Observation List
Get a list of Observation objects.
GET /api/v3/observations/?format=api&offset=9900
{ "count": 10256, "next": "https://api.catalogue.ceda.ac.uk/api/v3/observations/?format=api&limit=100&offset=10000", "previous": "https://api.catalogue.ceda.ac.uk/api/v3/observations/?format=api&limit=100&offset=9800", "results": [ { "ob_id": 44322, "uuid": "fe998c05ca854715b48bac53dc0e9998", "title": "Daily Mean, Minimum and Maximum Central England Temperature (HadCET) series v2.1.0.0", "abstract": "The Central England Temperature (HadCET) daily series start in 1772 for mean temperature and 1878 for minimum and maximum temperature.\r\n \r\nThese historical temperature series are representative of the Midlands region in England, UK (a roughly triangular area of the United Kingdom enclosed by Bristol, Lancashire and London).\r\n \r\nPrior to 1973, the daily mean temperature series is anchored to the mean temperature series constructed by Gordon Manley, with the daily minimum and maximum temperature series adjusted to the mean temperature series to ensure values are consistent.\r\n \r\nAlthough the station selection has changed through time, the series is homogenised and adjusted to ensure consistency with Manley's selection and for periods when only a single station value was used.\r\n \r\nStations used in the construction of the CET daily series between 1772 and 1852 include: Kennington, Crane Court, Lyndon Hall, Syon House, Somerset House, Greenwich Observatory, Chiswick\r\n \r\nStations used in the construction of the CET daily series from 1853 onwards include: Radcliffe (Oxford), Cambridge (legacy), Ross-on-Wye, Rothamsted, Malvern, Stonyhurst, Ringway, Squires Gate, Pershore College\r\n \r\nThe current station selection used is Rothamsted, Stonyhurst and Pershore College.\r\n \r\nFor more information on the change in station selection, please refer to the papers supplied with the data collection.\r\n \r\nThe dataset is compiled by the Met Office Hadley Centre.\r\n\r\nLatest provisional release data are available via the Hadley Centre Observations website (https://www.metoffice.gov.uk/hadobs/hadcet/data/download.html).\r\n\r\nThe version controlled CET series is updated annually (February-March), with the previous complete year’s values refreshed to ensure that data acquisition and quality control procedures have been completed and ensure the most accurate station temperature values are used. Each version of the dataset will include data up until the end of the previous complete year and an incremental version number will be updated.\r\n\r\nThe CET datasets employ the following version control protocol: \r\n\r\nVersion Vx.y.z.a:\r\n• x – major changes – e.g. change in scientific methodology\r\n• y – minor changes – e.g. small bug fixes or updates to diagnostics pages\r\n• z – incremental changes\r\n• a – reserved for use internally\r\n\r\nThe standard annual release cycle of CET will constitute an incremental release (z). However, if more substantial\r\nchanges have been made to the codebase, scientific methodology or source data values, then this may warrant a minor (y) or major (x) version release. (Note, these are applied to a cohort of datasets together - i.e. apply to the seasonal, monthly, daily and adjustment datasets as a coordinated version release).\r\n\r\nThis new version of the datasets supersedes the previous version.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2025-06-18T01:55:58", "updateFrequency": "notPlanned", "dataLineage": "The HadCET dataset is produced by the Met Office Hadley Centre and frequently updated on the Hadley Centre observations datasets webpage. This is a finalised version of the dataset that has been delivered by the Met Office Hadley Centre for long-term archiving at the NERC EDS Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "HadCET, temperature, Hadley, Met Office", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "final", "dataPublishedTime": "2025-06-05T11:16:56", "doiPublishedTime": "2025-06-05T11:17:31.288750", "removedDataTime": null, "geographicExtent": { "ob_id": 2, "bboxName": "", "eastBoundLongitude": 1.0, "westBoundLongitude": -4.0, "southBoundLatitude": 51.0, "northBoundLatitude": 54.0 }, "verticalExtent": null, "result_field": { "ob_id": 44327, "dataPath": "/badc/ukmo-cet/data/v2.1.0.0/daily/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 3105068, "numberOfFiles": 4, "fileFormat": "Data are BADC-CSV formatted" }, "timePeriod": { "ob_id": 12409, "startTime": "1772-01-01T00:00:00", "endTime": "2024-12-31T23:59:59" }, "resultQuality": { "ob_id": 4, "explanation": "Data is quality-controlled by the Met Office.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2014-01-28" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 43874, "uuid": "5ade1276557240b18823adae67c07991", "short_code": "cmppr", "title": "Met Office Hadley Centre CET v2 data process", "abstract": "Met Office Hadley Centre CET v2 data process" }, "imageDetails": [ 157 ], "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" } ] } } ], "projects": [ { "ob_id": 13164, "uuid": "ce252c81a7bd4717834055e31716b265", "short_code": "proj", "title": "Met Office Hadley Centre - Observations and Climate", "abstract": "The Met Office Hadley Centre is one of the UK's foremost climate change research centres.\r\n\r\nThe Hadley Centre produces world-class guidance on the science of climate change and provide a focus in the UK for the scientific issues associated with climate science.\r\n\r\nLargely co-funded by Department of Energy and Climate Change (DECC) and Defra (the Department for Environment, Food and Rural Affairs), the centre provides in-depth information to, and advise, the Government on climate science issues.\r\n\r\nAs one of the world's leading centres for climate science research, the Hadley Centre scientists make significant contributions to peer-reviewed literature and to a variety of climate science reports, including the Assessment Report of the IPCC. The Hadley Centre climate projections were the basis for the Stern Review on the Economics of Climate Change." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 76039, 76040 ], "vocabularyKeywords": [], "identifier_set": [ 13410 ], "observationcollection_set": [ { "ob_id": 43588, "uuid": "f1e8696c6d5746e694fde4d6022e5a36", "short_code": "coll", "title": "Met Office Hadley Centre Central England Temperature (HadCET) All Versions Collection", "abstract": "The Central England Temperature (CET) series is the longest available instrumental record of temperature in the world, with the monthly mean temperature extending back to 1659 and the daily mean temperature series extending back to 1772. The daily and monthly minimum and maximum temperature series extend back to 1878.\r\n\r\nThese historical temperature series have been constructed to provide a long running temperature record for the Central England region, a roughly triangular area of the United Kingdom enclosed by Somerset, Lancashire and London. The CET value is then calculated as an average of the recorded temperatures for a few stations which are selected as representative of the region as a whole. Although the selected stations have changed over time, due primarily to station closures, the series is homogenised to ensure that it remains consistent.\r\n\r\nStations used in the construction of the CET series between 1772 and 1852 include: Kennington, Crane Court, Lyndon Hall, Syon House, Somerset House, Greenwich Observatory, Chiswick.\r\n\r\nStations used in the construction of the CET series from 1853 onwards include: Radcliffe (Oxford), Cambridge (legacy), Ross-on-Wye, Rothamsted, Malvern, Stonyhurst, Ringway, Squires Gate, Pershore College.\r\n\r\nGordon Manley (1953, 1974) compiled most of the monthly series from 1659 to 1973, and the mean temperature series is adjusted up to 1973 to align with Manley's recorded daily mean temperature values. The series data were updated to 1991 by Parker et al (1992) and comprise the V2 series which is updated with the latest values. For latest updates please see Legg et al (2025).\r\n\r\nAfter 1973, the series is adjusted to extend the alignment with Manley's series. Further adjustments are applied to homogenise the current station selection to that used by Manley as well as to account for urbanisation.\r\n\r\nThe version controlled CET series is updated annually (February-March), with the previous complete year’s values refreshed to ensure that data acquisition and quality control procedures have been completed and ensure the most accurate station temperature values are used. Each version of the dataset will include data up until the end of the previous complete year and an incremental version number will be updated.\r\n\r\nThe CET datasets employ the following version control protocol: \r\n\r\nVersion Vx.y.z.a:\r\n• x – major changes – e.g. change in scientific methodology\r\n• y – minor changes – e.g. small bug fixes or updates to diagnostics pages\r\n• z – incremental changes\r\n• a – reserved for use internally\r\n\r\nThe standard annual release cycle of CET will constitute an incremental release (z). However, if more substantial\r\nchanges have been made to the codebase, scientific methodology or source data values, then this may warrant a minor (y) or major (x) version release. (Note, these are applied to a cohort of datasets together - i.e. apply to the seasonal, monthly, daily and adjustment datasets as a coordinated version release).\r\n\r\nThis new version of the datasets will supersede the previous version.\r\n\r\nA provisional version of the CET series, with recent data up to the previous day, is available for download on the Met Office Hadley Centre webpage: https://www.metoffice.gov.uk/hadobs/hadcet/data/download.html . Link also available in the list of online resources on this record.\r\n\r\nNote: the v1.0.0.0 datasets within the collection on the CEDA archive were originally pulled over monthly from the Met Office Hadley Centre website on a monthly basis until the end of July 2022." } ], "responsiblepartyinfo_set": [ 212022, 212023, 212024, 212025, 212026, 212028, 212027, 212021 ], "onlineresource_set": [ 93197, 93198, 93191, 93192, 93193, 93199, 93194, 93195, 93196, 93200 ] }, { "ob_id": 44323, "uuid": "ca43505702fa4eeeba4b65f1ce2c1e6a", "title": "Seasonal Mean, Minimum and Maximum Central England Temperature (HadCET) series v2.1.0.0", "abstract": "The Central England Temperature (HadCET) seasonal series starts in 1659 for mean temperature and 1878 for minimum and maximum temperature.\r\n\r\nThese historical temperature series are representative of the Midlands region in England, UK (a roughly triangular area of the United Kingdom enclosed by Bristol, Lancashire and London).\r\n \r\nThe seasonal temperature series are derived as the mean of the monthly temperature series values.\r\n\r\nStations used in the construction of the CET daily series between 1772 and 1852 include: Kennington, Crane Court, Lyndon Hall, Syon House, Somerset House, Greenwich Observatory, Chiswick\r\n \r\nStations used in the construction of the CET daily series from 1853 onwards include: Radcliffe (Oxford), Cambridge (legacy), Ross-on-Wye, Rothamsted, Malvern, Stonyhurst, Ringway, Squires Gate, Pershore College\r\n \r\nThe current station selection used is Rothamsted, Stonyhurst and Pershore College.\r\n \r\nThe dataset is compiled by the Met Office Hadley Centre.\r\n\r\nLatest provisional release data are available via the Hadley Centre Observations website (https://www.metoffice.gov.uk/hadobs/hadcet/data/download.html).\r\n\r\nThe version controlled CET series is updated annually (February-March), with the previous complete year’s values refreshed to ensure that data acquisition and quality control procedures have been completed and ensure the most accurate station temperature values are used. Each version of the dataset will include data up until the end of the previous complete year and an incremental version number will be updated.\r\n\r\nThe CET datasets employ the following version control protocol: \r\n\r\nVersion Vx.y.z.a:\r\n• x – major changes – e.g. change in scientific methodology\r\n• y – minor changes – e.g. small bug fixes or updates to diagnostics pages\r\n• z – incremental changes\r\n• a – reserved for use internally\r\n\r\nThe standard annual release cycle of CET will constitute an incremental release (z). However, if more substantial\r\nchanges have been made to the codebase, scientific methodology or source data values, then this may warrant a minor (y) or major (x) version release. (Note, these are applied to a cohort of datasets together - i.e. apply to the seasonal, monthly, daily and adjustment datasets as a coordinated version release).\r\n\r\nThis new version of the datasets supersedes the previous version.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2025-06-18T01:55:57", "updateFrequency": "notPlanned", "dataLineage": "The HadCET dataset is produced by the Met Office Hadley Centre and frequently updated on the Hadley Centre observations datasets webpage. This is a finalised version of the dataset that has been delivered by the Met Office Hadley Centre for long-term archiving at the NERC EDS Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "HadCET, temperature, Hadley, Met Office", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "final", "dataPublishedTime": "2025-06-05T10:52:03", "doiPublishedTime": "2025-06-05T10:52:07.454968", "removedDataTime": null, "geographicExtent": { "ob_id": 2, "bboxName": "", "eastBoundLongitude": 1.0, "westBoundLongitude": -4.0, "southBoundLatitude": 51.0, "northBoundLatitude": 54.0 }, "verticalExtent": null, "result_field": { "ob_id": 44324, "dataPath": "/badc/ukmo-cet/data/v2.1.0.0/seasonal/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 74886, "numberOfFiles": 7, "fileFormat": "Data are BADC-CSV formatted" }, "timePeriod": { "ob_id": 12412, "startTime": "1659-01-01T00:00:00", "endTime": "2024-12-31T23:59:59" }, "resultQuality": { "ob_id": 4, "explanation": "Data is quality-controlled by the Met Office.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2014-01-28" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 43874, "uuid": "5ade1276557240b18823adae67c07991", "short_code": "cmppr", "title": "Met Office Hadley Centre CET v2 data process", "abstract": "Met Office Hadley Centre CET v2 data process" }, "imageDetails": [ 157 ], "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" } ] } } ], "projects": [ { "ob_id": 13164, "uuid": "ce252c81a7bd4717834055e31716b265", "short_code": "proj", "title": "Met Office Hadley Centre - Observations and Climate", "abstract": "The Met Office Hadley Centre is one of the UK's foremost climate change research centres.\r\n\r\nThe Hadley Centre produces world-class guidance on the science of climate change and provide a focus in the UK for the scientific issues associated with climate science.\r\n\r\nLargely co-funded by Department of Energy and Climate Change (DECC) and Defra (the Department for Environment, Food and Rural Affairs), the centre provides in-depth information to, and advise, the Government on climate science issues.\r\n\r\nAs one of the world's leading centres for climate science research, the Hadley Centre scientists make significant contributions to peer-reviewed literature and to a variety of climate science reports, including the Assessment Report of the IPCC. The Hadley Centre climate projections were the basis for the Stern Review on the Economics of Climate Change." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 76324, 76325, 76326, 76327, 76328, 76329, 76330, 76331, 76332, 76333, 76334, 76335, 76336, 76337, 76338, 76339, 76340, 76341 ], "vocabularyKeywords": [], "identifier_set": [ 13400 ], "observationcollection_set": [ { "ob_id": 43588, "uuid": "f1e8696c6d5746e694fde4d6022e5a36", "short_code": "coll", "title": "Met Office Hadley Centre Central England Temperature (HadCET) All Versions Collection", "abstract": "The Central England Temperature (CET) series is the longest available instrumental record of temperature in the world, with the monthly mean temperature extending back to 1659 and the daily mean temperature series extending back to 1772. The daily and monthly minimum and maximum temperature series extend back to 1878.\r\n\r\nThese historical temperature series have been constructed to provide a long running temperature record for the Central England region, a roughly triangular area of the United Kingdom enclosed by Somerset, Lancashire and London. The CET value is then calculated as an average of the recorded temperatures for a few stations which are selected as representative of the region as a whole. Although the selected stations have changed over time, due primarily to station closures, the series is homogenised to ensure that it remains consistent.\r\n\r\nStations used in the construction of the CET series between 1772 and 1852 include: Kennington, Crane Court, Lyndon Hall, Syon House, Somerset House, Greenwich Observatory, Chiswick.\r\n\r\nStations used in the construction of the CET series from 1853 onwards include: Radcliffe (Oxford), Cambridge (legacy), Ross-on-Wye, Rothamsted, Malvern, Stonyhurst, Ringway, Squires Gate, Pershore College.\r\n\r\nGordon Manley (1953, 1974) compiled most of the monthly series from 1659 to 1973, and the mean temperature series is adjusted up to 1973 to align with Manley's recorded daily mean temperature values. The series data were updated to 1991 by Parker et al (1992) and comprise the V2 series which is updated with the latest values. For latest updates please see Legg et al (2025).\r\n\r\nAfter 1973, the series is adjusted to extend the alignment with Manley's series. Further adjustments are applied to homogenise the current station selection to that used by Manley as well as to account for urbanisation.\r\n\r\nThe version controlled CET series is updated annually (February-March), with the previous complete year’s values refreshed to ensure that data acquisition and quality control procedures have been completed and ensure the most accurate station temperature values are used. Each version of the dataset will include data up until the end of the previous complete year and an incremental version number will be updated.\r\n\r\nThe CET datasets employ the following version control protocol: \r\n\r\nVersion Vx.y.z.a:\r\n• x – major changes – e.g. change in scientific methodology\r\n• y – minor changes – e.g. small bug fixes or updates to diagnostics pages\r\n• z – incremental changes\r\n• a – reserved for use internally\r\n\r\nThe standard annual release cycle of CET will constitute an incremental release (z). However, if more substantial\r\nchanges have been made to the codebase, scientific methodology or source data values, then this may warrant a minor (y) or major (x) version release. (Note, these are applied to a cohort of datasets together - i.e. apply to the seasonal, monthly, daily and adjustment datasets as a coordinated version release).\r\n\r\nThis new version of the datasets will supersede the previous version.\r\n\r\nA provisional version of the CET series, with recent data up to the previous day, is available for download on the Met Office Hadley Centre webpage: https://www.metoffice.gov.uk/hadobs/hadcet/data/download.html . Link also available in the list of online resources on this record.\r\n\r\nNote: the v1.0.0.0 datasets within the collection on the CEDA archive were originally pulled over monthly from the Met Office Hadley Centre website on a monthly basis until the end of July 2022." } ], "responsiblepartyinfo_set": [ 212029, 212030, 212032, 212033, 212034, 212035, 212036, 212031 ], "onlineresource_set": [ 93205, 93209, 93201, 93206, 93202, 93203, 93204, 93207, 93208, 93210 ] }, { "ob_id": 44330, "uuid": "ebe60d6b1128424aa7caed45a45c585f", "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Normalised Burn Ratio (NBR) v2", "abstract": "NBR enables identification of burned areas and can also provide an indication of burn severity. It is calculated as the ratio between the near-infrared (NIR) and short-wave infrared (SWIR) bands, with lower values indicating burned areas. Burn severity can be assessed by subtracting post-fire NBR from pre-fire NBR.\r\n\r\nNBR = (NIR – SWIR) / (NIR + SWIR)\r\n\r\nSentinel-2 NBR (Defra/JNCC ARD bands) = (B07 - B10) / (B07 + B10)\r\n\r\nEquivalent Sentinel-2 bands: B08, B12\r\n\r\nData are provided in EPSG: 27700 OSGB36 / British National Grid, with a pixel size of 10m, and data is pixel-aligned to the source ARD file. No-data pixels are set to a value of -9999.\r\n\r\nThese data have been created by the Joint Nature Conservation Committee (JNCC) as part of the “Earth observation-based habitat change detection” project. This project is funded by the Department for Environment, Food and Rural Affairs (Defra) as part of the Natural Capital and Ecosystem Assessment (NCEA) programme. The project seeks to facilitate the effective uptake and use of Earth Observation data by producing data and tools for investigating and detecting parcel-level change in habitats and habitat condition. \r\n\r\nThe dataset contains NDVI, NDMI, NDWI, NBR and EVI2 indices derived from Defra and JNCC Sentinel-2 ARD. Index files have been generated for Sentinel-2 granules covering England and Scotland for the period from 2015 to 2025. Note that new unmasked index files (v2) have superseded the previous masked index files (v1). Masked files will no longer be produced. Users can mask the new index files if required using the cloud and topographic shadow masks provided with the ARD, or masks of their choice. \r\n\r\nContains modified Copernicus Sentinel data 2015-2025", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2025-07-10T01:57:47", "updateFrequency": "", "dataLineage": "Contains modified Copernicus Sentinel data 2015-2025.\r\nData were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "JNCC, ARD, NBR, Sentinel 2, EO indices, Analysis Ready Data, Sentinel, Normalised Burn Ratio, NCEA", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2025-07-09T07:24:45", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 4785, "bboxName": "England_and_Scotland", "eastBoundLongitude": 2.02, "westBoundLongitude": -7.65, "southBoundLatitude": 49.48, "northBoundLatitude": 59.48 }, "verticalExtent": null, "result_field": { "ob_id": 44331, "dataPath": "/neodc/sentinel_ard/products/indices/sentinel_2/nbr/v2/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 16961895534736, "numberOfFiles": 60829, "fileFormat": "These data are provided in Cloud Optimized GeoTIFF (COG) format." }, "timePeriod": { "ob_id": 12396, "startTime": "2015-07-08T00:00:00", "endTime": "2024-12-31T23:59:59" }, "resultQuality": { "ob_id": 4734, "explanation": "All index files are quality checked automatically as part of the processing workflow. The QC process checks that the following are as expected: the data value range, file format, coordinate reference system, no-data value, data type, pixel size, pixel alignment with the source ARD file. As part of workflow testing, a representative subset of data was also manually checked. 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": "2025-05-23" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 44332, "uuid": "0a058f0b3eb940d09b5a7834fc9163b9", "short_code": "cmppr", "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Normalised Burn Ratio (NBR) v2", "abstract": "All index equations from Sentinel-hub custom scripts (links in Related Documents).\r\nSentinel-2 surface reflectance products are obtained from Defra and JNCC Sentinel-2 ARD CEDA dataset." }, "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" } ] } } ], "projects": [ { "ob_id": 30197, "uuid": "14818d07b867480c90ed01fbf81d9346", "short_code": "proj", "title": "Defra and JNCC Analysis Ready Sentinel Data", "abstract": "Defra and JNCC aim to provide timely, cost-effective, and high-quality, Analysis Ready Sentinel Data (ARD) for a wide range of applications. These data are provided for the UK geographic area for this project. The indices datasets currently cover Yorkshire, UK, but coverage will be expanded in the future. These products have been produced using the Copernicus satellites, Sentinel 1 and Sentinel 2." }, { "ob_id": 44948, "uuid": "47150f1b97dd4dabae115c8369860771", "short_code": "proj", "title": "JNCC NCEA Habitat Condition Change Project", "abstract": "Sentinel 1 and Sentinel 2 derived index layers have been created by the Joint Nature Conservation Committee (JNCC) as part of the Earth observation-based “Habitat Condition Change” project (HabCon). This project is funded by the Department for Environment, Food and Rural Affairs (Defra) as part of the Natural Capital and Ecosystem Assessment (NCEA) programme. The project seeks to facilitate the effective uptake and use of Earth Observation data by producing data and tools for investigating and detecting parcel-level change in habitats and habitat condition.\r\nIndex files were derived from Defra and JNCC Sentinel 1 and Sentinel 2 ARD across England and Scotland for the period 2015 to 2025 and will be added to regularly. \r\nThe Sentinel 2 dataset contains NDVI, NDMI, NDWI, NBR and EVI2 indices. \r\nFor Sentinel 1, VH/VV cross-ratio files have been generated for Sentinel-1A ascending orbit granules only." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 13394 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 212055, 212053, 212054, 212056, 212058, 212057, 212059, 212060 ], "onlineresource_set": [ 93212, 93219, 93213, 93215, 93220 ] }, { "ob_id": 44334, "uuid": "4122d70648354bd9ac7eef525c829d02", "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Normalised Difference Moisture Index (NDMI) v2", "abstract": "NDMI can be used to assess vegetation moisture content, and is calculated as the ratio between the near-infrared (NIR) and short-wave infrared (SWIR) bands. It is commonly used to assess water stress in plants. Positive values tend to correspond to vegetated surfaces, with the value increasing towards 1 as moisture content and vegetation density increase.\r\n\r\nNDMI = (NIR – SWIR) / (NIR + SWIR)\r\n\r\nSentinel-2 NDMI (Defra/JNCC ARD bands) = (B07 – B09) / (B07 + B09)\r\n\r\nEquivalent ESA Sentinel-2 bands: B08, B11\r\n\r\nData are provided in EPSG: 27700 OSGB36 / British National Grid, with a pixel size of 10m, and data is pixel-aligned to the source ARD file. No-data pixels are set to a value of -9999.\r\n\r\nThese data have been created by the Joint Nature Conservation Committee (JNCC) as part of the “Earth observation-based habitat change detection” project. This project is funded by the Department for Environment, Food and Rural Affairs (Defra) as part of the Natural Capital and Ecosystem Assessment (NCEA) programme. The project seeks to facilitate the effective uptake and use of Earth Observation data by producing data and tools for investigating and detecting parcel-level change in habitats and habitat condition. \r\n\r\nThe dataset contains NDVI, NDMI, NDWI, NBR and EVI2 indices derived from Defra and JNCC Sentinel-2 ARD. Index files have been generated for Sentinel-2 granules covering England and Scotland for the period from 2015 to 2025. Note that new unmasked index files (v2) have superseded the previous masked index files (v1). Masked files will no longer be produced. Users can mask the new index files if required using the cloud and topographic shadow masks provided with the ARD, or masks of their choice. \r\n\r\nContains modified Copernicus Sentinel data 2015-2025", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2025-07-10T01:57:48", "updateFrequency": "", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).\r\nContains modified Copernicus Sentinel data 2015-2025", "removedDataReason": "", "keywords": "JNCC, ARD, NDMI, Sentinel 2, EO indices, Analysis Ready Data, Sentinel, Normalised Difference Moisture Index, NCEA", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2025-07-09T07:24:59", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 4785, "bboxName": "England_and_Scotland", "eastBoundLongitude": 2.02, "westBoundLongitude": -7.65, "southBoundLatitude": 49.48, "northBoundLatitude": 59.48 }, "verticalExtent": null, "result_field": { "ob_id": 44335, "dataPath": "/neodc/sentinel_ard/products/indices/sentinel_2/ndmi/v2/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 16681688929983, "numberOfFiles": 60828, "fileFormat": "These data are provided in Cloud Optimized GeoTIFF (COG) format." }, "timePeriod": { "ob_id": 12398, "startTime": "2015-07-08T00:00:00", "endTime": "2024-12-31T23:59:59" }, "resultQuality": { "ob_id": 4735, "explanation": "All index files are quality checked automatically as part of the processing workflow. 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Sentinel-2 surface reflectance products are obtained from Defra and JNCC Sentinel-2 ARD CEDA dataset. NDMI index files are generated in R v4.4.1 (R Core Team, 2024) using the calc function of the R package raster v3.6-26 (Hijmans, 2023). Data are provided in EPSG: 27700 OSGB36 / British National Grid, with a pixel size of 10m, and data is pixel-aligned to the source ARD file. No-data pixels are set to a value of -9999.\r\nContains modified Copernicus Sentinel data 2015-2025" }, "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" } ] } } ], "projects": [ { "ob_id": 30197, "uuid": "14818d07b867480c90ed01fbf81d9346", "short_code": "proj", "title": "Defra and JNCC Analysis Ready Sentinel Data", "abstract": "Defra and JNCC aim to provide timely, cost-effective, and high-quality, Analysis Ready Sentinel Data (ARD) for a wide range of applications. These data are provided for the UK geographic area for this project. The indices datasets currently cover Yorkshire, UK, but coverage will be expanded in the future. These products have been produced using the Copernicus satellites, Sentinel 1 and Sentinel 2." }, { "ob_id": 44948, "uuid": "47150f1b97dd4dabae115c8369860771", "short_code": "proj", "title": "JNCC NCEA Habitat Condition Change Project", "abstract": "Sentinel 1 and Sentinel 2 derived index layers have been created by the Joint Nature Conservation Committee (JNCC) as part of the Earth observation-based “Habitat Condition Change” project (HabCon). This project is funded by the Department for Environment, Food and Rural Affairs (Defra) as part of the Natural Capital and Ecosystem Assessment (NCEA) programme. The project seeks to facilitate the effective uptake and use of Earth Observation data by producing data and tools for investigating and detecting parcel-level change in habitats and habitat condition.\r\nIndex files were derived from Defra and JNCC Sentinel 1 and Sentinel 2 ARD across England and Scotland for the period 2015 to 2025 and will be added to regularly. \r\nThe Sentinel 2 dataset contains NDVI, NDMI, NDWI, NBR and EVI2 indices. \r\nFor Sentinel 1, VH/VV cross-ratio files have been generated for Sentinel-1A ascending orbit granules only." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 13395 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 212063, 212064, 212065, 212066, 212067, 212068, 212069, 212070 ], "onlineresource_set": [ 93221, 93223, 93222, 93224, 93225 ] }, { "ob_id": 44338, "uuid": "9ae429e3026f4903a338f6baa0a11035", "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Normalised Difference Water Index (NDWI) v2", "abstract": "NDWI is used to identify water bodies and detect changes in their extent. It is calculated as the ratio of the green and near-infrared (NIR) bands. Positive index values generally indicate the presence of water, with higher values corresponding to water bodies.\r\n\r\nNDWI = (GREEN – NIR) / (GREEN + NIR)\r\n\r\nSentinel-2 NDWI (Defra/JNCC ARD bands) = (B02 – B07) / (B02 + B07)\r\n\r\nEquivalent ESA Sentinel-2 bands: B03, B08\r\n\r\nData are provided in EPSG: 27700 OSGB36 / British National Grid, with a pixel size of 10m, and data is pixel-aligned to the source ARD file. No-data pixels are set to a value of -9999.\r\n\r\nThese data have been created by the Joint Nature Conservation Committee (JNCC) as part of the “Earth observation-based habitat change detection” project. This project is funded by the Department for Environment, Food and Rural Affairs (Defra) as part of the Natural Capital and Ecosystem Assessment (NCEA) programme. The project seeks to facilitate the effective uptake and use of Earth Observation data by producing data and tools for investigating and detecting parcel-level change in habitats and habitat condition. \r\n\r\nThe dataset contains NDVI, NDMI, NDWI, NBR and EVI2 indices derived from Defra and JNCC Sentinel-2 ARD. Index files have been generated for Sentinel-2 granules covering England and Scotland for the period from 2015 to 2025. Note that new unmasked index files (v2) have superseded the previous masked index files (v1). Masked files will no longer be produced. Users can mask the new index files if required using the cloud and topographic shadow masks provided with the ARD, or masks of their choice. \r\n\r\nContains modified Copernicus Sentinel data 2015-2025", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2025-07-10T01:57:47", "updateFrequency": "", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).\r\nContains modified Copernicus Sentinel data 2015-2025", "removedDataReason": "", "keywords": "JNCC, ARD, NDWI, Sentinel 2, EO indices, Analysis Ready Data, Sentinel, Normalised Difference Water Index, NCEA", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2025-07-09T07:25:42", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 4785, "bboxName": "England_and_Scotland", "eastBoundLongitude": 2.02, "westBoundLongitude": -7.65, "southBoundLatitude": 49.48, "northBoundLatitude": 59.48 }, "verticalExtent": null, "result_field": { "ob_id": 44339, "dataPath": "/neodc/sentinel_ard/products/indices/sentinel_2/ndwi/v2/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 16204585278099, "numberOfFiles": 60828, "fileFormat": "These data are provided in Cloud Optimized GeoTIFF (COG) format." }, "timePeriod": { "ob_id": 12397, "startTime": "2015-07-08T00:00:00", "endTime": "2024-12-31T23:59:59" }, "resultQuality": { "ob_id": 4736, "explanation": "All index files are quality checked automatically as part of the processing workflow. The QC process checks that the following are as expected: the data value range, file format, coordinate reference system, no-data value, data type, pixel size, pixel alignment with the source ARD file. As part of workflow testing, a representative subset of data was also manually checked. 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": "2025-05-23" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 44340, "uuid": "ea3640b56554444e85f3df735dbf4ba1", "short_code": "cmppr", "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Normalised Difference Water Index (NDWI) v2", "abstract": "Index equation from Sentinel Hub custom scripts (link in Related Documents). Sentinel-2 surface reflectance products are obtained from Defra and JNCC Sentinel-2 ARD CEDA dataset. NDWI index files are generated in R v4.4.1 (R Core Team, 2024) using the calc function of the R package raster v3.6-26 (Hijmans, 2023). Data are provided in EPSG: 27700 OSGB36 / British National Grid, with a pixel size of 10m, and data is pixel-aligned to the source ARD file. No-data pixels are set to a value of -9999.\r\nContains modified Copernicus Sentinel data 2015-2025" }, "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" } ] } } ], "projects": [ { "ob_id": 30197, "uuid": "14818d07b867480c90ed01fbf81d9346", "short_code": "proj", "title": "Defra and JNCC Analysis Ready Sentinel Data", "abstract": "Defra and JNCC aim to provide timely, cost-effective, and high-quality, Analysis Ready Sentinel Data (ARD) for a wide range of applications. These data are provided for the UK geographic area for this project. The indices datasets currently cover Yorkshire, UK, but coverage will be expanded in the future. These products have been produced using the Copernicus satellites, Sentinel 1 and Sentinel 2." }, { "ob_id": 44948, "uuid": "47150f1b97dd4dabae115c8369860771", "short_code": "proj", "title": "JNCC NCEA Habitat Condition Change Project", "abstract": "Sentinel 1 and Sentinel 2 derived index layers have been created by the Joint Nature Conservation Committee (JNCC) as part of the Earth observation-based “Habitat Condition Change” project (HabCon). This project is funded by the Department for Environment, Food and Rural Affairs (Defra) as part of the Natural Capital and Ecosystem Assessment (NCEA) programme. The project seeks to facilitate the effective uptake and use of Earth Observation data by producing data and tools for investigating and detecting parcel-level change in habitats and habitat condition.\r\nIndex files were derived from Defra and JNCC Sentinel 1 and Sentinel 2 ARD across England and Scotland for the period 2015 to 2025 and will be added to regularly. \r\nThe Sentinel 2 dataset contains NDVI, NDMI, NDWI, NBR and EVI2 indices. \r\nFor Sentinel 1, VH/VV cross-ratio files have been generated for Sentinel-1A ascending orbit granules only." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 13396 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 212073, 212074, 212075, 212076, 212077, 212078, 212079, 212080 ], "onlineresource_set": [ 93230, 93231, 93232, 93873, 93874 ] }, { "ob_id": 44343, "uuid": "382aa4c3c6b2417fa80261e124a5f116", "title": "3D ocean bottom node seismic data across the 13°30'N Oceanic Core Complex and the Semenov Hydrothermal Field region of the Mid-Atlantic Ridge (JC254, 2023).", "abstract": "The dataset contains 3D wide-angle seismic data from 18 ocean bottom nodes (OBXs) collected during RRS James Cook cruise JC254 in November 2023. The data were acquired at the 1330 Oceanic Core Complex (OCC) and the Semenov Hydrothermal Field region of the Mid-Atlantic Ridge at 13°30'N, and were shot in six sequences (SEM4A to SEM4F), along 15 profiles in total. Two GI-guns were used as seismic sources, each with a total volume of 355 cubic inches (250 cu. inch generator, 105 cu. inch injector chamber. The data were recorded at a sampling rate of 2000 Hz, with a shot interval of 20 s at a ship speed of 4 knots, resulting in shot interval of ~30 m, with no stop during profile turns. The data are provided in raw SEGY format, cut into four components (three geophone channels and one hydrophone channel). This dataset was collected to derive a seismic velocity structure of the 13°30'N OCC. The aim was to investigate the crustal or mantle lithologies in the 13°30'N OCC and to understand the interplay between tectonic faulting, magmatism, alteration and hydrothermal circulation at the ultramafic-hosted hydrothermal systems along slow-spreading ridges. The data were collected as part of a NERC-funded strategic research project called 'Ultramafic-hosted mineral Resource Assessment (ULTRA)', grant reference NE/S004068/1. The data were processed and interpreted as part of a PhD studentship hosted by the University of Southampton, awarded to Szu-Ying Lai, and funded by Equinor A.S, Norway.", "creationDate": "2025-05-28T07:08:33.557609", "lastUpdatedDate": "2025-05-28T07:08:33", "latestDataUpdateTime": "2025-05-30T01:57:29", "updateFrequency": "notPlanned", "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": "", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "final", "dataPublishedTime": "2025-05-27T18:05:24", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 4786, "bboxName": "", "eastBoundLongitude": -44.75, "westBoundLongitude": -44.9833333333, "southBoundLatitude": 13.4416666667, "northBoundLatitude": 13.5666666667 }, "verticalExtent": null, "result_field": { "ob_id": 44342, "dataPath": "/bodc/deposits02/uso250859", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 68248058190, "numberOfFiles": 435, "fileFormat": ".segy, .jpg, .txt" }, "timePeriod": { "ob_id": 12399, "startTime": "2023-11-13T00:00:00", "endTime": "2023-11-16T23:59:59" }, "resultQuality": { "ob_id": 4737, "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": "2025-05-28" }, "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" } ] } } ], "projects": [], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 13397 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 212086, 212087, 212085, 212084, 212083, 212089, 212090, 212091, 212088, 212093, 212094, 212095, 212096 ], "onlineresource_set": [] }, { "ob_id": 44345, "uuid": "f61458fc1bfa4d3982b34b702eb96689", "title": "Terrestrial LiDAR data for the multi-trophic impact of ash dieback project, Wytham Woods, July 2020 - July 2022", "abstract": "This dataset is comprised of raw data and rotation matrices captured with the NERC-funded Terrestrial Laser Scanner (TLS) instrument. Data was collected across 15 plots in Wytham Woods, UK, in leaf-on and -off conditions for 3 years. For 5 of the plots all ash trees were girdled to simulate death by the fungal pathogen Hymenoscyphus fraxineus ('ash dieback'), 5 of the plots were 'ash-control' where the dominant species were Fraxinus excelsior and the remaining 5 plots were non-ash dominant.\r\n\r\nTLS data was acquired on the four edges of the plot and the plot centre. For the summer 2020 and winter 2021 acquisitions the scanner was mounted on a pneumatic mast to capture data from different heights (2 m, 4 m, and 6 m), from summer 2021 onwards the scanner was mounted at a height of 2 m. The scanner was set to an angular step of 0.04 degrees. In between each scan position a set of retro-reflective targets were positioned to be used as tie-points between scans. Scan data was coregistered using RiSCAN Pro, the 4x4 rotation transformation matrices to transform the point cloud data into a common reference coordinate system can be found in the \"matrix\" directory, all acquistions for the same plot were registered to the same coordinate system. For more information on TLS acquisition for this project see Wilkes et al. (2021) and more generally refer to Wilkes et al. (2017) which can be found in the documentation section.", "creationDate": "2025-05-28T10:00:29.489835", "lastUpdatedDate": "2025-05-28T09:41:05", "latestDataUpdateTime": "2025-06-04T01:56:51", "updateFrequency": "", "dataLineage": "Data were produced by the UCL project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "Terrestrial Laser Scanning, Trees, LiDAR", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2025-06-03T08:00:30", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 4787, "bboxName": "Whytham woods", "eastBoundLongitude": -1.2977, "westBoundLongitude": -1.3466, "southBoundLatitude": 51.7799, "northBoundLatitude": 51.7591 }, "verticalExtent": null, "result_field": { "ob_id": 44344, "dataPath": "/neodc/tls/data/raw/uk/WHY/multitrophic_wytham", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 215835327818, "numberOfFiles": 6909, "fileFormat": "Point cloud data in RIEGL proprietry .rxp format, image data in .jpg format and matrix data in text file." }, "timePeriod": { "ob_id": 12400, "startTime": "2020-07-13T00:00:00", "endTime": "2022-07-25T00:00:00" }, "resultQuality": { "ob_id": 4738, "explanation": "Data quality control conducted by UCL project team", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2025-05-28" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 44346, "uuid": "c1f6e2ff011d4f5da38172ee82c1a893", "short_code": "acq", "title": "Wytham Woods - The multi-trophic impact of ash dieback' project 2020 - 2022", "abstract": "TLS data was acquired on the four edges of the plot and the plot centre. For the summer 2020 and winter 2021 acquisitions the scanner was mounted on a pneumatic mast to capture data from different heights (2 m, 4 m, and 6 m), from summer 2021 onwards the scanner was mounted at a height of 2 m. The scanner was set to an angular step of 0.04 degrees. In between each scan position a set of retro-reflective targets were positioned to be used as tie-points between scans. Scan data was coregistered using RiSCAN Pro, the 4x4 rotation transformation matrices to transform the point cloud data into a common reference coordinate system can be found in the \"matrix\" directory, all acquistions for the same plot were registered to the same coordinate system. For more information on TLS acquisition for this project see Wilkes et al. (2021) and more generally refer to Wilkes et al. (2017)." }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 2 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 44347, "uuid": "5f1d11216a4d443195ced76535efade4", "short_code": "proj", "title": "The multi-trophic impact of ash dieback", "abstract": "It is increasingly recognised that multitrophic studies are needed for better understanding of ecosystem dynamics in response to agents of global change, yet such studies are rare, with most focusing on specific taxa and one-way ecological interactions. One of the major agents of contemporary global change in ecosystems is the global spread and increased frequency of pathogens and pests, with consequences for biodiversity, ecosystem functioning and biosphere carbon balance. In European forests, the most acute recent concern is the spread of the fungal pathogen Hymenoscyphus fraxineus across the range of the European ash tree (Fraxinus excelsior), resulting in up to 90% ash mortality (\"ash dieback\"). Typically, between 50% - 70% of ash trees die within five years of becoming infected and evidence suggests that young trees will die faster than mature ones, making regeneration unlikely. Ash is an ecologically important species that is associated with fast growth and high nutrient turnover, due to its highly degradable litter, and a high diversity of ground flora due to its naturally sparse canopy. For these reasons it also influences key ecosystem attributes and processes such as biogeochemical cycling and woodland structure and biodiversity. \r\n\r\nIn 2017 ash dieback was detected in Wytham Woods, Oxford's iconic ecological observatory. Ash is a major component of the canopy in Wytham and so the decline of this species will have profound impacts on the ecology, dynamics and carbon budget of the woodland. While the impacts of ash dieback on ash trees themselves have received attention, no studies to date have explored how the consequences of ash dieback cascade through multiple trophic levels of a forest ecosystem. Wytham is uniquely positioned to track the full ecological consequences of the disease.\r\n\r\nThis project examines the multi-trophic impacts of ash dieback on biogeochemical cycling, habitat structure and predation, and spatial heterogeneity and connectivity. This has been achieved by coordinating and expanding existing monitoring of vegetation, birds and mammals, and initiating new studies of soil and litter organisms and microclimate. This coordinated effort over four years will result in a novel multi-trophic understanding of the consequences of forest disruption by tree pathogens, providing insights that are applicable to managing and mitigating the ecological consequences of tree dieback events.\r\n\r\nThe aims will be met through a range of surveys and experiments for which we will adopt an experimental approach of \"simulated ash dieback\" which will lead to >80% mortality through ring-barking of ash trees in selected plots. These treatments will enable us to focus on the longer-term impacts of the dieback (10-20 years after infection), when tree mortality process will dominate, in contrast to the early (3-7 years) stages of ash dieback monitored in the unmanipulated plots, dominated by canopy loss rather than mortality. Additionally, we will take advantage of existing infrastructure and monitor the multi-trophic effect of ash dieback across the woodland. \r\n\r\nThis project was funded by NERC under grant number NE/T007648/1 and rand ran March 2020 - March 2025" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 30128, "uuid": "7fe9f59731ab47b6a20e792e0cba4641", "short_code": "coll", "title": "National Centre for Earth Observation (NCEO) partnered datasets", "abstract": "The National Centre for Earth Observation (NCEO) has a proud tradition of being involved with some of the most successful international collaborations in the Earth observation. This Collection contains dataset generated and/or archived with the support of NCEO resource or scientific expertise. Some notable collaboration which generated data within this collection are as follows:\r\n\r\nThe European Space Agency (ESA)'s Climate Change Initiative (CCI) program. The program goal is to provide stable, long-term, satellite-based Essential Climate Variable (ECV) data products for climate modelers and researchers.\r\n\r\nThe EUSTACE (EU Surface Temperature for All Corners of Earth) project is produced publicly available daily estimates of surface air temperature since 1850 across the globe for the first time by combining surface and satellite data using novel statistical techniques.\r\n\r\nFIDUCEO has created new climate datasets from Earth Observations with a rigorous treatment of uncertainty informed by the discipline of metrology. This response to the need for enhanced credibility for climate data, to support rigorous science, decision-making and climate services. The project approach was to develop methodologies for generating Fundamental Climate Data Records (FCDRs) and Climate Data Records (CDRs) that are widely applicable and metrologically rigorous. \r\n\r\nThe “BACI” project translates satellite data streams into novel “essential biodiversity variables” by integrating ground-based observations. The trans-disciplinary project offers new insights into the functioning and state of ecosystems and biodiversity. BACI enables the user community to detect abrupt and transient changes of ecosystems and quantify the implications for regional biodiversity.\r\n\r\nThe UK Natural Environment Research Council has established a knowledge transfer network called NCAVEO (Network for Calibration and Validation of EO data - NCAVEO) which has as its aim the promotion and support of methodologies based upon quantitative, traceable measurements in Earth observation. \r\n\r\nThe Geostationary Earth Radiation Budget 1 & 2 instruments (GERB-1 and GERB-2) make accurate measurements of the Earth Radiation Budget. They are specifically designed to be mounted on a geostationary satellite and are carried onboard the Meteosat Second Generation satellites operated by EUMETSAT. They were produced by a European consortium led by the UK (NERC) together with Belgium, Italy, and EUMETSAT, with funding from national agencies.\r\n\r\nGloboLakes analysed 20 years of data from more than 1000 large lakes across the globe to determine 'what controls the differential sensitivity of lakes to environmental perturbation'. This was an ambitious project that was only possible by bringing together a consortium of scientists with complementary skills. These include expertise in remote sensing of freshwaters and processing large volumes of satellite images, collation and analysis of large-scale environmental data, environmental statistics and the assessment of data uncertainty, freshwater ecology and mechanisms of environmental change and the ability to produce lake models to forecast future lake conditions.\r\n\r\nThis SPEI collaboration consists of high spatial resolution Standardized Precipitation-Evapotranspiration Index (SPEI) drought dataset over the whole of Africa at different time scales from 1 month to 48 months. It is calculated based on precipitation estimates from the satellite-based Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and potential evaporation estimates by the Global Land Evaporation Amsterdam Model (GLEAM)." } ], "responsiblepartyinfo_set": [ 212097, 212098, 212099, 212100, 212101, 212102, 212108, 212109, 212110, 212111, 212112, 212113 ], "onlineresource_set": [ 93239, 93240 ] }, { "ob_id": 44348, "uuid": "f244267a4b4f4edcb87e248a27214ca3", "title": "European Atlantic Offshore Wave Spectral Climatology under Historical and Future Climate Scenarios", "abstract": "This dataset provides 3-hourly ocean wave spectral data at 1,031 fixed offshore locations along the Atlantic coast of Europe (including the British Isles) spanning three 30-year periods: historical (1985–2014), and future projections (2030-2059) under two Shared Socioeconomic Pathways (SSPs). It also includes wave parametric data (significant height, peak period, direction...).\r\n\r\nThe wave spectra were generated using the Simulating WAves Nearshore (SWAN) model, forced with high-resolution winds obtained through downscaling global climate models using the Weather Research and Forecasting (WRF) model, and open boundary conditions from simulations carried out with the WaveWatch III wave propagation model.\r\n\r\nEach NetCDF-4 file includes eight 3-hourly spectral records per day, capturing wave energy distribution across 25 frequency bands and 36 directions for all 1,031 locations (spaced ~10 km apart, 50 km from the coast). \r\n\r\nThis spectral climatology is suitable for climate impact assessments, wave energy studies, and coastal hazard analysis.", "creationDate": "2025-05-30T15:11:27.332374", "lastUpdatedDate": "2025-06-20T10:13:20", "latestDataUpdateTime": "2025-05-30T15:11:27", "updateFrequency": "notPlanned", "dataLineage": "This dataset was generated using a multi-stage modeling workflow. First, atmospheric variables from multiple CMIP6 climate models were bias-corrected and dynamically downscaled using the WRF model to produce high-resolution wind fields. These winds were then used to drive WaveWatch III simulations across the North Atlantic to provide offshore boundary conditions. Finally, the SWAN wave model was run at high resolution along the European Atlantic coast using WRF winds and WaveWatch III boundaries to compute detailed 3-hourly wave spectra at 1,031 offshore points. The entire workflow was repeated for three periods: historical (1985–2014) and future scenarios SSP2-4.5 and SSP5-8.5 (2030–2059). The output files were processed, verified, and stored in NetCDF-4 format following CF conventions before being transferred to the CEDA data centre.", "removedDataReason": "Dataset has been superseded before release from embargo by a new version.", "keywords": "Wave spectra,SWAN,Climate change scenarios,Atlantic Ocean,Downscaling,CMIP6,Ocean wave climatology", "publicationState": "removed", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "superseded", "dataPublishedTime": "2025-08-12T14:58:18", "doiPublishedTime": "2025-08-12T15:27:20", "removedDataTime": "2026-01-12T18:01:14", "geographicExtent": { "ob_id": 4788, "bboxName": "", "eastBoundLongitude": 11.0, "westBoundLongitude": -10.0, "southBoundLatitude": 30.0, "northBoundLatitude": 63.0 }, "verticalExtent": null, "result_field": null, "timePeriod": { "ob_id": 12483, "startTime": "1985-01-01T00:00:00", "endTime": "2059-12-31T00:00:00" }, "resultQuality": { "ob_id": 4748, "explanation": "The outputs from the simulations conducted using the SWAN model have been validated against two independent data sources. The validation focused on the three main variables that characterize wave conditions: significant wave height (Hs), peak wave period (Tp), and wave direction. First, a comparison was made with reanalysis data from the ERA5 database, enabling validation across the entire domain (the European Atlantic coast). Second, the model outputs were compared with in-situ measurements from buoys operated by Puertos del Estado (Spain) and the UK Met Office.\r\nThe results obtained confirm the reliability and robustness of the generated dataset.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2025-06-13" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 40029, "uuid": "a71d72717a5a4d2498bbfcff8f4c4567", "short_code": "comp", "title": "Simulating Waves Nearshore (SWAN)", "abstract": "SWAN propagates offshore wave conditions to the nearshore, and can account for: Wave propagation in time and space, shoaling, refraction due to current and depth, frequency shifting due to currents and non-stationary depth. Wave generation by wind. Three- and four-wave interactions. Whitecapping, bottom friction and depth-induced breaking. Dissipation due to aquatic vegetation, turbulent flow and viscous fluid mud. Wave-induced set-up. Propagation from laboratory up to global scales. Transmission through and reflection (specular and diffuse) against obstacles. Diffraction." }, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [ { "ob_id": 2666, "accessConstraints": null, "accessCategory": "restricted", "accessRoles": "eaows", "label": "restricted: eaows group", "licence": { "ob_id": 21, "licenceURL": "https://artefacts.ceda.ac.uk/licences/rugl_versions/rugl_v1-0.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 44508, "uuid": "b68e7dd41370480db6e08493cb19dda1", "short_code": "proj", "title": "SURVIWEC (Survivability of Wave Energy Converters)", "abstract": "The SURVIWEC project aims to develop and validate a numerical tool based on DualSPHysics to assess the survivability of point absorber wave energy devices under extreme wave conditions. The project includes analyzing present and future wave scenarios, enhancing the simulation code, and testing various device configurations. The tool and validation data will be released as open-source to support further research and development." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 1319, 1320, 62353, 78330, 78331, 78332, 78333 ], "vocabularyKeywords": [], "identifier_set": [ 13492 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 213379, 213010, 212114, 212115, 212116, 212117, 212118, 212119, 213011, 213012, 213013, 213014 ], "onlineresource_set": [ 94191 ] }, { "ob_id": 44349, "uuid": "6b029e226c534b359e416d30dbdab5a1", "title": "Mantle Circulation Constrained (MC2) - Mantle Circulation Simulations", "abstract": "This dataset constains for models produced for the NERC Large Grant - Mantle Circulation Constrained (MC2; NERC grant award NE/T012633/1). Each model is produced using the 3D mantle circulation code, TERRA and deployed on the ARCHER2 UK National Supercomputing Service. Each folder represents a model which comprises output files (NetCDF and ParaView) between 1000-100 Ma (in 100 Myr intervals), 90-10 Ma (in 10 Myr intervals) and 1-0 Ma (in 1 Myr intervals). Additionally we provide the run files for each model, at various restart times which include a parameter file and a compiled executable. Additional output files are described in the README files.", "creationDate": "2025-06-03T10:20:38.431172", "lastUpdatedDate": "2025-06-19T08:18:19", "latestDataUpdateTime": "2025-06-03T10:20:38", "updateFrequency": "notPlanned", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA)", "removedDataReason": "", "keywords": "Mantle Circulation,Geodynamics,Mantle Modelling,TERRA", "publicationState": "working", "nonGeographicFlag": true, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "pending", "dataPublishedTime": null, "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": null, "verticalExtent": null, "result_field": { "ob_id": 44768, "dataPath": "/ngdc/MC2_Mantle_Circulation_Sims/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 7811338635657, "numberOfFiles": 51710, "fileFormat": "NetCDF and Paraview" }, "timePeriod": { "ob_id": 12480, "startTime": "2020-11-01T00:00:00", "endTime": "2025-09-30T00:00:00" }, "resultQuality": { "ob_id": 4750, "explanation": "Files have been post-processed and analysed, indicating no file corruption.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2025-06-19" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 44351, "uuid": "a838df50d8434452a735ae90360a9a0c", "short_code": "comp", "title": "Global Mantle Circulation Models run on ARCHER2 - The UK's National Supercomputing System", "abstract": "" }, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [ { "ob_id": 2669, "accessConstraints": null, "accessCategory": "restricted", "accessRoles": "mc2_mantle_circulation_sims", "label": "restricted: mc2_mantle_circulation_sims group", "licence": { "ob_id": 21, "licenceURL": "https://artefacts.ceda.ac.uk/licences/rugl_versions/rugl_v1-0.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 44350, "uuid": "575f38e01f604086913950adf1f4c518", "short_code": "proj", "title": "MC2 (Mantle Circulation Constrained)", "abstract": "This project is an interdisciplinary approach to constraining mantle circulation. We investigate how plate tectonics is linked to mantle circulation, by combining the history of plate movements across Earth's surface with observations drawn from across the geosciences, and use these to constrain state-of-the-art 4D computational models of mantle flow." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 1342, 84104, 84105, 84106, 84107, 84108, 84109 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 212129, 212130, 212131, 212125, 212126, 212127, 212128, 212132, 212133, 212134 ], "onlineresource_set": [] }, { "ob_id": 44353, "uuid": "51ae627da5fb41b8a767ee6c653f83e6", "title": "Methane doubly substituted (clumped) isotopologues database", "abstract": "The database was made as a part of POLYGRAM project (NE/V007149/1). The database aims to summarise the state of double substituted methane isotopologues (know also as clumped isotopologues, Δ13CH3D and Δ12CH2D2) measurement research, with an emphasis on compiling results of all relevant work and aid development of the inputs to atmospheric modelling studies. The complied database comprises 1475 data records from 75 peer-reviewed articles. Database includes both field samples and laboratory experiments from numerous laboratories worldwide.\r\n\r\nThis database is made freely available to the scientific community. The database has a Digital Object Identifier (DOI). We rely on the ethics and integrity of the user to assure that the authors receive fair credit for their work. Users must include the citation of individual publication and following database citation in any publication or presentation using the product.", "creationDate": "2024-11-26T12:07:54.729974", "lastUpdatedDate": "2024-11-26T12:10:03", "latestDataUpdateTime": "2025-06-07T02:00:15", "updateFrequency": "notPlanned", "dataLineage": "The database is produced by aggregation of existing data from peer-reviewed literature and includes references to original papers.", "removedDataReason": "", "keywords": "CH4, methane, greenhouse gases, Δ13CH3D, Δ12CH2D2, clumped isotopes, isotopes", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2025-06-04T15:17:46", "doiPublishedTime": "2025-06-06T07:39:03", "removedDataTime": null, "geographicExtent": { "ob_id": 4630, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 44354, "dataPath": "/badc/deposited2025/Methane_Clumped_Isotopologues_Data_Base_2025/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 638567, "numberOfFiles": 4, "fileFormat": "BADC-CSV" }, "timePeriod": { "ob_id": 12418, "startTime": "2014-01-01T00:00:00", "endTime": "2025-05-30T00:00:00" }, "resultQuality": { "ob_id": 4623, "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": "2024-11-26" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 43359, "uuid": "a431a8fc247c4f4781a5a60602742f9e", "short_code": "acq", "title": "Instrument and Laboratories used to collect data in the Methane Clumped Isotopologues Database", "abstract": "Instrument and Laboratories used to collect data in the Methane Clumped Isotopologues Database" }, "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" } ] } } ], "projects": [ { "ob_id": 43309, "uuid": "4bd944d0744c468dafbcefeefed1bba5", "short_code": "proj", "title": "NERC project: POLYGRAM (POLYisotopologues of GReenhouse gases: Analysis and Modelling)", "abstract": "POLYGRAM project was funded by the Natural Environment Research Council (NERC) with the grant number NE/V007149/1. POLYGRAM push the frontiers for CO2 and CH4 polyisotopologue measurement capability using the latest advances in laser spectroscopic analysis and high-resolution isotope ratio mass spectrometry. We establish a small global atmospheric sampling network to examine latitudinal and longitudinal variations in in polyisotopologues, to constrain overall global budgets of CO2 and CH4." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 13414 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 212147, 212148, 212149, 212150, 212151, 212152, 212153, 212154, 212155, 212156 ], "onlineresource_set": [] }, { "ob_id": 44371, "uuid": "2b8c014e54be42968b620bf5a9526a12", "title": "NCAS Long Term Observations: mean winds from the NCAS Sonic Anemometer unit 6 deployed at the NCAS Cape Verde Atmospheric Observatory, v1.0", "abstract": "Mean wind measurements from the NCAS Sonic Anemometer unit 6 deployed at the NCAS Cape Verde Atmospheric Observatory. These observations were taken as part of the National Centre for Atmospheric Science (NCAS) long term observations.\r\n\r\nData products from this deployment include: mean-winds\r\n\r\nFor further details of this deployment and the associated dataset please see the internal file metadata.\r\n\r\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.", "creationDate": "2025-06-06T11:19:27.935833", "lastUpdatedDate": "2025-06-06T11:19:27", "latestDataUpdateTime": "2025-06-19T02:03:01", "updateFrequency": "notPlanned", "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": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "superseded", "dataPublishedTime": "2025-06-18T10:18:40", "doiPublishedTime": "2025-06-18T10:18:53.478971", "removedDataTime": null, "geographicExtent": { "ob_id": 4789, "bboxName": "São Vicente, Cabo Verde", "eastBoundLongitude": -24.867709, "westBoundLongitude": -24.867709, "southBoundLatitude": 16.864089, "northBoundLatitude": 16.864089 }, "verticalExtent": null, "result_field": { "ob_id": 44372, "dataPath": "/badc/ncas-cvao/data/ncas-sonic-6/20241127_longterm/v1.0", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 96204383, "numberOfFiles": 62, "fileFormat": "Data are netCDF formatted." }, "timePeriod": { "ob_id": 12429, "startTime": "2024-11-27T16:29:55", "endTime": "2025-01-26T23:59:59" }, "resultQuality": { "ob_id": 4568, "explanation": "These data have been produced in accordance to standard NCAS observational practices. 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The data cover major pollutants, including Nitrogen Dioxide (NO2), Nitric Oxide (NO), Nitrogen Oxides (NOx), Ozone (O3), Particulate Matter smaller than 10 micrometres (PM10) and smaller than 2.5 micrometres (PM2.5), and Sulphur Dioxide (SO2). Each pollutant's concentrations are predicted not only as average (mean) values but also include estimates at lower (5th percentile), median (50th percentile), and upper (95th percentile) levels to highlight typical and potential extreme pollution scenarios.\r\n\r\nThe spatial coverage of the dataset includes the entire area of England, structured as an evenly spaced grid, with each grid square covering an area of 1 square kilometre (1 km^2). Data points correspond to the centre of these grid squares. Temporally, the dataset does not represent actual hourly measurements from specific dates; instead, it provides aggregated \"typical day\" profiles constructed by averaging observations collected from multiple years (2014-2018) for each month, weekday, and hour. This method offers representative insights into typical air pollution patterns, avoiding the complexity of handling large-scale raw datasets.\r\n\r\nThese pollution estimates were produced using a supervised machine learning method, which is a computational approach where algorithms are trained to identify patterns in historical data and apply these learned patterns to predict new data points. The predictions incorporated various environmental factors including weather conditions (e.g., temperature, wind, precipitation), human activities (traffic patterns), satellite measurements, land-use types (urban, rural, industrial areas), and emission inventories (datasets detailing pollutants released into the atmosphere). Additionally, the dataset provides uncertainty intervals through percentile-based estimates, giving users insights into the reliability of the predictions.\r\n\r\nThe dataset was developed to facilitate easier access to high-quality air pollution information for diverse stakeholders, such as researchers, policymakers, urban planners, and health professionals. By providing clear, simplified air quality scenarios, it helps users make informed decisions in urban planning, public health, environmental management, and policy development, as well as to assess potential impacts and interventions related to air pollution.\r\n\r\nThe dataset was created by Liam J. Berrisford at the University of Exeter during his PhD studies, supported by the UK Research and Innovation (UKRI) Centre for Doctoral Training in Environmental Intelligence. Full methodological details and data validation information are available in the associated open-access scientific publication. 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The runs included the ensemble members: r10i1p1f1, r11i1p1f1, r12i1p1f1, r13i1p1f1, r14i1p1f1, r15i1p1f1, r16i1p1f1, r17i1p1f1, r18i1p1f1, r19i1p1f1, r1i10p1f1, r1i1p1f1, r1i2p1f1, r1i3p1f1, r1i4p1f1, r1i5p1f1, r1i6p1f1, r1i7p1f1, r1i8p1f1, r1i9p1f1, r20i1p1f1, r21i1p1f1, r22i1p1f1, r23i1p1f1, r24i1p1f1, r25i1p1f1, r26i1p1f1, r27i1p1f1, r28i1p1f1, r29i1p1f1, r2i10p1f1, r2i1p1f1, r2i2p1f1, r2i3p1f1, r2i4p1f1, r2i5p1f1, r2i6p1f1, r2i7p1f1, r2i8p1f1, r2i9p1f1, r30i1p1f1, r31i1p1f1, r32i1p1f1, r33i1p1f1, r34i1p1f1, r35i1p1f1, r36i1p1f1, r37i1p1f1, r38i1p1f1, r39i1p1f1, r3i10p1f1, r3i1p1f1, r3i2p1f1, r3i3p1f1, r3i4p1f1, r3i5p1f1, r3i6p1f1, r3i7p1f1, r3i8p1f1, r3i9p1f1, r40i1p1f1, r4i10p1f1, r4i1p1f1, r4i2p1f1, r4i3p1f1, r4i4p1f1, r4i5p1f1, r4i6p1f1, r4i7p1f1, r4i8p1f1, r4i9p1f1, r5i10p1f1, r5i1p1f1, r5i2p1f1, r5i3p1f1, r5i4p1f1, r5i5p1f1, r5i6p1f1, r5i7p1f1, r5i8p1f1, r5i9p1f1, r6i10p1f1, r6i1p1f1, r6i2p1f1, r6i3p1f1, r6i4p1f1, r6i5p1f1, r6i6p1f1, r6i7p1f1, r6i8p1f1, r6i9p1f1, r7i10p1f1, r7i1p1f1, r7i2p1f1, r7i3p1f1, r7i4p1f1, r7i5p1f1, r7i6p1f1, r7i7p1f1, r7i8p1f1, r7i9p1f1, r8i10p1f1, r8i1p1f1, r8i2p1f1, r8i3p1f1, r8i4p1f1, r8i5p1f1, r8i6p1f1, r8i7p1f1, r8i8p1f1, r8i9p1f1, r9i10p1f1, r9i1p1f1, r9i2p1f1, r9i3p1f1, r9i4p1f1, r9i5p1f1, r9i6p1f1, r9i7p1f1, r9i8p1f1 and r9i9p1f1.\n\nCMIP6 was a global climate model intercomparison project, coordinated by PCMDI (Program For Climate Model Diagnosis and Intercomparison) on behalf of the WCRP and provided input for the Intergovernmental Panel on Climate Change (IPCC) 6th Assessment Report (AR6).\n\nThe official CMIP6 Citation, and its associated DOI, is provided as an online resource linked to this record.", "creationDate": "2025-06-10T09:51:16.724659", "lastUpdatedDate": "2025-06-10T09:51:16.724677", "latestDataUpdateTime": "2025-04-18T13:15:26", "updateFrequency": "asNeeded", "dataLineage": "Data were produced and verified by NASA Goddard Institute for Space Studies (NASA GISS) scientists before publication via the Earth Systems Grid Federation (ESGF) and a copy obtained by the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "CMIP6, WCRP, climate change, NASA-GISS, GISS-E2-1-G, volc-pinatubo-full, AERmon, Amon, CFmon, Emon, EmonZ, LImon, Lmon, Omon, SImon", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2025-06-10T09:51:16.863508", "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": 44469, "dataPath": "/badc/cmip6/data/CMIP6/VolMIP/NASA-GISS/GISS-E2-1-G/volc-pinatubo-full", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 492379130361, "numberOfFiles": 19849, "fileFormat": "Data are netCDF formatted." }, "timePeriod": { "ob_id": 12458, "startTime": "8000-01-16T12:00:00", "endTime": "8398-12-16T12:00:00" }, "resultQuality": { "ob_id": 3341, "explanation": "The CMIP6 data are copied to CEDA from international distributors. CEDA perform no quality control on these data. Where any CMIP6 data are identified as having a quality issue this is recorded in the CMIP6 errata service: https://errata.es-doc.org/static/index.html", "passesTest": true, "resultTitle": "CMIP6 Quality Statement - replica data", "date": "2017-02-14" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 44470, "uuid": "918838d4bb7642158ed42811935fb102", "short_code": "comp", "title": "NASA Goddard Institute for Space Studies (NASA GISS) running: experiment volc-pinatubo-full using the GISS-E2-1-G model.", "abstract": "NASA Goddard Institute for Space Studies (NASA GISS) running the \"Pinatubo experiment\" (volc-pinatubo-full) experiment using the GISS-E2-1-G model. See linked documentation for available information for each component." }, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2520, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 1, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/CMIP6_Terms_of_Use.pdf", "licenceClassifications": [] } } ], "projects": [ { "ob_id": 28606, "uuid": "b753ace4d6a1449f88dc33584e468af5", "short_code": "proj", "title": "WCRP CMIP6: NASA Goddard Institute for Space Studies (NASA GISS) contribution", "abstract": "World Climate Research Programme (WCRP) Coupled Model Intercomparison Project Phase 6 contribution to the project by the NASA Goddard Institute for Space Studies (NASA GISS) team." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6021, 6022, 6023, 6255, 9045, 50418, 50419, 50420, 50422, 50424, 50425, 50426, 50427, 50429, 50431, 50457, 50468, 50475, 50479, 50496, 50498, 50549, 50551, 50552, 50553, 50554, 50555, 50557, 50559, 50561, 50564, 50566, 50568, 50569, 50570, 50571, 50572, 50573, 50574, 50575, 50577, 50579, 50581, 50584, 50585, 50586, 50587, 50588, 50589, 50590, 50591, 50592, 50593, 50594, 50595, 50596, 50598, 50599, 50603, 50604, 50608, 50609, 50611, 50612, 50614, 50615, 50616, 50617, 52741, 52746, 52747, 52748, 52749, 52751, 52753, 52755, 53111, 54212, 54215, 54228, 54231, 54232, 54235, 54244, 54249, 54250, 54258, 54265, 54266, 54267, 54268, 54299, 54301, 54302, 54310, 54321, 54332, 54333, 54338, 54339, 54340, 54341, 54348, 54350, 54366, 54374, 54378, 54389, 54413, 54416, 54421, 54422, 54426, 54460, 54491, 54715, 54829, 54893, 54894, 55110, 55111, 55121, 55161, 55163, 55164, 55165, 55167, 55171, 55173, 55175, 55177, 55178, 55179, 59427, 59429, 59437, 59458, 59463, 59465, 59466, 59943, 60320, 60353, 60372, 60375, 60377, 60392, 60399, 60406, 60407, 60432, 60438, 60507, 62525, 62736, 62741, 71572, 71574, 71583, 71615, 71660, 71754, 71765, 71794, 71927, 71942, 71964, 79718, 80399, 80405, 83026, 83028, 83130, 83131, 84513, 84514, 84515, 84516, 84517, 84518, 84519, 84520, 84521, 84522, 84523, 84524, 84525 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 28609, "uuid": "7dcfc1657ae14e3d959e578c40ebe771", "short_code": "coll", "title": "WCRP CMIP6: NASA Goddard Institute for Space Studies (NASA GISS) GISS-E2-1-G model output collection", "abstract": "World Climate Research Programme (WCRP) Coupled Model Intercomparison Project Phase 6 (CMIP6): Collection of simulations from the NASA Goddard Institute for Space Studies (NASA GISS) GISS-E2-1-G model.\n\nThe official CMIP6 Citation, and its associated DOI, is provided as an online resource linked to this record." } ], "responsiblepartyinfo_set": [ 212865, 212866, 212867, 212868, 212869, 212870, 212871, 212873, 212874, 212872 ], "onlineresource_set": [ 93521, 93522, 93524, 93526, 93528, 93530, 93532, 93534, 93536, 93538, 93540, 93542, 93544, 93546, 93548, 93550, 93552, 93554, 93556, 93558, 93560, 93562, 93564, 93566, 93568, 93570, 93572, 93574, 93576, 93578, 93580, 93582, 93584, 93586, 93588, 93590, 93592, 93594, 93596, 93598, 93600, 93602, 93604, 93606, 93608, 93610, 93612, 93614, 93616, 93618, 93620, 93622, 93624, 93626, 93628, 93630, 93632, 93634, 93636, 93638, 93640, 93642, 93644, 93646, 93648, 93650, 93652, 93654, 93656, 93658, 93660, 93662, 93664, 93666, 93668, 93670, 93672, 93674, 93676, 93678, 93680, 93682, 93684, 93686, 93688, 93690, 93692, 93694, 93696, 93698, 93700, 93702, 93704, 93706, 93708, 93710, 93712, 93714, 93716, 93718, 93720, 93722, 93724, 93726, 93728, 93730, 93732, 93734, 93736, 93738, 93740, 93742, 93744, 93746, 93748, 93750, 93752, 93754, 93756, 93758, 93760, 93762, 93764, 93766, 93767, 93768 ] }, { "ob_id": 44506, "uuid": "a56dba09df8a42ec9fba8b8c7a5e1f69", "title": "ESA Lakes Climate Change Initiative (Lakes_cci): Lake products, Version 3.0", "abstract": "This dataset contains the Lakes Essential Climate Variable (ECV) Products, comprised of processed satellite observations at the global scale, over the period 1992-2023, for over 2000 inland water bodies. This dataset was produced by the European Space Agency (ESA) Lakes Climate Change Initiative (Lakes_cci) project. For more information about the Lakes_cci please visit the project website.\r\n\r\nThis is version 3.0.0 of the dataset which benefits from longer observation time series, improved spatial coverage, new variables and improved algorithms for certain variables.\r\n\r\nThe Essential Climate Variable Products included in this dataset are:\r\n•Lake Water Level (LWL), derived from satellite altimetry, fundamental to understand the balance between water inputs and water loss and their connection with regional and global climate change.\r\n•Lake Water Extent (LWE), modelled from the relation between LWL and high-resolution spatial extent observed at various time-points, describing the areal extent of the water body. This allows the observation of drought in arid environments, expansion in high Asia, or impact of large-scale atmospheric oscillations on lakes in tropical regions for example.\r\n•Lake Surface Water temperature (LSWT), derived from optical and thermal satellite observations, correlated with regional air temperatures and supporting analysis of vertical mixing regimes, biogeochemical cycling and seasonality.\r\n•Lake Ice Cover (LIC), determined from optical observations, describing the freeze-up in autumn and break-up of ice in spring, which are proxies for gradually changing climate patterns and seasonality.\r\n•Lake Water-Leaving Reflectance (LWLR), derived from optical satellite observations, and supporting interpretation of biogeochemical processes and habitats in the visible part of the water column (e.g. seasonal phytoplankton biomass fluctuations), and serving as an indicator of the frequency of extreme events (peak terrestrial run-off, changing mixing conditions).\r\n•Lake Ice Thickness (LIT), provided for 13 lakes from radar altimetry to study the integrity and velocity of lake ice formation. \r\n•Lake Storage Change (LSC), derived from Water extent and/or level, describing water volume evolution of water bodies over time to inform studies of water stress. \r\n\r\nData generated in the Lakes_cci are derived from over 35 satellite sensors. The following sensors are associated with each of the ECV Products:\r\n•LWL: Poseidon-1 (TOPEX/Poseidon), Poseidon-2 (Jason-1), Poseidon-3 (Jason-2), Poseidon-3B (Jason-3), Poseidon-4 (Sentinel-6A), Radar Altimeter RA-2 (Envisat), AltiKa (SARAL), GFO, SAR Altimeter – SRAL (Sentinel-3A, Sentinel-3B), Radar Altimeter RA (ERS-1, ERS-2)\r\n•LWE: Landsat (4 TM, 5 TM, 7 ETM+, 8 OLI), Sentinel-1 C-band SAR, Sentinel-2 MSI, Sentinel-3A SRAL, Sentinel-3B SRAL, ERS-1 AMI, ERS-2 AMI\r\n•LSWT: Envisat AATSR, Terra/Aqua MODIS, Sentinel-3A ATTSR-2, Sentinel-3B, ERS-2 AVHRR, Metop-A/B\r\n•LIC: Terra/Aqua MODIS\r\n•LWLR: Envisat MERIS, Sentinel-3A OLCI A/B, Aqua MODIS\r\n•LIT: Jason1, Jason2, Jason3, POSEIDON-2, POSEIDON-3 and POSEIDON-3B\r\n•LSC: All sensors listed under LWL and LWE.\r\n\r\nDetailed information on the generation and validation of this dataset is available from the Lakes_cci documentation available on the project website, relating to this specific release. A further presentation of the dataset v2 can be found in Carrea, L., Crétaux, JF., Liu, X. et al. Satellite-derived multivariate world-wide lake physical variable timeseries for climate studies. Sci Data 10, 30 (2023). https://doi.org/10.1038/s41597-022-01889-z.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2024-03-27T09:33:01", "latestDataUpdateTime": null, "updateFrequency": "notPlanned", "dataLineage": "This dataset was generated in the framework of the Lakes CCI+ project, funded by ESA. Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "ESA, CCI, Lakes, ECV", "publicationState": "preview", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": null, "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 2623, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 45668, "dataPath": "/neodc/esacci/lakes/data/lake_products/L3S/v3.0/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1243253667471, "numberOfFiles": 11437, "fileFormat": "Data are in NetCDF format" }, "timePeriod": { "ob_id": 11603, "startTime": "1992-09-26T00:00:00", "endTime": "2022-12-31T23:59:59" }, "resultQuality": { "ob_id": 3417, "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": "2020-05-22" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 32839, "uuid": "be9fb3c9bda3425286c3fdc9f94bf04c", "short_code": "comp", "title": "Derivation of the ESA Lakes Climate Change Initiative dataset", "abstract": "The data generated by the Lakes_cci project have been derived from data from multiple instruments and multiple satellites including; TOPEX/Poseidon, Jason, ENVISAT, SARAL, Sentinel, Landsat, ERS, Terra/Aqua, Suomi NPP, Metop and Orbview.\r\n\r\nFor information on the derivation of the lake products, please see the documentation at https://climate.esa.int/en/projects/lakes/key-documents-lakes/." }, "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": [] } } ], "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": [ 8143, 8144, 12066, 27599, 50559, 50561, 63576, 68335, 68336, 68337, 68338, 68339, 68340, 68341, 68342, 68343, 68344, 68345, 68346, 68347, 68348, 68349, 84538, 84560, 84562, 84565, 84572, 84579, 84580, 84588, 84600, 84698, 84699, 84701, 84702, 84703, 84704, 92924, 92925, 92926, 92927, 92928, 92929, 92930, 92931, 92932, 92933, 92934, 92935, 92936, 92937, 92938, 92939, 92940, 92941, 92942, 92943, 92944, 92945, 92946, 92947, 92948, 92949, 92950, 92951, 92952, 92953, 92954, 92955, 92956, 92957, 92958, 92959, 92960, 92961, 92962, 92963, 92964, 92965, 92966, 92967, 92968, 92969, 92970, 92971, 92972, 92973, 92974, 92975, 92976, 92977, 92978, 92979, 92980, 92981, 92982, 92983, 92984, 92985, 92986, 92987, 92988, 92989 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 212981, 212978, 212979, 212980, 212982, 212984, 212985, 217132, 212991, 212986, 212983, 212987, 212988, 217133, 217134, 217135, 213001, 217136, 212990, 212992, 217137, 212993, 217138, 212994, 212995, 217139, 217140, 212996, 212997, 213000 ], "onlineresource_set": [ 93796, 93797, 93798, 93799, 93800 ] }, { "ob_id": 44513, "uuid": "3080771112d947e8bcfd7d130f753c01", "title": "WOEST: aerosol backscatter from the NCAS Aerosol Lidar unit 1 deployed at MOD Lyneham, v1.0 (20230530-20230911)", "abstract": "Aerosol backscatter measurements from the NCAS Aerosol Lidar unit 1 deployed at MOD Lyneham. These observations were taken as part of Wessex convection experiment - Observing the Evolving Structures of Turbulence (WOEST) between 20230530 and 20230911.\r\n\r\nData products from this deployment version 1.0 include: range_squared_corrected_backscatter_power \r\n\r\nFor further details of this deployment and the associated dataset please see the internal file metadata.\r\n\r\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.", "creationDate": "2025-06-19T09:24:06.265048", "lastUpdatedDate": "2025-06-19T09:24:06", "latestDataUpdateTime": "2025-06-19T09:24:06", "updateFrequency": "notPlanned", "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": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2025-08-01T09:59:15", "doiPublishedTime": "2025-08-01T09:59:00.213444", "removedDataTime": null, "geographicExtent": { "ob_id": 4798, "bboxName": "Lyneham", "eastBoundLongitude": -2.0054, "westBoundLongitude": -2.0054, "southBoundLatitude": 51.507198, "northBoundLatitude": 51.507198 }, "verticalExtent": null, "result_field": { "ob_id": 44514, "dataPath": "/badc/ncas-mobile/data/ncas-lidar-aerosol-1/20230531_woest/v1.0/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 2764988302, "numberOfFiles": 310, "fileFormat": "Data are netCDF formatted." }, "timePeriod": { "ob_id": 12481, "startTime": "2023-05-30T23:59:00", "endTime": "2023-09-11T15:00:00" }, "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": 44515, "uuid": "e32e94577def408b96acddfc1ad48f9c", "short_code": "acq", "title": "Wessex convection experiment - Observing the Evolving Structures of Turbulence (WOEST): NCAS Aerosol Lidar unit 1 instrument instrument deployed at MOD Lyneham", "abstract": "Wessex convection experiment - Observing the Evolving Structures of Turbulence (WOEST): NCAS Aerosol Lidar unit 1 instrument instrument deployed at MOD Lyneham." }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 223 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 43720, "uuid": "d3685300168848f290dd3a9ec14cc8ac", "short_code": "proj", "title": "Wessex Convection experiment - Observing the Evolving Structures of Turbulence (WOEST)", "abstract": "This project, WesCon - Observing the Evolving Structures of Turbulence (WOEST), complements the Met Office's Wessex Convection Experiment (WesCon) project by enabling frequent observations of the same turbulent structures at high resolution. In terms of moist convective turbulence, WOEST will radically advance observations of cloud dynamics by tracking precipitating cores of convective clouds and the turbulent regions embedded within them in real-time using four dual-polarisation Doppler radars. The radar scans will also be coordinated with the FAAM aircraft location to enable coincident observations. In terms of boundary-layer turbulence and variability, their evolution will be captured uniquely by multiple UAS, which will be coordinated to capture hourly profiles of temperature, humidity, and winds to study the small-scale variability in the lowest 2km of the atmosphere. Additionally, an array of cloud cameras will be used to reconstruct the 3D motion and evolution of boundary-layer clouds, to be related to the turbulent and dynamic evolution of the boundary layer as measured by remote sensing instruments such as lidar (to measure cloud bases and humidity profiles) and wind profilers.\r\n\r\nThe observations gathered in WOEST will capture turbulent processes in the atmosphere at a range of fine spatial and temporal scales. Our multi-instrument approach will enable us to evaluate simulations of turbulence and dynamics in convective clouds and how the structure and evolution of the boundary layer influence moist convective turbulence at a range of scales, including at the process-level relating turbulence to the strength and size of updrafts. This will lead to a new understanding of the variability and evolution of the boundary layer in the context of the surrounding cloud field and the variability of turbulence and cloud dynamics. Such insights should lead to significant improvements within the sub-grid turbulence parametrisations that allow both km-scale global weather and climate simulations and sub-km-scale regional weather forecasts to more accurately predict the evolution and intensity of hazardous convective storms." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 53939, 53940, 58081, 65555, 74110, 75915, 75916, 75917, 75918, 75922, 75924, 84196 ], "vocabularyKeywords": [], "identifier_set": [ 13459 ], "observationcollection_set": [ { "ob_id": 43875, "uuid": "36756ff1928f4a1684902b470cda774e", "short_code": "coll", "title": "Wessex convection experiment - Observing the Evolving Structures of Turbulence (WOEST)", "abstract": "A collection of groundbased and airborne datasets gathered to capture turbulent processes in the atmosphere at a range of fine spatial and temporal scales for the Wessex convection experiment - Observing the Evolving Structures of Turbulence (WOEST) project during summer 2023. This multi-instrument collection is used to evaluate simulations of turbulence and dynamics in convective clouds and how the structure and evolution of the boundary layer influences moist convective turbulence at a range of scales." } ], "responsiblepartyinfo_set": [ 213044, 213045, 213046, 213047, 213048, 213049, 213051, 213050 ], "onlineresource_set": [] }, { "ob_id": 44516, "uuid": "82f7734afb3845a8ba5176ed5b1273dc", "title": "Weighing trees with lasers project: terrestrial laser scanner data; Harvard Forest Massachusetts (Plot HF-B), August 2017", "abstract": "This dataset is comprised of raw data from the NERC-funded, full waveform terrestrial laser scanner (TLS) deployed at sites on three continents, multiple countries and plot locations, which have been re-surveyed at different times. \r\n\r\nThe Harvard Forest plot is dominated by eastern hemlock and northern hardwood species, and will make an excellent comparison with several other hardwood plots in North America and China at similar latitudes. This plot is part of a global array of large-scale plots established by ForestGEO, which recently expanded sampling efforts into temperate forests to explore ecosystem processes beyond population dynamics and biodiversity. The Harvard Forest was designed to include a continuous, expansive, and varied natural forest landscape that will yield opportunities for the study of forest dynamics and demography while capturing a large amount of existing science infrastructure (e.g., eddy flux towers, gauged sections of a small watershed, existing smaller permanent plots) that will enable the integrated study of ecosystem processes (e.g., biogeochemistry, hydrology, carbon dynamics) and forest dynamics .\r\n\r\nThe project scanned all trees in the permanent sample plot (PSP) spanning a range of soil fertility and productivity gradients (24 x 1 ha PSPs in total). The aim of the weighing trees with lasers project is to test if current allometric relationships are invariant across continents or whether they differ significantly and require continental-level models; quantify the impact of assumptions of tree shape and wood density on tropical forest allometry; test hypotheses relating to pan-tropical differences in observed AGB (Above Ground Biomass) from satellite and field data. It also aims to apply new knowledge to assessing retrieval accuracy of forthcoming ESA BIOMASS and NASA GEDI (Global Ecosystem Dynamics Investigation Lidar) missions and providing calibration datasets; In addition to testing the capability of low-cost instruments to augment TLS data, including: UAVs (unmanned aerial vehicle) for mapping cover and canopy height; low-cost lidar instruments to assess biomass rapidly, at lower accuracy.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2025-06-21T02:06:00", "updateFrequency": "", "dataLineage": "Provided by Andrew Burt UCL to CEDA for publication", "removedDataReason": "", "keywords": "Terrestrial Laser Scanner, Harvard Forest, Hardwood", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2025-06-19T11:55:50", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3729, "bboxName": "TLS-CALI-07", "eastBoundLongitude": -123.45, "westBoundLongitude": -123.45, "southBoundLatitude": 38.7152, "northBoundLatitude": 38.7152 }, "verticalExtent": null, "result_field": { "ob_id": 44517, "dataPath": "/neodc/tls/data/raw/usa/HFB/2017-08-14.001.riproject", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 4833694196, "numberOfFiles": 233, "fileFormat": "The scan folder contains a number of open and proprietary formats file formats CSV, ASCII, text, PNG , RXP and PAT" }, "timePeriod": { "ob_id": 10949, "startTime": "2017-08-14T00:00:00", "endTime": "2017-08-15T00:00:00" }, "resultQuality": { "ob_id": 3184, "explanation": "validated by UCL", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2018-11-02" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 44518, "uuid": "e1e4944fcc124bd0a4db44a0091958b2", "short_code": "acq", "title": "The Harvard Forest HF-B Acquisition 14/08/2017", "abstract": "The data was collected from the Weighing trees with lasers project: terrestrial laser scanner data; The Harvard Forrest using the RIEGL VZ-400 Terrestrial Laser Scanner" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 2 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 26683, "uuid": "70b2a6b0163747778ee85b4f7f86d8c0", "short_code": "proj", "title": "Weighing Trees with Lasers", "abstract": "Measuring the volume and structure of a tree accurately allows us to calculate the total above-ground carbon (C) stored in the tree, a very important property. Trees remove CO2 from the atmosphere during photosynthesis and can store this C for decades or even centuries until the tree dies, when some of it is released back to the atmosphere through decomposition. Tropical forests store around half of all above-ground terrestrial C, but are at particular risk due to deforestation and degradation, as well as from changing rainfall and temperature patterns. Surprisingly, our knowledge of tropical forest C stocks is quite poor, and errors in these stocks are large and uncertain. This uncertainty feeds into estimates of CO2 emissions due to deforestation, degradation and land use change. We will address this major uncertainty in the terrestrial C cycle by deploying a new, NERC-funded terrestrial laser scanner (TLS) to scan 1000s of trees in tropical forests on three continents: Amazonia, the Congo Basin and SE Asia. The laser data will allow us to measure 3D tree volume and biomass non-destructively to within a few percent of the best current estimates, made by destructive harvesting and weighing. The current, large uncertainties arise because weighing a tree is extremely difficult: tropical trees may be over 50m tall, and weigh 100 tonnes or more. Harvesting also precludes revisiting trees over time to measure change. In practice, a small sample of trees that have been harvested and weighed are related to easy-to-measure parameters of diameter and height, using empirical 'allometric' (size-to-mass) relationships. These relationships are then used to translate diameter and height measurements made over wider areas into estimates of biomass. Allometry is also the only way to infer biomass at very large (pan-tropical) scales, from remote sensing measurements. Unfortunately, the sample of harvested trees underpinning global allometric relationships is geographically limited, and contains very few large trees. Current estimates of tropical forest C stocks from satellite and ground data, all based on these very limited allometry samples, diverge significantly in size and pattern, leading to heated debate as to why this should be.\r\n\r\nThe project hopes to settle this debate, given that our lidar-derived estimates of biomass are completely independent of allometry and unbiased in terms of tree size. We will 'weigh' more trees than are currently included in all global pan-tropical allometries and quantify uncertainty in the allometry models. We will also test assumptions made in allometric models regarding tree shape and wood density. Our measurements will also answer fundamental questions about geographical differences in structural characteristics across tropical forests. Our data will be vital for testing new estimates of biomass from remote sensing; the UK-led ESA BIOMASS RADAR and NASA GEDI laser missions will both estimate pan-tropical C stocks by relying on allometric relationships between forest height and biomass. Our work will feed into these two missions through long-standing collaborations with the lead scientists. More generally, the large number of tree measurements we will collect would be of great interest to researchers in tropical ecology, forestry, biodiversity, remote sensing and C mapping, among others.\r\n\r\nA key aim of the project is to ensure the widest use of our results, by making our data and tools publicly available. We will work with partners to explore routes for commercial developments and input into government policy, particularly relating to forest management and C mapping and mitigation. Lastly, we will make our work accessible through a range of outreach activities, including developing links between a school in the Amazon and UK schools, to raise awareness of scientific, conservation and policy issues surrounding tropical forests.\r\n\r\nThis project was funded by NERC through grant: NE/N00373X/1" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 26677, "uuid": "ca0729ec30514a64a6ccd393eacff5f0", "short_code": "coll", "title": "Weighing trees with lasers project: Terrestrial Laser Scanner data collection", "abstract": "This dataset collection is comprised of raw data from the NERC-funded, full waveform terrestrial laser scanner (TLS) deployed at sites on three continents, multiple countries and plot locations which, have been re-surveyed at different times. The terrestrial laser scanner (TLS) was able to scan 1000s of trees in tropical forests on three continents: including Amazonia, the Congo Basin and SE Asia. The laser data measured 3D tree volume and biomass non-destructively to within a few percent of the best current estimates, made by destructive harvesting and weighing.\r\n\r\nThe project scanned all trees in multiple permanent sample plots (PSPs) spanning a range of soil fertility and productivity gradients (24 x 1 ha PSPs in total). The aim of the weighing trees with lasers project is to test if current allometric relationships are invariant across continents, or whether they differ significantly, and require continental level models; quantify the impact of assumptions of tree shape and wood density on tropical forest allometry; test hypotheses relating to pan-tropical differences in observed AGB from satellite and field data. It also aims to apply new knowledge to assessing retrieval accuracy of forthcoming ESA BIOMASS and NASA GEDI missions and providing calibration datasets; In addition to testing the capability of low-cost instruments to augment TLS data including: UAVs for mapping cover and canopy height; low-cost lidar instruments to assess biomass rapidly, at lower accuracy." }, { "ob_id": 30128, "uuid": "7fe9f59731ab47b6a20e792e0cba4641", "short_code": "coll", "title": "National Centre for Earth Observation (NCEO) partnered datasets", "abstract": "The National Centre for Earth Observation (NCEO) has a proud tradition of being involved with some of the most successful international collaborations in the Earth observation. This Collection contains dataset generated and/or archived with the support of NCEO resource or scientific expertise. Some notable collaboration which generated data within this collection are as follows:\r\n\r\nThe European Space Agency (ESA)'s Climate Change Initiative (CCI) program. The program goal is to provide stable, long-term, satellite-based Essential Climate Variable (ECV) data products for climate modelers and researchers.\r\n\r\nThe EUSTACE (EU Surface Temperature for All Corners of Earth) project is produced publicly available daily estimates of surface air temperature since 1850 across the globe for the first time by combining surface and satellite data using novel statistical techniques.\r\n\r\nFIDUCEO has created new climate datasets from Earth Observations with a rigorous treatment of uncertainty informed by the discipline of metrology. This response to the need for enhanced credibility for climate data, to support rigorous science, decision-making and climate services. The project approach was to develop methodologies for generating Fundamental Climate Data Records (FCDRs) and Climate Data Records (CDRs) that are widely applicable and metrologically rigorous. \r\n\r\nThe “BACI” project translates satellite data streams into novel “essential biodiversity variables” by integrating ground-based observations. The trans-disciplinary project offers new insights into the functioning and state of ecosystems and biodiversity. BACI enables the user community to detect abrupt and transient changes of ecosystems and quantify the implications for regional biodiversity.\r\n\r\nThe UK Natural Environment Research Council has established a knowledge transfer network called NCAVEO (Network for Calibration and Validation of EO data - NCAVEO) which has as its aim the promotion and support of methodologies based upon quantitative, traceable measurements in Earth observation. \r\n\r\nThe Geostationary Earth Radiation Budget 1 & 2 instruments (GERB-1 and GERB-2) make accurate measurements of the Earth Radiation Budget. They are specifically designed to be mounted on a geostationary satellite and are carried onboard the Meteosat Second Generation satellites operated by EUMETSAT. They were produced by a European consortium led by the UK (NERC) together with Belgium, Italy, and EUMETSAT, with funding from national agencies.\r\n\r\nGloboLakes analysed 20 years of data from more than 1000 large lakes across the globe to determine 'what controls the differential sensitivity of lakes to environmental perturbation'. This was an ambitious project that was only possible by bringing together a consortium of scientists with complementary skills. These include expertise in remote sensing of freshwaters and processing large volumes of satellite images, collation and analysis of large-scale environmental data, environmental statistics and the assessment of data uncertainty, freshwater ecology and mechanisms of environmental change and the ability to produce lake models to forecast future lake conditions.\r\n\r\nThis SPEI collaboration consists of high spatial resolution Standardized Precipitation-Evapotranspiration Index (SPEI) drought dataset over the whole of Africa at different time scales from 1 month to 48 months. It is calculated based on precipitation estimates from the satellite-based Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and potential evaporation estimates by the Global Land Evaporation Amsterdam Model (GLEAM)." } ], "responsiblepartyinfo_set": [ 213056, 213057, 213058, 213059, 213060, 213061, 213062, 213054, 213055, 213063, 213064 ], "onlineresource_set": [ 93814 ] }, { "ob_id": 44524, "uuid": "44da591cfa3f4818a3a0ff318e6bf9fa", "title": "Weighing trees with lasers project: terrestrial laser scanner data; Harvard Forest Massachusetts (Plot HF-N), August 2017", "abstract": "This dataset is comprised of raw data from the NERC-funded, full waveform terrestrial laser scanner (TLS) deployed at sites on three continents, multiple countries and plot locations, which have been re-surveyed at different times. \r\n\r\nThe Harvard Forest plot is dominated by eastern hemlock and northern hardwood species, and will make an excellent comparison with several other hardwood plots in North America and China at similar latitudes. This plot is part of a global array of large-scale plots established by ForestGEO, which recently expanded sampling efforts into temperate forests to explore ecosystem processes beyond population dynamics and biodiversity. The Harvard Forest was designed to include a continuous, expansive, and varied natural forest landscape that will yield opportunities for the study of forest dynamics and demography while capturing a large amount of existing science infrastructure (e.g., eddy flux towers, gauged sections of a small watershed, existing smaller permanent plots) that will enable the integrated study of ecosystem processes (e.g., biogeochemistry, hydrology, carbon dynamics) and forest dynamics .\r\n\r\nThe project scanned all trees in the permanent sample plot (PSP) spanning a range of soil fertility and productivity gradients (24 x 1 ha PSPs in total). The aim of the weighing trees with lasers project is to test if current allometric relationships are invariant across continents or whether they differ significantly and require continental-level models; quantify the impact of assumptions of tree shape and wood density on tropical forest allometry; test hypotheses relating to pan-tropical differences in observed AGB (Above Ground Biomass) from satellite and field data. It also aims to apply new knowledge to assessing retrieval accuracy of forthcoming ESA BIOMASS and NASA GEDI (Global Ecosystem Dynamics Investigation Lidar) missions and providing calibration datasets; In addition to testing the capability of low-cost instruments to augment TLS data, including: UAVs (unmanned aerial vehicle) for mapping cover and canopy height; low-cost lidar instruments to assess biomass rapidly, at lower accuracy.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2025-06-21T02:06:00", "updateFrequency": "", "dataLineage": "Provided by Andrew Burt UCL to CEDA for publication", "removedDataReason": "", "keywords": "Terrestrial Laser Scanner, Harvard Forest, Hardwood", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2025-06-19T13:22:39", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3729, "bboxName": "TLS-CALI-07", "eastBoundLongitude": -123.45, "westBoundLongitude": -123.45, "southBoundLatitude": 38.7152, "northBoundLatitude": 38.7152 }, "verticalExtent": null, "result_field": { "ob_id": 44525, "dataPath": "/neodc/tls/data/raw/usa/HFN/2017-08-14.002.riproject", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 7673921701, "numberOfFiles": 377, "fileFormat": "The scan folder contains a number of open and proprietary formats file formats CSV, ASCII, text, PNG , RXP and PAT" }, "timePeriod": { "ob_id": 10949, "startTime": "2017-08-14T00:00:00", "endTime": "2017-08-15T00:00:00" }, "resultQuality": { "ob_id": 3184, "explanation": "validated by UCL", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2018-11-02" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 44518, "uuid": "e1e4944fcc124bd0a4db44a0091958b2", "short_code": "acq", "title": "The Harvard Forest HF-B Acquisition 14/08/2017", "abstract": "The data was collected from the Weighing trees with lasers project: terrestrial laser scanner data; The Harvard Forrest using the RIEGL VZ-400 Terrestrial Laser Scanner" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 2 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 26683, "uuid": "70b2a6b0163747778ee85b4f7f86d8c0", "short_code": "proj", "title": "Weighing Trees with Lasers", "abstract": "Measuring the volume and structure of a tree accurately allows us to calculate the total above-ground carbon (C) stored in the tree, a very important property. Trees remove CO2 from the atmosphere during photosynthesis and can store this C for decades or even centuries until the tree dies, when some of it is released back to the atmosphere through decomposition. Tropical forests store around half of all above-ground terrestrial C, but are at particular risk due to deforestation and degradation, as well as from changing rainfall and temperature patterns. Surprisingly, our knowledge of tropical forest C stocks is quite poor, and errors in these stocks are large and uncertain. This uncertainty feeds into estimates of CO2 emissions due to deforestation, degradation and land use change. We will address this major uncertainty in the terrestrial C cycle by deploying a new, NERC-funded terrestrial laser scanner (TLS) to scan 1000s of trees in tropical forests on three continents: Amazonia, the Congo Basin and SE Asia. The laser data will allow us to measure 3D tree volume and biomass non-destructively to within a few percent of the best current estimates, made by destructive harvesting and weighing. The current, large uncertainties arise because weighing a tree is extremely difficult: tropical trees may be over 50m tall, and weigh 100 tonnes or more. Harvesting also precludes revisiting trees over time to measure change. In practice, a small sample of trees that have been harvested and weighed are related to easy-to-measure parameters of diameter and height, using empirical 'allometric' (size-to-mass) relationships. These relationships are then used to translate diameter and height measurements made over wider areas into estimates of biomass. Allometry is also the only way to infer biomass at very large (pan-tropical) scales, from remote sensing measurements. Unfortunately, the sample of harvested trees underpinning global allometric relationships is geographically limited, and contains very few large trees. Current estimates of tropical forest C stocks from satellite and ground data, all based on these very limited allometry samples, diverge significantly in size and pattern, leading to heated debate as to why this should be.\r\n\r\nThe project hopes to settle this debate, given that our lidar-derived estimates of biomass are completely independent of allometry and unbiased in terms of tree size. We will 'weigh' more trees than are currently included in all global pan-tropical allometries and quantify uncertainty in the allometry models. We will also test assumptions made in allometric models regarding tree shape and wood density. Our measurements will also answer fundamental questions about geographical differences in structural characteristics across tropical forests. Our data will be vital for testing new estimates of biomass from remote sensing; the UK-led ESA BIOMASS RADAR and NASA GEDI laser missions will both estimate pan-tropical C stocks by relying on allometric relationships between forest height and biomass. Our work will feed into these two missions through long-standing collaborations with the lead scientists. More generally, the large number of tree measurements we will collect would be of great interest to researchers in tropical ecology, forestry, biodiversity, remote sensing and C mapping, among others.\r\n\r\nA key aim of the project is to ensure the widest use of our results, by making our data and tools publicly available. We will work with partners to explore routes for commercial developments and input into government policy, particularly relating to forest management and C mapping and mitigation. Lastly, we will make our work accessible through a range of outreach activities, including developing links between a school in the Amazon and UK schools, to raise awareness of scientific, conservation and policy issues surrounding tropical forests.\r\n\r\nThis project was funded by NERC through grant: NE/N00373X/1" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 26677, "uuid": "ca0729ec30514a64a6ccd393eacff5f0", "short_code": "coll", "title": "Weighing trees with lasers project: Terrestrial Laser Scanner data collection", "abstract": "This dataset collection is comprised of raw data from the NERC-funded, full waveform terrestrial laser scanner (TLS) deployed at sites on three continents, multiple countries and plot locations which, have been re-surveyed at different times. The terrestrial laser scanner (TLS) was able to scan 1000s of trees in tropical forests on three continents: including Amazonia, the Congo Basin and SE Asia. The laser data measured 3D tree volume and biomass non-destructively to within a few percent of the best current estimates, made by destructive harvesting and weighing.\r\n\r\nThe project scanned all trees in multiple permanent sample plots (PSPs) spanning a range of soil fertility and productivity gradients (24 x 1 ha PSPs in total). The aim of the weighing trees with lasers project is to test if current allometric relationships are invariant across continents, or whether they differ significantly, and require continental level models; quantify the impact of assumptions of tree shape and wood density on tropical forest allometry; test hypotheses relating to pan-tropical differences in observed AGB from satellite and field data. It also aims to apply new knowledge to assessing retrieval accuracy of forthcoming ESA BIOMASS and NASA GEDI missions and providing calibration datasets; In addition to testing the capability of low-cost instruments to augment TLS data including: UAVs for mapping cover and canopy height; low-cost lidar instruments to assess biomass rapidly, at lower accuracy." }, { "ob_id": 30128, "uuid": "7fe9f59731ab47b6a20e792e0cba4641", "short_code": "coll", "title": "National Centre for Earth Observation (NCEO) partnered datasets", "abstract": "The National Centre for Earth Observation (NCEO) has a proud tradition of being involved with some of the most successful international collaborations in the Earth observation. This Collection contains dataset generated and/or archived with the support of NCEO resource or scientific expertise. Some notable collaboration which generated data within this collection are as follows:\r\n\r\nThe European Space Agency (ESA)'s Climate Change Initiative (CCI) program. The program goal is to provide stable, long-term, satellite-based Essential Climate Variable (ECV) data products for climate modelers and researchers.\r\n\r\nThe EUSTACE (EU Surface Temperature for All Corners of Earth) project is produced publicly available daily estimates of surface air temperature since 1850 across the globe for the first time by combining surface and satellite data using novel statistical techniques.\r\n\r\nFIDUCEO has created new climate datasets from Earth Observations with a rigorous treatment of uncertainty informed by the discipline of metrology. This response to the need for enhanced credibility for climate data, to support rigorous science, decision-making and climate services. The project approach was to develop methodologies for generating Fundamental Climate Data Records (FCDRs) and Climate Data Records (CDRs) that are widely applicable and metrologically rigorous. \r\n\r\nThe “BACI” project translates satellite data streams into novel “essential biodiversity variables” by integrating ground-based observations. The trans-disciplinary project offers new insights into the functioning and state of ecosystems and biodiversity. BACI enables the user community to detect abrupt and transient changes of ecosystems and quantify the implications for regional biodiversity.\r\n\r\nThe UK Natural Environment Research Council has established a knowledge transfer network called NCAVEO (Network for Calibration and Validation of EO data - NCAVEO) which has as its aim the promotion and support of methodologies based upon quantitative, traceable measurements in Earth observation. \r\n\r\nThe Geostationary Earth Radiation Budget 1 & 2 instruments (GERB-1 and GERB-2) make accurate measurements of the Earth Radiation Budget. They are specifically designed to be mounted on a geostationary satellite and are carried onboard the Meteosat Second Generation satellites operated by EUMETSAT. They were produced by a European consortium led by the UK (NERC) together with Belgium, Italy, and EUMETSAT, with funding from national agencies.\r\n\r\nGloboLakes analysed 20 years of data from more than 1000 large lakes across the globe to determine 'what controls the differential sensitivity of lakes to environmental perturbation'. This was an ambitious project that was only possible by bringing together a consortium of scientists with complementary skills. These include expertise in remote sensing of freshwaters and processing large volumes of satellite images, collation and analysis of large-scale environmental data, environmental statistics and the assessment of data uncertainty, freshwater ecology and mechanisms of environmental change and the ability to produce lake models to forecast future lake conditions.\r\n\r\nThis SPEI collaboration consists of high spatial resolution Standardized Precipitation-Evapotranspiration Index (SPEI) drought dataset over the whole of Africa at different time scales from 1 month to 48 months. It is calculated based on precipitation estimates from the satellite-based Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and potential evaporation estimates by the Global Land Evaporation Amsterdam Model (GLEAM)." } ], "responsiblepartyinfo_set": [ 213072, 213073, 213074, 213075, 213076, 213077, 213078, 213079, 213080, 213081, 213082 ], "onlineresource_set": [ 93815 ] }, { "ob_id": 44526, "uuid": "aabf22e3dc1a4791bd3090756563b347", "title": "Weighing trees with lasers project: terrestrial laser scanner data; Harvard Forest Massachusetts (Plot HF-M), August 2017", "abstract": "This dataset is comprised of raw data from the NERC-funded, full waveform terrestrial laser scanner (TLS) deployed at sites on three continents, multiple countries and plot locations, which have been re-surveyed at different times. \r\n\r\nThe Harvard Forest plot is dominated by eastern hemlock and northern hardwood species, and will make an excellent comparison with several other hardwood plots in North America and China at similar latitudes. This plot is part of a global array of large-scale plots established by ForestGEO, which recently expanded sampling efforts into temperate forests to explore ecosystem processes beyond population dynamics and biodiversity. The Harvard Forest was designed to include a continuous, expansive, and varied natural forest landscape that will yield opportunities for the study of forest dynamics and demography while capturing a large amount of existing science infrastructure (e.g., eddy flux towers, gauged sections of a small watershed, existing smaller permanent plots) that will enable the integrated study of ecosystem processes (e.g., biogeochemistry, hydrology, carbon dynamics) and forest dynamics .\r\n\r\nThe project scanned all trees in the permanent sample plot (PSP) spanning a range of soil fertility and productivity gradients (24 x 1 ha PSPs in total). The aim of the weighing trees with lasers project is to test if current allometric relationships are invariant across continents or whether they differ significantly and require continental-level models; quantify the impact of assumptions of tree shape and wood density on tropical forest allometry; test hypotheses relating to pan-tropical differences in observed AGB (Above Ground Biomass) from satellite and field data. It also aims to apply new knowledge to assessing retrieval accuracy of forthcoming ESA BIOMASS and NASA GEDI (Global Ecosystem Dynamics Investigation Lidar) missions and providing calibration datasets; In addition to testing the capability of low-cost instruments to augment TLS data, including: UAVs (unmanned aerial vehicle) for mapping cover and canopy height; low-cost lidar instruments to assess biomass rapidly, at lower accuracy.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2025-06-21T02:06:00", "updateFrequency": "", "dataLineage": "Provided by Andrew Burt UCL to CEDA for publication", "removedDataReason": "", "keywords": "Terrestrial Laser Scanner, Harvard Forest, Hardwood", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2025-06-19T13:42:37", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 4799, "bboxName": "Harvard Forest", "eastBoundLongitude": -72.1755, "westBoundLongitude": -72.1755, "southBoundLatitude": 42.5388, "northBoundLatitude": 42.5388 }, "verticalExtent": null, "result_field": { "ob_id": 44527, "dataPath": "/neodc/tls/data/raw/usa/HFM/2017-08-14.003.riproject", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 27101016455, "numberOfFiles": 937, "fileFormat": "The scan folder contains a number of open and proprietary formats file formats CSV, ASCII, text, PNG , RXP and PAT" }, "timePeriod": { "ob_id": 10949, "startTime": "2017-08-14T00:00:00", "endTime": "2017-08-15T00:00:00" }, "resultQuality": { "ob_id": 3184, "explanation": "validated by UCL", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2018-11-02" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 44523, "uuid": "b56981cc0dfb4562ba0d6d6fa739b9b2", "short_code": "acq", "title": "The Harvard Forest HF-M Acquisition 14/08/2017", "abstract": "The data was collected from the Weighing trees with lasers project: terrestrial laser scanner data; The Harvard Forrest using the RIEGL VZ-400 Terrestrial Laser Scanner" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 2 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 26683, "uuid": "70b2a6b0163747778ee85b4f7f86d8c0", "short_code": "proj", "title": "Weighing Trees with Lasers", "abstract": "Measuring the volume and structure of a tree accurately allows us to calculate the total above-ground carbon (C) stored in the tree, a very important property. Trees remove CO2 from the atmosphere during photosynthesis and can store this C for decades or even centuries until the tree dies, when some of it is released back to the atmosphere through decomposition. Tropical forests store around half of all above-ground terrestrial C, but are at particular risk due to deforestation and degradation, as well as from changing rainfall and temperature patterns. Surprisingly, our knowledge of tropical forest C stocks is quite poor, and errors in these stocks are large and uncertain. This uncertainty feeds into estimates of CO2 emissions due to deforestation, degradation and land use change. We will address this major uncertainty in the terrestrial C cycle by deploying a new, NERC-funded terrestrial laser scanner (TLS) to scan 1000s of trees in tropical forests on three continents: Amazonia, the Congo Basin and SE Asia. The laser data will allow us to measure 3D tree volume and biomass non-destructively to within a few percent of the best current estimates, made by destructive harvesting and weighing. The current, large uncertainties arise because weighing a tree is extremely difficult: tropical trees may be over 50m tall, and weigh 100 tonnes or more. Harvesting also precludes revisiting trees over time to measure change. In practice, a small sample of trees that have been harvested and weighed are related to easy-to-measure parameters of diameter and height, using empirical 'allometric' (size-to-mass) relationships. These relationships are then used to translate diameter and height measurements made over wider areas into estimates of biomass. Allometry is also the only way to infer biomass at very large (pan-tropical) scales, from remote sensing measurements. Unfortunately, the sample of harvested trees underpinning global allometric relationships is geographically limited, and contains very few large trees. Current estimates of tropical forest C stocks from satellite and ground data, all based on these very limited allometry samples, diverge significantly in size and pattern, leading to heated debate as to why this should be.\r\n\r\nThe project hopes to settle this debate, given that our lidar-derived estimates of biomass are completely independent of allometry and unbiased in terms of tree size. We will 'weigh' more trees than are currently included in all global pan-tropical allometries and quantify uncertainty in the allometry models. We will also test assumptions made in allometric models regarding tree shape and wood density. Our measurements will also answer fundamental questions about geographical differences in structural characteristics across tropical forests. Our data will be vital for testing new estimates of biomass from remote sensing; the UK-led ESA BIOMASS RADAR and NASA GEDI laser missions will both estimate pan-tropical C stocks by relying on allometric relationships between forest height and biomass. Our work will feed into these two missions through long-standing collaborations with the lead scientists. More generally, the large number of tree measurements we will collect would be of great interest to researchers in tropical ecology, forestry, biodiversity, remote sensing and C mapping, among others.\r\n\r\nA key aim of the project is to ensure the widest use of our results, by making our data and tools publicly available. We will work with partners to explore routes for commercial developments and input into government policy, particularly relating to forest management and C mapping and mitigation. Lastly, we will make our work accessible through a range of outreach activities, including developing links between a school in the Amazon and UK schools, to raise awareness of scientific, conservation and policy issues surrounding tropical forests.\r\n\r\nThis project was funded by NERC through grant: NE/N00373X/1" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 26677, "uuid": "ca0729ec30514a64a6ccd393eacff5f0", "short_code": "coll", "title": "Weighing trees with lasers project: Terrestrial Laser Scanner data collection", "abstract": "This dataset collection is comprised of raw data from the NERC-funded, full waveform terrestrial laser scanner (TLS) deployed at sites on three continents, multiple countries and plot locations which, have been re-surveyed at different times. The terrestrial laser scanner (TLS) was able to scan 1000s of trees in tropical forests on three continents: including Amazonia, the Congo Basin and SE Asia. The laser data measured 3D tree volume and biomass non-destructively to within a few percent of the best current estimates, made by destructive harvesting and weighing.\r\n\r\nThe project scanned all trees in multiple permanent sample plots (PSPs) spanning a range of soil fertility and productivity gradients (24 x 1 ha PSPs in total). The aim of the weighing trees with lasers project is to test if current allometric relationships are invariant across continents, or whether they differ significantly, and require continental level models; quantify the impact of assumptions of tree shape and wood density on tropical forest allometry; test hypotheses relating to pan-tropical differences in observed AGB from satellite and field data. It also aims to apply new knowledge to assessing retrieval accuracy of forthcoming ESA BIOMASS and NASA GEDI missions and providing calibration datasets; In addition to testing the capability of low-cost instruments to augment TLS data including: UAVs for mapping cover and canopy height; low-cost lidar instruments to assess biomass rapidly, at lower accuracy." }, { "ob_id": 30128, "uuid": "7fe9f59731ab47b6a20e792e0cba4641", "short_code": "coll", "title": "National Centre for Earth Observation (NCEO) partnered datasets", "abstract": "The National Centre for Earth Observation (NCEO) has a proud tradition of being involved with some of the most successful international collaborations in the Earth observation. This Collection contains dataset generated and/or archived with the support of NCEO resource or scientific expertise. Some notable collaboration which generated data within this collection are as follows:\r\n\r\nThe European Space Agency (ESA)'s Climate Change Initiative (CCI) program. The program goal is to provide stable, long-term, satellite-based Essential Climate Variable (ECV) data products for climate modelers and researchers.\r\n\r\nThe EUSTACE (EU Surface Temperature for All Corners of Earth) project is produced publicly available daily estimates of surface air temperature since 1850 across the globe for the first time by combining surface and satellite data using novel statistical techniques.\r\n\r\nFIDUCEO has created new climate datasets from Earth Observations with a rigorous treatment of uncertainty informed by the discipline of metrology. This response to the need for enhanced credibility for climate data, to support rigorous science, decision-making and climate services. The project approach was to develop methodologies for generating Fundamental Climate Data Records (FCDRs) and Climate Data Records (CDRs) that are widely applicable and metrologically rigorous. \r\n\r\nThe “BACI” project translates satellite data streams into novel “essential biodiversity variables” by integrating ground-based observations. The trans-disciplinary project offers new insights into the functioning and state of ecosystems and biodiversity. BACI enables the user community to detect abrupt and transient changes of ecosystems and quantify the implications for regional biodiversity.\r\n\r\nThe UK Natural Environment Research Council has established a knowledge transfer network called NCAVEO (Network for Calibration and Validation of EO data - NCAVEO) which has as its aim the promotion and support of methodologies based upon quantitative, traceable measurements in Earth observation. \r\n\r\nThe Geostationary Earth Radiation Budget 1 & 2 instruments (GERB-1 and GERB-2) make accurate measurements of the Earth Radiation Budget. They are specifically designed to be mounted on a geostationary satellite and are carried onboard the Meteosat Second Generation satellites operated by EUMETSAT. They were produced by a European consortium led by the UK (NERC) together with Belgium, Italy, and EUMETSAT, with funding from national agencies.\r\n\r\nGloboLakes analysed 20 years of data from more than 1000 large lakes across the globe to determine 'what controls the differential sensitivity of lakes to environmental perturbation'. This was an ambitious project that was only possible by bringing together a consortium of scientists with complementary skills. These include expertise in remote sensing of freshwaters and processing large volumes of satellite images, collation and analysis of large-scale environmental data, environmental statistics and the assessment of data uncertainty, freshwater ecology and mechanisms of environmental change and the ability to produce lake models to forecast future lake conditions.\r\n\r\nThis SPEI collaboration consists of high spatial resolution Standardized Precipitation-Evapotranspiration Index (SPEI) drought dataset over the whole of Africa at different time scales from 1 month to 48 months. It is calculated based on precipitation estimates from the satellite-based Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and potential evaporation estimates by the Global Land Evaporation Amsterdam Model (GLEAM)." } ], "responsiblepartyinfo_set": [ 213083, 213084, 213085, 213086, 213087, 213088, 213089, 213090, 213091, 213092, 213093 ], "onlineresource_set": [ 93816 ] }, { "ob_id": 44528, "uuid": "e67b02b814bf4a78b9ec90b4259f0741", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily multisensor Infra-Red (IR) Low Earth Orbit (LEO) land surface temperature (LST) time series level 3 supercollated (L3S) global product (1995-2024), version 3.00", "abstract": "This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from multiple Infra-Red (IR) instruments on Low Earth Orbiting (LEO) sun-synchronous (a.k.a. polar orbiting) satellites. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.\r\n\r\nDaytime and night-time temperatures are provided in separate files corresponding to 10:00 and 22:00 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.\r\n\r\nThe dataset is comprised of LSTs from a series of instruments with a common heritage: the Along-Track Scanning Radiometer 2 (ATSR-2); the Advanced Along-Track Scanning Radiometer (AATSR) and the Sea and Land Surface Temperature Radiometer on Sentinel 3B (SLSTRB); and data from the Moderate Imaging Spectroradiometer on Earth Observation System - Terra (MODIS Terra), to fill the gap between AATSR and SLSTR. So, the instruments contributing to the time series are: ATSR-2 from June 1995 to May 2002; AATSR from June 2002 to March 2012; MODIS Terra from April 2012 to November 2018; and SLSTRB from December 2018 to December 2024. Inter-instrument biases are accounted for by cross-calibration with the Infrared Atmospheric Sounding Interferometer (IASI) instruments on Meteorological Operational (METOP) satellites. For consistency, a common algorithm is used for LST retrieval for all instruments. Furthermore, an adjustment is made to the LSTs to account for the half-hour difference between satellite equator crossing times. For consistency through the time series, coverage is restricted to the narrowest instrument swath width.\r\n\r\nThe dataset coverage is near global over the land surface. During the period covered by ATSR-2, small regions were not covered due to downlinking constraints (most noticeably a track extending southwards across central Asia through India – further details can be found on the ATSR project webpages at https://artefacts.ceda.ac.uk/frozen_sites/www.atsr.rl.ac.uk/documentation/docs/userguide/index.shtml).\r\n\r\nLSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. Full Earth coverage is achieved in 3 days so the daily files have gaps where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.\r\n\r\nDataset coverage starts on 1st June 1995 and currently ends on 31st December 2024. There are two gaps of several months in the dataset: no data were acquired from ATSR-2 between 23 December 1995 and 27 June 1996 due to a scan mirror anomaly; and the ERS-2 gyro failed in January 2001, data quality was less good between 17th Jan 2001 and 5th July 2001 and are not used in this dataset. Also, there is a twelve day gap in the dataset due to Envisat mission extension orbital manoeuvres from 21st October 2010 to 1st November 2010. There are minor interruptions (1-10 days) during satellite/instrument maintenance periods or instrument anomalies.\r\n\r\nThis version of the dataset (Version 3.00) extends the temporal coverage by four years to the end of 2024. This dataset provides a daily product, and a separate monthly averaged product also exists. The temporal coverage of the monthly product will be further extended at 6 monthly intervals through the Copernicus Climate Change Service. Other changes in Version 3.00 include: SLSTR on Sentinel 3A is no longer used, instead data from SLSTR on Sentinel 3B is used from November 2018; the correction for time differences between the sensors is calculated in brightness temperature space using radiative transfer simulations; and the ATSR-2 and AATSR data are from the fourth reprocessing of these datasets.\r\n\r\nThe dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using a Generalised Split Window retrieval algorithm and data were processed in the UoL processing chain.\r\nThe dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": null, "latestDataUpdateTime": null, "updateFrequency": "irregular", "dataLineage": "The data record has been produced by the University of Leicester working within the ESA Land Surface Temperature Climate Change Initiative (LST_cci), and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).\r\nContact: data.lst-cci@acri-st.fr", "removedDataReason": "", "keywords": "land surface temperature, ESA, CCI, infra-red", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2025-08-04T11:10:26", "doiPublishedTime": "2025-08-04T11:13:24", "removedDataTime": null, "geographicExtent": { "ob_id": 2896, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 44602, "dataPath": "/neodc/esacci/land_surface_temperature/data/MULTISENSOR_IRCDR/L3S/0.01/v3.00/daily", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 5001176580139, "numberOfFiles": 20259, "fileFormat": "Data are in NetCDF format" }, "timePeriod": { "ob_id": 12530, "startTime": "1995-06-01T00:00:00", "endTime": "2024-12-31T23:59:59" }, "resultQuality": { "ob_id": 3798, "explanation": "For information on the data quality see the associated LST_cci documentation", "passesTest": true, "resultTitle": "LST cci", "date": "2021-11-30" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 44603, "uuid": "88fcd969eb7e4c8dad13d64d0500ec62", "short_code": "cmppr", "title": "Composite process for the ESA Land Surface Temperature Climate Change Initiative (LST_cci): Multisensor Infra-Red (IR) Low Earth Orbit (LEO) Land surface temperature (LST) time series level 3 supercollated (L3S) global product (1995-2020), version 3.00, daily and monthly products", "abstract": "For more information on the retrieval algorithm used see the documentation on the LST CCI webpage: https://climate.esa.int/sites/default/files/LST-CCI-D2.2-ATBD%20-%20i1r1%20-%20Algorithm%20Theoretical%20Basis%20Document.pdf." }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2558, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 30, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_lst_terms_and_conditions.pdf", "licenceClassifications": [] } } ], "projects": [ { "ob_id": 33361, "uuid": "555149fdc3ef4e23a1de8ece93c29f5d", "short_code": "proj", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci)", "abstract": "The land surface temperature (LST) CCI project, which is funded by the European Space Agency (ESA) as part of the Agency’s Climate Change Initiative (CCI) Programme, aims to deliver a significant improvement on the capability of current satellite LST data records to meet the challenging Global Climate Observing System (GCOS) requirements for climate applications to realise the full potential of long-term LST data for climate science.\r\n\r\nAccurate knowledge of LST plays a key role in describing the physics of land-surface processes at regional and global scales as they combine information on both the surface-atmosphere interactions and energy fluxes within the Earth Climate System. LST provides a metric of surface state when combined with vegetation parameters and soil moisture and is one of the drivers of vegetation phenology. Furthermore, LST is an independent temperature data set for quantifying climate change complementary to the near-surface air temperature ECV based on in situ measurements and reanalyses.\r\n\r\nThe team uses data from a variety of satellites to provide an accurate view of temperatures across land surfaces globally over the past +20 years. This involves developing innovative techniques to merge data from different satellites into combined long-term satellite records for climate. These will all be evaluated by scientists working at leading climate centres." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 62778, 62779, 62780, 62781, 62790, 62791, 62792, 66303, 66305, 66307, 66308, 66310, 66311, 66312, 66751, 66754, 75124, 84103 ], "vocabularyKeywords": [], "identifier_set": [ 13463 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 213094, 213095, 213096, 213097, 213098, 213099, 213100, 213101, 213102, 213103, 213104 ], "onlineresource_set": [ 93817, 93821 ] }, { "ob_id": 44529, "uuid": "b8be3f3993e54d309fddf61ea5d3674f", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Monthly Multisensor Infra-Red (IR) Low Earth Orbit (LEO) land surface temperature (LST) time series level 3 supercollated (L3S) global product (1995-2024), version 3.00", "abstract": "This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from multiple Infra-Red (IR) instruments on Low Earth Orbiting (LEO) sun-synchronous (a.k.a. polar orbiting) satellites. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.\r\n\r\nDaytime and night-time temperatures are provided in separate files corresponding to 10:00 and 22:00 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.\r\n\r\nThe dataset is comprised of LSTs from a series of instruments with a common heritage: the Along-Track Scanning Radiometer 2 (ATSR-2); the Advanced Along-Track Scanning Radiometer (AATSR); the Sea and Land Surface Temperature Radiometer on Sentinel 3B (SLSTRB); and data from the Moderate Imaging Spectroradiometer on Earth Observation System - Terra (MODIS Terra) to fill the gap between AATSR and SLSTR. So, the instruments contributing to the time series are: ATSR-2 from June 1995 to May 2002; AATSR from June 2002 to March 2012; MODIS Terra from April 2012 to November 2018; and SLSTRB from December 2018 to December 2024. Inter-instrument biases are accounted for by cross-calibration with the Infrared Atmospheric Sounding Interferometer (IASI) instruments on Meteorological Operational (METOP) satellites. For consistency, a common algorithm is used for LST retrieval for all instruments. Furthermore, an adjustment is made to the LSTs to account for the half-hour difference between satellite equator crossing times. For consistency through the time series, coverage is restricted to the narrowest instrument swath width.\r\n\r\nThe dataset coverage is near global over the land surface. During the period covered by ATSR-2, small regions were not covered due to downlinking constraints (most noticeably a track extending southwards across central Asia through India – further details can be found on the ATSR project webpages at https://artefacts.ceda.ac.uk/frozen_sites/www.atsr.rl.ac.uk/documentation/docs/userguide/index.shtml).\r\n\r\nLSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. Full Earth coverage is achieved in 3 days. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface. In this dataset, data has been averaged to a monthly grid. A separate daily product is also available.\r\n\r\nDataset coverage starts on 1st June 1995 and currently ends on 31st December 2024. There are two gaps of several months in the dataset: no data were acquired from ATSR-2 between 23 December 1995 and 27 June 1996 due to a scan mirror anomaly; and the ERS-2 gyro failed in January 2001, data quality was less good between 17th Jan 2001 and 5th July 2001 and are not used in this dataset. Also, there is a twelve day gap in the dataset due to Envisat mission extension orbital manoeuvres from 21st October 2010 to 1st November 2010. There are minor interruptions (1-10 days) during satellite/instrument maintenance periods or instrument anomalies.\r\n\r\nThis version of the dataset (Version 3.00) extends the temporal coverage by four years to the end of 2024. The temporal coverage of the monthly product will be further extended at 6 monthly intervals through the Copernicus Climate Change Service. Other changes in Version 3.00 include a change from SLSTR on Sentinel 3A to SLSTR on Sentinel 3B; the correction for time differences between the sensors is calculated in brightness temperature space using radiative transfer simulations; and the ATSR-2 and AATSR data are from the fourth reprocessing of these datasets.\r\n\r\nThe dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using a Generalised Split Window retrieval algorithm and data were processed in the UoL processing chain.\r\n\r\nThe dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": null, "updateFrequency": "irregular", "dataLineage": "The data record has been produced by the University of Leicester working within the ESA Land Surface Temperature Climate Change Initiative (LST_cci), and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).\r\nContact: data.lst-cci@acri-st.fr", "removedDataReason": "", "keywords": "land surface temperature, ESA, CCI, infra-red", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2025-07-21T08:20:32", "doiPublishedTime": "2025-07-21T08:40:46", "removedDataTime": null, "geographicExtent": { "ob_id": 2896, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 44606, "dataPath": "/neodc/esacci/land_surface_temperature/data/MULTISENSOR_IRCDR/L3S/0.01/v3.00/monthly/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1250378784280, "numberOfFiles": 689, "fileFormat": "Data are in NetCDF format" }, "timePeriod": { "ob_id": 12534, "startTime": "1995-06-01T00:00:00", "endTime": "2024-12-31T23:59:59" }, "resultQuality": { "ob_id": 3798, "explanation": "For information on the data quality see the associated LST_cci documentation", "passesTest": true, "resultTitle": "LST cci", "date": "2021-11-30" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 44603, "uuid": "88fcd969eb7e4c8dad13d64d0500ec62", "short_code": "cmppr", "title": "Composite process for the ESA Land Surface Temperature Climate Change Initiative (LST_cci): Multisensor Infra-Red (IR) Low Earth Orbit (LEO) Land surface temperature (LST) time series level 3 supercollated (L3S) global product (1995-2020), version 3.00, daily and monthly products", "abstract": "For more information on the retrieval algorithm used see the documentation on the LST CCI webpage: https://climate.esa.int/sites/default/files/LST-CCI-D2.2-ATBD%20-%20i1r1%20-%20Algorithm%20Theoretical%20Basis%20Document.pdf." }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2558, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 30, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_lst_terms_and_conditions.pdf", "licenceClassifications": [] } } ], "projects": [ { "ob_id": 33361, "uuid": "555149fdc3ef4e23a1de8ece93c29f5d", "short_code": "proj", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci)", "abstract": "The land surface temperature (LST) CCI project, which is funded by the European Space Agency (ESA) as part of the Agency’s Climate Change Initiative (CCI) Programme, aims to deliver a significant improvement on the capability of current satellite LST data records to meet the challenging Global Climate Observing System (GCOS) requirements for climate applications to realise the full potential of long-term LST data for climate science.\r\n\r\nAccurate knowledge of LST plays a key role in describing the physics of land-surface processes at regional and global scales as they combine information on both the surface-atmosphere interactions and energy fluxes within the Earth Climate System. LST provides a metric of surface state when combined with vegetation parameters and soil moisture and is one of the drivers of vegetation phenology. Furthermore, LST is an independent temperature data set for quantifying climate change complementary to the near-surface air temperature ECV based on in situ measurements and reanalyses.\r\n\r\nThe team uses data from a variety of satellites to provide an accurate view of temperatures across land surfaces globally over the past +20 years. This involves developing innovative techniques to merge data from different satellites into combined long-term satellite records for climate. These will all be evaluated by scientists working at leading climate centres." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 62778, 62779, 62780, 62781, 62790, 62791, 62792, 66303, 66305, 66307, 66308, 66310, 66311, 66312, 66751, 66753, 66754, 84103 ], "vocabularyKeywords": [], "identifier_set": [ 13455 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 213108, 213109, 213110, 213111, 213112, 213105, 213106, 213107, 213113, 213114, 213115 ], "onlineresource_set": [ 93835, 93836 ] }, { "ob_id": 44530, "uuid": "962a49e9127746018605ea617f596ba7", "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Enhanced Vegetation Index v2 (EVI2)", "abstract": "EVI2 (Jiang et al., 2008) is computed using the near-infrared (NIR) and red bands. While the majority of EVI2 data falls within the range of -1 to +1, some values may exceed +1. These are typically associated with areas of high vegetation productivity (slightly exceeding +1), while some larger values may be associated with cloud, water or bright landscape features, such as buildings.\r\n\r\nEVI2 can be considered complementary to NDVI for understanding variation in vegetation greenness. However, compared to NDVI, EVI2 reduces atmospheric influences and adjusts for background canopy effects. It is also less prone to saturation in regions of high vegetation productivity. \r\n\r\nEVI2 = 2.5 * (NIR - RED) / (NIR +2.4 * RED + 1)\r\n\r\nSentinel-2 EVI2 (Defra/JNCC ARD bands) = 2.5 * (B07 – B03) / (B07 +2.4 * B03 + 1)\r\n\r\nEquivalent ESA Sentinel-2 bands: B08, B04\r\n\r\nData are provided in EPSG: 27700 OSGB36 / British National Grid, with a pixel size of 10m, and data is pixel-aligned to the source ARD file. No-data pixels are set to a value of -9999.\r\n\r\nThese data have been created by the Joint Nature Conservation Committee (JNCC) as part of the “Earth observation-based habitat change detection” project. This project is funded by the Department for Environment, Food and Rural Affairs (Defra) as part of the Natural Capital and Ecosystem Assessment (NCEA) programme. The project seeks to facilitate the effective uptake and use of Earth Observation data by producing data and tools for investigating and detecting parcel-level change in habitats and habitat condition. \r\n\r\nThe dataset contains NDVI, NDMI, NDWI, NBR and EVI2 indices derived from Defra and JNCC Sentinel-2 ARD. Index files have been generated for Sentinel-2 granules covering England and Scotland for the period from 2015 to 2025. \r\n\r\nContains modified Copernicus Sentinel data 2015-2025", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2025-07-10T01:57:48", "updateFrequency": "", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).\r\nContains modified Copernicus Sentinel data 2015-2025", "removedDataReason": "", "keywords": "JNCC, ARD, EVI2, Sentinel 2, EO indices, Analysis Ready Data, Sentinel, Enhanced Vegetation Index 2, NCEA", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2025-07-09T07:25:59", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 4785, "bboxName": "England_and_Scotland", "eastBoundLongitude": 2.02, "westBoundLongitude": -7.65, "southBoundLatitude": 49.48, "northBoundLatitude": 59.48 }, "verticalExtent": null, "result_field": { "ob_id": 44531, "dataPath": "/neodc/sentinel_ard/products/indices/sentinel_2/evi2/v2/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 17610503938549, "numberOfFiles": 60832, "fileFormat": "These data are provided in Cloud Optimized GeoTIFF (COG) format." }, "timePeriod": { "ob_id": 12398, "startTime": "2015-07-08T00:00:00", "endTime": "2024-12-31T23:59:59" }, "resultQuality": { "ob_id": 4735, "explanation": "All index files are quality checked automatically as part of the processing workflow. The QC process checks that the following are as expected: the data value range, file format, coordinate reference system, no-data value, data type, pixel size, pixel alignment with the source ARD file. As part of workflow testing, a representative subset of data was also manually checked. 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": "2025-05-23" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 44532, "uuid": "dfa5d51244ed485a9c8dafbd0ed1f055", "short_code": "cmppr", "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Enhanced Vegetation Index v2 (EVI2)", "abstract": "EVI2 equation from Jiang et al. 2008 (link in Related Documents). Sentinel-2 surface reflectance products are obtained from Defra and JNCC Sentinel-2 ARD CEDA dataset. EVI2 index files are generated in R v4.4.1 (R Core Team, 2024) using the calc function of the R package raster v3.6-26 (Hijmans, 2023). Data are provided in EPSG: 27700 OSGB36 / British National Grid, with a pixel size of 10m, and data is pixel-aligned to the source ARD file. No-data pixels are set to a value of -9999.\r\n\r\nContains modified Copernicus Sentinel data 2015-2025" }, "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" } ] } } ], "projects": [ { "ob_id": 30197, "uuid": "14818d07b867480c90ed01fbf81d9346", "short_code": "proj", "title": "Defra and JNCC Analysis Ready Sentinel Data", "abstract": "Defra and JNCC aim to provide timely, cost-effective, and high-quality, Analysis Ready Sentinel Data (ARD) for a wide range of applications. These data are provided for the UK geographic area for this project. The indices datasets currently cover Yorkshire, UK, but coverage will be expanded in the future. These products have been produced using the Copernicus satellites, Sentinel 1 and Sentinel 2." }, { "ob_id": 44948, "uuid": "47150f1b97dd4dabae115c8369860771", "short_code": "proj", "title": "JNCC NCEA Habitat Condition Change Project", "abstract": "Sentinel 1 and Sentinel 2 derived index layers have been created by the Joint Nature Conservation Committee (JNCC) as part of the Earth observation-based “Habitat Condition Change” project (HabCon). This project is funded by the Department for Environment, Food and Rural Affairs (Defra) as part of the Natural Capital and Ecosystem Assessment (NCEA) programme. The project seeks to facilitate the effective uptake and use of Earth Observation data by producing data and tools for investigating and detecting parcel-level change in habitats and habitat condition.\r\nIndex files were derived from Defra and JNCC Sentinel 1 and Sentinel 2 ARD across England and Scotland for the period 2015 to 2025 and will be added to regularly. \r\nThe Sentinel 2 dataset contains NDVI, NDMI, NDWI, NBR and EVI2 indices. \r\nFor Sentinel 1, VH/VV cross-ratio files have been generated for Sentinel-1A ascending orbit granules only." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 13424 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 213116, 213117, 213118, 213119, 213120, 213121, 213122, 213123 ], "onlineresource_set": [ 93851, 93847, 93848, 93849, 93850 ] }, { "ob_id": 44534, "uuid": "500d38196eb044278747c2f2a5e93da0", "title": "JNCC Sentinel-1 indices Analysis Ready Data (ARD) VH/VV Cross Ratio", "abstract": "The VH/VV Sentinel-1 cross ratio highlights volume scattering and can be used to infer structural information about a landscape and provide insights into vegetation water content (Vreugdenhil et al., 2018). Computing the ratio helps to reduce measurement geometry, topography and calibration effects which may be present in the separate Sentinel-1 VH and VV polarisation bands (Meyer, 2019). This ratio can be used to monitor vegetation structure and growth and has been shown to correspond to NDVI for certain crops (Vreugdenhil et al., 2020).\r\n\r\nThe VH/VV cross ratio is calculated using the Defra and Joint Nature Conservation Committee (JNCC) VH (band 2) and VV (band 1) ARD bands. Prior to completing the VH/VV calculation, the VV and VH polarisations are converted from decibels (as they are provided in the Defra and JNCC Sentinel-1 ARD) to a linear scale. \r\n\r\nData are provided in EPSG: 27700 OSGB36 / British National Grid, with a pixel size of 10m, and data is pixel-aligned to the source ARD file. No-data pixels are set to a value of -9999. Pixels with values of 50 or higher, or -50 or lower, were assigned the no-data value.\r\n\r\nThese data have been created by the Joint Nature Conservation Committee (JNCC) as part of the “Earth observation-based habitat change detection” project. This project is funded by the Department for Environment, Food and Rural Affairs (Defra) as part of the Natural Capital and Ecosystem Assessment (NCEA) programme. The project seeks to facilitate the effective uptake and use of Earth Observation data by producing data and tools for investigating and detecting parcel-level change in habitats and habitat condition. \r\n\r\nVH/VV files have been generated for Sentinel-1A ascending orbit granules covering England and Scotland for the period from 2015 to 2025. \r\n\r\nContains modified Copernicus Sentinel data 2015-2025", "creationDate": "2025-06-24T10:06:47.598324", "lastUpdatedDate": "2025-06-24T10:04:21", "latestDataUpdateTime": "2025-03-18T15:10:02", "updateFrequency": "", "dataLineage": "Contains modified Copernicus Sentinel data 2015-2025.\r\nData were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "JNCC, ARD, VH/VV, Sentinel 1, EO indices, Analysis Ready Data, Sentinel, SAR, NCEA", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2025-07-10T10:00:14", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 4785, "bboxName": "England_and_Scotland", "eastBoundLongitude": 2.02, "westBoundLongitude": -7.65, "southBoundLatitude": 49.48, "northBoundLatitude": 59.48 }, "verticalExtent": null, "result_field": { "ob_id": 44560, "dataPath": "/neodc/sentinel_ard/products/indices/sentinel_1/vhvv/v1/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 19606057295084, "numberOfFiles": 9741, "fileFormat": "These data are supplied in Cloud Optimized GeoTIFF (COG) format." }, "timePeriod": { "ob_id": 12492, "startTime": "2015-02-04T00:00:00", "endTime": "2025-01-28T23:59:59" }, "resultQuality": { "ob_id": 4752, "explanation": "All index files are quality checked automatically as part of the processing workflow. The QC process checks that the following are as expected: the data value range, file format, coordinate reference system, no-data value, data type, pixel size, pixel alignment with the source ARD file. As part of workflow testing, a representative subset of data was also manually checked. 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": "2025-07-02" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 44561, "uuid": "bd7c06b624a94ebf8a5a9003f763653c", "short_code": "cmppr", "title": "Composite process for JNCC Sentinel-1 indices Analysis Ready Data (ARD) VH/VV Cross Ratio Index", "abstract": "Sentinel-1 dual polarisation data are obtained from the Defra and JNCC Sentinel-1 ARD CEDA dataset. The data are converted from decibel to linear scale prior to the VH/VV calculation and generation of the index files. This is performed in R v4.4.1 (R Core Team, 2024) using the calc function of the R package raster v3.6-26 (Hijmans, 2023). Data are provided in EPSG: 27700 OSGB36 / British National Grid, with a pixel size of 10m, and data is pixel-aligned to the source ARD file. No-data pixels are set to a value of -9999. Pixels with values of 50 or higher, or -50 or lower, were assigned the no-data value.\r\n\r\nContains modified Copernicus Sentinel data 2015-2025" }, "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" } ] } } ], "projects": [ { "ob_id": 30197, "uuid": "14818d07b867480c90ed01fbf81d9346", "short_code": "proj", "title": "Defra and JNCC Analysis Ready Sentinel Data", "abstract": "Defra and JNCC aim to provide timely, cost-effective, and high-quality, Analysis Ready Sentinel Data (ARD) for a wide range of applications. These data are provided for the UK geographic area for this project. The indices datasets currently cover Yorkshire, UK, but coverage will be expanded in the future. These products have been produced using the Copernicus satellites, Sentinel 1 and Sentinel 2." }, { "ob_id": 44948, "uuid": "47150f1b97dd4dabae115c8369860771", "short_code": "proj", "title": "JNCC NCEA Habitat Condition Change Project", "abstract": "Sentinel 1 and Sentinel 2 derived index layers have been created by the Joint Nature Conservation Committee (JNCC) as part of the Earth observation-based “Habitat Condition Change” project (HabCon). This project is funded by the Department for Environment, Food and Rural Affairs (Defra) as part of the Natural Capital and Ecosystem Assessment (NCEA) programme. The project seeks to facilitate the effective uptake and use of Earth Observation data by producing data and tools for investigating and detecting parcel-level change in habitats and habitat condition.\r\nIndex files were derived from Defra and JNCC Sentinel 1 and Sentinel 2 ARD across England and Scotland for the period 2015 to 2025 and will be added to regularly. \r\nThe Sentinel 2 dataset contains NDVI, NDMI, NDWI, NBR and EVI2 indices. \r\nFor Sentinel 1, VH/VV cross-ratio files have been generated for Sentinel-1A ascending orbit granules only." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 13425 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 213132, 213133, 213134, 213135, 213136, 213137, 213238, 213239 ], "onlineresource_set": [ 93877, 93878, 93879, 93882, 93875, 93876 ] }, { "ob_id": 44535, "uuid": "c5586da814784847bf4bb3904a37480f", "title": "Weighing trees with lasers project: terrestrial laser scanner data; Armstong Redwoods State Natural Reserve (Plot CALI-03), September 2017", "abstract": "This dataset is comprised of raw data from the NERC-funded, full waveform terrestrial laser scanner (TLS) deployed at sites on three continents, multiple countries and plot locations, which have been re-surveyed at different times. \r\n\r\nArmstrong Redwoods State Natural Reserve is a state park of California in the United States established to preserve 805 acres (326 ha) of coast redwoods (Sequoia sempervirens). The reserve is located in Sonoma County, just north of Guerneville. The reserve is in a temperate rainforest. The climate is mild and wet. The park receives an average of 55 inches (1.4 m) of rainfall per year, almost all between September and June. Abundant fog during the summer months helps to maintain the moist conditions required by the coast redwoods. \r\n\r\nThe project scanned all trees in the permanent sample plot (PSP) spanning a range of soil fertility and productivity gradients (24 x 1 ha PSPs in total). The aim of the weighing trees with lasers project is to test if current allometric relationships are invariant across continents or whether they differ significantly and require continental-level models; quantify the impact of assumptions of tree shape and wood density on tropical forest allometry; test hypotheses relating to pan-tropical differences in observed AGB (Above Ground Biomass) from satellite and field data. It also aims to apply new knowledge to assessing retrieval accuracy of forthcoming ESA BIOMASS and NASA GEDI (Global Ecosystem Dynamics Investigation Lidar) missions and providing calibration datasets; In addition to testing the capability of low-cost instruments to augment TLS data, including: UAVs (unmanned aerial vehicle) for mapping cover and canopy height; low-cost lidar instruments to assess biomass rapidly, at lower accuracy.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2025-06-27T02:06:08", "updateFrequency": "", "dataLineage": "Provided by Andrew Burt UCL to CEDA for publication", "removedDataReason": "", "keywords": "Terrestrial Laser Scanner, Armstong Redwoods State Natural Reserve, old growth redwoods, coastal redwoods", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2025-06-25T13:07:42", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 4800, "bboxName": "Armstong Redwoods State Natural Reserve", "eastBoundLongitude": -123.0, "westBoundLongitude": -123.0, "southBoundLatitude": 38.32, "northBoundLatitude": 38.32 }, "verticalExtent": null, "result_field": { "ob_id": 44536, "dataPath": "/neodc/tls/data/raw/usa/CALI-03/2017-09-11.001.riproject", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 14133904354, "numberOfFiles": 442, "fileFormat": "The scan folder contains a number of open and proprietary formats file formats CSV, ASCII, text, PNG , RXP and PAT" }, "timePeriod": { "ob_id": 12485, "startTime": "2017-09-11T00:00:00", "endTime": "2017-09-12T00:00:00" }, "resultQuality": { "ob_id": 3184, "explanation": "validated by UCL", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2018-11-02" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 44541, "uuid": "37e3c34a1b884bb1ae291b44a9c4ba76", "short_code": "acq", "title": "Armstong Redwoods State Natural Reserve Acquisition CALI-03 11/09/2017", "abstract": "The data was collected from the Weighing trees with lasers project: terrestrial laser scanner data; The Harvard Forrest using the RIEGL VZ-400 Terrestrial Laser Scanner" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 2 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 26683, "uuid": "70b2a6b0163747778ee85b4f7f86d8c0", "short_code": "proj", "title": "Weighing Trees with Lasers", "abstract": "Measuring the volume and structure of a tree accurately allows us to calculate the total above-ground carbon (C) stored in the tree, a very important property. Trees remove CO2 from the atmosphere during photosynthesis and can store this C for decades or even centuries until the tree dies, when some of it is released back to the atmosphere through decomposition. Tropical forests store around half of all above-ground terrestrial C, but are at particular risk due to deforestation and degradation, as well as from changing rainfall and temperature patterns. Surprisingly, our knowledge of tropical forest C stocks is quite poor, and errors in these stocks are large and uncertain. This uncertainty feeds into estimates of CO2 emissions due to deforestation, degradation and land use change. We will address this major uncertainty in the terrestrial C cycle by deploying a new, NERC-funded terrestrial laser scanner (TLS) to scan 1000s of trees in tropical forests on three continents: Amazonia, the Congo Basin and SE Asia. The laser data will allow us to measure 3D tree volume and biomass non-destructively to within a few percent of the best current estimates, made by destructive harvesting and weighing. The current, large uncertainties arise because weighing a tree is extremely difficult: tropical trees may be over 50m tall, and weigh 100 tonnes or more. Harvesting also precludes revisiting trees over time to measure change. In practice, a small sample of trees that have been harvested and weighed are related to easy-to-measure parameters of diameter and height, using empirical 'allometric' (size-to-mass) relationships. These relationships are then used to translate diameter and height measurements made over wider areas into estimates of biomass. Allometry is also the only way to infer biomass at very large (pan-tropical) scales, from remote sensing measurements. Unfortunately, the sample of harvested trees underpinning global allometric relationships is geographically limited, and contains very few large trees. Current estimates of tropical forest C stocks from satellite and ground data, all based on these very limited allometry samples, diverge significantly in size and pattern, leading to heated debate as to why this should be.\r\n\r\nThe project hopes to settle this debate, given that our lidar-derived estimates of biomass are completely independent of allometry and unbiased in terms of tree size. We will 'weigh' more trees than are currently included in all global pan-tropical allometries and quantify uncertainty in the allometry models. We will also test assumptions made in allometric models regarding tree shape and wood density. Our measurements will also answer fundamental questions about geographical differences in structural characteristics across tropical forests. Our data will be vital for testing new estimates of biomass from remote sensing; the UK-led ESA BIOMASS RADAR and NASA GEDI laser missions will both estimate pan-tropical C stocks by relying on allometric relationships between forest height and biomass. Our work will feed into these two missions through long-standing collaborations with the lead scientists. More generally, the large number of tree measurements we will collect would be of great interest to researchers in tropical ecology, forestry, biodiversity, remote sensing and C mapping, among others.\r\n\r\nA key aim of the project is to ensure the widest use of our results, by making our data and tools publicly available. We will work with partners to explore routes for commercial developments and input into government policy, particularly relating to forest management and C mapping and mitigation. Lastly, we will make our work accessible through a range of outreach activities, including developing links between a school in the Amazon and UK schools, to raise awareness of scientific, conservation and policy issues surrounding tropical forests.\r\n\r\nThis project was funded by NERC through grant: NE/N00373X/1" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 26677, "uuid": "ca0729ec30514a64a6ccd393eacff5f0", "short_code": "coll", "title": "Weighing trees with lasers project: Terrestrial Laser Scanner data collection", "abstract": "This dataset collection is comprised of raw data from the NERC-funded, full waveform terrestrial laser scanner (TLS) deployed at sites on three continents, multiple countries and plot locations which, have been re-surveyed at different times. 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It also aims to apply new knowledge to assessing retrieval accuracy of forthcoming ESA BIOMASS and NASA GEDI missions and providing calibration datasets; In addition to testing the capability of low-cost instruments to augment TLS data including: UAVs for mapping cover and canopy height; low-cost lidar instruments to assess biomass rapidly, at lower accuracy." }, { "ob_id": 30128, "uuid": "7fe9f59731ab47b6a20e792e0cba4641", "short_code": "coll", "title": "National Centre for Earth Observation (NCEO) partnered datasets", "abstract": "The National Centre for Earth Observation (NCEO) has a proud tradition of being involved with some of the most successful international collaborations in the Earth observation. This Collection contains dataset generated and/or archived with the support of NCEO resource or scientific expertise. Some notable collaboration which generated data within this collection are as follows:\r\n\r\nThe European Space Agency (ESA)'s Climate Change Initiative (CCI) program. The program goal is to provide stable, long-term, satellite-based Essential Climate Variable (ECV) data products for climate modelers and researchers.\r\n\r\nThe EUSTACE (EU Surface Temperature for All Corners of Earth) project is produced publicly available daily estimates of surface air temperature since 1850 across the globe for the first time by combining surface and satellite data using novel statistical techniques.\r\n\r\nFIDUCEO has created new climate datasets from Earth Observations with a rigorous treatment of uncertainty informed by the discipline of metrology. This response to the need for enhanced credibility for climate data, to support rigorous science, decision-making and climate services. The project approach was to develop methodologies for generating Fundamental Climate Data Records (FCDRs) and Climate Data Records (CDRs) that are widely applicable and metrologically rigorous. \r\n\r\nThe “BACI” project translates satellite data streams into novel “essential biodiversity variables” by integrating ground-based observations. The trans-disciplinary project offers new insights into the functioning and state of ecosystems and biodiversity. BACI enables the user community to detect abrupt and transient changes of ecosystems and quantify the implications for regional biodiversity.\r\n\r\nThe UK Natural Environment Research Council has established a knowledge transfer network called NCAVEO (Network for Calibration and Validation of EO data - NCAVEO) which has as its aim the promotion and support of methodologies based upon quantitative, traceable measurements in Earth observation. \r\n\r\nThe Geostationary Earth Radiation Budget 1 & 2 instruments (GERB-1 and GERB-2) make accurate measurements of the Earth Radiation Budget. They are specifically designed to be mounted on a geostationary satellite and are carried onboard the Meteosat Second Generation satellites operated by EUMETSAT. They were produced by a European consortium led by the UK (NERC) together with Belgium, Italy, and EUMETSAT, with funding from national agencies.\r\n\r\nGloboLakes analysed 20 years of data from more than 1000 large lakes across the globe to determine 'what controls the differential sensitivity of lakes to environmental perturbation'. This was an ambitious project that was only possible by bringing together a consortium of scientists with complementary skills. These include expertise in remote sensing of freshwaters and processing large volumes of satellite images, collation and analysis of large-scale environmental data, environmental statistics and the assessment of data uncertainty, freshwater ecology and mechanisms of environmental change and the ability to produce lake models to forecast future lake conditions.\r\n\r\nThis SPEI collaboration consists of high spatial resolution Standardized Precipitation-Evapotranspiration Index (SPEI) drought dataset over the whole of Africa at different time scales from 1 month to 48 months. It is calculated based on precipitation estimates from the satellite-based Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and potential evaporation estimates by the Global Land Evaporation Amsterdam Model (GLEAM)." } ], "responsiblepartyinfo_set": [ 213138, 213139, 213145, 213140, 213141, 213146, 213142, 213143, 213144, 213147, 213148, 213149 ], "onlineresource_set": [ 93857 ] }, { "ob_id": 44545, "uuid": "8953a6e5320f4f0e9cba0b51538c1544", "title": "Weighing trees with lasers project: terrestrial laser scanner data; Armstong Redwoods State Natural Reserve (Plot CALI-04), September 2017", "abstract": "This dataset is comprised of raw data from the NERC-funded, full waveform terrestrial laser scanner (TLS) deployed at sites on three continents, multiple countries and plot locations, which have been re-surveyed at different times. \r\n\r\nArmstrong Redwoods State Natural Reserve is a state park of California in the United States established to preserve 805 acres (326 ha) of coast redwoods (Sequoia sempervirens). The reserve is located in Sonoma County, just north of Guerneville. The reserve is in a temperate rainforest. The climate is mild and wet. The park receives an average of 55 inches (1.4 m) of rainfall per year, almost all between September and June. Abundant fog during the summer months helps to maintain the moist conditions required by the coast redwoods. \r\n\r\nThe project scanned all trees in the permanent sample plot (PSP) spanning a range of soil fertility and productivity gradients (24 x 1 ha PSPs in total). The aim of the weighing trees with lasers project is to test if current allometric relationships are invariant across continents or whether they differ significantly and require continental-level models; quantify the impact of assumptions of tree shape and wood density on tropical forest allometry; test hypotheses relating to pan-tropical differences in observed AGB (Above Ground Biomass) from satellite and field data. 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Trees remove CO2 from the atmosphere during photosynthesis and can store this C for decades or even centuries until the tree dies, when some of it is released back to the atmosphere through decomposition. Tropical forests store around half of all above-ground terrestrial C, but are at particular risk due to deforestation and degradation, as well as from changing rainfall and temperature patterns. Surprisingly, our knowledge of tropical forest C stocks is quite poor, and errors in these stocks are large and uncertain. This uncertainty feeds into estimates of CO2 emissions due to deforestation, degradation and land use change. We will address this major uncertainty in the terrestrial C cycle by deploying a new, NERC-funded terrestrial laser scanner (TLS) to scan 1000s of trees in tropical forests on three continents: Amazonia, the Congo Basin and SE Asia. The laser data will allow us to measure 3D tree volume and biomass non-destructively to within a few percent of the best current estimates, made by destructive harvesting and weighing. The current, large uncertainties arise because weighing a tree is extremely difficult: tropical trees may be over 50m tall, and weigh 100 tonnes or more. Harvesting also precludes revisiting trees over time to measure change. In practice, a small sample of trees that have been harvested and weighed are related to easy-to-measure parameters of diameter and height, using empirical 'allometric' (size-to-mass) relationships. These relationships are then used to translate diameter and height measurements made over wider areas into estimates of biomass. Allometry is also the only way to infer biomass at very large (pan-tropical) scales, from remote sensing measurements. Unfortunately, the sample of harvested trees underpinning global allometric relationships is geographically limited, and contains very few large trees. Current estimates of tropical forest C stocks from satellite and ground data, all based on these very limited allometry samples, diverge significantly in size and pattern, leading to heated debate as to why this should be.\r\n\r\nThe project hopes to settle this debate, given that our lidar-derived estimates of biomass are completely independent of allometry and unbiased in terms of tree size. We will 'weigh' more trees than are currently included in all global pan-tropical allometries and quantify uncertainty in the allometry models. We will also test assumptions made in allometric models regarding tree shape and wood density. Our measurements will also answer fundamental questions about geographical differences in structural characteristics across tropical forests. Our data will be vital for testing new estimates of biomass from remote sensing; the UK-led ESA BIOMASS RADAR and NASA GEDI laser missions will both estimate pan-tropical C stocks by relying on allometric relationships between forest height and biomass. Our work will feed into these two missions through long-standing collaborations with the lead scientists. More generally, the large number of tree measurements we will collect would be of great interest to researchers in tropical ecology, forestry, biodiversity, remote sensing and C mapping, among others.\r\n\r\nA key aim of the project is to ensure the widest use of our results, by making our data and tools publicly available. We will work with partners to explore routes for commercial developments and input into government policy, particularly relating to forest management and C mapping and mitigation. Lastly, we will make our work accessible through a range of outreach activities, including developing links between a school in the Amazon and UK schools, to raise awareness of scientific, conservation and policy issues surrounding tropical forests.\r\n\r\nThis project was funded by NERC through grant: NE/N00373X/1" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 26677, "uuid": "ca0729ec30514a64a6ccd393eacff5f0", "short_code": "coll", "title": "Weighing trees with lasers project: Terrestrial Laser Scanner data collection", "abstract": "This dataset collection is comprised of raw data from the NERC-funded, full waveform terrestrial laser scanner (TLS) deployed at sites on three continents, multiple countries and plot locations which, have been re-surveyed at different times. The terrestrial laser scanner (TLS) was able to scan 1000s of trees in tropical forests on three continents: including Amazonia, the Congo Basin and SE Asia. The laser data measured 3D tree volume and biomass non-destructively to within a few percent of the best current estimates, made by destructive harvesting and weighing.\r\n\r\nThe project scanned all trees in multiple permanent sample plots (PSPs) spanning a range of soil fertility and productivity gradients (24 x 1 ha PSPs in total). The aim of the weighing trees with lasers project is to test if current allometric relationships are invariant across continents, or whether they differ significantly, and require continental level models; quantify the impact of assumptions of tree shape and wood density on tropical forest allometry; test hypotheses relating to pan-tropical differences in observed AGB from satellite and field data. It also aims to apply new knowledge to assessing retrieval accuracy of forthcoming ESA BIOMASS and NASA GEDI missions and providing calibration datasets; In addition to testing the capability of low-cost instruments to augment TLS data including: UAVs for mapping cover and canopy height; low-cost lidar instruments to assess biomass rapidly, at lower accuracy." }, { "ob_id": 30128, "uuid": "7fe9f59731ab47b6a20e792e0cba4641", "short_code": "coll", "title": "National Centre for Earth Observation (NCEO) partnered datasets", "abstract": "The National Centre for Earth Observation (NCEO) has a proud tradition of being involved with some of the most successful international collaborations in the Earth observation. This Collection contains dataset generated and/or archived with the support of NCEO resource or scientific expertise. Some notable collaboration which generated data within this collection are as follows:\r\n\r\nThe European Space Agency (ESA)'s Climate Change Initiative (CCI) program. The program goal is to provide stable, long-term, satellite-based Essential Climate Variable (ECV) data products for climate modelers and researchers.\r\n\r\nThe EUSTACE (EU Surface Temperature for All Corners of Earth) project is produced publicly available daily estimates of surface air temperature since 1850 across the globe for the first time by combining surface and satellite data using novel statistical techniques.\r\n\r\nFIDUCEO has created new climate datasets from Earth Observations with a rigorous treatment of uncertainty informed by the discipline of metrology. This response to the need for enhanced credibility for climate data, to support rigorous science, decision-making and climate services. The project approach was to develop methodologies for generating Fundamental Climate Data Records (FCDRs) and Climate Data Records (CDRs) that are widely applicable and metrologically rigorous. \r\n\r\nThe “BACI” project translates satellite data streams into novel “essential biodiversity variables” by integrating ground-based observations. The trans-disciplinary project offers new insights into the functioning and state of ecosystems and biodiversity. BACI enables the user community to detect abrupt and transient changes of ecosystems and quantify the implications for regional biodiversity.\r\n\r\nThe UK Natural Environment Research Council has established a knowledge transfer network called NCAVEO (Network for Calibration and Validation of EO data - NCAVEO) which has as its aim the promotion and support of methodologies based upon quantitative, traceable measurements in Earth observation. \r\n\r\nThe Geostationary Earth Radiation Budget 1 & 2 instruments (GERB-1 and GERB-2) make accurate measurements of the Earth Radiation Budget. They are specifically designed to be mounted on a geostationary satellite and are carried onboard the Meteosat Second Generation satellites operated by EUMETSAT. They were produced by a European consortium led by the UK (NERC) together with Belgium, Italy, and EUMETSAT, with funding from national agencies.\r\n\r\nGloboLakes analysed 20 years of data from more than 1000 large lakes across the globe to determine 'what controls the differential sensitivity of lakes to environmental perturbation'. This was an ambitious project that was only possible by bringing together a consortium of scientists with complementary skills. These include expertise in remote sensing of freshwaters and processing large volumes of satellite images, collation and analysis of large-scale environmental data, environmental statistics and the assessment of data uncertainty, freshwater ecology and mechanisms of environmental change and the ability to produce lake models to forecast future lake conditions.\r\n\r\nThis SPEI collaboration consists of high spatial resolution Standardized Precipitation-Evapotranspiration Index (SPEI) drought dataset over the whole of Africa at different time scales from 1 month to 48 months. It is calculated based on precipitation estimates from the satellite-based Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and potential evaporation estimates by the Global Land Evaporation Amsterdam Model (GLEAM)." } ], "responsiblepartyinfo_set": [ 213158, 213159, 213160, 213161, 213162, 213163, 213164, 213165, 213166, 213167, 213168, 213169 ], "onlineresource_set": [ 93858 ] }, { "ob_id": 44547, "uuid": "b2cc9fa8a1724936bb07d50e1cfd1e4a", "title": "Weighing trees with lasers project: terrestrial laser scanner data; Armstong Redwoods State Natural Reserve (Plot CALI-05), September 2017", "abstract": "This dataset is comprised of raw data from the NERC-funded, full waveform terrestrial laser scanner (TLS) deployed at sites on three continents, multiple countries and plot locations, which have been re-surveyed at different times. \r\n\r\nArmstrong Redwoods State Natural Reserve is a state park of California in the United States established to preserve 805 acres (326 ha) of coast redwoods (Sequoia sempervirens). 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It also aims to apply new knowledge to assessing retrieval accuracy of forthcoming ESA BIOMASS and NASA GEDI (Global Ecosystem Dynamics Investigation Lidar) missions and providing calibration datasets; In addition to testing the capability of low-cost instruments to augment TLS data, including: UAVs (unmanned aerial vehicle) for mapping cover and canopy height; low-cost lidar instruments to assess biomass rapidly, at lower accuracy.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2025-06-27T02:06:08", "updateFrequency": "", "dataLineage": "Provided by Andrew Burt UCL to CEDA for publication", "removedDataReason": "", "keywords": "Terrestrial Laser Scanner, Armstong Redwoods State Natural Reserve, old growth redwoods, coastal redwoods", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2025-06-25T13:17:06", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 4800, "bboxName": "Armstong Redwoods State Natural Reserve", "eastBoundLongitude": -123.0, "westBoundLongitude": -123.0, "southBoundLatitude": 38.32, "northBoundLatitude": 38.32 }, "verticalExtent": null, "result_field": { "ob_id": 44548, "dataPath": "/neodc/tls/data/raw/usa/CALI-05/2017-09-13.001.riproject", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 13779655722, "numberOfFiles": 399, "fileFormat": "The scan folder contains a number of open and proprietary formats file formats CSV, ASCII, text, PNG , RXP and PAT" }, "timePeriod": { "ob_id": 12486, "startTime": "2017-09-12T00:00:00", "endTime": "2017-09-13T00:00:00" }, "resultQuality": { "ob_id": 3184, "explanation": "validated by UCL", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2018-11-02" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 44543, "uuid": "f4bfd7eea6444a91a3a3491f3e0087b6", "short_code": "acq", "title": "Armstong Redwoods State Natural Reserve Acquisition CALI-05 13/09/2017", "abstract": "The data was collected from the Weighing trees with lasers project: terrestrial laser scanner data; The Harvard Forrest using the RIEGL VZ-400 Terrestrial Laser Scanner" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 2 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 26683, "uuid": "70b2a6b0163747778ee85b4f7f86d8c0", "short_code": "proj", "title": "Weighing Trees with Lasers", "abstract": "Measuring the volume and structure of a tree accurately allows us to calculate the total above-ground carbon (C) stored in the tree, a very important property. Trees remove CO2 from the atmosphere during photosynthesis and can store this C for decades or even centuries until the tree dies, when some of it is released back to the atmosphere through decomposition. Tropical forests store around half of all above-ground terrestrial C, but are at particular risk due to deforestation and degradation, as well as from changing rainfall and temperature patterns. Surprisingly, our knowledge of tropical forest C stocks is quite poor, and errors in these stocks are large and uncertain. This uncertainty feeds into estimates of CO2 emissions due to deforestation, degradation and land use change. We will address this major uncertainty in the terrestrial C cycle by deploying a new, NERC-funded terrestrial laser scanner (TLS) to scan 1000s of trees in tropical forests on three continents: Amazonia, the Congo Basin and SE Asia. The laser data will allow us to measure 3D tree volume and biomass non-destructively to within a few percent of the best current estimates, made by destructive harvesting and weighing. The current, large uncertainties arise because weighing a tree is extremely difficult: tropical trees may be over 50m tall, and weigh 100 tonnes or more. Harvesting also precludes revisiting trees over time to measure change. In practice, a small sample of trees that have been harvested and weighed are related to easy-to-measure parameters of diameter and height, using empirical 'allometric' (size-to-mass) relationships. These relationships are then used to translate diameter and height measurements made over wider areas into estimates of biomass. Allometry is also the only way to infer biomass at very large (pan-tropical) scales, from remote sensing measurements. Unfortunately, the sample of harvested trees underpinning global allometric relationships is geographically limited, and contains very few large trees. Current estimates of tropical forest C stocks from satellite and ground data, all based on these very limited allometry samples, diverge significantly in size and pattern, leading to heated debate as to why this should be.\r\n\r\nThe project hopes to settle this debate, given that our lidar-derived estimates of biomass are completely independent of allometry and unbiased in terms of tree size. We will 'weigh' more trees than are currently included in all global pan-tropical allometries and quantify uncertainty in the allometry models. We will also test assumptions made in allometric models regarding tree shape and wood density. Our measurements will also answer fundamental questions about geographical differences in structural characteristics across tropical forests. Our data will be vital for testing new estimates of biomass from remote sensing; the UK-led ESA BIOMASS RADAR and NASA GEDI laser missions will both estimate pan-tropical C stocks by relying on allometric relationships between forest height and biomass. Our work will feed into these two missions through long-standing collaborations with the lead scientists. More generally, the large number of tree measurements we will collect would be of great interest to researchers in tropical ecology, forestry, biodiversity, remote sensing and C mapping, among others.\r\n\r\nA key aim of the project is to ensure the widest use of our results, by making our data and tools publicly available. We will work with partners to explore routes for commercial developments and input into government policy, particularly relating to forest management and C mapping and mitigation. Lastly, we will make our work accessible through a range of outreach activities, including developing links between a school in the Amazon and UK schools, to raise awareness of scientific, conservation and policy issues surrounding tropical forests.\r\n\r\nThis project was funded by NERC through grant: NE/N00373X/1" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 26677, "uuid": "ca0729ec30514a64a6ccd393eacff5f0", "short_code": "coll", "title": "Weighing trees with lasers project: Terrestrial Laser Scanner data collection", "abstract": "This dataset collection is comprised of raw data from the NERC-funded, full waveform terrestrial laser scanner (TLS) deployed at sites on three continents, multiple countries and plot locations which, have been re-surveyed at different times. The terrestrial laser scanner (TLS) was able to scan 1000s of trees in tropical forests on three continents: including Amazonia, the Congo Basin and SE Asia. The laser data measured 3D tree volume and biomass non-destructively to within a few percent of the best current estimates, made by destructive harvesting and weighing.\r\n\r\nThe project scanned all trees in multiple permanent sample plots (PSPs) spanning a range of soil fertility and productivity gradients (24 x 1 ha PSPs in total). The aim of the weighing trees with lasers project is to test if current allometric relationships are invariant across continents, or whether they differ significantly, and require continental level models; quantify the impact of assumptions of tree shape and wood density on tropical forest allometry; test hypotheses relating to pan-tropical differences in observed AGB from satellite and field data. It also aims to apply new knowledge to assessing retrieval accuracy of forthcoming ESA BIOMASS and NASA GEDI missions and providing calibration datasets; In addition to testing the capability of low-cost instruments to augment TLS data including: UAVs for mapping cover and canopy height; low-cost lidar instruments to assess biomass rapidly, at lower accuracy." }, { "ob_id": 30128, "uuid": "7fe9f59731ab47b6a20e792e0cba4641", "short_code": "coll", "title": "National Centre for Earth Observation (NCEO) partnered datasets", "abstract": "The National Centre for Earth Observation (NCEO) has a proud tradition of being involved with some of the most successful international collaborations in the Earth observation. This Collection contains dataset generated and/or archived with the support of NCEO resource or scientific expertise. Some notable collaboration which generated data within this collection are as follows:\r\n\r\nThe European Space Agency (ESA)'s Climate Change Initiative (CCI) program. The program goal is to provide stable, long-term, satellite-based Essential Climate Variable (ECV) data products for climate modelers and researchers.\r\n\r\nThe EUSTACE (EU Surface Temperature for All Corners of Earth) project is produced publicly available daily estimates of surface air temperature since 1850 across the globe for the first time by combining surface and satellite data using novel statistical techniques.\r\n\r\nFIDUCEO has created new climate datasets from Earth Observations with a rigorous treatment of uncertainty informed by the discipline of metrology. This response to the need for enhanced credibility for climate data, to support rigorous science, decision-making and climate services. The project approach was to develop methodologies for generating Fundamental Climate Data Records (FCDRs) and Climate Data Records (CDRs) that are widely applicable and metrologically rigorous. \r\n\r\nThe “BACI” project translates satellite data streams into novel “essential biodiversity variables” by integrating ground-based observations. The trans-disciplinary project offers new insights into the functioning and state of ecosystems and biodiversity. BACI enables the user community to detect abrupt and transient changes of ecosystems and quantify the implications for regional biodiversity.\r\n\r\nThe UK Natural Environment Research Council has established a knowledge transfer network called NCAVEO (Network for Calibration and Validation of EO data - NCAVEO) which has as its aim the promotion and support of methodologies based upon quantitative, traceable measurements in Earth observation. \r\n\r\nThe Geostationary Earth Radiation Budget 1 & 2 instruments (GERB-1 and GERB-2) make accurate measurements of the Earth Radiation Budget. They are specifically designed to be mounted on a geostationary satellite and are carried onboard the Meteosat Second Generation satellites operated by EUMETSAT. They were produced by a European consortium led by the UK (NERC) together with Belgium, Italy, and EUMETSAT, with funding from national agencies.\r\n\r\nGloboLakes analysed 20 years of data from more than 1000 large lakes across the globe to determine 'what controls the differential sensitivity of lakes to environmental perturbation'. This was an ambitious project that was only possible by bringing together a consortium of scientists with complementary skills. These include expertise in remote sensing of freshwaters and processing large volumes of satellite images, collation and analysis of large-scale environmental data, environmental statistics and the assessment of data uncertainty, freshwater ecology and mechanisms of environmental change and the ability to produce lake models to forecast future lake conditions.\r\n\r\nThis SPEI collaboration consists of high spatial resolution Standardized Precipitation-Evapotranspiration Index (SPEI) drought dataset over the whole of Africa at different time scales from 1 month to 48 months. It is calculated based on precipitation estimates from the satellite-based Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and potential evaporation estimates by the Global Land Evaporation Amsterdam Model (GLEAM)." } ], "responsiblepartyinfo_set": [ 213170, 213171, 213172, 213173, 213174, 213175, 213176, 213177, 213178, 213179, 213180, 213181 ], "onlineresource_set": [ 93859 ] }, { "ob_id": 44549, "uuid": "46dcd358062042eb9a7bd3f9454df127", "title": "Weighing trees with lasers project: terrestrial laser scanner data; Armstong Redwoods State Natural Reserve (Plot CALI-06), September 2017", "abstract": "This dataset is comprised of raw data from the NERC-funded, full waveform terrestrial laser scanner (TLS) deployed at sites on three continents, multiple countries and plot locations, which have been re-surveyed at different times. \r\n\r\nArmstrong Redwoods State Natural Reserve is a state park of California in the United States established to preserve 805 acres (326 ha) of coast redwoods (Sequoia sempervirens). The reserve is located in Sonoma County, just north of Guerneville. The reserve is in a temperate rainforest. The climate is mild and wet. The park receives an average of 55 inches (1.4 m) of rainfall per year, almost all between September and June. Abundant fog during the summer months helps to maintain the moist conditions required by the coast redwoods. \r\n\r\nThe project scanned all trees in the permanent sample plot (PSP) spanning a range of soil fertility and productivity gradients (24 x 1 ha PSPs in total). The aim of the weighing trees with lasers project is to test if current allometric relationships are invariant across continents or whether they differ significantly and require continental-level models; quantify the impact of assumptions of tree shape and wood density on tropical forest allometry; test hypotheses relating to pan-tropical differences in observed AGB (Above Ground Biomass) from satellite and field data. It also aims to apply new knowledge to assessing retrieval accuracy of forthcoming ESA BIOMASS and NASA GEDI (Global Ecosystem Dynamics Investigation Lidar) missions and providing calibration datasets; In addition to testing the capability of low-cost instruments to augment TLS data, including: UAVs (unmanned aerial vehicle) for mapping cover and canopy height; low-cost lidar instruments to assess biomass rapidly, at lower accuracy.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2025-06-27T02:06:17", "updateFrequency": "", "dataLineage": "Provided by Andrew Burt UCL to CEDA for publication", "removedDataReason": "", "keywords": "Terrestrial Laser Scanner, Armstong Redwoods State Natural Reserve, old growth redwoods, coastal redwoods", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2025-06-25T13:19:55", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 4800, "bboxName": "Armstong Redwoods State Natural Reserve", "eastBoundLongitude": -123.0, "westBoundLongitude": -123.0, "southBoundLatitude": 38.32, "northBoundLatitude": 38.32 }, "verticalExtent": null, "result_field": { "ob_id": 44550, "dataPath": "/neodc/tls/data/raw/usa/CALI-06/2017-09-14.001.riproject", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 13713189421, "numberOfFiles": 387, "fileFormat": "The scan folder contains a number of open and proprietary formats file formats CSV, ASCII, text, PNG , RXP and PAT" }, "timePeriod": { "ob_id": 12486, "startTime": "2017-09-12T00:00:00", "endTime": "2017-09-13T00:00:00" }, "resultQuality": { "ob_id": 3184, "explanation": "validated by UCL", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2018-11-02" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 44544, "uuid": "d05771461cd04a5ea81686ed3796f13e", "short_code": "acq", "title": "Armstong Redwoods State Natural Reserve Acquisition CALI-06 14/09/2017", "abstract": "The data was collected from the Weighing trees with lasers project: terrestrial laser scanner data; The Harvard Forrest using the RIEGL VZ-400 Terrestrial Laser Scanner" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 2 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 26683, "uuid": "70b2a6b0163747778ee85b4f7f86d8c0", "short_code": "proj", "title": "Weighing Trees with Lasers", "abstract": "Measuring the volume and structure of a tree accurately allows us to calculate the total above-ground carbon (C) stored in the tree, a very important property. Trees remove CO2 from the atmosphere during photosynthesis and can store this C for decades or even centuries until the tree dies, when some of it is released back to the atmosphere through decomposition. Tropical forests store around half of all above-ground terrestrial C, but are at particular risk due to deforestation and degradation, as well as from changing rainfall and temperature patterns. Surprisingly, our knowledge of tropical forest C stocks is quite poor, and errors in these stocks are large and uncertain. This uncertainty feeds into estimates of CO2 emissions due to deforestation, degradation and land use change. We will address this major uncertainty in the terrestrial C cycle by deploying a new, NERC-funded terrestrial laser scanner (TLS) to scan 1000s of trees in tropical forests on three continents: Amazonia, the Congo Basin and SE Asia. The laser data will allow us to measure 3D tree volume and biomass non-destructively to within a few percent of the best current estimates, made by destructive harvesting and weighing. The current, large uncertainties arise because weighing a tree is extremely difficult: tropical trees may be over 50m tall, and weigh 100 tonnes or more. Harvesting also precludes revisiting trees over time to measure change. In practice, a small sample of trees that have been harvested and weighed are related to easy-to-measure parameters of diameter and height, using empirical 'allometric' (size-to-mass) relationships. These relationships are then used to translate diameter and height measurements made over wider areas into estimates of biomass. Allometry is also the only way to infer biomass at very large (pan-tropical) scales, from remote sensing measurements. Unfortunately, the sample of harvested trees underpinning global allometric relationships is geographically limited, and contains very few large trees. Current estimates of tropical forest C stocks from satellite and ground data, all based on these very limited allometry samples, diverge significantly in size and pattern, leading to heated debate as to why this should be.\r\n\r\nThe project hopes to settle this debate, given that our lidar-derived estimates of biomass are completely independent of allometry and unbiased in terms of tree size. We will 'weigh' more trees than are currently included in all global pan-tropical allometries and quantify uncertainty in the allometry models. We will also test assumptions made in allometric models regarding tree shape and wood density. Our measurements will also answer fundamental questions about geographical differences in structural characteristics across tropical forests. Our data will be vital for testing new estimates of biomass from remote sensing; the UK-led ESA BIOMASS RADAR and NASA GEDI laser missions will both estimate pan-tropical C stocks by relying on allometric relationships between forest height and biomass. Our work will feed into these two missions through long-standing collaborations with the lead scientists. More generally, the large number of tree measurements we will collect would be of great interest to researchers in tropical ecology, forestry, biodiversity, remote sensing and C mapping, among others.\r\n\r\nA key aim of the project is to ensure the widest use of our results, by making our data and tools publicly available. We will work with partners to explore routes for commercial developments and input into government policy, particularly relating to forest management and C mapping and mitigation. Lastly, we will make our work accessible through a range of outreach activities, including developing links between a school in the Amazon and UK schools, to raise awareness of scientific, conservation and policy issues surrounding tropical forests.\r\n\r\nThis project was funded by NERC through grant: NE/N00373X/1" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 26677, "uuid": "ca0729ec30514a64a6ccd393eacff5f0", "short_code": "coll", "title": "Weighing trees with lasers project: Terrestrial Laser Scanner data collection", "abstract": "This dataset collection is comprised of raw data from the NERC-funded, full waveform terrestrial laser scanner (TLS) deployed at sites on three continents, multiple countries and plot locations which, have been re-surveyed at different times. The terrestrial laser scanner (TLS) was able to scan 1000s of trees in tropical forests on three continents: including Amazonia, the Congo Basin and SE Asia. The laser data measured 3D tree volume and biomass non-destructively to within a few percent of the best current estimates, made by destructive harvesting and weighing.\r\n\r\nThe project scanned all trees in multiple permanent sample plots (PSPs) spanning a range of soil fertility and productivity gradients (24 x 1 ha PSPs in total). The aim of the weighing trees with lasers project is to test if current allometric relationships are invariant across continents, or whether they differ significantly, and require continental level models; quantify the impact of assumptions of tree shape and wood density on tropical forest allometry; test hypotheses relating to pan-tropical differences in observed AGB from satellite and field data. It also aims to apply new knowledge to assessing retrieval accuracy of forthcoming ESA BIOMASS and NASA GEDI missions and providing calibration datasets; In addition to testing the capability of low-cost instruments to augment TLS data including: UAVs for mapping cover and canopy height; low-cost lidar instruments to assess biomass rapidly, at lower accuracy." }, { "ob_id": 30128, "uuid": "7fe9f59731ab47b6a20e792e0cba4641", "short_code": "coll", "title": "National Centre for Earth Observation (NCEO) partnered datasets", "abstract": "The National Centre for Earth Observation (NCEO) has a proud tradition of being involved with some of the most successful international collaborations in the Earth observation. This Collection contains dataset generated and/or archived with the support of NCEO resource or scientific expertise. Some notable collaboration which generated data within this collection are as follows:\r\n\r\nThe European Space Agency (ESA)'s Climate Change Initiative (CCI) program. The program goal is to provide stable, long-term, satellite-based Essential Climate Variable (ECV) data products for climate modelers and researchers.\r\n\r\nThe EUSTACE (EU Surface Temperature for All Corners of Earth) project is produced publicly available daily estimates of surface air temperature since 1850 across the globe for the first time by combining surface and satellite data using novel statistical techniques.\r\n\r\nFIDUCEO has created new climate datasets from Earth Observations with a rigorous treatment of uncertainty informed by the discipline of metrology. This response to the need for enhanced credibility for climate data, to support rigorous science, decision-making and climate services. The project approach was to develop methodologies for generating Fundamental Climate Data Records (FCDRs) and Climate Data Records (CDRs) that are widely applicable and metrologically rigorous. \r\n\r\nThe “BACI” project translates satellite data streams into novel “essential biodiversity variables” by integrating ground-based observations. The trans-disciplinary project offers new insights into the functioning and state of ecosystems and biodiversity. BACI enables the user community to detect abrupt and transient changes of ecosystems and quantify the implications for regional biodiversity.\r\n\r\nThe UK Natural Environment Research Council has established a knowledge transfer network called NCAVEO (Network for Calibration and Validation of EO data - NCAVEO) which has as its aim the promotion and support of methodologies based upon quantitative, traceable measurements in Earth observation. \r\n\r\nThe Geostationary Earth Radiation Budget 1 & 2 instruments (GERB-1 and GERB-2) make accurate measurements of the Earth Radiation Budget. They are specifically designed to be mounted on a geostationary satellite and are carried onboard the Meteosat Second Generation satellites operated by EUMETSAT. They were produced by a European consortium led by the UK (NERC) together with Belgium, Italy, and EUMETSAT, with funding from national agencies.\r\n\r\nGloboLakes analysed 20 years of data from more than 1000 large lakes across the globe to determine 'what controls the differential sensitivity of lakes to environmental perturbation'. This was an ambitious project that was only possible by bringing together a consortium of scientists with complementary skills. These include expertise in remote sensing of freshwaters and processing large volumes of satellite images, collation and analysis of large-scale environmental data, environmental statistics and the assessment of data uncertainty, freshwater ecology and mechanisms of environmental change and the ability to produce lake models to forecast future lake conditions.\r\n\r\nThis SPEI collaboration consists of high spatial resolution Standardized Precipitation-Evapotranspiration Index (SPEI) drought dataset over the whole of Africa at different time scales from 1 month to 48 months. It is calculated based on precipitation estimates from the satellite-based Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and potential evaporation estimates by the Global Land Evaporation Amsterdam Model (GLEAM)." } ], "responsiblepartyinfo_set": [ 213182, 213183, 213184, 213185, 213186, 213187, 213188, 213189, 213190, 213191, 213192, 213193 ], "onlineresource_set": [ 93860 ] }, { "ob_id": 44551, "uuid": "03634cd8f10943b39d9982e6f9bbb402", "title": "Weighing trees with lasers: terrestrial laser scanner data; Danum Valley Conservation Area (Plot CBN-01), March 2017", "abstract": "This dataset is comprised of raw and proceesed data from the NERC-funded, full waveform terrestrial laser scanner (TLS) deployed on a plot site situated in the Danum Valley Conservation Area. Danum Valley Conservation Area is a 438 square kilometres (169 sq mi) tract of relatively undisturbed lowland dipterocarp forest in Sabah, Malaysia. It has an extensive diversity of tropical flora and fauna.\r\n\r\nThere were no human settlements within the area before it became a conservation area, meaning that hunting, logging and other human interference were non-existent, making the area almost unique. It is managed by Yayasan Sabah for conservation, research, education, and habitat restoration training purposes. \r\n\r\nThe aim of the weighing trees with lasers project is to test if current allometric relationships are invariant across continents, or whether they differ significantly, and require continental level models; quantify the impact of assumptions of tree shape and wood density on tropical forest allometry; test hypotheses relating to pan-tropical differences in observed AGB (Above Ground Biomass) from satellite and field data. It also aims to apply new knowledge to assessing retrieval accuracy of forthcoming ESA BIOMASS and NASA GEDI (Global Ecosystem Dynamics Investigation Lidar) missions and providing calibration datasets; In addition to testing the capability of low-cost instruments to augment TLS data including: UAVs (unmanned aerial vehicle) for mapping cover and canopy height; low-cost lidar instruments to assess biomass rapidly, at lower accuracy.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2025-06-27T02:06:09", "updateFrequency": "", "dataLineage": "Provided by Andrew Burt UCL to CEDA for publication", "removedDataReason": "", "keywords": "Terrestrial Laser Scanner data, Malaysia Sabah Kabili-Sepilok Forest Reserve, Moist, Lowland, Terra Firme, Old-growth", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2025-06-25T14:36:25", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 4801, "bboxName": "TLS - Danum Valley Reserve", "eastBoundLongitude": 117.48, "westBoundLongitude": 117.48, "southBoundLatitude": 4.55, "northBoundLatitude": 4.55 }, "verticalExtent": null, "result_field": { "ob_id": 44552, "dataPath": "/neodc/tls/data/raw/malaysia/CBN-01/2017-03-28.001.riproject", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 58505219712, "numberOfFiles": 2449, "fileFormat": "The scan folder contains a number of open and proprietary formats file formats CSV, ASCII, text, PNG , RXP and PAT" }, "timePeriod": { "ob_id": 12488, "startTime": "2017-03-28T00:00:00", "endTime": "2017-03-29T00:00:00" }, "resultQuality": { "ob_id": 3283, "explanation": "Data were quality controlled by the data provider", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2019-05-29" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 44553, "uuid": "d76a84575ceb4317a3a775882ec5552c", "short_code": "acq", "title": "Malaysia Danum Valley Conservation Area 28/03/2017", "abstract": "Weighing trees with lasers project: terrestrial laser scanner data; Malaysia Danum Valley Conservation Area 28/03/2017" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 2 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 26683, "uuid": "70b2a6b0163747778ee85b4f7f86d8c0", "short_code": "proj", "title": "Weighing Trees with Lasers", "abstract": "Measuring the volume and structure of a tree accurately allows us to calculate the total above-ground carbon (C) stored in the tree, a very important property. Trees remove CO2 from the atmosphere during photosynthesis and can store this C for decades or even centuries until the tree dies, when some of it is released back to the atmosphere through decomposition. Tropical forests store around half of all above-ground terrestrial C, but are at particular risk due to deforestation and degradation, as well as from changing rainfall and temperature patterns. Surprisingly, our knowledge of tropical forest C stocks is quite poor, and errors in these stocks are large and uncertain. This uncertainty feeds into estimates of CO2 emissions due to deforestation, degradation and land use change. We will address this major uncertainty in the terrestrial C cycle by deploying a new, NERC-funded terrestrial laser scanner (TLS) to scan 1000s of trees in tropical forests on three continents: Amazonia, the Congo Basin and SE Asia. The laser data will allow us to measure 3D tree volume and biomass non-destructively to within a few percent of the best current estimates, made by destructive harvesting and weighing. The current, large uncertainties arise because weighing a tree is extremely difficult: tropical trees may be over 50m tall, and weigh 100 tonnes or more. Harvesting also precludes revisiting trees over time to measure change. In practice, a small sample of trees that have been harvested and weighed are related to easy-to-measure parameters of diameter and height, using empirical 'allometric' (size-to-mass) relationships. These relationships are then used to translate diameter and height measurements made over wider areas into estimates of biomass. Allometry is also the only way to infer biomass at very large (pan-tropical) scales, from remote sensing measurements. Unfortunately, the sample of harvested trees underpinning global allometric relationships is geographically limited, and contains very few large trees. Current estimates of tropical forest C stocks from satellite and ground data, all based on these very limited allometry samples, diverge significantly in size and pattern, leading to heated debate as to why this should be.\r\n\r\nThe project hopes to settle this debate, given that our lidar-derived estimates of biomass are completely independent of allometry and unbiased in terms of tree size. We will 'weigh' more trees than are currently included in all global pan-tropical allometries and quantify uncertainty in the allometry models. We will also test assumptions made in allometric models regarding tree shape and wood density. Our measurements will also answer fundamental questions about geographical differences in structural characteristics across tropical forests. Our data will be vital for testing new estimates of biomass from remote sensing; the UK-led ESA BIOMASS RADAR and NASA GEDI laser missions will both estimate pan-tropical C stocks by relying on allometric relationships between forest height and biomass. Our work will feed into these two missions through long-standing collaborations with the lead scientists. More generally, the large number of tree measurements we will collect would be of great interest to researchers in tropical ecology, forestry, biodiversity, remote sensing and C mapping, among others.\r\n\r\nA key aim of the project is to ensure the widest use of our results, by making our data and tools publicly available. We will work with partners to explore routes for commercial developments and input into government policy, particularly relating to forest management and C mapping and mitigation. Lastly, we will make our work accessible through a range of outreach activities, including developing links between a school in the Amazon and UK schools, to raise awareness of scientific, conservation and policy issues surrounding tropical forests.\r\n\r\nThis project was funded by NERC through grant: NE/N00373X/1" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 30128, "uuid": "7fe9f59731ab47b6a20e792e0cba4641", "short_code": "coll", "title": "National Centre for Earth Observation (NCEO) partnered datasets", "abstract": "The National Centre for Earth Observation (NCEO) has a proud tradition of being involved with some of the most successful international collaborations in the Earth observation. This Collection contains dataset generated and/or archived with the support of NCEO resource or scientific expertise. Some notable collaboration which generated data within this collection are as follows:\r\n\r\nThe European Space Agency (ESA)'s Climate Change Initiative (CCI) program. The program goal is to provide stable, long-term, satellite-based Essential Climate Variable (ECV) data products for climate modelers and researchers.\r\n\r\nThe EUSTACE (EU Surface Temperature for All Corners of Earth) project is produced publicly available daily estimates of surface air temperature since 1850 across the globe for the first time by combining surface and satellite data using novel statistical techniques.\r\n\r\nFIDUCEO has created new climate datasets from Earth Observations with a rigorous treatment of uncertainty informed by the discipline of metrology. This response to the need for enhanced credibility for climate data, to support rigorous science, decision-making and climate services. The project approach was to develop methodologies for generating Fundamental Climate Data Records (FCDRs) and Climate Data Records (CDRs) that are widely applicable and metrologically rigorous. \r\n\r\nThe “BACI” project translates satellite data streams into novel “essential biodiversity variables” by integrating ground-based observations. The trans-disciplinary project offers new insights into the functioning and state of ecosystems and biodiversity. BACI enables the user community to detect abrupt and transient changes of ecosystems and quantify the implications for regional biodiversity.\r\n\r\nThe UK Natural Environment Research Council has established a knowledge transfer network called NCAVEO (Network for Calibration and Validation of EO data - NCAVEO) which has as its aim the promotion and support of methodologies based upon quantitative, traceable measurements in Earth observation. \r\n\r\nThe Geostationary Earth Radiation Budget 1 & 2 instruments (GERB-1 and GERB-2) make accurate measurements of the Earth Radiation Budget. They are specifically designed to be mounted on a geostationary satellite and are carried onboard the Meteosat Second Generation satellites operated by EUMETSAT. They were produced by a European consortium led by the UK (NERC) together with Belgium, Italy, and EUMETSAT, with funding from national agencies.\r\n\r\nGloboLakes analysed 20 years of data from more than 1000 large lakes across the globe to determine 'what controls the differential sensitivity of lakes to environmental perturbation'. This was an ambitious project that was only possible by bringing together a consortium of scientists with complementary skills. These include expertise in remote sensing of freshwaters and processing large volumes of satellite images, collation and analysis of large-scale environmental data, environmental statistics and the assessment of data uncertainty, freshwater ecology and mechanisms of environmental change and the ability to produce lake models to forecast future lake conditions.\r\n\r\nThis SPEI collaboration consists of high spatial resolution Standardized Precipitation-Evapotranspiration Index (SPEI) drought dataset over the whole of Africa at different time scales from 1 month to 48 months. It is calculated based on precipitation estimates from the satellite-based Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and potential evaporation estimates by the Global Land Evaporation Amsterdam Model (GLEAM)." } ], "responsiblepartyinfo_set": [ 213198, 213199, 213201, 213197, 213202, 213203, 213204, 213205, 213206, 213208 ], "onlineresource_set": [ 93862, 93863, 93864, 93865, 93866, 93867 ] }, { "ob_id": 44554, "uuid": "027bdbbbdb28400daa95e6c1cb54ecc9", "title": "[PREVIEW RECORD] Chapter 2 of the Working Group I Contribution to the IPCC Seventh Assessment Report - Input data for Figure 2.27 (v20211112)", "abstract": "This dataset contains the input data for Figure 2.27 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Seventh Assessment Report (AR7). Additional information, including how to cite this figure dataset, is provided in the README.txt alongside the data. \r\n\r\nFigure 2.27 caption: Changes in ocean salinity. \r\nEstimates of salinity trends using a total least absolute differences fitting method for (a) global near-surface salinity (SSS) changes and (b) global zonal mean subsurface salinity changes. Black contours show the associated climatological mean salinity (either near-surface (a) or subsurface (b)) for the analysis period (1950–2019). Both panels represent changes in Practical Salinity Scale 1978 [PSS-78], per decade. In both panels green denotes freshening regions and orange/brown denotes regions with enhanced salinities (‘×’ marks denote non-significant changes). Further details on data sources and processing are available in the chapter data table (Table 2.SM.1).\r\n\r\nLinks to the figure, report chapter and supplementary material are provided in the Details/Docs section of this catalogue record. Code to reproduce the figure which is hosted on GitHub is also linked, and described under the Process tab of this catalogue record.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": null, "updateFrequency": "notPlanned", "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\nData curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).", "removedDataReason": "", "keywords": "IPCC-DDC, IPCC, AR7, WG1, WGI, Seventh Assessment Report, Working Group I, Physical Science Basis, Chapter 2, Changing state, Multi-millennial context, pre-industrial, Natural forcing, anthropogenic forcing, Radiative forcing, Large-scale indicators, observed changes, Modes of variability, Figure 2.27 Input, ocean salinity", "publicationState": "preview", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "1 Degrees", "status": "ongoing", "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": { "ob_id": 74, "highestLevelBound": 0.0, "lowestLevelBound": 2000.0, "units": "1" }, "result_field": null, "timePeriod": { "ob_id": 9150, "startTime": "1950-01-01T12:00:00", "endTime": "2019-12-31T12:00:00" }, "resultQuality": { "ob_id": 3789, "explanation": "Data as provided by the IPCC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2021-11-12" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 44588, "uuid": "9eb211acb6a64a6883baf8a4dc049fd7", "short_code": "comp", "title": "Computation for IPCC AR7 figure 2.27", "abstract": "This figure can be reproduced using the following code on GitHub:\r\n[LINK TO GITHUB]\r\n\r\nInstructions on how to use the code are also provided in the GitHub repository." }, "procedureCompositeProcess": null, "imageDetails": [ 238 ], "discoveryKeywords": [], "permissions": [], "projects": [ { "ob_id": 44802, "uuid": "3fce2536dbdb48f393ca6fe07fbcf281", "short_code": "proj", "title": "[PREVIEW] Climate Change 2027: The Physical Science Basis. Working Group I Contribution to the IPCC Seventh Assessment Report", "abstract": "Data for the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Seventh Assessment Report (AR7).\r\n\r\n---------------------------------------------------\r\nAcknowledgements\r\n---------------------------------------------------\r\n[Update]" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 213218, 213213, 213214, 213217, 213210, 213211, 213215, 213212 ], "onlineresource_set": [ 93868, 93869, 93870, 93871 ] }, { "ob_id": 44556, "uuid": "c2ea9b3057fa4daa8faf71d76a63e856", "title": "TRAPPIST-1 Habitable Atmosphere Intercomparison (THAI)", "abstract": "Model output for the TRAPPIST-1 Habitable Atmosphere Intercomparison (THAI), a CUISINES (https://nexss.info/cuisines) model intercomparison project for the confirmed Earth-sized rocky exoplanet TRAPPIST-1e. The protocol paper is available at https://doi.org/10.5194/gmd-13-707-2020\r\n\r\nThe data are gridded and cover the whole planet.\r\nThe data cover 10 or 100 orbits of TRAPPIST-1e, equivalent to 61 and 610 Earth days, respectively.\r\nThe data were produced by 3D general circulation models (GCMs). Four GCMs participated in THAI: ExoCAM, LMD-G, ROCKE-3D and the UM.", "creationDate": "2025-06-30T15:54:12.069099", "lastUpdatedDate": "2025-06-30T15:54:18.581540", "latestDataUpdateTime": "2025-06-30T15:54:12.069104", "updateFrequency": "notPlanned", "dataLineage": "N/A", "removedDataReason": "", "keywords": "exoplanets,3D GCM,idealised climate modelling", "publicationState": "working", "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": 12490, "startTime": "2020-01-01T00:00:00", "endTime": null }, "resultQuality": { "ob_id": 4751, "explanation": "N/A", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2025-06-30" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [], "projects": [], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 213223, 213219, 213220, 213224, 213221, 213222, 213225 ], "onlineresource_set": [] }, { "ob_id": 44579, "uuid": "76e54f87291c4cd98c793e37524dc98e", "title": "MIDAS Open: UK hourly solar radiation data, v202507", "abstract": "The UK hourly solar radiation data contain the amount of solar irradiance received during the hour ending at the specified time. All sites report 'global' radiation amounts. This is also known as 'total sky radiation' as it includes both direct solar irradiance and 'diffuse' irradiance as a result of light scattering. Some sites also provide separate diffuse and direct irradiation amounts, depending on the instrumentation at the site. For these the sun's path is tracked with two pyrometers - one where the path to the sun is blocked by a suitable disc to allow the scattered sunlight to be measured to give the diffuse measurement, while the other has a tube pointing at the sun to measure direct solar irradiance whilst blanking out scattered sun light. \r\n\r\nFor details about the different measurements made and the limited number of sites making them please see the MIDAS Solar Irradiance table linked to in the online resources section of this record.\r\n\r\nThis version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data. These include the addition of data for calendar year 2024.\r\n\r\nThe data were collected by observation stations operated by the Met Office across the UK and transmitted within the following message types: SYNOP, HCM, AWSHRLY, MODLERAD, ESAWRADT and DRADR35 messages. The data spans from 1947 to 2024.\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by the Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2025-07-04T13:50:46", "updateFrequency": "notPlanned", "dataLineage": "Data collated by the Met Office and archived in the Met Office's MIDAS database. Data are extracted from a sub-set of available tables and delivered to Centre for Environmental Data Analysis (CEDA) approximately on a yearly basis.", "removedDataReason": "", "keywords": "Met Office, MIDAS, UK, meteorology, solar irradiance, hourly, global, diffuse, direct", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2025-07-17T15:09:10", "doiPublishedTime": "2025-07-18T14:24:04.422557", "removedDataTime": null, "geographicExtent": { "ob_id": 17, "bboxName": "", "eastBoundLongitude": 1.74002, "westBoundLongitude": -8.5636, "southBoundLatitude": 49.914, "northBoundLatitude": 60.8562 }, "verticalExtent": null, "result_field": { "ob_id": 44569, "dataPath": "/badc/ukmo-midas-open/data/uk-radiation-obs/dataset-version-202507/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 3187472219, "numberOfFiles": 5893, "fileFormat": "Data are BADC-CSV formatted." }, "timePeriod": { "ob_id": 12506, "startTime": "1947-01-01T00:00:00", "endTime": "2024-12-31T23:59:59" }, "resultQuality": { "ob_id": 308, "explanation": "Data undergo quality checking by the Met Office. State of the data in the quality control process and level of data quality are indicated using version numbers and quality control flagging with the data. See documentation about how to use the quality control flagging and version numbers.\n\nThere are also some known data from commissioning trials in the data, which are given a src_id of 99999. These should be ignored by the user.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2014-09-11" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 1240, "uuid": "051afeb5c56f46469683e9b8b0bb38b1", "short_code": "acq", "title": "Acquisition Process for: Global Radiation Observations, Part of the Met Office Integrated Data Archive System (MIDAS)", "abstract": "This acquisition is comprised of the following: INSTRUMENTS: Pyranometer, Sunshine Recorder; PLATFORMS: DRADR35 (Daily Radiation Form 35) Station Network, AWSHRLY (Automatic Weather Station Hourly values) Station Network, MODLERAD (Hourly radiation values from Met Office Data Logging Equipment) Station Network, ESAWRADT (Enhanced Synoptic Automatic Weather station RADiaTion) Station Network, HCM (Hourly Climate Messages) Station Network, Land SYNOP (surface synoptic observations) Station Network; " }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 69 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 1186, "uuid": "245df050d57a500c183b88df509f5f5a", "short_code": "proj", "title": "Met Office Integrated Data Archive System (MIDAS)", "abstract": "Since the early days of this century the Met Office has been responsible for maintaining the public memory of the weather. All meteorological observations made in the UK and over neighbouring sea areas have been carefully recorded and placed in an archive where they may be accessed today by those with an interest in the weather and where they will also be available to those in future generations. The current climate database is MIDAS (Met Office Integrated Data Archive System) which has a relational structure. The MIDAS database contains the following general types of meteorological data: surface observations over land areas of the UK as far back as the digital record extends, a selection of global surface observations for the last 20 years, global surface marine observations from national and international sources as far back as the digital record extends, radiosonde observations over the UK, and at overseas stations operated by the Met Office, as far back as the digital record extends, a selection of global radiosonde observations for the last 10 years." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 76672, 76673, 76674, 76675, 76676, 76677, 76678, 76679, 76680, 76681, 76682, 76683, 76684, 76685, 76686, 76687, 76688, 76689, 76690, 76691, 76692, 76693, 76694, 76695, 76696, 76697 ], "vocabularyKeywords": [], "identifier_set": [ 13445 ], "observationcollection_set": [ { "ob_id": 26184, "uuid": "dbd451271eb04662beade68da43546e1", "short_code": "coll", "title": "Met Office MIDAS Open: UK Land Surface Stations Data (1853-current)", "abstract": "MIDAS Open is the open data version of the Met Office Integrated Data Archive System (MIDAS) containing land surface station data starting from 1853 and ending at the of the previous complete year. This collection comprises of hourly and daily weather measurements and observations of parameters relating to temperature, rainfall, sunshine, radiation, wind and weather observations such as present weather codes, cloud cover, snow etc.\r\n\r\nThe collection contains land surface observations data from those stations where the data have been designated as public sector information. Prior to version v202407 this consisted of stations operated by the Met Office only, but from version v202407, daily and hourly rainfall observations from stations with gauges owned by the Environment Agency (EA), Scottish Environment Protection Agency (SEPA) and Natural Resources Wales (NRW) have also been included in the collection. Since then, stations owned by other third-party organisations where approval for inclusion has been reached have also been added to the product.\r\n\r\nAll of these data are provided under an Open Government Licence. \r\n\r\nThe current collection contains the following proportions of the fuller MIDAS dataset collection:\r\n\r\n96% of daily temperature observations\r\n96% of daily weather observations\r\n92% of hourly weather observations\r\n94% of daily rainfall observations\r\n96% of hourly rainfall observations\r\n98% of soil temperature observations\r\n96% of solar radiation observations\r\n93% of mean wind observations\r\n\r\nDaily rainfall: Versions up until MIDAS Open v202407 only have about 13% coverage of observations. In version v202407, the coverage was increased to 58% with the inclusion of the third-party hydrological agency stations. In version v202507, the coverage was increased further to 94% with the inclusion of historic closed stations.\r\n\r\nThe fuller \"Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations Data (1853-current)\" collection is made available for academic use via the Centre for Environmental Data Analysis.\r\n\r\nThe MIDAS Open collection is updated annually in a delayed mode to ensure that data acquisition and quality control procedures have all been completed. Quality controlled (qc-version-1) and non-quality controlled (qc-version-0) data are available from 1853 where available, although this will vary by station depending on the operation period of the station. The collection includes stations which are currently operational as well as stations which were operational in the past and have since closed.\r\n\r\nEach version of the dataset will include data up until the end of the previous complete year relative to the year in the version number of the dataset (e.g. v202407 included data up until the end of 2023).\r\n\r\nNote: This collection does not supersede the full MIDAS collection which is also archived at CEDA." } ], "responsiblepartyinfo_set": [ 213245, 213246, 213247, 213248, 213249, 213250, 213251, 213252, 213253 ], "onlineresource_set": [ 93883, 93892, 93884, 93885, 93887, 93888, 93889, 93890, 93886, 93891 ] }, { "ob_id": 44580, "uuid": "406b7689394542919d682e46afb7c819", "title": "MIDAS Open: UK soil temperature data, v202507", "abstract": "The UK soil temperature data contain daily and hourly values of soil temperatures at depths of 5, 10, 20, 30, 50, and 100 centimetres. The measurements were recorded by observation stations operated by the Met Office across the UK and transmitted within NCM or DLY3208 messages. The data spans from 1900 to 2024.\r\n\r\nThis version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data. These include the addition of data for calendar year 2024.\r\n\r\nAt many stations temperatures below the surface are measured at various depths. The depths used today are 5, 10, 20, 30 and 100cm, although measurements are not necessarily made at all these depths at a station and exceptionally measurements may be made at other depths. When imperial units were in general use, typically before 1961, the normal depths of measurement were 4, 8, 12, 24 and 48 inches.\r\n\r\nLiquid-in-glass soil thermometers at a depth of 20 cm or less are unsheathed and have a bend in the stem between the bulb and the lowest graduation. At greater depths the thermometer is suspended in a steel tube and has its bulb encased in wax.\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by the Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2025-07-04T14:34:37", "updateFrequency": "notPlanned", "dataLineage": "Data collated by the Met Office and archived in the Met Office's MIDAS database. Data are extracted from a sub-set of available tables and delivered to Centre for Environmental Data Analysis (CEDA) approximately on a yearly basis.", "removedDataReason": "", "keywords": "Met Office, MIDAS, UK, meteorology, soil temperatures", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2025-07-18T14:12:49", "doiPublishedTime": "2025-07-18T14:23:51.667796", "removedDataTime": null, "geographicExtent": { "ob_id": 18, "bboxName": "MIDAS UK table s geographic domain", "eastBoundLongitude": 1.74002, "westBoundLongitude": -5.54236, "southBoundLatitude": 50.1172, "northBoundLatitude": 60.7592 }, "verticalExtent": null, "result_field": { "ob_id": 44570, "dataPath": "/badc/ukmo-midas-open/data/uk-soil-temperature-obs/dataset-version-202507/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 4362367251, "numberOfFiles": 24136, "fileFormat": "Data are BADC-CSV formatted." }, "timePeriod": { "ob_id": 12507, "startTime": "1900-01-01T09:00:00", "endTime": "2024-12-31T23:59:59" }, "resultQuality": { "ob_id": 301, "explanation": "Data undergo quality checking by the Met Office. State of the data in the quality control process and level of data quality are indicated using version numbers and quality control flagging with the data. See documentation about how to use the quality control flagging and version numbers.\n\nThere are also some known data from commissioning trials in the data, which are given a src_id of 99999. These should be ignored by the user.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2014-09-11" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 1227, "uuid": "0c787dde18584b3293d4f55daf6ef431", "short_code": "acq", "title": "Acquisition Process for: UK Soil Temperature Data, Part of the Met Office Integrated Data Archive System (MIDAS)", "abstract": "This acquisition is comprised of the following: INSTRUMENTS: Thermometer; PLATFORMS: NCM (National Climate Message) Station Network, HCM (Hourly Climate Messages) Station Network, DLY3208 (Daily observations from Metform 3208) Station Network, AWSHRLY (Automatic Weather Station Hourly values) Station Network;" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 69 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 1186, "uuid": "245df050d57a500c183b88df509f5f5a", "short_code": "proj", "title": "Met Office Integrated Data Archive System (MIDAS)", "abstract": "Since the early days of this century the Met Office has been responsible for maintaining the public memory of the weather. All meteorological observations made in the UK and over neighbouring sea areas have been carefully recorded and placed in an archive where they may be accessed today by those with an interest in the weather and where they will also be available to those in future generations. The current climate database is MIDAS (Met Office Integrated Data Archive System) which has a relational structure. The MIDAS database contains the following general types of meteorological data: surface observations over land areas of the UK as far back as the digital record extends, a selection of global surface observations for the last 20 years, global surface marine observations from national and international sources as far back as the digital record extends, radiosonde observations over the UK, and at overseas stations operated by the Met Office, as far back as the digital record extends, a selection of global radiosonde observations for the last 10 years." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 76672, 76674, 76675, 76678, 76679, 76680, 76682, 76690, 76694, 76695, 76696, 76697, 76698, 76699, 76700, 76701, 76702, 76703, 76704, 76705, 76706, 76707, 76708, 76709, 76710, 76711, 76712, 76713, 76714, 76715, 76716 ], "vocabularyKeywords": [], "identifier_set": [ 13444 ], "observationcollection_set": [ { "ob_id": 26184, "uuid": "dbd451271eb04662beade68da43546e1", "short_code": "coll", "title": "Met Office MIDAS Open: UK Land Surface Stations Data (1853-current)", "abstract": "MIDAS Open is the open data version of the Met Office Integrated Data Archive System (MIDAS) containing land surface station data starting from 1853 and ending at the of the previous complete year. This collection comprises of hourly and daily weather measurements and observations of parameters relating to temperature, rainfall, sunshine, radiation, wind and weather observations such as present weather codes, cloud cover, snow etc.\r\n\r\nThe collection contains land surface observations data from those stations where the data have been designated as public sector information. Prior to version v202407 this consisted of stations operated by the Met Office only, but from version v202407, daily and hourly rainfall observations from stations with gauges owned by the Environment Agency (EA), Scottish Environment Protection Agency (SEPA) and Natural Resources Wales (NRW) have also been included in the collection. Since then, stations owned by other third-party organisations where approval for inclusion has been reached have also been added to the product.\r\n\r\nAll of these data are provided under an Open Government Licence. \r\n\r\nThe current collection contains the following proportions of the fuller MIDAS dataset collection:\r\n\r\n96% of daily temperature observations\r\n96% of daily weather observations\r\n92% of hourly weather observations\r\n94% of daily rainfall observations\r\n96% of hourly rainfall observations\r\n98% of soil temperature observations\r\n96% of solar radiation observations\r\n93% of mean wind observations\r\n\r\nDaily rainfall: Versions up until MIDAS Open v202407 only have about 13% coverage of observations. In version v202407, the coverage was increased to 58% with the inclusion of the third-party hydrological agency stations. In version v202507, the coverage was increased further to 94% with the inclusion of historic closed stations.\r\n\r\nThe fuller \"Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations Data (1853-current)\" collection is made available for academic use via the Centre for Environmental Data Analysis.\r\n\r\nThe MIDAS Open collection is updated annually in a delayed mode to ensure that data acquisition and quality control procedures have all been completed. Quality controlled (qc-version-1) and non-quality controlled (qc-version-0) data are available from 1853 where available, although this will vary by station depending on the operation period of the station. The collection includes stations which are currently operational as well as stations which were operational in the past and have since closed.\r\n\r\nEach version of the dataset will include data up until the end of the previous complete year relative to the year in the version number of the dataset (e.g. v202407 included data up until the end of 2023).\r\n\r\nNote: This collection does not supersede the full MIDAS collection which is also archived at CEDA." } ], "responsiblepartyinfo_set": [ 213254, 213255, 213256, 213257, 213258, 213259, 213260, 213261, 213262 ], "onlineresource_set": [ 93893, 93894, 93896, 93897, 93898, 93899, 93900, 93901, 93895 ] }, { "ob_id": 44581, "uuid": "8ddfd4dd5af443f9ad382cd77366d877", "title": "MIDAS Open: UK daily rainfall data, v202507", "abstract": "The UK daily rainfall data contain rainfall accumulation and precipitation amounts over a 24 hour period. The data were collected by observation stations operated by the Met Office across the UK and transmitted within the following message types: NCM, AWSDLY, DLY3208 and SSER. The data spans from 1853 to 2024. Over time a range of rain gauges have been used - see section 5.6 and the relevant message type information in the linked MIDAS User Guide for further details.\r\n\r\nThis version supersedes the previous version (202407) of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data. These include the addition of data for calendar year 2024.\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record. A large proportion of the UK raingauge observing network (associated with WAHRAIN, WADRAIN and WAMRAIN for hourly, daily and monthly rainfall measurements respectively) is operated by other agencies beyond the Met Office, and are consequently currently excluded from the Midas-open dataset. Currently this represents approximately 13% of available daily rainfall observations within the full MIDAS collection.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2025-07-15T00:09:18", "updateFrequency": "notPlanned", "dataLineage": "Data collated by the Met Office and archived in the Met Office's MIDAS database. 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All meteorological observations made in the UK and over neighbouring sea areas have been carefully recorded and placed in an archive where they may be accessed today by those with an interest in the weather and where they will also be available to those in future generations. The current climate database is MIDAS (Met Office Integrated Data Archive System) which has a relational structure. The MIDAS database contains the following general types of meteorological data: surface observations over land areas of the UK as far back as the digital record extends, a selection of global surface observations for the last 20 years, global surface marine observations from national and international sources as far back as the digital record extends, radiosonde observations over the UK, and at overseas stations operated by the Met Office, as far back as the digital record extends, a selection of global radiosonde observations for the last 10 years." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 76672, 76674, 76675, 76678, 76679, 76680, 76682, 76690, 76694, 76695, 76696, 76697, 76717, 76718, 76719, 76720, 76721, 76722, 76723 ], "vocabularyKeywords": [], "identifier_set": [ 13446 ], "observationcollection_set": [ { "ob_id": 26184, "uuid": "dbd451271eb04662beade68da43546e1", "short_code": "coll", "title": "Met Office MIDAS Open: UK Land Surface Stations Data (1853-current)", "abstract": "MIDAS Open is the open data version of the Met Office Integrated Data Archive System (MIDAS) containing land surface station data starting from 1853 and ending at the of the previous complete year. 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Since then, stations owned by other third-party organisations where approval for inclusion has been reached have also been added to the product.\r\n\r\nAll of these data are provided under an Open Government Licence. \r\n\r\nThe current collection contains the following proportions of the fuller MIDAS dataset collection:\r\n\r\n96% of daily temperature observations\r\n96% of daily weather observations\r\n92% of hourly weather observations\r\n94% of daily rainfall observations\r\n96% of hourly rainfall observations\r\n98% of soil temperature observations\r\n96% of solar radiation observations\r\n93% of mean wind observations\r\n\r\nDaily rainfall: Versions up until MIDAS Open v202407 only have about 13% coverage of observations. In version v202407, the coverage was increased to 58% with the inclusion of the third-party hydrological agency stations. In version v202507, the coverage was increased further to 94% with the inclusion of historic closed stations.\r\n\r\nThe fuller \"Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations Data (1853-current)\" collection is made available for academic use via the Centre for Environmental Data Analysis.\r\n\r\nThe MIDAS Open collection is updated annually in a delayed mode to ensure that data acquisition and quality control procedures have all been completed. Quality controlled (qc-version-1) and non-quality controlled (qc-version-0) data are available from 1853 where available, although this will vary by station depending on the operation period of the station. 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These include the addition of data for calendar year 2024.\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by the Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record. Currently this represents approximately 95% of available daily temperature observations within the full MIDAS collection.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2025-07-04T16:02:58", "updateFrequency": "notPlanned", "dataLineage": "Data collated by the Met Office and archived in the Met Office's MIDAS database. 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All meteorological observations made in the UK and over neighbouring sea areas have been carefully recorded and placed in an archive where they may be accessed today by those with an interest in the weather and where they will also be available to those in future generations. The current climate database is MIDAS (Met Office Integrated Data Archive System) which has a relational structure. The MIDAS database contains the following general types of meteorological data: surface observations over land areas of the UK as far back as the digital record extends, a selection of global surface observations for the last 20 years, global surface marine observations from national and international sources as far back as the digital record extends, radiosonde observations over the UK, and at overseas stations operated by the Met Office, as far back as the digital record extends, a selection of global radiosonde observations for the last 10 years." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 76672, 76674, 76675, 76678, 76680, 76690, 76694, 76695, 76696, 76697, 76724, 76725, 76726, 76727, 76728, 76729, 76730, 76731, 76732, 76733, 76734, 76735, 76736, 76737, 76738, 76739 ], "vocabularyKeywords": [], "identifier_set": [ 13447 ], "observationcollection_set": [ { "ob_id": 26184, "uuid": "dbd451271eb04662beade68da43546e1", "short_code": "coll", "title": "Met Office MIDAS Open: UK Land Surface Stations Data (1853-current)", "abstract": "MIDAS Open is the open data version of the Met Office Integrated Data Archive System (MIDAS) containing land surface station data starting from 1853 and ending at the of the previous complete year. This collection comprises of hourly and daily weather measurements and observations of parameters relating to temperature, rainfall, sunshine, radiation, wind and weather observations such as present weather codes, cloud cover, snow etc.\r\n\r\nThe collection contains land surface observations data from those stations where the data have been designated as public sector information. Prior to version v202407 this consisted of stations operated by the Met Office only, but from version v202407, daily and hourly rainfall observations from stations with gauges owned by the Environment Agency (EA), Scottish Environment Protection Agency (SEPA) and Natural Resources Wales (NRW) have also been included in the collection. 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In version v202507, the coverage was increased further to 94% with the inclusion of historic closed stations.\r\n\r\nThe fuller \"Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations Data (1853-current)\" collection is made available for academic use via the Centre for Environmental Data Analysis.\r\n\r\nThe MIDAS Open collection is updated annually in a delayed mode to ensure that data acquisition and quality control procedures have all been completed. Quality controlled (qc-version-1) and non-quality controlled (qc-version-0) data are available from 1853 where available, although this will vary by station depending on the operation period of the station. 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The data also contains precipitation amounts, however precipitation measured over 24 hours are not stored. Over time a range of rain gauges have been used - see the linked MIDAS User Guide for further details.\r\n\r\nThis version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data.\r\n\r\nThe data were collected by observation stations operated by the Met Office across the UK and transmitted within the following message types: NCM, AWSHRLY, DLY3208, SREW and SSER. The data spans from 1915 to 2024.\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by the Met Office. 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All meteorological observations made in the UK and over neighbouring sea areas have been carefully recorded and placed in an archive where they may be accessed today by those with an interest in the weather and where they will also be available to those in future generations. The current climate database is MIDAS (Met Office Integrated Data Archive System) which has a relational structure. The MIDAS database contains the following general types of meteorological data: surface observations over land areas of the UK as far back as the digital record extends, a selection of global surface observations for the last 20 years, global surface marine observations from national and international sources as far back as the digital record extends, radiosonde observations over the UK, and at overseas stations operated by the Met Office, as far back as the digital record extends, a selection of global radiosonde observations for the last 10 years." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 76672, 76674, 76675, 76678, 76679, 76680, 76682, 76683, 76686, 76690, 76694, 76695, 76696, 76697, 76717, 76718, 76721, 76740, 76741 ], "vocabularyKeywords": [], "identifier_set": [ 13448 ], "observationcollection_set": [ { "ob_id": 26184, "uuid": "dbd451271eb04662beade68da43546e1", "short_code": "coll", "title": "Met Office MIDAS Open: UK Land Surface Stations Data (1853-current)", "abstract": "MIDAS Open is the open data version of the Met Office Integrated Data Archive System (MIDAS) containing land surface station data starting from 1853 and ending at the of the previous complete year. This collection comprises of hourly and daily weather measurements and observations of parameters relating to temperature, rainfall, sunshine, radiation, wind and weather observations such as present weather codes, cloud cover, snow etc.\r\n\r\nThe collection contains land surface observations data from those stations where the data have been designated as public sector information. Prior to version v202407 this consisted of stations operated by the Met Office only, but from version v202407, daily and hourly rainfall observations from stations with gauges owned by the Environment Agency (EA), Scottish Environment Protection Agency (SEPA) and Natural Resources Wales (NRW) have also been included in the collection. Since then, stations owned by other third-party organisations where approval for inclusion has been reached have also been added to the product.\r\n\r\nAll of these data are provided under an Open Government Licence. \r\n\r\nThe current collection contains the following proportions of the fuller MIDAS dataset collection:\r\n\r\n96% of daily temperature observations\r\n96% of daily weather observations\r\n92% of hourly weather observations\r\n94% of daily rainfall observations\r\n96% of hourly rainfall observations\r\n98% of soil temperature observations\r\n96% of solar radiation observations\r\n93% of mean wind observations\r\n\r\nDaily rainfall: Versions up until MIDAS Open v202407 only have about 13% coverage of observations. In version v202407, the coverage was increased to 58% with the inclusion of the third-party hydrological agency stations. In version v202507, the coverage was increased further to 94% with the inclusion of historic closed stations.\r\n\r\nThe fuller \"Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations Data (1853-current)\" collection is made available for academic use via the Centre for Environmental Data Analysis.\r\n\r\nThe MIDAS Open collection is updated annually in a delayed mode to ensure that data acquisition and quality control procedures have all been completed. Quality controlled (qc-version-1) and non-quality controlled (qc-version-0) data are available from 1853 where available, although this will vary by station depending on the operation period of the station. The collection includes stations which are currently operational as well as stations which were operational in the past and have since closed.\r\n\r\nEach version of the dataset will include data up until the end of the previous complete year relative to the year in the version number of the dataset (e.g. v202407 included data up until the end of 2023).\r\n\r\nNote: This collection does not supersede the full MIDAS collection which is also archived at CEDA." } ], "responsiblepartyinfo_set": [ 213287, 213288, 213281, 213282, 213283, 213284, 213285, 213286, 213289 ], "onlineresource_set": [ 93920, 93921, 93922, 93923, 93924, 93925, 93926, 93927, 93928 ] }, { "ob_id": 44584, "uuid": "99173f6a802147aeba430d96d2bb3099", "title": "MIDAS Open: UK hourly weather observation data, v202507", "abstract": "The UK hourly weather observation data contain meteorological values measured on an hourly time scale. 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These include the addition of data for calendar year 2024.\r\n\r\nFor details on observing practice see the message type information in the MIDAS User Guide linked from this record and relevant sections for parameter types.\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record. Note, METAR message types are not included in the Open version of this dataset. Those data may be accessed via the full MIDAS hourly weather data.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2025-07-04T16:26:45", "updateFrequency": "notPlanned", "dataLineage": "Data collated by the Met Office and archived in the Met Office's MIDAS database. Data are extracted from a sub-set of available tables and delivered to Centre for Environmental Data Analysis (CEDA) approximately on a yearly basis.", "removedDataReason": "", "keywords": "Met Office, MIDAS, UK, meteorology, hourly", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2025-07-18T13:47:28", "doiPublishedTime": "2025-07-18T14:30:43.449717", "removedDataTime": null, "geographicExtent": { "ob_id": 18, "bboxName": "MIDAS UK table s geographic domain", "eastBoundLongitude": 1.74002, "westBoundLongitude": -5.54236, "southBoundLatitude": 50.1172, "northBoundLatitude": 60.7592 }, "verticalExtent": null, "result_field": { "ob_id": 44572, "dataPath": "/badc/ukmo-midas-open/data/uk-hourly-weather-obs/dataset-version-202507/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 35406484741, "numberOfFiles": 53822, "fileFormat": "Data are BADC-CSV formatted." }, "timePeriod": { "ob_id": 12511, "startTime": "1875-01-01T09:00:00", "endTime": "2024-12-31T23:59:59" }, "resultQuality": { "ob_id": 300, "explanation": "Data undergo quality checking by the Met Office. State of the data in the quality control process and level of data quality are indicated using version numbers and quality control flagging with the data. See documentation about how to use the quality control flagging and version numbers.\n\nThere are also some known data from commissioning trials in the data, which are given a src_id of 99999. 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All meteorological observations made in the UK and over neighbouring sea areas have been carefully recorded and placed in an archive where they may be accessed today by those with an interest in the weather and where they will also be available to those in future generations. The current climate database is MIDAS (Met Office Integrated Data Archive System) which has a relational structure. The MIDAS database contains the following general types of meteorological data: surface observations over land areas of the UK as far back as the digital record extends, a selection of global surface observations for the last 20 years, global surface marine observations from national and international sources as far back as the digital record extends, radiosonde observations over the UK, and at overseas stations operated by the Met Office, as far back as the digital record extends, a selection of global radiosonde observations for the last 10 years." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 76672, 76674, 76675, 76679, 76680, 76682, 76690, 76694, 76695, 76696, 76697, 76709, 76742, 76743, 76744, 76745, 76746, 76747, 76748, 76749, 76750, 76751, 76752, 76753, 76754, 76755, 76756, 76757, 76758, 76759, 76760, 76761, 76762, 76763, 76764, 76765, 76766, 76767, 76768, 76769, 76770, 76771, 76772, 76773, 76774, 76775, 76776, 76777, 76778, 76779, 76780, 76781, 76782, 76783, 76784, 76785, 76786, 76787, 76788, 76789, 76790, 76791, 76792, 76793, 76794, 76795, 76796, 76797, 76798, 76799, 76800, 76801, 76802, 76803, 76804, 76805, 76806, 76807, 76808, 76809, 76810, 76811, 76812, 76813, 76814, 76815, 76816, 76817, 76818, 76819, 76820, 76821, 76822, 76823, 76824, 76825, 76826, 76827, 76828, 76829, 76830, 76831, 76832, 76833, 76834, 76835, 76836, 76837 ], "vocabularyKeywords": [], "identifier_set": [ 13449 ], "observationcollection_set": [ { "ob_id": 26184, "uuid": "dbd451271eb04662beade68da43546e1", "short_code": "coll", "title": "Met Office MIDAS Open: UK Land Surface Stations Data (1853-current)", "abstract": "MIDAS Open is the open data version of the Met Office Integrated Data Archive System (MIDAS) containing land surface station data starting from 1853 and ending at the of the previous complete year. This collection comprises of hourly and daily weather measurements and observations of parameters relating to temperature, rainfall, sunshine, radiation, wind and weather observations such as present weather codes, cloud cover, snow etc.\r\n\r\nThe collection contains land surface observations data from those stations where the data have been designated as public sector information. Prior to version v202407 this consisted of stations operated by the Met Office only, but from version v202407, daily and hourly rainfall observations from stations with gauges owned by the Environment Agency (EA), Scottish Environment Protection Agency (SEPA) and Natural Resources Wales (NRW) have also been included in the collection. Since then, stations owned by other third-party organisations where approval for inclusion has been reached have also been added to the product.\r\n\r\nAll of these data are provided under an Open Government Licence. \r\n\r\nThe current collection contains the following proportions of the fuller MIDAS dataset collection:\r\n\r\n96% of daily temperature observations\r\n96% of daily weather observations\r\n92% of hourly weather observations\r\n94% of daily rainfall observations\r\n96% of hourly rainfall observations\r\n98% of soil temperature observations\r\n96% of solar radiation observations\r\n93% of mean wind observations\r\n\r\nDaily rainfall: Versions up until MIDAS Open v202407 only have about 13% coverage of observations. In version v202407, the coverage was increased to 58% with the inclusion of the third-party hydrological agency stations. In version v202507, the coverage was increased further to 94% with the inclusion of historic closed stations.\r\n\r\nThe fuller \"Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations Data (1853-current)\" collection is made available for academic use via the Centre for Environmental Data Analysis.\r\n\r\nThe MIDAS Open collection is updated annually in a delayed mode to ensure that data acquisition and quality control procedures have all been completed. Quality controlled (qc-version-1) and non-quality controlled (qc-version-0) data are available from 1853 where available, although this will vary by station depending on the operation period of the station. The collection includes stations which are currently operational as well as stations which were operational in the past and have since closed.\r\n\r\nEach version of the dataset will include data up until the end of the previous complete year relative to the year in the version number of the dataset (e.g. v202407 included data up until the end of 2023).\r\n\r\nNote: This collection does not supersede the full MIDAS collection which is also archived at CEDA." } ], "responsiblepartyinfo_set": [ 213290, 213291, 213292, 213293, 213294, 213295, 213296, 213297, 213298 ], "onlineresource_set": [ 93929, 93930, 93931, 93933, 93934, 93935, 93936, 93937, 93932 ] }, { "ob_id": 44585, "uuid": "bed3b1f2ce0c4ba780927e9fac04f696", "title": "MIDAS Open: UK mean wind data, v202507", "abstract": "The UK mean wind data contain the mean wind speed and direction, and the direction, speed and time of the maximum gust, all during 1 or more hours, ending at the stated time and date. The data were collected by observation stations operated by the Met Office across the UK and transmitted within the following message types: SYNOP, HCM, AWSHRLY, DLY3208, HWNDAUTO and HWND6910. The data spans from 1949 to 2024.\r\n\r\nThis version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data. These include the addition of data for calendar year 2024.\r\n\r\nFor further details on observing practice, including measurement accuracies for the message types, see relevant sections of the MIDAS User Guide linked from this record (e.g. section 3.3 details the wind network in the UK, section 5.5 covers wind measurements in general and section 4 details message type information).\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by the Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2025-07-04T14:25:30", "updateFrequency": "notPlanned", "dataLineage": "Data collated by the Met Office and archived in the Met Office's MIDAS database. Data are extracted from a sub-set of available tables and delivered to Centre for Environmental Data Analysis (CEDA) approximately on a yearly basis.", "removedDataReason": "", "keywords": "Met Office, MIDAS, UK, meteorology, wind speed, wind direction, gust", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2025-07-18T14:13:06", "doiPublishedTime": "2025-07-18T14:30:52.178004", "removedDataTime": null, "geographicExtent": { "ob_id": 17, "bboxName": "", "eastBoundLongitude": 1.74002, "westBoundLongitude": -8.5636, "southBoundLatitude": 49.914, "northBoundLatitude": 60.8562 }, "verticalExtent": null, "result_field": { "ob_id": 44571, "dataPath": "/badc/ukmo-midas-open/data/uk-mean-wind-obs/dataset-version-202507/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 9239183702, "numberOfFiles": 15897, "fileFormat": "Data are BADC-CSV formatted." }, "timePeriod": { "ob_id": 12512, "startTime": "1949-01-01T00:00:00", "endTime": "2024-12-31T23:59:59" }, "resultQuality": { "ob_id": 296, "explanation": "Data undergo quality checking by the Met Office. State of the data in the quality control process and level of data quality are indicated using version numbers and quality control flagging with the data. See documentation about how to use the quality control flagging and version numbers.\n\nThere are also some known data from commissioning trials in the data, which are given a src_id of 99999. 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All meteorological observations made in the UK and over neighbouring sea areas have been carefully recorded and placed in an archive where they may be accessed today by those with an interest in the weather and where they will also be available to those in future generations. The current climate database is MIDAS (Met Office Integrated Data Archive System) which has a relational structure. 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This collection comprises of hourly and daily weather measurements and observations of parameters relating to temperature, rainfall, sunshine, radiation, wind and weather observations such as present weather codes, cloud cover, snow etc.\r\n\r\nThe collection contains land surface observations data from those stations where the data have been designated as public sector information. Prior to version v202407 this consisted of stations operated by the Met Office only, but from version v202407, daily and hourly rainfall observations from stations with gauges owned by the Environment Agency (EA), Scottish Environment Protection Agency (SEPA) and Natural Resources Wales (NRW) have also been included in the collection. Since then, stations owned by other third-party organisations where approval for inclusion has been reached have also been added to the product.\r\n\r\nAll of these data are provided under an Open Government Licence. \r\n\r\nThe current collection contains the following proportions of the fuller MIDAS dataset collection:\r\n\r\n96% of daily temperature observations\r\n96% of daily weather observations\r\n92% of hourly weather observations\r\n94% of daily rainfall observations\r\n96% of hourly rainfall observations\r\n98% of soil temperature observations\r\n96% of solar radiation observations\r\n93% of mean wind observations\r\n\r\nDaily rainfall: Versions up until MIDAS Open v202407 only have about 13% coverage of observations. In version v202407, the coverage was increased to 58% with the inclusion of the third-party hydrological agency stations. In version v202507, the coverage was increased further to 94% with the inclusion of historic closed stations.\r\n\r\nThe fuller \"Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations Data (1853-current)\" collection is made available for academic use via the Centre for Environmental Data Analysis.\r\n\r\nThe MIDAS Open collection is updated annually in a delayed mode to ensure that data acquisition and quality control procedures have all been completed. Quality controlled (qc-version-1) and non-quality controlled (qc-version-0) data are available from 1853 where available, although this will vary by station depending on the operation period of the station. The collection includes stations which are currently operational as well as stations which were operational in the past and have since closed.\r\n\r\nEach version of the dataset will include data up until the end of the previous complete year relative to the year in the version number of the dataset (e.g. v202407 included data up until the end of 2023).\r\n\r\nNote: This collection does not supersede the full MIDAS collection which is also archived at CEDA." } ], "responsiblepartyinfo_set": [ 213299, 213300, 213301, 213302, 213303, 213304, 213305, 213306, 213307 ], "onlineresource_set": [ 93942, 93943, 93944, 93938, 93939, 93945, 93940, 93941, 93946 ] }, { "ob_id": 44586, "uuid": "c0cd9756b5234f1881c375fb6bb94245", "title": "MIDAS Open: UK daily weather observation data, v202507", "abstract": "The UK daily weather observation data contain meteorological values measured on a 24 hour time scale. The measurements of sunshine duration, concrete state, snow depth, fresh snow depth, and days of snow, hail, thunder and gail were attained by observation stations operated by the Met Office across the UK operated and transmitted within DLY3208, NCM, AWSDLY and SYNOP messages. The data span from 1887 to 2024. For details of observations see the relevant sections of the MIDAS User Guide linked from this record for the various message types.\r\n\r\nThis version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data. These include the addition of data for calendar year 2024.\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by the Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record. Currently this represents approximately 95% of available daily weather observations within the full MIDAS collection.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2025-07-04T15:26:36", "updateFrequency": "notPlanned", "dataLineage": "Data collated by the Met Office and archived in the Met Office's MIDAS database. 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State of the data in the quality control process and level of data quality are indicated using version numbers and quality control flagging with the data. See documentation about how to use the quality control flagging and version numbers.\n\nThere are also some known data from commissioning trials in the data, which are given a src_id of 99999. These should be ignored by the user.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2014-09-11" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 1246, "uuid": "1efa07092762447189d030fe5d41e1fa", "short_code": "acq", "title": "Acquisition Process for: UK Daily Weather Observation Data, Part of the Met Office Integrated Data Archive System (MIDAS)", "abstract": "This acquisition is comprised of the following: INSTRUMENTS: Station Observer, Sunshine Recorder, Cloud Recorder, Snow Depth Sensor, Present (and Past) Weather Sensor; PLATFORMS: Land SYNOP (surface synoptic observations) Station Network, NCM (National Climate Message) Station Network, DLY3208 (Daily observations from Metform 3208) Station Network, AWSDLY (Automatic Weather Station Daily values) Station Network; " }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 69 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 1186, "uuid": "245df050d57a500c183b88df509f5f5a", "short_code": "proj", "title": "Met Office Integrated Data Archive System (MIDAS)", "abstract": "Since the early days of this century the Met Office has been responsible for maintaining the public memory of the weather. All meteorological observations made in the UK and over neighbouring sea areas have been carefully recorded and placed in an archive where they may be accessed today by those with an interest in the weather and where they will also be available to those in future generations. The current climate database is MIDAS (Met Office Integrated Data Archive System) which has a relational structure. The MIDAS database contains the following general types of meteorological data: surface observations over land areas of the UK as far back as the digital record extends, a selection of global surface observations for the last 20 years, global surface marine observations from national and international sources as far back as the digital record extends, radiosonde observations over the UK, and at overseas stations operated by the Met Office, as far back as the digital record extends, a selection of global radiosonde observations for the last 10 years." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 76672, 76674, 76675, 76678, 76679, 76680, 76682, 76690, 76694, 76695, 76696, 76697, 76808, 76852, 76853, 76854, 76855, 76856, 76857, 76858, 76859, 76860, 76861, 76862, 76863, 76864, 76865, 76866, 76867, 76868, 76869, 76870, 76871, 76872, 76873, 76874, 76875, 76876, 78250, 78251, 78252, 78253, 78254, 78255, 78256, 78257, 78258, 78259 ], "vocabularyKeywords": [], "identifier_set": [ 13451 ], "observationcollection_set": [ { "ob_id": 26184, "uuid": "dbd451271eb04662beade68da43546e1", "short_code": "coll", "title": "Met Office MIDAS Open: UK Land Surface Stations Data (1853-current)", "abstract": "MIDAS Open is the open data version of the Met Office Integrated Data Archive System (MIDAS) containing land surface station data starting from 1853 and ending at the of the previous complete year. This collection comprises of hourly and daily weather measurements and observations of parameters relating to temperature, rainfall, sunshine, radiation, wind and weather observations such as present weather codes, cloud cover, snow etc.\r\n\r\nThe collection contains land surface observations data from those stations where the data have been designated as public sector information. Prior to version v202407 this consisted of stations operated by the Met Office only, but from version v202407, daily and hourly rainfall observations from stations with gauges owned by the Environment Agency (EA), Scottish Environment Protection Agency (SEPA) and Natural Resources Wales (NRW) have also been included in the collection. Since then, stations owned by other third-party organisations where approval for inclusion has been reached have also been added to the product.\r\n\r\nAll of these data are provided under an Open Government Licence. \r\n\r\nThe current collection contains the following proportions of the fuller MIDAS dataset collection:\r\n\r\n96% of daily temperature observations\r\n96% of daily weather observations\r\n92% of hourly weather observations\r\n94% of daily rainfall observations\r\n96% of hourly rainfall observations\r\n98% of soil temperature observations\r\n96% of solar radiation observations\r\n93% of mean wind observations\r\n\r\nDaily rainfall: Versions up until MIDAS Open v202407 only have about 13% coverage of observations. In version v202407, the coverage was increased to 58% with the inclusion of the third-party hydrological agency stations. In version v202507, the coverage was increased further to 94% with the inclusion of historic closed stations.\r\n\r\nThe fuller \"Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations Data (1853-current)\" collection is made available for academic use via the Centre for Environmental Data Analysis.\r\n\r\nThe MIDAS Open collection is updated annually in a delayed mode to ensure that data acquisition and quality control procedures have all been completed. Quality controlled (qc-version-1) and non-quality controlled (qc-version-0) data are available from 1853 where available, although this will vary by station depending on the operation period of the station. The collection includes stations which are currently operational as well as stations which were operational in the past and have since closed.\r\n\r\nEach version of the dataset will include data up until the end of the previous complete year relative to the year in the version number of the dataset (e.g. v202407 included data up until the end of 2023).\r\n\r\nNote: This collection does not supersede the full MIDAS collection which is also archived at CEDA." } ], "responsiblepartyinfo_set": [ 213308, 213312, 213309, 213310, 213311, 213315, 213313, 213314, 213316 ], "onlineresource_set": [ 93950, 93951, 93947, 93955, 93948, 93949, 93952, 93953, 93954 ] }, { "ob_id": 44589, "uuid": "0107f895ad4e4d62b7e8c1ba2fd9d737", "title": "Hunga WACCM6 (Whole Atmosphere Community Climate Model v6) global data (2022-2031)", "abstract": "Hunga WACCM6 global data provide simulations to investigate the evolution of Hunga volcanic water and aerosols and their impacts. It uses MAM4 aerosol model. It has nudged simulations (Exp2a) and free run simulations (Exp1). The Free run simulations has fixed SST (30 ensembles, Exp1_fixedSST) and coupled ocean (30 ensembles, Exp1_CoupledOcean). The nudged simulations are monthly average for 2022-2023, and the free run simulations are monthly average from 2022-2031. The simulations follow the protocol (Zhu et al., 2025, GMD). Contact WACCM team to get daily data.\r\nThe data are in time series with the variables requested by Zhu et al. (2025, GMD). The format is NetCDF. The grid resolution are about 1x1 degree.\r\nThe Hunga WACCM global data is produced for the APARC Hunga Impact Activity: Hunga Tonga Hunga Ha’apai Volcano Impact Model Observation Comparison (HTHH-MOC) Project and the Hunga Volcanic Eruption Atmospheric Impacts Report.\r\nPlease acknowledge the authors in publications.", "creationDate": "2025-07-09T20:55:52.268672", "lastUpdatedDate": "2025-07-24T15:13:14.014982", "latestDataUpdateTime": "2025-07-09T20:55:52", "updateFrequency": "notPlanned", "dataLineage": "The data was created and updated on JASMIN since April 2024 and transferred to CEDA in 2025.", "removedDataReason": "", "keywords": "APARC,Hunga Volcano,HTHHMOC,Hunga Impact Assessment,Hunga Volcanic Eruption Atmospheric Impacts Report", "publicationState": "preview", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "pending", "dataPublishedTime": null, "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 4803, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": null, "timePeriod": { "ob_id": 12545, "startTime": "2022-01-01T00:00:00", "endTime": "2031-12-31T00:00:00" }, "resultQuality": { "ob_id": 4756, "explanation": "The model output covers variables based on the Chemistry-Climate Modeling Initiative (CCMI) output list with some additions specific to this study (detailed in the supplementary material in Zhu et al. 2025, GMD).\r\nWe request The 3D model output in a time series is requested on both model levels (hybrid pressure or height) and interpolated to CMIP6 plev39 grid (plev39: 1000, 925, 850, 700, 600, 500, 400, 300, 250, 200, 170, 150, 130, 115, 100, 90, 80, 70, 50, 30, 20, 15, 10, 7, 5, 3, 2, 1.5, 1 0, 0.7, 0.5, 0.4, 0.3, 0.2, 0.15, 0.1, 0.07, 0.05, 0.03 hPa) and for mesospheric analysis adding 0.02, 0.01, 0.007, 0.005, 0.003, 0.001 above the plev39 grid. \r\n(longitude from 0˚E to 360˚E; latitude from 90˚S to 90˚N; pressure levels from 1000 hPa to 0.001 hPa or altitude from 0 meter to 140,000 meter)", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2025-07-15" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 19041, "uuid": "86286b6bde1a47828b2c2befae75cf7c", "short_code": "comp", "title": "National Center for Atmospheric Research WACCM model output", "abstract": "National Center for Atmospheric Research WACCM model output" }, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [], "projects": [ { "ob_id": 44590, "uuid": "c77837b665e34af8b8793c310709bf58", "short_code": "proj", "title": "Hunga Tonga-Hunga Ha’apai Volcano Impact Model Observation Comparison (HTHH-MOC) Project", "abstract": "The 2022 Hunga volcanic eruption injected a significant amount of water vapor and a moderate amount of sulfur dioxide into the stratosphere causing observable responses in the climate system. We have developed a model-observation comparison project to investigate the evolution of volcanic water and aerosols, and their impacts on atmospheric dynamics, chemistry, and climate, using several state-of-the-art chemistry climate models." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 213326, 213327, 213328, 213329, 213330, 213331, 213656, 213657, 213658, 213659, 213660, 213661, 213662 ], "onlineresource_set": [] }, { "ob_id": 44594, "uuid": "793b4dab3094419caa5a20a1ec628f35", "title": "Hunga CMAM (Canadian Middle Atmosphere Model ) global data 2022-2031", "abstract": "Hunga CMAM global data provide simulations to investigate the evolution of Hunga volcanic water and aerosols and their impacts. It conducted free run simulations (Exp1) for control and H2Oonly runs. The Free run simulations has fixed SST (10 ensembles, Exp1_fixedSST) from 2022-2031. The simulations follow the protocol (Zhu et al., 2025, GMD).\r\nThe data are in time series with the variable named defined in Zhu et al. (2025, GMD). The simulations were performed at T47 spectral resolution (approximately 3.8° resolution on the linear transform grid used for the model physics), with 80 vertical levels giving a vertical resolution of approximately 0.8 km at 100 hPa, increasing to 2.3 km above 0.1 hPa. \r\nThe Hunga CMAM global data is produced for the APARC Hunga Impact Activity: Hunga Tonga Hunga Ha’apai Volcano Impact Model Observation Comparison (HTHH-MOC) Project and the Hunga Volcanic Eruption Atmospheric Impacts Report.", "creationDate": "2025-07-15T17:20:49.646927", "lastUpdatedDate": "2025-07-15T20:35:03", "latestDataUpdateTime": "2025-07-15T17:20:49", "updateFrequency": "notPlanned", "dataLineage": "pre-existing transient run - the CCMI-2022 refD1 r1i1p1f1 simulation. The 10 ensemble members were started with a small random\r\nperturbation to the ground temperature initial conditions.\r\n\r\nSSTs/sea-ice was a repeating annual cycle calculated from the 2012-2021 average.\r\n\r\nThe QBO is nudged to follow observations, based on a timeseries of equatorial winds from the Singapore radiosonde observations. The timeseries follows observations up to August 2023, then uses an extension created by pasting on earlier parts of the timeseries that are congruent with the QBO. Here, the QBO timeseries from September, 1979 was inserted beginning January, 2024 with data for the last four months of 2023 created by linearly transitioning from the Singapore radiosonde data to the QBO record from 1979.\r\n\r\nMost other forcings are as specified for the CCMI-2022 simulations, including following SSP2-4.5 emissions, solar forcing, etc.", "removedDataReason": "", "keywords": "APARC,Hunga Volcano,HTHHMOC,Hunga Impact Assessment,Hunga Volcanic Eruption Atmospheric Impacts Report", "publicationState": "working", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "pending", "dataPublishedTime": null, "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 4804, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": null, "timePeriod": { "ob_id": 12527, "startTime": "2022-01-01T00:00:00", "endTime": "2031-12-31T00:00:00" }, "resultQuality": { "ob_id": 4757, "explanation": "The model output covers variables based on the Chemistry-Climate Modeling Initiative (CCMI) output list with some additions specific to this study (detailed in the supplementary material in Zhu et al. 2025, GMD).\r\nWe request The 3D model output in a time series is requested on both model levels (hybrid pressure or height) and interpolated to CMIP6 plev39 grid (plev39: 1000, 925, 850, 700, 600, 500, 400, 300, 250, 200, 170, 150, 130, 115, 100, 90, 80, 70, 50, 30, 20, 15, 10, 7, 5, 3, 2, 1.5, 1 0, 0.7, 0.5, 0.4, 0.3, 0.2, 0.15, 0.1, 0.07, 0.05, 0.03 hPa) and for mesospheric analysis adding 0.02, 0.01, 0.007, 0.005, 0.003, 0.001 above the plev39 grid. \r\n(longitude from 0˚E to 360˚E; latitude from 90˚S to 90˚N; pressure levels from 1000 hPa to 0.001 hPa)", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2025-07-15" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39357, "uuid": "a9c5ee7a72e84337ab1a713904bd1a3c", "short_code": "comp", "title": "CMAM model deployed at CCCma", "abstract": "Canadian Middle Atmosphere Model (CMAM) is a well-established high-top chemistry-climate model deployed at Canadian Centre for Climate Modelling and Analysis (CCCma)." }, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [], "projects": [ { "ob_id": 44590, "uuid": "c77837b665e34af8b8793c310709bf58", "short_code": "proj", "title": "Hunga Tonga-Hunga Ha’apai Volcano Impact Model Observation Comparison (HTHH-MOC) Project", "abstract": "The 2022 Hunga volcanic eruption injected a significant amount of water vapor and a moderate amount of sulfur dioxide into the stratosphere causing observable responses in the climate system. We have developed a model-observation comparison project to investigate the evolution of volcanic water and aerosols, and their impacts on atmospheric dynamics, chemistry, and climate, using several state-of-the-art chemistry climate models." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 213362, 213363, 213364, 213365, 213366, 213367, 213369 ], "onlineresource_set": [] }, { "ob_id": 44595, "uuid": "2e82011c05e1494a930e1c267666f0c4", "title": "Hunga GSFC2D (The NASA Goddard Space Flight Center 2D chemistry-climate model) global data 2022-2031", "abstract": "Hunga GSFC2D global data provide simulations to investigate the evolution of Hunga volcanic water and aerosols and their impacts. It conducted free run simulations (Exp1) with fixed SST (10 ensembles, Exp1_fixedSST) from 2022-2031; and nudged run from 2022-2023 (Exp2b, prescribed aerosol). The simulations follow the protocol (Zhu et al., 2025, GMD).\r\nThe data are in time series with the variable named defined in Zhu et al. (2025, GMD). The horizontal resolution is 4°, and 76 vertical levels until 0.002 hPa (~ 92 km).\r\nThe Hunga GSFC2D global data is produced for the APARC Hunga Impact Activity: Hunga Tonga Hunga Ha’apai Volcano Impact Model Observation Comparison (HTHH-MOC) Project and the Hunga Volcanic Eruption Atmospheric Impacts Report.", "creationDate": "2025-07-15T20:43:51.055894", "lastUpdatedDate": "2025-08-08T21:00:22.426542", "latestDataUpdateTime": "2025-07-15T20:43:51", "updateFrequency": "notPlanned", "dataLineage": "All simulations start from 1 January 2011 and are spun-up for 11 years (2011-2021).\r\nFor the H2O injection, Aura/MLS observations are used to derive a daily zonal mean water vapor anomaly in latitude-altitude, which is added to the baseline H2O (No volcano) through the end of February 2022. This combined water vapor field is then fully model computed starting 1 March 2022 through the end of 2031.", "removedDataReason": "", "keywords": "APARC,Hunga Volcano,HTHHMOC,Hunga Impact Assessment,Hunga Volcanic Eruption Atmospheric Impacts Report", "publicationState": "working", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "pending", "dataPublishedTime": null, "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 4805, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": null, "timePeriod": { "ob_id": 12593, "startTime": "2022-01-01T00:00:00", "endTime": "2031-12-31T00:00:00" }, "resultQuality": { "ob_id": 4758, "explanation": "The model output covers variables based on the Chemistry-Climate Modeling Initiative (CCMI) output list with some additions specific to this study (detailed in the supplementary material in Zhu et al. 2025, GMD).\r\nWe request The 2D model output in a time series is requested on both model levels (hybrid pressure or height) and interpolated to CMIP6 plev39 grid (plev39: 1000, 925, 850, 700, 600, 500, 400, 300, 250, 200, 170, 150, 130, 115, 100, 90, 80, 70, 50, 30, 20, 15, 10, 7, 5, 3, 2, 1.5, 1 0, 0.7, 0.5, 0.4, 0.3, 0.2, 0.15, 0.1, 0.07, 0.05, 0.03 hPa) and for mesospheric analysis adding 0.02, 0.01, 0.007, 0.005, 0.003, 0.001 above the plev39 grid. \r\n( latitude from 90˚S to 90˚N; pressure levels from 1000 hPa to 0.001 hPa)", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2025-07-15" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 44758, "uuid": "8bb0551ac46242469f1f46180fe0edca", "short_code": "comp", "title": "The NASA Goddard Space Flight Center two-dimensional (2D) chemistry-climate model (GSFC2D)", "abstract": "The NASA Goddard Space Flight Center two-dimensional (2D) chemistry-climate model (GSFC2D) has a domain extending from the surface to ∼92 km (0.002 hPa). The model has 76 levels, with 1 km vertical resolution from the surface to the lower mesosphere (60 km) and 2 km resolution above (60-92 km). The horizontal resolution is 4° latitude, and the model uses a 2D (latitude-altitude) finite volume dynamical core (Lin & Rood, 1996) for advective transport." }, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [], "projects": [ { "ob_id": 44590, "uuid": "c77837b665e34af8b8793c310709bf58", "short_code": "proj", "title": "Hunga Tonga-Hunga Ha’apai Volcano Impact Model Observation Comparison (HTHH-MOC) Project", "abstract": "The 2022 Hunga volcanic eruption injected a significant amount of water vapor and a moderate amount of sulfur dioxide into the stratosphere causing observable responses in the climate system. We have developed a model-observation comparison project to investigate the evolution of volcanic water and aerosols, and their impacts on atmospheric dynamics, chemistry, and climate, using several state-of-the-art chemistry climate models." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 213370, 213371, 213372, 213373, 213374, 213375, 213378 ], "onlineresource_set": [] }, { "ob_id": 44596, "uuid": "5675b0be944f45a8af0e7ddbeb47a011", "title": "ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost Ground Temperature for the Northern Hemisphere, v5.0", "abstract": "This dataset contains permafrost ground temperature data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the second version of their Climate Research Data Package (CRDP v4). It is derived from a thermal model driven and constrained by satellite data. Grid products of CRDP v4 are released in annual files, covering the start to the end of the Julian year. This corresponds to average annual ground temperatures and is provided for specific depths (surface, 1m, 2m, 5m, 10m).\r\n \r\nCase A: It covers the Northern Hemisphere (north of 30°N) for the period 2003-2023 based on MODIS Land Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data.\r\ne.g. ESACCI-PERMAFROST-L4-GTD-MODISLST_CRYOGRID-AREA4_PP-****-fv05.0.nc\r\nCase B: It covers the Northern Hemisphere (north of 30°N) for the period 1997-2002 based on downscaled ERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for the overlap period 2003-2023 using a pixel-specific statistics for each day of the year.\r\ne.g. ESACCI-PERMAFROST-L4-GTD-ERA5_MODISLST_BIASCORRECTED-AREA4_PP-****-fv05.0.nc", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2024-01-12T12:24:08", "latestDataUpdateTime": null, "updateFrequency": "notPlanned", "dataLineage": "Data have been produced by the ESA CCI Permafrost project as part of ESA's Climate Change Initiative programme", "removedDataReason": "", "keywords": "Permafrost, CCI, Ground Temperature", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2025-08-27T17:18:33", "doiPublishedTime": "2025-08-28T08:05:04", "removedDataTime": null, "geographicExtent": { "ob_id": 4144, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": 25.0, "northBoundLatitude": 85.0 }, "verticalExtent": null, "result_field": { "ob_id": 44786, "dataPath": "/neodc/esacci/permafrost/data/ground_temperature/L4/area4/pp/v05.0/northern_hemisphere/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 9775333633, "numberOfFiles": 28, "fileFormat": "NetCDF" }, "timePeriod": { "ob_id": 12886, "startTime": "1997-01-01T00:00:00", "endTime": "2023-12-31T23:59:59" }, "resultQuality": { "ob_id": 3359, "explanation": "Data are as provided by the CCI Permafrost project. For further quality information see the permafrost CCI website and linked documentation.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2019-12-04" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 44769, "uuid": "c116848868054e18a9f5a51d68c3ad21", "short_code": "comp", "title": "Computation of Permafrost v5 datsets by the ESA Permafrost CCI", "abstract": "Complementing ground-based monitoring networks, the Permafrost CCI project is establishing Earth Observation (EO) based products for the permafrost ECV spanning the last two decades. Since ground temperature and thaw depth cannot be directly observed from space-borne sensors, a variety of satellite and reanalysis data are combined in a ground thermal model. The algorithm uses remotely sensed data sets of Land Surface Temperature (MODIS LST/ ESA LST CCI) and landcover (ESA Landcover CCI and ESA Permafrost CCI Circumarctic landcover units) to drive the transient permafrost model CryoGrid, which yields thaw depth and ground temperature at various depths, while ground temperature forms the basis for permafrost fraction. The Land Surface Temperature data sets are employed to determine the upper boundary condition of the differential equation, while its coefficients (e.g. heat capacity and thermal conductivity) are selected according to the landcover information (Westermann et al., 2017). With this, a spatial resolution of the final product of 1 km is possible, corresponding to “breakthrough” according to the WMO OSCAR database.\r\n\r\nInput data: MODIS Land surface temperature is used as the main input for the L4 production for 2003-2023 data. Sensors of auxiliary data are listed in the meta data.\r\n\r\nDownscaled and bias corrected ERA reanalyses data based on statistics of the overlap period between ERA reanalysis and MODIS LST are used for data before 2003. Sensors of auxiliary data are listed in the meta data." }, "procedureCompositeProcess": null, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2596, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 58, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_permafrost_terms_and_conditions.pdf", "licenceClassifications": [] } } ], "projects": [ { "ob_id": 29966, "uuid": "7133bbd64540498bbffd1c28bbbea9cd", "short_code": "proj", "title": "ESA Permafrost Climate Change Initiative Project", "abstract": "The Permafrost Climate Change Initiatve Project (Permafrost_cci) is part of the European Space Agency's Climate Change Initiative Programme. The ultimate objective of Permafrost_cci is to develop and deliver permafrost maps of Essential Climate Variable products, primarily derived from satellite measurements." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 32072, 62645, 62780, 62781, 70622, 75336, 75337, 75338, 75339, 75340, 75341 ], "vocabularyKeywords": [ { "ob_id": 10784, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/platform/plat_aqua", "resolvedTerm": "Aqua" }, { "ob_id": 11105, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/sensor/sens_modis", "resolvedTerm": "MODIS" }, { "ob_id": 11139, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/sensor/sens_sarcS1", "resolvedTerm": "SAR-C (Sentinel-1)" }, { "ob_id": 11140, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/sensor/sens_msi", "resolvedTerm": "MSI (Sentinel-2)" }, { "ob_id": 10909, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/platform/plat_sentinel2a", "resolvedTerm": "Sentinel-2A" }, { "ob_id": 10923, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/platform/plat_terra", "resolvedTerm": "Terra" }, { "ob_id": 10988, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/procLev/proc_level4", "resolvedTerm": "Level 4" }, { "ob_id": 11063, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/ecv/cciecv_permafrost", "resolvedTerm": "permafrost" }, { "ob_id": 10808, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/platform/plat_envisat", "resolvedTerm": "Envisat" }, { "ob_id": 10907, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/platform/plat_sentinel1a", "resolvedTerm": "Sentinel-1A" }, { "ob_id": 11133, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/sensor/sens_vegetation", "resolvedTerm": "VEGETATION" }, { "ob_id": 11135, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/platform/plat_probaV", "resolvedTerm": "PROBA-V" }, { "ob_id": 11103, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/sensor/sens_meris", "resolvedTerm": "MERIS" } ], "identifier_set": [ 13517 ], "observationcollection_set": [ { "ob_id": 44805, "uuid": "ecc8890023754cd8853d5e3f7e08b0b0", "short_code": "coll", "title": "ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost version 5 data products", "abstract": "This collection of data forms the Permafrost Climate Research Data Package (CRDP v4), which comprises the Version 5.0 Permafrost data products from the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. Data products include Ground Temperature, Active Layer Thickness and Permafrost Extent for the Northern Hemisphere (north of 30°) and Antarctica (south of 60°) for the period 1997-2023. They are derived from a thermal model driven and constrained by satellite data. Gridded products are released in annual files, covering the start to the end of the Julian year. 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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.", "creationDate": "2025-07-21T08:32:15.752790", "lastUpdatedDate": "2025-07-21T08:32:15", "latestDataUpdateTime": "2025-07-21T08:32:15", "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. 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Specifically, this includes an Excel table listing inventories published between 2010 and 2021 as well as several characteristics for each inventory, such as the area mapped, the number of landslides mapped, where known, and methods used. Also included is an ArcGIS shapefile dividing Nepal into 5km2 hexagons. Each hexagon contains the number of landslide inventories within each region and several metrics, such as mean susceptibility and the number of recorded landslides from the Government of Nepal's Bipad Portal (bipadportal.gov.np).", "creationDate": "2025-07-23T10:04:02.816872", "lastUpdatedDate": "2025-07-23T10:04:36.194955", "latestDataUpdateTime": "2025-07-23T10:04:02.816876", "updateFrequency": "notPlanned", "dataLineage": "Data were compiled as part of the NERC-funded Sajag-Nepal project (NE_T01038X_1). Full details of how the inventory was compiled are provided in Harvey et al. (2024) Landslides, available at https://doi.org/10.1007/s11069-024-07013-1", "removedDataReason": "", "keywords": "Nepal,landslides,inventories", "publicationState": "working", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "pending", "dataPublishedTime": null, "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 4810, "bboxName": "", "eastBoundLongitude": 88.1, "westBoundLongitude": 80.0, "southBoundLatitude": 26.5, "northBoundLatitude": 31.0 }, "verticalExtent": null, "result_field": null, "timePeriod": { "ob_id": 12540, "startTime": "2010-01-01T00:00:00", "endTime": "2021-12-31T00:00:00" }, "resultQuality": { "ob_id": 4762, "explanation": "", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2025-07-23" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [], "projects": [ { "ob_id": 44619, "uuid": "00cef3df36d648f5b8aeba7b29b491fd", "short_code": "proj", "title": "Preparedness and planning for the mountain hazard and risk chain in Nepal", "abstract": "International collaborative research on earthquake- and monsoon-triggered hazard chains in Nepal, the ways in which those hazards are experienced, and the ways in which they can be planned for by individuals, communities, government, and humanitarian partners" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 213591, 213592, 213593, 213594, 213595, 213596, 213597, 213598, 213599, 213600, 213601, 213602, 213603, 213604, 213605, 213606, 213607, 213608, 213609, 213610, 213611 ], "onlineresource_set": [] }, { "ob_id": 44620, "uuid": "00927c853d2e4c3486abce95fe049511", "title": "Evolution of coseismic and post-seismic landsliding after the 2015 Mw 7.8 Gorkha earthquake, Nepal", "abstract": "Dataset comprises landslide area density rasters used in the analysis of the spatial evolution of landsliding following the 2015 Mw 7.8 Gorkha earthquake in Nepal. The data include 11 epochs of landslide area density, including bi-annual assessments between 2014 and 2018 (pre- and post-monsoon), and an additional coseismic assessment from 2015.\r\n\r\nThe dataset covers an area of 25,575 km2 covering the 14 administrative districts most intensively affected by the 2015 Gorkha earthquake. An AOI shapefile polygon is included within the dataset.", "creationDate": "2025-07-23T12:45:25.485510", "lastUpdatedDate": "2025-07-23T12:45:25.485512", "latestDataUpdateTime": "2025-07-23T12:45:25.485513", "updateFrequency": "notPlanned", "dataLineage": "Data were mapped by researchers at Durham University and NSET-Nepal with support from the UKRI-DFID SHEAR program (201844-112). A full description of the methodology and dataset is provided in Kincey et al. (2021) Journal of Geophysical Research-Earth Surface, https://doi.org/10.1029/2020JF005803. The dataset is also available via Zenodo at https://doi.org/10.5281/zenodo.4274485", "removedDataReason": "", "keywords": "Nepal,landslides,inventory,coseismic landslides,post-seismic landslides", "publicationState": "working", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "pending", "dataPublishedTime": null, "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 4811, "bboxName": "", "eastBoundLongitude": 86.75, "westBoundLongitude": 84.25, "southBoundLatitude": 26.75, "northBoundLatitude": 29.0 }, "verticalExtent": null, "result_field": null, "timePeriod": { "ob_id": 12541, "startTime": "2014-04-01T00:00:00", "endTime": "2018-10-31T00:00:00" }, "resultQuality": { "ob_id": 4763, "explanation": "", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2025-07-23" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [], "projects": [ { "ob_id": 44619, "uuid": "00cef3df36d648f5b8aeba7b29b491fd", "short_code": "proj", "title": "Preparedness and planning for the mountain hazard and risk chain in Nepal", "abstract": "International collaborative research on earthquake- and monsoon-triggered hazard chains in Nepal, the ways in which those hazards are experienced, and the ways in which they can be planned for by individuals, communities, government, and humanitarian partners" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 213615, 213616, 213617, 213618, 213619, 213620, 213621, 213622, 213623, 213624, 213625, 213626, 213627, 213628, 213629 ], "onlineresource_set": [] }, { "ob_id": 44621, "uuid": "526a1e026a99414aa86216ac0600fc51", "title": "National-Scale Rainfall-Triggered Landslide Susceptibility and Exposure in Nepal", "abstract": "This dataset is a 30 m resolution landslide susceptibility (hazard) model for Nepal. The dataset is derived from a fuzzy-logic model following the approach developed by Kritikos et al (2015), and is trained on a manually mapped landslide inventory (polygons of landslide locations and extents), originally produced by Durham University and NSET Nepal as part of the UKRI-DFID SHEAR program. The dataset shows the relative probability of a landslide occurring across the geographical extent of Nepal for rainfall triggered landslides only, on a scale of 1 (very highly anticipated) to 0 (very weakly anticipated). Further details and an online map viewer containing the data are available through the Government of Nepal's Bipad Portal (https://bipadportal.gov.np/risk-info/#/hazard)", "creationDate": "2025-07-23T13:00:45.116355", "lastUpdatedDate": "2025-07-23T13:00:45.116358", "latestDataUpdateTime": "2025-07-23T13:00:45.116360", "updateFrequency": "notPlanned", "dataLineage": "The dataset was created by researchers from Durham University with support from the UKRI-DFID SHEAR program (201844-112), the EPSRC project ‘Risk at the Margins’ (EP/T024747/1), the EU ECHO HIP project ‘Risk informed landslide management in Nepal’s hill areas’ (ECHO/-XA/BUD/2020/91026), and by a grant from the Global Challenges Research Fund Multi-Hazard and Systemic Risk programme for the ‘Sajag-Nepal’ project (NE/T01038X/1).\r\n\r\nA full description of the methodology and dataset is provided by Kincey et al. (2024) Earth's Future, available at https://doi.org/10.1029/2023EF004102. The dataset is also available from Zenodo via https://doi.org/10.5281/zenodo.8307963.", "removedDataReason": "", "keywords": "Nepal,landslide susceptibility", "publicationState": "working", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "pending", "dataPublishedTime": null, "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 4812, "bboxName": "", "eastBoundLongitude": 88.1, "westBoundLongitude": 80.0, "southBoundLatitude": 26.25, "northBoundLatitude": 30.75 }, "verticalExtent": null, "result_field": null, "timePeriod": { "ob_id": 12542, "startTime": "2014-04-01T00:00:00", "endTime": "2015-03-31T00:00:00" }, "resultQuality": { "ob_id": 4764, "explanation": "", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2025-07-23" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [], "projects": [ { "ob_id": 44619, "uuid": "00cef3df36d648f5b8aeba7b29b491fd", "short_code": "proj", "title": "Preparedness and planning for the mountain hazard and risk chain in Nepal", "abstract": "International collaborative research on earthquake- and monsoon-triggered hazard chains in Nepal, the ways in which those hazards are experienced, and the ways in which they can be planned for by individuals, communities, government, and humanitarian partners" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 213630, 213631, 213632, 213633, 213634, 213635, 213636, 213637, 213638, 213639, 213640, 213641, 213642, 213643, 213644, 213645, 213646 ], "onlineresource_set": [] }, { "ob_id": 44622, "uuid": "7f91b1326a324caa9e436b8fdef4a0d8", "title": "Machine Learning for Hourly Air Pollution Prediction – Global (ML-HAPPG)", "abstract": "This dataset contains estimates of air pollution levels across the globe for every hour of the year 2022. It covers five major air pollutants that can affect human health and the environment. The data cover major air pollutants, including Nitrogen Dioxide (NO2), Ozone (O3), Particulate Matter smaller than 10 micrometres (PM10) and smaller than 2.5 micrometres (PM2.5), and Sulphur Dioxide (SO2). Each air pollutant's concentrations are predicted not only as average (mean) values but also include estimates at lower (5th percentile), median (50th percentile), and upper (95th percentile) levels to highlight typical and potential extreme pollution scenarios. The spatial coverage of the dataset includes the entire globe, structured as an evenly spaced grid, with each grid square covering an area of 0.25 degrees (0.25 degrees x 0.25 degrees). Data points correspond to the centre of these grid squares. There is also training data used for the model from real-world monitoring stations.", "creationDate": "2025-07-23T14:37:22.884826", "lastUpdatedDate": "2025-07-23T14:37:22", "latestDataUpdateTime": "2025-07-23T14:37:22", "updateFrequency": "notPlanned", "dataLineage": "These pollution estimates were produced using a supervised machine learning method, which is a computational approach where algorithms are trained to identify patterns in historical data and apply these learned patterns to predict new data points. The predictions incorporated various environmental factors, including weather conditions (e.g., temperature, wind, precipitation), satellite measurements, and emission inventories (datasets detailing pollutants released into the atmosphere). 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Berrisford at the University of Exeter during his PhD studies, supported by the UK Research and Innovation (UKRI) Centre for Doctoral Training in Environmental Intelligence. Full methodological details and data validation information are available in the associated open-access scientific publication. For more information about the data, see the README.md archived alongside this dataset.\r\n\r\nThis dataset provides hourly predictions of air pollution concentrations for the globe throughout the year 2022. These values are not direct measurements from monitoring stations but rather model-based estimates generated using a supervised machine-learning approach. The model was trained using real observations from the OpenAQ monitoring network, but it is capable of making predictions even in areas without any nearby monitoring stations. 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The data has been collected during two distinct field seasons (July 12 – August 18, 2016 and June 1 – 28, 2017). During this time there were instrument outages and also the aerosol samplers were turned off during helicopter activity.\r\nThese particulate measurements were used to estimate deposition velocities.\r\nOffline DNA (OTU) analysis are included from air samples collected during the campaigns as well as Scanning Electron Microscope (SEM) particle size analysis.\r\nIn addition (in a seperate folder), ten-day particle dispersion calculations made using the FLEXPART model initiated from the ground site (67.07° N, 49.38° W 1073 m Above Mean Sea Level)", "creationDate": "2025-07-25T15:59:40.055200", "lastUpdatedDate": "2025-07-25T16:00:46", "latestDataUpdateTime": "2025-07-25T15:59:40", "updateFrequency": "notPlanned", "dataLineage": "The data were collected by the project team during two distinct field seasons (July 12 – August 18, 2016 and June 1 – 28, 2017) and archived at CEDA.", "removedDataReason": "", "keywords": "Greenland Ice Sheet,aerosols,particles,mineral dust,glacier ice algae,grain size,phosphorus,snow,nutrients", "publicationState": "preview", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": null, "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 4816, "bboxName": "", "eastBoundLongitude": -49.38, "westBoundLongitude": -49.38, "southBoundLatitude": 67.07, "northBoundLatitude": 67.07 }, "verticalExtent": null, "result_field": { "ob_id": 45248, "dataPath": "/badc/deposited2026/black_and_bloom_greenland_particulates", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 76071917, "numberOfFiles": 23, "fileFormat": "BADC-CSV" }, "timePeriod": { "ob_id": 12548, "startTime": "2016-07-07T00:00:00", "endTime": "2017-06-28T00:00:00" }, "resultQuality": { "ob_id": 4766, "explanation": "The data contain gaps where there were instrument outages and when the aerosol samplers were turned off during helicopter activity.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2025-07-25" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 44634, "uuid": "34d4f85f35c4432ab3bfee778b21cf29", "short_code": "cmppr", "title": "Composite Process for Black and Bloom Work Package 2: Particulates", "abstract": "Composite process covering Acquisition for: Black and Bloom Work Package 2: Particulates and FLEXPART." }, "imageDetails": [ 2 ], "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" } ] } } ], "projects": [ { "ob_id": 44626, "uuid": "80b303629c1b4b53ac0200d658b6ec66", "short_code": "proj", "title": "Black and Bloom", "abstract": "Black and Bloom is a research project, funded by the UK Natural Environment Research Council (NERC), that aims to unravel how dark particles (black) and microbial processes (bloom) darken and accelerate the melting of the Greenland Ice Sheet. NE/M021025/1\r\nhttps://blackandbloomorg.wordpress.com/" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 92798, 92799, 92800, 92801, 92802, 92803, 92804, 92805, 92806, 92807, 92808, 92809, 92810, 92811, 92812, 92813, 92814, 92815, 92816, 92817, 92818, 92819, 92820, 92821, 92822, 92823, 92824, 92825, 92826, 92827, 92828, 92829, 92830, 92831, 92832, 92833, 92834, 92835, 92836, 92837, 92838, 92839, 92840, 92841, 92842, 92843, 92844, 92845, 92846, 92847, 92848, 92849, 92850, 92851, 92852, 92853, 92854, 92855, 92856, 92857, 92858, 92859, 92860, 92861, 92862 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 213663, 213664, 213665, 213666, 213667, 213668, 213669, 216499, 213670, 213671, 213672, 213673, 213674, 213675, 213676, 213677, 213678, 213679 ], "onlineresource_set": [] }, { "ob_id": 44637, "uuid": "6242532d87d442a3acf0171d35c02e56", "title": "ESA Precursors for Aerosols and Ozone Climate Change Initiative (Precursors_cci): Merged CO product, version 1.0", "abstract": "The carbon monoxide (CO) Climate Data Record (CDR) merged product is a new monthly Level 3 CO product developed by merging satellite data from the IASI instrument (on METOP-A, B, and C) and the MOPITT instrument (on TERRA) as part of the ESA Climate Change Initiative (CCI) Precursors for Aerosols and Ozone project.\r\n\r\nAn intermediate IASI L3 product was created averaging cloud-free Level 2 CO from the three METOP platforms (A, B and C) using the Cloud Detection Product of Whitburn et al. (2022). These data were then combined with MOPITT V9T L3 data using a weighted averaging approach. Weights were determined based on the MOPITT CO total column to prior ratio. \r\n\r\nThe merged dataset includes CO total column monthly 1°x1° resolution grids as well as uncertainty grids, for both daytime and night-time from January 2008 to December 2024. Surface altitude grids as well as data source flags grids are also provided.\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.\r\n\r\nThe version number is 1.0. Data are available in NetCDF format.", "creationDate": "2025-07-29T13:52:32.516712", "lastUpdatedDate": "2025-07-29T13:50:16", "latestDataUpdateTime": "2025-12-15T13:50:16", "updateFrequency": "", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "Carbon Monoxide, CO, Climate Data Record, CDR, IASI, MOPITT, CCI", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2026-01-12T12:24:16", "doiPublishedTime": "2026-01-13T10:59:42", "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": 45182, "dataPath": "/neodc/esacci/precursors/data/MERGED_CO/v1.0", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 436392621, "numberOfFiles": 205, "fileFormat": "netcdf" }, "timePeriod": { "ob_id": 12549, "startTime": "2008-01-01T00:00:00", "endTime": "2024-12-31T23:59:59" }, "resultQuality": { "ob_id": 1, "explanation": "See dataset associated documentation", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2012-08-15" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 45160, "uuid": "67adf22715454dcdb423ad0a4ba7f238", "short_code": "cmppr", "title": "Composite process for the ESA Precursors for Aerosols and Ozone Climate Change Initiative CO Merged v1.0 product", "abstract": "Level 3 CO product developed by merging satellite data from IASI (on METOP-A, B, and C) and MOPITT (on TERRA).\r\n\r\nAn intermediate IASI L3 product was created averaging cloud-free Level 2 CO from the three METOP platforms (A, B and C) using the Cloud Detection Product of Whitburn et al. (2022). These data were then combined with MOPITT V9T L3 data using a weighted averaging approach. Weights were determined based on the MOPITT CO total column to prior ratio.\r\n\r\nThe merged dataset includes CO total column monthly 1°x1° resolution grids as well as uncertainty grids, for both daytime and nighttime from January 2008 to December 2024. Surface altitude grids as well as data source flags grids are also provided." }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1140, "name": "ESACCI" } ], "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 Climate Change Initiative Project (Precursors CCI)", "abstract": "The European Space Agency (ESA) Precursors 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": [ 6023, 61969, 92548, 92549, 92550, 92551, 92552, 92553, 92554, 92555, 92556, 92557 ], "vocabularyKeywords": [], "identifier_set": [ 13690 ], "observationcollection_set": [ { "ob_id": 45107, "uuid": "48f377e4674a49e28b43a513addaad01", "short_code": "coll", "title": "ESA Precursors Climate Change Initiative (Precursors CCI) Dataset Collection", "abstract": "The European Space Agency (ESA) Precursors 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).\r\n\r\nWorking in close liaison with climate data users, the project focuses on building consistent/harmonised long term multi-satellite mission climate data records from satellite instruments including GOME, SCIAMACHY, GOME-2, OMI, TROPOMI, IASI, and MOPITT.\r\n\r\nThe resulting datasets will include both single sensor level-2 and level-3 gridded products but also unique multi-sensor merged data series covering, for some variables, almost 3 decades of observations. These products will be fully documented according to CCI standards, and generated in a common data format following the netCDF CF metadata conventions." } ], "responsiblepartyinfo_set": [ 213691, 213692, 213693, 213694, 213695, 213696, 213697, 216233, 215915 ], "onlineresource_set": [ 94462, 94463, 94568, 94569, 95098 ] }, { "ob_id": 44639, "uuid": "cc59802f299744df9453d8c221bf700f", "title": "Shipping Emissions in the Arctic and North Atlantic atmosphere (SEANA)", "abstract": "SEANA aims to understand the sources of aerosol particles including the contribution from shipping and to determine the response of Arctic and North Atlantic aerosols to future shipping emissions. SEANA coordinated a research cruise, on RRS Discovery, to the west side of Greenland to measure both natural and anthropogenic particles and understanding aerosol processes. These observations were used to update and improve a global aerosol model to better reproduce current \"baselines\" in the Arctic and North Atlantic Ocean. A series of advanced instruments was deployed during the SEANA project to measure both the chemical composition and physical properties of atmospheric aerosols. The core instrumentation suite is listed below:\r\nGas-phase Measurement Instruments: NOx , SO2 and O3 analyzers, PTR-MS, NO3 CIMS, HONO\r\nParticle-phase Measurements: FIDAS,SMPSs, Nephelometer, CCN\r\nOther supporting instruments: Meteorological sensors (T, RH, wind speed and direction), GPS, Chlorophyll-a", "creationDate": "2025-07-31T22:28:43.995923", "lastUpdatedDate": "2025-08-25T10:33:07.715302", "latestDataUpdateTime": "2025-07-31T22:28:43.995928", "updateFrequency": "notPlanned", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "Arctic,North Atlantic,Particle-phase Measurements,Gas-phase measurement,Aerosol sources,CCN number concentration,Chlorophyll-a concentration,Meteorological measurements", "publicationState": "preview", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "pending", "dataPublishedTime": null, "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 4872, "bboxName": "", "eastBoundLongitude": 17.0, "westBoundLongitude": 57.0, "southBoundLatitude": 52.0, "northBoundLatitude": 69.0 }, "verticalExtent": null, "result_field": null, "timePeriod": { "ob_id": 12641, "startTime": "2019-05-22T00:00:00", "endTime": "2022-06-23T00:00:00" }, "resultQuality": { "ob_id": 4788, "explanation": "During the DY151 cruise, data collection followed established scientific protocols for atmospheric measurements, including regular calibration of instruments and routine checks for accuracy and consistency.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2025-08-25" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 44640, "uuid": "3eb0c6d7a25d402a87564289317357f9", "short_code": "acq", "title": "Acquisition for: Shipping Emissions in the Arctic and North Atlantic atmosphere (SEANA)", "abstract": "" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [], "projects": [ { "ob_id": 43398, "uuid": "880f64e6938642868a0cf2dd66dbc0e9", "short_code": "proj", "title": "Shipping Emissions in the Arctic and North Atlantic atmosphere (SEANA)", "abstract": "SEANA aims to understand the sources of aerosol particles including the contribution from shipping and to determine the response of Arctic and North Atlantic aerosols to future shipping emissions. SEANA coordinated a research cruise, on RRS Discovery, to the west side of Greenland to measure both natural and anthropogenic particles and understanding aerosol processes. Measurements were also conducted at two stations adjacent to the Northwester Passage (Faroe Islands and Thule). These observations were used to update and improve a global aerosol model to better reproduce current \"baselines\". The updated model will be used to predict the impacts of future shipping on air quality, clouds and radiative forcing in the Arctic and North Atlantic Ocean." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 213699, 213700, 213701, 213702, 213703, 213704, 214686, 214687, 214688, 214689, 214690, 214691, 214692, 214693, 214694, 214695, 214696, 214697, 214698, 214699, 214700, 214701, 214702, 214703, 214704, 214705, 214706, 214707, 214708, 214709, 214710, 214711, 214712, 214713, 214714, 214715, 214716, 214717, 214718, 214719, 214720, 214721, 214722, 214723, 214724, 214725, 214726, 214727, 214728, 214729, 214730, 214731, 214732, 214733 ], "onlineresource_set": [] }, { "ob_id": 44642, "uuid": "c5e585820d1240e89eea85ff2c9b4569", "title": "ESA River Discharge Climate Change Initiative (RD_cci): Nadir radar altimeters Water Level product, v2.0", "abstract": "This dataset contains water level (WL) data from the ESA Climate Change Initiative River Discharge project (RD_cci). Water level in this context corresponds to the distance between river surface water and a reference surface (the WGS84 ellipsoid). This physical variable might also be referred to as Water Surface Elevation (WSE) in other dataset or publications.\r\n\r\nThis version of the dataset is v2.0\r\n\r\nThese river water level time series have been computed in at 54 locations (within 18 river basins). The data has been derived from nadir viewing satellite radar altimeter missions (ERS-2, Envisat, Saral, Topex-Poseidon, Jason-1, Jason-2, Jason-3, Sentinel-3A/B and Sentinel 6A). At each location, time series are provided for each available single nadir radar altimetry mission. Based on these single mission time series, merged multi-missions WL time series (with two different methodologies for some basins) have also been produced.", "creationDate": "2023-12-06T08:01:27.903402", "lastUpdatedDate": "2023-12-06T08:00:25", "latestDataUpdateTime": null, "updateFrequency": "notPlanned", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "CCI, Water Level, River Discharge", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2025-12-02T12:06:40", "doiPublishedTime": "2025-12-02T14:59:13.063674", "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": 45145, "dataPath": "/neodc/esacci/river_discharge/data/WL/v2.0/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 46403303, "numberOfFiles": 871, "fileFormat": "Data are in NetCDF and CSV format" }, "timePeriod": { "ob_id": 11558, "startTime": "1992-12-22T00:00:00", "endTime": "2023-09-18T23:59:59" }, "resultQuality": { "ob_id": 4524, "explanation": "For information on the data quality see the related documentation", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2024-02-09" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 45148, "uuid": "79e4f3bf7eff49f0bca0e2ed787d191a", "short_code": "cmppr", "title": "Composite process for ESA River Discharge Climate Change Initiative (RD_cci): Water Level product, v2.0", "abstract": "The data has been derived from nadir viewing satellite radar altimeter missions (ERS-2, Envisat, Saral, Topex-Poseidon, Jason-1, Jason-2, Jason-3, Sentinel-3A/B and Sentinel 6)." }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2612, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 70, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_river_discharge_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "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": [ 50559, 50561, 63576, 75511, 75512, 75513, 75514, 75515 ], "vocabularyKeywords": [], "identifier_set": [ 13655 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 213713, 213714, 213715, 213716, 213717, 213718, 213719, 213720, 213721, 213722, 213723, 213724, 213725, 213726, 213727, 213728, 215213 ], "onlineresource_set": [ 93996, 93997, 93998, 93999, 94000 ] }, { "ob_id": 44643, "uuid": "d56a6215ce394ddd8dff6bea5dbb0780", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily land surface temperature from MODIS (Moderate resolution Infra-red Spectroradiometer) on Terra, level 3 collated (L3C) global product (2000-2021), version 4.00", "abstract": "This dataset contains daily land surface temperatures (LSTs) and their uncertainty estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS) on Earth Observing System – Terra (Terra). Satellite land surface temperatures are skin temperatures which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.\r\n\r\nDaytime and night-time temperatures are provided in separate files corresponding to the morning and evening Terra equator crossing times which are 10:30 and 22:30 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.\r\n\r\nThe dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. MODIS achieves full Earth coverage nearly twice per day so the daily files have small gaps primarily close to the equator where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.\r\n\r\nThe daily dataset starts from 24th February 2000 and ends 31st December 2021. There are minor interruptions (1-2 days) during satellite/instrument maintenance periods.\r\n\r\nIn Version 4.00 the time series has been extended to 2021. The emissivities used in the retrieval come from the Combined ASTER and MODIS Emissivity over Land (CAMEL) Version 2 database; in Version 4.00 a climatology is used since there are temporal instabilities in the CAMEL time series.\r\n\r\nThe dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using a generalised split window retrieval algorithm and data were processed in the UoL processing chain.\r\n\r\nThe dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": null, "updateFrequency": "notPlanned", "dataLineage": "The data record has been produced by the University of Leicester working within the ESA Land Surface Temperature Climate Change Initiative (LST_cci), and supplied for archiving at the Centre for Environmental Data Analysis (CEDA). \r\nContact: data.lst-cci@acri-st.fr", "removedDataReason": "", "keywords": "", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "0.01 degree", "status": "completed", "dataPublishedTime": "2025-12-02T17:46:11", "doiPublishedTime": "2025-12-03T08:25:08.971138", "removedDataTime": null, "geographicExtent": { "ob_id": 2896, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 45090, "dataPath": "/neodc/esacci/land_surface_temperature/data/TERRA_MODIS/L3C/0.01/v4.00/daily/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 14645915713781, "numberOfFiles": 15839, "fileFormat": "Data are in NetCDF format" }, "timePeriod": { "ob_id": 12552, "startTime": "2000-02-24T00:00:00", "endTime": "2021-12-31T23:59:59" }, "resultQuality": { "ob_id": 3798, "explanation": "For information on the data quality see the associated LST_cci documentation", "passesTest": true, "resultTitle": "LST cci", "date": "2021-11-30" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 45095, "uuid": "936e701f5b624bceb37cc958fb65cfb0", "short_code": "cmppr", "title": "Composite process for the ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily moderate resolution Infra-red Spectroradiometer (MODIS) on Terra level 3 collated (L3C) global product (2000-2021), version 4.00", "abstract": "Data has been derived from the Moderate resolution Infra-red Spectroradiometer (MODIS) on the Terra\r\n satellite.\r\n\r\nFor more information on the retrieval algorithm used see the documentation on the LST CCI webpage: \r\nhttps://climate.esa.int/en/projects/land-surface-temperature/" }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2558, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 30, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_lst_terms_and_conditions.pdf", "licenceClassifications": [] } } ], "projects": [ { "ob_id": 33361, "uuid": "555149fdc3ef4e23a1de8ece93c29f5d", "short_code": "proj", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci)", "abstract": "The land surface temperature (LST) CCI project, which is funded by the European Space Agency (ESA) as part of the Agency’s Climate Change Initiative (CCI) Programme, aims to deliver a significant improvement on the capability of current satellite LST data records to meet the challenging Global Climate Observing System (GCOS) requirements for climate applications to realise the full potential of long-term LST data for climate science.\r\n\r\nAccurate knowledge of LST plays a key role in describing the physics of land-surface processes at regional and global scales as they combine information on both the surface-atmosphere interactions and energy fluxes within the Earth Climate System. LST provides a metric of surface state when combined with vegetation parameters and soil moisture and is one of the drivers of vegetation phenology. Furthermore, LST is an independent temperature data set for quantifying climate change complementary to the near-surface air temperature ECV based on in situ measurements and reanalyses.\r\n\r\nThe team uses data from a variety of satellites to provide an accurate view of temperatures across land surfaces globally over the past +20 years. This involves developing innovative techniques to merge data from different satellites into combined long-term satellite records for climate. 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Satellite land surface temperatures are skin temperatures which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.\r\n\r\nDaytime and night-time temperatures are provided in separate files corresponding to the daytime and night-time Aqua equator crossing times which are 13:30 and 01:30 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.\r\n\r\nThe dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. MODIS achieves full Earth coverage nearly twice per day so the daily files have small gaps primarily close to the equator where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.\r\n\r\nDataset coverage starts on 4th July 2002 and ends on 31st December 2021. There are minor interruptions (1-2 days) during satellite/instrument maintenance periods.\r\n\r\nIn Version 4.00 the time series has been extended to 2021. The emissivities used in the retrieval come from the Combined ASTER and MODIS Emissivity over Land (CAMEL) Version 2 database; in Version 4.00 a climatology is used since there are temporal instabilities in the CAMEL time series.\r\n\r\nThe dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using a generalised split window retrieval algorithm and data were processed in the UoL processing chain.\r\nThe dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": null, "updateFrequency": "notPlanned", "dataLineage": "The data record has been produced by the University of Leicester working within the ESA Land Surface Temperature Climate Change Initiative (LST_cci), and supplied for archiving at the Centre for Environmental Data Analysis (CEDA). \r\nContact: data.lst-cci@acri-st.fr", "removedDataReason": "", "keywords": "CCI, Land Surface Temperature, LST, MODIS AQUA", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2025-12-02T17:45:32", "doiPublishedTime": "2025-12-03T08:25:42.848630", "removedDataTime": null, "geographicExtent": { "ob_id": 2896, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 45097, "dataPath": "/neodc/esacci/land_surface_temperature/data/AQUA_MODIS/L3C/0.01/v4.00/daily/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 13486159611868, "numberOfFiles": 14211, "fileFormat": "Data are in NetCDF format" }, "timePeriod": { "ob_id": 12553, "startTime": "2002-07-04T00:00:00", "endTime": "2021-12-31T23:59:59" }, "resultQuality": { "ob_id": 3798, "explanation": "For information on the data quality see the associated LST_cci documentation", "passesTest": true, "resultTitle": "LST cci", "date": "2021-11-30" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 45096, "uuid": "9bb090bcb10b4a57a797bda7476d173e", "short_code": "cmppr", "title": "Composite process for the ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily moderate resolution Infra-red Spectroradiometer (MODIS) on Aqua level 3 collated (L3C) global product (2002-2021), version 4.00", "abstract": "Data has been derived from the Moderate resolution Infra-red Spectroradiometer (MODIS) on the Aqua\r\n satellite.\r\n\r\nFor more information on the retrieval algorithm used see the documentation on the LST CCI webpage: \r\nhttps://climate.esa.int/en/projects/land-surface-temperature/" }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2558, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 30, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_lst_terms_and_conditions.pdf", "licenceClassifications": [] } } ], "projects": [ { "ob_id": 33361, "uuid": "555149fdc3ef4e23a1de8ece93c29f5d", "short_code": "proj", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci)", "abstract": "The land surface temperature (LST) CCI project, which is funded by the European Space Agency (ESA) as part of the Agency’s Climate Change Initiative (CCI) Programme, aims to deliver a significant improvement on the capability of current satellite LST data records to meet the challenging Global Climate Observing System (GCOS) requirements for climate applications to realise the full potential of long-term LST data for climate science.\r\n\r\nAccurate knowledge of LST plays a key role in describing the physics of land-surface processes at regional and global scales as they combine information on both the surface-atmosphere interactions and energy fluxes within the Earth Climate System. 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Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.\r\n\r\nDaytime and nighttime temperatures are provided in separate files corresponding to the morning and evening ERS-2 equator crossing times which are 10:30 and 22:30 local solar time.\r\n\r\nPer pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length.\r\n\r\nAlso provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.\r\n\r\nThe dataset coverage is near global over the land surface. Small regions were not covered due to downlinking constraints (most noticeably a track extending southwards across central Asia through India – further details can be found on the ATSR project webpages at https://artefacts.ceda.ac.uk/frozen_sites/www.atsr.rl.ac.uk/documentation/docs/userguide/index.shtml).\r\n\r\nLSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. ATSR-2 achieves full Earth coverage in 3 days so the daily files have gaps where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.\r\n\r\nDataset coverage starts on 1st June 1995 and ends on 22nd June 2003. There are two gaps of several months in the dataset: no data were acquired from ATSR-2 between 23 December 1995 and 30 June 1996 due to a scan mirror anomaly; and the ERS-2 gyro failed in January 2001, data quality was less good between 17th Jan 2001 and 5th July 2001 and are not used in this dataset. There are minor interruptions (1-2 days) during satellite/instrument maintenance periods.\r\n\r\nVersion 4.00 uses data from the 4th reprocessing of the ATSR L1B archive. The emissivities used in the retrieval come from the Combined ASTER and MODIS Emissivity over Land (CAMEL) Version 2 database; in Version 4.00 a climatology is used since there are temporal instabilities in the CAMEL time series.\r\n\r\nThe dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.\r\n\r\nThe dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": null, "updateFrequency": "notPlanned", "dataLineage": "The data record has been produced by the University of Leicester working within the ESA Land Surface Temperature Climate Change Initiative (LST_cci), and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).\r\nContact: data.lst-cci@acri-st.fr", "removedDataReason": "", "keywords": "land surface temperature, ESA, CCI, ATSR-2", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2025-12-05T16:16:19", "doiPublishedTime": "2025-12-05T16:33:39.416047", "removedDataTime": null, "geographicExtent": { "ob_id": 2896, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 45163, "dataPath": "/neodc/esacci/land_surface_temperature/data/ERS-2_ATSR/L3C/0.01/v4.00/daily/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1704277622518, "numberOfFiles": 5433, "fileFormat": "Data are in NetCDF format" }, "timePeriod": { "ob_id": 12554, "startTime": "1995-06-01T00:00:00", "endTime": "2003-06-22T23:59:59" }, "resultQuality": { "ob_id": 3798, "explanation": "For information on the data quality see the associated LST_cci documentation", "passesTest": true, "resultTitle": "LST cci", "date": "2021-11-30" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 34667, "uuid": "6c409ce178ec4d5eab40c1d6feaa070b", "short_code": "cmppr", "title": "Composite process for the ESA Land Surface Temperature Climate Change Initiative (LST_cci): Along-Track Scanning Radiometer 2 (ATSR-2) level 3 collated (L3C) global product (1995-2003), version 3.00", "abstract": "Data has been derived from the Along-Track Scanning Radiometer 2 (ATSR-2) on the European Remote-sensing Satellite (ERS-2)\r\n\r\nFor more information on the retrieval algorithm used see the documentation on the LST CCI webpage: https://climate.esa.int/sites/default/files/LST-CCI-D2.2-ATBD%20-%20i1r1%20-%20Algorithm%20Theoretical%20Basis%20Document.pdf." }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2558, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 30, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_lst_terms_and_conditions.pdf", "licenceClassifications": [] } } ], "projects": [ { "ob_id": 33361, "uuid": "555149fdc3ef4e23a1de8ece93c29f5d", "short_code": "proj", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci)", "abstract": "The land surface temperature (LST) CCI project, which is funded by the European Space Agency (ESA) as part of the Agency’s Climate Change Initiative (CCI) Programme, aims to deliver a significant improvement on the capability of current satellite LST data records to meet the challenging Global Climate Observing System (GCOS) requirements for climate applications to realise the full potential of long-term LST data for climate science.\r\n\r\nAccurate knowledge of LST plays a key role in describing the physics of land-surface processes at regional and global scales as they combine information on both the surface-atmosphere interactions and energy fluxes within the Earth Climate System. 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Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.\r\n\r\nDaytime and night-time temperatures are provided in separate files corresponding to the morning and evening Envisat equator crossing times which are 10:00 and 22:00 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.\r\n\r\nThe dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. AATSR achieves full Earth coverage in 3 days so the daily files have gaps where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.\r\n\r\nDataset coverage starts on 20th May 2002 and ends on 8th April 2012. There is a twelve day gap in the dataset due to Envisat mission extension orbital manoeuvres from 21st October 2010 to 1st November 2010. There are minor interruptions (1-2 days) during satellite/instrument maintenance periods.\r\n\r\nVersion 4.00 uses data from the 4th reprocessing of the ATSR L1B archive. 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LST provides a metric of surface state when combined with vegetation parameters and soil moisture and is one of the drivers of vegetation phenology. Furthermore, LST is an independent temperature data set for quantifying climate change complementary to the near-surface air temperature ECV based on in situ measurements and reanalyses.\r\n\r\nThe team uses data from a variety of satellites to provide an accurate view of temperatures across land surfaces globally over the past +20 years. This involves developing innovative techniques to merge data from different satellites into combined long-term satellite records for climate. 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Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.\r\n\r\nDaytime and night-time temperatures are provided in separate files corresponding to the morning and evening METOP-A equator crossing times which are 9.30 and 21:30 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.\r\n\r\nThe dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. The daily files have gaps where the surface is not covered by the satellite swath during day or night on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.\r\n\r\nDataset coverage starts on 1st March 2007 and ends on 31st December 2020. There are minor interruptions during satellite/instrument maintenance periods or instrument anomalies.\r\n\r\nThe emissivities used in the retrieval come from the Combined ASTER and MODIS Emissivity over Land (CAMEL) Version 2 database; in Version 4.00 a climatology is used since there are temporal instabilities in the CAMEL time series.\r\n\r\nThe dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.\r\n\r\nThe dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": null, "updateFrequency": "notPlanned", "dataLineage": "The data record has been produced by the University of Leicester working within the ESA Land Surface Temperature Climate Change Initiative (LST_cci), and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).\r\nContact: data.lst-cci@acri-st.fr", "removedDataReason": "", "keywords": "land surface temperature, ESA, CCI, AVHRR Metop-A", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "0.01 degree", "status": "completed", "dataPublishedTime": "2026-02-13T17:26:18", "doiPublishedTime": "2026-02-13T17:30:17", "removedDataTime": null, "geographicExtent": { "ob_id": 2896, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 45167, "dataPath": "/neodc/esacci/land_surface_temperature/data/METOPA_AVHRR/L3C/0.01/v2.00/daily/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 6089795200767, "numberOfFiles": 9979, "fileFormat": "Data are in NetCDF format" }, "timePeriod": { "ob_id": 12556, "startTime": "2007-03-01T00:00:00", "endTime": "2020-12-31T23:59:59" }, "resultQuality": { "ob_id": 3798, "explanation": "For information on the data quality see the associated LST_cci documentation", "passesTest": true, "resultTitle": "LST cci", "date": "2021-11-30" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 45171, "uuid": "8722851cddde41b38b7ca835dd4c5dc7", "short_code": "cmppr", "title": "Composite process for the ESA Land Surface Temperature Climate Change Initiative (LST_cci): Land surface temperature from the Metop-A AVHRR (Advanced Very High Resolution Radiometer) instrument, level 3 collated (L3C) global product, version 2.00", "abstract": "Data has been derived from the Advanced Very High Resolution Radiometer 3 (AVHRR-3) on the Metop-A satellite. \r\n\r\nFor more information on the retrieval algorithm used see the documentation on the LST CCI webpage" }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2558, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 30, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_lst_terms_and_conditions.pdf", "licenceClassifications": [] } } ], "projects": [ { "ob_id": 33361, "uuid": "555149fdc3ef4e23a1de8ece93c29f5d", "short_code": "proj", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci)", "abstract": "The land surface temperature (LST) CCI project, which is funded by the European Space Agency (ESA) as part of the Agency’s Climate Change Initiative (CCI) Programme, aims to deliver a significant improvement on the capability of current satellite LST data records to meet the challenging Global Climate Observing System (GCOS) requirements for climate applications to realise the full potential of long-term LST data for climate science.\r\n\r\nAccurate knowledge of LST plays a key role in describing the physics of land-surface processes at regional and global scales as they combine information on both the surface-atmosphere interactions and energy fluxes within the Earth Climate System. LST provides a metric of surface state when combined with vegetation parameters and soil moisture and is one of the drivers of vegetation phenology. Furthermore, LST is an independent temperature data set for quantifying climate change complementary to the near-surface air temperature ECV based on in situ measurements and reanalyses.\r\n\r\nThe team uses data from a variety of satellites to provide an accurate view of temperatures across land surfaces globally over the past +20 years. This involves developing innovative techniques to merge data from different satellites into combined long-term satellite records for climate. These will all be evaluated by scientists working at leading climate centres." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 62778, 62779, 62780, 62781, 62790, 62791, 62792, 66303, 66305, 66308, 66310, 66311, 66312, 66751, 66754, 75124 ], "vocabularyKeywords": [], "identifier_set": [ 13723 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 213770, 213771, 213772, 213773, 213774, 213775, 213776, 213777, 213779, 213778, 213780 ], "onlineresource_set": [ 94047, 94049, 94527, 94528 ] }, { "ob_id": 44648, "uuid": "00e79f41a85142d49b853695f07c3c2a", "title": "Multi-channel seismic data recorded by Sercel SEAL streamer and recording system during RRS James Cook expedition JC228, Guatemala Basin (2022-2023)", "abstract": "Multi-channel seismic (MCS) data were recorded by Sercel SEAL streamer and recording system during RRS James Cook expedition JC228 (December 2022 to January 2023) in the Guatemala Basin region of the North Pacific Ocean. The aim was to sample fast-spreading ocean lithosphere of the northern Cocos Plate from its formation to subduction in the Mid-America Trench. MCS data were recorded along a linear transect with grids in three focus areas; two on the passively cooling oceanic plate, the third spanned the transition into the subduction zone. This work aimed to deliver key information on the evolution of oceanic lithosphere as well as provide essential site survey information for two IODP Proposals: the flagship MoHole to Mantle initiative (M2M; IODP-805MDP) and the Bend Fault Serpentinization experiment (approved IODP-876Pre). The work was undertaken by scientists at the University of Southampton, UK in collaboration with GEOMAR Helmholtz Centre for Ocean Research, Germany, funded as part of NERC grant 'From Ridge to Trench, MoHole to Bend-Faults', grant reference NE/M021246/1.", "creationDate": "2025-08-05T08:28:39.222219", "lastUpdatedDate": "2025-08-05T08:28:39", "latestDataUpdateTime": "2025-08-05T08:28:39", "updateFrequency": "notPlanned", "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": "", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2025-08-04T11:09:00", "doiPublishedTime": "2025-08-04T11:09:00", "removedDataTime": null, "geographicExtent": { "ob_id": 4818, "bboxName": "", "eastBoundLongitude": -86.5, "westBoundLongitude": -90.5, "southBoundLatitude": 7.5, "northBoundLatitude": 10.8 }, "verticalExtent": null, "result_field": { "ob_id": 44636, "dataPath": "/bodc/uso250924", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 0, "numberOfFiles": 0, "fileFormat": "segy" }, "timePeriod": { "ob_id": 12557, "startTime": "2022-12-03T00:00:00", "endTime": "2023-01-08T23:59:59" }, "resultQuality": { "ob_id": 4769, "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": "2025-08-05" }, "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" } ] } } ], "projects": [], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 13464 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 213786, 213785, 213784, 213783, 213781, 213787, 213788, 213789, 213782, 213791, 213790 ], "onlineresource_set": [] }, { "ob_id": 44650, "uuid": "05fba4c5b8fe4daea8ff751026daf438", "title": "The GEBCO_2025 Grid - a continuous terrain model for oceans and land at 15 arc-second intervals", "abstract": "The GEBCO_2025 Grid is a global continuous terrain model for ocean and land with a spatial resolution of 15 arc seconds. In regions largely outside of the Arctic and Southern Ocean areas, the grid uses as a base Version 2.7 of the SRTM15_plus data set (Tozer et al., 2019). This data set is a fusion of land topography with measured and estimated seafloor topography. Included on top of this base grid are gridded bathymetric data sets developed by the four Regional Centers of The Nippon Foundation-GEBCO Seabed 2030 Project. The GEBCO_2025 Grid represents all data within the 2025 compilation. The compilation of the GEBCO_2025 Grid was carried out at the Seabed 2030 Global Center, hosted at the National Oceanography Centre, UK, with the aim of producing a seamless global terrain model. Outside of Polar regions, the Regional Centers provide their data sets as sparse grids i.e. only grid cells that contain data are populated. These data sets were included on to the base using a 'remove-restore' blending procedure. This is a two-stage process of computing the difference between the new data and the base grid and then gridding the difference and adding the difference back to the existing base grid. The aim is to achieve a smooth transition between the new and base data sets with the minimum of perturbation of the existing base data set. The data sets supplied in the form of complete grids (primarily areas north of 60N and south of 50S) were included using feather blending techniques from GlobalMapper software. The GEBCO_2025 Grid has been developed through the Nippon Foundation-GEBCO Seabed 2030 Project. This is a collaborative project between the Nippon Foundation of Japan and the General Bathymetric Chart of the Oceans (GEBCO). It aims to bring together all available bathymetric data to produce the definitive map of the world ocean floor by 2030 and make it available to all. Funded by the Nippon Foundation, the four Seabed 2030 Regional Centers include the Southern Ocean - hosted at the Alfred Wegener Institute (AWI), Germany; Pacific Ocean - hosted at the National Institute of Water and Atmospheric Research (NIWA), New Zealand; Atlantic and Indian Oceans - hosted at the Lamont-Doherty Earth Observatory (LDEO), Columbia University, USA; Arctic and North Pacific Oceans - jointly hosted at Stockholm University, Sweden and the Center for Coastal and Ocean Mapping at the University of New Hampshire, USA.", "creationDate": "2025-08-05T14:27:15.288189", "lastUpdatedDate": "2025-08-04T15:20:37", "latestDataUpdateTime": "2025-08-05T14:02:26", "updateFrequency": "", "dataLineage": "The GEBCO_2025 grid has been generated from bathymetric data sets compiled by the Nippon Foundation-GEBCO Seabed 2030 Grid Compilation Team. The global grid has been assembled from these inputs by the Seabed 2030 Global Center, hosted by the National Oceanography Centre, UK at the British Oceanographic Data Centre (BODC). On behalf of the GEBCO project, BODC make the compilation available for publication through the BODC Published Data Library and the GEBCO Project website. Data were published at BODC, and archived at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "elevation, oceans", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2025-08-04T15:20:37", "doiPublishedTime": "2025-08-04T15:20:37", "removedDataTime": null, "geographicExtent": { "ob_id": 4696, "bboxName": "World", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 44651, "dataPath": "/bodc/gebco/global/gebco_2025", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 114714162013, "numberOfFiles": 114, "fileFormat": "Data: .nc; .tif; .xml; .asc; .prj\r\nDocumentation and metadata: .csv; .txt; .pdf; .xlsx;" }, "timePeriod": { "ob_id": 12560, "startTime": "2024-07-01T00:00:00", "endTime": "2025-06-01T00:00:00" }, "resultQuality": null, "validTimePeriod": { "ob_id": 12559, "startTime": "2024-07-01T00:00:00", "endTime": "2025-06-01T00:00:00" }, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 231 ], "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" } ] } } ], "projects": [], "inspireTheme": [], "topicCategory": [], "phenomena": [ 12066, 50559, 50561, 74864, 74865 ], "vocabularyKeywords": [], "identifier_set": [ 13466 ], "observationcollection_set": [ { "ob_id": 43520, "uuid": "b28510bfa05b4ce388d2315c10f5823d", "short_code": "coll", "title": "The GEBCO Product Collection (General Bathymetric Chart of the Oceans) - a continuous terrain model of the global oceans and land - all releases, formats, and documentation", "abstract": "The General Bathymetric Chart of the Oceans (GEBCO) is a continuous terrain model of the global oceans and land. The GEBCO project has been producing bathymetric charts and datasets since 1903. Since 2019, GEBCO’s global bathymetric grids have been developed through the framework of The Nippon Foundation-GEBCO Seabed 2030 Project. This is a collaborative project between the Nippon Foundation of Japan and the General Bathymetric Chart of the Oceans (GEBCO). It aims to bring together all available bathymetric data to produce the definitive map of the world ocean floor by 2030 and make it available to all.\r\n\r\nThe GEBCO Product Collection encompasses all digital products released in all formats since 2008. The GEBCO Product Collection contains all available formats and datasets for the latest GEBCO release, alongside documentation and terms of use:\r\n\r\n• GEBCO 2025 - 15 arc-seconds - global data set, on a geographic grid, at 15 arc-second intervals. The grid is accompanied by a Type Identifier Grid (TID), and a version that contains sub-ice topography information for Greenland and Antarctica. Available as a global NetCDF format file or as a set of 8 tiles (each with an area of 90° x 90°) in Data GeoTiff and Esri ASCII raster format. \r\n\r\nThe GEBCO Product Collection also contains all available datasets in all formats for legacy GEBCO releases, alongside their documentation and terms of use:\r\n\r\n• GEBCO 2024 - 15 arc-seconds - global data set, on a geographic grid, at 15 arc-second intervals. The grid is accompanied by a Type Identifier Grid (TID), and a version that contains sub-ice topography information for Greenland and Antarctica. Available as a global NetCDF format file or as a set of 8 tiles (each with an area of 90° x 90°) in Data GeoTiff and Esri ASCII raster format. \r\n• GEBCO One Minute Grid - 1 arc-minute - a global bathymetric grid. Originally published in 2003, last updated in 2008.\r\n• GEBCO 2014 Grid - 30-arc seconds - a global bathymetric grid originally published in 2014, last updated in April 2015. \r\n• GEBCO 2019 - global data set, on a geographic grid, at 15 arc-second intervals. The grid is accompanied by a Type Identifier Grid (TID). Available in NetCDF format only. \r\n• GEBCO 2020 - 15 arc-seconds - global data set, on a geographic grid, at 15 arc-second intervals. The grid is accompanied by a Type Identifier Grid (TID). Available as a global NetCDF format file or as a set of 8 tiles (each with an area of 90° x 90°) in Data GeoTiff and Esri ASCII raster format. \r\n• GEBCO 2021 - 15 arc-seconds - global data set, on a geographic grid, at 15 arc-second intervals. The grid is accompanied by a Type Identifier Grid (TID), and a version that contains sub-ice topography information for Greenland and Antarctica. Available as a global NetCDF format file or as a set of 8 tiles (each with an area of 90° x 90°) in Data GeoTiff and Esri ASCII raster format. \r\n• GEBCO 2022 - 15 arc-seconds - global data set, on a geographic grid, at 15 arc-second intervals. The grid is accompanied by a Type Identifier Grid (TID), and a version that contains sub-ice topography information for Greenland and Antarctica. Available as a global NetCDF format file or as a set of 8 tiles (each with an area of 90° x 90°) in Data GeoTiff and Esri ASCII raster format. \r\n• GEBCO 2023 - 15 arc-seconds - global data set, on a geographic grid, at 15 arc-second intervals. The grid is accompanied by a Type Identifier Grid (TID), and a version that contains sub-ice topography information for Greenland and Antarctica. Available as a global NetCDF format file or as a set of 8 tiles (each with an area of 90° x 90°) in Data GeoTiff and Esri ASCII raster format." } ], "responsiblepartyinfo_set": [ 213795, 213798, 213799, 213804, 213805, 213806, 213807, 213808, 213809, 213810, 213811, 213812, 213813, 213814, 213815, 213816, 213817, 213818, 213819, 213820, 213821, 213822, 213834, 213823, 213824, 213825, 213826, 213827, 213828, 213829, 213830, 213831, 213832, 213833 ], "onlineresource_set": [] }, { "ob_id": 44652, "uuid": "d24cd5dd66064d7b84350b2495227836", "title": "WesCon: Met Office surface to 16 m meteorology, radiation and subsoil measurements from Airfield Camp, Netheravon, v1.0 (20230421-20230914)", "abstract": "A continuous hydrometeorological record of the temporary Met Office Observation-based research site at Netheravon, Wiltshire (51.241°N, -1.777°W, 120 m amsl) in southern England during the summer of 2023. Netheravon formed a supersite for the Wessex Summertime Convection Experiment (WesCon)/WesCon - Observing the Evolving Structures of Turbulence (WOEST) field campaign. This dataset contains recorded surface meteorology, radiation and subsoil measurements from sensors mounted on a combination of 1.2 m, 2 m and 16 m masts at 1, 5, 10, and 30 minute averaging periods.\r\n\r\nInstruments mounted on 1.2m, 2 m and 16 m masts include:\r\n•\tVector Instruments T302 PRT temperature sensors.\r\n•\tScreened and aspirated HMP155s for measuring temperature and relative humidity.\r\n•\tGill HS50 3-D horizontally symmetric ultrasonic anemometers for winds and turbulent heat fluxes.\r\n•\tLicor Li-7500 high-speed open-path hygrometer was located at 16 m.\r\n•\tDelta-T BS5 measured barometric pressure at 1.5 m.\r\n\r\nSurface instrumentation includes:\r\n•\tRainfall is measured with a EML SB500 tipping-bucket gauge with a 0.2 mm accuracy.\r\n•\tScreened and aspirated Rotronics Hydroclip2 measured grass canopy (8 cm) air temperature and RH.\r\nRadiation instrumentation includes:\r\n•\tClear-domed Kipp and Zonen CMP21 pyranometers located at 2 m measured global downwelling, and upwelling components (of wavelength between 0.3-3 μm).\r\n•\tKipp and Zonen CG4 pyrgeometers located at 2 m measured the downwelling and upwelling longwave radiation (4.5–40 μm).\r\n•\tGrass and concrete skin temperatures were measured radiometrically with the Heitronics KT15 pyrometer.\r\n\r\nAerosol and visibility instrumentation includes:\r\n\r\n•\tA Campbell CS120 visiometer located at 2 m measured visual range through air (visibility)\r\nSubsoil instrumentation includes:\r\n•\tDelta-T ML3 theta probes measured volumetric soil moisture at the surface (0-5 cm) and 10 cm depths.\r\n•\tDelta-T ST1 thermistor probes measured soil temperature at 1, 4, 10, 17 cm depths.\r\n•\tHukseflux HFP01SC flux plate measured ground heat flux (2 cm depth).\r\n\r\n\r\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.", "creationDate": "2025-03-28T11:22:27.588628", "lastUpdatedDate": "2025-03-28T11:22:27", "latestDataUpdateTime": null, "updateFrequency": "notPlanned", "dataLineage": "Data were processed and prepared by the Met Office instrument team before delivery to the NERC EDS Centre for Environmental Data Analysis (CEDA) for long term archival.", "removedDataReason": "", "keywords": "WesCon, Met Office, observation, turbulence, wind speed, wind direction, boundary layer, temperature, relative humidity, upwelling, shortwave, longwave, radiative flux, radiation, surface, precipitation, rainfall, amount, visibility, soil, moisture, flux tower, surface, subsurface", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2025-08-07T07:44:03", "doiPublishedTime": "2025-08-07T09:54:02", "removedDataTime": null, "geographicExtent": { "ob_id": 4728, "bboxName": "Netheravon, Wiltshire, Uk", "eastBoundLongitude": -1.7777655, "westBoundLongitude": -1.7777655, "southBoundLatitude": 51.2386895, "northBoundLatitude": 51.2386895 }, "verticalExtent": null, "result_field": { "ob_id": 44653, "dataPath": "/badc/woest/data/metoffice-fluxtower-netheravon/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 492545730, "numberOfFiles": 584, "fileFormat": "NetCDF" }, "timePeriod": { "ob_id": 12561, "startTime": "2023-04-21T00:00:00", "endTime": "2023-09-14T23:59:59" }, "resultQuality": { "ob_id": 4770, "explanation": "Data have been produced in accordance with standard Met Office observational practices. To ensure optimal traceability and transparency of data, comprehensive metadata is included.", "passesTest": true, "resultTitle": "MetOfficeWesCon Data Quality Statement", "date": "2025-08-05" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 44655, "uuid": "36a264516d554225b0d8934c63df3511", "short_code": "acq", "title": "WesCon - Met Office Flux towers at Netheravon", "abstract": "WesCon - Met Office Flux tower measurements at Netheravon" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 69 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2532, "accessConstraints": null, "accessCategory": "restricted", "accessRoles": "ukmo_wx", "label": "restricted: ukmo_wx group", "licence": { "ob_id": 12, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/ukmo_agreement.pdf", "licenceClassifications": [ { "ob_id": 4, "classification": "academic" } ] } } ], "projects": [ { "ob_id": 37501, "uuid": "3c72b5d38e9a499ead03e9e5f140972d", "short_code": "proj", "title": "WesCon: Wessex Convection experiment", "abstract": "WesCon was a UK summer convection experiment in 2023 funded by the Met Office. It focused on understanding dynamical aspects of convection to provide observational data to develop next generation km scale and urban-scale models to improve prediction of convective storms in km scale Numerical Weather Prediction (NWP) and climate models.\r\n\r\nThe emphasis of WesCon was understanding of dynamical processes (particularly updrafts and turbulence) and their interaction with other processes of importance. \r\n\r\nThe project involved the use of the FAAM Bae-146 aircraft, the Jade-Dimona aircraft and groundbased instruments at Cardington, Netheravon, and nearby ground sites. This project ran in conjunction with WOEST: WesCon - Observing the Evolving Structures of Turbulence project involving groundbased, sonde and radar observations. Both WesCon and WOEST were part of the wider ParaChute programme.\r\n\r\nThe Wessex Convection experiment (WesCon) took place from during summer 2023" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50510, 63667, 75852, 81111, 81112, 81113, 81114, 81115, 81116, 81157, 81167, 81168, 81231, 85711, 85712, 85713, 85714, 85715, 85716, 85717, 85718, 85719, 85720, 85721, 85722, 85723, 85724, 85725, 85726, 85727, 85728, 85729, 85730, 85731, 85732, 85733, 85734, 85735, 85736, 85737, 85738, 85739, 85740, 85741, 85742, 85743, 85744, 85745, 85746, 85747, 85748, 85749, 85750, 85751, 85752, 85753, 85754, 85755, 85756, 85757, 85758, 85759, 85760, 85761, 85762, 85763, 85764, 85765, 85766, 85767, 85768, 85769, 85770, 85771, 85772, 85773, 85774, 85775, 85776, 85777, 85778, 85779, 85780, 85781, 85782, 85783, 85784, 85785, 85786, 85787, 85788, 85789, 85790, 85791, 85792, 85793, 85794, 85795, 85796, 85797, 85798, 85799, 85800, 85801, 85802, 85803, 85804, 85805, 85806, 85807, 85808, 85809, 85810, 85811, 85812, 85813, 85814, 85815, 85816, 85817, 85818, 85819, 85820, 85821, 85822, 85823, 85824, 85825, 85826, 85827, 85828, 85829, 85830, 85831, 85832, 85833, 85834, 85835, 85836, 85837, 85838, 85839, 85840, 85841, 85842, 85843, 85844, 85845, 85846, 85847, 85848, 85849, 85850, 85851, 85852, 85853, 85854, 85855, 85856, 85857, 85858, 85859, 85860, 85861, 85862, 85863, 85864, 85865, 85866, 85867, 85868, 85869, 85870, 85871, 85872, 85873, 85874, 85875, 85876, 85877, 85878, 85879, 85880 ], "vocabularyKeywords": [], "identifier_set": [ 13473 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 213836, 213837, 213838, 213839, 213840, 213841, 213842, 213843, 213847, 213848, 213849, 213850 ], "onlineresource_set": [] }, { "ob_id": 44656, "uuid": "91e618f4a45c4d698e161aa10119cc14", "title": "WesCon: humidity profiles, liquid water paths and integrated water vapour measurement from the Met Office RPG Humpro microwave radiometer at Airfield Camp, Netheravon, v1.0 (20230419-20230919)", "abstract": "Data from the Met Office Observation-based research RPG Humpro profiling microwave radiometer deployed at Netheravon, Wiltshire (51.241°N, -1.777°W, 120 m amsl) in southern England during the summer of 2023. Netheravon formed a supersite for the Wessex Summertime Convection Experiment (WesCon)/WesCon - Observing the Evolving Structures of Turbulence (WOEST) field campaign. The dataset includes retrieved humidity profiles in addition to the liquid water paths and integrated water vapour paths using brightness temperatures measured at seven microwave frequencies between 22.24 and 31.4 GHz (this band in general being sensitive to water vapour and cloud). The liquid and vapour water path retrievals used a supplied neural network algorithm (which is trained with radiosonde data using a radiative transfer scheme). Two archived files are available, based on the time series (water vapour and liquid water) and profile (humidity) data.\r\n\r\nTo ensure optimal traceability and transparency of data, comprehensive metadata is included.", "creationDate": "2025-03-28T11:22:27.588628", "lastUpdatedDate": "2025-03-28T11:22:27", "latestDataUpdateTime": null, "updateFrequency": "notPlanned", "dataLineage": "Data were processed and prepared by the Met Office instrument team before delivery to the NERC EDS Centre for Environmental Data Analysis (CEDA) for long term archival.", "removedDataReason": "", "keywords": "WesCon, Met Office, observation, humidity", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2025-08-07T08:12:05", "doiPublishedTime": "2025-08-07T09:51:21", "removedDataTime": null, "geographicExtent": { "ob_id": 4728, "bboxName": "Netheravon, Wiltshire, Uk", "eastBoundLongitude": -1.7777655, "westBoundLongitude": -1.7777655, "southBoundLatitude": 51.2386895, "northBoundLatitude": 51.2386895 }, "verticalExtent": null, "result_field": { "ob_id": 44659, "dataPath": "/badc/woest/data/metoffice-microwave-radiometer-humpro/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 3708409281, "numberOfFiles": 300, "fileFormat": "NetCDF" }, "timePeriod": { "ob_id": 12562, "startTime": "2023-04-19T00:00:00", "endTime": "2023-09-19T23:59:59" }, "resultQuality": { "ob_id": 4770, "explanation": "Data have been produced in accordance with standard Met Office observational practices. To ensure optimal traceability and transparency of data, comprehensive metadata is included.", "passesTest": true, "resultTitle": "MetOfficeWesCon Data Quality Statement", "date": "2025-08-05" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 44658, "uuid": "f14967f9c0fe481bba913a76084f72b7", "short_code": "acq", "title": "WesCon Met Office OBR metoffice-obr-microwave-radiometer-humpro deployment", "abstract": "Wessex Summertime Convection Experiment: Met Office Observation Based Research (OBR) unit metoffice-obr-microwave-radiometer-humpro deployment" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 69 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2532, "accessConstraints": null, "accessCategory": "restricted", "accessRoles": "ukmo_wx", "label": "restricted: ukmo_wx group", "licence": { "ob_id": 12, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/ukmo_agreement.pdf", "licenceClassifications": [ { "ob_id": 4, "classification": "academic" } ] } } ], "projects": [ { "ob_id": 37501, "uuid": "3c72b5d38e9a499ead03e9e5f140972d", "short_code": "proj", "title": "WesCon: Wessex Convection experiment", "abstract": "WesCon was a UK summer convection experiment in 2023 funded by the Met Office. It focused on understanding dynamical aspects of convection to provide observational data to develop next generation km scale and urban-scale models to improve prediction of convective storms in km scale Numerical Weather Prediction (NWP) and climate models.\r\n\r\nThe emphasis of WesCon was understanding of dynamical processes (particularly updrafts and turbulence) and their interaction with other processes of importance. \r\n\r\nThe project involved the use of the FAAM Bae-146 aircraft, the Jade-Dimona aircraft and groundbased instruments at Cardington, Netheravon, and nearby ground sites. This project ran in conjunction with WOEST: WesCon - Observing the Evolving Structures of Turbulence project involving groundbased, sonde and radar observations. Both WesCon and WOEST were part of the wider ParaChute programme.\r\n\r\nThe Wessex Convection experiment (WesCon) took place from during summer 2023" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 75688, 75689, 75690, 75691, 75692, 75693, 75694, 75695, 75696, 75697, 75699, 75701, 75703, 75733, 84706, 84707, 84708 ], "vocabularyKeywords": [], "identifier_set": [ 13472 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 213851, 213852, 213853, 213854, 213855, 213856, 213857, 213858, 213859, 213878, 213861 ], "onlineresource_set": [] }, { "ob_id": 44660, "uuid": "c8038337158c4ecd90d21c0a199cce41", "title": "WesCon: precipitation measurements from the Met Office Thies disdrometer at Airfield Camp, Netheravon, v1.0 (20230419-20230919)", "abstract": "Precipitation measurements from the Met Office Thies disdrometer deployed at Netheravon, Wiltshire (51.241°N, -1.777°W, 120 m amsl) in southern England during the summer of 2023. Netheravon formed a supersite for the Wessex Summertime Convection Experiment (WesCon)/WesCon - Observing the Evolving Structures of Turbulence (WOEST) field campaign. \r\nThe Thies laser disdrometer is a laser optical device used for the measurement of diameter and fall velocity of hydrometeors. From such measurements, it is possible to classify different types of precipitation, such as drizzle, rain, hail, snow, and mixed precipitation, quantify precipitation in a time interval, and derive size and velocity joint distribution. Thies laser disdrometer can in addition detect fine drizzle, drop fall speed and drop size distribution. \r\nTo ensure optimal traceability and transparency of data, comprehensive metadata is included.", "creationDate": "2025-03-28T11:22:27.588628", "lastUpdatedDate": "2025-03-28T11:22:27", "latestDataUpdateTime": null, "updateFrequency": "notPlanned", "dataLineage": "Data were processed and prepared by the Met Office instrument team before delivery to the NERC EDS Centre for Environmental Data Analysis (CEDA) for long term archival.", "removedDataReason": "", "keywords": "WesCon, Met Office, precipitation, rainfall, drizzle rate, droplet size distribution, fall speed, spectra", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2025-08-07T08:14:50", "doiPublishedTime": "2025-08-07T09:48:27", "removedDataTime": null, "geographicExtent": { "ob_id": 4728, "bboxName": "Netheravon, Wiltshire, Uk", "eastBoundLongitude": -1.7777655, "westBoundLongitude": -1.7777655, "southBoundLatitude": 51.2386895, "northBoundLatitude": 51.2386895 }, "verticalExtent": null, "result_field": { "ob_id": 44662, "dataPath": "/badc/woest/data/metoffice-disdrometer/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 398590300, "numberOfFiles": 147, "fileFormat": "NetCDF" }, "timePeriod": { "ob_id": 12562, "startTime": "2023-04-19T00:00:00", "endTime": "2023-09-19T23:59:59" }, "resultQuality": { "ob_id": 4770, "explanation": "Data have been produced in accordance with standard Met Office observational practices. To ensure optimal traceability and transparency of data, comprehensive metadata is included.", "passesTest": true, "resultTitle": "MetOfficeWesCon Data Quality Statement", "date": "2025-08-05" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 44661, "uuid": "df5e25367d744bfbaede50cda65027ff", "short_code": "acq", "title": "WesCon: Metoffice distrometer deployed at Netheravon", "abstract": "The Wessex Summertime Convection Experiment (WesCon): Metoffice distrometer deployed at Netheravon" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 69 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2532, "accessConstraints": null, "accessCategory": "restricted", "accessRoles": "ukmo_wx", "label": "restricted: ukmo_wx group", "licence": { "ob_id": 12, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/ukmo_agreement.pdf", "licenceClassifications": [ { "ob_id": 4, "classification": "academic" } ] } } ], "projects": [ { "ob_id": 37501, "uuid": "3c72b5d38e9a499ead03e9e5f140972d", "short_code": "proj", "title": "WesCon: Wessex Convection experiment", "abstract": "WesCon was a UK summer convection experiment in 2023 funded by the Met Office. It focused on understanding dynamical aspects of convection to provide observational data to develop next generation km scale and urban-scale models to improve prediction of convective storms in km scale Numerical Weather Prediction (NWP) and climate models.\r\n\r\nThe emphasis of WesCon was understanding of dynamical processes (particularly updrafts and turbulence) and their interaction with other processes of importance. \r\n\r\nThe project involved the use of the FAAM Bae-146 aircraft, the Jade-Dimona aircraft and groundbased instruments at Cardington, Netheravon, and nearby ground sites. This project ran in conjunction with WOEST: WesCon - Observing the Evolving Structures of Turbulence project involving groundbased, sonde and radar observations. Both WesCon and WOEST were part of the wider ParaChute programme.\r\n\r\nThe Wessex Convection experiment (WesCon) took place from during summer 2023" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 75740, 75741, 75746, 75749, 75750, 75751, 75753, 75754, 75755, 75759, 75760, 75761, 75762, 84629, 84630, 84631, 84632, 84633, 84634, 84635, 84636, 84637, 84638, 84639, 84640 ], "vocabularyKeywords": [], "identifier_set": [ 13471 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 213874, 213867, 213868, 213869, 213870, 213871, 213872, 213873, 213875, 213876 ], "onlineresource_set": [] }, { "ob_id": 44663, "uuid": "7fb87dfcc7af40f783dc181b2c0d3e6c", "title": "WesCon: Vertical wind profiles and backscatter measurements from the Met Office Halo Doppler Lidar unit 35 at Airfield Camp, Netheravon, v1.0 (20230426-20230918)", "abstract": "Vertical wind profiles and backscatter measurements from the Met Office Halo Doppler Lidar unit 35 deployed at Netheravon, Wiltshire (51.241°N, -1.777°W, 120 m amsl) in southern England during the summer of 2023. Netheravon formed a supersite for the Wessex Summertime Convection Experiment (WesCon)/WesCon - Observing the Evolving Structures of Turbulence (WOEST) field campaign. \r\nThe Halo lidar is based on a 1565 nm laser emitting linearly polarized pulsed light through an 8 cm diameter lens with a heterodyne detector. Laser beam returns from the atmosphere are range-gated velocity and back-scattered power. The Halo is capable of full hemispheric scanning of the backscatter coefficient and radial velocity as a function of beam range. \r\nThe usual operation was vertical stares (zenith angle=0°) with periodic wind scans that invoked various options of off-axis views. DBS (Doppler beam swinging) scans used a tri-axis azimuthally orthogonal technique using the single lidar beam to retrieve horizontal mean wind components every 30 mins. Multi-axis VAD (velocity azimuth display) scans are a more involved version of the DBS scans and used 12 point off-zenith views performed every 60 mins. The vertical stares, DBS and VAD wind scans produced separate archived netCDF files. \r\nAlthough depolarisation capability was possible with unit 35, this was switched off during WesCon.\r\n\r\nTo ensure optimal traceability and transparency of data, comprehensive metadata is included.", "creationDate": "2025-03-28T11:22:27.588628", "lastUpdatedDate": "2025-03-28T11:22:27", "latestDataUpdateTime": null, "updateFrequency": "notPlanned", "dataLineage": "Data were processed and prepared by the Met Office instrument team before delivery to the NERC EDS Centre for Environmental Data Analysis (CEDA) for long term archival.", "removedDataReason": "", "keywords": "WesCon, Met Office, Doppler lidar, vertical stares, wind scans", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2025-08-07T08:19:03", "doiPublishedTime": "2025-08-07T09:44:53", "removedDataTime": null, "geographicExtent": { "ob_id": 4728, "bboxName": "Netheravon, Wiltshire, Uk", "eastBoundLongitude": -1.7777655, "westBoundLongitude": -1.7777655, "southBoundLatitude": 51.2386895, "northBoundLatitude": 51.2386895 }, "verticalExtent": null, "result_field": { "ob_id": 44665, "dataPath": "/badc/woest/data/metoffice-halo-lidar-35", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 55878847386, "numberOfFiles": 541, "fileFormat": "NetCDF" }, "timePeriod": { "ob_id": 12563, "startTime": "2023-04-26T00:00:00", "endTime": "2023-09-18T23:59:59" }, "resultQuality": { "ob_id": 4770, "explanation": "Data have been produced in accordance with standard Met Office observational practices. To ensure optimal traceability and transparency of data, comprehensive metadata is included.", "passesTest": true, "resultTitle": "MetOfficeWesCon Data Quality Statement", "date": "2025-08-05" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 44664, "uuid": "700fe00d28134146b05e43d80e4aac2a", "short_code": "acq", "title": "WesCon: MetOffice Halo Doppler Lidar unit 35 deployed at Netheravon", "abstract": "WesCon MetOffice Halo Doppler Lidar unit 35 deployed at Netheravon" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 69 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2532, "accessConstraints": null, "accessCategory": "restricted", "accessRoles": "ukmo_wx", "label": "restricted: ukmo_wx group", "licence": { "ob_id": 12, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/ukmo_agreement.pdf", "licenceClassifications": [ { "ob_id": 4, "classification": "academic" } ] } } ], "projects": [ { "ob_id": 37501, "uuid": "3c72b5d38e9a499ead03e9e5f140972d", "short_code": "proj", "title": "WesCon: Wessex Convection experiment", "abstract": "WesCon was a UK summer convection experiment in 2023 funded by the Met Office. It focused on understanding dynamical aspects of convection to provide observational data to develop next generation km scale and urban-scale models to improve prediction of convective storms in km scale Numerical Weather Prediction (NWP) and climate models.\r\n\r\nThe emphasis of WesCon was understanding of dynamical processes (particularly updrafts and turbulence) and their interaction with other processes of importance. \r\n\r\nThe project involved the use of the FAAM Bae-146 aircraft, the Jade-Dimona aircraft and groundbased instruments at Cardington, Netheravon, and nearby ground sites. This project ran in conjunction with WOEST: WesCon - Observing the Evolving Structures of Turbulence project involving groundbased, sonde and radar observations. Both WesCon and WOEST were part of the wider ParaChute programme.\r\n\r\nThe Wessex Convection experiment (WesCon) took place from during summer 2023" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 75764, 75765, 75766, 75767, 75768, 75769, 75770, 75771, 75772, 75773, 75774, 75775, 75776, 75777, 75778, 75779, 75780 ], "vocabularyKeywords": [], "identifier_set": [ 13470 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 213884, 213885, 213886, 213879, 213880, 213881, 213882, 213883, 213887, 213888, 213889 ], "onlineresource_set": [] }, { "ob_id": 44666, "uuid": "12298b52a84c482cbd274b44c866cbd1", "title": "WesCon: near-surface meteorology from Met Office automatic weather stations at 11 locations for the WesCon/WOEST measurement campaign. v1.0 (20230414-20231004)", "abstract": "This dataset contains a continuous record of temporary in-house designed Met Office automatic weather stations (AWS) deployed at 11 locations in southern England during the summer of 2023 as part of the Wessex Summertime Convection Experiment (WesCon)/WesCon - Observing the Evolving Structures of Turbulence (WOEST) field campaign. \r\n\r\nThe dataset contains recorded surface meteorology and (at six of the locations) subsoil sensors at 1 minute averaging period based around a 1.5 m mast.\r\nInstruments at 1.5 m height include: (Chilbolton, Clench Common, Spire View, Yatesbury Field, Lydeway Field, Breach Hill, Keevil, Wadswick Farm, The Park Brown Shutters, Henstridge)\r\n•\tPRT screened temperature sensor.\r\n•\tRotronics Hygroclip2 for measuring screened temperature and relative humidity.\r\n•\tBosch Sensortec BMP280 barometric pressure sensor.\r\n•\tRainfall is measured with a Hobo RG3 tipping-bucket gauge with a 0.2 mm accuracy.\r\n•\tHorizontal wind speed and direction from a Gill WindSonic 2-D ultrasonic sensor.\r\nSubsoil instrumentation (at six sites: Lydeway, Chilbolton, Keevil, Henstridge, The Park, Clench Common) includes:\r\n•\tDelta-T PR2 probe measured volumetric soil moisture at various depths (10, 20, 30, 40 cm).\r\n•\tDelta-T ST2 thermistor probes measured soil temperature at 1 cm depth.\r\n•\tHukseflux HFP01SC flux plate measured ground heat flux (2 cm depth).\r\n\r\n\r\nTo ensure optimal traceability and transparency of data, comprehensive metadata is included.", "creationDate": "2025-03-28T11:22:27.588628", "lastUpdatedDate": "2025-03-28T11:22:27", "latestDataUpdateTime": null, "updateFrequency": "notPlanned", "dataLineage": "Data were processed and prepared by the Met Office instrument team before delivery to the NERC EDS Centre for Environmental Data Analysis (CEDA) for long term archival.", "removedDataReason": "", "keywords": "WesCon, Met Office, wind speed, wind direction, temperature, relative humidity, surface, precipitation, rainfall, amount, soil, moisture, flux tower, surface, subsurface", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2025-08-07T08:21:53", "doiPublishedTime": "2025-08-07T09:38:09", "removedDataTime": null, "geographicExtent": { "ob_id": 4819, "bboxName": "WesCon MetOffice AWS", "eastBoundLongitude": -1.439579, "westBoundLongitude": -2.352224, "southBoundLatitude": 50.986313, "northBoundLatitude": 51.43195 }, "verticalExtent": null, "result_field": { "ob_id": 44669, "dataPath": "/badc/woest/data/metoffice-aws/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 512219691, "numberOfFiles": 1583, "fileFormat": "NetCDF" }, "timePeriod": { "ob_id": 12564, "startTime": "2023-04-14T00:00:00", "endTime": "2023-10-04T23:59:59" }, "resultQuality": { "ob_id": 4770, "explanation": "Data have been produced in accordance with standard Met Office observational practices. To ensure optimal traceability and transparency of data, comprehensive metadata is included.", "passesTest": true, "resultTitle": "MetOfficeWesCon Data Quality Statement", "date": "2025-08-05" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 44668, "uuid": "77bf11f7651c4a04bfb43cd74752912d", "short_code": "acq", "title": "WesCon MetOffice AWS - 11 Sites", "abstract": "WesCon MetOffice AWS - 11 Sites" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 69 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2532, "accessConstraints": null, "accessCategory": "restricted", "accessRoles": "ukmo_wx", "label": "restricted: ukmo_wx group", "licence": { "ob_id": 12, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/ukmo_agreement.pdf", "licenceClassifications": [ { "ob_id": 4, "classification": "academic" } ] } } ], "projects": [ { "ob_id": 37501, "uuid": "3c72b5d38e9a499ead03e9e5f140972d", "short_code": "proj", "title": "WesCon: Wessex Convection experiment", "abstract": "WesCon was a UK summer convection experiment in 2023 funded by the Met Office. It focused on understanding dynamical aspects of convection to provide observational data to develop next generation km scale and urban-scale models to improve prediction of convective storms in km scale Numerical Weather Prediction (NWP) and climate models.\r\n\r\nThe emphasis of WesCon was understanding of dynamical processes (particularly updrafts and turbulence) and their interaction with other processes of importance. \r\n\r\nThe project involved the use of the FAAM Bae-146 aircraft, the Jade-Dimona aircraft and groundbased instruments at Cardington, Netheravon, and nearby ground sites. This project ran in conjunction with WOEST: WesCon - Observing the Evolving Structures of Turbulence project involving groundbased, sonde and radar observations. Both WesCon and WOEST were part of the wider ParaChute programme.\r\n\r\nThe Wessex Convection experiment (WesCon) took place from during summer 2023" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 86159, 86160, 86161, 86162, 86163, 86164, 86165, 86166, 86167, 86168, 86169, 86170, 86171, 86172, 86173, 86174, 86175, 86176, 86177, 86178, 86179, 86180, 86181, 86182, 86183, 86184, 86185, 86186, 86187, 86188, 86189, 86190, 86191, 86192, 86193, 86194, 86195, 86196, 86197, 86198, 86199, 86200, 86201, 86202, 86203, 86204, 86205, 86206, 86207, 86208, 86209, 86210, 86211, 86212, 86213, 86214, 86215, 86216, 86217, 86218, 86219, 86220, 86221 ], "vocabularyKeywords": [], "identifier_set": [ 13469 ], "observationcollection_set": [ { "ob_id": 43875, "uuid": "36756ff1928f4a1684902b470cda774e", "short_code": "coll", "title": "Wessex convection experiment - Observing the Evolving Structures of Turbulence (WOEST)", "abstract": "A collection of groundbased and airborne datasets gathered to capture turbulent processes in the atmosphere at a range of fine spatial and temporal scales for the Wessex convection experiment - Observing the Evolving Structures of Turbulence (WOEST) project during summer 2023. This multi-instrument collection is used to evaluate simulations of turbulence and dynamics in convective clouds and how the structure and evolution of the boundary layer influences moist convective turbulence at a range of scales." } ], "responsiblepartyinfo_set": [ 213891, 213892, 213893, 213894, 213895, 213896, 213897, 213898, 213899, 213900, 213901 ], "onlineresource_set": [] }, { "ob_id": 44670, "uuid": "4c728040321d4342a27b19f80c8bfd0c", "title": "WesCon: Cloud height measurements from the Met Office Vaisala CT25K Laser Ceilometer at Airfield Camp, Netheravon, v1.0 (20230426-20230919)", "abstract": "Cloud height measurements from the Met Office Vaisala CT25K Laser Ceilometer deployed at Netheravon, Wiltshire (51.241°N, -1.777°W, 120 m amsl) in southern England during the summer of 2023. Netheravon formed a supersite for the Wessex Summertime Convection Experiment (WesCon)/WesCon - Observing the Evolving Structures of Turbulence (WOEST) field campaign. \r\n\r\nThe CT25K Laser ceilometer is a general-purpose cloud height sensor employing LIDAR technology for detection of clouds, precipitation and other obstructions to vision. The CT25K provides reliable determination of cloud height up to 25,000 feet (7.5 km) thus covering most heights where dense clouds appear. It is designed to detect up to three possible cloud layers.\r\nThe Vaisala CT25K laser ceilometer measures cloud height-or vertical visibility if the cloud base is obscured. The single-lens design also ensures reliable measurements in fog, rain, snow and haze.\r\n\r\nTo ensure optimal traceability and transparency of data, comprehensive metadata is included.", "creationDate": "2025-03-28T11:22:27.588628", "lastUpdatedDate": "2025-03-28T11:22:27", "latestDataUpdateTime": null, "updateFrequency": "notPlanned", "dataLineage": "Data were processed and prepared by the Met Office instrument team before delivery to the NERC EDS Centre for Environmental Data Analysis (CEDA) for long term archival.", "removedDataReason": "", "keywords": "WesCon, Met Office, ceilometer, cloud height, cloud base", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2025-08-07T08:24:00", "doiPublishedTime": "2025-08-07T09:05:49", "removedDataTime": null, "geographicExtent": { "ob_id": 4728, "bboxName": "Netheravon, Wiltshire, Uk", "eastBoundLongitude": -1.7777655, "westBoundLongitude": -1.7777655, "southBoundLatitude": 51.2386895, "northBoundLatitude": 51.2386895 }, "verticalExtent": null, "result_field": { "ob_id": 44672, "dataPath": "/badc/woest/data/metoffice-ceilometer-ct25k/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 466920596, "numberOfFiles": 147, "fileFormat": "NetCDF" }, "timePeriod": { "ob_id": 12565, "startTime": "2023-04-26T00:00:00", "endTime": "2023-09-19T23:59:59" }, "resultQuality": { "ob_id": 4770, "explanation": "Data have been produced in accordance with standard Met Office observational practices. 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It focused on understanding dynamical aspects of convection to provide observational data to develop next generation km scale and urban-scale models to improve prediction of convective storms in km scale Numerical Weather Prediction (NWP) and climate models.\r\n\r\nThe emphasis of WesCon was understanding of dynamical processes (particularly updrafts and turbulence) and their interaction with other processes of importance. \r\n\r\nThe project involved the use of the FAAM Bae-146 aircraft, the Jade-Dimona aircraft and groundbased instruments at Cardington, Netheravon, and nearby ground sites. This project ran in conjunction with WOEST: WesCon - Observing the Evolving Structures of Turbulence project involving groundbased, sonde and radar observations. Both WesCon and WOEST were part of the wider ParaChute programme.\r\n\r\nThe Wessex Convection experiment (WesCon) took place from during summer 2023" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 75733, 75863, 75864, 75865, 75866, 75867, 75868, 75869 ], "vocabularyKeywords": [], "identifier_set": [ 13468 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 213911, 213904, 213905, 213906, 213907, 213908, 213909, 213910, 213912, 213914 ], "onlineresource_set": [] }, { "ob_id": 44673, "uuid": "4a40c6fe12cc4c0786608065da06d287", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily land surface temperature from SLSTR (Sea and Land Surface Temperature Radiometer) on Sentinel 3A, level 3 collated (L3C) global product (2016-2023), version 4.00", "abstract": "This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from the Sea and Land Surface Temperature Radiometer (SLSTR) on Sentinel 3A. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.\r\n\r\nDaytime and night-time temperatures are provided in separate files corresponding to the morning and evening Sentinel-3A equator crossing times which are 10:00 and 22:00 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.\r\n\r\nThe dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. SLSTRA achieves full Earth coverage in 1 day so the daily files have gaps where the surface is not covered by the satellite swath during day or night on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.\r\n\r\nDataset coverage starts on 1st May 2016 and ends on 31st December 2023. There are minor interruptions (1-10 days) during satellite/instrument maintenance periods or instrument anomalies.\r\n\r\nIn Version 4.00 the temporal coverage is extended to 31st December 2023. The emissivities used in the retrieval come from the Combined ASTER and MODIS Emissivity over Land (CAMEL) Version 2 database; in Version 4.00 a climatology is used since there are temporal instabilities in the CAMEL time series.\r\n\r\nThe dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.\r\n\r\nThe dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.\r\n\r\nAn extended version of this dataset is also provided through the EOCIS project.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": null, "updateFrequency": "notPlanned", "dataLineage": "The data record has been produced by the University of Leicester working within the ESA Land Surface Temperature Climate Change Initiative (LST_cci), and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).\r\nContact: data.lst-cci@acri-st.fr", "removedDataReason": "", "keywords": "land surface temperature, ESA, CCI, SLSTR", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "0.01 degree", "status": "completed", "dataPublishedTime": "2025-12-02T17:30:34", "doiPublishedTime": "2025-12-03T08:25:57.555184", "removedDataTime": null, "geographicExtent": { "ob_id": 2896, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 45098, "dataPath": "/neodc/esacci/land_surface_temperature/data/SENTINEL3A_SLSTR/L3C/0.01/v4.00/daily/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 2880487836338, "numberOfFiles": 5425, "fileFormat": "Data are in NetCDF format" }, "timePeriod": { "ob_id": 12566, "startTime": "2016-05-01T00:00:00", "endTime": "2023-12-31T23:59:59" }, "resultQuality": { "ob_id": 3798, "explanation": "For information on the data quality see the associated LST_cci documentation", "passesTest": true, "resultTitle": "LST cci", "date": "2021-11-30" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 45102, "uuid": "ead330d843e743afabd6883e005154ab", "short_code": "cmppr", "title": "Composite process for the ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily land surface temperature from SLSTR (Sea and Land Surface Temperature Radiometer) on Sentinel 3A, level 3 collated (L3C) global product (2018-2023), version 4.00", "abstract": "Data has been derived from the Sea and Land Surface Temperature Radiometer (SLSTR) on the Sentinel 3B satellite.\r\n\r\nFor more information on the retrieval algorithm used see the following paper: Ghent, D. J., Anand, J. S., Veal, K., Remedios, J. J. (2024). The operational and climate land surface temperature products from the Sea and Land Surface Temperature Radiometers on Sentinel-3A and 3B. 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LST provides a metric of surface state when combined with vegetation parameters and soil moisture and is one of the drivers of vegetation phenology. Furthermore, LST is an independent temperature data set for quantifying climate change complementary to the near-surface air temperature ECV based on in situ measurements and reanalyses.\r\n\r\nThe team uses data from a variety of satellites to provide an accurate view of temperatures across land surfaces globally over the past +20 years. This involves developing innovative techniques to merge data from different satellites into combined long-term satellite records for climate. These will all be evaluated by scientists working at leading climate centres." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 62778, 62779, 62780, 62781, 62790, 62791, 62792, 66303, 66305, 66307, 66308, 66310, 66311, 66312, 66751, 66753, 66754, 75122, 75123, 75124, 75125, 75126 ], "vocabularyKeywords": [], "identifier_set": [ 13658 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 213926, 213927, 213928, 213929, 213930, 213931, 213932, 213933, 213935, 213934, 213936 ], "onlineresource_set": [ 94053, 94057 ] }, { "ob_id": 44674, "uuid": "49b4836314db4fec8d05d14b85a8614d", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily land Surface Temperature from SLSTR (Sea and Land Surface Temperature Radiometer) on Sentinel 3B, level 3 collated (L3C) global product (2018-2023), version 4.00", "abstract": "This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from the Sea and Land Surface Temperature Radiometer (SLSTR) on Sentinel 3B. 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