK’s National Centre for Earth Observation (NCEO) under the Natural Environment Research Council (NERC), with additional funding from EUMETSAT contract EUM/CO/13/4600001252/THH. This version of the data was produced as part of the Earth Observation Climate Information Service (EOCIS) project.", "removedDataReason": "", "keywords": "RAL, IMS, atmospheric and surface properties, METOP-A", "publicationState": "working", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": null, "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": null, "timePeriod": { "ob_id": 9178, "startTime": "2018-04-01T00:00:00", "endTime": "2018-12-31T23:59:59" }, "resultQuality": { "ob_id": 3813, "explanation": "Data are as given by the data provider, no quality control has been performed by the Centre for Environmental Data Analysis (CEDA)", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2021-12-10" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 43471, "uuid": "0c2e0c54eab246deb6ffbd43bba6f2b4", "short_code": "cmppr", "title": "The RAL extended IMS retrieval scheme applied to the IASI, AMSU and MHS instruments on METOP-A", "abstract": "The Rutherford Appleton Laboratory (RAL) extended Infrared Microwave Sounder (IMS) data set contains vertical profiles of temperature, water vapour (H2O), ozone (O3), carbon monoxide (CO), together with estimated total columns of other minor gases, cloud optical depth and effective radius, dust and sulfuric acid aerosol optical depth. The scheme also provides surface temperature and surface spectral emissivity spanning infrared and microwave. Data are retrieved from the infra-red and microwave sounders on platforms Metop (IASI, AMSU and MHS) and Suomi-NPP (CrIS and ATMS).\r\n\r\nColumn amounts of the following minor gases are retrieved: Nitric acid (HNO3), ammonia (NH3), sulfur dioxide (SO2), methanol (CH3OH), formic acid (HCOOH) and (for Suomi-NPP only) isoprene (C5H8).\r\n\r\nIn this dataset, the scheme has been applied to the IASI, AMSU and MHS instruments on the METOP-A satellite." }, "imageDetails": [ 233 ], "discoveryKeywords": [], "permissions": [], "projects": [ { "ob_id": 43216, "uuid": "8bffaba46c4a4b8c82e4be2c91c637b9", "short_code": "proj", "title": "Earth Observation Climate Information Service (EOCIS)", "abstract": "The UK Earth Observation Climate Information Service exploits the observations available from environmental sensors orbiting in space to create climate data records and climate information. EOCIS was announced by the government in November 2022, and formally launched in March 2023. It is funded currently until March 2025. \r\n\r\nEOCIS is a collaboration led by the National Centre for Earth Observation, and involving over a dozen research organisations. EOCIS addresses 12 categories of global and regional essential climate variables, which are the following:\r\n- Sea surface temperature\r\n- Ocean reflectance\r\n- Fire occurrence and emissions\r\n- Aerosol and particulate\r\n- Cloud-aerosol-radiation\r\n- Methane\r\n- Land surface temperature\r\n- Water vapour, ozone\r\n- Arctic: ice sheet mass and sea ice\r\n- Eurasia: surface methane\r\n- Africa: soil water balance\r\n- Antarctic: ice sheet mass and ice velocity\r\n\r\nEOCIS is also creating new climate data at high resolution for the UK specifically. This includes both rapid-response information for climate-linked events (fire early warning and urban flood mapping) and longer term climate data linked to human and ecosystem health and landscape greenhouse gas emissions." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 221067, 221068, 221069, 221070, 221071, 221072, 221073, 221074, 221075, 221076, 221077, 221078 ], "onlineresource_set": [ 96011 ] }, { "ob_id": 46275, "uuid": "d89357be415441589167609a651496e1", "title": "ESA Land Cover Climate Change Initiative (Land_Cover_cci): Global Plant Functional Types (PFT) Dataset, v2.0.82", "abstract": "This dataset provides yearly global Plant Functional Type (PFT) fractional-cover maps for 1992–2025 (34 years), superseding v2.0.81 (1992–2020). It is derived by combining auxiliary high-resolution (~30 m) datasets with the ESA CCI Medium Resolution Land Cover (MRLC) map series of the same years, adding within-class spatial variability by quantifying PFT fractional composition at each 300 m pixel globally, while remaining consistent with the source Land_Cover_cci class legends.\r\n\r\nVersion 2.0.82 introduces the following changes relative to v2.0.81:\r\n\r\n•\tRebalanced shrub and natural-grass fractions. \r\nA comparison between PFT v2.0.81 of Harper et al. (2023) and the PFT derived from the CWT of Poulter et al. (2015) identified a global underestimation of shrub cover and a corresponding overestimation of grassland extent in v2.0.81. Both PFTs were rebalanced across the full time series. The correction is most pronounced in Australia, where shrubland replaces grassland in areas where it better reflects actual land cover.\r\n\r\n•\tExplicit C3/C4 grassland partitioning. \r\nNatural and managed grasslands, previously delivered as single undifferentiated layers, are now each split into C3 and C4 photosynthetic-pathway fractions (percentage of the 300 m pixel), allowing mixed C3/C4 pixel compositions. This is relevant for productivity and carbon-flux applications.\r\n\r\n•\tRefined broadleaved evergreen (BE) tree fraction. \r\nBE trees were previously underestimated outside the pantropical domain, notably in the Mediterranean (e.g., *Quercus ilex*) and other Northern Hemisphere areas; tree type/seasonality assignments were revised accordingly. Applied consistently across the full 1992–2025 series.\r\n\r\n•\tTemporal extension. \r\nCoverage extended by 5 years, from 2020 to 2025, following the extension of the Land Cover map series under the Copernicus Climate Change Service contract.\r\n\r\nThe explicit LAND, WATER_INLAND and WATER_OCEAN layers continue to complement the single WATER layer.\r\n\r\nAs a result, the dataset now comprises 19 layers (up from 14 in v2.0.81), each representing percentage cover (0–100%) of a PFT at 300 m spatial resolution.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2023-01-25T17:29:48", "latestDataUpdateTime": null, "updateFrequency": "annually", "dataLineage": "Data were processed by the ESA CCI Medium Resolution Land Cover project and catalogued here as part of the CCI Open Data Portal Project", "removedDataReason": "", "keywords": "Land Cover, CCI, Global, 300 m, PFT, Plant Functional Types, Vegetation Fractions, C3, C4, Photosynthetic Pathway, Climate Change", "publicationState": "preview", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "pending", "dataPublishedTime": null, "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": null, "timePeriod": { "ob_id": 13350, "startTime": "1992-01-01T00:00:00", "endTime": "2025-12-31T23:59:59" }, "resultQuality": { "ob_id": 3953, "explanation": "For more information on the data quality see the documentation from the ESA Land Cover project", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-05-19" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 46276, "uuid": "f0f59a4371cd4752aad9d318561375c7", "short_code": "comp", "title": "Derivation of the ESA Land Cover Climate Change Initiative (Land_Cover_cci): Global Plant Function Types (PFT) Dataset, v2.0.82", "abstract": "The methodology used to derive the v2.0.82 Pland Functional Type (PFT) data is unchanged in principle from v2.0.81. The PFT distributions were derived by combining auxiliary datasets with the ESA MRLC map series; see Harper et al. (2022, ESSD) for the base methodology. \r\n\r\nv2.0.82 refines the shrub/grass balance, adds explicit C3/C4 partitioning, and refines BE tree assignment (see Abstract). It has been reviewed in collaboration with LSCE and is intended to support ORCHIDEE and contribute to CMIP7 vegetation-climate feedback analyses." }, "procedureCompositeProcess": null, "imageDetails": [ 111 ], "discoveryKeywords": [], "permissions": [], "projects": [ { "ob_id": 14431, "uuid": "40e37317e38d4264ae57ecb515b781fa", "short_code": "proj", "title": "ESA Land Cover Climate Change Initiative Project", "abstract": "The ESA Land Cover Climate Change Initiative Project is part of the European Space Agency's Climate Change Initiative to produce long term datasets of Essential Climate Variables (ECV's) from historic satellite data.\r\n\r\nLand cover is defined as the (bio) physical cover at the earth surface including grass, trees, bare ground and water. Land cover is fundamental to better understand the climate through the estimation and validation of fluxes of water, carbon, and energy. It plays a role in adaptation and mitigation assessments at various scales.\r\n\r\nThe projects objective is to critically revisit all algorithms required for the generation of global land product in the light of GCOS requirements, and to design and demonstrate a mature system delivering in a consistent way over years and from multi-mission Earth Observation instruments, the longest possible global land cover map series at 300m, matching the needs of key users belonging to the climate change and land cover communities. The focus is placed on ESA and Member States missions, providing near daily global surface reflectance observation at moderate spatial resolution from 1999 onwards (MERIS Full Resolution (FR) & Reduced Resolution (RR), SPOT VEGETATION, PROBA-V and ASAR data), while the contribution of the AVHRR sensor tackles specific past years back to 1992." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 221084, 221085, 221086, 221087, 221088, 221089, 221092, 221091, 221104, 221105, 221096, 221095, 221106, 221094, 221107, 221108, 221103 ], "onlineresource_set": [ 96015, 96016, 96017, 96018 ] }, { "ob_id": 46277, "uuid": "de87f734e87e427cb27c34fe5578c032", "title": "TCOM-O3", "abstract": "The TCOM-O3 Version 2.0 dataset provides a gap-free daily zonal stratospheric profile record of ozone (O3) volume mixing ratios spanning 9,132 daily time steps (2000–2024) across 64 latitude bands (87.86°N to 87.86°S) as part of the NCEO Core Datasets collection. The dataset is distributed in two CF-1.7 compliant NetCDF files containing daily zonal median mixing ratios (zmo3, in mol/mol) alongside level-specific uncertainty estimates (zmo3_uncertainty) derived using quantile regression. Height-level profiles are stored in \"zmo3_tcom_hlev_2000-2024_V2.0.nc\" (~234 MB) across 50 geometric altitude levels (1 to 50 km at 1 km resolution), while pressure-level profiles are stored in zmo3_tcom_plev_2000-2024_V2.0.nc\" (~103 MB) interpolated onto 22 standard pressure levels ranging from 300 to 1 hPa\r\n. Outside the scientifically valid observational retrieval range (5 km to 50 km), uncertainty values are set to NaN to explicitly denote uncorrected TOMCAT CTM model data.", "creationDate": "2026-09-14T12:38:27.369034", "lastUpdatedDate": "2026-09-14T12:42:15.636045", "latestDataUpdateTime": "2026-09-14T12:38:27.369038", "updateFrequency": "notPlanned", "dataLineage": "• Dhomse, S. S. and Chipperfield, M. P.: TCOM-CFC11 and TCOM-CFC12: a gap-free, observationally constrained global dataset of stratospheric CFC-11 and CFC-12 profiles (v2.0), Earth Syst. Sci. Data, 18, 6171–6189, https://doi.org/10.5194/essd-18-6171-2026, 2026.\r\n• Dhomse, S. S. and Chipperfield, M. P.: Using machine learning to construct TOMCAT model and occultation measurement-based stratospheric methane (TCOM-CH4) and nitrous oxide (TCOM-N2O) profile data sets, Earth Syst. Sci. Data, 15, 5105–5120, https://doi.org/10.5194/essd-15-5105-2023, 2023.\r\n• Dhomse, S. S., Arosio, C., Feng, W., Rozanov, A., Weber, M., and Chipperfield, M. P.: ML-TOMCAT: machine-learning-based satellite-corrected global stratospheric ozone profile data set from a chemical transport model, Earth Syst. Sci. Data, 13, 5711–5729, https://doi.org/10.5194/essd-13-5711-2021, 2021.", "removedDataReason": "", "keywords": "machine learning,ozone,satellite data,TOMCAT", "publicationState": "preview", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "pending", "dataPublishedTime": null, "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 5205, "bboxName": "", "eastBoundLongitude": 0.0, "westBoundLongitude": 0.0, "southBoundLatitude": 89.86, "northBoundLatitude": 87.86 }, "verticalExtent": null, "result_field": null, "timePeriod": { "ob_id": 13354, "startTime": "2000-01-01T00:00:00", "endTime": "2024-12-31T00:00:00" }, "resultQuality": null, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 46279, "uuid": "743405a7f0ce46b1870d92b325d84598", "short_code": "comp", "title": "https://catalogue.ceda.ac.uk/uuid/0dae9a32133941daa422df4e1c8598db/", "abstract": "" }, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [], "projects": [ { "ob_id": 46278, "uuid": "f7083c0e579f44119a081665c53a9385", "short_code": "proj", "title": "https://catalogue.ceda.ac.uk/uuid/0dae9a32133941daa422df4e1c8598db/", "abstract": "" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 221113, 221114, 221115, 221121, 221110, 221111, 221112, 221122, 221123 ], "onlineresource_set": [] }, { "ob_id": 46280, "uuid": "6d8c904239544c9fa848e9dffd14a1a5", "title": "TROPFLUXv2 sst, taux, CMORised to monthly frequency 1940-2025", "abstract": "TROPFLUX version2 of variables sst and taux 1940-2025 in the tropical oceans (30N - 30S). TropFlux provides surface heat and momentum flux data of tropical oceans (30°N-30°S)\r\nThe data provided by National Institute of Oceanography/CSIR (NIO, Goa, India), contact: praveen.b@incois.gov.in. \r\nWhile reference paper describing version 2 is under revision, sst and taux v2 were provided and then CMORised through ESMValTool to monthly frequency, to be used in ENSO recipes by felicity.chun@anu.edu.au (ACCESS-NRI). This data is used in ClimateREF in the ENSO recipes.", "creationDate": "2026-09-17T03:36:37.013152", "lastUpdatedDate": "2026-09-17T03:36:43.827858", "latestDataUpdateTime": "2026-09-17T03:36:37.013158", "updateFrequency": "notPlanned", "dataLineage": "The data provided by National Institute of Oceanography/CSIR (NIO, Goa, India).\r\nTropFlux relies on data provided by the ECMWF Re-Analysis interim (ERA-I) and ISCCP projects.\r\nWhile reference paper describing version 2 is under revision, sst and taux v2 daily were provided and then CMORised through ESMValTool to monthly frequency.", "removedDataReason": "", "keywords": "sea surface temperature,wind stress,tropical ocean", "publicationState": "preview", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "pending", "dataPublishedTime": null, "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 5206, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -30.0, "northBoundLatitude": 30.0 }, "verticalExtent": null, "result_field": null, "timePeriod": { "ob_id": 13356, "startTime": "1940-01-01T00:00:00", "endTime": "2025-12-31T00:00:00" }, "resultQuality": null, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [], "projects": [], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 221124, 221125, 221126, 221127, 221128, 221129, 221130, 221131, 221132, 221133 ], "onlineresource_set": [] }, { "ob_id": 46282, "uuid": "c5b861754839410792f56c80389828a1", "title": "jesse paleo", "abstract": "This dataset contains a range of parameters from a 1 km gridded output from runs of version 3.6.1 of the Weather Research and Forecasting (WRF) model deployed on the ARCHER UK National Supercomputing Service. These runs were part of the NERC funded BBUBL project (Biotelemetry/Bio-aerial-platforms for the Urban Boundary Layer - also known as City Flocks, NERC grant award NE/N003195/1). The domain of the model runs was over the set over Birmingham conurbation for all of 2015. This geo-temporal domain encompasses measurements of the urban boundary layer obtained from instrumentation attached to birds flown around the area. See related dataset.", "creationDate": "2026-09-17T14:30:36.831525", "lastUpdatedDate": "2026-09-17T14:30:58.973586", "latestDataUpdateTime": "2026-09-17T14:30:36.831528", "updateFrequency": "notPlanned", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "urban meteorology,boundary layer", "publicationState": "working", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "pending", "dataPublishedTime": null, "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 5208, "bboxName": "", "eastBoundLongitude": -1.26129, "westBoundLongitude": -2.53084, "southBoundLatitude": 52.1007, "northBoundLatitude": 52.8473 }, "verticalExtent": null, "result_field": null, "timePeriod": { "ob_id": 13358, "startTime": "0001-01-01T00:00:00", "endTime": null }, "resultQuality": { "ob_id": 4945, "explanation": "Model output has only been checked for CF convention compliance, otherwise supplied as-is.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2026-09-17" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [], "projects": [], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 221140, 221141, 221142, 221143, 221144, 221145, 221146, 221147 ], "onlineresource_set": [ 96020, 96021, 96022 ] }, { "ob_id": 46283, "uuid": "64d0efd5e64545fc8000a679101d7084", "title": "Cirrus fraction ratio derived from susceptibility of cirrus fraction to aviation change (2019-2020)", "abstract": "This is a global gridded dataset of the cirrus fraction ratio between 2019 (Business as Usual (BAU)) and the 2020 (COVID) period. It is derived from regional (6 regions: Europe, China, Central Asia, North America, North Atlantic and North Pacific) values of cirrus fraction susceptibilities to aviation fuel burn. The aviation data for March and April of 2019 and 2020 is downloaded from ACP Teoh 2024 ADS-B (GAIA) monthly for (2019–2021) https://doi.org/10.5194/acp-24-725-2024. \r\nTo quantify the cirrus fraction, regional susceptibility values were used for the 6 regions (2 oceanic and 4 land regions) covering most regions with frequent contrail occurrence worldwide. For the places outside the 6 regions, we adopt Monte Carlo random sampling of susceptibility (assuming Gaussian distribution) ten thousand times over the land and oceanic regions. Only aviation active places, with flight density greater than 1 km⁻¹ month⁻¹, are analysed, which cover almost 60% of the global surface area.", "creationDate": "2026-09-18T12:13:07.244705", "lastUpdatedDate": "2026-09-18T12:13:07.244707", "latestDataUpdateTime": "2026-09-18T12:13:07.244709", "updateFrequency": "notPlanned", "dataLineage": "Regional susceptibility values were calculated based on the response of the cirrus fraction (machine learning counterfactual) to the change in the aviation fuel burn (kg). To translate the regional cirrus fraction and uncertainty ranges to the global cirrus fraction with uncertainty range, Monte Carlo 10000 ensembles of random sampling of individual susceptibility were used at the 6 regions, and the mean susceptibility of land regions (North America, Europe, Central Asia and China) and ocean regions (North Atlantic and North Pacific) was used for the land and oceanic regions elsewhere. The grid points with traffic density less than 1 km⁻¹ month⁻¹ were neglected from the calculations. The formula used here is Cirrus_2019 = Cirrus_2020 * (aviation_2019 / aviation_2020)^S. The final NetCDF file contains global gridded maps of cirrus fraction ratio (BAU / COVID), cirrus fraction ratio + uncertainty and cirrus fraction ratio - uncertainty products.", "removedDataReason": "", "keywords": "Contrail Cirrus,Susceptibility,Cirrus Fraction Ratio,Aviation Traffic Density", "publicationState": "working", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "pending", "dataPublishedTime": null, "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 5209, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": 180.0, "southBoundLatitude": 90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": null, "timePeriod": { "ob_id": 13359, "startTime": "2020-03-01T00:00:00", "endTime": "2020-04-30T00:00:00" }, "resultQuality": { "ob_id": 4946, "explanation": "The regions with aviation traffic density less than 1 km⁻¹ month⁻¹ were neglected from the dataset.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2026-09-18" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [], "projects": [ { "ob_id": 46284, "uuid": "1b0456a9b4d6412d91fdee564edb0de1", "short_code": "proj", "title": "Quantifying and Reducing Aviation COntrail raDiativE forcing (QR-CODE)", "abstract": "This project aims to quantify the radiation contribution from aviation's contrail cirrus clouds." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 221148, 221149, 221150, 221151, 221152, 221153, 221154, 221155 ], "onlineresource_set": [] }, { "ob_id": 46285, "uuid": "145695fee82242328d2bd0c7472636e6", "title": "ESA Fire Climate Change Initiative (FireCCI): Long-term Small Fire Dataset (SFDL) Burned Area pixel product for Test Sites: Amazonia and Sahel, version 2.0", "abstract": "To be updated", "creationDate": "2024-09-13T13:04:23.192362", "lastUpdatedDate": "2024-09-13T13:03:26", "latestDataUpdateTime": null, "updateFrequency": "", "dataLineage": "Data was produced by the ESA Fire CCI team as part of the ESA Climate Change Initiative (CCI) and is being held on the CEDA (Centre for Environmental Data Analysis) archive as part of the ESA CCI Open Data Portal.", "removedDataReason": "", "keywords": "ESA, CCI, Pixel, Burned Area, Fire Disturbance, Climate Change", "publicationState": "preview", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": null, "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 4632, "bboxName": "SFDL10 - Amazonia, Sahel, Siberia", "eastBoundLongitude": 140.625, "westBoundLongitude": -78.75, "southBoundLatitude": -27.059126, "northBoundLatitude": 72.073911 }, "verticalExtent": null, "result_field": null, "timePeriod": null, "resultQuality": { "ob_id": 4610, "explanation": "See the associated dataset documentation at https://climate.esa.int/projects/fire/key-documents/", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2024-10-24" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 111 ], "discoveryKeywords": [], "permissions": [], "projects": [ { "ob_id": 13255, "uuid": "6c3584d985bd484e8beb23ff0df91292", "short_code": "proj", "title": "ESA Fire Climate Change Initiative Project (Fire CCI)", "abstract": "The European Space Agency (ESA) Fire Climate Change Initiative (Fire CCI) project, led by University of Alcala (Spain), is part of ESA's Climate Change Initiative (CCI) to produce long term datasets of Essential Climate Variables derived from global satellite data.\r\n\r\nThe Fire CCI focuses on the following issues relating to Fire Disturbance: Analysis and specification of scientific requirements relating to climate; Development and improvement of pre-processing and burned area algorithms; Inter-comparison and selection of burned area algorithms; System prototyping and production of burned area datasets; Product validation and product assessment\r\n" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 221159, 221160, 221161, 221162, 221163, 221164, 221165, 221166, 221167, 221168, 221169 ], "onlineresource_set": [ 96025, 96026 ] }, { "ob_id": 46286, "uuid": "d461b0b994d04edb9d7900bf3202b64a", "title": "Data from MPCM HCl and ozone simulations - Benne et al. (2025, 2026)", "abstract": "This repository contains all the main outputs from simulations conducted during the \"Improving understanding of Mars photochemistry: using models to interpret reactive trace gas retrievals from instruments aboard the Trace Gas Orbiter\" project funded by UKSA. We mainly focused on two main atmospheric species and their seasonal cycles in the Martian atmosphere: HCl and ozone (O3). These have been studied using the Mars Planetary Climate Model (MPCM), a GCM of the Martian atmosphere, for Martian Years 34--37. For more details about the different simulations, please refer to the provided README file.", "creationDate": "2026-09-21T13:42:24.200730", "lastUpdatedDate": "2026-09-21T13:42:24", "latestDataUpdateTime": "2026-09-21T13:42:24", "updateFrequency": "notPlanned", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "Mars,Photochemistry,GCM,HCl,Ozone", "publicationState": "preview", "nonGeographicFlag": true, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "pending", "dataPublishedTime": null, "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": null, "verticalExtent": null, "result_field": null, "timePeriod": { "ob_id": 13360, "startTime": "2023-11-01T00:00:00", "endTime": "2026-09-30T00:00:00" }, "resultQuality": { "ob_id": 4947, "explanation": "Please refer to the provided README file for details about our data processing methods.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2026-09-21" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 46288, "uuid": "c83f9b0539224a83862c368076f6b564", "short_code": "comp", "title": "Mars Planetary Climate Model (MPCM) - non-UK version", "abstract": "Please refer to the provided README file for more details about the setup used in our different simulations." }, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [], "projects": [ { "ob_id": 46287, "uuid": "e9cd12788eb14d15859437f0677c35ce", "short_code": "proj", "title": "Improving understanding of Mars photochemistry: using models to interpret trace gases from TGO data", "abstract": "The project aim was to study the seasonal cycles of two trace species of the Martian atmosphere: HCl and O3. The former was recently detected by ExoMars TGO and is the first halogen detected in the Martian atmosphere. Model work was then needed to try to reproduce and explain spacecraft observations. Ozone is a well-known compound of the Martian atmosphere, whose abundance is underestimated by models. We used newly measured vertical profiles from TGO to investigate this deficit." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 221170, 221171, 221172, 221173, 221174, 221175, 221176, 221184, 221177, 221178, 221179 ], "onlineresource_set": [] }, { "ob_id": 46289, "uuid": "9a71c4654d2a4d338e2ba0f8caa1df30", "title": "TCOM-O3: TOMCAT CTM and occultation measurements based daily zonal stratospheric ozone profile data", "abstract": "This dataset contains daily zonal stratospheric ozone (O3) profile outputs between 2000 and 2024 generated by combining 3D TOMCAT Chemical Transport Model (CTM) simulations with solar occultation observations from the ACE-FTS instrument on SCISAT. \r\nThe resulting dataset provides daily zonal median mixing ratios across 64 latitude bands (87.86°N to 87.86°S) spanning 9,132 days (2000–2024) and is distributed in two NetCDF files:\r\n\r\n • zmo3_tcom_hlev_2000-2024_V2.0.nc - height-level data across 50 altitude levels (1 to 50 km at 1 km resolution, ~234 MB)\r\n • zmo3_tcom_plev_2000-2024_V2.0.nc - pressure-level data interpolated onto 22 standard pressure levels (300 to 1 hPa, ~103 MB)\r\nCore variables store daily zonal median volume mixing ratios (zmo3, in mol/mol) and quantile-regression derived uncertainty estimates (zmo3_uncertainty). Outside the valid observational retrieval range (5 km to 50 km), uncertainty values are set to NaN to explicitly denote uncorrected TOMCAT CTM data.", "creationDate": "2026-09-23T10:12:53.969777", "lastUpdatedDate": "2026-09-23T10:16:53.547013", "latestDataUpdateTime": "2026-09-23T10:12:53.969782", "updateFrequency": "notPlanned", "dataLineage": "Data were produced by the project team at the University of Leeds using the offline 3D TOMCAT Chemical Transport Model (CTM) driven by ECMWF ERA5 meteorological reanalyses, bias-corrected against solar occultation measurements from the ACE-FTS satellite instrument (Version 5.2/5.3 retrievals, restricted to 980–1130 cm⁻¹) via Extreme Gradient Boosting (XGBoost) machine learning regression across five latitude bins. The dataset was supplied for long-term archiving at the NERC EDS Centre for Environmental Data Analysis (CEDA) as part of the National Centre for Earth Observation (NCEO) Core Datasets collection.\r\n\r\nReference Publications:\r\n • Dhomse et al. (2021, ESSD): https://doi.org/10.5194/essd-13-5711-2021\r\n • Dhomse & Chipperfield (2023, ESSD): https://doi.org/10.5194/essd-15-5105-2023\r\n • Dhomse & Chipperfield (2026, ESSD): https://doi.org/10.5194/essd-18-6171-2026\r\n • Boone et al. (2023, ACE-SOC-0043): https://www.ace.uwaterloo.ca/publications.html", "removedDataReason": "", "keywords": "TCOM,Stratosphere,Ozone Profiles,Satellite,machine learning,TOMCAT CTM,ACE-FTS,ERA5,NCEO core dataset", "publicationState": "preview", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "pending", "dataPublishedTime": null, "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 5210, "bboxName": "", "eastBoundLongitude": -180.0, "westBoundLongitude": 180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": null, "timePeriod": { "ob_id": 13362, "startTime": "2000-01-01T00:00:00", "endTime": "2024-12-31T00:00:00" }, "resultQuality": { "ob_id": 4948, "explanation": "Level-specific uncertainties are quantified using 25th and 75th percentile quantile regression models. For altitudes outside the scientifically valid ACE-FTS observational retrieval range for ozone (5 km to 50 km), uncertainty values are set to NaN to explicitly indicate that mixing ratios represent uncorrected TOMCAT CTM model output. Feature engineering is applied prior to training to adjust for multicollinearity among chemical predictors. SHAP value analysis demonstrates that XGBoost effectively corrects model transport and chemical biases, raising the correlation with ACE-FTS observations from 0.81 (raw CTM) to 0.96 (TCOM) in high-latitude regions.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2026-09-23" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 46279, "uuid": "743405a7f0ce46b1870d92b325d84598", "short_code": "comp", "title": "https://catalogue.ceda.ac.uk/uuid/0dae9a32133941daa422df4e1c8598db/", "abstract": "" }, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [], "projects": [], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 221185, 221186, 221187, 221188, 221189, 221190, 221191, 221192, 221193, 221194 ], "onlineresource_set": [ 96027, 96028, 96029, 96030 ] }, { "ob_id": 46290, "uuid": "eac4f125ae6d4909b706a130e20ceeab", "title": "ESA Lakes Climate Change Initiative (Lakes_cci): Harmonised lake surface water temperature and lake ice cover gap-filled product derived from Lake_cci CRDP v3.0 , Version 1.0", "abstract": "This dataset provides a global Level4 harmonised, gap-filled Lake Surface Water Temperature (LSWT) and Lake Ice Cover (LIC) dataset for 2,000 inland water bodies over the period 2000-2023 as part of the European Space Agency (ESA) Lakes Climate Change Initiative (Lakes_cci) project.\r\n\r\nThe dataset includes two thematic Lake ECVs: LSWT, derived from optical and thermal satellite observations, supporting analysis of mixing regimes, biogeochemical cycling; and LIC derived from optical satellite observations, describing the ice freeze-up and break-up. Together, these variables provide key indicators of climate variability and change.\r\n\r\nThis version 1.0 of the harmonised dataset is derived from the ESA CCI Lakes ECV Products Climate Data Research Package (CRDP) v3.0, specifically the LSWT and LIC products. The LSWT and the LIC were gap-filled independently and subsequently harmonised to ensure consistency between the variables. The gap-filling and harmonisation methods are described in three journal articles.\r\n\r\nBecause the gap-filled LSWT and LIC products are harmonised, they are fully consistent. Therefore, no water temperature is reported for grid cells classified as ice-covered.\r\n\r\nThe data are reported in two forms: 1) daily global files and 2) per lake time series files. The catalogue includes a CSV file listing all lakes for which the product is available. The lake mask for each lake is provided in all the files.", "creationDate": "2026-09-23T10:24:04.979626", "lastUpdatedDate": "2026-09-23T10:42:16", "latestDataUpdateTime": "2026-10-27T14:57:46", "updateFrequency": "notPlanned", "dataLineage": "The dataset was derived from the Level3 LSWT and LIC product of the ESA Lakes Climate Change Initiative (Lakes_cci): Lake products, Version 3.0 as part of the ESA Lakes Climate Change Initiative project.\r\n\r\nData was upplied for archiving at the Centre for Environmental Data Analysis (CEDA) as part of the CCI Open Data Portal.", "removedDataReason": "", "keywords": "European Space Agency,Climate Change Initiative,Lakes,Essential Climate Variables,Analysis product", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2026-10-28T11:38:55", "doiPublishedTime": "2026-10-28T11:54:34.239259", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 46376, "dataPath": "/neodc/esacci/lakes/data/gapfilled_lswt_lic/L4/v1.0/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 253058167081, "numberOfFiles": 10707, "fileFormat": "Data are in NetCDF format" }, "timePeriod": { "ob_id": 13365, "startTime": "2000-02-24T00:00:00", "endTime": "2023-12-31T00:00:00" }, "resultQuality": { "ob_id": 4949, "explanation": "The L4 gap-filled LSWT+LIC product was validated through manual inspection, comparison with other existing product and comparison with in situ data. Full description of the validation can be found in three journal articles.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2026-09-23" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 46377, "uuid": "23c1ca43ed014506871a3ef257ad2181", "short_code": "comp", "title": "Derivation of the ESA Lakes Climate Change Initiative (Lakes_cci): Harmonised lake surface water temperature and lake Ice cover gap-filled product, Version 1.0", "abstract": "The dataset was derived from the Level3 Lake Surface Water Temperature (LSWT) and Lake Ice Cover (LIC) product from the ESA Lakes Climate Change Initiative (Lakes_cci): Lake products, Version 3.0 dataset (https://doi.org/10.5285/a56dba09df8a42ec9fba8b8c7a5e1f69)\r\n \r\nLake Surface Water Temperature (LSWT) and Lake Ice Cover (LIC) were gap-filled independently using separate algorithms and subsequently harmonised to produce a consistent LSWT+LIC dataset.\r\n\r\nDetailed descriptions of the gap-filling methods, algorithm selection process, and harmonisation approach are provided in three journal articles. Further information on the dataset is available in these publications and in the Product User Guide of the ESA Lakes Climate Change Initiative (Lakes_cci): Lake products, Version 3.0." }, "procedureCompositeProcess": null, "imageDetails": [ 111 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2631, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 80, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_lakes_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 30309, "uuid": "09c9b617a2d6462f9954a3c3a34fcc27", "short_code": "proj", "title": "ESA Lakes Climate Change Initiative Project", "abstract": "The Lakes Climate Change Initiative Project (Lakes_cci) is part of the European Space Agency's Climate Change Initiative Programme to produce long term datasets of Essential Climate Variables (ECV's) derived from global satellite data..\r\n\r\nLakes are of significant interest to the scientific community, local to national governments, industries and the wider public. A range of scientific disciplines including hydrology, limnology, climatology, biogeochemistry and geodesy are interested in distribution and functioning of the millions of lakes (from small ponds to inland seas), from the local to the global scale. Remote sensing provides an opportunity to extend the spatio-temporal scale of lake observation. In this context, the Lakes_cci develops products for the following five thematic climate variables:\r\n•\tLake Water Level (LWL): a proxy fundamental to understand the balance between water inputs and water loss and their connection with regional and global climate changes.\r\n•\tLake Water Extent (LWE): a proxy for change in glacial regions (lake expansion) and drought in many arid environments, water extent relates to local climate for the cooling effect that water bodies provide.\r\n•\tLake Surface Water temperature (LSWT): correlated with regional air temperatures and a proxy for mixing regimes, driving biogeochemical cycling and seasonality. \r\n•\tLake Ice Cover (LIC): freeze-up in autumn and advancing break-up in spring are proxies for gradually changing climate patterns and seasonality. \r\n•\tLake Water-Leaving Reflectance (LWLR): a direct indicator of biogeochemical processes and habitats in the visible part of the water column (e.g. seasonal phytoplankton biomass fluctuations), and an indicator of the frequency of extreme events (peak terrestrial run-off, changing mixing conditions).\r\n\r\nIn this context, Lakes_cci represents a unique framework to provide consistent and homogenous data to the multiple communities of lake scientists. The project actively engages with this community to assess the utility and future improvement of Lakes_cci products." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 12066, 50559, 50561, 63576, 103372, 103373, 103374, 103375, 103376 ], "vocabularyKeywords": [], "identifier_set": [ 13982 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 221195, 221196, 221197, 221198, 221199, 221200, 221201, 221521, 221202, 221203, 221204, 221205, 221206 ], "onlineresource_set": [ 96531, 96532, 96533 ] }, { "ob_id": 46294, "uuid": "8c6be65e91f146d6877a78b36a1fb6a4", "title": "IMITATE Gross Primary Productivity (2009-2020, Europe) v1.0.", "abstract": "This dataset contains gridded daily Gross Primary Productivity (GPP) over Europe distributed on a regular latitude-longitude grid with a resolution of 0.05°x0.05° and time range from January 2009 to June 2020, inclusive. \r\n\r\nGross primary productivity (GPP) is the total rate at which primary producers capture energy and convert it into organic matter through photosynthesis. The GPP data were generated using machine-learning emulators of GPP from the Joint UK Land Environment Simulator (JULES) combined with Earth Observation datasets (Land Surface Temperature (LST), Fraction of Photosynthetically Active Radiation (FAPAR), soil moisture, and land cover). Further information on the dataset can be found in the Data Product User Guide archived with the data.\r\n\r\nThe data were generated as a proof of concept for an innovative model-data fusion approach within the ESA EO4Society project ‘IMITATE’ (Introducing Machine learning Into Targeted Analysis for Terrestrial Ecosystems).\r\n\r\nThe dataset was produced by a team of scientists from the National Centre for Earth Observation with support from the UK Met Office Hadley Centre.", "creationDate": "2026-09-28T15:58:35.302510", "lastUpdatedDate": "2026-09-28T15:06:20", "latestDataUpdateTime": "2026-09-28T15:06:20", "updateFrequency": "", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "Gross Primary Productivity, GPP Carbon cycle, IMITATE, ESA EO4Society, Emulator, Machine Learning, JULES Land surface", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2026-10-08T14:15:17", "doiPublishedTime": "2026-10-09T13:13:54.488823", "removedDataTime": null, "geographicExtent": { "ob_id": 5212, "bboxName": "NCEO GPP European", "eastBoundLongitude": 39.925, "westBoundLongitude": -9.725, "southBoundLatitude": 30.025, "northBoundLatitude": 70.725 }, "verticalExtent": null, "result_field": { "ob_id": 46293, "dataPath": "/neodc/gpp_eds/data/v1.0", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 185459230439, "numberOfFiles": 14, "fileFormat": "NetCDF" }, "timePeriod": { "ob_id": 13374, "startTime": "2009-01-10T00:00:00", "endTime": "2020-06-30T00:00:00" }, "resultQuality": { "ob_id": 4952, "explanation": "The data were validated by the University of Leicester project team. The GPP dataset was generated using the JULES emulator and the EO inputs, were validated against a number of FLUXNET sites and two existing GPP EO products (see Table 3). Some examples of how the datasets compare can be seen in Figures Figure 4 and Figure 5 data product user guide archived with the data.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2026-09-30" }, "validTimePeriod": { "ob_id": 13373, "startTime": "2009-01-10T00:00:00", "endTime": "2020-06-30T00:00:00" }, "procedureAcquisition": null, "procedureComputation": { "ob_id": 46299, "uuid": "f0bee719747e4cc380c601794a82e4c8", "short_code": "comp", "title": "Gross Primary Productivity, European Data Set (2009-2020), V1.0", "abstract": "This dataset was generated following a two-step process. First, a machine-learning emulator with 18 years of JULES data (2000-2018) was trained. \r\n\r\nThe emulator was evaluated and validated using data from 2019 and 2020, respectively. The input features for the emulator are: surface downward short-wave (SW) radiation, minimum and maximum daily land surface temperature, Fraction of Photosynthetically Active Radiation (FAPAR), soil moisture content of the top layer, and a soil moisture availability factor. \r\n\r\nThese inputs are listed in Table 1 of the data product user guide. The output of the emulator is GPP in kg m-2 s-1. The project developed 13 emulators, which correspond to the 13 different Plant Functional Types (PFTs) from JULES (listed in Table 2), where each emulator has inputs and output specific to a particular PFT. The outputs of all 13 emulators are then combined using the PFT fraction to provide the total gridbox GPP.\r\n\r\nThe second step involved running the emulators using inputs from equivalent EO datasets to generate the final GPP dataset. The EO datasets used in this project are listed in Table 3 of the data product user guide, including two existing GPP datasets used for comparison to validate the product. There are two exceptions of inputs that do not come from EO, but we take them from JULES: surface downward SW radiation, which is part of the TRENDY forcing data used to drive JULES, and the soil moisture availability factor (fsmc), which is generated by JULES and does not currently have an EO equivalent. Further work is required to replace fsmc with another related variable that is observable." }, "procedureCompositeProcess": null, "imageDetails": [ 111, 130 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" }, { "ob_id": 11, "classification": "attribution required" } ] } } ], "projects": [ { "ob_id": 5002, "uuid": "60e718d3f2957f742c89b2b4fc159718", "short_code": "proj", "title": "National Centre for Earth Observation (NCEO)", "abstract": "The National Centre for Earth Observation is a partnership of scientists and institutions, from a range of disciplines, who are using data from Earth observation satellites to monitor global and regional changes in the environment and to improve understanding of the Earth system so that we can predict future environmental conditions.\r\n\r\nNCEO's Vision is to unlock the full potential of Earth observation to monitor, diagnose and predict climate and environmental changes, ensuring that these scientific advances are delivered to the wider community embedded in world class science." }, { "ob_id": 46298, "uuid": "b16f0fdc58a64fffaf1e3ad960d3d1b3", "short_code": "proj", "title": "Introducing Machine learning Into Targeted Analysis for Terrestrial Ecosystems (IMITATE)", "abstract": "The IMITATE project, led by the University of Leicester under ESA’s EO4Society programme, explored how machine learning can be used to emulate and enhance physical land surface models of terrestrial ecosystems. Focusing on Europe and the carbon cycle, the project developed AI-based “emulators” of the JULES Earth System Model and compared their outputs with both model simulations and Earth Observation data. IMITATE aimed to improve understanding of key land-atmosphere processes and assess how well data-driven relationships align with established Earth system models. The project also sought to generate new EO-based products, including estimates of ecosystem productivity, to support climate and environmental research." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 62780, 62781, 103267, 103268, 103269, 103270, 103271, 103272, 103273, 103274, 103275, 103276, 103277, 103278, 103279, 103280, 103281 ], "vocabularyKeywords": [], "identifier_set": [ 13976 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 221209, 221210, 221211, 221212, 221213, 221214, 221233, 221234, 221235 ], "onlineresource_set": [] }, { "ob_id": 46296, "uuid": "25e812daf0984e409a3955fc5255c017", "title": "Model output from the 1/12° global integration of the NOC \"Near Present Day\" configuration forced with ERA-5 from 1976", "abstract": "This dataset contains annual, monthly and 5-day ocean and ice output from an integration of the National Oceanography Centre (NOC) \"Near Present Day\" configuration using the Nucleus for European Modelling of the Ocean (NEMO) modelling framework version 4.2 with SI3 (Sea Ice modelling Integrated Initiative), and based on the recent UK Global Ocean and Sea Ice configuration version 9 (GOSI9), which has been documented comprehensively in Guiavarc’h et al. (2025). Forcing is provided by a version of the ERA-5 atmospheric reanalysis (Hersbach et al., 2020) with a climatologically adjusted surface air temperature field. Integrations start from 1976 following three passes through the 1976 forcing with temperature and salinity conditions reset to the initial state at the end of each pass to allow an initial adjustment of the dynamics and ice field. The end of the simulation will continue to remain close to present day, with a typical time lag of 1-3 months. The complete dataset includes: full 5-day, monthly and annual mean ocean fields; and daily mean values of a subset of 2-dimensional fields, including surface fields and bottom pressure. The integration is on the 1/12° eORCA12 global grid, initialised from the World Ocean Atlas 2023 (Reagen et al., 2024) Climate Normal (30-year average) corresponding to 1971-2000. The NOC \"Near Present Day\" configuration and simulations were supported by the Natural Environment Research Council's AtlantiS project (NE/Y005589/1).", "creationDate": "2026-09-29T15:34:52.959293", "lastUpdatedDate": "2026-09-29T15:34:52", "latestDataUpdateTime": "2026-09-29T15:34:52", "updateFrequency": "", "dataLineage": "The data are archived on the British Oceanographic Data Centre (BODC)'s space at the Centre for Environmental Data Analysis (CEDA) and assigned a DOI. No quality control procedures were applied by BODC.", "removedDataReason": "", "keywords": "climatologyMeteorologyAtmosphere, location, elevation, oceans, geoscientificInformation", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "final", "dataPublishedTime": "2026-09-28T00:00:00", "doiPublishedTime": "2026-09-28T00:00:00", "removedDataTime": null, "geographicExtent": { "ob_id": 1, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 46295, "dataPath": "/bodc/soc260212", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 111995427616226, "numberOfFiles": 28203, "fileFormat": null }, "timePeriod": { "ob_id": 13367, "startTime": "1976-01-01T00:00:00", "endTime": null }, "resultQuality": { "ob_id": 4950, "explanation": "The data are provided as-is with no quality control undertaken by the British Oceanographic Data Centre (BODC). The data suppliers have not indicated if any quality control has been undertaken on these data.", "passesTest": true, "resultTitle": "BODC Data Quality Statement", "date": "2026-09-29" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" }, { "ob_id": 11, "classification": "attribution required" } ] } } ], "projects": [], "inspireTheme": [], "topicCategory": [], "phenomena": [ 36269, 36270, 36324, 36331, 36334, 36337, 50612, 50623, 50624, 52949, 52960, 52977, 54483, 59641, 59645, 59647, 59649, 59655, 59657, 59658, 59659, 59660, 59665, 59669, 59671, 59673, 59692, 59693, 59694, 59698, 59779, 59781, 59785, 59787, 59788, 59789, 59791, 59792, 60131, 60132, 60135, 60136, 60148, 62272, 62276, 63050, 63051, 63053, 63054, 63055, 63062, 63063, 63065, 63066, 63068, 63070, 63071, 63073, 63074, 63075, 63079, 63080, 63081, 63082, 63083, 63084, 63085, 63086, 63087, 63088, 63089, 63090, 63091, 63092, 63093, 63094, 63095, 63096, 63097, 63100, 63101, 63102, 63103, 63104, 63105, 63106, 63108, 63109, 63112, 63118, 63119, 63120, 63121, 63122, 63123, 63131, 64130, 64131, 75346, 75347, 75348, 75349, 75353, 75354, 75357, 75359, 75364, 75373, 75399, 75401, 75404, 75409, 75410, 75414, 75424, 75427, 93058, 93061, 93062, 93079, 93081, 93082, 93095, 93098, 93100, 93102, 93105, 93111, 93118, 103282, 103283, 103284, 103285, 103286, 103287, 103288, 103289, 103290, 103291, 103292 ], "vocabularyKeywords": [], "identifier_set": [ 13974 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 221218, 221220, 221219, 221217, 221216, 221215, 221221, 221222, 221223, 221224 ], "onlineresource_set": [] }, { "ob_id": 46300, "uuid": "52519ec172b94c309697c9b7b187f1c5", "title": "JC251 Goban spur multi-method geophysical dataset", "abstract": "Cruise JC251 collected the multi-method geophysical dataset for the project including coincident high-quality controlled source electromagnetic (CSEM) and wide-angle seismic datasets along a carefully chosen profile at the magma-poor Goban Spur rifted margin to quantify evolution of magmatism and serpentinisation during the onset of seafloor spreading. The dataset includes wide-angle seismic, electric field and magnetometer datasets recorded using ocean-bottom instruments (OBI) (14 OBIC UK), along with multi-physics ship dataset along a ~200 km line with a temporal span of 24 days (31-Aug-2024 to 23-Sep-2023). The 14 OBIC instruments were equipped with 3-component geophones and 2-component horizontal electrode dipoles with 12 m antennas and ten of them had additional 3 component fluxgate magnetometers. All measurements were recorded at 250 Hz sampling rate.", "creationDate": "2026-10-02T13:44:40.220710", "lastUpdatedDate": "2026-10-02T13:44:40.220713", "latestDataUpdateTime": "2026-10-02T13:44:40.220714", "updateFrequency": "notPlanned", "dataLineage": "The dataset is from the first (northern profile: profile-1) only.", "removedDataReason": "", "keywords": "gobanspur,wide-angle-seismic,CSEM,serpentinisation,magmatism", "publicationState": "working", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "pending", "dataPublishedTime": null, "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 5213, "bboxName": "", "eastBoundLongitude": -13.3289333, "westBoundLongitude": -15.3680833, "southBoundLatitude": 48.3418, "northBoundLatitude": 49.3269833 }, "verticalExtent": null, "result_field": null, "timePeriod": { "ob_id": 13368, "startTime": "2023-08-31T00:00:00", "endTime": "2023-09-23T00:00:00" }, "resultQuality": { "ob_id": 4953, "explanation": "The data acquisition was avoided during bad weather to ensure good quality of data.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2026-10-02" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 46302, "uuid": "e235d8762f724d9999ef79610e038cfb", "short_code": "acq", "title": "Acquisition for: JC251 Goban spur multi-method geophysical dataset", "abstract": "" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [], "projects": [ { "ob_id": 46301, "uuid": "bca898a16dfa4fc3aeca70dd0fac13cb", "short_code": "proj", "title": "Quantifying evolution of magmatism and serpentinisation during the onset of seafloor spreading", "abstract": "The overall aim is to quantify the evolution of serpentinisation and magmatism at magma-poor rifted margins as seafloor spreading is initiated, including whether serpentinisation of exhumed mantle is focused in discrete fault zones, and whether magmatism increases abruptly or gradually." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 221237, 221238, 221239, 221240, 221241, 221242, 221243, 221244, 221245, 221246, 221247, 221248 ], "onlineresource_set": [ 96050 ] }, { "ob_id": 46305, "uuid": "2856b5dd4b7d4c528d36e3679d2d706e", "title": "ESA Precursors for Aerosols and Ozone Climate Change Initiative (Precursors_cci): daily L3 HCHO from OMI, version 2.0", "abstract": "The Formaldehyde (HCHO) Climate Data Record (CDR) product is a Level 3 HCHO product developed by using satellite data from the OMI instrument (on AURA) as part of the ESA Climate Change Initiative (CCI) Precursors for Aerosols and Ozone project.\r\n\r\nThis dataset provides gridded HCHO tropospherical column densities of daily 0.25°x0.25° resolution grids from October 2004 to December 2024. \r\n\r\nThe L3 product is based on the Level 2 HCHO product created for the Precursors_cci project. \r\n\r\nCompared to the operational TROPOMI product, the air mass factors have been reprocessed using an update albedo climatology, and the CAMS Reanalysis Model for the a priori vertical profiles. The background correction and the quality values have also been updated.\r\n\r\nIn addition to the main product results, such as HCHO slant column, vertical column and air mass factor, the Level 3 data files contain several additional parameters and diagnostic information such as uncertainties, a priori profiles and averaging kernels.\r\n \r\nThe version number is 2.0. Data are available in NetCDF format. https://doi.org/10.18758/bf145884\r\n \r\nThe European Space Agency (ESA) Precursors for Aerosol and Ozone Climate Change Initiative (Precursors CCI) project is part of ESA's Climate Change Initiative (CCI) to produce long term datasets of Essential Climate Variables derived from global satellite data (https://climate.esa.int/en/projects/precursors-for-aerosols-and-ozone/)", "creationDate": "2026-10-05T13:20:04.050581", "lastUpdatedDate": null, "latestDataUpdateTime": "2026-10-05T13:16:33", "updateFrequency": "", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2026-10-21T09:53:18", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 46326, "dataPath": "/neodc/esacci/precursors/data/OMI_HCHO_L3/v2.0/daily/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1576511005504, "numberOfFiles": 6313, "fileFormat": "netcdf" }, "timePeriod": { "ob_id": 13369, "startTime": "2004-10-01T00:00:00", "endTime": "2024-12-30T23:59:59" }, "resultQuality": null, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 46327, "uuid": "59a231d640c44181bb224d48981731e8", "short_code": "cmppr", "title": "Composite process for ESA Precursors for Aerosols and Ozone Climate Change Initiative (Precursors_cci): daily L3 HCHO from OMI, version 2.0", "abstract": "Composite process for ESA Precursors for Aerosols and Ozone Climate Change Initiative (Precursors_cci): daily L3 HCHO from OMI, version 2.0" }, "imageDetails": [ 111 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2674, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 105, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_precursors_for_aerosols_and_ozone_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 45106, "uuid": "48df3bc5abf34360b4acf1343c2bbc77", "short_code": "proj", "title": "ESA Precursors for Aerosols and Ozone Climate Change Initiative Project (Precursors CCI)", "abstract": "The European Space Agency (ESA) Precursors for Aerosols and Ozone Climate Change Initiative (Precursors CCI) project is part of ESA's Climate Change Initiative (CCI) to produce long term datasets of Essential Climate Variables derived from global satellite data.\r\n\r\nThis project is developing long-term climate data records of the GCOS Precursors for Aerosol and Ozone Essential Climate Variable. Precursors include the short-lived atmospheric trace gases: nitrogen dioxide (NO2), sulphur dioxide (SO2), carbon monoxide (CO), formaldehyde (HCHO), and ammonia (NH3)." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 51157, 51158, 92995, 92998, 93003, 93015, 93017, 93018, 93019, 93020, 93021, 93022, 93023, 93024, 93025, 93027, 93028, 93029, 93030, 93031, 93032, 93033, 93035, 93037, 93038, 93040, 93041, 93042, 93044, 93045, 93046, 93047, 93049, 93050, 93051, 93053, 93054, 93055, 103293, 103294, 103295, 103296, 103297, 103298 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 45688, "uuid": "ea10a8b33cd34d3b80e57c0f6a06f41a", "short_code": "coll", "title": "ESA Precursors for Aerosols and Ozone Climate Change Initiative (Precursors_cci): L3 HCHO products", "abstract": "The Formaldehyde (HCHO) Climate Data Record (CDR) product is a Level 3 (L3) HCHO product developed by using satellite data as part of the ESA (European Space Agency) Climate Change Initiative (CCI) Precursors for Aerosols and Ozone project.\r\n\r\nThis collection record includes:\r\nMonthly L3 HCHO from TROPOMI, version 2.0\r\nDaily L3 from TROPOMI, version 2.0\r\nMonthly L3 from OMI, version 2.0\r\nDaily L3 from OMI, version 2.0\r\nMonthly L3 HCHO from GOME, version 2.0\r\nMonthly L3 HCHO from SCIAMACHY, version 2.0" } ], "responsiblepartyinfo_set": [ 221255, 221256, 221257, 221258, 221259, 221260, 221261, 221267, 221262, 221263, 221264, 221265, 221266 ], "onlineresource_set": [ 96051, 96052, 96069, 96070, 96071, 96516 ] }, { "ob_id": 46306, "uuid": "579a8da293824389be927157e0391eef", "title": "ESA Precursors for Aerosols and Ozone Climate Change Initiative (Precursors_cci): monthly L3 HCHO from OMI, version 2.0", "abstract": "The Formaldehyde (HCHO) Climate Data Record (CDR) product is a Level 3 HCHO product developed by using satellite data from the OMI instrument (on AURA) as part of the ESA Climate Change Initiative (CCI) Precursors for Aerosols and Ozone project.\r\n\r\nThis dataset provides gridded HCHO tropospherical column densities of monthly 0.125°x0.125° resolution grids from October 2004 to December 2024. \r\n\r\nThe L3 product is based on the Level 2 HCHO product created for the Precursors_cci project.\r\n\r\nCompared to the operational TROPOMI product, the air mass factors have been reprocessed using an update albedo climatology, and the CAMS Reanalysis Model for the a priori vertical profiles. 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The global climatic impact of all clouds types is estimated as a cooling effect. This net cooling effect results from the opposing impacts from liquid clouds (which cool by reflecting sunlight back into space) and ice clouds (which warm through a \"greenhouse\" effect). Unfortunately there is a lack of understanding of many of the physical processes occurring in ice clouds due to the complexity of ice particle processes and interactions between atmospheric motions, water vapour, and aerosol particles. This means ice clouds are a source of significant uncertainty in climate simulations, and can lead to errors in weather forecasts. Establishing which models are the most accurate remains difficult due to the lack of observations of ice cloud properties. \r\n\r\nRemote sensing techniques (e.g. radar) can provide observations of ice clouds over large areas on a continuous basis, making them ideal for assessing model skill. However, these techniques do not typically directly measure the atmospherically relevant quantity (e.g. mass of condensed water in a volume of air), and a retrieval must be used to obtain comparable data. These retrievals must invoke several assumptions about the properties of the ice clouds, properties which in reality are highly uncertain. We propose a project to collect a new dataset using in-situ observations from a research aircraft to directly observed ice cloud properties. At the same time, three different radars operating at difference wavelengths will scan the same clouds to allow a variety of radar retrievals to be developed and evaluated. We will obtain data during overpasses from a variety of different satellites which observe ice clouds from space. This work will improve fundamental understanding of ice cloud properties, and lead to improved remote sensing retrievals, both of which will lead to improved model accuracy and reduced uncertainty." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 10201, 13200, 13201, 25388, 103371 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 29923, "uuid": "400efba73c1d40c78f44918429ce9c99", "short_code": "coll", "title": "PICASSO: data from radiosonde flights, Chilbolton weather radar and in-situ airborne observations by the FAAM BAE-146 aircraft", "abstract": "In-situ airborne observations by the FAAM BAE-146 aircraft, radiosonde launches and the Chilbolton weather radar for the Parameterizing Ice Clouds using Airborne obServationS and triple-frequency dOppler radar data (PICASSO) project.\r\n\r\nThis dataset collection also links to a range of third party datasets that were also utilised by the project participants (Met Office rain radar, surface charts, Meteosat images, surface data from Chilbolton instruments and data from the X-band radar deployed at the site at the same time as the PICASSO observation days)." } ], "responsiblepartyinfo_set": [ 221458, 221459, 221460, 221461, 221462, 221463, 221464, 221478 ], "onlineresource_set": [] }, { "ob_id": 46362, "uuid": "8bb4af868a6b499dbe9324b35b1f1510", "title": "ESA River Discharge Climate Change Initiative (RD_cci): Altimetry-based River Discharge product, v2.1", "abstract": "This dataset comprises the altimetry-based river discharge (RD-alti) Climate Research Data Package (CRDP) v2.1, developed under the ESA CCI River Discharge precursor project phase 2 (RD_cci+). It provides long-term satellite-derived river discharge time series spanning at least last two decades and, at best, from 1992 to today, at designated locations. The Water Surface Elevation (WSE) time series are computed by LEGOS in collaboration with EOLA and Hydro Matters, while the river discharge time series are derived by Hydro Matters and EOLA.\r\n\r\nThe methodology, detailed in the associated ATBD (Algorithm Theoretical Baseline Document), generates two distinct RD-alti products:\r\n1.\tStation-level discharge estimates: A rating curve has been established for each station using a calibration period based on in-situ discharge time series and merged Water Surface Elevation (WSE) time series. This rating curve was then applied to the entire merged WSE series to produce a continuous river discharge (RD) time series per station.\r\n2.\tMission-track-specific discharge estimates: For each station, independent rating curves were derived for individual satellite mission-tracks, using the same calibration period. These rating curves were computed between in-situ discharge time series and single-mission WSE time series, then applied to the full single-mission WSE dataset to generate river discharge estimates per mission-track and per station.\r\n\r\nThe resulting CRDP delivers two complementary river discharge products, enabling robust analysis of river discharge dynamics over two decades.", "creationDate": "2023-12-06T08:01:27.903402", "lastUpdatedDate": "2023-12-06T08:00:25", "latestDataUpdateTime": null, "updateFrequency": "", "dataLineage": "Data were produced by the River Discharge CCI precursor project team, comprising Hydro Matters, EOLA and LEGOS.\r\n\r\nData were supplied for archiving at the Centre for Environmental Data Analysis (CEDA) as part of the CCI Open Data Portal in the context of the CCI Knowledge Exchange Project.", "removedDataReason": "", "keywords": "CCI, River Discharge, Altimeter", "publicationState": "working", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "pending", "dataPublishedTime": null, "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": null, "timePeriod": { "ob_id": 13323, "startTime": "1992-12-24T00:00:00", "endTime": "2025-06-13T23:59:59" }, "resultQuality": { "ob_id": 4580, "explanation": "For information on the data quality see the project documentation", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2024-07-02" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 46198, "uuid": "0675b0820e4b4eb18d72a50aa92fa0a7", "short_code": "comp", "title": "Computation for the ESA River Discharge Climate Change Initiative (RD_cci): Altimetry-based River Discharge product, v2.0", "abstract": "Long-term satellite river discharge (RD) time series have been derived at specified locations, utilizing the methodology outlined in detail in the Algorithm Theoretical Baseline Documents. \r\n\r\nFor more information see the attached documentation." }, "procedureCompositeProcess": null, "imageDetails": [ 111 ], "discoveryKeywords": [], "permissions": [], "projects": [ { "ob_id": 41537, "uuid": "dbba9cfe8d104648b19e39f4c2da1a27", "short_code": "proj", "title": "ESA River Discharge Climate Change Initiative (RD_cci)", "abstract": "The ESA river discharge Climate Change Initiative project aims to derive long term climate data records (at least over 20-years) of river discharge for some selected river basins (and some locations in the river network) using satellite remote sensing observations (altimetry and multispectral images) and ancillary data." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 221465, 221466, 221467, 221468, 221469, 221470, 221471, 221472, 221473, 221474, 221475, 221476, 221477 ], "onlineresource_set": [ 96528, 96521, 96522, 96523, 96524, 96525, 96526, 96527 ] }, { "ob_id": 46366, "uuid": "00bb3fa86ee44472b04f6f526d3a1175", "title": "MOMENTUM Mobile Observations and quantification of Methane Emissions", "abstract": "The focus of the project is on the measurement of CH4 concentrations, and on the measurement of other species and isotopes that help to distinguish between different sources of CH4. These include CO2, C2H6, NH3 and 13C of CH4.\r\nMobile measurements using either the MIGGAS facility (NERC-Capital) or by walkover survey.", "creationDate": "2026-10-26T13:15:27.590723", "lastUpdatedDate": "2026-10-26T13:18:01.189267", "latestDataUpdateTime": "2026-10-26T13:15:27.590730", "updateFrequency": "notPlanned", "dataLineage": "Mobile measurements using either the MIGGAS facility (NERC-Capital) or by walkover survey.\r\nHarmonisation of data with GPS using python scripts developed specifically for the MOMENTUM project.", "removedDataReason": "", "keywords": "methane,mobile measurements,carbon isotopes,MIGGAS,source characterisation", "publicationState": "preview", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "pending", "dataPublishedTime": null, "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 5221, "bboxName": "", "eastBoundLongitude": 2.0, "westBoundLongitude": -8.0, "southBoundLatitude": 50.0, "northBoundLatitude": 60.0 }, "verticalExtent": null, "result_field": null, "timePeriod": { "ob_id": 13390, "startTime": "2023-09-01T00:00:00", "endTime": null }, "resultQuality": { "ob_id": 4958, "explanation": "Calibration of measurements for all instruments to the WMO2024X scale for methane using cylinders calibrated by NOAA, MPI-Jena and NPL.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2026-10-26" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 46373, "uuid": "f60594c059f54887b2dd21f88263a02e", "short_code": "acq", "title": "Acquisition for: MOMENTUM Mobile Observations and quantification of Methane Emissions", "abstract": "" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [], "projects": [ { "ob_id": 46367, "uuid": "4de0919629d24e0e97a721e4fc981c89", "short_code": "proj", "title": "MOMENTUM Mobile Observations and quantification of Methane Emissions", "abstract": "The focus of the project is on the measurement of CH4 concentrations, and on the measurement of other species and isotopes that help to distinguish between different sources of CH4. These include CO2, C2H6, NH3 and 13C of CH4.\r\nMobile measurements using either the MIGGAS facility (NERC-Capital) or by walkover survey." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 221485, 221486, 221487, 221488, 221489, 221490, 221491, 221492, 221493, 221494 ], "onlineresource_set": [] }, { "ob_id": 46374, "uuid": "cf8b06bc0e3e44a4a0184804346a9338", "title": "Model Uncertainty–Model Intercomparison Project (MU-MIP), version 2: Indian Ocean simulations", "abstract": "Initial conditions, boundary conditions, forcing profiles and model output of the Single Column Model (SCM) atmospheric simulations carried out under the Model Uncertainty–Model Intercomparison Project (MU-MIP). Four models are archived:\r\n\r\n- The atmospheric component of ARPEGE-climat, version 6.3.\r\n- The Global Forecasting System physics suite, version 16.\r\n- The Rapid Refresh physics suite, version 5.\r\n- The Integrated Forecasting System (OpenIFS), CY48R1. \r\n\r\nAll data are archived as single time-stamp maps in netCDF files. \r\n\r\nThe data span an Indian Ocean domain, 35S-5N, 51-95E.\r\n\r\nThe data are available hourly from 11 August 2016 0000 UTC for a period of 31 days and twice hourly for OpenIFS. The forecasts are six hours long, initialised every 3 hours\r\n\r\nThe input files are derived by coarse graining a 2.5 km ICON simulation to a resolution of 0.2 degrees. The coarse graining procedure is detailed in Christensen et al, 2018, JAMES https://doi.org/10.1029/2017MS001189", "creationDate": "2026-10-27T06:56:57.436130", "lastUpdatedDate": "2026-10-28T05:52:55.380348", "latestDataUpdateTime": "2026-10-27T06:56:57.436139", "updateFrequency": "notPlanned", "dataLineage": "Data produced for the Model Uncertainty - Model Intercomparison Project.\r\n\r\nARPEGE simulations produced by W. Lfarh and R. Roehrig\r\nIFS simulations produced by E. Groot\r\nGFS and RAP simulations produced by K. Newman and X. Sun\r\n\r\nThe high-resolution ICON parent simulation was produced and published under the DYAMOND project and coarse grained by H. Christensen.\r\n\r\nDelivered to the CEDA Archive by the authors.", "removedDataReason": "", "keywords": "Indian Ocean,Single Column Model,Model Uncertainty - MIP", "publicationState": "working", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "pending", "dataPublishedTime": null, "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 5222, "bboxName": "", "eastBoundLongitude": 95.0, "westBoundLongitude": 51.0, "southBoundLatitude": -35.0, "northBoundLatitude": 5.0 }, "verticalExtent": null, "result_field": null, "timePeriod": { "ob_id": 13396, "startTime": "2016-08-11T00:00:00", "endTime": "2016-09-10T00:00:00" }, "resultQuality": { "ob_id": 4959, "explanation": "All simulations were cross-checked and analysed by all authors.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2026-10-27" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [], "projects": [ { "ob_id": 46375, "uuid": "453b01a5412f4f86aeea2eecd82ad5fd", "short_code": "proj", "title": "Model Uncertainty - Model Intercomparison Project (MU-MIP)", "abstract": "The Model Uncertainty - Model Intercomparison Project (MU-MIP), is an international initiative to characterise the systematic and random components of model error across many different climate models. \r\n\r\nFunded by:\r\n- Natural Environment Research Council, grant number NE/P018238/1\r\n- Leverhulme Trust, grant_number RPG-2022-192\r\n- National Oceanographic and Atmospheric Administration cooperative agreement NA22OAR4320151.\r\n- NOAA-NCAR Developmental Testbed Center." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 221504, 221505, 221506, 221507, 221508, 221509, 221510, 221511, 221512, 221513, 221514, 221515 ], "onlineresource_set": [ 96530 ] }, { "ob_id": 46381, "uuid": "16058684167843a1b98aa07c5017f0ca", "title": "NEX0102361:Topic A. Hydrogen Emissions: Constraining The Earth system Response (HECTER)", "abstract": "The provided data consists of processed model output data for a range of simulations associated with work under the HECTER project (NEX0102361:Topic A. Hydrogen Emissions: Constraining The Earth system Response). Raw model data were generated using STOCHEM-CRI, a 3D tropospheric chemistry and transport model operating at a resolution of 5x5 degrees. Simulations span a single production year using archived meteorological data from 1998, results for global burden of species and fluxes are reported at a monthly temporal resolution. Further model details will become available at point of publication.", "creationDate": "2026-10-28T16:42:47.377218", "lastUpdatedDate": "2026-10-30T10:24:52.077461", "latestDataUpdateTime": "2026-10-28T16:42:47.377225", "updateFrequency": "notPlanned", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "atmosphere,hydrogen,modelling", "publicationState": "preview", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "pending", "dataPublishedTime": null, "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 5223, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": null, "timePeriod": { "ob_id": 13403, "startTime": "1998-01-01T00:00:00", "endTime": "1998-12-31T00:00:00" }, "resultQuality": null, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 46383, "uuid": "88bb8f86e608464bbda231a507a98056", "short_code": "comp", "title": "STOCHEM-CRI", "abstract": "" }, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [], "projects": [ { "ob_id": 46382, "uuid": "a9e3a397202844f7b63cf8e3e5d239f2", "short_code": "proj", "title": "NEX0102361:Topic A. Hydrogen Emissions: Constraining The Earth system Response (HECTER)", "abstract": "" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 221528, 221529, 221530, 221531, 221532, 221533, 221534, 221535, 221536, 221537 ], "onlineresource_set": [] }, { "ob_id": 46387, "uuid": "21c9b819499644868e739fcc4f3db541", "title": "rf_predictors_modis_overpass_v1.0", "abstract": "Global predictors fields for studying cirrus and the effect of aviation on it: 327 NetCDF (Network Common Data Form) files on a 1° grid, with two time steps per month, 2000-2024. Content: 10 variables on 30 pressure levels and 19 surface variables from the fifth generation reanalysis (ERA5) of the European Centre for Medium-Range Weather Forecasts (ECMWF); 200-850 hPa wind shear; 200 hPa dust from the Copernicus Atmosphere Monitoring Service (CAMS) reanalysis, 2003-2022; aviation black carbon emissions from the Emissions Database for Global Atmospheric Research (EDGAR), 2000-2022 (2020-2022 estimated without COVID-19); and three ocean climate indices. ERA5 values are monthly means at the hour nearest the local 10:30 and 13:30 overpass times of the Moderate Resolution Imaging Spectroradiometer (MODIS) satellites, so time stamps are local-time labels, not Coordinated Universal Time. Other fields are monthly values repeated at both times. Author: Jaswant Moher (University of Birmingham)", "creationDate": "2026-10-29T14:48:15.658740", "lastUpdatedDate": "2026-10-29T16:31:33.664127", "latestDataUpdateTime": "2026-10-29T14:48:15.658750", "updateFrequency": "notPlanned", "dataLineage": "Download: ERA5 monthly averaged reanalysis by hour of day (pressure levels and single levels) was obtained from the Copernicus Climate Data Store; CAMS EAC4 monthly dust mixing ratios (200 hPa) from the Copernicus Atmosphere Data Store; EDGAR v8.1 annual aviation black carbon emission fluxes from the European Commission Joint Research Centre; IEA 2019 monthly passenger load factors; and Niño 3.4, Dipole Mode and PDO indices from NOAA PSL. 2) Processing: at each grid point, the ERA5 UTC hour closest to local 10:30 (Terra) and 13:30 (Aqua) was selected, and all fields were regridded to 1° × 1°. Vertical wind shear was calculated as sqrt((u200-u850)^2 + (v200-v850)^2). EDGAR annual totals were split into months using IEA load-factor ratios; 2020–2022 values were extrapolated from the 2003–2019 trend. Monthly CAMS, EDGAR and index values were repeated at both time steps; indices were repeated over all grid points. 3) Archive preparation: metadata was added.", "removedDataReason": "", "keywords": "ERA5,CAMS,EDGAR,MODIS,Cirrus,Contrails,Aviation,Random Forest,Counterfactuals", "publicationState": "working", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "pending", "dataPublishedTime": null, "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 5224, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": 180.0, "southBoundLatitude": 90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": null, "timePeriod": { "ob_id": 13402, "startTime": "2000-01-01T00:00:00", "endTime": "2024-12-31T00:00:00" }, "resultQuality": { "ob_id": 4960, "explanation": "No new measurements were made, so data quality follows that of the source products: ERA5 and CAMS EAC4 (ECMWF quality-controlled reanalyses), EDGAR v8.1, and NOAA PSL climate indices; see their documentation for known uncertainties. No additional quality control or bias correction was applied to the source values. Known limitations: (1) ERA5 values are monthly means at a fixed UTC hour, selected as the hour nearest local overpass time, so they can differ from the true overpass time by up to about 30 minutes; (2) time stamps are local-time labels, not UTC; (3) CAMS, EDGAR and index fields are monthly values and do not vary between the two daily time steps; (4) aviation emissions for 2020–2022 are trend-based business-as-usual estimates, not reported values; (5) regridding to 1° smooths small-scale features; (6) ERA5 relative humidity is with respect to ice below −23 °C and water above 0 °C. All files follow the CF metadata conventions, with units and sources documented in each file.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2026-10-29" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [], "projects": [ { "ob_id": 46284, "uuid": "1b0456a9b4d6412d91fdee564edb0de1", "short_code": "proj", "title": "Quantifying and Reducing Aviation COntrail raDiativE forcing (QR-CODE)", "abstract": "This project aims to quantify the radiation contribution from aviation's contrail cirrus clouds." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 221551, 221552, 221553, 221554, 221555, 221556, 221557, 221558, 221559, 221560, 221561, 221562, 221563, 221564, 221565 ], "onlineresource_set": [] }, { "ob_id": 46388, "uuid": "61a46ef80f63449397c9825676f3cc5a", "title": "Land-cover classifications and chainage-level vegetation metrics for islands of the middle Congo River derived from Sentinel-2 imagery for 2020, 2024 and 2025", "abstract": "Land-cover classifications of vegetated islands along a ~400 km section of the middle Congo River, derived from cloud-free Sentinel-2 Level-2A Surface Reflectance Harmonized imagery for the dry seasons of 2020 (pre-drought), 2024 (drought) and 2025 (post-drought). The dataset accompanies dataset accompanies Steadman et al. (in submission), \"Vegetation Loss and Response to Hydrological Drought on the Islands of the Congo River.\"\r\n\r\n The deposit consists of three classified raster maps (GeoTIFF, 10 m resolution) and one tabular dataset (CSV):\r\n\r\n - 2020_Landcover.tif, 2024_Landcover.tif, 2025_Landcover.tif: two-class supervised land-cover classifications based on a Random Forest classifier (500 trees) trained on Sentinel-2 bands B2, B3, B4, B8 and B11. Class codes: 1 = vegetated, 2 = non-vegetated.\r\n\r\n - chainage_vegetation_metrics.csv: per-chainage summary table, with one row per 10 km chainage interval (260-640 km along the Congo River centreline upstream from Kinshasa, after Carr et al., 2019). Columns include percentage vegetated cover for each year, June-August 2024 mean precipitation (mm, from Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS)) and canopy-height composition (proportion of <1 m, 1-10 m, and >10 m vegetation derived from the ETH Global Sentinel-2 10 m Canopy Height dataset, 2020).\r\n\r\n The study area covers a ~400 km corridor of the middle Congo River from just downstream of Mbandaka to a few kilometres upstream of Mpouya, spanning the border region between the Democratic Republic of the Congo and the Republic of the Congo. The corridor contains approximately 800 islands. Sentinel-2 imagery was mosaicked across MGRS tiles 33MXT, 33MXU, 33MYU, 33MYV and 33MZV.\r\n\r\n Dry-season composites were generated from images acquired on 16 June, 1 July, 31 July and 8 August 2020; 20 June, 10 July, 25 July and 8 August 2024; and 13 May and 10 June 2025. The chainage CSV summarises conditions for these three years and additionally includes June-August 2024 cumulative precipitation derived from CHIRPS daily data.\r\n\r\n Sentinel-2 Level-2A Surface Reflectance Harmonized imagery was accessed via Google Earth Engine. Cloud, cloud-shadow, cirrus and saturated pixels were masked using the Scene Classification Layer (SCL) with a 1-2 pixel buffer. Annual median composites were classified with a Random Forest model (500 trees, 70/30 train-test split) trained on manually digitised polygons of the four land-cover classes, using high-resolution historical imagery from Google Earth Pro and Bing Aerial Imagery as references. A majority filter was applied to smooth isolated misclassified pixels. Per-chainage metrics were derived in ArcGIS Pro using the Tabulate Area tool, with 10 km chainage polygons intersecting the basin-scale Congo River centreline (Carr et al., 2019) as zones. Percentage vegetated cover was computed as the vegetated area divided by the sum of vegetated and non-vegetated areas (water and sand classes were masked from the analysis).\r\n\r\nThe classifications and chainage metrics were produced to characterise the spatial extent and structural pattern of vegetation loss across Congo River islands during the extreme 2024 hydrological drought, and to test the hypothesis that vegetation loss is jointly controlled by hydroclimatic exposure (rainfall gradient) and vegetation structure.", "creationDate": "2026-10-30T14:55:48.968855", "lastUpdatedDate": "2026-10-30T14:50:24", "latestDataUpdateTime": "2026-10-30T14:50:24", "updateFrequency": "", "dataLineage": "Sentinel-2 Level-2A Surface Reflectance Harmonized scenes (COPERNICUS/S2_SR_HARMONIZED) were acquired via Google Earth Engine, filtered by cloud cover and date for the dry season of each target year, and mosaicked across MGRS tiles 33MXT, 33MXU, 33MYU, 33MYV and 33MZV. Cloud, shadow, cirrus and saturated pixels were masked using the Scene Classification Layer with a 1-2 pixel buffer, and annual dry-season median composites were derived. A Random Forest classifier (500 trees) was trained on manually digitised training polygons for four land-cover classes (vegetated, non-vegetated, water, sand) using bands B2, B3, B4, B8 and B11, with a 70/30 train-test split. Water and sand were then masked from the classification. Resulting rasters were aligned to a common 10 m grid, smoothed with a majority filter and exported as GeoTIFFs. Chainage-level metrics were generated in ArcGIS Pro using the Tabulate Area tool with 10 km chainage polygons (derived from the basin-scale Congo River centreline of Carr et al., 2019) as zones, applied to the classified rasters and the reclassified ETH Global Sentinel-2 10 m Canopy Height dataset (Lang et al., 2023). Per-chainage June-August 2024 rainfall was derived from CHIRPS daily precipitation aggregated to JJA totals and reduced over each chainage polygon.\r\n\r\nData were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "Land Cover, Congo, Sentinel, Sentinel 2", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2026-11-06T12:02:59", "doiPublishedTime": "2026-11-06T12:34:31.615411", "removedDataTime": null, "geographicExtent": { "ob_id": 5225, "bboxName": "Congo Basin", "eastBoundLongitude": 18.213, "westBoundLongitude": 16.118, "southBoundLatitude": -2.627, "northBoundLatitude": 0.0 }, "verticalExtent": null, "result_field": { "ob_id": 46389, "dataPath": "/neodc/deposited2026/Congo_Islands_Analysis/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 2044081373, "numberOfFiles": 5, "fileFormat": "These data are given in GeoTIFF and CSV format." }, "timePeriod": { "ob_id": 13404, "startTime": "2020-06-16T00:00:00", "endTime": "2025-06-10T23:59:59" }, "resultQuality": { "ob_id": 4961, "explanation": "Data are as given by the data provider, no quality control has been performed by the Centre for Environmental Data Analysis (CEDA).", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2026-10-30" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 46392, "uuid": "f94e106d9dd74d13874dd12e6241ac66", "short_code": "cmppr", "title": "Composite process for Land-cover classifications and chainage-level vegetation metrics for islands of the middle Congo River derived from Sentinel-2 imagery for 2020, 2024 and 2025", "abstract": "Land-cover mapping was performed in Google Earth Engine using ee.Classifier.smileRandomForest (500 trees) was trained on manually digitised polygons sampling four classes (vegetated, non-vegetated, water, sand) from Sentinel-2 L2A Surface Reflectance Harmonized median composites of June-August each target year. Cloud and shadow masking used the Scene Classification Layer with a 1-2 pixel buffer. Classification used bands B2, B3, B4, B8 and B11 with a 70/30 train-test split, validated using overall accuracy, Cohen's Kappa, Producer's Accuracy and User's Accuracy (Congalton, 1991). A majority filter was applied to reduce salt-and-pepper noise. Chainage-level metrics were generated in ArcGIS Pro using the Tabulate Area tool, with 10 km chainage polygons as zone features and the classified rasters / canopy-height raster as class rasters. The CHIRPS June-August 2024 cumulative rainfall raster was aggregated using Zonal Statistics (mean) over the same chainage polygons." }, "imageDetails": [], "discoveryKeywords": [], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" }, { "ob_id": 11, "classification": "attribution required" } ] } } ], "projects": [ { "ob_id": 46390, "uuid": "3040ed225b28477b9609fd02e6e42479", "short_code": "proj", "title": "Vegetation response to hydrological drought on the islands of the Congo River (NERC PANORAMA DTP PhD project, University of Leeds)", "abstract": "PhD research project investigating the vulnerability of floodplain island vegetation along the middle Congo River to extreme hydroclimatic events. The project combines satellite remote sensing, hydrometeorological analysis and altimetry to characterise the propagation of meteorological drought into hydrological drought during the 2024 event and to quantify the resulting vegetation response. Funded by the Natural Environment Research Council through the PANORAMA Doctoral Training Programme.\r\n\r\nNERC grant number: NE/S007458/1" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 13986 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 221566, 221567, 221568, 221569, 221570, 221571, 221572, 221573, 221574, 221575, 221589, 221576, 221577, 221578, 221579 ], "onlineresource_set": [ 96536, 96537, 96538, 96539 ] }, { "ob_id": 46394, "uuid": "59867cf784a7402faecc3bbba522c2d3", "title": "Model outputs containing seabed mobility projections from the North Western European shelf from 1992-2093 version 3", "abstract": "This dataset consists of outputs from a hydrodynamic and wave model for the North-West European Shelf. It contains simulation periods conducted over monthly time spans. These include: historic reanalysis periods: January 2017; June 2018 and climate projection periods: June 1992; January 1996; January 2050; June 2053; January 2090; June 2093. The outputs contain hourly combined mean and maximum bed shear stresses (Tau), and monthly derived seabed motion, or 'capacity to move'. Wave and hydrodynamic seabed stresses are calculated following the approach of Soulsby (1997). This indicates what sediment grain size could be mobilised at the seabed as the given value of shear stress. The data are delivered on a regular grid at approximately 1.5km resolution. The hydrodynamic model is NEMO (Nucleus for European Modelling of the Ocean) and wave model is WAVEWATCH III and the North-West Shelf configuration is based on a rotated 1.5 km grid used for NORTHWESTSHELF_ANALYSIS_FORECAST_PHY_004_013. The model is forced by lateral boundary conditions from a consistent Global wave hindcast. The reanalysis atmospheric forcing is given by the ECMWF ERA-5 Numerical Weather Prediction reanalysis. Future projections are forced by HadGEM3-GC3.05 PPE derived surface forcing and ocean lateral boundaries (Tinker et. al 2023). The wave model is forced by lateral boundary conditions from a consistent Global wave simulation. The outputs were generated under the ECOWind ACCELERATE project funded by the Natural Environment Research Council (NERC) under reference NE/X008886/1. A file within the archive (small_Jun2018_hourly_stress.nc) was replaced due to being corrupted, and this DOI represents the third version.", "creationDate": "2026-11-05T10:02:02.221005", "lastUpdatedDate": "2026-11-05T10:02:02", "latestDataUpdateTime": "2026-11-05T10:02:02", "updateFrequency": "", "dataLineage": "The data are archived on the British Oceanographic Data Centre (BODC)'s space at the Centre for Environmental Data Analysis (CEDA) and assigned a DOI. No quality control procedures were applied by BODC.", "removedDataReason": "", "keywords": "oceans, geoscientificInformation, environment", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "final", "dataPublishedTime": "2026-11-04T00:00:00", "doiPublishedTime": "2026-11-04T00:00:00", "removedDataTime": null, "geographicExtent": { "ob_id": 4807, "bboxName": "", "eastBoundLongitude": 13.7338, "westBoundLongitude": -25.4682, "southBoundLatitude": 44.0648, "northBoundLatitude": 63.3498 }, "verticalExtent": null, "result_field": { "ob_id": 46393, "dataPath": "/bodc/pol250902", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 6512231743, "numberOfFiles": 3, "fileFormat": null }, "timePeriod": { "ob_id": 12535, "startTime": "1992-06-01T00:00:00", "endTime": "2093-06-30T23:59:59" }, "resultQuality": { "ob_id": 4962, "explanation": "The data are provided as-is with no quality control undertaken by the British Oceanographic Data Centre (BODC). The data suppliers have not indicated if any quality control has been undertaken on these data.", "passesTest": true, "resultTitle": "BODC Data Quality Statement", "date": "2026-11-05" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" }, { "ob_id": 11, "classification": "attribution required" } ] } } ], "projects": [], "inspireTheme": [], "topicCategory": [], "phenomena": [ 103378, 103379, 103380, 103381, 103382 ], "vocabularyKeywords": [], "identifier_set": [ 13985 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 221593, 221595, 221594, 221592, 221591, 221590, 221596, 221597, 221598, 221600, 221599, 221601, 221602, 221603 ], "onlineresource_set": [] } ] }