Observation List
Get a list of Observation objects.
GET /api/v3/observations/?format=api&offset=1700
{ "count": 10256, "next": "https://api.catalogue.ceda.ac.uk/api/v3/observations/?format=api&limit=100&offset=1800", "previous": "https://api.catalogue.ceda.ac.uk/api/v3/observations/?format=api&limit=100&offset=1600", "results": [ { "ob_id": 12208, "uuid": "e96225e330764c16a4c4b160f56f4202", "title": "Cobbacombe Cross C-band rain radar single polar products", "abstract": "Single-polar products from the Met Office's Cobbacombe Cross C-band rain radar, Devon, England. Data include reflectivity (from August 2009), Doppler (from June 2008), CPAPR (from March 2011) and augmented refractivity (from March 2011) products. The radar is a C-band (5.3 cm wavelength) radar and data are received by the Nimrod system at 5 minute intervals.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2020-05-04T04:45:28", "updateFrequency": "continual", "dataLineage": "Data are acquired from rain radar network in the UK and Europe by the Met Office's NIMROD system. Single site data are then sent to the BADC by the Met Office's DART operational dissemination system. Raw polar data from single sites are sent through from the RADARNET team at the Met Office. On arrival at the BADC the data are ingested after a one day quarantine, having been compressed and then compiled into daily tar balls for each data type.", "removedDataReason": "", "keywords": "Met Office, NIMROD, rain radar", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2011-03-24T00:00:00", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 126, "bboxName": "Cobbacombe Cross rain radar ranage", "eastBoundLongitude": -1.27, "westBoundLongitude": -5.27, "southBoundLatitude": 48.58, "northBoundLatitude": 52.58 }, "verticalExtent": null, "result_field": { "ob_id": 12210, "dataPath": "/badc/ukmo-nimrod/data/single-site/cobbacombe/raw-polar/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1391571006419, "numberOfFiles": 6912, "fileFormat": "NIMROD product data are in the non-standard NIMROD binary format. Refer to linked documentation for further details.\r\n" }, "timePeriod": { "ob_id": 3297, "startTime": "2008-06-09T00:00:00", "endTime": "2018-07-10T23:59:59" }, "resultQuality": { "ob_id": 1497, "explanation": "Data are from the Met Office's operational NIMROD system", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2013-12-16" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 5778, "uuid": "308e557d568942cdb2168ead70bc825c", "short_code": "acq", "title": "Acquisition Process for: Cobbacombe Rainfall Data from the Met Office Nimrod System", "abstract": "This acquisition is comprised of the following: INSTRUMENTS: Met Office C-band radar, NIMROD system; PLATFORMS: Cobbacombe Cross Radar Station, UK;" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 69 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "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" } ] } }, { "ob_id": 2533, "accessConstraints": null, "accessCategory": "restricted", "accessRoles": "ukmo_wx_gov", "label": "restricted: ukmo_wx_gov group", "licence": { "ob_id": 13, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/ukmo_agreement_gov.pdf", "licenceClassifications": [ { "ob_id": 5, "classification": "policy" } ] } } ], "projects": [ { "ob_id": 5736, "uuid": "ced06d1e5a8eacb69bf0029bf5f0e17a", "short_code": "proj", "title": "Met Office NIMROD Database", "abstract": "The Met Office run the NIMROD system as a short-term forcasting tool, primarily used for collating observational data such as rain radar data from across Europe" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 25899 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 5709, "uuid": "82adec1f896af6169112d09cc1174499", "short_code": "coll", "title": "Met Office Rain Radar Data from the NIMROD System", "abstract": "A collection of products from rain radars operated by the Met Office and other European agencies for the UK and Europe. This collection includes rain composite plots and data for the UK and Europe, plus single site radar data including rain rate data, single and dual-polar data products. \r\n\r\nThese were produced by the Met Office's Nimrod system. Nimrod is a fully automated system for weather analysis and nowcasting based around a network of C-band rainfall radars. This dataset has the fine-resolution analyses of rain rate for the UK and Europe.\r\n\r\nThe UK has a network of C-band rainfall radars and data form these are processed by the Met Office NIMROD system. Four or five radar scans at different elevations at each site are processed to give the best possible estimate of rainfall at the ground. The main quality checking method is routine evaluation using rain gauges as ground truth.\r\n\r\nThe BADC holds the analyses of rainfall rate at a time resolution of 5 or 15 minutes. Data are available from late 2002. Images are available for the UK as well as a further image including neighbouring European countries from 1999. Data files are available on a 1 km and 5 km Cartesian grid. Single radar site data are available for 2 and 5 km Cartesian grids for various UK radar sites.\r\n\r\nThe value of radar-based data from the Nimrod system has been highlighted repeatedly. For example, in two severe flooding events during 1998 (at Easter over the Midlands and in late October over Wales), estimates of surface rainfall derived from radar data provided evidence of the extent and severity of the rainfall events. The 2 km data files reach to 100 km from the radar, the 5km files to 250 km.\r\n\r\nDetailed radar site location are given in the Met Office Weather Radar Factsheet.\r\n\r\nTime resolution is 5 or 15 minutes depending on the product.\r\n\r\nVarious scripts have been made available under the software directory to aid use of these data, including within GIS applications." } ], "responsiblepartyinfo_set": [ 46557, 46558, 46561, 46562, 46563, 46560, 46559, 46556 ], "onlineresource_set": [ 5515, 5514, 5513 ] }, { "ob_id": 12209, "uuid": "c3e80be157644981a0b6badda8d2e454", "title": "Cobbacombe Cross C-band rain radar dual polar products", "abstract": "Dual-polar products from the Met Office's Cobbacombe Cross C-band rain radar, Devon, England. Data include augmented ldr and zdr scan data (both long and short pulse). The radar is a C-band (5.3 cm wavelength) radar and data are received by the Nimrod system at 5 minute intervals.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2025-07-18T01:57:48", "updateFrequency": "continual", "dataLineage": "Data are acquired from rain radar network in the UK and Europe by the Met Office's NIMROD system. Single site data are then sent to the BADC by the Met Office's DART operational dissemination system. Raw polar data from single sites are sent through from the RADARNET team at the Met Office. On arrival at the BADC the data are ingested after a one day quarantine, having been compressed and then compiled into daily tar balls for each data type.", "removedDataReason": "", "keywords": "Met Office, NIMROD, rain radar", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2014-06-12T23:00:00", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 126, "bboxName": "Cobbacombe Cross rain radar ranage", "eastBoundLongitude": -1.27, "westBoundLongitude": -5.27, "southBoundLatitude": 48.58, "northBoundLatitude": 52.58 }, "verticalExtent": null, "result_field": { "ob_id": 12211, "dataPath": "/badc/ukmo-nimrod/data/single-site/cobbacombe/raw-dual-polar/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 536297104337, "numberOfFiles": 2568, "fileFormat": "NIMROD product data are in the non-standard NIMROD binary format. 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This collection includes rain composite plots and data for the UK and Europe, plus single site radar data including rain rate data, single and dual-polar data products. \r\n\r\nThese were produced by the Met Office's Nimrod system. Nimrod is a fully automated system for weather analysis and nowcasting based around a network of C-band rainfall radars. This dataset has the fine-resolution analyses of rain rate for the UK and Europe.\r\n\r\nThe UK has a network of C-band rainfall radars and data form these are processed by the Met Office NIMROD system. Four or five radar scans at different elevations at each site are processed to give the best possible estimate of rainfall at the ground. The main quality checking method is routine evaluation using rain gauges as ground truth.\r\n\r\nThe BADC holds the analyses of rainfall rate at a time resolution of 5 or 15 minutes. Data are available from late 2002. Images are available for the UK as well as a further image including neighbouring European countries from 1999. Data files are available on a 1 km and 5 km Cartesian grid. Single radar site data are available for 2 and 5 km Cartesian grids for various UK radar sites.\r\n\r\nThe value of radar-based data from the Nimrod system has been highlighted repeatedly. For example, in two severe flooding events during 1998 (at Easter over the Midlands and in late October over Wales), estimates of surface rainfall derived from radar data provided evidence of the extent and severity of the rainfall events. The 2 km data files reach to 100 km from the radar, the 5km files to 250 km.\r\n\r\nDetailed radar site location are given in the Met Office Weather Radar Factsheet.\r\n\r\nTime resolution is 5 or 15 minutes depending on the product.\r\n\r\nVarious scripts have been made available under the software directory to aid use of these data, including within GIS applications." } ], "responsiblepartyinfo_set": [ 46569, 46570, 46571, 46564, 46565, 46566, 46568, 46567 ], "onlineresource_set": [ 5518, 5517, 5516, 7141 ] }, { "ob_id": 12212, "uuid": "1bc73bbf64b6427fb8652bdc3de0ecc1", "title": "Jersey C-band rain radar 5 km rainfall rate data", "abstract": "5 km resolution data from the NIMROD system data describe rain-rate observations recorded by the Jersery rain radar, Channel Islands, by NIMROD, which is a very short range forecasting system used by the Met Office. 5 km rain rate data are available from 2004 until present. Radar images from the C-band (5.3 cm wavelength) radar are received by the Nimrod system 5 minute intervals.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2018-01-02T00:15:05", "updateFrequency": "continual", "dataLineage": "Data are acquired from rain radar network in the UK and Europe by the Met Office's NIMROD system. Composite products (images and data) and single site data are then sent to the BADC by the Met Office's DART operational dissemination system. Raw polar data from single sites are sent through from the RADARNET team at the Met Office. On arrival at the BADC the data are ingested after a one day quarantine.", "removedDataReason": "", "keywords": "Met Office, NIMROD, rain radar", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "5 km", "status": "ongoing", "dataPublishedTime": "2011-02-21T00:00:00", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 121, "bboxName": "Jersey rain radar ranage", "eastBoundLongitude": -0.196, "westBoundLongitude": -4.196, "southBoundLatitude": 47.208, "northBoundLatitude": 51.208 }, "verticalExtent": null, "result_field": { "ob_id": 12213, "dataPath": "/badc/ukmo-nimrod/data/single-site/jersey/5km/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 2817117131, "numberOfFiles": 4399, "fileFormat": "NIMROD product data are in the non-standard NIMROD binary format. Refer to linked documentation for further details.\r\n" }, "timePeriod": { "ob_id": 3301, "startTime": "2004-04-07T00:00:00", "endTime": null }, "resultQuality": { "ob_id": 1498, "explanation": "Data are from the Met Office's operational NIMROD system", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2013-12-16" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 5762, "uuid": "41e88d6f5db5484898aa9076e4529df9", "short_code": "acq", "title": "Acquisition Process for: Jersey Rainfall Data from the Met Office Nimrod System", "abstract": "This acquisition is comprised of the following: INSTRUMENTS: Met Office C-band radar, NIMROD system; PLATFORMS: Jersey radar station, Channel Islands; " }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 69 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "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" } ] } }, { "ob_id": 2533, "accessConstraints": null, "accessCategory": "restricted", "accessRoles": "ukmo_wx_gov", "label": "restricted: ukmo_wx_gov group", "licence": { "ob_id": 13, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/ukmo_agreement_gov.pdf", "licenceClassifications": [ { "ob_id": 5, "classification": "policy" } ] } } ], "projects": [ { "ob_id": 5736, "uuid": "ced06d1e5a8eacb69bf0029bf5f0e17a", "short_code": "proj", "title": "Met Office NIMROD Database", "abstract": "The Met Office run the NIMROD system as a short-term forcasting tool, primarily used for collating observational data such as rain radar data from across Europe" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 25899 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 5709, "uuid": "82adec1f896af6169112d09cc1174499", "short_code": "coll", "title": "Met Office Rain Radar Data from the NIMROD System", "abstract": "A collection of products from rain radars operated by the Met Office and other European agencies for the UK and Europe. This collection includes rain composite plots and data for the UK and Europe, plus single site radar data including rain rate data, single and dual-polar data products. \r\n\r\nThese were produced by the Met Office's Nimrod system. Nimrod is a fully automated system for weather analysis and nowcasting based around a network of C-band rainfall radars. This dataset has the fine-resolution analyses of rain rate for the UK and Europe.\r\n\r\nThe UK has a network of C-band rainfall radars and data form these are processed by the Met Office NIMROD system. Four or five radar scans at different elevations at each site are processed to give the best possible estimate of rainfall at the ground. The main quality checking method is routine evaluation using rain gauges as ground truth.\r\n\r\nThe BADC holds the analyses of rainfall rate at a time resolution of 5 or 15 minutes. Data are available from late 2002. Images are available for the UK as well as a further image including neighbouring European countries from 1999. Data files are available on a 1 km and 5 km Cartesian grid. Single radar site data are available for 2 and 5 km Cartesian grids for various UK radar sites.\r\n\r\nThe value of radar-based data from the Nimrod system has been highlighted repeatedly. For example, in two severe flooding events during 1998 (at Easter over the Midlands and in late October over Wales), estimates of surface rainfall derived from radar data provided evidence of the extent and severity of the rainfall events. The 2 km data files reach to 100 km from the radar, the 5km files to 250 km.\r\n\r\nDetailed radar site location are given in the Met Office Weather Radar Factsheet.\r\n\r\nTime resolution is 5 or 15 minutes depending on the product.\r\n\r\nVarious scripts have been made available under the software directory to aid use of these data, including within GIS applications." } ], "responsiblepartyinfo_set": [ 46572, 46573, 46574, 46577, 46578, 46579, 46576, 46575 ], "onlineresource_set": [ 5527, 5529, 5528 ] }, { "ob_id": 12214, "uuid": "40e29b7a717646f18b2315a6178f063c", "title": "Jersey C-band rain radar 1 km rainfall rate data", "abstract": "1 km resolution data from the NIMROD system data describe rain-rate observations recorded by the Jersery rain radar, Channel Islands, by NIMROD, which is a very short range forecasting system used by the Met Office. 1 km rain rate data are available from 2011 until present. Radar images from the C-band (5.3 cm wavelength) radar are received by the Nimrod system 5 minute intervals.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2018-01-02T00:15:04", "updateFrequency": "continual", "dataLineage": "Data are acquired from rain radar network in the UK and Europe by the Met Office's NIMROD system. Composite products (images and data) and single site data are then sent to the BADC by the Met Office's DART operational dissemination system. Raw polar data from single sites are sent through from the RADARNET team at the Met Office. On arrival at the BADC the data are ingested after a one day quarantine.", "removedDataReason": "", "keywords": "Met Office, NIMROD, rain radar", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "1 km", "status": "ongoing", "dataPublishedTime": "2011-02-23T00:00:00", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 121, "bboxName": "Jersey rain radar ranage", "eastBoundLongitude": -0.196, "westBoundLongitude": -4.196, "southBoundLatitude": 47.208, "northBoundLatitude": 51.208 }, "verticalExtent": null, "result_field": { "ob_id": 12215, "dataPath": "/badc/ukmo-nimrod/data/single-site/jersey/1km/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1546681291, "numberOfFiles": 2346, "fileFormat": "NIMROD product data are in the non-standard NIMROD binary format. Refer to linked documentation for further details." }, "timePeriod": { "ob_id": 3302, "startTime": "2011-02-22T00:00:00", "endTime": null }, "resultQuality": { "ob_id": 1498, "explanation": "Data are from the Met Office's operational NIMROD system", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2013-12-16" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 5762, "uuid": "41e88d6f5db5484898aa9076e4529df9", "short_code": "acq", "title": "Acquisition Process for: Jersey Rainfall Data from the Met Office Nimrod System", "abstract": "This acquisition is comprised of the following: INSTRUMENTS: Met Office C-band radar, NIMROD system; PLATFORMS: Jersey radar station, Channel Islands; " }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 69 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "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" } ] } }, { "ob_id": 2533, "accessConstraints": null, "accessCategory": "restricted", "accessRoles": "ukmo_wx_gov", "label": "restricted: ukmo_wx_gov group", "licence": { "ob_id": 13, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/ukmo_agreement_gov.pdf", "licenceClassifications": [ { "ob_id": 5, "classification": "policy" } ] } } ], "projects": [ { "ob_id": 5736, "uuid": "ced06d1e5a8eacb69bf0029bf5f0e17a", "short_code": "proj", "title": "Met Office NIMROD Database", "abstract": "The Met Office run the NIMROD system as a short-term forcasting tool, primarily used for collating observational data such as rain radar data from across Europe" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 25899 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 5709, "uuid": "82adec1f896af6169112d09cc1174499", "short_code": "coll", "title": "Met Office Rain Radar Data from the NIMROD System", "abstract": "A collection of products from rain radars operated by the Met Office and other European agencies for the UK and Europe. This collection includes rain composite plots and data for the UK and Europe, plus single site radar data including rain rate data, single and dual-polar data products. \r\n\r\nThese were produced by the Met Office's Nimrod system. Nimrod is a fully automated system for weather analysis and nowcasting based around a network of C-band rainfall radars. This dataset has the fine-resolution analyses of rain rate for the UK and Europe.\r\n\r\nThe UK has a network of C-band rainfall radars and data form these are processed by the Met Office NIMROD system. Four or five radar scans at different elevations at each site are processed to give the best possible estimate of rainfall at the ground. The main quality checking method is routine evaluation using rain gauges as ground truth.\r\n\r\nThe BADC holds the analyses of rainfall rate at a time resolution of 5 or 15 minutes. Data are available from late 2002. Images are available for the UK as well as a further image including neighbouring European countries from 1999. Data files are available on a 1 km and 5 km Cartesian grid. Single radar site data are available for 2 and 5 km Cartesian grids for various UK radar sites.\r\n\r\nThe value of radar-based data from the Nimrod system has been highlighted repeatedly. For example, in two severe flooding events during 1998 (at Easter over the Midlands and in late October over Wales), estimates of surface rainfall derived from radar data provided evidence of the extent and severity of the rainfall events. The 2 km data files reach to 100 km from the radar, the 5km files to 250 km.\r\n\r\nDetailed radar site location are given in the Met Office Weather Radar Factsheet.\r\n\r\nTime resolution is 5 or 15 minutes depending on the product.\r\n\r\nVarious scripts have been made available under the software directory to aid use of these data, including within GIS applications." } ], "responsiblepartyinfo_set": [ 46580, 46581, 46582, 46584, 46585, 46586, 46587, 46583 ], "onlineresource_set": [ 5531, 5533, 5532 ] }, { "ob_id": 12216, "uuid": "96775747be204b4fa7b69b7526eaae8b", "title": "Predannack C-band rain radar 2 km rainfall rate data", "abstract": "2 km resolution rain rate data from Met Office's Predannack C-band rain radar, Cornwall, England as part of the NIMROD, very short range forecasting system used by the Met Office. 2 km rain rate data are available from 2004 until present. 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The experiments follow the WMO (2011) A1 baseline scenario for ozone depleting substances and the RCP 6.0 (Meinshausen et al., 2011) for other greenhouse gases (GHGs), tropospheric ozone (O3) precursors, and aerosol and aerosol precursor emissions.\r\nsenC2CH4rcp85: Similar to ref-C2 but the methane surface-mixing ratio follows the RCP 8.5 scenario (Meinshausen et al., 2011), all other GHGs and forcings follow RCP 6.0.\r\nsenC2fCH4: Similar to ref-C2 but the methane surface-mixing ratio is fixed to its 1960 value.\r\nsenC2fEmis: Similar to ref-C2 but with surface and aircraft emissions fixed to their respective 1960 levels.\r\nsenC2fN2O: Similar to ref-C2 but the nitrous oxide surface-mixing ratio is fixed to its 1960 value.\r\nsenC2rcp26: The same as ref-C2, but with the GHG scenario changed to RCP 2.6 (Meinshausen et al., 2011). \r\nsenC2rcp45: The same as ref-C2, but with the GHG scenario changed to RCP 4.5 (Meinshausen et al., 2011). \r\nsenC2rcp85: The same as ref-C2, but with the GHG scenario changed to RCP 8.5 (Meinshausen et al., 2011).", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2020-05-29T18:54:14", "updateFrequency": "", "dataLineage": "The CCMI-1 model output uses CMOR to convert data to CF netCDF. The CMOR conversion is performed by the individual modelling groups and the resulting CF netCDF files are archived at the BADC.", "removedDataReason": "", "keywords": "IGAC, SPARC, ETH-PMOD SOCOL3 CCMI CCMI-1", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2015-03-24T12:27:37", "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": 12260, "dataPath": "/badc/wcrp-ccmi/data/CCMI-1/output/ETH-PMOD", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 3218436875142, "numberOfFiles": 69922, "fileFormat": "NetCDF" }, "timePeriod": { "ob_id": 8775, "startTime": "1960-01-01T00:00:00", "endTime": "2100-12-31T23:59:59" }, "resultQuality": null, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 12263, "uuid": "274cba2cd5654e569e85c477e7693f48", "short_code": "comp", "title": "SOCOL3", "abstract": "The third generation of the coupled chemistry-climate model SOCOL (modeling tools for studies of SOlar Climate Ozone Links)" }, "procedureCompositeProcess": null, "imageDetails": [ 146 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2546, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 12257, "uuid": "d982781218704637a8c180fdeb598984", "short_code": "proj", "title": "The IGAC/SPARC Chemistry Climate Model Initiative (CCMI)", "abstract": "Increasingly, the chemistry and dynamics of the stratosphere and troposphere are being studied and modeled as a single entity in global models. As evidence, in support of the Intergovernmental Panel on Climate Change Fifth Assessment Report (IPCC AR5), several groups performed simulations in the Coupled Model Intercomparison Project Phase 5 (CMIP5) using global models with interactive chemistry spanning the surface through the stratosphere and above. In addition, tropospheric and stratospheric global chemistry-climate models are continuously being challenged by new observations and process analyses. Some recent intercomparison exercises have for example highlighted shortcomings in our understanding and/or modeling of long-term ozone trends and methane lifetime. Furthermore, there is growing interest in the impact of stratospheric ozone changes on tropospheric chemistry via both ozone fluxes (e.g. from the projected strengthening of the Brewer-Dobson circulation) and actinic fluxes. This highlights that there is a need to better coordinate activities focusing on the two domains and to assess scientific questions in the context of the more comprehensive stratosphere-troposphere resolving models with chemistry. 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As evidence, in support of the Intergovernmental Panel on Climate Change Fifth Assessment Report (IPCC AR5), several groups performed simulations in the Coupled Model Intercomparison Project Phase 5 (CMIP5) using global models with interactive chemistry spanning the surface through the stratosphere and above. In addition, tropospheric and stratospheric global chemistry-climate models are continuously being challenged by new observations and process analyses. Some recent intercomparison exercises have for example highlighted shortcomings in our understanding and/or modeling of long-term ozone trends and methane lifetime. Furthermore, there is growing interest in the impact of stratospheric ozone changes on tropospheric chemistry via both ozone fluxes (e.g. from the projected strengthening of the Brewer-Dobson circulation) and actinic fluxes. This highlights that there is a need to better coordinate activities focusing on the two domains and to assess scientific questions in the context of the more comprehensive stratosphere-troposphere resolving models with chemistry. To address the issues, the joint IGAC/SPARC Chemistry-Climate Model Initiative (CCMI) was established to coordinate future (and to some extent existing) IGAC and SPARC chemistry-climate model evaluation and associated modeling activities." } ], "responsiblepartyinfo_set": [ 46855, 46856, 46937, 79057, 104813, 105010, 105203, 141641, 104790, 46857 ], "onlineresource_set": [ 5651, 5652, 5653, 5658, 41731, 41732, 41733, 41746 ] }, { "ob_id": 12264, "uuid": "5e878b3b9e584bccbc71b2e5094acfdb", "title": "905 nm Vaisala CT75K Lidar Ceilometer Data from the Chilbolton Facility for Atmospheric and Radio Research (CFARR) site, Hampshire, England, 1996 - present", "abstract": "The Chilbolton Observatory, Hampshire, have had a Vaisala CT75K lidar ceilometer deployed since 13th September 1996. This dataset contains measurements of the range of first, second, and third cloud base from the lidar and attenuated backscatter coefficients of aerosols within the atmosphere. Plots of the attenuated backscatter coefficient at different heights are also available.\r\n\r\nThe instrument has been regularly calibrated using the method described by O'Connor, Ewan J., Anthony J. Illingworth, Robin J. Hogan, 2004: A Technique for Auto-calibration of Cloud Lidar. J. Atmos. Oceanic Technol., 21, 777–786. doi: http://dx.doi.org/10.1175/1520-0426(2004)021<0777:ATFAOC>2.0.CO;2 . \r\n\r\nPrior to April 2014 this technique had been applied manually, but from 2014 this was automated to provide a routine, automated application of O'Connor et al's calibration technique. This also highlighted an instrument calibration drift not previously spotted in earlier data and so a corrected data have been added to the archive for the following periods (denoted by \"_cor1\" in the filename): 1st July 2003 – 31st December 2003, January 2006 to December 2012 and February and March 2013. Users should see the data quality notes for further details.\r\n\r\nThis dataset incorporates the earlier published and citable 1996 - 2013 collection of lidar data, but continues this dataset to present", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2024-03-09T03:06:22", "updateFrequency": "continual", "dataLineage": "Data are recorded continuously by the instrument at the Chilbolton site before post-processing using signals from optically thick strato-cumulus clouds to calibrate according to the method described by O'Connor, Ewan J., Anthony J. Illingworth, Robin J. Hogan, 2004: A Technique for Auto-calibration of Cloud Lidar. J. Atmos. Oceanic Technol., 21, 777–786. doi: http://dx.doi.org/10.1175/1520-0426(2004)021<0777:ATFAOC>2.0.CO;2. The data are then deposited at the BADC for ingestion into the BADC archives.", "removedDataReason": "", "keywords": "clouds, lidar, ceilometer, infrared, elastic backscattering, precipitation, cloud base", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2015-03-25T19:38:02", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 92, "bboxName": "Chilbolton", "eastBoundLongitude": -1.427, "westBoundLongitude": -1.427, "southBoundLatitude": 51.145, "northBoundLatitude": 51.145 }, "verticalExtent": { "ob_id": 2, "highestLevelBound": 11500.0, "lowestLevelBound": 30.0, "units": "m" }, "result_field": { "ob_id": 12265, "dataPath": "/badc/chilbolton/data/lidar-ct75k-all-years/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 3224687379, "numberOfFiles": 1399, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 1024, "startTime": "1996-09-12T23:00:00", "endTime": null }, "resultQuality": { "ob_id": 883, "explanation": "Data are checked by CFARR staff prior to submission to BADC", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2014-09-21" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 3599, "uuid": "1270a56a70d34c179589d6379b0f668e", "short_code": "acq", "title": "Acquisition Process for: Chilbolton Facility for Atmospheric and Radio Research (CFARR) 905 nm Vaisala CT75K Lidar Ceilometer Data", "abstract": "This acquisition is comprised of the following: INSTRUMENTS: Chilbolton Facility for Atmospheric and Radio Research (CFARR) 905 nm Vaisala CT75K Lidar Ceilometer; PLATFORMS: Chilbolton Facility for Atmospheric and Radio Research (CFARR), UK; " }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 50 ], "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": 3464, "uuid": "493185a4f967ee2a34516d9c5da9331e", "short_code": "proj", "title": "Chilbolton Facility for Atmospheric and Radio Research (CFARR)", "abstract": "The STFC facility at Chilbolton, Hampshire (51.1445N, 1.4270W) is the site of several observation systems for meteorological studies. The main system is the 3 GHz Doppler radar (CAMRa). A supporting 94 GHz radar (Galileo) has been located close to the main dish to allow dual frequency studies of precipitating particles. The system is complemented by a 905 nm Vaisala CT75K lidar, a 355nm UV Raman Lidar, multiple raingauge and meteorological sensors. This dataset also holds attenuation time-series data from vertically polarised links from South Wonston to Sparsholt. Sparsholt meteorological sensor and raingauge data is also archived. Cloud camera data from the Chilbolton site is available for examining weather patterns." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 22455, 53051, 69767, 69768, 69769, 69770, 69771, 69772, 69773, 69774, 69775, 69776, 69777, 69778, 69779, 69780, 69781, 69782, 69783, 69784, 69785, 69786, 69787 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 3461, "uuid": "7cbc3fc19bfa037a48ba4cba4b93544d", "short_code": "coll", "title": "Chilbolton Facility for Atmospheric and Radio Research (CFARR): surface, radar and lidar measurements (1998-present)", "abstract": "Data from observations made using Chilbolton Facility for Atmospheric and Radio Research (CFARR).The Science and Technology Facilities Council (STFC) facility at Chilbolton Observatory, Hampshire (51.1445N, 1.4270W) is the home of many observation systems for meteorological and atmospheric science research. There are 4 radar systems designed to study precipitation, clouds and clear air, of which the largest is the 3 GHz Doppler radar (CAMRa) on the 25 m dish. There are also 4 lidar systems providing data on elastic backscattering, Doppler velocity, water vapour profiles and depolarisation. A wide range of meteorological and multiple raingauge data are available from both Chilbolton and the nearby Sparsholt field site. There is a wide range of radiometers at the site: microwave (for water vapour and liquid water measurements) and downwelling infra-red and visible detectors for radiation budget measurements. This dataset holds attenuation time-series data from vertically polarised 5 km links from South Wonston to Sparsholt. Cloud camera data from the Chilbolton site are available to provide visual information on weather conditions.\r\n\r\nCFARR is funded by the Natural Environment Research Council (NERC) and is owned and operated by the Space Science and Technology Department of the STFC." }, { "ob_id": 7378, "uuid": "ac73aca8c271d8c763eef1040987be62", "short_code": "coll", "title": "Cirrus and Anvils: European Satellite and Airborne Radiation measurements (CAESAR) Project Data from FAAM (Facility for Airborne Atmospheric Measurements) and Chilbolton Observatory", "abstract": "The CAESAR project aimed to investigate the radiative properties of cirrus cloud over a wide range of wavelengths in combination with airborne in situ measurements of cirrus microphysical properties. \r\n\r\nThis dataset collection contains atmospheric measurements of vertical and horizontal distribution of ice crystal size, shape and ice water content and meteorology during summer and winter periods. \r\n\r\nData were obtained by Flights using the FAAM BAE-146 to observe frontal and anvil cirrus co-incident with the CloudSat Aqua-train and Advanced Along-Track Scanning Radiometer (AATSR) satellites over the Chilbolton cloud radars and lidars as well as ocean/sea surrounding the UK. The winter FAAM campaign ran from 14 November 2005 17 December 2005 and the summer FAAM campaign ran from 3-17 May 2006." }, { "ob_id": 32603, "uuid": "ea21f06880c345e0bba8862c03fdf54a", "short_code": "coll", "title": "GRACES: Collection of remote sensing and surface observations", "abstract": "Collection of datasets produced by the NERC funded GRACES (G-band RAdar for Cloud and prEcipitation Studies) project and third-party datasets utilised by the project.\r\n\r\nIn addition to 3 GHz and 94 GHz radar data collected specifically for the GRACES project at the Chilbolton Atmospheric Observatory (CAO) the project made use of standard data products from a range of surface instrumentation from the CAO site. These were augmented with the use of data from the Met Office NIMROD rain radar network for specific case studies." } ], "responsiblepartyinfo_set": [ 46864, 46865, 46866, 46870, 46871, 46872, 46873, 46868, 46867, 46869 ], "onlineresource_set": [] }, { "ob_id": 12267, "uuid": "46d70a7636284690882e4ba01cd997c2", "title": "Global Vegetation Height Frequency Distributions (v1.1) from the ICESAT GLAS instrument produced under NCEO", "abstract": "This dataset provides global (between 60 S and 80 N) vegetation height (or object height) frequency distributions per half by half degree longitude and latitude derived from ICESAT Geoscience Laser Altimeter System (GLAS) waveform data.\r\n\r\nThis version 1.1 data was produced as part of the National Centre for Earth Observation (NCEO) ", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2015-06-23T16:28:47.071045", "updateFrequency": "notPlanned", "dataLineage": "Data prepared by authors and sent to NERC Earth Observations Data Centre (NEODC) for archiving. Data files are checked for CF conventions compliance.", "removedDataReason": "", "keywords": "NCEO; vegetation height", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "0.5x0.5 degree", "status": "completed", "dataPublishedTime": "2015-06-28T09:58:40", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 538, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -60.0, "northBoundLatitude": 80.0 }, "verticalExtent": null, "result_field": { "ob_id": 12400, "dataPath": "/neodc/glas_veg_height/data/veg_height_distribution/v1-1", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 147248636, "numberOfFiles": 3, "fileFormat": "Data are netCDF formatted" }, "timePeriod": null, "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": 12271, "uuid": "7af15ba3881b454bb1544f1ae2d76c7d", "short_code": "cmppr", "title": "Global Vegetation Height Frequency Distributions derived from ICESAT GLAS data", "abstract": "This vegetation height frequency distribution dataset was derived from data from the Geoscience Laser Altimeter System (GLAS) on the Ice, Cloud and land Elevation Satellite (ICESat), with data available from the NSIDC (nsidc.org)\r\n\r\nICESAT Geoscience Laser Altimeter System (GLAS) waveform data contain information on topography (slope) and objects on the surface (mostly vegetation). GLAS emits a laser pulse that is absorbed scattered and reflected by vegetation and the land surface. Vegetation height is derived from the GLA14 product release 31. This product contains the decomposition of waveforms in up to 6 Gaussians. Height is estimated from the difference between the first return, indicative of the top of the vegetation canopy or object, and the ground return. Ground return is estimated from the lowest of the two gaussians. The Gaussian with maximum amplitude of the last two Gaussians is thought to represent the ground return (Rosette et al. 2008). \r\n\r\nSpurious data were eliminated by applying a set of filters. Filters test for slope, difference in elevation between the wave form and the ground (indicative of clouds) and signal strength. An adjustment is applied for the shape of the last Gaussian. Further details can be found in Los et al 2012." }, "imageDetails": [ 130 ], "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": 12078, "uuid": "d9c0d70d93fee8786e0c6474e9975bfa", "short_code": "proj", "title": "NCEO Theme 1: Climate", "abstract": "This Theme of the National Centre for Earth Observation aims to exploit EO to improve national capability for climate prediction over timescales from months to decades.\r\n\r\nIts priorities are to: \r\n- Compare how computer models represent water vapour, clouds and the Earth's radiation budget with satellite data\r\n- Combine ocean-surface data from space with data taken from instruments on-board ships and subsurface ocean monitoring networks to understand changes in ocean circulation and ocean warming\r\n- Improve how climate models represent cryosphere interactions with the oceans and atmosphere\r\n- Improve how climate models represent the land surface, including life processes, and the resulting heat, water and gas exchanges with the atmosphere" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 51157, 51158, 88529, 88530, 88531 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 12266, "uuid": "85e7d70a74244c73b71446940e05cde6", "short_code": "coll", "title": "Global Vegetation Height Frequency Distributions from the ICESAT GLAS instrument produced as part of the National Centre for Earth Observation (NCEO)", "abstract": "This dataset collection provides global (between 60 S and 80 N) vegetation height (or object height) frequency distributions per half by half degree longitude and latitude derived from ICESAT Geoscience Laser Altimeter System (GLAS) waveform data.\r\n\r\nThis data was produced as part of the National Centre for Earth Observation (NCEO) " }, { "ob_id": 30127, "uuid": "82b29f96b8c94db28ecc51a479f8c9c6", "short_code": "coll", "title": "National Centre for Earth Observation (NCEO) Core datasets", "abstract": "This NCEO Core data set collection contains data generated by the National Centre for Earth Observation core scientific programmes. NCEO is a National Environment Research Council (NERC) research centre with more than 80 scientists distributed across leading UK universities and research organisations and led by Professor John Remedios at the University of Leicester.\r\n\r\nNCEO provides the UK with core expertise in Earth Observation science, data sets and merging techniques, and model evaluation to underpin Earth System research and the UK’s international contribution to environmental science. NCEO scientists work strategically with space agencies, play significant roles in mission planning, and generate internationally-recognised data products from 20 different satellite instruments." } ], "responsiblepartyinfo_set": [ 46887, 46888, 46891, 46893, 47318, 46889, 46890, 46892, 46886 ], "onlineresource_set": [ 5812 ] }, { "ob_id": 12279, "uuid": "4c9705ffd7d24e72ba00dc8bb53af528", "title": "GSFC (Goddard Space Flight Centre) Data, Part of the IGAC/SPARC Chemistry-Climate Model Initiative Phase-1 (CCMI-1) Project Database", "abstract": "Data from the GSFC (Goddard Space Flight Centre) GEOSCCM model simulations, part of the International Global Atmospheric Chemistry (IGAC)/ Stratosphere-troposphere Processes and their Role in Climate (SPARC) Chemistry-Climate Model Initiative phase 1 (CCMI-1).\r\n\r\nCCMI-1 is a global chemistry climate model intercomparison project, coordinated by the University of Reading on behalf of the World Climate Research Programme (WCRP).\r\n\r\nThe dataset includes data for the CCMI-1 reference experiments ref-C1 and ref-C2. \r\n\r\nref-C1: Using state-of-knowledge historic forcings and observed sea surface conditions, the models simulate the recent past (1960–2010).\r\nref-C2: Simulations spanning the period 1960–2100. The experiments follow the WMO (2011) A1 baseline scenario for ozone depleting substances and the RCP 6.0 (Meinshausen et al., 2011) for other greenhouse gases, tropospheric ozone (O3) precursors, and aerosol and aerosol precursor emissions.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2024-03-09T01:41:36", "updateFrequency": "", "dataLineage": "The CCMI-1 model output uses CMOR to convert data to CF netCDF. 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Lastly, microphysical measurements made with the FAAM aircraft will be used to derive latent heating/cooling rates as a function of the microphysical environment and used to improve the model simulations in the first WP and to improve microphysical parameterisations in the UM The final WP addresses the problem of predictability, using a combination of ensemble and data assimilation techniques. A unique archive of forecast ensembles produced at the Met Office will be exploited to determine how well the forecast ensemble actually generates realistic mesoscale features, and the skill with which this is done (using standard measures of skill). Model errors in representing convection, air-sea fluxes and microphysics will be investigated to determine their impact on the forecasts for different flow conditions. 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DIAMET used the FAAM (Facility for Airborne Atmospheric Measurements) BAE-146 aircraft, ground-based and airborne instruments and radar together with modelling studies to forge a better understanding and prediction of mesoscale structures in synoptic-scale storms. This is determined by focusing an incident laser beam on particles, and whenever a particle passes through the beam, a shadow is generated and imaged onto the diode array. These images are part of this dataset along with flight summaries.\r\n\r\nThe DIAMET project aimed to better the understanding and prediction of mesoscale structures in synoptic-scale storms. Such structures include fronts, rain bands, secondary cyclones, sting jets etc, and are important because much of the extreme weather we experience (e.g. strong winds, heavy rain) comes from such regions. Weather forecasting models are able to capture some of this activity correctly, but there is much still to learn. By a combination of measurements and modelling, mainly using the Met Office Unified Model (UM), the project worked to better understand how mesoscale processes in cyclones give rise to severe weather and how they can be better represented in models and better forecast. The project is organised into three sections.\r\n\r\nReal mesoscale structures in the atmosphere have been examined, using high-resolution in situ and radar measurements to derive their morphology and dynamics. The key to the latter is to calculate the production of potential vorticity by diabatic processes - especially phase changes of water (vapour/liquid/ice) and air-sea fluxes of sensible and latent heat. The associated high-resolution modelling programme will use the UM to simulate a representative number of events, diagnosing the PV tendency in the model and comparing with the measurements. Sensitivity studies and further diagnostics with the model will reveal the sensitivity of the forecasts to the correct representation of these processes and the dynamical consequences of diabatically-generated PV, both on the mesoscale and larger scales. Two student projects have investigated the role of boundary-layer processes in storm behaviour and conduct a statistical investigation of mesoscale precipitation features, based on archived radar and wind profiler data.\r\n\r\nExamination of particular physical processes and the way these are represented in forecast models. Convection cannot be explicitly represented in current large-scale models (it is just beginning to be resolvable by high-resolution local-area models) so it needs to be parameterised. The schemes that are used are not optimised for mid-latitude storms, where convection often initiates at altitude rather than at the Earth's surface. A combination of novel diagnostics and new (or modified) schemes aimed at improving the representation of convection will be developed. Also addressed here will be the derivation of air-sea fluxes of heat and momentum from aircraft flights, and their use (as part of a larger, ongoing international project) to derive a better parameterisation for these quantities in high wind conditions. Lastly, microphysical measurements made with the FAAM aircraft will be used to derive latent heating/cooling rates as a function of the microphysical environment and used to improve the model simulations in the first WP and to improve microphysical parameterisations in the UM.\r\n\r\nThe problem of predictability will be addressed using a combination of ensemble and data assimilation techniques. A unique archive of forecast ensembles produced at the Met Office will be exploited to determine how well the forecast ensemble actually generates realistic mesoscale features, and the skill with which this is done (using standard measures of skill). Model errors in representing convection, air-sea fluxes and microphysics will be investigated to determine their impact on the forecasts for different flow conditions. The relationship between different model variables on the mesoscale is poorly known at present and this will be investigated using ensembles and the results of the measurement programme. Finally, novel approaches to data assimilation will be investigated." } ], "responsiblepartyinfo_set": [ 55312, 55314, 55315, 55313, 104815, 105012, 105205, 104792, 55316 ], "onlineresource_set": [ 5705, 5704 ] }, { "ob_id": 12292, "uuid": "6d2cbe9f93404c5e9152f4f92ea6cbdb", "title": "High accuracy line intensity data for carbon dioxide", "abstract": "High accuracy line intensity for carbon dioxide project was NERC (Natural Environment Research Council) funded. The aim of the project was to provide an accurate theoretical solution to the problem of CO2 line intensities based on the application of high accuracy, first principles quantum mechanical calculations for the intensities and experimental data for the line positions.\r\n\r\nThis dataset contains measurements of high accuracy line intensity for carbon dioxide.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2018-11-19T14:32:57.378558", "updateFrequency": "notPlanned", "dataLineage": "Data received from project group as is.", "removedDataReason": "", "keywords": "CO2 , carbon dioxide, nerc", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "superseded", "dataPublishedTime": "2015-04-28T11:05:31", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": null, "verticalExtent": null, "result_field": { "ob_id": 12416, "dataPath": "/badc/hi-acc-co2/data/old", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 25958998, "numberOfFiles": 2, "fileFormat": "HITRAN format" }, "timePeriod": null, "resultQuality": null, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 25056, "uuid": "07f4246e004a426aafe524aeda2147ed", "short_code": "comp", "title": "High accuracy line intensity data for carbon dioxide", "abstract": "The Lodi-Tennyson method [33] for validating linelists on a purely theoretical basis relies on the use of accurate, ab initio transition intensity calculations require an accurate procedures for obtaining nuclear motion wavefunctions together with the use of at least two DMSs and two PESs. This is described in this paperE. Zak, J. Tennyson, O.L. Polyansky, L. Lodi, S.A. Tashkun and V.I. Perevalov, A room temperature CO2 line list with ab initio computed intensities, J. Quant. Spectrosc. Rad. Transf, 177, 31-42 (2016). doi 10.1016/j.jqsrt.2015.12.022 \r\n" }, "procedureCompositeProcess": null, "imageDetails": [ 2 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 12148, "uuid": "407621ad08204f96a68708ceaf8cac2e", "short_code": "proj", "title": "High accuracy line intensities for carbon dioxide", "abstract": "This project was funded by the Natural Environment Research Council (NERC) with the grant reference - NE/J010316/1 - led by Professor Jonathan Tennyson (University College London). \r\n\r\nAll CO2 remote sensing activity, from both the ground and space, relies on monitoring how CO2 absorbs light. All this monitoring is therefore heavily dependent on understanding the absorption properties of the CO2 molecule which is usually obtained by measurements performed in the laboratory. In particular the accurate knowledge of the strength of individual absorption lines is crucial to determining how much CO2 is present and allowing the atmospheric data to be interpreted. Without high accuracy values for line intensities, reliable CO2 retrievals are simply not possible. Particularly with their emphasis on variation of CO2 concentrations with time, current missions and proposed missions require CO2 line intensities to be determined to significantly better than 1% accuracy if they are to fulfill their stated goals: intensities accurate to better than 0.5% are really required. Current line intensities measured in the laboratory simply do not gives this level of accuracy: most are accurate to about 5% with a few high quality measurements being good to 1 - 3%. Hence current CO2 retrievals values are limited by the available laboratory data. \r\n\r\nThe aim of this project was to provide an accurate theoretical solution to the problem of CO2 line intensities based on the application of high accuracy, first principles quantum mechanical calculations for the intensities and experimental data for the line positions. The resulting new lists of CO2 transition intensities were made widely available and, in particular, used to inform atmospheric databases which are used for the majority of atmospheric applications of molecular spectroscopy.\r\n\r\nThe main objectives of the project were to determine a complete set of high accuracy line intensities for the CO2 molecule for use in atmospheric studies. With the following specific objectives:\r\n\r\n1. Develop a theoretical model to compute a high accuracy dipole moments for asymmetric geometries of CO2.\r\n\r\n2. Compute dipole moments as a function of geometry and fit them to give a high accuracy dipole moment surface.\r\n\r\n3. Develop a model for and calculate high accuracy vibration-rotation wavefunctions and hence transition intensities\r\nfor CO2.\r\n\r\n4. Compare our results with laboratory data, particularly ultrahigh accuracy studies currently being performed in NIST.\r\n\r\n5. Generate a comprehensive linelist of CO2 transitions.\r\n\r\n6. Distribute our CO2 linelist widely via the web and via data compilations such as HITRAN, GEISA and BADC." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 23086 ], "vocabularyKeywords": [], "identifier_set": [ 8396 ], "observationcollection_set": [ { "ob_id": 13147, "uuid": "b7d3fffe79a34394becbb43feb163e7b", "short_code": "coll", "title": "High accuracy line intensity data for carbon dioxide", "abstract": "High accuracy line intensity for carbon dioxide project was NERC (Natural Environment Research Council) funded. The aim of the project was to provide an accurate theoretical solution to the problem of CO2 line intensities based on the application of high accuracy, first principles quantum mechanical calculations for the intensities and experimental data for the line positions. \r\n\r\nThis dataset collection contains measurements of high accuracy line intensity for carbon dioxide.\r\n\r\nAtmospheric CO2 concentrations are being closely monitored by remote sensing experiments which rely on knowing line intensities with an uncertainty of 0.5% or better. Most available laboratory measurements have uncertainties much larger than this. The generated data is a result of a joint experimental and theoretical study providing rotation-vibration line intensities with the required accuracy. The calculations are extendable to all atmospherically important bands of CO2 and to its isotologues. As such, they will form the basis for detailed CO2 spectroscopic line lists for future studies." } ], "responsiblepartyinfo_set": [ 47388, 47386, 47387, 47390, 47391, 47392, 47393, 47389 ], "onlineresource_set": [ 7064, 15322 ] }, { "ob_id": 12294, "uuid": "022a3d5ba0204c4c9bbe983b437289b7", "title": "Met Office MSLP Charts images June 2014 to present from the Met Office's SWIFT system", "abstract": "UK Met Office charts analyses pertaining to Mean Surface Level Pressure and 24 hour Weather Frontal Forecasting for the UK and Western Europe for the 00 and 12UT forecast runs. The charts in this dataset, at time steps at T+00, 24, 48 and 72 hours, have been produced by the Met Office's SWIFT system using VisualWeather, following a switch from the feed provided by the Met Office's GCPS Commercial System (see related dataset). The data in this collection cover the period 30th June 2014 to present, though initially with some data gaps.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2025-07-18T02:01:38", "updateFrequency": "continual", "dataLineage": "Data, over the period June 2014 to the present, are produced by the Met Office's operational model system, processed by the Met Office's SWITFT system and produced using VisualWeather before dissemination to the BADC through the Met Office's operational dissemination system. Data are ingested daily.", "removedDataReason": "", "keywords": "mlsp, charts, atmospheric pressure, met office, fronts, depressions, lows, highs", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2015-05-07T13:11:04", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 30, "bboxName": "", "eastBoundLongitude": 100.0, "westBoundLongitude": -120.0, "southBoundLatitude": 35.0, "northBoundLatitude": 80.0 }, "verticalExtent": null, "result_field": { "ob_id": 12295, "dataPath": "/badc/ukmo-charts/data/swift-mslp-charts_2014-present/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 3598200592, "numberOfFiles": 28866, "fileFormat": "Files are GIF formatted image files" }, "timePeriod": { "ob_id": 3327, "startTime": "2014-06-29T23:00:00", "endTime": null }, "resultQuality": { "ob_id": 427, "explanation": "Charts are produced automatically by Met Office's operational system", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2014-09-22" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 12296, "uuid": "29e400ee2dbe4a57bd546f0bd2f9df72", "short_code": "comp", "title": "Production of MSLP charts from the Met Office's SWIFT system", "abstract": "Mean Sea-level pressure (MSLP) charts are produced routinely from the Met Office's forecasting system via the Met Office's SWIFT system using VisualWeather." }, "procedureCompositeProcess": null, "imageDetails": [ 69 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "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" } ] } }, { "ob_id": 2533, "accessConstraints": null, "accessCategory": "restricted", "accessRoles": "ukmo_wx_gov", "label": "restricted: ukmo_wx_gov group", "licence": { "ob_id": 13, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/ukmo_agreement_gov.pdf", "licenceClassifications": [ { "ob_id": 5, "classification": "policy" } ] } } ], "projects": [ { "ob_id": 555, "uuid": "f46cfa4784fb454e105f336981f1a82b", "short_code": "proj", "title": "Met Office Numerical Weather Prediction (NWP) System", "abstract": "All forecasts, of whatever type, are ultimately based on the predictions from the Met Office suite of sophisticated atmospheric and oceanic models, run on their powerful supercomputer. This form of forecasting is known as numerical weather prediction (NWP). Numerical weather prediction (NWP) is the process of obtaining an objective forecast of the future state of the atmosphere by running a computer model. The Met Office Unified Model is run operationally, in a number of configurations, for weather forecasting at the Met Office." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 25886 ], "vocabularyKeywords": [], "identifier_set": [ 8395 ], "observationcollection_set": [ { "ob_id": 1872, "uuid": "267264a0036052fe71f5f25e384f0339", "short_code": "coll", "title": "Met Office Mean Sea Level Pressure (MSLP) Charts Collection 1999 to present.", "abstract": "UK Met Office charts analyses pertaining to Mean Surface Level Pressure and 24 hour Weather Frontal Forecasting for the UK and Western Europe (see chart samples below). The charts have been produced by two systems at the Met Office and so are provided in two distinct datasets within this collection. The first set was delivered by the Met Office's GPCS Commercial Suite and covers the period 7th June 1999 to 24th June 2014. At this point the Met Office turned off that service and switched to providing images produced by the Met Office's SWIFT system using VisualWeather. These later data cover the period 30th June 2015 to present, though initially with some data gaps." }, { "ob_id": 29923, "uuid": "400efba73c1d40c78f44918429ce9c99", "short_code": "coll", "title": "PICASSO: data from radiosonde flights, Chilbolton weather radar and in-situ airborne observations by the FAAM BAE-146 aircraft", "abstract": "In-situ airborne observations by the FAAM BAE-146 aircraft, radiosonde launches and the Chilbolton weather radar for the Parameterizing Ice Clouds using Airborne obServationS and triple-frequency dOppler radar data (PICASSO) project.\r\n\r\nThis dataset collection also links to a range of third party datasets that were also utilised by the project participants (Met Office rain radar, surface charts, Meteosat images, surface data from Chilbolton instruments and data from the X-band radar deployed at the site at the same time as the PICASSO observation days)." } ], "responsiblepartyinfo_set": [ 46956, 46957, 46958, 46959, 46953, 46954, 46952, 46955 ], "onlineresource_set": [] }, { "ob_id": 12297, "uuid": "42a4ab662c734acfa46d5a1b231364ad", "title": "NEXTMap British Difference model (dsm - dtm) Data by Intermap", "abstract": "This dataset consists of the Difference Model (dsm - dtm) - known as diff in the archive - from the NEXMap British Digital Terrain Model project produced by Intermap.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2005-09-22T18:06:43", "updateFrequency": "notPlanned", "dataLineage": "Data were purchased by the Natural Environmental Research Council from Intermap who produced the data. These data were then delivered to the NEODC for curation and to permit NERC funded researchers to access these data centrally.", "removedDataReason": "", "keywords": "NEXTMap, elevation", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2009-09-16T23:00:00", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 674, "bboxName": "UK OS Grid (UKCP grid)", "eastBoundLongitude": 4.41954, "westBoundLongitude": -8.56493, "southBoundLatitude": 49.53098, "northBoundLatitude": 61.11007 }, "verticalExtent": null, "result_field": { "ob_id": 12298, "dataPath": "/neodc/nextmap/by_product/diff", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 47717808848, "numberOfFiles": 35245, "fileFormat": "Data are ArcInfo binary Grid formatted" }, "timePeriod": { "ob_id": 3020, "startTime": "2001-12-30T00:00:00", "endTime": "2003-10-12T23:00:00" }, "resultQuality": null, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 133 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2587, "accessConstraints": null, "accessCategory": "restricted", "accessRoles": "nextmap_eula", "label": "restricted: nextmap_eula group", "licence": { "ob_id": 50, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/nextmap_eula.pdf", "licenceClassifications": [ { "ob_id": 7, "classification": "specific" }, { "ob_id": 4, "classification": "academic" } ] } } ], "projects": [ { "ob_id": 13986, "uuid": "7a4861633a3f490abf2fbeef4a444ffa", "short_code": "proj", "title": "NEXTMap British Digital Terrain Model", "abstract": "NEXTMap Britain digital terrain model dataset, produced by Intermap, was derived from airborne Interferometric Synthetic Aperture Radar (IFSAR). It enabled large areas of the country to be covered rapidly and at very high spatial resolution. The data were acquired by the British Geological Survey (BGS) and are for use by NERC funded researchers only.\r\n\r\nIt was originally commissioned by Norwich Union Insurance to aid in the estimation of flood risk, but has tremendous potential as a scientific research tool.\r\n\r\nThe dataset covers all of England, Wales and Scotland.\r\n\r\nData are arranged into Ordnance Survey / British National Grid tiles of 10 x 10 km, each denoted by a 4-charcter name, e.g. TL63 based on the OS National Grid coordinates of the southwest corner of the square. Products provided include: \r\n\r\nDigital surface mode (dsm)\r\nDigital terrain model (dtm)\r\nDifference model (dsm - dtm)\r\nDigital terrain model at 10m resolution\r\nDigital terrain model at 50m resolution\r\nDigital terrain model, enhanced (manually edited to remove embankments etc.)\r\nOrthorectified radar image (ori)\r\n\r\nThe data covering Northern Scotland (North of the Midland Valley) are only available as diff, dsm, dtm and ori products.\r\n\r\nThis dataset has been archived at the NEODC for the use of NERC scientists outside of BGS and CEH (who have made their own arrangements for internal distribution of the data)." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6868 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 10883, "uuid": "8f6e1598372c058f07b0aeac2442366d", "short_code": "coll", "title": "NEXTMap British Digital Terrain Model Dataset Produced by Intermap", "abstract": "NEXTMap Britain digital terrain model dataset, produced by Intermap, was derived from airborne Interferometric Synthetic Aperture Radar (IFSAR). It enabled large areas of the country to be covered rapidly and at very high spatial resolution. The data were acquired by the British Geological Survey (BGS) and are for use by NERC funded researchers only.\r\n\r\nIt was originally commissioned by Norwich Union Insurance to aid in the estimation of flood risk, but has tremendous potential as a scientific research tool.\r\n\r\nThe dataset covers all of England, Wales and Scotland.\r\n\r\nData are arranged into Ordnance Survey / British National Grid tiles of 10 x 10 km, each denoted by a 4-charcter name, e.g. TL63 based on the OS National Grid coordinates of the southwest corner of the square. Products provided include: \r\n\r\nDigital surface mode (dsm)\r\nDigital terrain model (dtm)\r\nDifference model (dsm - dtm)\r\nDigital terrain model at 10m resolution\r\nDigital terrain model at 50m resolution\r\nDigital terrain model, enhanced (manually edited to remove embankments etc.)\r\nOrthorectified radar image (ori)\r\n\r\nThe data covering Northern Scotland (North of the Midland Valley) are only available as diff, dsm, dtm and ori products.\r\n\r\nThis dataset has been archived at the NEODC for the use of NERC scientists outside of BGS and CEH (who have made their own arrangements for internal distribution of the data)." }, { "ob_id": 30129, "uuid": "3b0630c7fa264164868d4da5c9f90bed", "short_code": "coll", "title": "National Centre for Earth Observation (NCEO) Third Party Data", "abstract": "The National Centre for Earth Observation (NCEO) Third Party data contains a broad range remotely sensed data acquired by satellite for use by the Earth Observation Scientific community supported by NCEO. The Centre for Environmental Data Analysis (CEDA) has archived and provides access to extensive Earth observation datasets under strict licensing conditions. Please see the individual dataset records for conditions of use." } ], "responsiblepartyinfo_set": [ 46962, 46963, 46964, 46965, 46967, 46968, 46969, 46966 ], "onlineresource_set": [ 5719 ] }, { "ob_id": 12299, "uuid": "176e32eaa5ab4d4385a7370b58ccecf4", "title": "NEXTMap British Digital Surface Model (DSM) Data by Intermap", "abstract": "This dataset consists of the Digital Surface Model (DSM) from the NEXMap British Digital Terrain Model project produced by Intermap. These data have a spatial resolution of 5m and cover the British Isles.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2005-09-22T18:06:48", "updateFrequency": "notPlanned", "dataLineage": "Data were purchased by the Natural Environmental Research Council from Intermap who produced the data. These data were then delivered to the NEODC for curation and to permit NERC funded researchers to access these data centrally.", "removedDataReason": "", "keywords": "NEXTMap, elevation", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2009-09-16T23:00:00", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 674, "bboxName": "UK OS Grid (UKCP grid)", "eastBoundLongitude": 4.41954, "westBoundLongitude": -8.56493, "southBoundLatitude": 49.53098, "northBoundLatitude": 61.11007 }, "verticalExtent": null, "result_field": { "ob_id": 12300, "dataPath": "/neodc/nextmap/by_product/dsm", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 48821414568, "numberOfFiles": 35444, "fileFormat": "Data are ArcInfo Binary Grid formatted" }, "timePeriod": { "ob_id": 3020, "startTime": "2001-12-30T00:00:00", "endTime": "2003-10-12T23:00:00" }, "resultQuality": null, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 133 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2587, "accessConstraints": null, "accessCategory": "restricted", "accessRoles": "nextmap_eula", "label": "restricted: nextmap_eula group", "licence": { "ob_id": 50, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/nextmap_eula.pdf", "licenceClassifications": [ { "ob_id": 7, "classification": "specific" }, { "ob_id": 4, "classification": "academic" } ] } } ], "projects": [ { "ob_id": 13986, "uuid": "7a4861633a3f490abf2fbeef4a444ffa", "short_code": "proj", "title": "NEXTMap British Digital Terrain Model", "abstract": "NEXTMap Britain digital terrain model dataset, produced by Intermap, was derived from airborne Interferometric Synthetic Aperture Radar (IFSAR). It enabled large areas of the country to be covered rapidly and at very high spatial resolution. The data were acquired by the British Geological Survey (BGS) and are for use by NERC funded researchers only.\r\n\r\nIt was originally commissioned by Norwich Union Insurance to aid in the estimation of flood risk, but has tremendous potential as a scientific research tool.\r\n\r\nThe dataset covers all of England, Wales and Scotland.\r\n\r\nData are arranged into Ordnance Survey / British National Grid tiles of 10 x 10 km, each denoted by a 4-charcter name, e.g. TL63 based on the OS National Grid coordinates of the southwest corner of the square. Products provided include: \r\n\r\nDigital surface mode (dsm)\r\nDigital terrain model (dtm)\r\nDifference model (dsm - dtm)\r\nDigital terrain model at 10m resolution\r\nDigital terrain model at 50m resolution\r\nDigital terrain model, enhanced (manually edited to remove embankments etc.)\r\nOrthorectified radar image (ori)\r\n\r\nThe data covering Northern Scotland (North of the Midland Valley) are only available as diff, dsm, dtm and ori products.\r\n\r\nThis dataset has been archived at the NEODC for the use of NERC scientists outside of BGS and CEH (who have made their own arrangements for internal distribution of the data)." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6868 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 10883, "uuid": "8f6e1598372c058f07b0aeac2442366d", "short_code": "coll", "title": "NEXTMap British Digital Terrain Model Dataset Produced by Intermap", "abstract": "NEXTMap Britain digital terrain model dataset, produced by Intermap, was derived from airborne Interferometric Synthetic Aperture Radar (IFSAR). 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These data products are on various resolutions and include the following products:\r\n\r\nDifference model (dsm - dtm) Data\r\nDigital Surface Model (DSM) Data \r\nDigital Terrain 10m resolution (DTM10) Model Data \r\nDigital Terrain 50m resolution (DTM10) Model Data \r\nDigital Terrain (DTM) Model Data \r\nEnhanced Digital Terrain (DTME) Model Data\r\nOrthorectified Radar Image (ORI)", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2024-12-19T01:59:56", "updateFrequency": "notPlanned", "dataLineage": "Data were purchased by the Natural Environmental Research Council from Intermap who produced the data. These data were then delivered to the NEODC for curation and to permit NERC funded researchers to access these data centrally.", "removedDataReason": "", "keywords": "NEXTMap, elevation", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2009-09-16T23:00:00", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 674, "bboxName": "UK OS Grid (UKCP grid)", "eastBoundLongitude": 4.41954, "westBoundLongitude": -8.56493, "southBoundLatitude": 49.53098, "northBoundLatitude": 61.11007 }, "verticalExtent": null, "result_field": { "ob_id": 12310, "dataPath": "/neodc/nextmap/by_tile/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 825, "numberOfFiles": 1, "fileFormat": "Data are either ArcInfo Binary Grid formatted or GeoTIFF image files" }, "timePeriod": { "ob_id": 3020, "startTime": "2001-12-30T00:00:00", "endTime": "2003-10-12T23:00:00" }, "resultQuality": null, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 133 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2587, "accessConstraints": null, "accessCategory": "restricted", "accessRoles": "nextmap_eula", "label": "restricted: nextmap_eula group", "licence": { "ob_id": 50, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/nextmap_eula.pdf", "licenceClassifications": [ { "ob_id": 7, "classification": "specific" }, { "ob_id": 4, "classification": "academic" } ] } } ], "projects": [ { "ob_id": 13986, "uuid": "7a4861633a3f490abf2fbeef4a444ffa", "short_code": "proj", "title": "NEXTMap British Digital Terrain Model", "abstract": "NEXTMap Britain digital terrain model dataset, produced by Intermap, was derived from airborne Interferometric Synthetic Aperture Radar (IFSAR). It enabled large areas of the country to be covered rapidly and at very high spatial resolution. The data were acquired by the British Geological Survey (BGS) and are for use by NERC funded researchers only.\r\n\r\nIt was originally commissioned by Norwich Union Insurance to aid in the estimation of flood risk, but has tremendous potential as a scientific research tool.\r\n\r\nThe dataset covers all of England, Wales and Scotland.\r\n\r\nData are arranged into Ordnance Survey / British National Grid tiles of 10 x 10 km, each denoted by a 4-charcter name, e.g. TL63 based on the OS National Grid coordinates of the southwest corner of the square. Products provided include: \r\n\r\nDigital surface mode (dsm)\r\nDigital terrain model (dtm)\r\nDifference model (dsm - dtm)\r\nDigital terrain model at 10m resolution\r\nDigital terrain model at 50m resolution\r\nDigital terrain model, enhanced (manually edited to remove embankments etc.)\r\nOrthorectified radar image (ori)\r\n\r\nThe data covering Northern Scotland (North of the Midland Valley) are only available as diff, dsm, dtm and ori products.\r\n\r\nThis dataset has been archived at the NEODC for the use of NERC scientists outside of BGS and CEH (who have made their own arrangements for internal distribution of the data)." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6868 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 10883, "uuid": "8f6e1598372c058f07b0aeac2442366d", "short_code": "coll", "title": "NEXTMap British Digital Terrain Model Dataset Produced by Intermap", "abstract": "NEXTMap Britain digital terrain model dataset, produced by Intermap, was derived from airborne Interferometric Synthetic Aperture Radar (IFSAR). It enabled large areas of the country to be covered rapidly and at very high spatial resolution. The data were acquired by the British Geological Survey (BGS) and are for use by NERC funded researchers only.\r\n\r\nIt was originally commissioned by Norwich Union Insurance to aid in the estimation of flood risk, but has tremendous potential as a scientific research tool.\r\n\r\nThe dataset covers all of England, Wales and Scotland.\r\n\r\nData are arranged into Ordnance Survey / British National Grid tiles of 10 x 10 km, each denoted by a 4-charcter name, e.g. TL63 based on the OS National Grid coordinates of the southwest corner of the square. Products provided include: \r\n\r\nDigital surface mode (dsm)\r\nDigital terrain model (dtm)\r\nDifference model (dsm - dtm)\r\nDigital terrain model at 10m resolution\r\nDigital terrain model at 50m resolution\r\nDigital terrain model, enhanced (manually edited to remove embankments etc.)\r\nOrthorectified radar image (ori)\r\n\r\nThe data covering Northern Scotland (North of the Midland Valley) are only available as diff, dsm, dtm and ori products.\r\n\r\nThis dataset has been archived at the NEODC for the use of NERC scientists outside of BGS and CEH (who have made their own arrangements for internal distribution of the data)." }, { "ob_id": 30129, "uuid": "3b0630c7fa264164868d4da5c9f90bed", "short_code": "coll", "title": "National Centre for Earth Observation (NCEO) Third Party Data", "abstract": "The National Centre for Earth Observation (NCEO) Third Party data contains a broad range remotely sensed data acquired by satellite for use by the Earth Observation Scientific community supported by NCEO. The Centre for Environmental Data Analysis (CEDA) has archived and provides access to extensive Earth observation datasets under strict licensing conditions. Please see the individual dataset records for conditions of use." } ], "responsiblepartyinfo_set": [ 47010, 47011, 47012, 47013, 47015, 47016, 47017, 47014 ], "onlineresource_set": [ 5727 ] }, { "ob_id": 12312, "uuid": "2c6cb5a4a03f45edac5756222dacdae2", "title": "HadGEM1-2 model output prepared for APPOSITE ensemble prediction experiment", "abstract": "Atmosphere and Ocean components; Frequency: daily and monthly \r\nLand component, Frequency: Monthly.\r\n\r\nVariables Atmosphere daily:\r\npr psl sfcWind sfcWindmax snw tas tasmax tasmin ts uas vas zg\r\n\r\nVariables Atmosphere monthly:\r\nbmelt clt hfls hur hus prc prw psl rldscs rlus rlutcs rsdscs rsus rsut sci snw tas tauv ua va wap cl evspsbl hfss hurs pr prsn ps rlds rlns rlut rsds rsdt rsuscs rsutcs sfcWind ta tauu ts uas vas zg\r\n\r\nVariables Land monthly:\r\ngpp mrlsl mrro mrros mrsos tsl\r\n\r\nVariables Ocean daily:\r\nsic sim sit snd usi vsi\r\n\r\nVariables Ocean monthly:\r\ndsicdtadv dsitdtadv friver hfsifrazil meadtemp prsn sic sit snoToIce sos tos usi vsi wfonocorr\r\ndsicdtthd dsitdtthd fsitherm meadsal mlotst sblsi sim snd so thetao uo vo wfo", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2024-03-09T03:06:08", "updateFrequency": "", "dataLineage": "Supplied model data ingested as is in the CEDA Archive.", "removedDataReason": "", "keywords": "APPOSITE prediction Hadgem1-2 model arctic", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2015-06-22T17:33:20", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 1, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 12311, "dataPath": "/badc/apposite/data/hadgem1-2/pred", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 2851388566559, "numberOfFiles": 41503, "fileFormat": "NetCDF" }, "timePeriod": null, "resultQuality": null, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 2 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 11971, "uuid": "a2f5db43a5f37651aae1a3808a7be5e8", "short_code": "proj", "title": "Arctic Predictability and Prediction On Seasonal-to-Interannual Time-scales (APPOSITE) as part of the NERC Arctic Research Programme (ARP)", "abstract": "APPOSITE is a 3-year NERC Arctic research project that aims to address fundamental questions about the extent to which the climate and state of the Arctic system is predictable on seasonal to inter-annual timescales, and about the physical processes that govern this predictability. Model data output from this project are archived at the BADC.\r\n\r\nAPPOSITE is funded until March 2016.\r\n\r\nGrant ref: NE/I029137/1" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 1319, 1320, 1325, 6021, 6022, 6023, 6255, 9045, 11306, 11307, 50416, 50418, 50419, 50424, 50426, 50427, 50429, 50431, 50468, 50475, 50496, 50498, 50509, 50549, 50552, 50554, 50555, 50557, 50566, 50568, 50569, 50570, 50575, 50577, 50579, 50583, 50586, 50587, 50588, 50589, 50590, 50591, 50592, 50595, 50596, 50598, 50599, 50600, 50603, 50604, 50608, 51582, 51583, 52741, 52744, 52745, 52746, 52747, 52749, 52750, 52751, 52753, 52755, 53107, 53109, 53110, 53111, 53112, 53137, 53138, 53874, 54228, 54232, 54257, 54268, 54282, 54338, 54339, 55173, 57948, 57949, 57950, 57951, 57952, 57953, 57954, 57955, 57956, 57957, 57958, 57959, 57960, 57961, 57962 ], "vocabularyKeywords": [], "identifier_set": [ 8377 ], "observationcollection_set": [ { "ob_id": 11700, "uuid": "d330c7873c3f4880893bdedb547bea20", "short_code": "coll", "title": "Collection of Multi-model Data from the Arctic Predictability and Prediction On Seasonal-to-Interannual Time-scales (APPOSITE) Project", "abstract": "How feasible is it to predict Arctic climate at seasonal-to-interannual timescales? As part of the APPOSITE project a multi-model ensemble prediction experiment was conducted in order to answer this question.\r\n\r\nThe main goal of APPOSITE was to quantify the timescales on which Arctic climate is predictable. In order to achieve this, a coordinated set of idealised initial condition predictability experiments with seven general circulation models was conducted. This was the first intercomparison project designed to quantify the predictability of Arctic climate on seasonal to interannual timescales.\r\n\r\nSeveral different coupled climate models performed simulations for APPOSITE (see Doc below for Details of simulations submitted to the APPOSITE database). Six of these models followed the same experimental protocol (see Doc below for Control Simulations details and for Ensemble Predictions). One model, CanCM4 followed a slightly different protocol.\r\n\r\nThe Model data output from the APPOSITE project are now archived at CEDA. The collection of model outputs (control and prediction) include data from:\r\n\r\n- Canadian Centre for Climate Modelling and Analysis (CanCM4)\r\n- ECHAM6-FESOM (E6F), run and developed by the Alfred Wegener Institute.\r\n- EC-Earth consortium (ec-earth_v2_3) \r\n- Geophysical Fluid Dynamics Laboratory (gfdlcm3) \r\n- Met Office (hadgem1-2) \r\n- Model for Interdisciplinary Research on Climate (MIROC5-2) \r\n- Max-Planck-Institut for Meteorologie (mpiesm)\r\n\r\nAlthough designed to address Arctic predictability, this data set could also be used to assess the predictability of other regions and modes of climate variability on these timescales, such as the El Nino Southern Oscillation.\r\n\r\nA paper describing the simulations for APPOSITE is in preparation to be submitted to the Geoscientific Model Development Journal.\r\n\r\nNote: These data do not correspond to a particular time period since the studies are all conducted in the model world. They are not predictions or attempts to simulate a particular period of time. So the dates in the files are completely arbitrary. " } ], "responsiblepartyinfo_set": [ 47018, 47200, 47210, 47211, 47209, 47207, 47206, 47208, 54757 ], "onlineresource_set": [] }, { "ob_id": 12315, "uuid": "56ba0755afc54c1ba88e1ca73dcf2df5", "title": "Sentinel 1A C-band Synthetic Aperture Radar (SAR): SM mode SLC Level 1 data, Instrument Processing Facility (IPF) v3", "abstract": "This dataset contains Stripmap Mode (SM) C-band Synthetic Aperture Radar (SAR) Single Look Complex (SLC) data from the European Space Agency (ESA) Sentinel 1A satellite. Sentinel 1A was launched on 3rd April 2014 and provides continuous all-weather, day and night imaging radar data. The SM mode is used only on special request for extraordinary events such as emergency management. The SM mode supports single (HH or VV) and dual (HH+HV or VV+VH) polarisation. \r\n\r\nStripmap SLCs contain one image per polarisation band from one of six overlapping beams. Each beam covers 80.1 km, covering a combined range of 375 km. Pixel spacing is determined, in azimuth by the pulse repetition frequency (PRF), and in range by the radar range sampling frequency, providing natural pixel spacing.\r\n\r\nThese data are available via CEDA to any registered user.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2024-04-07T14:00:02", "updateFrequency": "", "dataLineage": "Data collected and prepared by European Space Agency (ESA). Downloaded from the Collaborative Hub for use by the UK scientific community.", "removedDataReason": "", "keywords": "Sentinel, radar, Synthetic Aperture Radar, Stripmap, Single Look Complex", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2021-06-25T15:50:31", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 1, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 12348, "dataPath": "/neodc/sentinel1a/data/SM/L1_SLC/IPF_v3/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 4229129545305, "numberOfFiles": 8111, "fileFormat": "Image data files are in a binary format. Quicklook images are in png format. Manifest files with relevant metadata are in SAFE format." }, "timePeriod": { "ob_id": 8996, "startTime": "2019-06-27T00:00:00", "endTime": null }, "resultQuality": { "ob_id": 3004, "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": "2014-09-28" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 32773, "uuid": "07df50df040f4559a7d4da60620bb9f0", "short_code": "cmppr", "title": "Composite Process for: Level 1 data from the Sentinel 1A C-band Synthetic Aperture Radar (SAR), Stripmap (SM) mode, Instrument Processing Facility (IPF) v3", "abstract": "Composite process for Level 1 data from the C-band Synthetic Aperture Radar (SAR) deployed on Sentinel 1. This consists of the Acquisition process for raw radar data from the Sentinel 1 SAR and the computation component to produce processed Level 1 radar data." }, "imageDetails": [ 148 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2586, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 49, "licenceURL": "https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 12321, "uuid": "7896ea1117dc4fa9bb95485ca9b1c6be", "short_code": "proj", "title": "Copernicus Programme", "abstract": "Copernicus, formerly known as the Global Monitoring for Environment and Security (GMES) programme, is headed by the European Commission (EC) in partnership with the European Space Agency (ESA). Within the Copernicus Space Component, ESA is developing a series of Sentinel satellite missions. Data from the Sentinel missions, as well as from Contributing Missions from other space agencies, are made freely available through a unified ground segment. Each Sentinel mission is comprised of a constallation of two or more satellites to fulfil the timeliness and reliability requirements of the Copernicus Services environmental monitoring and civil security activities. As well as operational and monitoring capabilities, the Sentinel missions will provide a wealth of Earth Observation data for scientific exploitation. The Sentinel 1 mission provides all weather, day and night radar imagery with scientific applications in sea-ice measurements, biomass observations and earthquake analysis. Sentinel 2 is a high resolution imaging mission to provide imagery of vegetation, soil and water cover, inland waterways and coastal areas. Sentinel 3 is a multi-instrument mission to measure sea-surface topography, sea- and land-surface temperature, ocean colour and land colour with high-end accuracy and reliability. Sentinel 4 is devoted to atmospheric monitoring and will be flown on a Meteosat Third Generation-Sounder (MTG-S) satellite in geostationary orbit. Sentinel 5 will monitor the atmosphere from polar orbit on board a MetOp Second Generation satellite. The Sentinel 5 precursor satellite mission is being developed to reduce data gaps between Envisat, in particular the Sciamachy instrument, and the launch of Sentinel 5. The Sentinel 5 mission will be dedicated to atmospheric monitoring. Sentinel 6 carries a radar altimeter to measure global sea-surface height, primarily for operational oceanography and for climate studies." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 25995 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 12314, "uuid": "06d1c86f906e42f58172de32c2640be2", "short_code": "coll", "title": "Sentinel 1A: C-band Synthetic Aperture Radar (SAR) data", "abstract": "This dataset collection contains radar data from the C-band Synthetic Aperture Radar (SAR) on the European Space Agency (ESA) Sentinel 1A satellite. Sentinel 1A was launched on 3rd April 2014 and provides continuous all-weather, day and night imaging radar data. Three acquisition modes are available: Stripmap (SM), Interferometric Wide swath (IW), and Extra Wide swath (EW). The main operational mode is IW. The EW mode is primarily used for wide area coastal monitoring. The SM mode is only used on special request for extraordinary events such as emergency management. The SM, IW and EW modes are available in single (HH or VV) and dual (HH+HV or VV+VH) polarisation. The C-band Synthetic Aperture Radar images the Earth with enhanced frequency and revisit times obtaining full Earth coverage every two weeks. Timeliness and reliability is optimised for emergency response and operational applications with Europe’s coastal zones and shipping routes being monitored on a daily basis. The data has a wide range of scientific applications including sea-ice monitoring, imaging of forests and investigation into the carbon cycle, and the analysis of earthquakes. Data are provided by the European Space Agency (ESA) and are made available via CEDA to any registered user." }, { "ob_id": 30129, "uuid": "3b0630c7fa264164868d4da5c9f90bed", "short_code": "coll", "title": "National Centre for Earth Observation (NCEO) Third Party Data", "abstract": "The National Centre for Earth Observation (NCEO) Third Party data contains a broad range remotely sensed data acquired by satellite for use by the Earth Observation Scientific community supported by NCEO. The Centre for Environmental Data Analysis (CEDA) has archived and provides access to extensive Earth observation datasets under strict licensing conditions. Please see the individual dataset records for conditions of use." } ], "responsiblepartyinfo_set": [ 47029, 79194, 143577, 143578, 47032, 47030, 47031, 146217, 47104, 146218 ], "onlineresource_set": [ 5767, 23911, 5734 ] }, { "ob_id": 12322, "uuid": "d170a0de0c89436f971e2bca6a3b1f36", "title": "Sentinel 1A C-band Synthetic Aperture Radar (SAR): Stripmap (SM) mode Ground Range Detected (GRD) Full Resolution (FR) Level 1 data", "abstract": "This dataset contains Stripmap Mode (SM) C-band Synthetic Aperture Radar (SAR) Ground Range Detected (GRD) Full Resolution (FR) data from the European Space Agency (ESA) Sentinel 1A satellite. Sentinel 1A was lanched on 3rd April 2014 and provides continuous all-weather, day and night imaging radar data. The SM mode is used only on special request for extraordinary events such as emergency management. The SM mode supports single (HH or VV) and dual (HH+HV or VV+VH) polarisation. These data are available via CEDA to any registered scientific user in the UK.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": null, "updateFrequency": "notPlanned", "dataLineage": "Data collected and prepared by European Space Agency (ESA). Downloaded from the Collaborative Hub for use by the UK scientific community. ", "removedDataReason": "", "keywords": "Sentinel, radar, Synthetic Aperture Radar, SAR, Stripmap, SM, Ground Range Detected, GRD", "publicationState": "working", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": null, "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 1, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 12347, "dataPath": "/neodc/sentinel1a/data/SM/L1_GRD/f/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 0, "numberOfFiles": 0, "fileFormat": "Image data files are in a binary format. Quicklook images are in png format. Manifest files with relevant metadata are in SAFE format. " }, "timePeriod": { "ob_id": 3332, "startTime": "2014-04-10T23:00:00", "endTime": null }, "resultQuality": { "ob_id": 3004, "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": "2014-09-28" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 12316, "uuid": "ec8ab4b0461c4e2aba76fc17e6803b8d", "short_code": "cmppr", "title": "Composite Process for: Level 1 data from the Sentinel 1A C-band Synthetic Aperture Radar (SAR), Interferometric Wide (IW), Instrument Processing Facility (IPF) v2", "abstract": "Composite process for Level 1 data from the C-band Synthetic Aperture Radar (SAR) deployed on Sentinel 1. This consists of the Acquisition process for raw radar data from the Sentinel 1 SAR and the computation component to produce processed Level 1 radar data." }, "imageDetails": [ 148 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2586, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 49, "licenceURL": "https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 12321, "uuid": "7896ea1117dc4fa9bb95485ca9b1c6be", "short_code": "proj", "title": "Copernicus Programme", "abstract": "Copernicus, formerly known as the Global Monitoring for Environment and Security (GMES) programme, is headed by the European Commission (EC) in partnership with the European Space Agency (ESA). Within the Copernicus Space Component, ESA is developing a series of Sentinel satellite missions. 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Please see the individual dataset records for conditions of use." } ], "responsiblepartyinfo_set": [ 50357, 50605, 47084, 47083, 47086, 105015, 47085, 146216, 50604, 47095, 146275 ], "onlineresource_set": [ 6362, 5755, 23916, 5754, 5749 ] }, { "ob_id": 12330, "uuid": "500608c3f0e9428eb624b6bfb6ecf5a8", "title": "Sentinel 1A C-band Synthetic Aperture Radar (SAR): Extra Wide (EW) mode Ground Range Detected (GRD) High Resolution (HR) Level 1 data", "abstract": "This dataset contains Extra Wide swath (EW) Ground Range Detected (GRD) High Resolution (HR) C-band Synthetic Aperture Radar (SAR) data from the Sentinel 1A satellite. Sentinel 1A was lanched on 3rd April 2014 and provides continuous all-weather, day and night imaging radar data. The EW mode is primarily used for wide area coastal monitoring. The EW mode supports single (HH or VV) and dual (HH+HV or VV+VH) polarisation. These data are provided by the European Space Agency (ESA) and made available via CEDA to any registered scientific user in the UK.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2015-06-19T13:42:25", "updateFrequency": "notPlanned", "dataLineage": "Data collected and prepared by European Space Agency (ESA). Downloaded from the Collaborative Hub for use by the UK scientific community. ", "removedDataReason": "", "keywords": "Sentinel, radar, Synthetic Aperture Radar, SAR, Extra Wide, EW, Ground Range Detected, GRD", "publicationState": "working", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": null, "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 1, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 12336, "dataPath": "/neodc/sentinel1a/data/EW/L1_GRD/h/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1001, "numberOfFiles": 2, "fileFormat": "Image data files are in a binary format. Quicklook images are in png format. Manifest files with relevant metadata are in SAFE format. " }, "timePeriod": { "ob_id": 3332, "startTime": "2014-04-10T23:00:00", "endTime": null }, "resultQuality": { "ob_id": 3004, "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": "2014-09-28" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 12316, "uuid": "ec8ab4b0461c4e2aba76fc17e6803b8d", "short_code": "cmppr", "title": "Composite Process for: Level 1 data from the Sentinel 1A C-band Synthetic Aperture Radar (SAR), Interferometric Wide (IW), Instrument Processing Facility (IPF) v2", "abstract": "Composite process for Level 1 data from the C-band Synthetic Aperture Radar (SAR) deployed on Sentinel 1. This consists of the Acquisition process for raw radar data from the Sentinel 1 SAR and the computation component to produce processed Level 1 radar data." }, "imageDetails": [ 148 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2586, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 49, "licenceURL": "https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 12321, "uuid": "7896ea1117dc4fa9bb95485ca9b1c6be", "short_code": "proj", "title": "Copernicus Programme", "abstract": "Copernicus, formerly known as the Global Monitoring for Environment and Security (GMES) programme, is headed by the European Commission (EC) in partnership with the European Space Agency (ESA). Within the Copernicus Space Component, ESA is developing a series of Sentinel satellite missions. Data from the Sentinel missions, as well as from Contributing Missions from other space agencies, are made freely available through a unified ground segment. Each Sentinel mission is comprised of a constallation of two or more satellites to fulfil the timeliness and reliability requirements of the Copernicus Services environmental monitoring and civil security activities. As well as operational and monitoring capabilities, the Sentinel missions will provide a wealth of Earth Observation data for scientific exploitation. The Sentinel 1 mission provides all weather, day and night radar imagery with scientific applications in sea-ice measurements, biomass observations and earthquake analysis. Sentinel 2 is a high resolution imaging mission to provide imagery of vegetation, soil and water cover, inland waterways and coastal areas. Sentinel 3 is a multi-instrument mission to measure sea-surface topography, sea- and land-surface temperature, ocean colour and land colour with high-end accuracy and reliability. Sentinel 4 is devoted to atmospheric monitoring and will be flown on a Meteosat Third Generation-Sounder (MTG-S) satellite in geostationary orbit. Sentinel 5 will monitor the atmosphere from polar orbit on board a MetOp Second Generation satellite. The Sentinel 5 precursor satellite mission is being developed to reduce data gaps between Envisat, in particular the Sciamachy instrument, and the launch of Sentinel 5. The Sentinel 5 mission will be dedicated to atmospheric monitoring. Sentinel 6 carries a radar altimeter to measure global sea-surface height, primarily for operational oceanography and for climate studies." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 25947, 25995 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 12314, "uuid": "06d1c86f906e42f58172de32c2640be2", "short_code": "coll", "title": "Sentinel 1A: C-band Synthetic Aperture Radar (SAR) data", "abstract": "This dataset collection contains radar data from the C-band Synthetic Aperture Radar (SAR) on the European Space Agency (ESA) Sentinel 1A satellite. Sentinel 1A was launched on 3rd April 2014 and provides continuous all-weather, day and night imaging radar data. Three acquisition modes are available: Stripmap (SM), Interferometric Wide swath (IW), and Extra Wide swath (EW). The main operational mode is IW. The EW mode is primarily used for wide area coastal monitoring. The SM mode is only used on special request for extraordinary events such as emergency management. The SM, IW and EW modes are available in single (HH or VV) and dual (HH+HV or VV+VH) polarisation. The C-band Synthetic Aperture Radar images the Earth with enhanced frequency and revisit times obtaining full Earth coverage every two weeks. Timeliness and reliability is optimised for emergency response and operational applications with Europe’s coastal zones and shipping routes being monitored on a daily basis. The data has a wide range of scientific applications including sea-ice monitoring, imaging of forests and investigation into the carbon cycle, and the analysis of earthquakes. Data are provided by the European Space Agency (ESA) and are made available via CEDA to any registered user." }, { "ob_id": 30129, "uuid": "3b0630c7fa264164868d4da5c9f90bed", "short_code": "coll", "title": "National Centre for Earth Observation (NCEO) Third Party Data", "abstract": "The National Centre for Earth Observation (NCEO) Third Party data contains a broad range remotely sensed data acquired by satellite for use by the Earth Observation Scientific community supported by NCEO. The Centre for Environmental Data Analysis (CEDA) has archived and provides access to extensive Earth observation datasets under strict licensing conditions. Please see the individual dataset records for conditions of use." } ], "responsiblepartyinfo_set": [ 50621, 50622, 47089, 47096, 47090, 47092, 47091, 143575, 50623 ], "onlineresource_set": [ 5750, 5752, 6361, 5753 ] }, { "ob_id": 12352, "uuid": "57f5fd7e369e4467a56f0f227060cd44", "title": "SPECS - CNRM-CM5 model output prepared for SPECS seaIceInit (1979-2012)", "abstract": "This dataset includes the CNRM-CM5 model output prepared for SPECS seaIceInit (1979-2012). These data were prepared by the Centre National de Recherches Meteorologiques, as part of the SPECS project. \r\n\r\nThe model used is CNRM-CM, combining ARPEGEv6.0.3 + NEM0v3.2 + GELATO6.0.1 + SURFEX7.2, frequency is monthly.\r\n\r\nAtmospheric variables are:\r\nclt evlwr hfls hfss hus mrro mrso pr prsn psl rlds rls rlut rsds rsdt rss ta tas tasmax tasmin tauu tauv ua uas va vas zg\r\n\r\nOceanic variables are:\r\nhfnorth mlotst msftmyz msftmyza msftmyzba sltnorth so t20d thetao tos uo vo\r\n\r\nSeaIce variables are:\r\nsic sit snld strairx strairy tsice usi vsi\r\n\r\n", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2015-06-26T09:57:15.481456", "updateFrequency": "", "dataLineage": "Data were supplied by SPECS participants to CEDA for archiving in 2015.", "removedDataReason": "", "keywords": "specs, cnrm, climate", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2015-09-24T20:31:43", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 528, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 13124, "dataPath": "/badc/specs/data/SPECS/output/CNRM/CNRM-CM5/seaIceInit", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 318989490982, "numberOfFiles": 73019, "fileFormat": "NetCDF4" }, "timePeriod": { "ob_id": 3566, "startTime": "1979-04-30T23:00:00", "endTime": "2012-04-30T23:00:00" }, "resultQuality": null, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 153 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" }, { "ob_id": 1139, "name": "CLIPC" } ], "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": 12970, "uuid": "c9b4b1fcab734987bcbfb36437734ca7", "short_code": "proj", "title": "Seasonal-to-decadal climate Prediction for the improvement of European Climate Services (SPECS)", "abstract": "SPECS will undertake research and dissemination activities to deliver a new generation of European climate forecast systems, with improved forecast quality and efficient regionalisation tools to produce reliable, local climate information over land at seasonal-to-decadal time scales, and provide an enhanced communication protocol and services to satisfy the climate information needs of a wide range of public and private stakeholders.\r\n\r\nThe improved understanding and seamless predictions will offer better estimates of the future frequency of high-impact, extreme climatic events and of the prediction uncertainty. New services to convey climate information and its quality will be used.\r\n\r\nSPECS will be, among other things, the glue to coalesce the outcome of previous research efforts that hardly took climate prediction into account. It will ensure interoperability so as to easily incorporate their application in an operational context, provide the basis for improving the capacity of European policy making, industry and society to adapt to near-future climate variations and a coordinated response to some of the GFCS components.\r\n\r\nThis project is funded by the Seventh Framework Programme (FP7) of the European Commission (GA 308378)." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6023, 6255, 11044, 11045, 50418, 50426, 50427, 50429, 50431, 50475, 50496, 50498, 50554, 50566, 50568, 50569, 50579, 50586, 50587, 50588, 50589, 50591, 50592, 50596, 50598, 50603, 50608, 52741, 52747, 52748, 52755, 53094, 53105, 53107, 53108, 53109, 53110, 53112, 53113, 53128, 53129, 53130, 53131, 53134, 53135, 53137, 53138, 53140, 53142, 54798, 57970, 68067, 68086, 79918, 82362, 89563, 89564, 89565, 89566, 89567 ], "vocabularyKeywords": [], "identifier_set": [ 8529 ], "observationcollection_set": [ { "ob_id": 6086, "uuid": "2d9c5f2cc621fb9bc0062356851b31b9", "short_code": "coll", "title": "SPECS: Seasonal-to-decadal climate prediction model outputs", "abstract": "SPECS will undertake research and dissemination activities to deliver a new generation of European climate forecast systems, with improved forecast quality and efficient regionalisation tools to produce reliable, local climate information over land at seasonal-to-decadal time scales, and provide an enhanced communication protocol and services to satisfy the climate information needs of a wide range of public and private stakeholders.\r\n\r\nA core set of common experiments has been defined, to which most forecast systems will contribute. Another set of coordinated experiments, tier 1, includes the experiments that one or more forecast systems are planning to run. \r\n\r\nA standard seasonal experimental set up will consist of ten-member ensembles, with two start dates per year (first of May and November) over the 1981-2012 period and seven-month forecast length. \r\n\r\nThe standard decadal experimental set up consists in five-member ensembles, starting on the first of November (or some time close to that date) of the years 1960, 1963, 1965, 1968, 1970, 1973, 1975, 1978, 1980, 1983, 1985, 1988, 1990, 1993, 1995, 1998, 2000, 2003, 2005, 2008, 2010, 2013, with a five-year forecast length. \r\n\r\nA description of the main experiments, with the minimum contribution in terms of start dates, forecast length and ensemble size follows: \r\n1 - Assessment of the impact of soil-moisture initial conditions (seasonal): contributing EC-Earth, IFS/NEMO (ECMWF), CNRM-CM5 (MeteoF), UM, MPI-ESM (MPG);\r\n2 - Assessment of the impact of sea-ice initialization (interannual); contributing EC-Earth (IC3), IPSL-CM5, CNRM-CM5 (MeteoF), UM, MPI-ESM (MPG)\r\n3 - Assessment of impact of increased horizontal resolution (seasonal and decadal); contributing CNRM-CM5 (CERFACS, decadal; MeteoF, seasonal), EC-Earth (IC3, seasonal; KNMI and SMHI, decadal), MPI-ESM (MPG, seasonal and decadal), IPSL-CM5 (decadal), UM (seasonal and decadal); \r\n4 - Assessment of impact of an improved stratosphere (seasonal and decadal) including interannually-varying ozone; contributing EC-Earth (KNMI seasonal with ozone; SMHI decadal), IFS/NEMO (ECMWF, seasonal), CNRM-CM5 (MeteoF, seasonal), UM (seasonal, decadal);\r\n5 - Assessment of impact of additional start dates (decadal); contributing EC-Earth (KNMI, SMHI), MPI-ESM (MPG), IPSL-CM5.\r\n\r\nSPECS research has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under SPECS project (grant agreement n° 308378)." } ], "responsiblepartyinfo_set": [ 50261, 47132, 47127, 47126, 47129, 47130, 47131, 47128, 54755 ], "onlineresource_set": [ 7037 ] }, { "ob_id": 12354, "uuid": "5b62f5050350429191f8300d6be24924", "title": "SPECS - CNRM-CM5-HRA-HRO model output prepared for SPECS horizlResImpact (1993-2009)", "abstract": "This dataset includes the CNRM-CM5-HRA-HRO model output prepared for SPECS horizlResImpact (1993-2009). These data were prepared by the Centre National de Recherches Meteorologiques, as part of the SPECS project. Model id is CNRM-CM5-HRA-HRO, frequency is daily, associated model is atmo HR ocean HR. Arpege V5.1 is the underlying model (CNRM-CM5 2013 atmosphere:Arpege (tl359l31r); ocean:Nemo (Orca0.25); sea-ice:none; land:isba).\r\n\r\nvariables available are:\r\n - Precipitation flux (pr) in kg m-2 s-1\r\n - Air Temperature (tas) in degree Kelvin\r\n - Geopotential height (zg) in metres\r\n\r\n", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2016-01-18T11:44:34.065314", "updateFrequency": "notPlanned", "dataLineage": "Data were supplied to CEDA-BADC for archiving in 2015.", "removedDataReason": "", "keywords": "SPECS, climate", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2015-09-24T19:47:32", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 528, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 13119, "dataPath": "/badc/specs/data/SPECS/output/CNRM/CNRM-CM5-HRA-HRO/horizlResImpact", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 83864362032, "numberOfFiles": 28561, "fileFormat": "NetCDF" }, "timePeriod": { "ob_id": 3565, "startTime": "1993-02-01T00:00:00", "endTime": "2009-11-01T00:00:00" }, "resultQuality": null, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 153 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" }, { "ob_id": 1139, "name": "CLIPC" } ], "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": 12970, "uuid": "c9b4b1fcab734987bcbfb36437734ca7", "short_code": "proj", "title": "Seasonal-to-decadal climate Prediction for the improvement of European Climate Services (SPECS)", "abstract": "SPECS will undertake research and dissemination activities to deliver a new generation of European climate forecast systems, with improved forecast quality and efficient regionalisation tools to produce reliable, local climate information over land at seasonal-to-decadal time scales, and provide an enhanced communication protocol and services to satisfy the climate information needs of a wide range of public and private stakeholders.\r\n\r\nThe improved understanding and seamless predictions will offer better estimates of the future frequency of high-impact, extreme climatic events and of the prediction uncertainty. New services to convey climate information and its quality will be used.\r\n\r\nSPECS will be, among other things, the glue to coalesce the outcome of previous research efforts that hardly took climate prediction into account. It will ensure interoperability so as to easily incorporate their application in an operational context, provide the basis for improving the capacity of European policy making, industry and society to adapt to near-future climate variations and a coordinated response to some of the GFCS components.\r\n\r\nThis project is funded by the Seventh Framework Programme (FP7) of the European Commission (GA 308378)." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50426, 50475, 50498, 50542, 50543, 53094, 53095, 53133, 56884 ], "vocabularyKeywords": [], "identifier_set": [ 8526 ], "observationcollection_set": [ { "ob_id": 6086, "uuid": "2d9c5f2cc621fb9bc0062356851b31b9", "short_code": "coll", "title": "SPECS: Seasonal-to-decadal climate prediction model outputs", "abstract": "SPECS will undertake research and dissemination activities to deliver a new generation of European climate forecast systems, with improved forecast quality and efficient regionalisation tools to produce reliable, local climate information over land at seasonal-to-decadal time scales, and provide an enhanced communication protocol and services to satisfy the climate information needs of a wide range of public and private stakeholders.\r\n\r\nA core set of common experiments has been defined, to which most forecast systems will contribute. Another set of coordinated experiments, tier 1, includes the experiments that one or more forecast systems are planning to run. \r\n\r\nA standard seasonal experimental set up will consist of ten-member ensembles, with two start dates per year (first of May and November) over the 1981-2012 period and seven-month forecast length. \r\n\r\nThe standard decadal experimental set up consists in five-member ensembles, starting on the first of November (or some time close to that date) of the years 1960, 1963, 1965, 1968, 1970, 1973, 1975, 1978, 1980, 1983, 1985, 1988, 1990, 1993, 1995, 1998, 2000, 2003, 2005, 2008, 2010, 2013, with a five-year forecast length. \r\n\r\nA description of the main experiments, with the minimum contribution in terms of start dates, forecast length and ensemble size follows: \r\n1 - Assessment of the impact of soil-moisture initial conditions (seasonal): contributing EC-Earth, IFS/NEMO (ECMWF), CNRM-CM5 (MeteoF), UM, MPI-ESM (MPG);\r\n2 - Assessment of the impact of sea-ice initialization (interannual); contributing EC-Earth (IC3), IPSL-CM5, CNRM-CM5 (MeteoF), UM, MPI-ESM (MPG)\r\n3 - Assessment of impact of increased horizontal resolution (seasonal and decadal); contributing CNRM-CM5 (CERFACS, decadal; MeteoF, seasonal), EC-Earth (IC3, seasonal; KNMI and SMHI, decadal), MPI-ESM (MPG, seasonal and decadal), IPSL-CM5 (decadal), UM (seasonal and decadal); \r\n4 - Assessment of impact of an improved stratosphere (seasonal and decadal) including interannually-varying ozone; contributing EC-Earth (KNMI seasonal with ozone; SMHI decadal), IFS/NEMO (ECMWF, seasonal), CNRM-CM5 (MeteoF, seasonal), UM (seasonal, decadal);\r\n5 - Assessment of impact of additional start dates (decadal); contributing EC-Earth (KNMI, SMHI), MPI-ESM (MPG), IPSL-CM5.\r\n\r\nSPECS research has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under SPECS project (grant agreement n° 308378)." } ], "responsiblepartyinfo_set": [ 47141, 47143, 47144, 47145, 47146, 47140, 50244, 47142, 54756 ], "onlineresource_set": [] }, { "ob_id": 12361, "uuid": "a7aebcabdce9416a9ccacfb13853a724", "title": "Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIR): Land remote sensing level 1 data", "abstract": "Landsat 8 was a joint project between the National Aeronautics and Space Administration (NASA) and the United States Geological Survey (USGS). Data was originally obtained from the USGS via the Catapult Satellite Applications Facility and is now held by Centre for Environmental Data Analysis (CEDA). The data are in GeoTIFF format.\r\n\r\nThis Landsat 8 dataset corresponds to the standard Level 1 terrain corrected data compatible with data acquired using Landsat 1 to Landsat 7. The Landsat 8 payload comprises two sensors: the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS). The OLI provides measurements in the visible, near infrared and shortwave infrared at 30 m spatial resolution (bands 1-7 and 9), as well as in a panchromatic band (band 8) at 15 m spatial resolution. The OLI provides a new infrared channel (band 9) for cirrus detection and a new deep blue band (band 1) for coastal monitoring. The TIRS instrument provides thermal infrared measurements in two bands (bands 10 and 11) at 100 m spatial resolution resampled to 30 m to match the OLI multispectral bands. \r\n\r\nThe images are in North up (MAP) orientation. The data is supplied using the Universal Transverse Mercator (UTM) map projection. Polar Stereographic projection is used for polar scenes. The dataset uses the World Geodetic System (WGS) 84 datum. Pixel values are 16 bit. \r\n\r\nData is catalogued according to path and row number according to the Worldwide Reference System 2 (WRS-2).", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-20T01:46:49", "updateFrequency": "notPlanned", "dataLineage": "Data collected from the United States Geological Survey and prepared by the Catapult Satellite Applications Facility before being acquired by Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "Landsat 8, land surface", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2015-07-21T09:24:55", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 634, "bboxName": "Landsat 8", "eastBoundLongitude": 28.0, "westBoundLongitude": -41.0, "southBoundLatitude": 35.0, "northBoundLatitude": 70.0 }, "verticalExtent": null, "result_field": { "ob_id": 12528, "dataPath": "/neodc/landsat8/data", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 12761244810732, "numberOfFiles": 84077, "fileFormat": "Images are GeoTIFF formatted." }, "timePeriod": { "ob_id": 3343, "startTime": "2014-02-25T00:00:00", "endTime": "2014-08-09T23:00:00" }, "resultQuality": { "ob_id": 2198, "explanation": "", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2014-09-11" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 12362, "uuid": "3d394741768542ada07e418ef17f73d5", "short_code": "cmppr", "title": "Composite process for: Landsat 8", "abstract": "This process is comprised of multiple procedures: 1. Acquisition: Acquisition Process for: Landsat 8; \r\n2. Computation: deployed on Landsat 8; \r\n" }, "imageDetails": [ 121 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2641, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 87, "licenceURL": "https://www.usgs.gov/information-policies-and-instructions/copyrights-and-credits", "licenceClassifications": [] } } ], "projects": [ { "ob_id": 8334, "uuid": "4e5dea57e8f01c2824b2169683822b56", "short_code": "proj", "title": "Landsat", "abstract": "Landsat satellites have been collecting images of the Earth's surface for more than thirty years. NASA launched the first Landsat satellite in 1972, and the most recent one, Landsat 8, in 2013. Instruments onboard the satellites have acquired millions of images of the Earth. These images provide a unique resource for people who work in agriculture, geology, forestry, regional planning, education, mapping, and global change research." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 25843, 25855, 26107 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 12359, "uuid": "62f41d30d9d344749b693e98cb79efc6", "short_code": "coll", "title": "Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIR): Land remote sensing data", "abstract": "The Landsat 8 mission was a collaboration between the National Aeronautics and Space Administration (NASA) and the United States Geological Survey (USGS) which ensures the continued availability of Landsat data. \r\n\r\nThis dataset collection contains moderate resolution images of the Earth’s surface in the visible to thermal infrared. \r\n\r\nLandsat 8 carries two science instruments: the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS). The OLI provides measurements in the visible, near infrared and shortwave infrared including a panchromatic band with 15 m spatial resolution and multispectral bands with 30 m spatial resolution. The TIRS provide a new infrared channel (band 9) for cirrus detection and a new deep blue band (band 1) for coastal monitoring. The TIRS instrument provides measurements at 100 m resolution in two bands in the thermal infrared previously covered by a single wide band. Some data from 2014 is now held by the Centre for Environmental Data Analysis (CEDA)." }, { "ob_id": 30129, "uuid": "3b0630c7fa264164868d4da5c9f90bed", "short_code": "coll", "title": "National Centre for Earth Observation (NCEO) Third Party Data", "abstract": "The National Centre for Earth Observation (NCEO) Third Party data contains a broad range remotely sensed data acquired by satellite for use by the Earth Observation Scientific community supported by NCEO. The Centre for Environmental Data Analysis (CEDA) has archived and provides access to extensive Earth observation datasets under strict licensing conditions. Please see the individual dataset records for conditions of use." } ], "responsiblepartyinfo_set": [ 47168, 47167, 47171, 74186, 74188, 74189, 74190, 74460, 47170, 74187 ], "onlineresource_set": [ 5787, 5788, 5789, 5790 ] }, { "ob_id": 12370, "uuid": "c92a088715184487a2eab79949dfe497", "title": "MINERVA: North-West European shelf seas marine climate projections data: spatial two-dimensional 30-year mean climatology ensemble statistics", "abstract": "These climate projections for the North-West European Shelf Seas update the shelf seas component of UKCP09 Marine Report (Lowe et al, 2009) and were funded by the MINERVA project.\r\n\r\nThis dataset contains ensemble statistics for model output based on the QUMP (Quantifying Uncertainties in Model Projections) ensemble of HadCM3 (Hadley Centre Coupled Model version 3) runs downscaled with the POLCOMS (Proudman Oceanographic Laboratory Coastal Ocean Modelling System) under SRES A1B (Special Report on Emissons Scenarios - A1B business-as-usual with medium emissions) conditions, from 1952-2098 for which 30-year means anomalies have been calculated from monthly mean data for each of the 12 months. \r\n\r\nA Perturbed Physics Ensemble (PPE) of HadCM3 has been downscaled with the shelf seas model POLCOMS. Each of the 11 ensemble members has been downscaled as transient simulations (from 1952-2098) under the SRES A1B emissions scenario. The PPE (QUMP) was designed to span the range of uncertainty associated with model parameter uncertainty in the atmosphere of the driving global climate model. POLCOMS was run at 12 km resolution, with 32 vertical levels using s-coordinates over the NW European Shelf Seas domain (-18.3 to 14 degrees East, 43 to 63.56 degrees North). Monthly statistics of the model results were recorded. \r\nFurther details can be found in Tinker et al (2015).\r\n", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2016-08-08T11:46:39.701096", "updateFrequency": "notPlanned", "dataLineage": "A Perturbed Physics Ensemble (PPE) of the Atmosphere-Ocean General Circulation Model (AO-GCM, or GCM) HadCM3 is downscaled by the shelf seas model POLCOMS. Monthly mean (and time-step minimum, maximum and variance) 3D (temperature, salinity, u- and v- components of currents) and 2D (surface elevation) fields were saved and the following variables were calculated: \r\n SST: Sea Surface Temperature – surface model layer\r\n\tSSS: Sea Surface Salinity – surface model layer\r\n\tNBT: Near-Bed Temperature – bottom model layer\r\n\tNBS: Near-Bed Salinity – bottom model layer\r\n\tDFT: Difference between SST and NBT – surface model layer minus bottom model layer\r\n\tDFS: Difference between SSS and NBS – surface model layer minus bottom model layer\r\n\tPEA: Potential Energy Anomaly (a measure of stratification).\r\n\tMLD: Mixed Layer Depth\r\n\r\nFor the present day and the future:\r\nThe ensemble mean (ens_mean) is the average of the 30-year means from each of the 11 ensemble members.\r\nThe ensemble variance (ens_var) is the variance of the 30-year means from each of the 11 ensemble members.\r\nThe interannual variance (int_var) is the mean of the 30-year variances from each of the 11 ensemble members.\r\nThe ensemble standard deviation (std_dev) is the standard deviation of the 30-year means from each of the 11 ensemble members. This is the square root of the ensemble variance, and is redundant in the present day and future period – it is included for completeness.\r\n\r\nFor the difference fields (future minus present day):\r\nThe ensemble mean, ensemble variance and interannual variance are simply the difference between the future and present day fields.\r\nThe ensemble standard deviation is different - the differences in the future and present day 30-year means from each of the 11 ensemble members are calculated, and these differences are used to calculated the anomaly ensemble standard deviation. It is included to allow the ensemble spread to be given for a warming, or freshening.", "removedDataReason": "", "keywords": "MINERVA, UKCP, Marine Climate, Climate Projection, Uncertainty, Climate Downscaling, Shelf Seas, Temperature, SST, Salinity, Stratification, Sea Surface Temperature", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "12 km", "status": "completed", "dataPublishedTime": "2016-09-05T11:26:35", "doiPublishedTime": "2016-09-20T09:08:24", "removedDataTime": null, "geographicExtent": { "ob_id": 537, "bboxName": "NW European Shelf Seas", "eastBoundLongitude": 13.0, "westBoundLongitude": -18.33, "southBoundLatitude": 43.0, "northBoundLatitude": 63.56 }, "verticalExtent": null, "result_field": { "ob_id": 19815, "dataPath": "/badc/ukcp09/data/marine/minerva/dataset_1/ens_stats", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 343215532, "numberOfFiles": 385, "fileFormat": "NetCDF" }, "timePeriod": { "ob_id": 3335, "startTime": "1960-01-01T00:00:00", "endTime": "2098-12-31T23:59:00" }, "resultQuality": { "ob_id": 1, "explanation": "See dataset associated documentation", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2012-08-15" }, "validTimePeriod": { "ob_id": 3334, "startTime": "1960-01-01T00:00:00", "endTime": "2098-12-31T23:59:00" }, "procedureAcquisition": null, "procedureComputation": { "ob_id": 19988, "uuid": "42283041b306455cb05955abb7d8b95f", "short_code": "comp", "title": "MINERVA: North-West European shelf seas marine climate projection simulations", "abstract": "A Perturbed Physics Ensemble (PPE) of the Atmosphere-Ocean General Circulation Model (AO-GCM, or GCM) HadCM3 is downscaled by the shelf seas model POLCOMS. Each ensemble member is run as a transient experiment, from 1952-2098, under the SRES (Nakicenovic et al. 2000) business-as-usual, medium emission scenario A1B. This work extends that of Holt et al. (2010), released as UKCP09 (Lowe et al. 2009).\r\nHadCM3 (Gordon et al. 2000; Pope et al. 2000) is a CMIP3 model (third phase of the Coupled Model Intercomparison Project). 30 parameters within the HadCM3 atmosphere were perturbed within an expert-specified range to explore the uncertainty in Climate Sensitivity (Collins et al. 2011). 16 parameter-sets were selected that spanned the full range of Climate Sensitivity whilst still validating against present day. These, with the standard parameter set, formed a 17-member global PPE. \r\nThe atmosphere of each ensemble member was downscaled with the regional version of HadCM3, HadRM3 (Jones et al. 2004), with equivalent parameter perturbations. One parameter perturbation, common to 6 HadRM3 ensemble members, did not validate, and so was excluded from the rest of the work.\r\nThe atmospheric run-off fields from HadRM3 were used to drive the river routing model TRIP (Oki and Sud 1998; Oki et al. 1999) to provide river forcings.\r\nPOLCOMS (Proudman Oceanographic Laboratory Coastal Ocean Modelling System) (Holt and James 2001) downscaled each of the 11 member from the HadRM3 PPE. Atmospheric forcings were taken from HadRM3, lateral ocean forcings were taken from the HadCM3 ocean, and riverine forcings were taken from TRIP. The Baltic was treated as a climatological river, with a seasonal cycle of temperature, salinity and volume flux that did not change through time, or across the ensemble. This is a limitation of the study, and has implications downstream (in the Skagerrak, Kattegat and in the Norwegian Trench)." }, "procedureCompositeProcess": null, "imageDetails": [], "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": 12372, "uuid": "045cf918f7084c568f90d76f3f6d95c0", "short_code": "proj", "title": "MINERVA: North-West European Shelf Seas Marine Climate Projections", "abstract": "Climate projections for the North-West European Shelf Seas. An update to the shelf seas component of UKCP09 Marine Report (Lowe et al, 2009) funded by the MINERVA project.\r\n\r\n" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 7563, 11390, 11391, 11392, 11394, 11396, 11397, 11398, 11399, 11400, 11401, 11402, 11403, 11404, 11408, 11409, 25149, 25150, 25151, 25152, 25153, 25900 ], "vocabularyKeywords": [], "identifier_set": [ 8995 ], "observationcollection_set": [ { "ob_id": 12371, "uuid": "9eba512621144dbaacda1ddb470f885b", "short_code": "coll", "title": "MINERVA: Update to the UKCP09 Marine Report North-West European Shelf Seas Marine Climate Projections", "abstract": "An update to the shelf seas component of UKCP09 Marine Report (Lowe et al. 2009) funded by the Minerva Project. \r\nA Perturbed Physics Ensemble (PPE) of HadCM3 has been downscaled with the shelf seas model POLCOMS. Each of the 11 ensemble members have been downscaled as transient simulations (from 1952-2098) under the SRES A1B emissions scenario. The PPE (QUMP: Quantifying Uncertainty in Model Projections) was designed to span the range of uncertainty associated with model parameter uncertainty in the atmosphere of the driving global climate model. POLCOMS was run at a 12km resolution, with 32 vertical levels using s-coordinates. Monthly statistics of the model results were recorded. \r\n" } ], "responsiblepartyinfo_set": [ 47183, 47193, 47185, 48403, 48407, 48404, 48406, 48402, 48405, 76014 ], "onlineresource_set": [ 5798, 15800, 15806, 15807, 15801, 15802, 16291, 94843 ] }, { "ob_id": 12373, "uuid": "a61fc5c5fcb54f02b35019d8de941cae", "title": "Model for Interdisciplinary Research on Climate (MIROC) 5.2 model output prepared for APPOSITE ensemble prediction experiment", "abstract": "Atmosphere and Ocean components; Frequency: daily and monthly \r\n\r\nVariables Atmosphere daily:\r\ntas uas vas zg500\r\n\r\nVariables Atmosphere monthly:\r\ncl clt evspsbl hfss hus prc prw rlds rlut rsds rsdt rsuscs rsutcs snc ta tauu ua va wap cli clw hfls hurs pr prsn psl rlus rlutcs rsdscs rsus rsut sfcWind snw tas tauv uas vas zg500\r\n\r\nVariables Ocean daily:\r\nsic sit smelt snd tmelt transix transiy tsice usi vsi\r\n\r\nVariables Ocean monthly:\r\nfriver mlotst msftmyza msftmyzg msftmyzp rhopoto sic sit smelt snd so sos thetao tmelt tos transix transiy tsice uo usi vo vsi", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2021-06-14T17:02:06", "updateFrequency": "", "dataLineage": "Supplied model data ingested as is in the CEDA Archive.", "removedDataReason": "", "keywords": "APPOSITE prediction MIROC5.2 model arctic", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2015-06-23T01:57:30", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 1, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 12374, "dataPath": "/badc/apposite/data/MIROC5-2/pred", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1268386313405, "numberOfFiles": 9475, "fileFormat": "NetCDF" }, "timePeriod": null, "resultQuality": null, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 2 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 11971, "uuid": "a2f5db43a5f37651aae1a3808a7be5e8", "short_code": "proj", "title": "Arctic Predictability and Prediction On Seasonal-to-Interannual Time-scales (APPOSITE) as part of the NERC Arctic Research Programme (ARP)", "abstract": "APPOSITE is a 3-year NERC Arctic research project that aims to address fundamental questions about the extent to which the climate and state of the Arctic system is predictable on seasonal to inter-annual timescales, and about the physical processes that govern this predictability. Model data output from this project are archived at the BADC.\r\n\r\nAPPOSITE is funded until March 2016.\r\n\r\nGrant ref: NE/I029137/1" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 1050, 6019, 6020, 6021, 6022, 6023, 6024, 6025, 6030, 6031, 6059, 6086, 6087, 6088, 6089, 6090, 6091, 6092, 6093, 6249, 6250, 6251, 6252, 6255, 6277, 6284, 6285, 6506, 7767, 7768, 7769, 7770, 7771, 7776, 7777, 7778, 8230, 8231, 9616, 9746, 9749, 9754, 9756, 9760, 9874, 10402, 10410, 10411, 10935, 10988, 11040, 11041, 11042, 11043, 11044, 11045, 11046, 11047, 11048, 11049, 11050, 11051, 11052, 11053, 11054, 11055, 11056, 11057, 11058, 11059, 11060, 11061, 11062, 11063, 11064, 11065, 11066, 11067, 11068, 11069, 11070, 11071, 11072, 11073, 11074, 11075, 11076, 11077, 11078, 11079, 11080, 11081, 11082, 11083, 11084, 11085 ], "vocabularyKeywords": [], "identifier_set": [ 8380 ], "observationcollection_set": [ { "ob_id": 11700, "uuid": "d330c7873c3f4880893bdedb547bea20", "short_code": "coll", "title": "Collection of Multi-model Data from the Arctic Predictability and Prediction On Seasonal-to-Interannual Time-scales (APPOSITE) Project", "abstract": "How feasible is it to predict Arctic climate at seasonal-to-interannual timescales? As part of the APPOSITE project a multi-model ensemble prediction experiment was conducted in order to answer this question.\r\n\r\nThe main goal of APPOSITE was to quantify the timescales on which Arctic climate is predictable. In order to achieve this, a coordinated set of idealised initial condition predictability experiments with seven general circulation models was conducted. This was the first intercomparison project designed to quantify the predictability of Arctic climate on seasonal to interannual timescales.\r\n\r\nSeveral different coupled climate models performed simulations for APPOSITE (see Doc below for Details of simulations submitted to the APPOSITE database). Six of these models followed the same experimental protocol (see Doc below for Control Simulations details and for Ensemble Predictions). One model, CanCM4 followed a slightly different protocol.\r\n\r\nThe Model data output from the APPOSITE project are now archived at CEDA. The collection of model outputs (control and prediction) include data from:\r\n\r\n- Canadian Centre for Climate Modelling and Analysis (CanCM4)\r\n- ECHAM6-FESOM (E6F), run and developed by the Alfred Wegener Institute.\r\n- EC-Earth consortium (ec-earth_v2_3) \r\n- Geophysical Fluid Dynamics Laboratory (gfdlcm3) \r\n- Met Office (hadgem1-2) \r\n- Model for Interdisciplinary Research on Climate (MIROC5-2) \r\n- Max-Planck-Institut for Meteorologie (mpiesm)\r\n\r\nAlthough designed to address Arctic predictability, this data set could also be used to assess the predictability of other regions and modes of climate variability on these timescales, such as the El Nino Southern Oscillation.\r\n\r\nA paper describing the simulations for APPOSITE is in preparation to be submitted to the Geoscientific Model Development Journal.\r\n\r\nNote: These data do not correspond to a particular time period since the studies are all conducted in the model world. They are not predictions or attempts to simulate a particular period of time. So the dates in the files are completely arbitrary. " } ], "responsiblepartyinfo_set": [ 47212, 47213, 47214, 47215, 47217, 47218, 47219, 47216, 54760 ], "onlineresource_set": [] }, { "ob_id": 12375, "uuid": "ef3353d179e848fba4da4fdd4d6664e6", "title": "MPI-ESM model output prepared for APPOSITE ensemble prediction experiment", "abstract": "Atmosphere and Ocean components; Frequency: daily and monthly \r\n\r\nVariables Atmosphere daily:\r\nrsnssi snw tas tsice zg500\r\n\r\nVariables Atmosphere monthly:\r\nalsom ameltfrac cli clw evspsbl hfls htae hus prc prw rlds rlut rsds rsnscs rsut sfcWind sicepdw ta tsicepdi va wtae ameltdepth cl clt dewts friver hfss htan pr prsn psl rlus rlutcs rsdt rsus rsutcs sicepdi snw tas ua wap wtan\r\n\r\nVariables Ocean daily:\r\nsic sit snd transix transiy usi vsi\r\n\r\nVariables Ocean monthly:\r\nbsit bthetao dsicdtadv dsitdtadv flum hfxba hfy hfydiff msftmyza msftmyzp sic snd sos thkcello transix uo vo bsnd bthkcello dsicdtthd dsitdtthd hfx hfxdiff hfyba mlotst msftmyzg rhopoto sit so thetao tos transiy usi vsi", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2024-03-09T03:20:00", "updateFrequency": "", "dataLineage": "Supplied model data ingested as is in the CEDA Archive.", "removedDataReason": "", "keywords": "APPOSITE prediction Hadgem1-2 model arctic", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2015-06-23T02:09:08", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 1, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 12376, "dataPath": "/badc/apposite/data/mpiesm/pred", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1719960955477, "numberOfFiles": 29590, "fileFormat": "NetCDF" }, "timePeriod": null, "resultQuality": null, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 2 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 11971, "uuid": "a2f5db43a5f37651aae1a3808a7be5e8", "short_code": "proj", "title": "Arctic Predictability and Prediction On Seasonal-to-Interannual Time-scales (APPOSITE) as part of the NERC Arctic Research Programme (ARP)", "abstract": "APPOSITE is a 3-year NERC Arctic research project that aims to address fundamental questions about the extent to which the climate and state of the Arctic system is predictable on seasonal to inter-annual timescales, and about the physical processes that govern this predictability. Model data output from this project are archived at the BADC.\r\n\r\nAPPOSITE is funded until March 2016.\r\n\r\nGrant ref: NE/I029137/1" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 11087, 52942, 52965, 60405, 61391, 61409, 61438, 61439, 61441, 61442, 61443, 61444, 61445, 61446, 61447, 61448, 61449, 61450, 61451, 61452, 61453, 61454, 61455, 61456, 61457, 61458, 61459, 61460, 61461, 61462, 61463, 61464, 61465, 61466, 61467, 61468, 61469, 61470, 61471, 61472, 61473, 61474, 61476, 61477, 61478, 61479, 61480, 61481, 61482, 61484, 61485, 61486, 61487, 61488, 61489, 61510, 61511, 61512, 61513, 61514, 61515, 61516, 61517, 61518, 61519, 61520, 61521, 61522, 61523, 61524, 61525, 61526, 61527, 61528 ], "vocabularyKeywords": [], "identifier_set": [ 8378 ], "observationcollection_set": [ { "ob_id": 11700, "uuid": "d330c7873c3f4880893bdedb547bea20", "short_code": "coll", "title": "Collection of Multi-model Data from the Arctic Predictability and Prediction On Seasonal-to-Interannual Time-scales (APPOSITE) Project", "abstract": "How feasible is it to predict Arctic climate at seasonal-to-interannual timescales? As part of the APPOSITE project a multi-model ensemble prediction experiment was conducted in order to answer this question.\r\n\r\nThe main goal of APPOSITE was to quantify the timescales on which Arctic climate is predictable. In order to achieve this, a coordinated set of idealised initial condition predictability experiments with seven general circulation models was conducted. This was the first intercomparison project designed to quantify the predictability of Arctic climate on seasonal to interannual timescales.\r\n\r\nSeveral different coupled climate models performed simulations for APPOSITE (see Doc below for Details of simulations submitted to the APPOSITE database). Six of these models followed the same experimental protocol (see Doc below for Control Simulations details and for Ensemble Predictions). One model, CanCM4 followed a slightly different protocol.\r\n\r\nThe Model data output from the APPOSITE project are now archived at CEDA. The collection of model outputs (control and prediction) include data from:\r\n\r\n- Canadian Centre for Climate Modelling and Analysis (CanCM4)\r\n- ECHAM6-FESOM (E6F), run and developed by the Alfred Wegener Institute.\r\n- EC-Earth consortium (ec-earth_v2_3) \r\n- Geophysical Fluid Dynamics Laboratory (gfdlcm3) \r\n- Met Office (hadgem1-2) \r\n- Model for Interdisciplinary Research on Climate (MIROC5-2) \r\n- Max-Planck-Institut for Meteorologie (mpiesm)\r\n\r\nAlthough designed to address Arctic predictability, this data set could also be used to assess the predictability of other regions and modes of climate variability on these timescales, such as the El Nino Southern Oscillation.\r\n\r\nA paper describing the simulations for APPOSITE is in preparation to be submitted to the Geoscientific Model Development Journal.\r\n\r\nNote: These data do not correspond to a particular time period since the studies are all conducted in the model world. They are not predictions or attempts to simulate a particular period of time. So the dates in the files are completely arbitrary. 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As part of the APPOSITE project a multi-model ensemble prediction experiment was conducted in order to answer this question.\r\n\r\nThe main goal of APPOSITE was to quantify the timescales on which Arctic climate is predictable. In order to achieve this, a coordinated set of idealised initial condition predictability experiments with seven general circulation models was conducted. This was the first intercomparison project designed to quantify the predictability of Arctic climate on seasonal to interannual timescales.\r\n\r\nSeveral different coupled climate models performed simulations for APPOSITE (see Doc below for Details of simulations submitted to the APPOSITE database). Six of these models followed the same experimental protocol (see Doc below for Control Simulations details and for Ensemble Predictions). One model, CanCM4 followed a slightly different protocol.\r\n\r\nThe Model data output from the APPOSITE project are now archived at CEDA. 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They are not predictions or attempts to simulate a particular period of time. So the dates in the files are completely arbitrary. 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As part of the APPOSITE project a multi-model ensemble prediction experiment was conducted in order to answer this question.\r\n\r\nThe main goal of APPOSITE was to quantify the timescales on which Arctic climate is predictable. In order to achieve this, a coordinated set of idealised initial condition predictability experiments with seven general circulation models was conducted. This was the first intercomparison project designed to quantify the predictability of Arctic climate on seasonal to interannual timescales.\r\n\r\nSeveral different coupled climate models performed simulations for APPOSITE (see Doc below for Details of simulations submitted to the APPOSITE database). Six of these models followed the same experimental protocol (see Doc below for Control Simulations details and for Ensemble Predictions). One model, CanCM4 followed a slightly different protocol.\r\n\r\nThe Model data output from the APPOSITE project are now archived at CEDA. The collection of model outputs (control and prediction) include data from:\r\n\r\n- Canadian Centre for Climate Modelling and Analysis (CanCM4)\r\n- ECHAM6-FESOM (E6F), run and developed by the Alfred Wegener Institute.\r\n- EC-Earth consortium (ec-earth_v2_3) \r\n- Geophysical Fluid Dynamics Laboratory (gfdlcm3) \r\n- Met Office (hadgem1-2) \r\n- Model for Interdisciplinary Research on Climate (MIROC5-2) \r\n- Max-Planck-Institut for Meteorologie (mpiesm)\r\n\r\nAlthough designed to address Arctic predictability, this data set could also be used to assess the predictability of other regions and modes of climate variability on these timescales, such as the El Nino Southern Oscillation.\r\n\r\nA paper describing the simulations for APPOSITE is in preparation to be submitted to the Geoscientific Model Development Journal.\r\n\r\nNote: These data do not correspond to a particular time period since the studies are all conducted in the model world. They are not predictions or attempts to simulate a particular period of time. So the dates in the files are completely arbitrary. 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As part of the APPOSITE project a multi-model ensemble prediction experiment was conducted in order to answer this question.\r\n\r\nThe main goal of APPOSITE was to quantify the timescales on which Arctic climate is predictable. In order to achieve this, a coordinated set of idealised initial condition predictability experiments with seven general circulation models was conducted. This was the first intercomparison project designed to quantify the predictability of Arctic climate on seasonal to interannual timescales.\r\n\r\nSeveral different coupled climate models performed simulations for APPOSITE (see Doc below for Details of simulations submitted to the APPOSITE database). Six of these models followed the same experimental protocol (see Doc below for Control Simulations details and for Ensemble Predictions). One model, CanCM4 followed a slightly different protocol.\r\n\r\nThe Model data output from the APPOSITE project are now archived at CEDA. The collection of model outputs (control and prediction) include data from:\r\n\r\n- Canadian Centre for Climate Modelling and Analysis (CanCM4)\r\n- ECHAM6-FESOM (E6F), run and developed by the Alfred Wegener Institute.\r\n- EC-Earth consortium (ec-earth_v2_3) \r\n- Geophysical Fluid Dynamics Laboratory (gfdlcm3) \r\n- Met Office (hadgem1-2) \r\n- Model for Interdisciplinary Research on Climate (MIROC5-2) \r\n- Max-Planck-Institut for Meteorologie (mpiesm)\r\n\r\nAlthough designed to address Arctic predictability, this data set could also be used to assess the predictability of other regions and modes of climate variability on these timescales, such as the El Nino Southern Oscillation.\r\n\r\nA paper describing the simulations for APPOSITE is in preparation to be submitted to the Geoscientific Model Development Journal.\r\n\r\nNote: These data do not correspond to a particular time period since the studies are all conducted in the model world. 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As part of the APPOSITE project a multi-model ensemble prediction experiment was conducted in order to answer this question.\r\n\r\nThe main goal of APPOSITE was to quantify the timescales on which Arctic climate is predictable. In order to achieve this, a coordinated set of idealised initial condition predictability experiments with seven general circulation models was conducted. This was the first intercomparison project designed to quantify the predictability of Arctic climate on seasonal to interannual timescales.\r\n\r\nSeveral different coupled climate models performed simulations for APPOSITE (see Doc below for Details of simulations submitted to the APPOSITE database). Six of these models followed the same experimental protocol (see Doc below for Control Simulations details and for Ensemble Predictions). One model, CanCM4 followed a slightly different protocol.\r\n\r\nThe Model data output from the APPOSITE project are now archived at CEDA. The collection of model outputs (control and prediction) include data from:\r\n\r\n- Canadian Centre for Climate Modelling and Analysis (CanCM4)\r\n- ECHAM6-FESOM (E6F), run and developed by the Alfred Wegener Institute.\r\n- EC-Earth consortium (ec-earth_v2_3) \r\n- Geophysical Fluid Dynamics Laboratory (gfdlcm3) \r\n- Met Office (hadgem1-2) \r\n- Model for Interdisciplinary Research on Climate (MIROC5-2) \r\n- Max-Planck-Institut for Meteorologie (mpiesm)\r\n\r\nAlthough designed to address Arctic predictability, this data set could also be used to assess the predictability of other regions and modes of climate variability on these timescales, such as the El Nino Southern Oscillation.\r\n\r\nA paper describing the simulations for APPOSITE is in preparation to be submitted to the Geoscientific Model Development Journal.\r\n\r\nNote: These data do not correspond to a particular time period since the studies are all conducted in the model world. They are not predictions or attempts to simulate a particular period of time. So the dates in the files are completely arbitrary. " } ], "responsiblepartyinfo_set": [ 47252, 47259, 47253, 47254, 47255, 47258, 47257, 47256, 54763 ], "onlineresource_set": [] }, { "ob_id": 12385, "uuid": "bb773c3420994ca3b2201ca4688860a5", "title": "HadGEM1-2 model output prepared for APPOSITE control experiment", "abstract": "Atmosphere and Ocean components; Frequency: daily and monthly \r\nLand component, Frequency: Monthly.\r\n\r\nVariables Atmosphere daily:\r\npr psl sfcWind sfcWindmax snw tas tasmax tasmin ts uas vas zg\r\n\r\nVariables Atmosphere monthly:\r\ncl evspsbl hfss hurs pr prsn ps rlds rlus rlutcs rsdscs rsus rsut sci snw tas tauv ua va wap clt hfls hur hus prc prw psl rldscs rlut rsds rsdt rsuscs rsutcs sfcWind ta tauu ts uas vas zg\r\n\r\nVariables Land monthly:\r\nevspsblveg gpp mrlsl mrro mrros mrsos nep npp rGrowth rh tsl\r\n\r\nVariables Ocean daily:\r\nsic sim sit snd usi vsi\r\n\r\nVariables Ocean monthly:\r\nfriver fsitherm hfsifrazil mlotst prsn sblsi sic sim sit snd snoToIce so sos thetao tos uo usi vo vsi wfo wfonocorr", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2021-06-14T17:28:55", "updateFrequency": "", "dataLineage": "Supplied model data ingested as is in the CEDA Archive.", "removedDataReason": "", "keywords": "APPOSITE control Hadgem1-2 model arctic", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2015-06-23T02:50:48", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 1, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 12386, "dataPath": "/badc/apposite/data/hadgem1-2/ctrl", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 519360822647, "numberOfFiles": 22414, "fileFormat": "NetCDF" }, "timePeriod": null, "resultQuality": null, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 2 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 11971, "uuid": "a2f5db43a5f37651aae1a3808a7be5e8", "short_code": "proj", "title": "Arctic Predictability and Prediction On Seasonal-to-Interannual Time-scales (APPOSITE) as part of the NERC Arctic Research Programme (ARP)", "abstract": "APPOSITE is a 3-year NERC Arctic research project that aims to address fundamental questions about the extent to which the climate and state of the Arctic system is predictable on seasonal to inter-annual timescales, and about the physical processes that govern this predictability. Model data output from this project are archived at the BADC.\r\n\r\nAPPOSITE is funded until March 2016.\r\n\r\nGrant ref: NE/I029137/1" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6021, 6022, 6023, 6255, 9045, 50416, 50418, 50419, 50424, 50426, 50427, 50429, 50431, 50468, 50475, 50496, 50498, 50509, 50549, 50552, 50554, 50555, 50557, 50559, 50561, 50566, 50568, 50569, 50570, 50575, 50577, 50579, 50583, 50586, 50587, 50588, 50589, 50590, 50591, 50592, 50595, 50596, 50598, 50599, 50600, 50603, 50604, 50608, 52741, 52744, 52745, 52746, 52747, 52749, 52750, 52751, 52753, 52755, 53107, 53109, 53110, 53111, 53112, 53137, 53138, 54228, 54231, 54232, 54257, 54268, 54282, 54338, 54339, 55173, 57950, 57951, 57952, 57953, 57954, 57955, 57961, 57962, 62525, 62736, 71572, 71574, 71590, 71595, 71615, 71709, 71710, 71754, 71769, 71933, 72009 ], "vocabularyKeywords": [], "identifier_set": [ 8384 ], "observationcollection_set": [ { "ob_id": 11700, "uuid": "d330c7873c3f4880893bdedb547bea20", "short_code": "coll", "title": "Collection of Multi-model Data from the Arctic Predictability and Prediction On Seasonal-to-Interannual Time-scales (APPOSITE) Project", "abstract": "How feasible is it to predict Arctic climate at seasonal-to-interannual timescales? As part of the APPOSITE project a multi-model ensemble prediction experiment was conducted in order to answer this question.\r\n\r\nThe main goal of APPOSITE was to quantify the timescales on which Arctic climate is predictable. In order to achieve this, a coordinated set of idealised initial condition predictability experiments with seven general circulation models was conducted. This was the first intercomparison project designed to quantify the predictability of Arctic climate on seasonal to interannual timescales.\r\n\r\nSeveral different coupled climate models performed simulations for APPOSITE (see Doc below for Details of simulations submitted to the APPOSITE database). Six of these models followed the same experimental protocol (see Doc below for Control Simulations details and for Ensemble Predictions). One model, CanCM4 followed a slightly different protocol.\r\n\r\nThe Model data output from the APPOSITE project are now archived at CEDA. The collection of model outputs (control and prediction) include data from:\r\n\r\n- Canadian Centre for Climate Modelling and Analysis (CanCM4)\r\n- ECHAM6-FESOM (E6F), run and developed by the Alfred Wegener Institute.\r\n- EC-Earth consortium (ec-earth_v2_3) \r\n- Geophysical Fluid Dynamics Laboratory (gfdlcm3) \r\n- Met Office (hadgem1-2) \r\n- Model for Interdisciplinary Research on Climate (MIROC5-2) \r\n- Max-Planck-Institut for Meteorologie (mpiesm)\r\n\r\nAlthough designed to address Arctic predictability, this data set could also be used to assess the predictability of other regions and modes of climate variability on these timescales, such as the El Nino Southern Oscillation.\r\n\r\nA paper describing the simulations for APPOSITE is in preparation to be submitted to the Geoscientific Model Development Journal.\r\n\r\nNote: These data do not correspond to a particular time period since the studies are all conducted in the model world. They are not predictions or attempts to simulate a particular period of time. So the dates in the files are completely arbitrary. " } ], "responsiblepartyinfo_set": [ 47260, 47267, 47261, 47262, 47263, 47265, 47266, 47264, 54764 ], "onlineresource_set": [] }, { "ob_id": 12387, "uuid": "1350827326184430abca2098e6bf38ea", "title": "EC-EARTH v2.3 model output prepared for APPOSITE control experiment", "abstract": "Atmosphere and Ocean components; Frequency: daily and monthly \r\n\r\n\r\nVariables Atmosphere monthly:\r\ncl clt d2m hfls hus prc psl rls rltcs rsds rsscs rstcs snd ta tos uas vas\r\ncli clw evspsbl hfss pr prsn rlds rlt rlus rss rst rsus snr tas ua va wap\r\n\r\n\r\nVariables Ocean monthly:\r\nfriver ialb mlotst sic sit smelt snd so somixhgt sos tauu tauv thetao tos tsice uo usi vo vsi", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2021-06-14T16:49:52", "updateFrequency": "", "dataLineage": "Supplied model data ingested as is in the CEDA Archive.", "removedDataReason": "", "keywords": "APPOSITE control EC-EARTH model arctic", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2015-06-23T02:55:39", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 1, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 12388, "dataPath": "/badc/apposite/data/ec-earth_v2_3/ctrl", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 378133094016, "numberOfFiles": 10903, "fileFormat": "NetCDF" }, "timePeriod": null, "resultQuality": null, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 2 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 11971, "uuid": "a2f5db43a5f37651aae1a3808a7be5e8", "short_code": "proj", "title": "Arctic Predictability and Prediction On Seasonal-to-Interannual Time-scales (APPOSITE) as part of the NERC Arctic Research Programme (ARP)", "abstract": "APPOSITE is a 3-year NERC Arctic research project that aims to address fundamental questions about the extent to which the climate and state of the Arctic system is predictable on seasonal to inter-annual timescales, and about the physical processes that govern this predictability. 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As part of the APPOSITE project a multi-model ensemble prediction experiment was conducted in order to answer this question.\r\n\r\nThe main goal of APPOSITE was to quantify the timescales on which Arctic climate is predictable. In order to achieve this, a coordinated set of idealised initial condition predictability experiments with seven general circulation models was conducted. This was the first intercomparison project designed to quantify the predictability of Arctic climate on seasonal to interannual timescales.\r\n\r\nSeveral different coupled climate models performed simulations for APPOSITE (see Doc below for Details of simulations submitted to the APPOSITE database). Six of these models followed the same experimental protocol (see Doc below for Control Simulations details and for Ensemble Predictions). One model, CanCM4 followed a slightly different protocol.\r\n\r\nThe Model data output from the APPOSITE project are now archived at CEDA. The collection of model outputs (control and prediction) include data from:\r\n\r\n- Canadian Centre for Climate Modelling and Analysis (CanCM4)\r\n- ECHAM6-FESOM (E6F), run and developed by the Alfred Wegener Institute.\r\n- EC-Earth consortium (ec-earth_v2_3) \r\n- Geophysical Fluid Dynamics Laboratory (gfdlcm3) \r\n- Met Office (hadgem1-2) \r\n- Model for Interdisciplinary Research on Climate (MIROC5-2) \r\n- Max-Planck-Institut for Meteorologie (mpiesm)\r\n\r\nAlthough designed to address Arctic predictability, this data set could also be used to assess the predictability of other regions and modes of climate variability on these timescales, such as the El Nino Southern Oscillation.\r\n\r\nA paper describing the simulations for APPOSITE is in preparation to be submitted to the Geoscientific Model Development Journal.\r\n\r\nNote: These data do not correspond to a particular time period since the studies are all conducted in the model world. They are not predictions or attempts to simulate a particular period of time. So the dates in the files are completely arbitrary. 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As part of the APPOSITE project a multi-model ensemble prediction experiment was conducted in order to answer this question.\r\n\r\nThe main goal of APPOSITE was to quantify the timescales on which Arctic climate is predictable. In order to achieve this, a coordinated set of idealised initial condition predictability experiments with seven general circulation models was conducted. This was the first intercomparison project designed to quantify the predictability of Arctic climate on seasonal to interannual timescales.\r\n\r\nSeveral different coupled climate models performed simulations for APPOSITE (see Doc below for Details of simulations submitted to the APPOSITE database). Six of these models followed the same experimental protocol (see Doc below for Control Simulations details and for Ensemble Predictions). One model, CanCM4 followed a slightly different protocol.\r\n\r\nThe Model data output from the APPOSITE project are now archived at CEDA. The collection of model outputs (control and prediction) include data from:\r\n\r\n- Canadian Centre for Climate Modelling and Analysis (CanCM4)\r\n- ECHAM6-FESOM (E6F), run and developed by the Alfred Wegener Institute.\r\n- EC-Earth consortium (ec-earth_v2_3) \r\n- Geophysical Fluid Dynamics Laboratory (gfdlcm3) \r\n- Met Office (hadgem1-2) \r\n- Model for Interdisciplinary Research on Climate (MIROC5-2) \r\n- Max-Planck-Institut for Meteorologie (mpiesm)\r\n\r\nAlthough designed to address Arctic predictability, this data set could also be used to assess the predictability of other regions and modes of climate variability on these timescales, such as the El Nino Southern Oscillation.\r\n\r\nA paper describing the simulations for APPOSITE is in preparation to be submitted to the Geoscientific Model Development Journal.\r\n\r\nNote: These data do not correspond to a particular time period since the studies are all conducted in the model world. They are not predictions or attempts to simulate a particular period of time. So the dates in the files are completely arbitrary. " } ], "responsiblepartyinfo_set": [ 47276, 47283, 47277, 47278, 47279, 47281, 47282, 47280, 54766 ], "onlineresource_set": [] }, { "ob_id": 12391, "uuid": "0192c72e48734d838e85dd8ffbd5ab43", "title": "Model for Interdisciplinary Research on Climate (MIROC) 5.2 model output prepared for APPOSITE control experiment", "abstract": "Atmosphere and Ocean components; Frequency: daily and monthly \r\n\r\nVariables Atmosphere daily:\r\ntas uas vas zg500\r\n\r\nVariables Atmosphere monthly:\r\ncl clt evspsbl hfss hus prc prw rlds rlut rsds rsdt rsuscs rsutcs snc ta tauu ua va wap cli clw hfls hurs pr prsn psl rlus rlutcs rsdscs rsus rsut sfcWind snw tas tauv uas vas zg500\r\n\r\nVariables Ocean daily:\r\nsic sit smelt snd tmelt transix transiy tsice usi vsi\r\n\r\nVariables Ocean monthly:\r\nfriver mlotst msftmyza msftmyzg msftmyzp rhopoto sic sit smelt snd so sos thetao tmelt tos transix transiy tsice uo usi vo vsi", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2024-03-09T01:59:45", "updateFrequency": "", "dataLineage": "Supplied model data ingested as is in the CEDA Archive.", "removedDataReason": "", "keywords": "APPOSITE control MIROC5.2 model arctic", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2015-06-23T03:06:43", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 1, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 12392, "dataPath": "/badc/apposite/data/MIROC5-2/ctrl", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 312448716081, "numberOfFiles": 7403, "fileFormat": "NetCDF" }, "timePeriod": null, "resultQuality": null, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 2 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 11971, "uuid": "a2f5db43a5f37651aae1a3808a7be5e8", "short_code": "proj", "title": "Arctic Predictability and Prediction On Seasonal-to-Interannual Time-scales (APPOSITE) as part of the NERC Arctic Research Programme (ARP)", "abstract": "APPOSITE is a 3-year NERC Arctic research project that aims to address fundamental questions about the extent to which the climate and state of the Arctic system is predictable on seasonal to inter-annual timescales, and about the physical processes that govern this predictability. Model data output from this project are archived at the BADC.\r\n\r\nAPPOSITE is funded until March 2016.\r\n\r\nGrant ref: NE/I029137/1" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6021, 6022, 6023, 6255, 11044, 11045, 11046, 11047, 50416, 50418, 50419, 50420, 50422, 50424, 50425, 50427, 50429, 50431, 50475, 50496, 50498, 50549, 50551, 50552, 50553, 50554, 50555, 50557, 50559, 50561, 50566, 50568, 50569, 50570, 50579, 50583, 50586, 50587, 50588, 50589, 50590, 50591, 50595, 50596, 50598, 50599, 50600, 52741, 52746, 52747, 52753, 52755, 53107, 53109, 53110, 53111, 53112, 53130, 53131, 53137, 53138, 53142, 54061, 54062, 54228, 54336, 54338, 54339, 54416, 54802, 54803, 54804, 54805, 54806, 54807, 54808, 54809, 55173, 57953, 57955, 61066, 61071, 61492, 61493, 61494, 61495, 61496, 61497, 61498 ], "vocabularyKeywords": [], "identifier_set": [ 8387 ], "observationcollection_set": [ { "ob_id": 11700, "uuid": "d330c7873c3f4880893bdedb547bea20", "short_code": "coll", "title": "Collection of Multi-model Data from the Arctic Predictability and Prediction On Seasonal-to-Interannual Time-scales (APPOSITE) Project", "abstract": "How feasible is it to predict Arctic climate at seasonal-to-interannual timescales? As part of the APPOSITE project a multi-model ensemble prediction experiment was conducted in order to answer this question.\r\n\r\nThe main goal of APPOSITE was to quantify the timescales on which Arctic climate is predictable. In order to achieve this, a coordinated set of idealised initial condition predictability experiments with seven general circulation models was conducted. This was the first intercomparison project designed to quantify the predictability of Arctic climate on seasonal to interannual timescales.\r\n\r\nSeveral different coupled climate models performed simulations for APPOSITE (see Doc below for Details of simulations submitted to the APPOSITE database). Six of these models followed the same experimental protocol (see Doc below for Control Simulations details and for Ensemble Predictions). One model, CanCM4 followed a slightly different protocol.\r\n\r\nThe Model data output from the APPOSITE project are now archived at CEDA. The collection of model outputs (control and prediction) include data from:\r\n\r\n- Canadian Centre for Climate Modelling and Analysis (CanCM4)\r\n- ECHAM6-FESOM (E6F), run and developed by the Alfred Wegener Institute.\r\n- EC-Earth consortium (ec-earth_v2_3) \r\n- Geophysical Fluid Dynamics Laboratory (gfdlcm3) \r\n- Met Office (hadgem1-2) \r\n- Model for Interdisciplinary Research on Climate (MIROC5-2) \r\n- Max-Planck-Institut for Meteorologie (mpiesm)\r\n\r\nAlthough designed to address Arctic predictability, this data set could also be used to assess the predictability of other regions and modes of climate variability on these timescales, such as the El Nino Southern Oscillation.\r\n\r\nA paper describing the simulations for APPOSITE is in preparation to be submitted to the Geoscientific Model Development Journal.\r\n\r\nNote: These data do not correspond to a particular time period since the studies are all conducted in the model world. They are not predictions or attempts to simulate a particular period of time. So the dates in the files are completely arbitrary. " } ], "responsiblepartyinfo_set": [ 47284, 47291, 47286, 47287, 47289, 47290, 47285, 47288, 54767 ], "onlineresource_set": [] }, { "ob_id": 12393, "uuid": "89fb055ff9684240bad6c5c3b3544d38", "title": "MPI-ESM model output prepared for APPOSITE control experiment", "abstract": "Atmosphere and Ocean components; Frequency: daily and monthly \r\n\r\n\r\nVariables Atmosphere monthly:\r\ncl clt dewts friver hfss pr prsn psl rlus rlutcs rsdt rsus rsutcs snw tas va\r\ncli clw evspsbl hfls hus prc prw rlds rlut rsds rsnscs rsut sfcWind ta ua wap\r\n\r\nVariables Ocean daily:\r\nsic sit usi vsi\r\n\r\nVariables Ocean monthly:\r\ndsicdtadv dsitdtadv flum hfxba hfy hfydiff msftmyza msftmyzp sic snd sos thkcello transix uo dsicdtthd dsitdtthd hfx hfxdiff hfyba mlotst msftmyzg rhopoto sit so thetao tos transiy vo", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2024-03-09T01:41:39", "updateFrequency": "", "dataLineage": "Supplied model data ingested as is in the CEDA Archive.", "removedDataReason": "", "keywords": "APPOSITE control mpiesm model arctic", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2015-06-23T03:11:21", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 1, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 12394, "dataPath": "/badc/apposite/data/mpiesm/ctrl", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 239656481977, "numberOfFiles": 12802, "fileFormat": "NetCDF" }, "timePeriod": null, "resultQuality": null, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 2 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 11971, "uuid": "a2f5db43a5f37651aae1a3808a7be5e8", "short_code": "proj", "title": "Arctic Predictability and Prediction On Seasonal-to-Interannual Time-scales (APPOSITE) as part of the NERC Arctic Research Programme (ARP)", "abstract": "APPOSITE is a 3-year NERC Arctic research project that aims to address fundamental questions about the extent to which the climate and state of the Arctic system is predictable on seasonal to inter-annual timescales, and about the physical processes that govern this predictability. Model data output from this project are archived at the BADC.\r\n\r\nAPPOSITE is funded until March 2016.\r\n\r\nGrant ref: NE/I029137/1" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 52942, 52965, 60354, 60405, 61147, 61391, 61409, 61438, 61439, 61440, 61441, 61442, 61443, 61444, 61445, 61446, 61447, 61448, 61449, 61450, 61451, 61452, 61453, 61454, 61455, 61456, 61457, 61458, 61459, 61460, 61461, 61462, 61463, 61464, 61465, 61466, 61467, 61468, 61469, 61470, 61471, 61472, 61473, 61474, 61475, 61476, 61477, 61478, 61479, 61480, 61481, 61482, 61483, 61484, 61485, 61486, 61487, 61488, 61489, 61490, 61491, 72063, 72064, 72065, 72066, 72067 ], "vocabularyKeywords": [], "identifier_set": [ 8388 ], "observationcollection_set": [ { "ob_id": 11700, "uuid": "d330c7873c3f4880893bdedb547bea20", "short_code": "coll", "title": "Collection of Multi-model Data from the Arctic Predictability and Prediction On Seasonal-to-Interannual Time-scales (APPOSITE) Project", "abstract": "How feasible is it to predict Arctic climate at seasonal-to-interannual timescales? As part of the APPOSITE project a multi-model ensemble prediction experiment was conducted in order to answer this question.\r\n\r\nThe main goal of APPOSITE was to quantify the timescales on which Arctic climate is predictable. In order to achieve this, a coordinated set of idealised initial condition predictability experiments with seven general circulation models was conducted. This was the first intercomparison project designed to quantify the predictability of Arctic climate on seasonal to interannual timescales.\r\n\r\nSeveral different coupled climate models performed simulations for APPOSITE (see Doc below for Details of simulations submitted to the APPOSITE database). Six of these models followed the same experimental protocol (see Doc below for Control Simulations details and for Ensemble Predictions). One model, CanCM4 followed a slightly different protocol.\r\n\r\nThe Model data output from the APPOSITE project are now archived at CEDA. The collection of model outputs (control and prediction) include data from:\r\n\r\n- Canadian Centre for Climate Modelling and Analysis (CanCM4)\r\n- ECHAM6-FESOM (E6F), run and developed by the Alfred Wegener Institute.\r\n- EC-Earth consortium (ec-earth_v2_3) \r\n- Geophysical Fluid Dynamics Laboratory (gfdlcm3) \r\n- Met Office (hadgem1-2) \r\n- Model for Interdisciplinary Research on Climate (MIROC5-2) \r\n- Max-Planck-Institut for Meteorologie (mpiesm)\r\n\r\nAlthough designed to address Arctic predictability, this data set could also be used to assess the predictability of other regions and modes of climate variability on these timescales, such as the El Nino Southern Oscillation.\r\n\r\nA paper describing the simulations for APPOSITE is in preparation to be submitted to the Geoscientific Model Development Journal.\r\n\r\nNote: These data do not correspond to a particular time period since the studies are all conducted in the model world. They are not predictions or attempts to simulate a particular period of time. So the dates in the files are completely arbitrary. " } ], "responsiblepartyinfo_set": [ 47292, 47299, 47293, 47294, 47295, 47298, 47297, 47296, 54768 ], "onlineresource_set": [ 6937 ] }, { "ob_id": 12395, "uuid": "140ee66f0d5b49bb95047b3fed8d1b2a", "title": "GFDL-CM3 model output prepared for APPOSITE control experiment", "abstract": "Atmosphere and Ocean components; Frequency: daily and monthly \r\n\r\nVariables Atmosphere daily:\r\ntas zg500\r\n\r\nVariables Atmosphere monthly:\r\nclt hfls hurs pr prsn psl rlus rlutcs rsdt rsuscs rsutcs ta tot_cloud_area tot_liq_amt va\r\nevspsbl hfss hus prc prw rlds rlut rsds rsus rsut sfcWind tas tot_ice_amt ua wap\r\n\r\nVariables Ocean daily:\r\nsic sit snd usi vsi\r\n\r\nVariables Ocean monthly:\r\nalbedo rhopoto sic snd temp_xflux_adv_int_z temp_yflux_adv_int_z thetao transix tx_trans tx_trans_rho_gm vo friver rsdssi sit so temp_xflux_neutral_int_z temp_yflux_neutral_int_z tmelt transiy tx_trans_gm uo vsi mlotst rsussi smelt sos temp_xflux_sigma temp_yflux_sigma tos tsice tx_trans_rho usi", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2021-06-14T16:52:34", "updateFrequency": "", "dataLineage": "Supplied model data ingested as is in the CEDA Archive.", "removedDataReason": "", "keywords": "APPOSITE control GFDLCM3 model arctic", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2015-06-23T03:16:33", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 1, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 12396, "dataPath": "/badc/apposite/data/gfdlcm3/ctrl", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 872609628646, "numberOfFiles": 23385, "fileFormat": "NetCDF" }, "timePeriod": null, "resultQuality": null, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 2 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 11971, "uuid": "a2f5db43a5f37651aae1a3808a7be5e8", "short_code": "proj", "title": "Arctic Predictability and Prediction On Seasonal-to-Interannual Time-scales (APPOSITE) as part of the NERC Arctic Research Programme (ARP)", "abstract": "APPOSITE is a 3-year NERC Arctic research project that aims to address fundamental questions about the extent to which the climate and state of the Arctic system is predictable on seasonal to inter-annual timescales, and about the physical processes that govern this predictability. Model data output from this project are archived at the BADC.\r\n\r\nAPPOSITE is funded until March 2016.\r\n\r\nGrant ref: NE/I029137/1" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6021, 6022, 11044, 11045, 11200, 19835, 53130, 53131, 54061, 54062, 54411, 61367, 61368, 61369, 61370, 61371, 61372, 61373, 61374, 61375, 61376, 61377, 61378, 61379, 61380, 61381, 61382, 61383, 61384, 61385, 61386, 61387, 61388, 61389, 61390, 61391, 61392, 61393, 61394, 61395, 61396, 61397, 61398, 61399, 61400, 61401, 61402, 61403, 61404, 61405, 61406, 61407, 61408, 61409, 61410, 61411, 61412, 61413, 61414, 61415, 61416, 61417, 61418, 61419, 61420, 61421, 61422, 61423, 61424, 61425, 61426, 61427, 61428, 61429, 61430, 61431, 61432, 61433, 61434, 61435, 61436, 72068, 72069 ], "vocabularyKeywords": [], "identifier_set": [ 8389 ], "observationcollection_set": [ { "ob_id": 11700, "uuid": "d330c7873c3f4880893bdedb547bea20", "short_code": "coll", "title": "Collection of Multi-model Data from the Arctic Predictability and Prediction On Seasonal-to-Interannual Time-scales (APPOSITE) Project", "abstract": "How feasible is it to predict Arctic climate at seasonal-to-interannual timescales? As part of the APPOSITE project a multi-model ensemble prediction experiment was conducted in order to answer this question.\r\n\r\nThe main goal of APPOSITE was to quantify the timescales on which Arctic climate is predictable. In order to achieve this, a coordinated set of idealised initial condition predictability experiments with seven general circulation models was conducted. This was the first intercomparison project designed to quantify the predictability of Arctic climate on seasonal to interannual timescales.\r\n\r\nSeveral different coupled climate models performed simulations for APPOSITE (see Doc below for Details of simulations submitted to the APPOSITE database). Six of these models followed the same experimental protocol (see Doc below for Control Simulations details and for Ensemble Predictions). One model, CanCM4 followed a slightly different protocol.\r\n\r\nThe Model data output from the APPOSITE project are now archived at CEDA. The collection of model outputs (control and prediction) include data from:\r\n\r\n- Canadian Centre for Climate Modelling and Analysis (CanCM4)\r\n- ECHAM6-FESOM (E6F), run and developed by the Alfred Wegener Institute.\r\n- EC-Earth consortium (ec-earth_v2_3) \r\n- Geophysical Fluid Dynamics Laboratory (gfdlcm3) \r\n- Met Office (hadgem1-2) \r\n- Model for Interdisciplinary Research on Climate (MIROC5-2) \r\n- Max-Planck-Institut for Meteorologie (mpiesm)\r\n\r\nAlthough designed to address Arctic predictability, this data set could also be used to assess the predictability of other regions and modes of climate variability on these timescales, such as the El Nino Southern Oscillation.\r\n\r\nA paper describing the simulations for APPOSITE is in preparation to be submitted to the Geoscientific Model Development Journal.\r\n\r\nNote: These data do not correspond to a particular time period since the studies are all conducted in the model world. They are not predictions or attempts to simulate a particular period of time. So the dates in the files are completely arbitrary. " } ], "responsiblepartyinfo_set": [ 47300, 47301, 47302, 47303, 47305, 47306, 47307, 47304, 54769 ], "onlineresource_set": [ 6935 ] }, { "ob_id": 12399, "uuid": "a3931d0dfcc243ab8d93603c8ee58811", "title": "Gridded Global Surface Ozone Metrics data (1971-2015) for Atmospheric Chemistry Model Evaluation - version 2.4", "abstract": "This dataset consists of version 2.4 gridded surface ozone metrics for atmospheric chemistry model evaluation. These data have global coverage on both monthly and annual timescales from 1971-2015. Multiple model resolutions were used and can be easily identified within the filename. \r\n\r\nData are stored as NetCDF-4 files, with one file per model resolution at each time resolution. The files hold gridded surface ozone data from multiple databases (WMO GAW, EPA CASTNET, EPA AQS, Canadian CAPMON, Canadian NAPS, European EMEP, European AirBase, East Asian Acid Deposition Monitoring Network EANET), plus metadata. Metrics include the moments of the distribution, percentiles, maximum daily eight-hour average (MDA8), SOMO35, AOT40, and metrics related to air quality regulatory thresholds. In addition, metadata for all contributing sites (e.g. Site name, latitude, longitude) are included in each file.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2015-06-12T17:21:31", "updateFrequency": "notPlanned", "dataLineage": "Data were processed by the authors and delivered to CEDA for archiving", "removedDataReason": "", "keywords": "Ozone", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "various grid resolutions", "status": "completed", "dataPublishedTime": "2015-06-30T15:14:56", "doiPublishedTime": "2015-06-30T23:00:00", "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": 12403, "dataPath": "/badc/proc-earth-model/data/ozone-metrics/v2.4", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 15131965112, "numberOfFiles": 17, "fileFormat": "Data is formatted in NetCDF-4" }, "timePeriod": { "ob_id": 3336, "startTime": "1970-12-31T23:00:00", "endTime": "2015-12-31T23:59:59" }, "resultQuality": null, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 2 ], "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": 12368, "uuid": "0a5fb98570a0459fb326deb173f50b2f", "short_code": "proj", "title": "Process Based Earth System Model (ESM) Evaluation", "abstract": "This project was funded by the Natural Environment Research Council (NERC) with the grant reference - NE/K016008/1 - led by Professor Mathew Evans (University of York).\r\n\r\nClimate change and air pollution are two of the biggest challenges facing humanity today. Ozone and particulate matter are pollutants that are particularly harmful to human health. Recent studies have suggested that in the UK alone they cause 50,000 extra deaths and result in a financial burden of 8-22 billion pounds per year. Both ozone and particulate matter also play an important role in climate change. Ozone absorbs infra-red radiation resulting in a warming of the climate. Particles scatter and absorb incoming solar radiation and alter the properties of clouds. This results in complex interactions with the Earth's climate, with some types of aerosol pollution warming climate whereas others cool climate. Future air quality depends both on changes to emissions of pollutants and to changes in climate. Furthermore, a warming climate can result in worsened air pollution, which in turn can drive additional warming, meaning that complex feedbacks are possible between air pollution and climate.\r\n\r\nTo help understand these complex interactions and feedbacks scientists have developed Earth System Models that include a description of the important physical and biogeochemical processes. These models are increasingly being used by policy makers to make predictions about future air quality and climate and to guide policy decisions. It is therefore important that the models are rigorously tested. \r\n\r\nThis testing involves using detailed observations of atmospheric composition that have been made over the past few decades at locations around the world. Most model evaluation to date has involved testing whether the models simulate current average concentrations of atmospheric pollutants. Whilst this is a useful and necessary first step in model evaluation it does not test whether the model accurately simulates the change in concentration of a pollutant under changing emissions or changing climate. For example, does the model capture the real-world change in concentrations of a pollutant given a particular change in emission or under a future climate change scenario? This is particularly important as these predictions under-pin policy recommendations for air quality abatement. \r\n\r\nThis project synthesised long-term (multi-decadal) observations of ozone and particulate matter and their atmospheric precursors. They used these observations to explore trends and variability that have been observed over the past few decades. A model-observation framework was developed that can be used to evaluate how well models simulate observed variability and trends. The project tested state-of-the-art Earth System Models using existing model output from model intercomparison exercises. Finally, they explored the model processes that are driving simulated variability and trends.\r\n\r\nThe results inform the scientific community as to the fidelity of Earth System Models. This project helped to improve our models and give us more confidence in our predictions.\r\n\r\nThe overall objective of this project was to develop and implement a framework capable of evaluating the sensitivity of atmospheric composition simulated by ESMs to changing climate and emissions. \r\n\r\nOur scientific objectives were to:\r\n\r\nO1. Develop observationally-based metrics and relationships with which to evaluate variability and trends in atmospheric composition and its drivers in ESMs. \r\n\r\nO2. Understand the sensitivity of observed and simulated atmospheric composition to environmental drivers.\r\n\r\nO3. Quantify the ability of ESMs to capture observed temporal variability and trends in atmospheric composition.\r\n\r\nO4. Improve our understanding of the processes driving observed variability in atmospheric composition." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 1050, 1635, 1636, 11360, 11361, 11362, 11363, 11364, 11365, 11366, 11367, 11368, 11369, 11370, 11371, 11372, 11373, 11374, 11375, 11376, 11377, 11378, 11379, 11380, 11381, 11382, 11383, 11384, 25394 ], "vocabularyKeywords": [], "identifier_set": [ 8392, 8393 ], "observationcollection_set": [ { "ob_id": 12398, "uuid": "5af3f51efc1d46c88d35be217b20fad3", "short_code": "coll", "title": "Process Based Earth System Model Evaluation: Gridded Global Surface Ozone Metrics and Precursor species data collection for Atmospheric Chemistry Model Evaluation", "abstract": "This dataset collection presents a global surface ozone compilation for long-term trends and ESM (Earth System Model) evaluation.\r\n\r\nThe project (Process Based Earth System Model Evaluation) brought together all publicly available surface ozone observations from online databases from the modern era to build a consistent dataset for the evaluation of chemical transport and chemistry-climate (Earth System) models for projects such as the Chemistry-Climate Model Initiative (CCMI) and Aer-Chem-MIP. \r\n\r\nFrom a total dataset of approximately 6600 sites and 500 million hourly observations from 1971-2015, approximately 2200 sites and 200 million hourly observations pass screening as high-quality sites in regional background locations that are appropriate for use in global model evaluation. There was generally good data volume in the datasets since the start of air quality monitoring networks in 1990 through to 2013. Ozone observations are biased heavily toward North America and Europe with sparse coverage over the rest of the globe. \r\n\r\nThis dataset collection was made available for the purposes of model evaluation as a set of gridded metrics intended to describe the distribution of ozone concentrations on monthly and annual timescales. This collection currently holds version 2.4 data only, but future versions may follow." } ], "responsiblepartyinfo_set": [ 47335, 47337, 47338, 47341, 47336, 47340, 129584, 47339, 47334 ], "onlineresource_set": [ 5831, 5832, 6078, 94659 ] }, { "ob_id": 12411, "uuid": "ecbecb0fbf6e4a4e93bb3ee2cf9854cd", "title": "Vertical wind profile data from 6th November 2013 to 18th January 2016 measured by the University of Manchester 1290 mhz mobile wind profiler deployed on long term observations at Met Office Research Unit, Cardington, Bedfordshire", "abstract": "Vertical profiles of horizontal and vertical wind components as well as signal-to-noise (SNR) and spectal width measurements were collected at the Met Office Research Unit, Cardington, Bedfordshire, UK, from 6th November 2013 to 18th January 2016 as part of ongoing long term observations made by the NERC National Centre for Atmospheric Science (NCAS). These data were collected by the NCAS Atmospheric Measurement Facility's (AMF) 1290 MHz Mobile Wind Profiler, owned and operated by the University of Manchester and previously known as the aber-radar-1290mhz at the time of these observations. The data are available at 15 minute intervals as netCDF files to all registered BADC users under the Open Government License.\r\n\r\nPlease note, the data between 1st September and 31st December 2014 should be treated with caution as there were various logged errors during this period.\r\n\r\nThe dataset contains the following measurements:\r\n\r\nEastward wind velocity component\r\nNorthward wind velocity component\r\nUpward air velocity\r\nDirection the wind is from\r\nSignal to noise ratio\r\nAltitude of instrument above the ground\r\nLongitude of instrument\r\nLatitude of instrument\r\nSpectral width", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2019-03-21T14:01:25.407584", "updateFrequency": "notPlanned", "dataLineage": "Data were collected by the wind profiler before being processed by the instrument scientist, Emily Norton, and then provided to the BADC for archiving.", "removedDataReason": "", "keywords": "NCAS, AMF, radar, wind profiler, boundary layer wind profiler, turbulence, thermals, boundary layer structure, radar wind profiler, Doppler radar, UHF radar, 'Clear' air radar, morning transition, convective boundary layer, mixed layer, entrainment zone, transitional boundary layer, nocturnal boundary layer, lower level jet, nocturnal jet, weather fronts, sting jets", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "superseded", "dataPublishedTime": "2015-07-01T15:01:44", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 39, "bboxName": "Met Office Cardington site", "eastBoundLongitude": -0.42161, "westBoundLongitude": -0.42161, "southBoundLatitude": 52.10469, "northBoundLatitude": 52.10469 }, "verticalExtent": null, "result_field": { "ob_id": 42819, "dataPath": "/badc/ncas-longterm-obs/data/man-radar-1290mhz/20131106-20160118", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 8496511, "numberOfFiles": 1029, "fileFormat": "Data are netCDF formatted." }, "timePeriod": { "ob_id": 3338, "startTime": "2013-11-06T00:00:00", "endTime": "2016-01-18T23:59:59" }, "resultQuality": { "ob_id": 1409, "explanation": "The data are provided as-is with no quality control undertaken by the Centre for Environmental Data Analysis (CEDA).", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2014-07-08" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 5429, "uuid": "f03bcf662a7a432eb357504a87e98402", "short_code": "acq", "title": "Acquisition Process for: Vertical wind profile data from 14th April 2010 to 25th May 2011 measured by the University of Manchester 1290 mhz mobile wind profiler deployed on long term observations at Met Office Research Unit, Cardington, Bedfordshire", "abstract": "This acquisition is comprised of the following: INSTRUMENTS: University of Manchester Degreane 1290mhz Mobile Wind Profiler Radar - formerly aber-radar-1290mhz; PLATFORMS: Met Office Meteorologial Research Unit, Cardington; " }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 13 ], "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": 5407, "uuid": "4afb58c7bc065928378a16415e69081a", "short_code": "proj", "title": "NCAS-AMF: Long term observations at Cardington, Bedfordshire", "abstract": "The Natural Environment Research Council's (NERC) National Centre for Atmospheric Science (NCAS) undertake a number of long term measurements by a suite of instruments to support ongoing atmospheric research at a variety of locations. These include a long-term observation mode of instruments from the NCAS Atmospheric Measurement Facility (AMF) when not deployed on specific field campaign duties for other projects. One such long-term deployment covers the NCAS mobile wind profiler deployed at the Met Office's Cardington site in Bedfordshire. This complements other long-term wind profilers in the UK, incuding the NERC Mesosphere-Stratosphere-Troposphere (MST) radar located near Absersystwyth, mid-Wales - an alternative site also used for the NCAS AMF mobile wind profiler for long-term observations." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 3037, 3038, 3039, 22318, 52195, 52196, 52197, 52198, 52199, 52200, 52201, 52202, 52203, 52204, 52205, 52206, 52207, 52208, 52209, 52210, 52211, 52212, 83844, 83845 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 5404, "uuid": "a0c7a4c46a83992cfdf9820a4b253923", "short_code": "coll", "title": "NCAS Long Term Observations of atmospheric dynamics and composition", "abstract": "The UK's Natural Environment Research Council's (NERC) National Centre for Atmospheric Sciences (NCAS) operates a suite of instrumentation to monitor the atmospheric dynamics and composition of the atmosphere. This dataset brings together all the long term routine observations made by NCAS instruments covering surface based instruments as well as remote sensing instruments such as radars and lidars. Some of the instruments may also be deployed elsewhere on field campaigns, for which the data will be available under the associated field campaign dataset. Links are also available to pages describing the instruments from which links to all data from that particular instrument can be found." } ], "responsiblepartyinfo_set": [ 47378, 47379, 47380, 47382, 47383, 47384, 47385, 47381 ], "onlineresource_set": [] }, { "ob_id": 12414, "uuid": "84d4f66b668241328df0c43f8f3b3e16", "title": "GloboLakes: high-resolution global limnology dataset v1", "abstract": "These data are high-resolution datasets related to in-land water for limnology (study of in-land waters) and remote sensing applications. This includes: distance-to-land, distance-to-water, water-body identifier and lake-centre co-ordinates on a high-resolution (1/360x1/360 degree) grid, produced by the Department of Meteorology at the University of Reading. Data was derived using the ESA CCI Land Cover Map (see linked documentation). \r\n\r\nDatasets containing information to locate and identify water bodies have been generated from high-resolution (1/360x1/360 degree, about 300mx300m) data locating static-water-bodies recently released by the Land Cover Climate Change Initiative (LC CCI) of the European Space Agency. The new datasets provide: distance to land, distance to water, water body identifiers and lake centre locations. The lake identifiers (IDs) are from the Global Lakes and Wetlands Database (GLWD), and lake centres are defined for in-land waters for which GLWD IDs were determined. The new datasets therefore link recent lake/reservoir/wetlands extent to the GLWD, together with a set of coordinates which locates unambiguously the water bodies in the database. \r\n\r\nThe LC CCI water bodies dataset has been obtained from multi-temporal metrics based on time series of the backscattered intensity recorded by ASAR (Advanced Synthetic Aperture Radar) on Envisat between 2005 and 2010. Temporal change in water body extent is common. Future versions of the LC CCI dataset are planned to represent temporal variation, and this will permit these derived datasets to be updated.\r\n\r\nThe paper associated with this dataset is: \r\nL.Carrea O. Embury C.J. Merchant \"High-resolution datasets related to in-land water for limnology and remote sensing applications: distance-to-land, distance-to-water, water-body identifier and lake-centre co-ordinates\" Geoscience Data Journal, vol. 2 issue 2, pp. 83-97, November 2015. DOI: 10.1002/gdj3.32\r\n", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2015-07-21T11:36:49", "updateFrequency": "notPlanned", "dataLineage": "Data were processed by the authors and delivered to CEDA for archiving", "removedDataReason": "", "keywords": "limnology, lake, freshwater, satellite, environmental change, surface water temperature", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "high-resolution (1/360x1/360 degree) grid", "status": "completed", "dataPublishedTime": "2015-07-21T10:28:54", "doiPublishedTime": "2015-07-21T10:28:44", "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": 12413, "dataPath": "/neodc/globolakes/data/v1/limnology", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1690133055, "numberOfFiles": 5, "fileFormat": "Files are formatted in NetCDF and BADC-CSV" }, "timePeriod": { "ob_id": 3341, "startTime": "2005-01-01T00:00:00", "endTime": "2010-01-01T00:00:00" }, "resultQuality": { "ob_id": 3037, "explanation": "Please see paper: L.Carrea O. Embury C.J. Merchant \"High-resolution datasets related to in-land water for limnology and remote sensing applications: distance-to-land, distance-to-water, water-body identifier and lake-centre co-ordinates\" Geoscience Data Journal, vol. 2 issue 2, pp. 83-97, November 2015. DOI: 10.1002/gdj3.32", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2015-07-20" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 2 ], "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": 12408, "uuid": "a525f9c2b77043b3a12514da2a30aa86", "short_code": "proj", "title": "GloboLakes Project", "abstract": "Global Observatory of Lake Responses to Environmental Change (GloboLakes) was a project funded by the Natural Environment Research Council (NERC) with the following grant references; NE/J023345/2, NE/J02211X/1, NE/J023396/1, NE/J021717/1 and NE/J022810/1. These grants were led by Professor Christopher Merchant, Dr Mark Cutler, Mr Stephen Groom, Professor Stephen Maberly and Dr Claire Miller respectively. \r\n\r\nThere are around 304 million lakes globally. These provide essential resources for human survival and are an important component of global biogeochemical cycles. Lakes are also fragile systems that are sensitive to multiple pressures including nutrient enrichment, climate change and hydrological modification, making them important 'sentinels' of environmental perturbation. However, traditional monitoring has only produced data from a tiny fraction of the global population of lakes and disentangling the causes of change requires consistently-produced data from a large number of lakes, along with measurements of possible causes of change. Satellite observations (remote sensing) and the establishment of a global lake observatory would produce a step-change in our ability to detect and attribute the causes of changes in lakes world-wide. \r\n\r\nThis is now possible for three reasons: \r\n(1) the improved wavebands, spatial resolution and frequency of data collection from satellite sensors is now sufficient to monitor inland waters; \r\n(2) formulae to correct for atmospheric properties and to convert the detected reflected light to useful lake properties have been developed; and \r\n(3) computing power has increased to the point that allows near real time and archived information from satellites to be processed. \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'. \r\n\r\nThis was an ambitious project that was only possible by bringing together a consortium of scientists with complementary skills. These included 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\nThe eight objectives of GloboLakes were to:\r\n(i) develop remote sensing algorithms to estimate lake biogeochemical and physical parameters;\r\n(ii) make these algorithms operational and process satellite data;\r\n(iii) compile integrated spatio-temporal information on climatic and catchment data for >1000 lakes;\r\n(iv) integrate data and assess uncertainty in data sources;\r\n(v) detect spatial and temporal patterns in lake water quality;\r\n(vi) attribute the causes of lake response to environmental conditions;\r\n(vii) forecast lake sensitivity to environmental change;\r\n(viii) apply data to lake management and the monitoring of freshwater resources.\r\n\r\nThe project focused on the retrieval of surface water temperature as this has a fundamental effect on lake ecology, the concentration of coloured dissolved organic matter and suspended solids that derive largely from the catchment, the abundance of phytoplankton measured as the concentration of the pigment, chlorophyll a, and the abundance of cyanobacteria (blue-green algae) that can potentially be toxic. Knowledge of the conditions of lakes and their sensitivity to change is also extremely valuable for the management of lakes and reservoirs and GloboLakes provided information and products specifically for environmental managers. \r\n\r\nA satellite launched during the course of the project, called Sentinel 2, provided even greater spatial resolution allowing data to be collected and exploited from even smaller lakes. This was investigated by GloboLakes and incorporated into the framework of a global lake observatory." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50559, 50561, 70070, 70071, 91961, 91962, 91963, 91964, 91965, 91966, 91967, 91968, 91969, 91970, 91971, 91972 ], "vocabularyKeywords": [], "identifier_set": [ 8397 ], "observationcollection_set": [ { "ob_id": 12415, "uuid": "06cef537c5b14a2e871a333b9bc0b482", "short_code": "coll", "title": "GloboLakes: high-resolution global limnology data", "abstract": "This dataset collection holds high-resolution datasets related to in-land water for limnology (study of in-land waters) and remote sensing applications. These were produced by the Department of Meteorology at the University of Reading. \r\n\r\nInformation on distance-to-land for each water cell and the distance-to-water for each land cell has many potential applications in remote sensing, where the applicability of geophysical retrieval algorithms may be affected by the presence of water or land within a satellite field of view (image pixel).\r\n\r\nThe data was recorded over a 5 year period from 2005-2010 on a global scale. It is expected that new and updated datasets will be added in the future.\r\n\r\n" }, { "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)." }, { "ob_id": 30127, "uuid": "82b29f96b8c94db28ecc51a479f8c9c6", "short_code": "coll", "title": "National Centre for Earth Observation (NCEO) Core datasets", "abstract": "This NCEO Core data set collection contains data generated by the National Centre for Earth Observation core scientific programmes. NCEO is a National Environment Research Council (NERC) research centre with more than 80 scientists distributed across leading UK universities and research organisations and led by Professor John Remedios at the University of Leicester.\r\n\r\nNCEO provides the UK with core expertise in Earth Observation science, data sets and merging techniques, and model evaluation to underpin Earth System research and the UK’s international contribution to environmental science. NCEO scientists work strategically with space agencies, play significant roles in mission planning, and generate internationally-recognised data products from 20 different satellite instruments." } ], "responsiblepartyinfo_set": [ 47403, 47404, 47405, 47406, 47410, 47409, 47408, 47407, 47503, 47411, 47412 ], "onlineresource_set": [ 5835, 5874, 8723, 87595, 94852, 94853 ] }, { "ob_id": 12421, "uuid": "94f757d9b28846b5ac810a277a916fa7", "title": "UKCP09: Met Office gridded land surface climate observations - monthly climate variables at 5km resolution", "abstract": "This dataset contains a set of observed monthly climate variables on a 5km resolution grid. The observations are derived from 16 daily climate variables that have been averaged (e.g. daily maximum temperature) or summed (e.g. monthly total precipitation) over calendar months. The input station data originate from the Met Office Integrated Data Archive System (A version of MIDAS is also available through CEDA, although incremental developments to the database such as quality control and data recovery activities may result in some differences compared to the database at the time of production of the UKCP09 data).\r\n\r\nThe gridding process accounts for effects such as latitude, longitude, altitude, coastal influence, and the effect of urban land through the use of normalisation with respect to monthly 1961 – 1990 climate normals, and in the case of some variables a regression model. For more details about the construction see Perry and Hollis (2005). \r\n\r\nThe data are provide in CF-1.5 compliant NetCDF format. The data are additionally provided in ESRI-ascii format, suitable for ingestion in GIS applications, and a simple timeseries format for users requiring a limited number of points.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2017-08-17T16:07:13", "updateFrequency": "notPlanned", "dataLineage": "Data were produced by the Met Office for the UKCP09 service and then prepared for archiving with Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "UKCP09, Met Office, land surface, climate observations", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2017-08-21T11:38:37", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 674, "bboxName": "UK OS Grid (UKCP grid)", "eastBoundLongitude": 4.41954, "westBoundLongitude": -8.56493, "southBoundLatitude": 49.53098, "northBoundLatitude": 61.11007 }, "verticalExtent": null, "result_field": { "ob_id": 12486, "dataPath": "/badc/ukcp09/data/gridded-land-obs/gridded-land-obs-monthly", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 11530273862, "numberOfFiles": 16839, "fileFormat": "The data are provide in CF-1.5 compliant NetCDF format. The data are additionally provided in ESRI-ascii format, suitable for ingestion in GIS applications, and a simple timeseries format for users requiring a limited number of points." }, "timePeriod": { "ob_id": 6748, "startTime": "1910-01-01T00:00:00", "endTime": "2016-12-31T23:59:59" }, "resultQuality": { "ob_id": 3094, "explanation": "For details on data quality checks, grid accuracy etc. please see the linked \"UKCP09 Frequently Asked Questions\".", "passesTest": true, "resultTitle": "UKCP09 Gridded Land Observations Quality Info", "date": "2017-08-17" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 24942, "uuid": "01c5c729c64046af967a201b9652437a", "short_code": "comp", "title": "UKCP09 gridded land surface climate observations methodology", "abstract": "The gridded data sets are based on the archive of UK weather observations held at the Met Office. The density of the station network used varies through time, and for different climate variables — for example, for the temperature variables the number of stations rises from about 270 in 1914 to 600 in the mid-1990s, before falling to 450 in 2006.\r\n\r\nRegression and interpolation are used to generate values on a regular grid from the irregular station network, taking into account factors such as latitude and longitude, altitude and terrain shape, coastal influence, and urban land use. This alleviates the impact of station openings and closures on homogeneity, but the impacts of a changing station network cannot be removed entirely, especially in areas of complex topography or sparse station coverage. The methods used to generate the monthly and annual grids are described in more detail in a paper published in the International Journal of Climatology, vol. 25 (2005), pages 1,041-1,054, which can be downloaded here: Generation of monthly gridded data sets for a range of climatic variables over the UK (PDF, 590 kB). The methods used to generate the daily grids are described in more detail in the report 'The generation of the daily gridded data sets of temperature and rainfall for the UK' (PDF, 2.27 MB).\r\n\r\nThe 5 × 5 km grids of the 1961-1990 baseline climate average available on these webpages are simply calculated by averaging or summing the 30 monthly or annual gridded data sets for each variable. They are not the same as the 1 × 1 km data sets presented on our UK Climate pages. The methods used to generate the 1 × 1 km data sets for 30-year average periods, which were used to normalise the station data prior to the generation of monthly and annual grids, are described in a paper published in the International Journal of Climatology, vol. 25 (2005), pages 1,023-1,039, which can be downloaded here: Development of a new set of long-term averages for the UK\r\n(PDF, 1.08 MB)\r\n\r\nTo help users combine the 5 × 5 km baseline data sets with the UK Climate Projections, values of the 1961-1990 baseline climate average have also been generated for the 25 × 25 km grid boxes of the HadRM3 regional climate model and for administrative regions and river basins.\r\n\r\nEach 25 × 25 km grid box value is an average of the 5 × 5 km grid cell values that fall within it. Averages have been calculated for each month, season and the year as a whole (17 data sets). For the days of frost and days of rain variables the seasonal and annual averages are the total of the individual monthly averages. For the remaining variables the seasonal and annual averages are the mean of the monthly averages (allowing for differences in month length). To facilitate combining the baseline data with the UKCP09 climate projections, the 25 km baseline averages for rainfall have been expressed in units of millimetres per day (rather than total millimetres, as for the 5 km data sets).\r\n\r\nEach regional value is also an average of the 5 × 5 km grid cell values that fall within it. Monthly averages (12 values) have been calculated for each monthly variable and an annual average has been calculated for each annual variable. As with the 25 km data, the averages for rainfall have been expressed in units of millimetres per day." }, "procedureCompositeProcess": null, "imageDetails": [ 69 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 12420, "uuid": "25a32d835f7c4831b0c624dd992fb8f7", "short_code": "proj", "title": "Met Office UKCP09 Contribution Project", "abstract": "Met Office contribution to UKCP09 Project" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6023, 11484, 11485, 11486, 21974, 51188, 51189, 52664, 52665, 52667, 52668, 54994, 54996, 54997, 55539, 55540, 55541, 55542, 55543, 55544, 55545, 55546, 55547, 55548, 55549, 55550, 62353, 79929, 79930, 79931, 79932 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 12422, "uuid": "87f43af9d02e42f483351d79b3d6162a", "short_code": "coll", "title": "UKCP09: Met Office gridded and regional land surface climate observation datasets", "abstract": "This collection contains datasets of climate variables derived from the network of UK land surface observations. The data have been interpolated from meteorological station data onto a uniform grid to provide complete and consistent coverage across the UK. The data sets cover the UK at 5 x 5 km resolution and span the period 1910 - 2015. They are available at daily, monthly and annual timescales, as well as long-term averages for the periods 1961 - 1990, 1971 - 2000, and 1981 - 2010. Baseline averages are also available at 25 x 25 km resolution to match the UKCP09 climate change projections. \r\n\r\nThe primary purpose of this data resource is to encourage and facilitate research into climate change impacts and adaptation. The datasets have been created by the Met Office with financial support from the Department for Environment, Food and Rural Affairs (Defra) and are promoted within the UK Climate Projections (UKCP09). The UKCP09 report The climate of the UK and recent trends uses these gridded data sets to describe UK climatoloagies and regional trends." } ], "responsiblepartyinfo_set": [ 47823, 47822, 47821, 47819, 47818, 47817, 102098, 105207, 47820, 102099 ], "onlineresource_set": [ 23665, 23666, 23667, 23668, 23669 ] }, { "ob_id": 12424, "uuid": "620f6ed379d543098be1126769111007", "title": "UKCP09: Met Office gridded land surface climate observations - long term averages at 5km resolution", "abstract": "This dataset contains a set of reference long term averages for observed climate variables on a 5km resolution grid. Where possible, reference climatologies are available for the periods 1961-1990, 1971-2000 and 1981-2010 for each variable. The availability of the variable for the given reference period was dependant on sufficient data being available for the variable for that period. \r\n\r\nThe data were derived from the Met Office gridded land surface climate observations - monthly variables at 5km resolution (see related dataset). The averages are calculated by averaging the 30 monthly or annual gridded datasets for each variable for each averaging period.\r\n\r\nThe data are provide in CF-1.5 compliant NetCDF format. The data are additionally provided in ESRI-ascii format, suitable for ingestion in GIS applications.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2017-08-17T13:57:45.129833", "updateFrequency": "notPlanned", "dataLineage": "Data were produced by the Met Office for the UKCP09 service and then prepared for archiving with Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "UKCP09, Met Office, DEFRA, land surface, climate observations", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2017-08-21T11:36:47", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 674, "bboxName": "UK OS Grid (UKCP grid)", "eastBoundLongitude": 4.41954, "westBoundLongitude": -8.56493, "southBoundLatitude": 49.53098, "northBoundLatitude": 61.11007 }, "verticalExtent": null, "result_field": { "ob_id": 12423, "dataPath": "/badc/ukcp09/data/gridded-land-obs/gridded-land-obs-averages-5km", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 361287165, "numberOfFiles": 639, "fileFormat": "The data are provide in CF-1.5 compliant NetCDF format. The data are additionally provided in ESRI-ascii format, suitable for ingestion in GIS applications." }, "timePeriod": { "ob_id": 6746, "startTime": "1960-01-01T00:00:00", "endTime": "2010-12-31T23:59:59" }, "resultQuality": { "ob_id": 3094, "explanation": "For details on data quality checks, grid accuracy etc. please see the linked \"UKCP09 Frequently Asked Questions\".", "passesTest": true, "resultTitle": "UKCP09 Gridded Land Observations Quality Info", "date": "2017-08-17" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 24942, "uuid": "01c5c729c64046af967a201b9652437a", "short_code": "comp", "title": "UKCP09 gridded land surface climate observations methodology", "abstract": "The gridded data sets are based on the archive of UK weather observations held at the Met Office. The density of the station network used varies through time, and for different climate variables — for example, for the temperature variables the number of stations rises from about 270 in 1914 to 600 in the mid-1990s, before falling to 450 in 2006.\r\n\r\nRegression and interpolation are used to generate values on a regular grid from the irregular station network, taking into account factors such as latitude and longitude, altitude and terrain shape, coastal influence, and urban land use. This alleviates the impact of station openings and closures on homogeneity, but the impacts of a changing station network cannot be removed entirely, especially in areas of complex topography or sparse station coverage. The methods used to generate the monthly and annual grids are described in more detail in a paper published in the International Journal of Climatology, vol. 25 (2005), pages 1,041-1,054, which can be downloaded here: Generation of monthly gridded data sets for a range of climatic variables over the UK (PDF, 590 kB). The methods used to generate the daily grids are described in more detail in the report 'The generation of the daily gridded data sets of temperature and rainfall for the UK' (PDF, 2.27 MB).\r\n\r\nThe 5 × 5 km grids of the 1961-1990 baseline climate average available on these webpages are simply calculated by averaging or summing the 30 monthly or annual gridded data sets for each variable. They are not the same as the 1 × 1 km data sets presented on our UK Climate pages. The methods used to generate the 1 × 1 km data sets for 30-year average periods, which were used to normalise the station data prior to the generation of monthly and annual grids, are described in a paper published in the International Journal of Climatology, vol. 25 (2005), pages 1,023-1,039, which can be downloaded here: Development of a new set of long-term averages for the UK\r\n(PDF, 1.08 MB)\r\n\r\nTo help users combine the 5 × 5 km baseline data sets with the UK Climate Projections, values of the 1961-1990 baseline climate average have also been generated for the 25 × 25 km grid boxes of the HadRM3 regional climate model and for administrative regions and river basins.\r\n\r\nEach 25 × 25 km grid box value is an average of the 5 × 5 km grid cell values that fall within it. Averages have been calculated for each month, season and the year as a whole (17 data sets). For the days of frost and days of rain variables the seasonal and annual averages are the total of the individual monthly averages. For the remaining variables the seasonal and annual averages are the mean of the monthly averages (allowing for differences in month length). To facilitate combining the baseline data with the UKCP09 climate projections, the 25 km baseline averages for rainfall have been expressed in units of millimetres per day (rather than total millimetres, as for the 5 km data sets).\r\n\r\nEach regional value is also an average of the 5 × 5 km grid cell values that fall within it. Monthly averages (12 values) have been calculated for each monthly variable and an annual average has been calculated for each annual variable. As with the 25 km data, the averages for rainfall have been expressed in units of millimetres per day." }, "procedureCompositeProcess": null, "imageDetails": [ 69 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 12420, "uuid": "25a32d835f7c4831b0c624dd992fb8f7", "short_code": "proj", "title": "Met Office UKCP09 Contribution Project", "abstract": "Met Office contribution to UKCP09 Project" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 1633, 3301, 3302, 6023, 11482, 11483, 11484, 11485, 11486, 11487, 11488, 11489, 11490, 11491, 11492, 11493, 11494, 11495, 11496, 11497, 11498, 11499, 11500, 11501, 11502, 11503, 11504, 11505, 11506, 11507, 11508, 11509, 11510, 11511, 11512, 11513, 11514, 11515, 11516, 11517, 21978, 25154 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 12422, "uuid": "87f43af9d02e42f483351d79b3d6162a", "short_code": "coll", "title": "UKCP09: Met Office gridded and regional land surface climate observation datasets", "abstract": "This collection contains datasets of climate variables derived from the network of UK land surface observations. The data have been interpolated from meteorological station data onto a uniform grid to provide complete and consistent coverage across the UK. The data sets cover the UK at 5 x 5 km resolution and span the period 1910 - 2015. They are available at daily, monthly and annual timescales, as well as long-term averages for the periods 1961 - 1990, 1971 - 2000, and 1981 - 2010. Baseline averages are also available at 25 x 25 km resolution to match the UKCP09 climate change projections. \r\n\r\nThe primary purpose of this data resource is to encourage and facilitate research into climate change impacts and adaptation. The datasets have been created by the Met Office with financial support from the Department for Environment, Food and Rural Affairs (Defra) and are promoted within the UK Climate Projections (UKCP09). The UKCP09 report The climate of the UK and recent trends uses these gridded data sets to describe UK climatoloagies and regional trends." } ], "responsiblepartyinfo_set": [ 47416, 47417, 47804, 47803, 47802, 47800, 47799, 102092, 47801, 102093 ], "onlineresource_set": [ 23654, 23655, 23656, 23657 ] }, { "ob_id": 12426, "uuid": "453ffa392f6b43299cbb59779eae4fe3", "title": "ACSOE OXICOA EASE-96: Airborne Atmospheric Oxidants Data from on-board the Cranfield Jetstream Research Aircraft", "abstract": "This data set is comprised of observations from Cranfield Jetstream Research Aircraft which carried three NOy monitors, an ozone monitor, a solarimeter, a gas chromatograph-flame ionisation detector and a temperature sensor off the eastern coast of Ireland to measure the vertical and horizontal homogeneity of the air mass for EASE-96.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "1999-04-21T10:55:56", "updateFrequency": "notPlanned", "dataLineage": "Data collected by instruments on-board the Cranfield Jetstream Research Aircraft. Data acquired by BADC for archiving as programme was underway.", "removedDataReason": "", "keywords": "ACSOE, OXICOA, EASE, Tropospheric Chemistry", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2007-03-24T21:44:49", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 635, "bboxName": "ACSOE EASE-96 Jetstream", "eastBoundLongitude": -8.9312, "westBoundLongitude": -10.8008, "southBoundLatitude": 53.0983, "northBoundLatitude": 53.527 }, "verticalExtent": null, "result_field": { "ob_id": 12432, "dataPath": "/badc/acsoe/data/ease-96/jetstream/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 4100061, "numberOfFiles": 27, "fileFormat": "The data files are NASA-Ames formatted." }, "timePeriod": { "ob_id": 3344, "startTime": "1996-07-15T23:00:00", "endTime": "1996-07-24T23:00:00" }, "resultQuality": { "ob_id": 908, "explanation": "Data files archived as is. Data quality information may be available within each data file.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2014-09-21" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 12430, "uuid": "b5547199b2364644abd6c513f45f7d9f", "short_code": "acq", "title": "Acquisition Process for: Atmospheric Oxidants Observations on-board Cranfield Jetstream Research Aircraft from the ACSOE OXICOA EASE 96 campaign", "abstract": "This acquisition is comprised of the following: INSTRUMENTS: " }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 39 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 12801, "uuid": "80d1282e07bd49d795df48f38c09b371", "short_code": "proj", "title": "ACSOE OXICOA Eastern Atlantic Spring/Summer Experiments (EASE)", "abstract": "The Atmospheric Chemistry Studies in the Oceanic Environment (ACSOE) OXIdising Capacity of the Ocean Atmosphere (OXICOA) Eastern Atlantic Spring/Summer Experiments (EASE) were carried out from the Mace Head Atmospheric Research Station, Ireland and from on-board the Cranfield Jetstream Research Aircraft. The objectives of the project were: to investigate the chemical production and loss mechanisms of atmospheric oxidants in the marine boundary layer; to determine the role of photochemically produced radical species in the ozone cycle in the marine boundary layer; to investigate the speciation of oxidised nitrogen and its consequences for long range transport and photochemical processing; to characterise the air masses arriving off the West coast of Ireland and determine whether trace gas composition is indicative of the source of the air mass; to quantify the extent of NOy chemistry in the marine atmosphere over the Atlantic Ocean; to investigate the extent of halogenation chemistry occurring over the Atlantic Ocean; and to study the extent of NO3 chemistry, particularly its impact on DMS and hydrocarbons. The experiments at the Mace Head Atmospheric Research Station focused on ozone, OH and nitrogen species active in the oxidant chemistry, while the Cranfield Jetstream Research Aircraft was used to characterise the vertical and horizontal homogeneity of the air mass in the troposphere." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 8403 ], "observationcollection_set": [ { "ob_id": 3636, "uuid": "c82574d742b6172e7abd7d01524eb22c", "short_code": "coll", "title": "ACSOE OXICOA EASE-96 and EASE-97: Airborne and Ground-based Measurements of Atmospheric Oxidants", "abstract": "The Atmospheric Chemistry Studies in the Oceanic Environment (ACSOE) was a 5-year Natural Environment Research Council (NERC) programme on tropospheric chemistry coordinated by the University of East Anglia and involving research groups from a number of UK universities and research institutes. The project had three consortia of UK institutes and universities, each of which focused on a different scientific topic. OXICOA (OXIdising Capacity of the Ocean Atmosphere) was a study of oxidant, radical and related gas-phase chemistry in the clean and moderately polluted marine atmosphere. The Eastern Atlantic Spring/Summer Experiments (EASE-96 and EASE-97) were carried to collect data. The dataset includes measurements of the OH and HO2 radicals, measurements of halogen oxide radicals at Mace Head in conjunction with a survey of potential organic halogen source gases. In EASE 96 the Cranfield Jetstream aircraft was deployed to measure the vertical and horizontal homogeneity of the air mass." } ], "responsiblepartyinfo_set": [ 47421, 47422, 47420, 47435, 47426, 105017, 47427, 47424, 47423, 50142, 47437, 50143, 47458 ], "onlineresource_set": [ 6941 ] }, { "ob_id": 12434, "uuid": "f4545eca3bf343a8acbeb4f71b1f2e72", "title": "MIPAS ENVISAT Level 1b data (July 2002 up to April 2012) IPF v7 data", "abstract": "Level 1b consolidated, near-real time and reprocessed data from the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) instrument on the ENVISAT satellite operated by ESA. These data are Instrument Processor Facility (IPF) version 7.1 data. Improvements with respect to the previous version 5.06 data include a new offset validation algorithm, addition of alignment matrix correction and scan mirror non-linearity correction in geolocation pointing, a new spike detection and correction algorithm, enabling of generation of Optimised Resolution (OR) products from Full Resolution (FR) products for the FR acquisition period, and an update of the format function for the number of sweeps per scan. Data are from 1st July 2002 to 8th April 2012.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2020-04-30T23:25:51", "updateFrequency": "notPlanned", "dataLineage": "Data acquired directly from ESA (European Space Agency) via a number of routes via FTP.", "removedDataReason": "", "keywords": "ATMOSPHERE EARTHSCIENCE SPECTRAL", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2015-08-17T13:03:22", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 1, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 12440, "dataPath": "/neodc/mipas/data/l1b/reprocessed/v7", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 8716668333364, "numberOfFiles": 35019, "fileFormat": "Data are binary formatted." }, "timePeriod": { "ob_id": 2440, "startTime": "2002-03-01T00:00:00", "endTime": "2012-04-07T23:00:00" }, "resultQuality": { "ob_id": 2251, "explanation": "Operational data.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2014-08-12" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 8542, "uuid": "38540b15ec714e46a2751696247d9256", "short_code": "acq", "title": "Acquisition Process for: ENVISAT MIPAS Level 1b consolidated, near-real time and reprocessed data (March 2002 - April 2012)", "abstract": "This acquisition is comprised of the following: INSTRUMENTS: MIPAS Level 1b data processing, Envisat - MIPAS (Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) Instrument; PLATFORMS: ESA ENVISAT Processing Ground Station; " }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2574, "accessConstraints": null, "accessCategory": "restricted", "accessRoles": "mipas", "label": "restricted: mipas group", "licence": { "ob_id": 41, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/mipas.pdf", "licenceClassifications": [ { "ob_id": 2, "classification": "unstated" } ] } } ], "projects": [ { "ob_id": 7819, "uuid": "dc4b92f73c225f5cff79f832699cea02", "short_code": "proj", "title": "ENVISAT", "abstract": "An ARIANE 5 rocket launched the ENVISAT satellite on the 1st March, 2002 at 02:07:59 (CET) hrs. The satellite follows in the footsteps of ERS 1 and 2 in its mission to provide support for research into global warming and climate change and in pollution and disaster monitoring. It provides complete coverage of the earth within one to three days. Its nine instruments deliver data about the Earth's land, oceans, atmosphere and ice caps." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6868, 26107 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 8536, "uuid": "4a9da084adf4252752e5fe77a5cfd0a9", "short_code": "coll", "title": "Emission Spectra Data and Derived Products from the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) on-board the European Space Agency (ESA) Envisat Satellite", "abstract": "The Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) is one of the ten instruments onboard the Envisat satellite launched from Kourou (French Guyana) on the 28th February 2002 and operated by the European Space Agency (ESA). MIPAS is a Fourier transform spectrometer for the measurement of high-resolution gaseous emission spectra at the Earth's limb. It operates in the near- to mid-infrared, where many of the main atmospheric trace gases have important emission features. Its main objectives are to provide simultaneous and global measurements of the middle atmosphere dynamics, radiation budget and chemical composition (O3, H2O, CH4, N2O, HNO3, NO2), and to monitor stratospheric ozone and CFCs.\r\n\r\nThe data issues by ESA include both Level 1B (emission spectra) and Level 2 (derived quantities) products. They come in a number of versions: a near real time (nrt) version, a consolidated version, result of a first processing by ESA to eliminate bad data and perform a few basic checks, and fully reprocessed spectra. MIPAS is a Fourier transform spectrometer for the measurement of high-resolution gaseous emission spectra at the Earth's limb. It operates in the near- to mid-infrared spectra. \r\n\r\nThe data routinely archived at the NEODC is received from ESA through different routes (DDS, FTP, tapes). The archive currently includes:\r\n\r\nLevel 1B data (emission spectra)\r\nNRT data - September 2002 - May 2011\r\nconsolidated data - June 2002 - January 2004\r\nreprocessed data - July 2002 - April 2012 - processing versions v4.x (2002-2010) and v5.x (2002-2012)\r\nLevel 2 data (derived products)\r\nconsolidated data - July 2002 - February 2004\r\nreprocessed data - July 2002 - March 2004" }, { "ob_id": 30129, "uuid": "3b0630c7fa264164868d4da5c9f90bed", "short_code": "coll", "title": "National Centre for Earth Observation (NCEO) Third Party Data", "abstract": "The National Centre for Earth Observation (NCEO) Third Party data contains a broad range remotely sensed data acquired by satellite for use by the Earth Observation Scientific community supported by NCEO. The Centre for Environmental Data Analysis (CEDA) has archived and provides access to extensive Earth observation datasets under strict licensing conditions. Please see the individual dataset records for conditions of use." } ], "responsiblepartyinfo_set": [ 47514, 47515, 47513, 47517, 47518, 47519, 47520, 47516 ], "onlineresource_set": [ 5913, 5914, 5915 ] }, { "ob_id": 12435, "uuid": "d2adcb41c8944dc99c43e77c974c9330", "title": "MIPAS ENVISAT Level 1b data (July 2002 up to April 2012) IPF v5 data", "abstract": "Level 1b consolidated, near-real time and reprocessed data from the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) instrument on the ENVISAT satellite operated by ESA. These data are Instrument Processor Facility (IPF) version 5 data. Original release is version 5.02 with bug fixes implemented in versions 5.05 and 5.06. Improvements with respect to the previous version 4.67 data include trunctation of the interferogram at 8 cm to avoid undersampling for the Optimised Resolution (OR) mission, improved engineering heights calculation, calculation of quadratic terms for spectral calibration included in output products, and addition of L0 house keeping data packets. Data are from 1st July 2002 to 8th April 2012.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2020-04-30T23:24:36", "updateFrequency": "notPlanned", "dataLineage": "Data acquired directly from ESA (European Space Agency) via File Transfer Protocol.", "removedDataReason": "", "keywords": "ATMOSPHERE EARTHSCIENCE SPECTRAL", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2015-08-17T13:02:57", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 1, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 12441, "dataPath": "/neodc/mipas/data/l1b/reprocessed/v5", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 8870286687158, "numberOfFiles": 71477, "fileFormat": "Data are binary formatted." }, "timePeriod": { "ob_id": 2440, "startTime": "2002-03-01T00:00:00", "endTime": "2012-04-07T23:00:00" }, "resultQuality": { "ob_id": 2251, "explanation": "Operational data.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2014-08-12" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 8542, "uuid": "38540b15ec714e46a2751696247d9256", "short_code": "acq", "title": "Acquisition Process for: ENVISAT MIPAS Level 1b consolidated, near-real time and reprocessed data (March 2002 - April 2012)", "abstract": "This acquisition is comprised of the following: INSTRUMENTS: MIPAS Level 1b data processing, Envisat - MIPAS (Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) Instrument; PLATFORMS: ESA ENVISAT Processing Ground Station; " }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2574, "accessConstraints": null, "accessCategory": "restricted", "accessRoles": "mipas", "label": "restricted: mipas group", "licence": { "ob_id": 41, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/mipas.pdf", "licenceClassifications": [ { "ob_id": 2, "classification": "unstated" } ] } } ], "projects": [ { "ob_id": 7819, "uuid": "dc4b92f73c225f5cff79f832699cea02", "short_code": "proj", "title": "ENVISAT", "abstract": "An ARIANE 5 rocket launched the ENVISAT satellite on the 1st March, 2002 at 02:07:59 (CET) hrs. The satellite follows in the footsteps of ERS 1 and 2 in its mission to provide support for research into global warming and climate change and in pollution and disaster monitoring. It provides complete coverage of the earth within one to three days. Its nine instruments deliver data about the Earth's land, oceans, atmosphere and ice caps." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 25878, 26107 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 8536, "uuid": "4a9da084adf4252752e5fe77a5cfd0a9", "short_code": "coll", "title": "Emission Spectra Data and Derived Products from the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) on-board the European Space Agency (ESA) Envisat Satellite", "abstract": "The Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) is one of the ten instruments onboard the Envisat satellite launched from Kourou (French Guyana) on the 28th February 2002 and operated by the European Space Agency (ESA). MIPAS is a Fourier transform spectrometer for the measurement of high-resolution gaseous emission spectra at the Earth's limb. It operates in the near- to mid-infrared, where many of the main atmospheric trace gases have important emission features. Its main objectives are to provide simultaneous and global measurements of the middle atmosphere dynamics, radiation budget and chemical composition (O3, H2O, CH4, N2O, HNO3, NO2), and to monitor stratospheric ozone and CFCs.\r\n\r\nThe data issues by ESA include both Level 1B (emission spectra) and Level 2 (derived quantities) products. They come in a number of versions: a near real time (nrt) version, a consolidated version, result of a first processing by ESA to eliminate bad data and perform a few basic checks, and fully reprocessed spectra. MIPAS is a Fourier transform spectrometer for the measurement of high-resolution gaseous emission spectra at the Earth's limb. It operates in the near- to mid-infrared spectra. \r\n\r\nThe data routinely archived at the NEODC is received from ESA through different routes (DDS, FTP, tapes). The archive currently includes:\r\n\r\nLevel 1B data (emission spectra)\r\nNRT data - September 2002 - May 2011\r\nconsolidated data - June 2002 - January 2004\r\nreprocessed data - July 2002 - April 2012 - processing versions v4.x (2002-2010) and v5.x (2002-2012)\r\nLevel 2 data (derived products)\r\nconsolidated data - July 2002 - February 2004\r\nreprocessed data - July 2002 - March 2004" }, { "ob_id": 30129, "uuid": "3b0630c7fa264164868d4da5c9f90bed", "short_code": "coll", "title": "National Centre for Earth Observation (NCEO) Third Party Data", "abstract": "The National Centre for Earth Observation (NCEO) Third Party data contains a broad range remotely sensed data acquired by satellite for use by the Earth Observation Scientific community supported by NCEO. The Centre for Environmental Data Analysis (CEDA) has archived and provides access to extensive Earth observation datasets under strict licensing conditions. Please see the individual dataset records for conditions of use." } ], "responsiblepartyinfo_set": [ 47522, 47523, 47521, 47525, 47526, 47527, 47528, 47524 ], "onlineresource_set": [ 5910, 5911, 5912 ] }, { "ob_id": 12436, "uuid": "0c9e286726bd46eca52d9dc39245fcb3", "title": "MIPAS ENVISAT Level 1b data (July 2002 up to April 2012) IPF v4 data", "abstract": "Level 1b consolidated, near-real time and reprocessed data from the Michelson Interferometer for Passive Atmospheric Sounding instrument on the ENVISAT satellite operated by ESA. These data are Instrument Processor Facility (IPF) version 4 data. Latest release is IPF v4.67. Data are from 1st July 2002 to 8th April 2012.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2020-04-30T23:52:05", "updateFrequency": "notPlanned", "dataLineage": "Data acquired directly from ESA (European Space Agency) via File Transfer Protocol.", "removedDataReason": "", "keywords": "ATMOSPHERE EARTHSCIENCE SPECTRAL", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2015-08-17T13:01:56", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 1, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 12442, "dataPath": "/neodc/mipas/data/l1b/reprocessed/v4", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 5407072942192, "numberOfFiles": 35030, "fileFormat": "Data are binary formatted." }, "timePeriod": { "ob_id": 2440, "startTime": "2002-03-01T00:00:00", "endTime": "2012-04-07T23:00:00" }, "resultQuality": { "ob_id": 2251, "explanation": "Operational data.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2014-08-12" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 8542, "uuid": "38540b15ec714e46a2751696247d9256", "short_code": "acq", "title": "Acquisition Process for: ENVISAT MIPAS Level 1b consolidated, near-real time and reprocessed data (March 2002 - April 2012)", "abstract": "This acquisition is comprised of the following: INSTRUMENTS: MIPAS Level 1b data processing, Envisat - MIPAS (Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) Instrument; PLATFORMS: ESA ENVISAT Processing Ground Station; " }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2574, "accessConstraints": null, "accessCategory": "restricted", "accessRoles": "mipas", "label": "restricted: mipas group", "licence": { "ob_id": 41, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/mipas.pdf", "licenceClassifications": [ { "ob_id": 2, "classification": "unstated" } ] } } ], "projects": [ { "ob_id": 7819, "uuid": "dc4b92f73c225f5cff79f832699cea02", "short_code": "proj", "title": "ENVISAT", "abstract": "An ARIANE 5 rocket launched the ENVISAT satellite on the 1st March, 2002 at 02:07:59 (CET) hrs. The satellite follows in the footsteps of ERS 1 and 2 in its mission to provide support for research into global warming and climate change and in pollution and disaster monitoring. It provides complete coverage of the earth within one to three days. Its nine instruments deliver data about the Earth's land, oceans, atmosphere and ice caps." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 25878, 26107 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 8536, "uuid": "4a9da084adf4252752e5fe77a5cfd0a9", "short_code": "coll", "title": "Emission Spectra Data and Derived Products from the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) on-board the European Space Agency (ESA) Envisat Satellite", "abstract": "The Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) is one of the ten instruments onboard the Envisat satellite launched from Kourou (French Guyana) on the 28th February 2002 and operated by the European Space Agency (ESA). MIPAS is a Fourier transform spectrometer for the measurement of high-resolution gaseous emission spectra at the Earth's limb. It operates in the near- to mid-infrared, where many of the main atmospheric trace gases have important emission features. Its main objectives are to provide simultaneous and global measurements of the middle atmosphere dynamics, radiation budget and chemical composition (O3, H2O, CH4, N2O, HNO3, NO2), and to monitor stratospheric ozone and CFCs.\r\n\r\nThe data issues by ESA include both Level 1B (emission spectra) and Level 2 (derived quantities) products. They come in a number of versions: a near real time (nrt) version, a consolidated version, result of a first processing by ESA to eliminate bad data and perform a few basic checks, and fully reprocessed spectra. MIPAS is a Fourier transform spectrometer for the measurement of high-resolution gaseous emission spectra at the Earth's limb. It operates in the near- to mid-infrared spectra. \r\n\r\nThe data routinely archived at the NEODC is received from ESA through different routes (DDS, FTP, tapes). The archive currently includes:\r\n\r\nLevel 1B data (emission spectra)\r\nNRT data - September 2002 - May 2011\r\nconsolidated data - June 2002 - January 2004\r\nreprocessed data - July 2002 - April 2012 - processing versions v4.x (2002-2010) and v5.x (2002-2012)\r\nLevel 2 data (derived products)\r\nconsolidated data - July 2002 - February 2004\r\nreprocessed data - July 2002 - March 2004" }, { "ob_id": 30129, "uuid": "3b0630c7fa264164868d4da5c9f90bed", "short_code": "coll", "title": "National Centre for Earth Observation (NCEO) Third Party Data", "abstract": "The National Centre for Earth Observation (NCEO) Third Party data contains a broad range remotely sensed data acquired by satellite for use by the Earth Observation Scientific community supported by NCEO. The Centre for Environmental Data Analysis (CEDA) has archived and provides access to extensive Earth observation datasets under strict licensing conditions. Please see the individual dataset records for conditions of use." } ], "responsiblepartyinfo_set": [ 47530, 47531, 47529, 47534, 47535, 47536, 47533, 47532 ], "onlineresource_set": [ 5918, 5916, 5917 ] }, { "ob_id": 12439, "uuid": "781bb3981efa48dcae5e14546b312e4b", "title": "Met Office LIDARNET Aberporth Vaisala CL31 ceilometer cloud base and backscatter data", "abstract": "Cloud base and backscatter data from the Met Office's Aberporth Cl31 ceilometer located at Aberporth, mid-Wales. The Met Office's laser cloud base recorders network (LCBRs), or ceilometers, returns a range of products for use in forecasting and hazard detection. The backscatter profiles can allow detection of aerosol species such as volcanic ash where suitable instrumentation is deployed.\r\n", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2025-01-17T01:00:06", "updateFrequency": "daily", "dataLineage": "Data were collected by the Met Office before processed through the Met Office's Hermes system. A copy of these data were then delivered by the Met Office to CEDA for archiving.", "removedDataReason": "", "keywords": "Met Office, lidar, ceilometer, ash", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2015-07-28T13:43:11", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 636, "bboxName": "Aberporth airfield", "eastBoundLongitude": -4.565, "westBoundLongitude": -4.565, "southBoundLatitude": 52.136, "northBoundLatitude": 52.136 }, "verticalExtent": null, "result_field": { "ob_id": 12445, "dataPath": "/badc/ukmo-lidarnet/data/aberporth/vaisala-cl31", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 47832562517, "numberOfFiles": 87296, "fileFormat": "These data are netCDF formatted" }, "timePeriod": { "ob_id": 3347, "startTime": "2015-07-13T08:00:15", "endTime": null }, "resultQuality": null, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 12447, "uuid": "f43edea6af4143ea9e52b69e3822cd1a", "short_code": "acq", "title": "Vaisala Cl31 Ceilometer at Aberporth", "abstract": "Vaisala Cl31 Ceilometer deployed at Aberporth as part of the Met Office's LIDARNET network." }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 69 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "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" } ] } }, { "ob_id": 2533, "accessConstraints": null, "accessCategory": "restricted", "accessRoles": "ukmo_wx_gov", "label": "restricted: ukmo_wx_gov group", "licence": { "ob_id": 13, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/ukmo_agreement_gov.pdf", "licenceClassifications": [ { "ob_id": 5, "classification": "policy" } ] } } ], "projects": [ { "ob_id": 12429, "uuid": "9124ac0ba62d4c87990e0d5cd5c38f92", "short_code": "proj", "title": "Met Office LIDARNET system", "abstract": "Met Office operate a network of ceilometers from which they obtain both cloud base and backscatter information." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 1635, 1636, 4865, 8483, 9767, 9768, 9769, 9770, 9773, 9778, 9783, 9784, 9790, 11523, 11524, 11525, 11526, 11527, 11528, 11529, 11530 ], "vocabularyKeywords": [], "identifier_set": [ 8454 ], "observationcollection_set": [ { "ob_id": 10994, "uuid": "38a6e76871fca4c58d0f831e532bff41", "short_code": "coll", "title": "Met Office LIDARNET ceilometer network cloud base and backscatter data", "abstract": "Data from the Met Office's laser cloud base recorders network (LCBRs), or ceilometers, returns a range of products for use in forecasting and hazard detection. These include cloud base height and also backscatter profiles. The backscatter profiles can allow detection of aerosol species such as volcanic ash where suitable instrumentation is deployed. The Met Offices operational LIDARNET network consists of Vaisala CT25k and CL31 ceilometers and the Jenoptik CHM15k Nimbus. In addition, this dataset also contains data from the development ceilometers operated by the Met Office." } ], "responsiblepartyinfo_set": [ 47537, 47538, 47539, 47541, 47542, 47543, 47544, 47540 ], "onlineresource_set": [ 5919, 15758 ] }, { "ob_id": 12444, "uuid": "92d6475925224211b255fabe55161ea1", "title": "NCAS AMOF: Noctilucent Cloud (NLC) camera images from the Chilbolton Observatory, Hampshire", "abstract": "The 'ncas-cam5' camera is operated between dusk and dawn (typically at 5 minute intervals) during the mid-summer months (principally June and July) with the aim of observing Noctilucent Clouds for scientific study. It is located within the Receive Cabin of the Chilbolton Observatory, Hampshire, UK (51.145168°N,-1.4397500°E). It is approximately 4.3 m above the ground level, which is approximately 84 m above mean sea level. The field of view (taking the 1.6 crop factor of the camera into account) is 67.9° in the horizontal and 45.3° in the vertical. The centre of each image is directed towards an azimuth of approximately +15°. However, since the camera body is not locked in place, this value can vary by a few degrees (typically from one year to the next). The azimuth at which stars reach their lowest elevation corresponds to true north. This has been used to estimate an azimuth of +17.6° for where the mast in th e foreground crosses the skyline. The +0.4° elevation of the skyline has been measured with an inclinometer. Date-time information is derived from the image capture computer and given in Coordinated Universal Time (UTC).", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2015-07-23T13:07:33", "updateFrequency": "asNeeded", "dataLineage": "Data collected and processed by an NCAS AMOF instrument scientist before delivery to CEDA Archive for archiving.", "removedDataReason": "", "keywords": "NCAS, AMOF, NLC, noctilucent cloud, camera", "publicationState": "preview", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": null, "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 1649, "bboxName": "MST NLC field of view", "eastBoundLongitude": 9.75, "westBoundLongitude": -7.15, "southBoundLatitude": 51.15, "northBoundLatitude": 59.35 }, "verticalExtent": { "ob_id": 20, "highestLevelBound": 83.0, "lowestLevelBound": 0.0, "units": "km" }, "result_field": { "ob_id": 12443, "dataPath": "/badc/mst/data/nerc-mstrf-nlc-camera", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 936, "numberOfFiles": 1, "fileFormat": "Files are JPEG formatted" }, "timePeriod": { "ob_id": 5185, "startTime": "2012-06-07T10:17:11", "endTime": null }, "resultQuality": { "ob_id": 3048, "explanation": "Data are checked by the instrument scientist prior to delivery to CEDA for ingestion into the CEDA archives", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2016-07-21" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 19757, "uuid": "a8c18158f541431e8492d4cf0cc04153", "short_code": "acq", "title": "MST NLC Camera at Chilbolton", "abstract": "MST NLC Camera at Chilbolton" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 223 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2543, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 2, "licenceURL": "https://artefacts.ceda.ac.uk/licences/missing_licence.pdf", "licenceClassifications": [ { "ob_id": 2, "classification": "unstated" } ] } } ], "projects": [ { "ob_id": 25330, "uuid": "bda9d9df0f6d4fed86102c2479ce0a93", "short_code": "proj", "title": "NERC MST Radar Facility (MSTRF)", "abstract": "The Natural Environment Research Council's Mesosphere-Stratosphere-Troposphere (MST) Radar Facility at Capel Dewi, near Aberystwyth, Ceredigion, Mid-Wales (52.42°N, 4.01°W), is an atmospheric observatory site of several observation systems for meteorological studies, managed and operated by the RAL Space department of the STFC Rutherford Appleton Laboratory on behalf of the UK's environmental science community. The facility operates a range of instruments and hosts additional instrumentation from other institutes.\r\n\r\nThe principal instrument is the NERC Mesosphere-Stratosphere-Troposphere (MST) Radar, the UK's most powerful and most versatile wind-profiling instrument from which wind data are operationally assimilated by a number of European meteorological services (including the UK's Met Office) for the purposes of numerical weather prediction. The MSTRF also operates a number of auxiliary instruments, including those for measuring surface wind, temperature, pressure, humidity, and rainfall at the site as well as surface wind speed and direction at the nearby Fron Goch site and a noctilucent cloud camera located at the Chilbolton Observatory in Hampshire (to allow images of such clouds to be seen at similar latitudes as the MSTRF site).\r\n\r\nHosted instrumentation has varied over time, but has included or continues to include instruments operated by the University of Manchester Centre for Atmospheric Science, the Met Office, the National Centre for Atmospheric Science (NCAS) Atmospheric Measurement Facility (AMF) and the RAL SPace department of the STFC Rutherford Appleton Laboratory.\r\n\r\nThe facility makes its main data products open-access (i.e. freely available) through the Centre for Environmental Data Analysis (CEDA) to all registered users, and may additionally assist users to access, to analyse, and to interpret the data as well as producing bespoke data products. Enquiries for further information about additional support form the facility should be addresses directly to the facility manager. Data from other instruments hosted by the site may be available under alternative access conditions and may not necessarily be available through CEDA." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 6117, "uuid": "bd095d86e4a9f0c706b08058dbad3b31", "short_code": "coll", "title": "Natural Environment Research Council (NERC) Mesosphere-Stratosphere-Troposphere (MST) Radar Facility: surface and upper air meteorology measurements from Aberystwyth, mid-Wales", "abstract": "This collection contains data from the Natural Environment Research Council (NERC) Mesosphere-Stratosphere-Troposphere (MST) Radar Facility at Capel Dewi, near Aberystwyth in West Wales.\r\n\r\nThe principal measurements made by the MST radar, a 46.5 MHz pulsed Doppler radar), ideally suited for studied of atmospheric winds, waves and turbulence. It is run predominantly in the ST mode (approximately 2 - 20 km altitude) for which MST radars are unique in their ability to give continuous measurements of the three dimensional wind vector at high resolution (typically 2 - 3 minutes in time and 300 m in altitude). Under certain circumstances they can additionally provide information about humidity, static stability (thus allowing monitoring of the altitude and sharpness of the tropopause) and turbulence of at least moderate intensity. Surface meteorological measurements from the radar site, ceilometer data, sky camera images and wind speed and direction recorded from a 10m tower located at Frongoch (6km away) are also available. Other instruments at the facility have included one of the Met Office's boundary layer wind profilers and, on occasion, the NCAS's boundary layer wind profiler. \r\n\r\nThe collection also includes high-resolution radiosonde data from RAF Aberporth, approximately 45 km to the south-west of the radar site, are available for the period April 1990 - June 2000 and data from the RAL Cloud Radar. Facility data are available to all registered users under the UK Open Government License; Met Office data are available under the NERC-Met Office agreement and the RAL Cloud Radar is restricted to registered users at present.\r\n\r\n" } ], "responsiblepartyinfo_set": [ 47545, 47546, 47547, 74857, 74858, 74860, 104981, 74859 ], "onlineresource_set": [ 15735, 15957 ] }, { "ob_id": 12448, "uuid": "b1e2f90ef234469fa313b4fc3630581e", "title": "Met Office LIDARNET Northolt Vaisala CT25k ceilometer cloud base and backscatter data", "abstract": "Cloud base and backscatter data from the Met Office's Vaisala Ct25k ceilometer located at RAF Northolt, near Heathrow, southern England. The Met Office's laser cloud base recorders network (LCBRs), or ceilometers, returns a range of products for use in forecasting and hazard detection. The backscatter profiles can allow detection of aerosol species such as volcanic ash where suitable instrumentation is deployed.\r\n", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2020-05-29T21:14:55", "updateFrequency": "notPlanned", "dataLineage": "Data were collected by the Met Office before processed through the Met Office's Hermes system. A copy of these data were then delivered by the Met Office to CEDA for archiving.", "removedDataReason": "", "keywords": "Met Office, lidar, ceilometer, ash", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2015-07-28T13:45:44", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 637, "bboxName": "RAF Northolt", "eastBoundLongitude": -0.417, "westBoundLongitude": -0.417, "southBoundLatitude": 51.55, "northBoundLatitude": 51.55 }, "verticalExtent": null, "result_field": { "ob_id": 12449, "dataPath": "/badc/ukmo-lidarnet/data/northolt/vaisala-ct25k/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 4461485437, "numberOfFiles": 10674, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 3348, "startTime": "2015-07-13T08:00:15", "endTime": "2016-10-05T06:00:00" }, "resultQuality": null, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 12452, "uuid": "070e9ab352b048b5983780ce1ad24ffb", "short_code": "acq", "title": "Vaisala CT25k Ceilometer at RAF Northolt", "abstract": "Vaisala CT25k Ceilometer deployed at RAF Northolt as part of the Met Office's LIDARNET network." }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 69 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "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" } ] } }, { "ob_id": 2533, "accessConstraints": null, "accessCategory": "restricted", "accessRoles": "ukmo_wx_gov", "label": "restricted: ukmo_wx_gov group", "licence": { "ob_id": 13, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/ukmo_agreement_gov.pdf", "licenceClassifications": [ { "ob_id": 5, "classification": "policy" } ] } } ], "projects": [ { "ob_id": 12429, "uuid": "9124ac0ba62d4c87990e0d5cd5c38f92", "short_code": "proj", "title": "Met Office LIDARNET system", "abstract": "Met Office operate a network of ceilometers from which they obtain both cloud base and backscatter information." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 1635, 1636, 4865, 8483, 9767, 9768, 9769, 9770, 9773, 9778, 9783, 9784, 9790, 11523, 11524, 11526, 11527, 11529, 11734, 11735, 11736 ], "vocabularyKeywords": [], "identifier_set": [ 8453 ], "observationcollection_set": [ { "ob_id": 10994, "uuid": "38a6e76871fca4c58d0f831e532bff41", "short_code": "coll", "title": "Met Office LIDARNET ceilometer network cloud base and backscatter data", "abstract": "Data from the Met Office's laser cloud base recorders network (LCBRs), or ceilometers, returns a range of products for use in forecasting and hazard detection. These include cloud base height and also backscatter profiles. The backscatter profiles can allow detection of aerosol species such as volcanic ash where suitable instrumentation is deployed. The Met Offices operational LIDARNET network consists of Vaisala CT25k and CL31 ceilometers and the Jenoptik CHM15k Nimbus. In addition, this dataset also contains data from the development ceilometers operated by the Met Office." } ], "responsiblepartyinfo_set": [ 47553, 47554, 47555, 47557, 47558, 47559, 47560, 47556, 102590 ], "onlineresource_set": [ 5921, 15762 ] }, { "ob_id": 12487, "uuid": "319b3f878c7d4cbfbdb356e19d8061d6", "title": "UKCP09: Met Office gridded land surface climate observations - daily temperature and precipitation at 5km resolution", "abstract": "This dataset contains a set of daily observations of temperature (daily maximum, daily minimum and daily mean temperature) and rainfall (24 hour accumulation) interpolated to a uniform 5km grid resolution covering the period 1960 to 2014.\r\n\r\nThe input station data originate from the Met Office Integrated Data Archive System - MIDAS - a database at the Met Office of observation station data stretching back to the 18th century. (A version of MIDAS is also available through CEDA, although incremental developments to the database such as quality control and data recovery activities may result in some differences compared to the database at the time of production of the UKCP09 data - see linked datasets for access to these equivalent datasets held by CEDA).\r\n \r\nThe input station data used provide observations relating to periods 0900 to 0900 UTC, so the gridded output stored against day \"dd\" are as follows:\r\n•\tMaximum temperature between 0900 on day dd and 0900 on day dd+1 (normally expect to occur during the afternoon of day dd)\r\n•\tMinimum temperature between 0900 on day dd-1 and 0900 on day dd (normally expect to occur just before dawn on day dd)\r\n•\tMean temperature that is the average of the maximum and minimum temperature\r\n•\tRainfall (or rainfall equivalent in cases of frozen precipitation) amount between 0900 day dd and 0900 day dd+1\r\n\r\nThe gridding process accounts for effects such as latitude, longitude, altitude, coastal influence, and the effect of urban land through the use of normalisation with respect to monthly 1961 – 1990 climate normals, and in the case of temperature, a regression model.\r\n\r\nThe data are provide in CF-1.5 compliant NetCDF format. The data are additionally provided in ESRI-ascii format, suitable for ingestion in GIS applications, and a simple timeseries format for users requiring a limited number of points.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2020-01-13T10:33:20", "updateFrequency": "notPlanned", "dataLineage": "Data were produced by the Met Office for the UKCP09 service and then prepared for archiving with Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "UKCP09, Met Office, land surface, climate observations", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2017-08-21T11:10:21", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 674, "bboxName": "UK OS Grid (UKCP grid)", "eastBoundLongitude": 4.41954, "westBoundLongitude": -8.56493, "southBoundLatitude": 49.53098, "northBoundLatitude": 61.11007 }, "verticalExtent": null, "result_field": { "ob_id": 12488, "dataPath": "/badc/ukcp09/data/gridded-land-obs/gridded-land-obs-daily", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 67319706309, "numberOfFiles": 84921, "fileFormat": "The data are provide in CF-1.5 compliant NetCDF format. 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The density of the station network used varies through time, and for different climate variables — for example, for the temperature variables the number of stations rises from about 270 in 1914 to 600 in the mid-1990s, before falling to 450 in 2006.\r\n\r\nRegression and interpolation are used to generate values on a regular grid from the irregular station network, taking into account factors such as latitude and longitude, altitude and terrain shape, coastal influence, and urban land use. This alleviates the impact of station openings and closures on homogeneity, but the impacts of a changing station network cannot be removed entirely, especially in areas of complex topography or sparse station coverage. 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For the days of frost and days of rain variables the seasonal and annual averages are the total of the individual monthly averages. For the remaining variables the seasonal and annual averages are the mean of the monthly averages (allowing for differences in month length). To facilitate combining the baseline data with the UKCP09 climate projections, the 25 km baseline averages for rainfall have been expressed in units of millimetres per day (rather than total millimetres, as for the 5 km data sets).\r\n\r\nEach regional value is also an average of the 5 × 5 km grid cell values that fall within it. Monthly averages (12 values) have been calculated for each monthly variable and an annual average has been calculated for each annual variable. 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The data have been interpolated from meteorological station data onto a uniform grid to provide complete and consistent coverage across the UK. The data sets cover the UK at 5 x 5 km resolution and span the period 1910 - 2015. They are available at daily, monthly and annual timescales, as well as long-term averages for the periods 1961 - 1990, 1971 - 2000, and 1981 - 2010. Baseline averages are also available at 25 x 25 km resolution to match the UKCP09 climate change projections. \r\n\r\nThe primary purpose of this data resource is to encourage and facilitate research into climate change impacts and adaptation. The datasets have been created by the Met Office with financial support from the Department for Environment, Food and Rural Affairs (Defra) and are promoted within the UK Climate Projections (UKCP09). 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The density of the station network used varies through time, and for different climate variables — for example, for the temperature variables the number of stations rises from about 270 in 1914 to 600 in the mid-1990s, before falling to 450 in 2006.\r\n\r\nRegression and interpolation are used to generate values on a regular grid from the irregular station network, taking into account factors such as latitude and longitude, altitude and terrain shape, coastal influence, and urban land use. This alleviates the impact of station openings and closures on homogeneity, but the impacts of a changing station network cannot be removed entirely, especially in areas of complex topography or sparse station coverage. 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The methods used to generate the 1 × 1 km data sets for 30-year average periods, which were used to normalise the station data prior to the generation of monthly and annual grids, are described in a paper published in the International Journal of Climatology, vol. 25 (2005), pages 1,023-1,039, which can be downloaded here: Development of a new set of long-term averages for the UK\r\n(PDF, 1.08 MB)\r\n\r\nTo help users combine the 5 × 5 km baseline data sets with the UK Climate Projections, values of the 1961-1990 baseline climate average have also been generated for the 25 × 25 km grid boxes of the HadRM3 regional climate model and for administrative regions and river basins.\r\n\r\nEach 25 × 25 km grid box value is an average of the 5 × 5 km grid cell values that fall within it. Averages have been calculated for each month, season and the year as a whole (17 data sets). 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The data have been interpolated from meteorological station data onto a uniform grid to provide complete and consistent coverage across the UK. The data sets cover the UK at 5 x 5 km resolution and span the period 1910 - 2015. They are available at daily, monthly and annual timescales, as well as long-term averages for the periods 1961 - 1990, 1971 - 2000, and 1981 - 2010. Baseline averages are also available at 25 x 25 km resolution to match the UKCP09 climate change projections. \r\n\r\nThe primary purpose of this data resource is to encourage and facilitate research into climate change impacts and adaptation. The datasets have been created by the Met Office with financial support from the Department for Environment, Food and Rural Affairs (Defra) and are promoted within the UK Climate Projections (UKCP09). 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The methods used to generate the 1 × 1 km data sets for 30-year average periods, which were used to normalise the station data prior to the generation of monthly and annual grids, are described in a paper published in the International Journal of Climatology, vol. 25 (2005), pages 1,023-1,039, which can be downloaded here: Development of a new set of long-term averages for the UK\r\n(PDF, 1.08 MB)\r\n\r\nTo help users combine the 5 × 5 km baseline data sets with the UK Climate Projections, values of the 1961-1990 baseline climate average have also been generated for the 25 × 25 km grid boxes of the HadRM3 regional climate model and for administrative regions and river basins.\r\n\r\nEach 25 × 25 km grid box value is an average of the 5 × 5 km grid cell values that fall within it. Averages have been calculated for each month, season and the year as a whole (17 data sets). For the days of frost and days of rain variables the seasonal and annual averages are the total of the individual monthly averages. For the remaining variables the seasonal and annual averages are the mean of the monthly averages (allowing for differences in month length). To facilitate combining the baseline data with the UKCP09 climate projections, the 25 km baseline averages for rainfall have been expressed in units of millimetres per day (rather than total millimetres, as for the 5 km data sets).\r\n\r\nEach regional value is also an average of the 5 × 5 km grid cell values that fall within it. Monthly averages (12 values) have been calculated for each monthly variable and an annual average has been calculated for each annual variable. 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The data have been interpolated from meteorological station data onto a uniform grid to provide complete and consistent coverage across the UK. The data sets cover the UK at 5 x 5 km resolution and span the period 1910 - 2015. They are available at daily, monthly and annual timescales, as well as long-term averages for the periods 1961 - 1990, 1971 - 2000, and 1981 - 2010. Baseline averages are also available at 25 x 25 km resolution to match the UKCP09 climate change projections. \r\n\r\nThe primary purpose of this data resource is to encourage and facilitate research into climate change impacts and adaptation. The datasets have been created by the Met Office with financial support from the Department for Environment, Food and Rural Affairs (Defra) and are promoted within the UK Climate Projections (UKCP09). The UKCP09 report The climate of the UK and recent trends uses these gridded data sets to describe UK climatoloagies and regional trends." } ], "responsiblepartyinfo_set": [ 47854, 47853, 47852, 47850, 47849, 47848, 47847, 102094, 47851, 102095 ], "onlineresource_set": [ 16750, 23658, 23659, 23660 ] }, { "ob_id": 12493, "uuid": "51132aea5ed0433ca338d32a912e3976", "title": "UKCP09: Met Office regional land surface climate observations - long term averages for administrative regions and river basins", "abstract": "This dataset contains a set of reference long term averages for observed climate variables that have been spatially averaged over a set of 14 administrative regions and 23 river basins across the British Isles (see links to maps and lists below). Where possible, reference climatologies for each of the periods 1961-1990, 1971-2000 and 1981-2010 are available for each variable. The availability of a variable for a given reference period was dependant on sufficient data for that variable within the reference period. Each regional value is an average of the 5km x 5km grid cell values that fall within it. \r\n\r\nThe data were derived from the Met Office gridded land surface climate observations – long term averages at 5km resolution (See related dataset) and are provided as space delimited text files.\r\n\r\nThe 14 administrative regions used within UKCP09 were:\r\n- East Midlands \r\n- East of England \r\n- East Scotland \r\n- London \r\n- North East England \r\n- North Scotland \r\n- North West England \r\n- Northern Ireland \r\n- South East England \r\n- South West England \r\n- Wales \r\n- West Midlands \r\n- West Scotland \r\n- Yorkshire & The Humber \r\n\r\nThe 23 river basin regions used within UKCP09 were: \r\n- Anglian \r\n- Argyll \r\n- Clyde \r\n- Dee \r\n- Forth \r\n- Humber \r\n- Neagh Bann \r\n- North East Scotland \r\n- North Eastern Ireland \r\n- North Highland \r\n- North West England \r\n- North Western Ireland \r\n- Northumbria \r\n- Orkney and Shetland \r\n- Severn \r\n- Solway \r\n- South East England \r\n- South West England \r\n- Tay \r\n- Thames \r\n- Tweed \r\n- West Highland \r\n- Western Wales", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2017-08-17T16:07:15.442123", "updateFrequency": "notPlanned", "dataLineage": "Data were produced by the Met Office for the UKCP09 service and then prepared for archiving with Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "UKCP09, Met Office, DEFRA, land surface, climate observations", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2017-08-21T11:41:06", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 674, "bboxName": "UK OS Grid (UKCP grid)", "eastBoundLongitude": 4.41954, "westBoundLongitude": -8.56493, "southBoundLatitude": 49.53098, "northBoundLatitude": 61.11007 }, "verticalExtent": null, "result_field": { "ob_id": 12494, "dataPath": "/badc/ukcp09/data/gridded-land-obs/gridded-land-obs-regional-averages", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 297955, "numberOfFiles": 79, "fileFormat": "Data are provided in the original ASCII format used within the UKCP09 project. Please see linked documentation for further details." }, "timePeriod": { "ob_id": 6747, "startTime": "1961-01-01T00:00:00", "endTime": "2010-12-31T23:59:59" }, "resultQuality": { "ob_id": 3094, "explanation": "For details on data quality checks, grid accuracy etc. please see the linked \"UKCP09 Frequently Asked Questions\".", "passesTest": true, "resultTitle": "UKCP09 Gridded Land Observations Quality Info", "date": "2017-08-17" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 24942, "uuid": "01c5c729c64046af967a201b9652437a", "short_code": "comp", "title": "UKCP09 gridded land surface climate observations methodology", "abstract": "The gridded data sets are based on the archive of UK weather observations held at the Met Office. The density of the station network used varies through time, and for different climate variables — for example, for the temperature variables the number of stations rises from about 270 in 1914 to 600 in the mid-1990s, before falling to 450 in 2006.\r\n\r\nRegression and interpolation are used to generate values on a regular grid from the irregular station network, taking into account factors such as latitude and longitude, altitude and terrain shape, coastal influence, and urban land use. This alleviates the impact of station openings and closures on homogeneity, but the impacts of a changing station network cannot be removed entirely, especially in areas of complex topography or sparse station coverage. The methods used to generate the monthly and annual grids are described in more detail in a paper published in the International Journal of Climatology, vol. 25 (2005), pages 1,041-1,054, which can be downloaded here: Generation of monthly gridded data sets for a range of climatic variables over the UK (PDF, 590 kB). The methods used to generate the daily grids are described in more detail in the report 'The generation of the daily gridded data sets of temperature and rainfall for the UK' (PDF, 2.27 MB).\r\n\r\nThe 5 × 5 km grids of the 1961-1990 baseline climate average available on these webpages are simply calculated by averaging or summing the 30 monthly or annual gridded data sets for each variable. They are not the same as the 1 × 1 km data sets presented on our UK Climate pages. The methods used to generate the 1 × 1 km data sets for 30-year average periods, which were used to normalise the station data prior to the generation of monthly and annual grids, are described in a paper published in the International Journal of Climatology, vol. 25 (2005), pages 1,023-1,039, which can be downloaded here: Development of a new set of long-term averages for the UK\r\n(PDF, 1.08 MB)\r\n\r\nTo help users combine the 5 × 5 km baseline data sets with the UK Climate Projections, values of the 1961-1990 baseline climate average have also been generated for the 25 × 25 km grid boxes of the HadRM3 regional climate model and for administrative regions and river basins.\r\n\r\nEach 25 × 25 km grid box value is an average of the 5 × 5 km grid cell values that fall within it. Averages have been calculated for each month, season and the year as a whole (17 data sets). 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The data have been interpolated from meteorological station data onto a uniform grid to provide complete and consistent coverage across the UK. The data sets cover the UK at 5 x 5 km resolution and span the period 1910 - 2015. They are available at daily, monthly and annual timescales, as well as long-term averages for the periods 1961 - 1990, 1971 - 2000, and 1981 - 2010. Baseline averages are also available at 25 x 25 km resolution to match the UKCP09 climate change projections. \r\n\r\nThe primary purpose of this data resource is to encourage and facilitate research into climate change impacts and adaptation. The datasets have been created by the Met Office with financial support from the Department for Environment, Food and Rural Affairs (Defra) and are promoted within the UK Climate Projections (UKCP09). 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The data is of use for those interested in historical burned patterns, fire management and emissions analysis and climate change research, by providing a consistent burned area time series. \r\n\r\nCurrent datasets consist of maps of global burned area for the years 1982 to 2019. Products are available at different spatial resolutions: the Pixel product (at the original resolution of the sensor data) and the Grid product (0.25 degrees resolution), the latter of which is produced from the Pixel product. They are based upon spectral information from different sensors, and in many cases also thermal information from active fires.\r\n\r\nGlobal products: \r\n\r\nFireCCI41: Medium Resolution Imaging Spectrometer (MERIS) reflectance, on board the ENVISAT ESA satellite, 300m spatial resolution, and MODIS active fires. 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