Related Observation Info List
Get a list of RelatedObservationInfo objects.
GET /api/v3/relatedobservationinfos/?format=api&offset=800
{ "count": 1153, "next": "https://api.catalogue.ceda.ac.uk/api/v3/relatedobservationinfos/?format=api&limit=100&offset=900", "previous": "https://api.catalogue.ceda.ac.uk/api/v3/relatedobservationinfos/?format=api&limit=100&offset=700", "results": [ { "ob_id": 861, "relationType": "IsSupplementTo", "subjectObservation": { "ob_id": 41268, "uuid": "6285564c34a246fc9ba5ce053d85e5e7", "short_code": "ob", "title": "ACSIS: Merged airborne chemistry data from instruments on board the FAAM aircraft", "abstract": "A collection of collated chemistry measurements made onboard the FAAM BAE-146 aircraft for The North Atlantic Climate System Integrated Study: ACSIS project. These data were collected by core and non-core instruments and compiled into a merged dataset. \r\n\r\nFlights included are from the measurement campaigns: ACSIS1 - February 2017, ACSIS2 - October 2017, ACSIS3 - May 2018, ACSIS4 - February 2019, ACSIS5 - August 2019, ACSIS6 - February 2020 and ACSIS7 - May 2022. Parameters include Carbon Dioxide (CO2), Methane (CH4), Nitric oxide (NO), Nitric dioxide (NO2), Total Organic Aerosol, Sulphate Aerosol, Nitrate Aerosol, Ammonium Aerosol, Chloride Aerosol, Urea (CH4N2O), Hydrogen cyanide (HCN), Nitryl chloride (ClNO2), and photolysis rates." }, "objectObservation": { "ob_id": 25493, "uuid": "8434b678f8844f14adbb6e7cba3501c9", "short_code": "ob", "title": "FAAM C066 ACSIS Transit flight: Airborne atmospheric measurements from core and non-core instrument suites on board the BAE-146 aircraft", "abstract": "Airborne atmospheric measurements from core and non-core instrument suites data on board the FAAM BAE-146 aircraft collected for The North Atlantic Climate System Integrated Study: ACSIS project." } }, { "ob_id": 862, "relationType": "IsSupplementTo", "subjectObservation": { "ob_id": 41268, "uuid": "6285564c34a246fc9ba5ce053d85e5e7", "short_code": "ob", "title": "ACSIS: Merged airborne chemistry data from instruments on board the FAAM aircraft", "abstract": "A collection of collated chemistry measurements made onboard the FAAM BAE-146 aircraft for The North Atlantic Climate System Integrated Study: ACSIS project. These data were collected by core and non-core instruments and compiled into a merged dataset. \r\n\r\nFlights included are from the measurement campaigns: ACSIS1 - February 2017, ACSIS2 - October 2017, ACSIS3 - May 2018, ACSIS4 - February 2019, ACSIS5 - August 2019, ACSIS6 - February 2020 and ACSIS7 - May 2022. Parameters include Carbon Dioxide (CO2), Methane (CH4), Nitric oxide (NO), Nitric dioxide (NO2), Total Organic Aerosol, Sulphate Aerosol, Nitrate Aerosol, Ammonium Aerosol, Chloride Aerosol, Urea (CH4N2O), Hydrogen cyanide (HCN), Nitryl chloride (ClNO2), and photolysis rates." }, "objectObservation": { "ob_id": 25502, "uuid": "5b937be3db27419197336ccac0f087ee", "short_code": "ob", "title": "FAAM C067 ACSIS flight: Airborne atmospheric measurements from core and non-core instrument suites on board the BAE-146 aircraft", "abstract": "Airborne atmospheric measurements from core and non-core instrument suites data on board the FAAM BAE-146 aircraft collected for The North Atlantic Climate System Integrated Study: ACSIS project." } }, { "ob_id": 863, "relationType": "IsSupplementTo", "subjectObservation": { "ob_id": 41268, "uuid": "6285564c34a246fc9ba5ce053d85e5e7", "short_code": "ob", "title": "ACSIS: Merged airborne chemistry data from instruments on board the FAAM aircraft", "abstract": "A collection of collated chemistry measurements made onboard the FAAM BAE-146 aircraft for The North Atlantic Climate System Integrated Study: ACSIS project. These data were collected by core and non-core instruments and compiled into a merged dataset. \r\n\r\nFlights included are from the measurement campaigns: ACSIS1 - February 2017, ACSIS2 - October 2017, ACSIS3 - May 2018, ACSIS4 - February 2019, ACSIS5 - August 2019, ACSIS6 - February 2020 and ACSIS7 - May 2022. Parameters include Carbon Dioxide (CO2), Methane (CH4), Nitric oxide (NO), Nitric dioxide (NO2), Total Organic Aerosol, Sulphate Aerosol, Nitrate Aerosol, Ammonium Aerosol, Chloride Aerosol, Urea (CH4N2O), Hydrogen cyanide (HCN), Nitryl chloride (ClNO2), and photolysis rates." }, "objectObservation": { "ob_id": 25498, "uuid": "3450271614774081bb538de48618bb9e", "short_code": "ob", "title": "FAAM C068 ACSIS flight: Airborne atmospheric measurements from core and non-core instrument suites on board the BAE-146 aircraft", "abstract": "Airborne atmospheric measurements from core and non-core instrument suites data on board the FAAM BAE-146 aircraft collected for The North Atlantic Climate System Integrated Study: ACSIS project." } }, { "ob_id": 864, "relationType": "IsSupplementTo", "subjectObservation": { "ob_id": 41268, "uuid": "6285564c34a246fc9ba5ce053d85e5e7", "short_code": "ob", "title": "ACSIS: Merged airborne chemistry data from instruments on board the FAAM aircraft", "abstract": "A collection of collated chemistry measurements made onboard the FAAM BAE-146 aircraft for The North Atlantic Climate System Integrated Study: ACSIS project. These data were collected by core and non-core instruments and compiled into a merged dataset. \r\n\r\nFlights included are from the measurement campaigns: ACSIS1 - February 2017, ACSIS2 - October 2017, ACSIS3 - May 2018, ACSIS4 - February 2019, ACSIS5 - August 2019, ACSIS6 - February 2020 and ACSIS7 - May 2022. Parameters include Carbon Dioxide (CO2), Methane (CH4), Nitric oxide (NO), Nitric dioxide (NO2), Total Organic Aerosol, Sulphate Aerosol, Nitrate Aerosol, Ammonium Aerosol, Chloride Aerosol, Urea (CH4N2O), Hydrogen cyanide (HCN), Nitryl chloride (ClNO2), and photolysis rates." }, "objectObservation": { "ob_id": 25514, "uuid": "eb136b5006a34b2693187e5e5d7aceaf", "short_code": "ob", "title": "FAAM C070 ACSIS flight: Airborne atmospheric measurements from core and non-core instrument suites on board the BAE-146 aircraft", "abstract": "Airborne atmospheric measurements from core and non-core instrument suites data on board the FAAM BAE-146 aircraft collected for The North Atlantic Climate System Integrated Study: ACSIS project." } }, { "ob_id": 865, "relationType": "IsSupplementTo", "subjectObservation": { "ob_id": 41268, "uuid": "6285564c34a246fc9ba5ce053d85e5e7", "short_code": "ob", "title": "ACSIS: Merged airborne chemistry data from instruments on board the FAAM aircraft", "abstract": "A collection of collated chemistry measurements made onboard the FAAM BAE-146 aircraft for The North Atlantic Climate System Integrated Study: ACSIS project. These data were collected by core and non-core instruments and compiled into a merged dataset. \r\n\r\nFlights included are from the measurement campaigns: ACSIS1 - February 2017, ACSIS2 - October 2017, ACSIS3 - May 2018, ACSIS4 - February 2019, ACSIS5 - August 2019, ACSIS6 - February 2020 and ACSIS7 - May 2022. Parameters include Carbon Dioxide (CO2), Methane (CH4), Nitric oxide (NO), Nitric dioxide (NO2), Total Organic Aerosol, Sulphate Aerosol, Nitrate Aerosol, Ammonium Aerosol, Chloride Aerosol, Urea (CH4N2O), Hydrogen cyanide (HCN), Nitryl chloride (ClNO2), and photolysis rates." }, "objectObservation": { "ob_id": 25518, "uuid": "677ca252ac8d44468bb473316c578ec7", "short_code": "ob", "title": "FAAM C071 ACSIS flight: Airborne atmospheric measurements from core and non-core instrument suites on board the BAE-146 aircraft", "abstract": "Airborne atmospheric measurements from core and non-core instrument suites data on board the FAAM BAE-146 aircraft collected for The North Atlantic Climate System Integrated Study: ACSIS project." } }, { "ob_id": 866, "relationType": "IsSupplementTo", "subjectObservation": { "ob_id": 41268, "uuid": "6285564c34a246fc9ba5ce053d85e5e7", "short_code": "ob", "title": "ACSIS: Merged airborne chemistry data from instruments on board the FAAM aircraft", "abstract": "A collection of collated chemistry measurements made onboard the FAAM BAE-146 aircraft for The North Atlantic Climate System Integrated Study: ACSIS project. These data were collected by core and non-core instruments and compiled into a merged dataset. \r\n\r\nFlights included are from the measurement campaigns: ACSIS1 - February 2017, ACSIS2 - October 2017, ACSIS3 - May 2018, ACSIS4 - February 2019, ACSIS5 - August 2019, ACSIS6 - February 2020 and ACSIS7 - May 2022. Parameters include Carbon Dioxide (CO2), Methane (CH4), Nitric oxide (NO), Nitric dioxide (NO2), Total Organic Aerosol, Sulphate Aerosol, Nitrate Aerosol, Ammonium Aerosol, Chloride Aerosol, Urea (CH4N2O), Hydrogen cyanide (HCN), Nitryl chloride (ClNO2), and photolysis rates." }, "objectObservation": { "ob_id": 26411, "uuid": "9a586aea2b59413599b4b3d5e7b98711", "short_code": "ob", "title": "FAAM C103 ACSIS Transit flight: Airborne atmospheric measurements from core instrument suite on board the BAE-146 aircraft", "abstract": "Airborne atmospheric measurements from core instrument suite data on board the FAAM BAE-146 aircraft collected for The North Atlantic Climate System Integrated Study: ACSIS project." } }, { "ob_id": 867, "relationType": "IsSupplementTo", "subjectObservation": { "ob_id": 41268, "uuid": "6285564c34a246fc9ba5ce053d85e5e7", "short_code": "ob", "title": "ACSIS: Merged airborne chemistry data from instruments on board the FAAM aircraft", "abstract": "A collection of collated chemistry measurements made onboard the FAAM BAE-146 aircraft for The North Atlantic Climate System Integrated Study: ACSIS project. These data were collected by core and non-core instruments and compiled into a merged dataset. \r\n\r\nFlights included are from the measurement campaigns: ACSIS1 - February 2017, ACSIS2 - October 2017, ACSIS3 - May 2018, ACSIS4 - February 2019, ACSIS5 - August 2019, ACSIS6 - February 2020 and ACSIS7 - May 2022. Parameters include Carbon Dioxide (CO2), Methane (CH4), Nitric oxide (NO), Nitric dioxide (NO2), Total Organic Aerosol, Sulphate Aerosol, Nitrate Aerosol, Ammonium Aerosol, Chloride Aerosol, Urea (CH4N2O), Hydrogen cyanide (HCN), Nitryl chloride (ClNO2), and photolysis rates." }, "objectObservation": { "ob_id": 26294, "uuid": "f22c74b77ea34adea7628dd088a4f767", "short_code": "ob", "title": "FAAM C105 ACSIS flight: Airborne atmospheric measurements from core instrument suite on board the BAE-146 aircraft", "abstract": "Airborne atmospheric measurements from core instrument suite data on board the FAAM BAE-146 aircraft collected for The North Atlantic Climate System Integrated Study: ACSIS project." } }, { "ob_id": 868, "relationType": "IsSupplementTo", "subjectObservation": { "ob_id": 41268, "uuid": "6285564c34a246fc9ba5ce053d85e5e7", "short_code": "ob", "title": "ACSIS: Merged airborne chemistry data from instruments on board the FAAM aircraft", "abstract": "A collection of collated chemistry measurements made onboard the FAAM BAE-146 aircraft for The North Atlantic Climate System Integrated Study: ACSIS project. These data were collected by core and non-core instruments and compiled into a merged dataset. \r\n\r\nFlights included are from the measurement campaigns: ACSIS1 - February 2017, ACSIS2 - October 2017, ACSIS3 - May 2018, ACSIS4 - February 2019, ACSIS5 - August 2019, ACSIS6 - February 2020 and ACSIS7 - May 2022. Parameters include Carbon Dioxide (CO2), Methane (CH4), Nitric oxide (NO), Nitric dioxide (NO2), Total Organic Aerosol, Sulphate Aerosol, Nitrate Aerosol, Ammonium Aerosol, Chloride Aerosol, Urea (CH4N2O), Hydrogen cyanide (HCN), Nitryl chloride (ClNO2), and photolysis rates." }, "objectObservation": { "ob_id": 26290, "uuid": "130637c33a27475093552bf76c088150", "short_code": "ob", "title": "FAAM C106 ACSIS flight: Airborne atmospheric measurements from core instrument suite on board the BAE-146 aircraft", "abstract": "Airborne atmospheric measurements from core instrument suite data on board the FAAM BAE-146 aircraft collected for The North Atlantic Climate System Integrated Study: ACSIS project." } }, { "ob_id": 869, "relationType": "IsSupplementTo", "subjectObservation": { "ob_id": 41268, "uuid": "6285564c34a246fc9ba5ce053d85e5e7", "short_code": "ob", "title": "ACSIS: Merged airborne chemistry data from instruments on board the FAAM aircraft", "abstract": "A collection of collated chemistry measurements made onboard the FAAM BAE-146 aircraft for The North Atlantic Climate System Integrated Study: ACSIS project. These data were collected by core and non-core instruments and compiled into a merged dataset. \r\n\r\nFlights included are from the measurement campaigns: ACSIS1 - February 2017, ACSIS2 - October 2017, ACSIS3 - May 2018, ACSIS4 - February 2019, ACSIS5 - August 2019, ACSIS6 - February 2020 and ACSIS7 - May 2022. Parameters include Carbon Dioxide (CO2), Methane (CH4), Nitric oxide (NO), Nitric dioxide (NO2), Total Organic Aerosol, Sulphate Aerosol, Nitrate Aerosol, Ammonium Aerosol, Chloride Aerosol, Urea (CH4N2O), Hydrogen cyanide (HCN), Nitryl chloride (ClNO2), and photolysis rates." }, "objectObservation": { "ob_id": 27893, "uuid": "735dd23c7bbc40d59ee568d1363a03e4", "short_code": "ob", "title": "FAAM C141 ACSIS-4 flight: Airborne atmospheric measurements from core and non-core instrument suites on board the BAE-146 aircraft", "abstract": "Airborne atmospheric measurements from core and non-core instrument suites data on board the FAAM BAE-146 aircraft collected for The North Atlantic Climate System Integrated Study: ACSIS (ACSIS-4) project." } }, { "ob_id": 870, "relationType": "IsSupplementTo", "subjectObservation": { "ob_id": 41268, "uuid": "6285564c34a246fc9ba5ce053d85e5e7", "short_code": "ob", "title": "ACSIS: Merged airborne chemistry data from instruments on board the FAAM aircraft", "abstract": "A collection of collated chemistry measurements made onboard the FAAM BAE-146 aircraft for The North Atlantic Climate System Integrated Study: ACSIS project. These data were collected by core and non-core instruments and compiled into a merged dataset. \r\n\r\nFlights included are from the measurement campaigns: ACSIS1 - February 2017, ACSIS2 - October 2017, ACSIS3 - May 2018, ACSIS4 - February 2019, ACSIS5 - August 2019, ACSIS6 - February 2020 and ACSIS7 - May 2022. Parameters include Carbon Dioxide (CO2), Methane (CH4), Nitric oxide (NO), Nitric dioxide (NO2), Total Organic Aerosol, Sulphate Aerosol, Nitrate Aerosol, Ammonium Aerosol, Chloride Aerosol, Urea (CH4N2O), Hydrogen cyanide (HCN), Nitryl chloride (ClNO2), and photolysis rates." }, "objectObservation": { "ob_id": 27897, "uuid": "7ae42edd3d854ca98424c7bd3824805d", "short_code": "ob", "title": "FAAM C143 ACSIS-4 flight: Airborne atmospheric measurements from core and non-core instrument suites on board the BAE-146 aircraft", "abstract": "Airborne atmospheric measurements from core and non-core instrument suites data on board the FAAM BAE-146 aircraft collected for The North Atlantic Climate System Integrated Study: ACSIS (ACSIS-4) project." } }, { "ob_id": 871, "relationType": "IsSupplementTo", "subjectObservation": { "ob_id": 41268, "uuid": "6285564c34a246fc9ba5ce053d85e5e7", "short_code": "ob", "title": "ACSIS: Merged airborne chemistry data from instruments on board the FAAM aircraft", "abstract": "A collection of collated chemistry measurements made onboard the FAAM BAE-146 aircraft for The North Atlantic Climate System Integrated Study: ACSIS project. These data were collected by core and non-core instruments and compiled into a merged dataset. \r\n\r\nFlights included are from the measurement campaigns: ACSIS1 - February 2017, ACSIS2 - October 2017, ACSIS3 - May 2018, ACSIS4 - February 2019, ACSIS5 - August 2019, ACSIS6 - February 2020 and ACSIS7 - May 2022. Parameters include Carbon Dioxide (CO2), Methane (CH4), Nitric oxide (NO), Nitric dioxide (NO2), Total Organic Aerosol, Sulphate Aerosol, Nitrate Aerosol, Ammonium Aerosol, Chloride Aerosol, Urea (CH4N2O), Hydrogen cyanide (HCN), Nitryl chloride (ClNO2), and photolysis rates." }, "objectObservation": { "ob_id": 30167, "uuid": "1015965e30bf4c1f8eb7f02bedcd865a", "short_code": "ob", "title": "FAAM C226 ACSIS-6 Transit flight: Airborne atmospheric measurements from core instrument suite on board the BAE-146 aircraft", "abstract": "Airborne atmospheric measurements from core instrument suite data on board the FAAM BAE-146 aircraft collected for ACSIS-6 FAAM Aircraft Project project." } }, { "ob_id": 872, "relationType": "IsSupplementTo", "subjectObservation": { "ob_id": 41268, "uuid": "6285564c34a246fc9ba5ce053d85e5e7", "short_code": "ob", "title": "ACSIS: Merged airborne chemistry data from instruments on board the FAAM aircraft", "abstract": "A collection of collated chemistry measurements made onboard the FAAM BAE-146 aircraft for The North Atlantic Climate System Integrated Study: ACSIS project. These data were collected by core and non-core instruments and compiled into a merged dataset. \r\n\r\nFlights included are from the measurement campaigns: ACSIS1 - February 2017, ACSIS2 - October 2017, ACSIS3 - May 2018, ACSIS4 - February 2019, ACSIS5 - August 2019, ACSIS6 - February 2020 and ACSIS7 - May 2022. Parameters include Carbon Dioxide (CO2), Methane (CH4), Nitric oxide (NO), Nitric dioxide (NO2), Total Organic Aerosol, Sulphate Aerosol, Nitrate Aerosol, Ammonium Aerosol, Chloride Aerosol, Urea (CH4N2O), Hydrogen cyanide (HCN), Nitryl chloride (ClNO2), and photolysis rates." }, "objectObservation": { "ob_id": 30171, "uuid": "2fad8124836b44b7860598397f29c132", "short_code": "ob", "title": "FAAM C227 ACSIS-6 Transit flight: Airborne atmospheric measurements from core instrument suite on board the BAE-146 aircraft", "abstract": "Airborne atmospheric measurements from core instrument suite data on board the FAAM BAE-146 aircraft collected for ACSIS-6 FAAM Aircraft Project project." } }, { "ob_id": 873, "relationType": "IsSupplementTo", "subjectObservation": { "ob_id": 41268, "uuid": "6285564c34a246fc9ba5ce053d85e5e7", "short_code": "ob", "title": "ACSIS: Merged airborne chemistry data from instruments on board the FAAM aircraft", "abstract": "A collection of collated chemistry measurements made onboard the FAAM BAE-146 aircraft for The North Atlantic Climate System Integrated Study: ACSIS project. These data were collected by core and non-core instruments and compiled into a merged dataset. \r\n\r\nFlights included are from the measurement campaigns: ACSIS1 - February 2017, ACSIS2 - October 2017, ACSIS3 - May 2018, ACSIS4 - February 2019, ACSIS5 - August 2019, ACSIS6 - February 2020 and ACSIS7 - May 2022. Parameters include Carbon Dioxide (CO2), Methane (CH4), Nitric oxide (NO), Nitric dioxide (NO2), Total Organic Aerosol, Sulphate Aerosol, Nitrate Aerosol, Ammonium Aerosol, Chloride Aerosol, Urea (CH4N2O), Hydrogen cyanide (HCN), Nitryl chloride (ClNO2), and photolysis rates." }, "objectObservation": { "ob_id": 30169, "uuid": "d9d91c40bcbb46468db8de7da6e55bb4", "short_code": "ob", "title": "FAAM C228 ACSIS-6 Transit flight: Airborne atmospheric measurements from core instrument suite on board the BAE-146 aircraft", "abstract": "Airborne atmospheric measurements from core instrument suite data on board the FAAM BAE-146 aircraft collected for ACSIS-6 FAAM Aircraft Project project." } }, { "ob_id": 874, "relationType": "IsSupplementTo", "subjectObservation": { "ob_id": 41268, "uuid": "6285564c34a246fc9ba5ce053d85e5e7", "short_code": "ob", "title": "ACSIS: Merged airborne chemistry data from instruments on board the FAAM aircraft", "abstract": "A collection of collated chemistry measurements made onboard the FAAM BAE-146 aircraft for The North Atlantic Climate System Integrated Study: ACSIS project. These data were collected by core and non-core instruments and compiled into a merged dataset. \r\n\r\nFlights included are from the measurement campaigns: ACSIS1 - February 2017, ACSIS2 - October 2017, ACSIS3 - May 2018, ACSIS4 - February 2019, ACSIS5 - August 2019, ACSIS6 - February 2020 and ACSIS7 - May 2022. Parameters include Carbon Dioxide (CO2), Methane (CH4), Nitric oxide (NO), Nitric dioxide (NO2), Total Organic Aerosol, Sulphate Aerosol, Nitrate Aerosol, Ammonium Aerosol, Chloride Aerosol, Urea (CH4N2O), Hydrogen cyanide (HCN), Nitryl chloride (ClNO2), and photolysis rates." }, "objectObservation": { "ob_id": 30173, "uuid": "e8c2a0e24726480fb65b34d03a8b7d06", "short_code": "ob", "title": "FAAM C229 ACSIS-6 Transit flight: Airborne atmospheric measurements from core instrument suite on board the BAE-146 aircraft", "abstract": "Airborne atmospheric measurements from core instrument suite data on board the FAAM BAE-146 aircraft collected for ACSIS-6 FAAM Aircraft Project project." } }, { "ob_id": 875, "relationType": "IsSupplementTo", "subjectObservation": { "ob_id": 41268, "uuid": "6285564c34a246fc9ba5ce053d85e5e7", "short_code": "ob", "title": "ACSIS: Merged airborne chemistry data from instruments on board the FAAM aircraft", "abstract": "A collection of collated chemistry measurements made onboard the FAAM BAE-146 aircraft for The North Atlantic Climate System Integrated Study: ACSIS project. These data were collected by core and non-core instruments and compiled into a merged dataset. \r\n\r\nFlights included are from the measurement campaigns: ACSIS1 - February 2017, ACSIS2 - October 2017, ACSIS3 - May 2018, ACSIS4 - February 2019, ACSIS5 - August 2019, ACSIS6 - February 2020 and ACSIS7 - May 2022. Parameters include Carbon Dioxide (CO2), Methane (CH4), Nitric oxide (NO), Nitric dioxide (NO2), Total Organic Aerosol, Sulphate Aerosol, Nitrate Aerosol, Ammonium Aerosol, Chloride Aerosol, Urea (CH4N2O), Hydrogen cyanide (HCN), Nitryl chloride (ClNO2), and photolysis rates." }, "objectObservation": { "ob_id": 37474, "uuid": "7b16d480d42248ccbf2e144456de38db", "short_code": "ob", "title": "FAAM C288 ACSIS Transit flight: Airborne atmospheric measurements from core instrument suite on board the BAE-146 aircraft", "abstract": "Airborne atmospheric measurements from core instrument suite data on board the FAAM BAE-146 aircraft collected for The North Atlantic Climate System Integrated Study: ACSIS project." } }, { "ob_id": 876, "relationType": "IsSupplementTo", "subjectObservation": { "ob_id": 41268, "uuid": "6285564c34a246fc9ba5ce053d85e5e7", "short_code": "ob", "title": "ACSIS: Merged airborne chemistry data from instruments on board the FAAM aircraft", "abstract": "A collection of collated chemistry measurements made onboard the FAAM BAE-146 aircraft for The North Atlantic Climate System Integrated Study: ACSIS project. These data were collected by core and non-core instruments and compiled into a merged dataset. \r\n\r\nFlights included are from the measurement campaigns: ACSIS1 - February 2017, ACSIS2 - October 2017, ACSIS3 - May 2018, ACSIS4 - February 2019, ACSIS5 - August 2019, ACSIS6 - February 2020 and ACSIS7 - May 2022. Parameters include Carbon Dioxide (CO2), Methane (CH4), Nitric oxide (NO), Nitric dioxide (NO2), Total Organic Aerosol, Sulphate Aerosol, Nitrate Aerosol, Ammonium Aerosol, Chloride Aerosol, Urea (CH4N2O), Hydrogen cyanide (HCN), Nitryl chloride (ClNO2), and photolysis rates." }, "objectObservation": { "ob_id": 37498, "uuid": "e525619875834d63a2a6ce84288cb130", "short_code": "ob", "title": "FAAM C294 ACSIS flight: Airborne atmospheric measurements from core instrument suite on board the BAE-146 aircraft", "abstract": "Airborne atmospheric measurements from core instrument suite data on board the FAAM BAE-146 aircraft collected for The North Atlantic Climate System Integrated Study: ACSIS project." } }, { "ob_id": 877, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 41388, "uuid": "b82b58d085d0433b821f4ae31cb608de", "short_code": "ob", "title": "HadISD: Global sub-daily, surface meteorological station data, 1931-2023, v3.4.0.2023f", "abstract": "This is version v3.4.0.2023f of Met Office Hadley Centre's Integrated Surface Database, HadISD. These data are global sub-daily surface meteorological data.\r\n\r\nThis update (v3.4.0.2023f) to HadISD corrects a long-standing bug which was discovered in autumn 2023 whereby the neighbour checks (and associated [un]flagging for some other tests) were not being implemented. For more details see the posts on the HadISD blog: https://hadisd.blogspot.com/2023/10/bug-in-buddy-checks.html & https://hadisd.blogspot.com/2024/01/hadisd-v3402023f-future-look.html\r\n\r\nThe quality controlled variables in this dataset are: temperature, dewpoint temperature, sea-level pressure, wind speed and direction, cloud data (total, low, mid and high level). Past significant weather and precipitation data are also included, but have not been quality controlled, so their quality and completeness cannot be guaranteed. Quality control flags and data values which have been removed during the quality control process are provided in the qc_flags and flagged_values fields, and ancillary data files show the station listing with a station listing with IDs, names and location information. \r\n\r\nThe data are provided as one NetCDF file per station. Files in the station_data folder station data files have the format \"station_code\"_HadISD_HadOBS_19310101-20240101_v3.4.1.2023f.nc. The station codes can be found under the docs tab. The station codes file has five columns as follows: 1) station code, 2) station name 3) station latitude 4) station longitude 5) station height.\r\n\r\nTo keep informed about updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS.\r\n\r\nFor more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISD blog: http://hadisd.blogspot.co.uk/\r\n\r\nReferences:\r\nWhen using the dataset in a paper you must cite the following papers (see Docs for link to the publications) and this dataset (using the \"citable as\" reference) :\r\n\r\nDunn, R. J. H., (2019), HadISD version 3: monthly updates, Hadley Centre Technical Note.\r\n\r\nDunn, R. J. H., Willett, K. M., Parker, D. E., and Mitchell, L.: Expanding HadISD: quality-controlled, sub-daily station data from 1931, Geosci. Instrum. Method. Data Syst., 5, 473-491, doi:10.5194/gi-5-473-2016, 2016.\r\n\r\nDunn, R. J. H., et al. (2012), HadISD: A Quality Controlled global synoptic report database for selected variables at long-term stations from 1973-2011, Clim. Past, 8, 1649-1679, 2012, doi:10.5194/cp-8-1649-2012\r\n\r\nSmith, A., N. Lott, and R. Vose, 2011: The Integrated Surface Database: Recent Developments and Partnerships. Bulletin of the American Meteorological Society, 92, 704–708, doi:10.1175/2011BAMS3015.1\r\n\r\nFor a homogeneity assessment of HadISD please see this following reference\r\n\r\nDunn, R. J. H., K. M. Willett, C. P. Morice, and D. E. Parker. \"Pairwise homogeneity assessment of HadISD.\" Climate of the Past 10, no. 4 (2014): 1501-1522. doi:10.5194/cp-10-1501-2014, 2014." }, "objectObservation": { "ob_id": 39557, "uuid": "60c28523d8c54c58831b2608164cf35e", "short_code": "ob", "title": "HadISD: Global sub-daily, surface meteorological station data, 1931-2022, v3.3.0.2022f", "abstract": "This is version v3.3.0.2022f of Met Office Hadley Centre's Integrated Surface Database, HadISD. These data are global sub-daily surface meteorological data.\r\n\r\nThe quality controlled variables in this dataset are: temperature, dewpoint temperature, sea-level pressure, wind speed and direction, cloud data (total, low, mid and high level). Past significant weather and precipitation data are also included, but have not been quality controlled, so their quality and completeness cannot be guaranteed. Quality control flags and data values which have been removed during the quality control process are provided in the qc_flags and flagged_values fields, and ancillary data files show the station listing with a station listing with IDs, names and location information. \r\n\r\nThe data are provided as one NetCDF file per station. Files in the station_data folder station data files have the format \"station_code\"_HadISD_HadOBS_19310101-20230101_v3.3.1.2022f.nc. The station codes can be found under the docs tab. The station codes file has five columns as follows: 1) station code, 2) station name 3) station latitude 4) station longitude 5) station height.\r\n\r\nTo keep informed about updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS.\r\n\r\nFor more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISD blog: http://hadisd.blogspot.co.uk/\r\n\r\nReferences:\r\nWhen using the dataset in a paper you must cite the following papers (see Docs for link to the publications) and this dataset (using the \"citable as\" reference) :\r\n\r\nDunn, R. J. H., (2019), HadISD version 3: monthly updates, Hadley Centre Technical Note.\r\n\r\nDunn, R. J. H., Willett, K. M., Parker, D. E., and Mitchell, L.: Expanding HadISD: quality-controlled, sub-daily station data from 1931, Geosci. Instrum. Method. Data Syst., 5, 473-491, doi:10.5194/gi-5-473-2016, 2016.\r\n\r\nDunn, R. J. H., et al. (2012), HadISD: A Quality Controlled global synoptic report database for selected variables at long-term stations from 1973-2011, Clim. Past, 8, 1649-1679, 2012, doi:10.5194/cp-8-1649-2012\r\n\r\nSmith, A., N. Lott, and R. Vose, 2011: The Integrated Surface Database: Recent Developments and Partnerships. Bulletin of the American Meteorological Society, 92, 704–708, doi:10.1175/2011BAMS3015.1\r\n\r\nFor a homogeneity assessment of HadISD please see this following reference\r\n\r\nDunn, R. J. H., K. M. Willett, C. P. Morice, and D. E. Parker. \"Pairwise homogeneity assessment of HadISD.\" Climate of the Past 10, no. 4 (2014): 1501-1522. doi:10.5194/cp-10-1501-2014, 2014." } }, { "ob_id": 879, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 41232, "uuid": "0d0f4a942a144d9cab9263de3949a5d6", "short_code": "ob", "title": "ESA Sea Surface Salinity Climate Change Initiative (Sea_Surface_Salinity_cci): Weekly sea surface salinity product on a global grid, v04.41, for 2010 to 2022", "abstract": "This dataset contains Sea Surface Salinity (SSS) v04.41 data at a spatial resolution of 50km and a time resolution of 1 week. It is spatially sampled on a 0.25 degree grid and 1 day of time sampling. This product is also available separately on polar 25km EASE (Equal Area Scalable Earth) grids. A monthly product is also available. In addition to salinity, information on uncertainties are provided. For more information see the user guide and other product documentation available from the linked Sea Surface Salinity CCI web page.\r\n\r\nCompared to version 3.21 of the data, version 04.41 SSS is of similar or improved quality. The main improvements concern high latitude regions (reduced seasonal biases and better ice flagging). The v04.41 dataset also covers a longer period (Jan 2010-Oct 2022)." }, "objectObservation": { "ob_id": 32842, "uuid": "fad2e982a59d44788eda09e3c67ed7d5", "short_code": "ob", "title": "ESA Sea Surface Salinity Climate Change Initiative (Sea_Surface_Salinity_cci): Weekly sea surface salinity product, v03.21, for 2010 to 2020", "abstract": "The ESA Sea Surface Salinity Climate Change Initiative (CCI) consortium has produced global, level 4, multi-sensor Sea Surface Salinity maps covering the 2010-2020 period.\r\n\r\nThis dataset contains Sea Surface Salinity (SSS) v03.21 data at a spatial resolution of 50 km and a time resolution of 1 week. It has been spatially sampled on a 25 km EASE (Equal Area Scalable Earth) grid and 1 day of time sampling. A monthly product is also available. In addition to salinity, information on errors are provided. For more information see the user guide and other product documentation available from the linked Sea Surface Salinity CCI web page).\r\n\r\nCompared to the previous version of the data, version 3 SSS and associated uncertainties are more precise and cover a longer period (Jan 2010-sept 2020); version 3 SSS are provided closer to land than version 2 SSS, with a possible degraded quality. Users might remove these additional near land data by using the lsc_qc flag." } }, { "ob_id": 880, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 41234, "uuid": "7cc16c0d8d2f49278ed5ebf8341ed40b", "short_code": "ob", "title": "ESA Sea Surface Salinity Climate Change Initiative (Sea_Surface_Salinity_cci): Monthly sea surface salinity product on a global grid, v04.41, for 2010 to 2022", "abstract": "This dataset contains Sea Surface Salinity (SSS) v04.41 data at a spatial resolution of 50km and a time resolution of 1 month. It is spatially sampled on a 0.25 degree grid and 15 days of time sampling. This product is also available separately on polar 25km EASE (Equal Area Scalable Earth) grids. A weekly product is also available. In addition to salinity, information on uncertainties are provided. For more information see the user guide and other product documentation available from the linked Sea Surface Salinity CCI web page.\r\n\r\nCompared to version 3.21 of the data, version 04.41 SSS is of similar or improved quality. The main improvements concern high latitude regions (reduced seasonal biases and better ice flagging). The v04.41 dataset covers a longer period (Jan 2010-Oct 2022)." }, "objectObservation": { "ob_id": 32841, "uuid": "7da8723b16e94771be1a2717d8a6e2fe", "short_code": "ob", "title": "ESA Sea Surface Salinity Climate Change Initiative (Sea_Surface_Salinity_cci): Monthly sea surface salinity product, v03.21, for 2010 to 2020", "abstract": "The ESA Sea Surface Salinity Climate Change Initiative (CCI) consortium has produced global, level 4, multi-sensor Sea Surface Salinity maps covering the 2010-2020 period.\r\n\r\nThis dataset provides Sea Surface Salinity (SSS) data at a spatial resolution of 25 km and a time resolution of 1 month. This has been spatially sampled on a 25 km EASE (Equal Area Scalable Earth) grid and 15 days of time sampling. A weekly product is also available. In addition to salinity, information on errors are provided. For more information, see the user guide and other product documentation available from the linked Sea Surface Salinity CCI web page.\r\n\r\nCompared to the previous version of the data, version 3 SSS and associated uncertainties are more precise and cover a longer period (Jan 2010-sept 2020); version 3 SSS are provided closer to land than version 2 SSS, with a possible degraded quality. Users might remove these additional near land data by using the lsc_qc flag." } }, { "ob_id": 881, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 41234, "uuid": "7cc16c0d8d2f49278ed5ebf8341ed40b", "short_code": "ob", "title": "ESA Sea Surface Salinity Climate Change Initiative (Sea_Surface_Salinity_cci): Monthly sea surface salinity product on a global grid, v04.41, for 2010 to 2022", "abstract": "This dataset contains Sea Surface Salinity (SSS) v04.41 data at a spatial resolution of 50km and a time resolution of 1 month. It is spatially sampled on a 0.25 degree grid and 15 days of time sampling. This product is also available separately on polar 25km EASE (Equal Area Scalable Earth) grids. A weekly product is also available. In addition to salinity, information on uncertainties are provided. For more information see the user guide and other product documentation available from the linked Sea Surface Salinity CCI web page.\r\n\r\nCompared to version 3.21 of the data, version 04.41 SSS is of similar or improved quality. The main improvements concern high latitude regions (reduced seasonal biases and better ice flagging). The v04.41 dataset covers a longer period (Jan 2010-Oct 2022)." }, "objectObservation": { "ob_id": 32841, "uuid": "7da8723b16e94771be1a2717d8a6e2fe", "short_code": "ob", "title": "ESA Sea Surface Salinity Climate Change Initiative (Sea_Surface_Salinity_cci): Monthly sea surface salinity product, v03.21, for 2010 to 2020", "abstract": "The ESA Sea Surface Salinity Climate Change Initiative (CCI) consortium has produced global, level 4, multi-sensor Sea Surface Salinity maps covering the 2010-2020 period.\r\n\r\nThis dataset provides Sea Surface Salinity (SSS) data at a spatial resolution of 25 km and a time resolution of 1 month. This has been spatially sampled on a 25 km EASE (Equal Area Scalable Earth) grid and 15 days of time sampling. A weekly product is also available. In addition to salinity, information on errors are provided. For more information, see the user guide and other product documentation available from the linked Sea Surface Salinity CCI web page.\r\n\r\nCompared to the previous version of the data, version 3 SSS and associated uncertainties are more precise and cover a longer period (Jan 2010-sept 2020); version 3 SSS are provided closer to land than version 2 SSS, with a possible degraded quality. Users might remove these additional near land data by using the lsc_qc flag." } }, { "ob_id": 882, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 41425, "uuid": "c14874e943cc453a8e63ce5841ecc9b0", "short_code": "ob", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged methane from GOSAT-2, generated with the SRFP (RemoTeC) full physics retrieval algorithm (CH4_GO2_SRFP), version 2.0.2", "abstract": "This dataset contains column-averaged dry-air mole fractions (mixing ratios) of methane (XCH4). It has been produced using Near Infrared (NIR) and Shortwave Infrared (SWIR) spectra acquired from the Thermal and Near Infrared Sensor for Carbon Observations - Fourier Transform Spectrometer-2 (TANSO-FTS-2) onboard the Japanese Greenhouse gases Observing Satellite (GOSAT-2), using the Remote Sensing of Greenhouse Gases for Carbon Cycle Modeling (RemoTeC) SRON Full Physics (SRFP) retrieval algorithm. Results are provided for the individual GOSAT-2 spatial footprints.\r\n\r\nThese data have been produced as part of the European Space Agency (ESA)'s Climate Change Initiative (CCI) programme." }, "objectObservation": { "ob_id": 38314, "uuid": "c8037223dfff49db9fcdd8a9f6dd8d41", "short_code": "ob", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged methane from GOSAT-2, generated with the SRFP (RemoTeC) full physics retrieval algorithm (CH4_GO2_SRFP), version 2.0.0", "abstract": "This dataset contains column-averaged dry-air mole fractions (mixing ratios) of methane (XCH4). It has been produced using data acquired from the Thermal and Near Infrared Sensor for Carbon Observations - Fourier Transform Spectrometer-2 (TANSO-FTS-2) Near Infrared (NIR) and Shortwave Infrared (SWIR) spectra, onboard the Japanese Greenhouse gases Observing Satellite (GOSAT-2), using the Remote Sensing of Greenhouse Gases for Carbon Cycle Modeling (RemoTeC) SRON Full Physics (SRFP) retrieval algorithm. Results are provided for the individual GOSAT-2 spatial footprints.\r\n\r\nThese data have been produced as part of the European Space Agency (ESA)'s Climate Change Initiative (CCI) programme." } }, { "ob_id": 883, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 41429, "uuid": "3fc7927499fa49e0b6ace6c807972259", "short_code": "ob", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged methane from GOSAT-2, generated with the SRPR (RemoTeC) proxy retrieval algorithm (CH4_GO2_SRPR), version 2.0.2", "abstract": "This dataset contains column-averaged dry-air mole fractions (mixing ratios) of methane (XCH4). It has been produced using Near Infrared (NIR) and Shortwave Infrared (SWIR) spectra acquired from the Thermal and Near Infrared Sensor for Carbon Observations - Fourier Transform Spectrometer-2 (TANSO-FTS-2) onboard the Japanese Greenhouse gases Observing Satellite (GOSAT-2), using the Remote Sensing of Greenhouse Gases for Carbon Cycle Modeling (RemoTeC) SRON Proxy (SRPR) retrieval algorithm. Results are provided for the individual GOSAT-2 spatial footprints.\r\n\r\nThese data have been produced as part of the European Space Agency (ESA)'s Climate Change Initiative (CCI) programme." }, "objectObservation": { "ob_id": 38313, "uuid": "6ecb706ac16c4e05aab75ed2cc3ec119", "short_code": "ob", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged methane from GOSAT-2, generated with the SRPR (RemoTeC) proxy retrieval algorithm (CH4_GO2_SRPR), version 2.0.0", "abstract": "This dataset contains column-averaged dry-air mole fractions (mixing ratios) of methane (XCH4). It has been produced using data acquired from the Thermal and Near Infrared Sensor for Carbon Observations - Fourier Transform Spectrometer-2 (TANSO-FTS-2) Near Infrared (NIR) and Shortwave Infrared (SWIR) spectra, onboard the Japanese Greenhouse gases Observing Satellite (GOSAT-2), using the Remote Sensing of Greenhouse Gases for Carbon Cycle Modeling (RemoTeC) SRON Proxy (SRPR) retrieval algorithm. Results are provided for the individual GOSAT-2 spatial footprints.\r\n\r\nThese data have been produced as part of the European Space Agency (ESA)'s Climate Change Initiative (CCI) programme." } }, { "ob_id": 884, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 41427, "uuid": "875f25069b5d4bd9a7101ca1206ee4f0", "short_code": "ob", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged carbon dioxide from GOSAT-2, derived using the SRFP (RemoTeC) full physics algorithm (CO2_GO2_SRFP), version 2.0.2", "abstract": "This dataset contains column-averaged dry-air mole fractions (mixing ratios) of carbon dioxide (XCO2). It has been produced using Near Infrared (NIR) and Shortwave Infrared (SWIR) spectra acquired from the Thermal and Near Infrared Sensor for Carbon Observations - Fourier Transform Spectrometer-2 (TANSO-FTS-2) onboard the Japanese Greenhouse gases Observing Satellite (GOSAT-2), using the Remote Sensing of Greenhouse Gases for Carbon Cycle Modeling (RemoTeC) SRON Full Physics (SRFP) retrieval algorithm. Results are provided for the individual GOSAT-2 spatial footprints.\r\n\r\nThese data have been produced as part of the European Space Agency (ESA)'s Climate Change Initiative (CCI) programme." }, "objectObservation": { "ob_id": 38315, "uuid": "169c76a05fa247eebc5ee53f239871a7", "short_code": "ob", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged carbon dioxide from GOSAT-2, derived using the SRFP (RemoTeC) full physics algorithm (CO2_GO2_SRFP), version 2.0.0", "abstract": "This dataset contains column-averaged dry-air mole fractions (mixing ratios) of carbon dioxide (XCO2). It has been produced using data acquired from the Thermal and Near Infrared Sensor for Carbon Observations - Fourier Transform Spectrometer-2 (TANSO-FTS-2) Near Infrared (NIR) and Shortwave Infrared (SWIR) spectra, onboard the Japanese Greenhouse gases Observing Satellite (GOSAT-2), using the Remote Sensing of Greenhouse Gases for Carbon Cycle Modeling (RemoTeC) SRON Full Physics (SRFP) retrieval algorithm. Results are provided for the individual GOSAT-2 spatial footprints.\r\n\r\nThese data have been produced as part of the European Space Agency (ESA)'s Climate Change Initiative (CCI) programme." } }, { "ob_id": 885, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 41499, "uuid": "43eeb6248d7f435da2300edb34cc4bea", "short_code": "ob", "title": "Daily Colour and Intensity Georeferenced Orthophotos of the Cliff and Beach at Happisburgh, Norfolk, UK (April-December 2019) version 2.", "abstract": "This dataset contains georeferenced orthophotos collected daily along a 450 metre coastal stretch at Happisburgh, UK, over a time span of 9 months (April 6, 2019 to December 23, 2019). The dataset contains 190 colour images and 190 intensity images in GEOTIFF format. The orthophotos are produced by projection of LiDAR (Light Detection And Ranging) scans of the coastal stretch. There are 190 images out of a possible 262 days, since only days when scans were performed from two locations are included, which did not happen every day due to weather conditions. The orthophotos are point-cloud renders of the scan data created using ScanLAB's proprietary point-cloud rendering engine. The colour orthophotos are rendered using the colour information projected onto the scan during post-process colourisation. The intensity orthophotos are rendered using the intensity data for each scan. The orthophotos are rendered using an orthographic virtual camera which frames the useful extents of the scan data and is orientated such that the rendered orthophoto is \"north-up\". The orthophotos are georeferenced using pythons GDAL library and ground-truth GPS measurements taken at the two TLS positions onsite. The procedure for this follows; 1) the ground-truth GPS positions were converted from OSGB 1936 / British National Grid to Latitude and Longitude., 2) The pixel resolution of the orthophotos was calculated from the pixel position of the two TLS in the orthophotos and the two ground truth GPS positions., 3) The GPS position of the top left pixel of the orthophoto was calculated using pixel position of a TLS and the pixel resolution., 4) A python script using the GDAL library applied the GPS metadata to the orthophoto and saved it in GEOTIFF format. These data were collected to better understand the dynamic of beach-cliff and shore platform interaction along soft cliffed coasts. This research was funded by the UK Natural Environment Research Council (NE/M004996/1; BLUE-coast project). The on-location LiDAR Scanning and Technical R&D operated by ScanLAB Projects Ltd was funded by Innovate UK's Audience of the Future Program (Multiscale 3D Scanning with Framerate for TV and Immersive Applications project). The first 6 months of LiDAR scans (April to September 2019) were funded by Innovate UK, and this project was continued by the NERC BLUE-coast funding for the last 3 months (October to December 2019). The files were re-submitted and this DOI represents the second version of the data." }, "objectObservation": { "ob_id": 40039, "uuid": "5768b4e7462f4facbcf447c8cd3929b9", "short_code": "ob", "title": "Daily Colour and Intensity Orthophotos of the Cliff and Beach at Happisburgh, Norfolk, UK (April-December 2019).", "abstract": "This dataset contains orthophotos collected daily along a 450 metre coastal stretch at Happisburgh, UK, over a time span of 9 months (April 6, 2019 to December 23, 2019). The dataset contains 190 colour images and 190 intensity images in .png format. The orthophotos are produced by projection of LiDAR (Light Detection And Ranging) scans of the coastal stretch. There are 190 images out of a possible 262 days, since only days when scans were performed from two locations are included, which didn't happen every day due to weather conditions. These data were collected to better understand the dynamic of beach-cliff and shore platform interaction along soft cliffed coasts. ScanLAB Projects Ltd and the British Geological Survey (BGS) were responsible for the collection of the data, funded by the UK Natural Environment Research Council (NE/M004996/1; BLUE-coast project)." } }, { "ob_id": 886, "relationType": "Continues", "subjectObservation": { "ob_id": 41500, "uuid": "e652f0109f21401680bc3c0ac834a96e", "short_code": "ob", "title": "Hydro-JULES: Global high-resolution drought datasets from 1981-2022 - Amendment to CHIRPS_GLEAM subset", "abstract": "This is a revised version of the Climate Hazards group InfraRed Precipitation with Station data (CHIRPS) Global Land Evaporation Amsterdam Model (GLEAM) data from the Hydro-JULES: Global high-resolution drought dataset (doi:10.5285/ac43da11867243a1bb414e1637802dec).\r\n\r\nThis version corrects some errors found in the previous version due to the model run errors in some areas. The model has been re-run to accurately produce the dataset. \r\n\r\nThese are global scale high-resolution drought indices developed from a combination of precipitation and potential evapotranspiration datasets for the Hydro-JULES project. Climate Hazards group InfraRed Precipitation with Station data (CHIRPS), Multi-Source Weighted-Ensemble Precipitation (MSWEP) precipitation estimates, Global Land Evaporation Amsterdam Model (GLEAM) and Bristol Hourly potential evapotranspiration (hPET) estimates were used. The drought index is developed using the Standardized Precipitation-Evapotranspiration Index (SPEI). These high-resolution global scale drought indices are available from 1981-2022 at a monthly and 5km spatial resolution. The SPEI indices are available from 1-48 months. The datasets provide valuable information for the study and analysis of droughts at much higher resolution from global to local scale. \r\n\r\nThese data were produced for Hydro-Jules (NE/S017380/1) and REACH (Foreign, Commonwealth and Development Office): Programme Code 201880." }, "objectObservation": { "ob_id": 40082, "uuid": "ac43da11867243a1bb414e1637802dec", "short_code": "ob", "title": "Hydro-JULES: Global high-resolution drought datasets from 1981-2022", "abstract": "These are global scale high-resolution drought indices developed from a combination of precipitation and potential evapotranspiration datasets for the Hydro-JULES project. Climate Hazards group InfraRed Precipitation with Station data (CHIRPS), Multi-Source Weighted-Ensemble Precipitation (MSWEP) precipitation estimates, Global Land Evaporation Amsterdam Model (GLEAM) and Bristol Hourly potential evapotranspiration (hPET) estimates were used. The drought index is developed using the Standardized Precipitation-Evapotranspiration Index (SPEI). These high-resolution global scale drought indices are available from 1981-2022 at a monthly and 5km spatial resolution. The SPEI indices are available from 1-48 months. The datasets provide valuable information for the study and analysis of droughts at much higher resolution from global to local scale. \r\nThese data were produced for Hydro-Jules (NE/S017380/1) and REACH (Foreign, Commonwealth and Development Office): Programme Code 201880." } }, { "ob_id": 887, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 41262, "uuid": "2c1cb1d606c4421e9339a3028839a41f", "short_code": "ob", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column averaged carbon dioxide from OCO-2 generated with the FOCAL algorithm, version 10.1", "abstract": "This dataset contains column-average dry-air mole fractions of atmospheric carbon dioxide (XCO2), using the fast atmospheric trace gas retrieval for OCO2 (FOCAL-OCO2). The FOCAL-OCO2 algorithm which has been setup to retrieve XCO2 by analysing hyper spectral solar backscattered radiance measurements from NASA's Orbiting Carbon Observatory 2 (OCO-2) satellite. FOCAL includes a radiative transfer model which has been developed to approximate light scattering effects by multiple scattering at an optically thin scattering layer. This reduces the computational costs by several orders of magnitude. FOCAL's radiative transfer model is utilised to simulate the radiance in all three OCO-2 spectral bands allowing the simultaneous retrieval of CO2, H2O, and solar induced chlorophyll fluorescence. The product is limited to cloud-free scenes on the Earth's day side. This dataset is also referred to as CO2_OC2_FOCA.\r\n\r\nThis version of the data (v10.1) was produced as part of the European Space Agency's (ESA) \r\nClimate Change Initiative (CCI) Greenhouse Gases (GHG) project (GHG-CCI+, http://cci.esa.int/ghg)\r\nand got co-funding from the University of Bremen and EU H2020 projects CHE (grant agreement no. 776186) and VERIFY (grant agreement no. 776810).\r\n\r\nWhen citing this data, please also cite the following peer-reviewed publications:\r\n\r\nM.Reuter, M.Buchwitz, O.Schneising, S.Noël, V.Rozanov, H.Bovensmann and J.P.Burrows: A Fast Atmospheric Trace Gas Retrieval for Hyperspectral Instruments Approximating Multiple Scattering - Part 1: Radiative Transfer and a Potential OCO-2 XCO2 Retrieval Setup, Remote Sensing, 9(11), 1159; doi:10.3390/rs9111159, 2017\r\n\r\nM.Reuter, M.Buchwitz, O.Schneising, S.Noël, H.Bovensmann and J.P.Burrows: A Fast Atmospheric Trace Gas Retrieval for Hyperspectral Instruments Approximating Multiple Scattering - Part 2: Application to XCO2 Retrievals from OCO-2, Remote Sensing, 9(11), 1102; doi:10.3390/rs9111102, 2017" }, "objectObservation": { "ob_id": 37846, "uuid": "070522ac6a5d4973a95c544beef714b4", "short_code": "ob", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column averaged carbon dioxide from OCO-2 generated with the FOCAL algorithm, version 10", "abstract": "This dataset contains column-average dry-air mole fractions of atmospheric carbon dioxide (XCO2), using the fast atmospheric trace gas retrieval for OCO2 (FOCAL-OCO2). The FOCAL-OCO2 algorithm which has been setup to retrieve XCO2 by analysing hyper spectral solar backscattered radiance measurements from NASA's Orbiting Carbon Observatory 2 (OCO-2) satellite. FOCAL includes a radiative transfer model which has been developed to approximate light scattering effects by multiple scattering at an optically thin scattering layer. This reduces the computational costs by several orders of magnitude. FOCAL's radiative transfer model is utilised to simulate the radiance in all three OCO-2 spectral bands allowing the simultaneous retrieval of CO2, H2O, and solar induced chlorophyll fluorescence. The product is limited to cloud-free scenes on the Earth's day side. This dataset is also referred to as CO2_OC2_FOCA.\r\n\r\nThis version of the data (v10) was produced as part of the European Space Agency's (ESA) \r\nClimate Change Initiative (CCI) Greenhouse Gases (GHG) project (GHG-CCI+, http://cci.esa.int/ghg)\r\nand got co-funding from the Univ. Bremen and EU H2020 projects CHE (grant agreement no. 776186) and VERIFY (grant agreement no. 776810).\r\n\r\nWhen citing this data, please also cite the following peer-reviewed publications:\r\n\r\nM.Reuter, M.Buchwitz, O.Schneising, S.Noël, V.Rozanov, H.Bovensmann and J.P.Burrows: A Fast Atmospheric Trace Gas Retrieval for Hyperspectral Instruments Approximating Multiple Scattering - Part 1: Radiative Transfer and a Potential OCO-2 XCO2 Retrieval Setup, Remote Sensing, 9(11), 1159; doi:10.3390/rs9111159, 2017\r\n\r\nM.Reuter, M.Buchwitz, O.Schneising, S.Noël, H.Bovensmann and J.P.Burrows: A Fast Atmospheric Trace Gas Retrieval for Hyperspectral Instruments Approximating Multiple Scattering - Part 2: Application to XCO2 Retrievals from OCO-2, Remote Sensing, 9(11), 1102; doi:10.3390/rs9111102, 2017" } }, { "ob_id": 888, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 40037, "uuid": "da8e669a74334c82a56e0b470bc4ef04", "short_code": "ob", "title": "ESA Fire Climate Change Initiative (Fire_cci): Sentinel-3 SYN Burned Area Grid product, version 1.1", "abstract": "The ESA Fire Disturbance Climate Change Initiative (CCI) project has produced maps of global burned area derived from satellite observations. The Sentinel-3 SYN Fire_cci v1.1 grid product described here contains gridded data on global burned area derived from surface reflectance data from the OLCI and SLSTR instruments (combined as the Synergy (SYN) product) onboard the Sentinel-3 A&B satellites, complemented by VIIRS thermal information. This product, called FireCCIS311 for short, is available for the years 2019 to 2024.\r\n\r\nThis gridded dataset has been derived from the FireCCIS311 pixel product (also available) by summarising its burned area information into a regular grid covering the Earth at 0.25 x 0.25 degrees resolution and at monthly temporal resolution. Information on burned area is included in 22 individual quantities: sum of burned area, standard error, fraction of burnable area, fraction of observed area, and the burned area for 18 land cover classes, as defined by the Copernicus Climate Change Initiative (C3S) Land Cover v2.1.1 product. For further information on the product and its format see the Product User Guide in the linked documentation." }, "objectObservation": { "ob_id": 34729, "uuid": "3aaaaf94813e48f18f2b83242a8dacbe", "short_code": "ob", "title": "ESA Fire Climate Change Initiative (Fire_cci): Sentinel-3 SYN Burned Area Grid product, version 1.0", "abstract": "The ESA Fire Disturbance Climate Change Initiative (CCI) project has produced maps of global burned area derived from satellite observations. The Sentinel-3 SYN Fire_cci v1.0 grid product described here contains gridded data on global burned area derived from surface reflectance data from the OLCI and SLSTR instruments (combined as the Synergy (SYN) product) onboard the Sentinel-3 A&B satellites, complemented by VIIRS thermal information. This product, called FireCCIS310 for short, is currently available for 2019, but it is foreseen to be extended for additional years.\r\n\r\nThis gridded dataset has been derived from the FireCCIS310 pixel product (also available) by summarising its burned area information into a regular grid covering the Earth at 0.25 x 0.25 degrees resolution and at monthly temporal resolution. Information on burned area is included in 22 individual quantities: sum of burned area, standard error, fraction of burnable area, fraction of observed area, and the burned area for 18 land cover classes, as defined by the Copernicus Climate Change Initiative(C3S) Land Cover v2.1.1 product. For further information on the product and its format see the Product User Guide in the linked documentation." } }, { "ob_id": 889, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 40038, "uuid": "d441079fc77f49fabeb41330612b252f", "short_code": "ob", "title": "ESA Fire Climate Change Initiative (Fire_cci): Sentinel-3 SYN Burned Area Pixel product, version 1.1", "abstract": "The ESA Fire Disturbance Climate Change Initiative (CCI) project has produced maps of global burned area derived from satellite observations. The Sentinel-3 SYN Fire_cci v1.1 pixel product is distributed as 6 continental tiles and is based upon surface reflectance data from the OLCI and SLSTR instruments (combined as the Synergy (SYN) product) onboard the Sentinel-3 A&B satellites. This information is complemented by VIIRS thermal information. This product, called FireCCIS311 for short, is available for the years 2019 to 2024.\r\n\r\nThe FireCCIS311 Pixel product described here includes maps at 0.002777-degree (approx. 300m) resolution. Burned area (BA) information includes 3 individual files, packed in a compressed tar.gz file: date of BA detection (labelled JD), the confidence level (CL, a probability value estimating the confidence that a pixel is actually burned), and the land cover (LC) information as defined in the Copernicus Climate Change Service (C3S) Land Cover v2.1.1 product. An unpacked version of the data is also available. For further information on the product and its format see the Product User Guide in the linked documentation." }, "objectObservation": { "ob_id": 34730, "uuid": "c98515f1934a4db68d2007b47c5a8d04", "short_code": "ob", "title": "ESA Fire Climate Change Initiative (Fire_cci): Sentinel-3 SYN Burned Area Pixel product, version 1.0", "abstract": "The ESA Fire Disturbance Climate Change Initiative (CCI) project has produced maps of global burned area derived from satellite observations. The Sentinel-3 SYN Fire_cci v1.0 pixel product is distributed as 6 continental tiles and is based upon surface reflectance data from the OLCI and SLSTR instruments (combined as the Synergy (SYN) product) onboard the Sentinel-3 A&B satellites. This information is complemented by VIIRS thermal information. This product, called FireCCIS310 for short, is currently available for 2019, but it is foreseen to be extended for additional years.\r\n\r\nThe FireCCIS310 Pixel product described here includes maps at 0.002777-degree (approx. 300m) resolution. Burned area (BA) information includes 3 individual files, packed in a compressed tar.gz file: date of BA detection (labelled JD), the confidence level (CL, a probability value estimating the confidence that a pixel is actually burned), and the land cover (LC) information as defined in the Copernicus Climate Change Service (C3S) Land Cover v2.1.1 product. An unpacked version of the data is also available. For further information on the product and its format see the Product User Guide in the linked documentation." } }, { "ob_id": 890, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 41358, "uuid": "8175ede3a1d642deba8f4cce49d7bda8", "short_code": "ob", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged methane from Sentinel-5P, generated with the WFM-DOAS algorithm, version 1.8, November 2017 - October 2023", "abstract": "This product is the column-average dry-air mole fraction of atmospheric methane, denoted XCH4. It has been retrieved from radiance measurements from the TROPOspheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor satellite in the 2.3 µm spectral range of the solar spectral range, using the Weighting Function Modified Differential Optical Absorption Spectroscopy (WFM-DOAS or WFMD) retrieval algorithm. This dataset is also referred to as CH4_S5P_WFMD. This version of the product is version 1.8, and covers the period from November 2017 - October 2023. \r\n\r\nThe WFMD algorithm is based on iteratively fitting a simulated radiance spectrum to the measured spectrum using a least-squares method. The algorithm is very fast as it is based on a radiative transfer model based look-up table scheme. The product is limited to cloud-free scenes on the Earth's day side.\r\n\r\nThese data were produced as part of the European Space Agency's (ESA) Greenhouse Gases (GHG) Climate Change Initiative (CCI) project.\r\n\r\nWhen citing this dataset, please also cite the following peer-reviewed publication: \r\nSchneising, O., Buchwitz, M., Hachmeister, J., Vanselow, S., Reuter, M., Buschmann, M., Bovensmann, H., and Burrows, J. P.: Advances in retrieving XCH4 and XCO from Sentinel-5 Precursor: improvements in the scientific TROPOMI/WFMD algorithm, Atmos. Meas. Tech., 16, 669–694, https://doi.org/10.5194/amt-16-669-2023, 2023." }, "objectObservation": { "ob_id": 38312, "uuid": "0f51318b226546c3a13e7d8a1451bbd3", "short_code": "ob", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged methane from Sentinel-5P, generated with the WFM-DOAS algorithm, version 1.5, November 2017 - December 2020", "abstract": "This product is the column-average dry-air mole fraction of atmospheric methane, denoted XCH4. It has been retrieved from radiance measurements from the TROPOspheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor satellite in the 2.3 µm spectral range of the solar spectral range, using the Weighting Function Modified Differential Optical Absorption Spectroscopy (WFM-DOAS or WFMD) retrieval algorithm. This dataset is also referred to as CH4_S5P_WFMD. This version of the product is version 1.5, and covers the period from November 2017 - December 2020. \r\n\r\nThe WFMD algorithm is based on iteratively fitting a simulated radiance spectrum to the measured spectrum using a least-squares method. The algorithm is very fast as it is based on a radiative transfer model based look-up table scheme. The product is limited to cloud-free scenes on the Earth's day side.\r\n\r\nThese data were produced as part of the European Space Agency's (ESA) Greenhouse Gases (GHG) Climate Change Initiative (CCI) project.\r\n\r\nWhen citing this dataset, please also cite the following peer-reviewed publication: \r\nSchneising, O., Buchwitz, M., Reuter, M., Bovensmann, H., Burrows, J. P., Borsdorff, T., Deutscher, N. M., Feist, D. G., Griffith, D. W. T., Hase, F., Hermans, C., Iraci, L. T., Kivi, R., Landgraf, J., Morino, I., Notholt, J., Petri, C., Pollard, D. F., Roche, S., Shiomi, K., Strong, K., Sussmann, R., Velazco, V. A., Warneke, T., and Wunch, D.: A scientific algorithm to simultaneously retrieve carbon monoxide and methane from TROPOMI onboard Sentinel-5 Precursor, Atmos. Meas. Tech., 12, 6771–6802, https://doi.org/10.5194/amt-12-6771-2019, 2019." } }, { "ob_id": 891, "relationType": "IsSupplementTo", "subjectObservation": { "ob_id": 41396, "uuid": "2bfbdba03d9b423f99cadf404ca2daab", "short_code": "ob", "title": "HadEX3: global land-surface climate extremes indices v3.0.4 (1901-2018); ETSCI extension", "abstract": "HadEX3-ETSCI is a land-surface dataset of climate extremes indices available on a 1.875 x 1.25 longitude-latitude grid. The indices in this extension to HadEX3 are those which are recommended by the World Meteorological Organization (WMO) Expert Team on Sector-specific Climate Indices (ET-SCI). Daily precipitation, as well as maximum and minimum temperature observations, are used to calculate these indices at each station. The daily data, as well as indices, have been supplied, quality controlled and combined to make a gridded set of NetCDF files covering 1901-2018 (inclusive). There are minor changes in the input data and also quality control checks which have been included in this dataset which are different to those in HadEX3, version 3.0.4, which contains parameters as defined by the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices (ETCCDI).\r\n\r\nSpatial coverage is determined by the number of stations present at each time point as well as the spatial correlation structure between the stations for each index. The spatial coverage is lowest at the beginning of the dataset, rising until around 1960 where it plateaus, and then declines slightly after 2010.\r\n\r\nIndices are available as annual and/or monthly quantities. A number of the indices use a reference period to determine thresholds. For these, we provide two versions, one set using 1961-1990 and another using the more recent 1981-2010 (these reference periods have been indicated in the file name as either 'ref-6190' or 'ref-8110').\r\n\r\nTo align this extension with the existing ETCCDI indices in HadEX3, we commence versioning at 3.0.4." }, "objectObservation": { "ob_id": 34669, "uuid": "115d5e4ebf7148ec941423ec86fa9f26", "short_code": "ob", "title": "HadEX3: Global land-surface climate extremes indices v3.0.4 (1901-2018)", "abstract": "HadEX3 is a land-surface dataset of climate extremes indices available on a 1.875 x 1.25 longitude-latitude grid. These 29 indices have been developed by the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices (ETCCDI). Daily precipitation, as well as maximum and minimum temperature observations, are used to calculate these indices at each station. The daily data, as well as indices, have been supplied, quality controlled and combined to make a gridded set of NetCDF files covering 1901-2018 (inclusive). \r\n\r\nSpatial coverage is determined by the number of stations present at each time point as well as the spatial correlation structure between the stations for each index. The spatial coverage is lowest at the beginning of the dataset, rising until around 1960 where it plateaus, and then declines slightly after 2010.\r\n\r\nAll indices are available as annual quantities, with a subset also available on a monthly basis. A number of the indices use a reference period to determine thresholds. For these, we provide two versions, one set using 1961-1990 and another using the more recent 1981-2010 (these reference periods have been indicated in the file name as either 'ref-6190' or 'ref-8110').\r\n\r\nVersion 3.0.4 was added due to an error in how the Rx1day and Rx5day data were being handled for one of the West African data sources. More details can be found in the HadEX3 blog under 'Details/Docs' tab.\r\n\r\nAdditionally, an extension to HadEX3, comprising additional indices recommended by the WMO Expert Team on Sector-specific Climate Indices (ET-SCI), has been produced. These data are available in a separate dataset connected to this record, marked as supplemental to this dataset." } }, { "ob_id": 892, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 34669, "uuid": "115d5e4ebf7148ec941423ec86fa9f26", "short_code": "ob", "title": "HadEX3: Global land-surface climate extremes indices v3.0.4 (1901-2018)", "abstract": "HadEX3 is a land-surface dataset of climate extremes indices available on a 1.875 x 1.25 longitude-latitude grid. These 29 indices have been developed by the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices (ETCCDI). Daily precipitation, as well as maximum and minimum temperature observations, are used to calculate these indices at each station. The daily data, as well as indices, have been supplied, quality controlled and combined to make a gridded set of NetCDF files covering 1901-2018 (inclusive). \r\n\r\nSpatial coverage is determined by the number of stations present at each time point as well as the spatial correlation structure between the stations for each index. The spatial coverage is lowest at the beginning of the dataset, rising until around 1960 where it plateaus, and then declines slightly after 2010.\r\n\r\nAll indices are available as annual quantities, with a subset also available on a monthly basis. A number of the indices use a reference period to determine thresholds. For these, we provide two versions, one set using 1961-1990 and another using the more recent 1981-2010 (these reference periods have been indicated in the file name as either 'ref-6190' or 'ref-8110').\r\n\r\nVersion 3.0.4 was added due to an error in how the Rx1day and Rx5day data were being handled for one of the West African data sources. More details can be found in the HadEX3 blog under 'Details/Docs' tab.\r\n\r\nAdditionally, an extension to HadEX3, comprising additional indices recommended by the WMO Expert Team on Sector-specific Climate Indices (ET-SCI), has been produced. These data are available in a separate dataset connected to this record, marked as supplemental to this dataset." }, "objectObservation": { "ob_id": 41396, "uuid": "2bfbdba03d9b423f99cadf404ca2daab", "short_code": "ob", "title": "HadEX3: global land-surface climate extremes indices v3.0.4 (1901-2018); ETSCI extension", "abstract": "HadEX3-ETSCI is a land-surface dataset of climate extremes indices available on a 1.875 x 1.25 longitude-latitude grid. The indices in this extension to HadEX3 are those which are recommended by the World Meteorological Organization (WMO) Expert Team on Sector-specific Climate Indices (ET-SCI). Daily precipitation, as well as maximum and minimum temperature observations, are used to calculate these indices at each station. The daily data, as well as indices, have been supplied, quality controlled and combined to make a gridded set of NetCDF files covering 1901-2018 (inclusive). There are minor changes in the input data and also quality control checks which have been included in this dataset which are different to those in HadEX3, version 3.0.4, which contains parameters as defined by the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices (ETCCDI).\r\n\r\nSpatial coverage is determined by the number of stations present at each time point as well as the spatial correlation structure between the stations for each index. The spatial coverage is lowest at the beginning of the dataset, rising until around 1960 where it plateaus, and then declines slightly after 2010.\r\n\r\nIndices are available as annual and/or monthly quantities. A number of the indices use a reference period to determine thresholds. For these, we provide two versions, one set using 1961-1990 and another using the more recent 1981-2010 (these reference periods have been indicated in the file name as either 'ref-6190' or 'ref-8110').\r\n\r\nTo align this extension with the existing ETCCDI indices in HadEX3, we commence versioning at 3.0.4." } }, { "ob_id": 893, "relationType": "IsPartOf", "subjectObservation": { "ob_id": 41529, "uuid": "44a0f04f9b37423e8534cc96a8cbe990", "short_code": "ob", "title": "Daily Transects and Areas from Light Detection and Ranging (LiDAR) scans of an eroding soft cliff at Happisburgh, UK (April-December 2019).", "abstract": "This dataset contains 236 transects and areas point-cloud elevation and colour intensity data collected daily along a 450 metre coastal stretch at Happisburgh, UK, over a time span of 9 months (April 6, 2019 to December 23, 2019). Included are subsets of these point-clouds, named transects and grids. Scans were taken approximately daily, and on some days only one scanner was run resulting in half-size scans. A single FARO S350 LiDAR scanner was placed at two fixed locations on the beach, spaced 178 metres alongshore and between 30 to 40 metres from the 10 metre high cliff. The duration of the scanning at each location was around 30 minutes. These data were collected to better understand the dynamic of beach-cliff and shore platform interaction along soft cliffed coasts. The shapefiles of the transects and areas are also included for a more complete description. ScanLAB Projects Ltd were responsible for the collection of the data, along with the British Geological Survey (BGS), funded by the UK Natural Environment Research Council (NE/M004996/1; BLUE-coast project)." }, "objectObservation": { "ob_id": 40044, "uuid": "b8cf940850164ebeb4cba343384f88b8", "short_code": "ob", "title": "Daily Light Detection and Ranging (LiDAR) scans of an eroding soft cliff at Happisburgh, UK (October-December 2019)", "abstract": "This dataset contains 67 point-cloud elevation and colour intensity data collected daily along a 450 metre coastal stretch at Happisburgh, UK, over a time span of 3 months (October 2, 2019 to December 23, 2019). Also included are subsets of these point-clouds, named slices and grids. Scans were taken approximately daily, and on some days only one scanner was run resulting in half-size scans. A single FARO S350 LiDAR scanner was placed at two fixed locations on the beach, spaced 178 metres alongshore and between 30 to 40 metres from the 10 metre high cliff. The duration of the scanning at each location was around 30 minutes. These data were collected to better understand the dynamic of beach-cliff and shore platform interaction along soft cliffed coasts. ScanLAB Projects Ltd were responsible for the collection of the data, along with the British Geological Survey (BGS), funded by the UK Natural Environment Research Council (NE/M004996/1; BLUE-coast project). This was a continuation of an Innovate UK project undertaken by ScanLAB Projects Ltd." } }, { "ob_id": 894, "relationType": "IsPartOf", "subjectObservation": { "ob_id": 41529, "uuid": "44a0f04f9b37423e8534cc96a8cbe990", "short_code": "ob", "title": "Daily Transects and Areas from Light Detection and Ranging (LiDAR) scans of an eroding soft cliff at Happisburgh, UK (April-December 2019).", "abstract": "This dataset contains 236 transects and areas point-cloud elevation and colour intensity data collected daily along a 450 metre coastal stretch at Happisburgh, UK, over a time span of 9 months (April 6, 2019 to December 23, 2019). Included are subsets of these point-clouds, named transects and grids. Scans were taken approximately daily, and on some days only one scanner was run resulting in half-size scans. A single FARO S350 LiDAR scanner was placed at two fixed locations on the beach, spaced 178 metres alongshore and between 30 to 40 metres from the 10 metre high cliff. The duration of the scanning at each location was around 30 minutes. These data were collected to better understand the dynamic of beach-cliff and shore platform interaction along soft cliffed coasts. The shapefiles of the transects and areas are also included for a more complete description. ScanLAB Projects Ltd were responsible for the collection of the data, along with the British Geological Survey (BGS), funded by the UK Natural Environment Research Council (NE/M004996/1; BLUE-coast project)." }, "objectObservation": { "ob_id": 40331, "uuid": "d3018891eac34460a7723811a2b69580", "short_code": "ob", "title": "Daily Light Detection and Ranging (LiDAR) scans of an eroding soft cliff at Happisburgh, UK (April-September 2019)", "abstract": "This dataset contains 169 point-cloud elevation and colour intensity data collected daily along a 450 metre coastal stretch at Happisburgh, UK, over a time span of 6 months (April 6, 2019 to September 30, 2019). Also included are subsets of these point-clouds, named slices and grids. Scans were taken approximately daily, and on some days only one scanner was run resulting in half-size scans. A single FARO S350 LiDAR scanner was placed at two fixed locations on the beach, spaced 178 metres alongshore and between 30 to 40 metres from the 10 metre high cliff. The duration of the scanning at each location was around 30 minutes. This data was collected to better understand the dynamic of beach-cliff and shore platform interaction along soft cliffed coasts. ScanLAB Projects Ltd were responsible for the collection of the data under the United Kingdom Research and Innovation (UKRI) Innovate UK funded project “Multiscale 3D Scanning with Framerate for TV and Immersive Applications”. The data are restricted to non-commercial use." } }, { "ob_id": 895, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 41400, "uuid": "45b5b1e556da448089e2b57452f277f5", "short_code": "ob", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Northern hemisphere sea ice thickness from the CryoSat-2 satellite on a monthly grid (L3C), v3.0", "abstract": "This dataset provides a Climate Data Record of Sea Ice Thickness for the Northern Hemisphere polar region, derived from the SIRAL (SAR Interferometer Radar ALtimeter) instrument on the CryoSat-2 satellite at Level 3C (L3C). This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.\r\n\r\nIt provides monthly gridded sea ice thickness data on a Lambeth Azimuthal Equal Area grid for the period November 2010 to April 2020. Data are only available for the NH winter months, October - April." }, "objectObservation": { "ob_id": 25872, "uuid": "ff79d140824f42dd92b204b4f1e9e7c2", "short_code": "ob", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Northern hemisphere sea ice thickness from the CryoSat-2 satellite on a monthly grid (L3C), v2.0", "abstract": "This dataset provides a Climate Data Record of Sea Ice Thickness for the Northern Hemisphere polar region, derived from the SIRAL (SAR Interferometer Radar ALtimeter) instrument on the CryoSat-2 satellite at Level 3C (L3C). This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.\r\n\r\nIt provides monthly gridded sea ice thickness data on a Lambeth Azimuthal Equal Area grid for the period November 2010 to April 2017. Data are only available for the NH winter months, October - April." } }, { "ob_id": 896, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 41401, "uuid": "83b11005a3d7472eb57df4f90933c462", "short_code": "ob", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Northern hemisphere sea ice thickness from the Envisat satellite on a monthly grid (L3C), v3.0", "abstract": "This dataset provides a Climate Data Record of Sea Ice Thickness for the northern hemisphere polar region, derived from the RA-2 (Radar Altimeter -2) instrument on the ENVISAT satellite at Level 3C (L3C). This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.\r\n\r\nIt provides monthly gridded sea ice thickness data on a Lambeth Azimuthal Equal Area grid for the period October 2002 to March 2012. Data is only available for the NH winter months, October - April." }, "objectObservation": { "ob_id": 25867, "uuid": "f4c34f4f0f1d4d0da06d771f6972f180", "short_code": "ob", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Northern hemisphere sea ice thickness from the Envisat satellite on a monthly grid (L3C), v2.0", "abstract": "This dataset provides a Climate Data Record of Sea Ice Thickness for the northern hemisphere polar region, derived from the RA-2 (Radar Altimeter -2) instrument on the ENVISAT satellite at Level 3C (L3C). This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.\r\n\r\nIt provides monthly gridded sea ice thickness data on a Lambeth Azimuthal Equal Area grid for the period October 2002 to March 2012. Data is only available for the NH winter months, October - April." } }, { "ob_id": 897, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 41402, "uuid": "67b003a864cd4e9ebeccd29fbdf4447e", "short_code": "ob", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Southern hemisphere sea ice thickness from the CryoSat-2 satellite on a monthly grid (L3C), v3.0", "abstract": "This dataset provides a Climate Data Record of Sea Ice Thickness for the SH polar region, derived from the SIRAL (SAR Interferometer Radar ALtimeter) instrument on the CryoSat-2 satellite at Level 3C (L3C). This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.\r\n\r\nIt provides daily sea ice thickness data gridded on a Lambeth Azimuthal Equal Area grid for the period November 2010 to April 2020. 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The CDR accurately maps the surface temperature of the global oceans over the period 1980 to 2021 using observations from many satellites, with a high degree of independence from in situ measurements. The data provide independently quantified SSTs to a quality suitable for climate research. \r\n\r\nThis CDR Version 3.0 product supersedes the CDR v2.2 product. Compared to the previous version the major changes are: \r\n\r\n* Longer time series: 1980 to 2021 (previous CDR was Sept 1981 to 2016) \r\n* Improved retrieval to reduce systematic biases using bias-aware optimal methods (for single view sensors) \r\n* Improved retrieval with respect to desert-dust aerosols \r\n* Addition of dual-view SLSTR data from 2016 onwards \r\n* Addition of early AVHRR/1 data in 1980s, and improved AVHRR processing to reduce data gaps in 1980s \r\n* Use of full-resolution MetOp AVHRR data (previously used ‘global area coverage’ Level 1 data) \r\n* Inclusion of L2P passive microwave AMSR data \r\n\r\nData are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ \r\n\r\nWhen citing this dataset please also cite the associated data paper: \r\n\r\nEmbury, O., Merchant, C.J., Good, S.A., Rayner, N.A., Høyer, J.L., Atkinson, C., Block, T., Alerskans, E., Pearson, K.J., Worsfold, M., McCarroll, N., Donlon, C. Satellite-based time-series of sea-surface temperature since 1980 for climate applications. Scientific Data 11, 326 (2024). https://doi.org/10.1038/s41597-024-03147-w" }, "objectObservation": { "ob_id": 40619, "uuid": "dc11996a68c446abb342e917efdaac30", "short_code": "ob", "title": "ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Climatology Climate Data Record, version 2.2", "abstract": "This v2.2 SST_cci Climatology Data Record (CDR) consists of daily climatological mean sea surface temperature on a global 0.05 degree latitude-longitude grid, derived from the SST CCI analysis data for the period 1982 to 2010 (29 years). This climatology includes the post-hoc dust corrections from Merchant and Embury (2020) https://doi.org/10.3390/rs12162554.\r\n\r\nThe changes from climatology v2.1 are:\r\n* Inclusion of post-hoc dust corrections from Merchant and Embury (2020) reduces biases in affected regions (tropical Atlantic Ocean and the Mediterranean, Red, and Arabian Seas).\r\n* Improved compliance with CF Conventions.\r\n\r\nData are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ . \r\n\r\nWhen citing this dataset please also cite the associated data paper: Merchant, C.J., Embury, O., Bulgin, C.E., Block T., Corlett, G.K., Fiedler, E., Good, S.A., Mittaz, J., Rayner, N.A., Berry, D., Eastwood, S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R., Donlon, C. (2019) Satellite-based time-series of sea-surface temperature since 1981 for climate applications, Scientific Data 6:223. http://doi.org/10.1038/s41597-019-0236-x" } }, { "ob_id": 915, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 40866, "uuid": "4a9654136a7148e39b7feb56f8bb02d2", "short_code": "ob", "title": "ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Level 4 Analysis product, version 3.0", "abstract": "This dataset provides daily-mean sea surface temperatures (SST), presented on global 0.05° latitude-longitude grid, spanning 1980 to present. This is a Level 4 product, with gaps between available daily observations filled by statistical means.\r\n\r\nThe SST CCI Analysis product contains estimates of daily mean SST and sea ice concentration. Each SST value has an associated uncertainty estimate. \r\n\r\nThe dataset has been produced as part of the version 3 Climate Data Record (CDR) produced by the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project (ESA SST_cci). The CDR accurately maps the surface temperature of the global oceans over the period 1980 to 2021 using observations from many satellites, with a high degree of independence from in situ measurements. The data provide independently quantified SSTs to a quality suitable for climate research. \r\n\r\nData from 2022 onwards are provided as an Interim Climate Data Record (ICDR) and will be updated daily at one month behind present. The Copernicus Climate Change Service (C3S) funded the development of the ICDR extension and production of the ICDR during 2022. From 2023 onwards the production of the ICDR is funded by the UK Earth Observation Climate Information Service (EOCIS) and Marine and Climate Advisory Service (MCAS). \r\n\r\nThis CDR Version 3.0 product supersedes the CDR v2.1 product. Compared to the previous version the major changes are: \r\n\r\n* Longer time series: 1980 to 2021 (previous CDR was Sept 1981 to 2016) \r\n\r\n* Improved retrieval to reduce systematic biases using bias-aware optimal methods (for single view sensors) \r\n\r\n* Improved retrieval with respect to desert-dust aerosols \r\n\r\n* Addition of dual-view SLSTR data from 2016 onwards \r\n\r\n* Addition of early AVHRR/1 data in 1980s, and improved AVHRR processing to reduce data gaps in 1980s \r\n\r\n* Use of full-resolution MetOp AVHRR data (previously used ‘global area coverage’ Level 1 data) \r\n\r\n* Inclusion of L2P passive microwave AMSR data \r\n\r\nData are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ \r\n\r\nWhen citing this dataset please also cite the associated data paper: \r\n\r\nEmbury, O., Merchant, C.J., Good, S.A., Rayner, N.A., Høyer, J.L., Atkinson, C., Block, T., Alerskans, E., Pearson, K.J., Worsfold, M., McCarroll, N., Donlon, C. Satellite-based time-series of sea-surface temperature since 1980 for climate applications. Scientific Data 11, 326 (2024). https://doi.org/10.1038/s41597-024-03147-w" }, "objectObservation": { "ob_id": 27532, "uuid": "62c0f97b1eac4e0197a674870afe1ee6", "short_code": "ob", "title": "ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Level 4 Analysis Climate Data Record, version 2.1", "abstract": "This v2.1 SST_cci Level 4 Analysis Climate Data Record (CDR) provides a globally-complete daily analysis of sea surface temperature (SST) on a 0.05 degree regular latitude - longitude grid. It combines data from both the Advanced Very High Resolution Radiometer (AVHRR ) and Along Track Scanning Radiometer (ATSR) SST_cci Climate Data Records, using a data assimilation method to provide SSTs where there were no measurements. These data cover the period between 09/1981 and 12/2016.\r\n\r\nThe dataset has been produced as part of the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project(ESA SST_cci). The data products from SST_cci accurately map the surface temperature of the global oceans over the period 1981 to 2016 using observations from many satellites. The data provide independently quantified SSTs to a quality suitable for climate research.\r\n\r\nThe CDR Version 2.1 product supercedes the CDR Version 2.0 product. Data are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/\r\n\r\nWhen citing this dataset please also cite the associated data paper: Merchant, C.J., Embury, O., Bulgin, C.E., Block T., Corlett, G.K., Fiedler, E., Good, S.A., Mittaz, J., Rayner, N.A., Berry, D., Eastwood, S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R., Donlon, C. Satellite-based time-series of sea-surface temperature since 1981 for climate applications, Scientific Data 6:223 (2019). http://doi.org/10.1038/s41597-019-0236-x" } }, { "ob_id": 916, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 41271, "uuid": "20ec12f5d1f94e99aff2ed796264ee65", "short_code": "ob", "title": "ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost Ground Temperature for the Northern Hemisphere, v4.0", "abstract": "This dataset contains v4.0 permafrost ground temperature data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the third version of their Climate Research Data Package (CRDP v3). It is derived from a thermal model driven and constrained by satellite data. CRDPv3 covers the years from 1997 to 2021. Grid products of CDRP v3 are released in annual files, covering the start to the end of the Julian year. This corresponds to average annual ground temperatures and is provided for specific depths (surface, 1m, 2m, 5m , 10m). \r\n\r\nCase A: It covers the Northern Hemisphere (north of 30°) for the period 2003-2021 based on MODIS \r\nLand Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data. \r\nCase B: It covers the Northern Hemisphere (north of 30°) for the period 1997-2002 based on downscaled \r\nERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for \r\nthe overlap period 2003-2021 using a pixel-specific statistics for each day of the year." }, "objectObservation": { "ob_id": 32619, "uuid": "b25d4a6174de4ac78000d034f500a268", "short_code": "ob", "title": "ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost Ground Temperature for the Northern Hemisphere, v3.0", "abstract": "This dataset contains permafrost ground temperature data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the second version of their Climate Research Data Package (CRDP v2). It is derived from a thermal model driven and constrained by satellite data. Grid products of CDRP v2 are released in annual files, covering the start to the end of the Julian year. This corresponds to average annual ground temperatures and is provided for specific depths (surface, 1m, 2m, 5m , 10m).\r\n\r\nCase A: This covers the Northern Hemisphere (north of 30°) for the period 2003-2019 based on MODIS Land Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data.\r\nCase B: This covers the Northern Hemisphere (north of 30°) for the period 1997-2002 based on downscaled ERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for the overlap period 2003-2019 using a pixel-specific statistics for each day of the year." } }, { "ob_id": 917, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 41269, "uuid": "93444bc1c4364a59869e004bf9bfd94a", "short_code": "ob", "title": "ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost extent for the Northern Hemisphere, v4.0", "abstract": "This dataset contains v4.0 permafrost extent data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the third version of their Climate Research Data Package (CRDP v3). It is derived from a thermal model driven and constrained by satellite data. CRDPv3 covers the years from 1997 to 2021. Grid products of CDRP v3 are released in annual files, covering the start to the end of the Julian year. This corresponds to average annual ground temperatures (at 2 m depth) which forms the basis for the retrieval of yearly fraction of permafrost-underlain and permafrost-free area within a pixel. A classification according to the IPA (International Permafrost Association) zonation delivers the well-known permafrost zones, distinguishing isolated (0-10%) sporadic (10-50%), discontinuous (50-90%) and continuous permafrost (90-100%). \r\n\r\nCase A: It covers the Northern Hemisphere (north of 30°) for the period 2003-2021 based on MODIS \r\nLand Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data.\r\nCase B: It covers the Northern Hemisphere (north of 30°) for the period 1997-2002 based on downscaled ERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for the overlap period 2003-2021 using a pixel-specific statistics for each day of the year." }, "objectObservation": { "ob_id": 32614, "uuid": "6e2091cb0c8b4106921b63cd5357c97c", "short_code": "ob", "title": "ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost extent for the Northern Hemisphere, v3.0", "abstract": "This dataset contains permafrost extent data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the second version of their Climate Research Data Package (CRDP v2). It is derived from a thermal model driven and constrained by satellite data. Grid products of CDRP v2 are released in annual files, covering the start to the end of the Julian year. This corresponds to average annual ground temperatures (at 2 m depth) which forms the basis for the retrieval of yearly fraction of permafrost-underlain and permafrost-free area within a pixel. A classification according to the IPA (International Permafrost Association) zonation delivers the well-known permafrost zones, distinguishing isolated (0-10%) sporadic (10-50%), discontinuous (50-90%) and continuous permafrost (90-100%).\r\n\r\nCase A: This covers the Northern Hemisphere (north of 30°) for the period 2003-2019 based on MODIS Land Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data. \r\nCase B: This covers the Northern Hemisphere (north of 30°) for the period 1997-2002 based on downscaled ERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for the overlap period 2003-2019 using a pixel-specific statistics for each day of the year." } }, { "ob_id": 918, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 41270, "uuid": "d34330ce3f604e368c06d76de1987ce5", "short_code": "ob", "title": "ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost active layer thickness for the Northern Hemisphere, v4.0", "abstract": "This dataset contains v4.0 permafrost active layer thickness data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the third version of their Climate Research Data Package (CRDP v3). It is derived from a thermal model driven and constrained by satellite data. CRDPv3 covers the years from 1997 to 2021. Grid products of CDRP v3 are released in annual files, covering the start to the end of the Julian year. The maximum depth of seasonal thaw is provided, which corresponds to the active layer thickness. \r\n\r\nCase A: It covers the Northern Hemisphere (north of 30°) for the period 2003-2021 based on MODIS \r\nLand Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data. \r\nCase B: It covers the Northern Hemisphere (north of 30°) for the period 1997-2002 based on downscaled \r\nERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for \r\nthe overlap period 2003-2021 using a pixel-specific statistics for each day of the year." }, "objectObservation": { "ob_id": 32612, "uuid": "67a3f8c8dc914ef99f7f08eb0d997e23", "short_code": "ob", "title": "ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost active layer thickness for the Northern Hemisphere, v3.0", "abstract": "This dataset contains permafrost active layer thickness data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the second version of their Climate Research Data Package (CRDP v2). It is derived from a thermal model driven and constrained by satellite data. Grid products of CDRP v2 are released in annual files, covering the start to the end of the Julian year. The maximum depth of seasonal thaw is provided, which corresponds to the active layer thickness.\r\n\r\nCase A: This covers the Northern Hemisphere (north of 30°) for the period 2003-2019 based on MODIS Land Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data.\r\nCase B: This covers the Northern Hemisphere (north of 30°) for the period 1997-2002 based on downscaled ERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for the overlap period 2003-2019 using a pixel-specific statistics for each day of the year." } }, { "ob_id": 919, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 40843, "uuid": "7fc9df8070d34cacab8092e45ef276f1", "short_code": "ob", "title": "ESA Lakes Climate Change Initiative (Lakes_cci): Lake products, Version 2.1", "abstract": "This dataset contains the Lakes Essential Climate Variable, which is comprised of processed satellite observations at the global scale, over the period 1992-2022, for over 2000 inland water bodies. This dataset was produced by the European Space Agency (ESA) Lakes Climate Change Initiative (Lakes_cci) project. For more information about the Lakes_cci please visit the project website. \r\n\r\nThis is version 2.1.0 of the dataset.\r\n\r\nThe six thematic climate variables included in this dataset are:\r\n• Lake Water Level (LWL), derived from satellite altimetry, is fundamental to understand the balance between water inputs and water loss and their connection with regional and global climate change.\r\n• Lake Water Extent (LWE), modelled from the relation between LWL and high-resolution spatial extent observed at set time-points, describes the areal extent of the water body. This allows the observation of drought in arid environments, expansion in high Asia, or impact of large-scale atmospheric oscillations on lakes in tropical regions for example. .\r\n• Lake Surface Water temperature (LSWT), derived from optical and thermal satellite observations, is correlated with regional air temperatures and is informative about vertical mixing regimes, driving biogeochemical cycling and seasonality.\r\n• Lake Ice Cover (LIC), determined from optical observations, describes the freeze-up in autumn and break-up of ice in spring, which are proxies for gradually changing climate patterns and seasonality.\r\n• Lake Water-Leaving Reflectance (LWLR), derived from optical satellite observations, is a direct indicator of biogeochemical processes and habitats in the visible part of the water column (e.g. seasonal phytoplankton biomass fluctuations), and an indicator of the frequency of extreme events (peak terrestrial run-off, changing mixing conditions).\r\n• Lake Ice Thickness (LIT), containing LIT information over Great Slave lake from 2002-2022.\r\n\r\nData generated in the Lakes_cci are derived from multiple satellite sensors including: TOPEX/Poseidon, Jason, ENVISAT, SARAL, Sentinel 2-3, Landsat 4, 5, 7 and 8, ERS-1, ERS-2, Terra/Aqua and Metop-A/B.\r\n\r\nSatellite sensors associated with the thematic climate variables are as follows:\r\nLWL: TOPEX/Poseidon, Jason-1, Jason-2, Jason-3, Sentinel-6A, Envisat RA/RA-2, SARAL AltiKa, GFO, Sentinel-3A SRAL, Sentinel-3B SRAL, ERS-1 RA, ERS-2; \r\nLWE: Landsat 4 TM, 5 TM, 7 ETM+, 8 OLI, Sentinel-1 C-band SAR, Sentinel-2 MSI, Sentinel-3A SRAL, Sentinel-3B SRAL, ERS-1 AMI, ERS-2 AMI;\r\nLSWT: Envisat AATSR, Terra/Aqua MODIS, Sentinel-3A ATTSR-2, Sentinel-3B, ERS-2 AVHRR, Metop-A/B; \r\nLIC: Terra/Aqua MODIS; \r\nLWLR: Envisat MERIS, Sentinel-3A OLCI A/B, Aqua MODIS;\r\nLIT: Jason1, Jason2, Jason3, POSEIDON-2, POSEIDON-3 and POSEIDON-3B.\r\n\r\nDetailed information about the generation and validation of this dataset is available from the Lakes_cci documentation available on the project website and in Carrea, L., Crétaux, JF., Liu, X. et al. Satellite-derived multivariate world-wide lake physical variable timeseries for climate studies. Sci Data 10, 30 (2023). https://doi.org/10.1038/s41597-022-01889-z" }, "objectObservation": { "ob_id": 37381, "uuid": "a07deacaffb8453e93d57ee214676304", "short_code": "ob", "title": "ESA Lakes Climate Change Initiative (Lakes_cci): Lake products, Version 2.0.2", "abstract": "This dataset contains the Lakes Essential Climate Variable, which is comprised of processed satellite observations at the global scale, over the period 1992-2020, for over 2000 inland water bodies. This dataset was produced by the European Space Agency (ESA) Lakes Climate Change Initiative (Lakes_cci) project. For more information about the Lakes_cci please visit the project website. \r\n\r\nThis is version 2.0.2 of the dataset. \r\n\r\nThe five thematic climate variables included in this dataset are:\r\n• Lake Water Level (LWL), derived from satellite altimetry, is fundamental to understand the balance between water inputs and water loss and their connection with regional and global climate change.\r\n• Lake Water Extent (LWE), modelled from the relation between LWL and high-resolution spatial extent observed at set time-points, describes the areal extent of the water body. This allows the observation of drought in arid environments, expansion in high Asia, or impact of large-scale atmospheric oscillations on lakes in tropical regions for example. .\r\n• Lake Surface Water temperature (LSWT), derived from optical and thermal satellite observations, is correlated with regional air temperatures and is informative about vertical mixing regimes, driving biogeochemical cycling and seasonality.\r\n• Lake Ice Cover (LIC), determined from optical observations, describes the freeze-up in autumn and break-up of ice in spring, which are proxies for gradually changing climate patterns and seasonality.\r\n• Lake Water-Leaving Reflectance (LWLR), derived from optical satellite observations, is a direct indicator of biogeochemical processes and habitats in the visible part of the water column (e.g. seasonal phytoplankton biomass fluctuations), and an indicator of the frequency of extreme events (peak terrestrial run-off, changing mixing conditions).\r\n\r\nData generated in the Lakes_cci are derived from multiple satellite sensors including: TOPEX/Poseidon, Jason, ENVISAT, SARAL, Sentinel 2-3, Landsat OLI, ERS, MODIS Terra/Aqua and Metop.\r\n\r\nDetailed information about the generation and validation of this dataset is available from the Lakes_cci documentation available on the project website and in Carrea, L., Crétaux, JF., Liu, X. et al. Satellite-derived multivariate world-wide lake physical variable timeseries for climate studies. Sci Data 10, 30 (2023). https://doi.org/10.1038/s41597-022-01889-z" } }, { "ob_id": 920, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 40865, "uuid": "a104ed92bddd4c56b11127d4cc49b8d4", "short_code": "ob", "title": "ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Sea and Land Surface Temperature Radiometer (SLSTR) Level 3 Collated (L3C) product, version 3.0", "abstract": "This dataset provides global sea surface temperatures (SST) from Sea and Land Surface Temperature Radiometers (SLSTR), daily collations on a 0.05° latitude-longitude grid, spanning 2016 to present, and separated into daytime and night-time files. \r\n\r\nThe SST CCI SLSTR product contains two different SST estimates. The first is the skin temperature of the water at the time it was observed. The second is an estimate of the temperature at 20 cm depth at either 1030h or 2230h local time, which closely approximates the daily mean SST. Each SST value has an associated total uncertainty estimate, and uncertainty estimates for various contributions to that total. \r\n \r\nThe dataset has been produced as part of the version 3 Climate Data Record (CDR) produced by the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project (ESA SST_cci). The CDR accurately maps the surface temperature of the global oceans over the period 1980 to 2021 using observations from many satellites, with a high degree of independence from in situ measurements. The data provide independently quantified SSTs to a quality suitable for climate research. \r\n\r\nData from 2022 onwards are provided as an Interim Climate Data Record (ICDR) and will be updated daily at one month behind present. The Copernicus Climate Change Service (C3S) funded the development of the ICDR extension and production of the ICDR during 2022. From 2023 onwards the production of the ICDR is funded by the UK Earth Observation Climate Information Service (EOCIS) and Marine and Climate Advisory Service (MCAS). \r\n\r\nData are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ \r\n\r\nWhen citing this dataset please also cite the associated data paper: \r\n\r\nEmbury, O., Merchant, C.J., Good, S.A., Rayner, N.A., Høyer, J.L., Atkinson, C., Block, T., Alerskans, E., Pearson, K.J., Worsfold, M., McCarroll, N., Donlon, C. Satellite-based time-series of sea-surface temperature since 1980 for climate applications. Scientific Data 11, 326 (2024). https://doi.org/10.1038/s41597-024-03147-w" }, "objectObservation": { "ob_id": 39984, "uuid": "04f822f5cce4436ab88ac1f9a5109441", "short_code": "ob", "title": "Copernicus Climate Change Service Dataset: Sea Surface Temperature Integrated Climate Data Record (ICDR) from the SLSTR instrument on Sentinel 3, Level 3C (L3C), version 2.1", "abstract": "This dataset provides gridded Sea Surface Temperature data derived from the Sea and Land Surface Temperature Radiometer(SLSTR) on the Sentinel-3 series of satellites. \r\n\r\nThis dataset is produced as an Intermediate Climate Data Record for the Copernicus Climate Change Service (C3S). V2.1 extends from 2017-2022.\r\n\r\nA historic Climate Data Record (CDR) has also been produced under the ESA Climate Change Initiative Sea Surface Temperature (CCI_sst). This is available as a separate dataset in the CEDA catalogue and through the ESA CCI Open Data Portal." } }, { "ob_id": 921, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 40862, "uuid": "be418645dfa542df86165a7caad24284", "short_code": "ob", "title": "ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Advanced Very High Resolution Radiometer (AVHRR) Level 3 Collated (L3C) product, version 3.0", "abstract": "This dataset provides global sea surface temperatures (SST) from Advanced Very High Resolution Radiometers (AVHRR), daily collations on a 0.05° latitude-longitude grid, spanning 1980 to present, and separated into daytime and night-time files. \r\n\r\nThe SST CCI AVHRR product contains two different SST estimates. The first is the skin temperature of the water at the time it was observed. The second is an estimate of the temperature at 20 cm depth at either 1030h or 2230h local time, which closely approximates the daily mean SST. Each SST value has an associated total uncertainty estimate, and uncertainty estimates for various contributions to that total. \r\n\r\nThe dataset has been produced as part of the version 3 Climate Data Record (CDR) produced by the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project (ESA SST_cci). The CDR accurately maps the surface temperature of the global oceans over the period 1980 to 2021 using observations from many satellites, with a high degree of independence from in situ measurements. The data provide independently quantified SSTs to a quality suitable for climate research. \r\n\r\nData from 2022 onwards are provided as an Interim Climate Data Record (ICDR) and will be updated daily at one month behind present. The Copernicus Climate Change Service (C3S) funded the development of the ICDR extension and production of the ICDR during 2022. From 2023 onwards the production of the ICDR is funded by the UK Earth Observation Climate Information Service (EOCIS) and Marine and Climate Advisory Service (MCAS). \r\n\r\nThis CDR Version 3.0 product supersedes the CDR v2.1 product. Compared to the previous version the major changes are: \r\n\r\n* Longer time series: 1980 to 2021 (previous CDR was Sept 1981 to 2016) \r\n\r\n* Improved retrieval to reduce systematic biases using bias-aware optimal methods (for single view sensors) \r\n\r\n* Improved retrieval with respect to desert-dust aerosols \r\n\r\n* Addition of early AVHRR/1 data in 1980s, and improved AVHRR processing to reduce data gaps in 1980s \r\n\r\n* Use of full-resolution MetOp AVHRR data (previously used ‘global area coverage’ Level 1 data) \r\n\r\nData are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ \r\n\r\nWhen citing this dataset please also cite the associated data paper: \r\n\r\nEmbury, O., Merchant, C.J., Good, S.A., Rayner, N.A., Høyer, J.L., Atkinson, C., Block, T., Alerskans, E., Pearson, K.J., Worsfold, M., McCarroll, N., Donlon, C. Satellite-based time-series of sea-surface temperature since 1980 for climate applications. Scientific Data 11, 326 (2024). https://doi.org/10.1038/s41597-024-03147-w" }, "objectObservation": { "ob_id": 39978, "uuid": "86d55a4576284365906f661aa547e566", "short_code": "ob", "title": "Copernicus Climate Change Service Dataset: Sea Surface Temperature Integrated Climate Data Record (ICDR) from the Advanced Very High Resolution Radiometer (AVHRR), Level 3C (L3C), version 2.1", "abstract": "This dataset provides gridded Sea Surface Temperature data derived from the Advance Very High Resolution Radiometer (AVHRR) series of satellites. Data is available separately for the AVHRR instruments on NOAA-19, METOP-A and METOP-B.\r\n\r\nThis dataset is produced as an Intermediate Climate Data Record for the Copernicus Climate Change Service (C3S). V2.1 extends from 2017-2022.\r\n\r\nA historic Climate Data Record (CDR) has also been produced under the ESA Climate Change Initiative Sea Surface Temperature (CCI_sst). This is available as a separate dataset in the CEDA catalgoue and through the ESA CCI Open Data Portal." } }, { "ob_id": 922, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 40862, "uuid": "be418645dfa542df86165a7caad24284", "short_code": "ob", "title": "ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Advanced Very High Resolution Radiometer (AVHRR) Level 3 Collated (L3C) product, version 3.0", "abstract": "This dataset provides global sea surface temperatures (SST) from Advanced Very High Resolution Radiometers (AVHRR), daily collations on a 0.05° latitude-longitude grid, spanning 1980 to present, and separated into daytime and night-time files. \r\n\r\nThe SST CCI AVHRR product contains two different SST estimates. The first is the skin temperature of the water at the time it was observed. The second is an estimate of the temperature at 20 cm depth at either 1030h or 2230h local time, which closely approximates the daily mean SST. Each SST value has an associated total uncertainty estimate, and uncertainty estimates for various contributions to that total. \r\n\r\nThe dataset has been produced as part of the version 3 Climate Data Record (CDR) produced by the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project (ESA SST_cci). The CDR accurately maps the surface temperature of the global oceans over the period 1980 to 2021 using observations from many satellites, with a high degree of independence from in situ measurements. The data provide independently quantified SSTs to a quality suitable for climate research. \r\n\r\nData from 2022 onwards are provided as an Interim Climate Data Record (ICDR) and will be updated daily at one month behind present. The Copernicus Climate Change Service (C3S) funded the development of the ICDR extension and production of the ICDR during 2022. From 2023 onwards the production of the ICDR is funded by the UK Earth Observation Climate Information Service (EOCIS) and Marine and Climate Advisory Service (MCAS). \r\n\r\nThis CDR Version 3.0 product supersedes the CDR v2.1 product. Compared to the previous version the major changes are: \r\n\r\n* Longer time series: 1980 to 2021 (previous CDR was Sept 1981 to 2016) \r\n\r\n* Improved retrieval to reduce systematic biases using bias-aware optimal methods (for single view sensors) \r\n\r\n* Improved retrieval with respect to desert-dust aerosols \r\n\r\n* Addition of early AVHRR/1 data in 1980s, and improved AVHRR processing to reduce data gaps in 1980s \r\n\r\n* Use of full-resolution MetOp AVHRR data (previously used ‘global area coverage’ Level 1 data) \r\n\r\nData are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ \r\n\r\nWhen citing this dataset please also cite the associated data paper: \r\n\r\nEmbury, O., Merchant, C.J., Good, S.A., Rayner, N.A., Høyer, J.L., Atkinson, C., Block, T., Alerskans, E., Pearson, K.J., Worsfold, M., McCarroll, N., Donlon, C. Satellite-based time-series of sea-surface temperature since 1980 for climate applications. Scientific Data 11, 326 (2024). https://doi.org/10.1038/s41597-024-03147-w" }, "objectObservation": { "ob_id": 27530, "uuid": "7db4459605da4665b6ab9a7102fb4875", "short_code": "ob", "title": "ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Advanced Very High Resolution Radiometer (AVHRR) Level 3 Collated (L3C) Climate Data Record, version 2.1", "abstract": "This v2.1 SST_cci Advanced Very High Resolution Radiometer (AVHRR) Level 3 Collated (L3C) Climate Data Record (CDR) consists of stable, low-bias sea surface temperature (SST) data from the AVHRR series of satellite instruments. It covers the period between 08/1981 and 12/2016. This L3C product provides these SST data on a 0.05 regular latitude-longitude grid and collated to include all orbits for a day (separated into daytime and nighttime files).\r\n\r\nThe dataset has been produced as part of the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project(ESA SST_cci). The data products from SST_cci accurately map the surface temperature of the global oceans over the period 1981 to 2016 using observations from many satellites. The data provide independently quantified SSTs to a quality suitable for climate research.\r\n\r\nThis CDR Version 2.1 product supercedes the CDR Version 2.0 product. Data are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ .\r\n\r\nWhen citing this dataset please also cite the associated data paper: Merchant, C.J., Embury, O., Bulgin, C.E., Block T., Corlett, G.K., Fiedler, E., Good, S.A., Mittaz, J., Rayner, N.A., Berry, D., Eastwood, S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R., Donlon, C. Satellite-based time-series of sea-surface temperature since 1981 for climate applications, Scientific Data 6:223 (2019). http://doi.org/10.1038/s41597-019-0236-x" } }, { "ob_id": 923, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 40861, "uuid": "c1d393f990fb4b6688b048222833d92f", "short_code": "ob", "title": "ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Advanced Very High Resolution Radiometer (AVHRR) Level 3 Uncollated (L3U) product, version 3.0", "abstract": "This dataset provides global sea surface temperatures (SST) from Advanced Very High Resolution Radiometers (AVHRR), presented on a 0.05° latitude-longitude grid, and spanning 1980 to 2021. \r\n\r\nThe SST CCI AVHRR product contains two different SST estimates. The first is the skin temperature of the water at the time it was observed. The second is an estimate of the temperature at 20 cm depth at either 1030h or 2230h local time, which closely approximates the daily mean SST. Each SST value has an associated total uncertainty estimate, and uncertainty estimates for various contributions to that total. \r\n\r\nThe dataset has been produced as part of the version 3 Climate Data Record (CDR) produced by the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project (ESA SST_cci). The CDR accurately maps the surface temperature of the global oceans over the period 1980 to 2021 using observations from many satellites, with a high degree of independence from in situ measurements. The data provide independently quantified SSTs to a quality suitable for climate research. \r\n\r\nThis CDR Version 3.0 product supersedes the CDR v2.1 product. Compared to the previous version the major changes are: \r\n\r\n* Longer time series: 1980 to 2021 (previous CDR was Sept 1981 to 2016) \r\n\r\n* Improved retrieval to reduce systematic biases using bias-aware optimal methods (for single view sensors) \r\n\r\n* Improved retrieval with respect to desert-dust aerosols \r\n\r\n* Addition of early AVHRR/1 data in 1980s, and improved AVHRR processing to reduce data gaps in 1980s \r\n\r\n* Use of full-resolution MetOp AVHRR data (previously used ‘global area coverage’ Level 1 data) \r\n\r\nData are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ \r\n\r\nWhen citing this dataset please also cite the associated data paper: \r\n\r\nEmbury, O., Merchant, C.J., Good, S.A., Rayner, N.A., Høyer, J.L., Atkinson, C., Block, T., Alerskans, E., Pearson, K.J., Worsfold, M., McCarroll, N., Donlon, C. Satellite-based time-series of sea-surface temperature since 1980 for climate applications. Scientific Data 11, 326 (2024). https://doi.org/10.1038/s41597-024-03147-w" }, "objectObservation": { "ob_id": 27528, "uuid": "42f7230ab55641cdac1bba84eabd446a", "short_code": "ob", "title": "ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Advanced Very High Resolution Radiometer (AVHRR) Level 3 Uncollated (L3U) Climate Data Record, version 2.1", "abstract": "This v2.1 SST_cci Advanced Very High Resolution Radiometer (AVHRR) level 3 uncollated data (L3U) Climate Data Record (CDR) consists of stable, low-bias sea surface temperature (SST) data from the AVHRR series of satellite instruments. It covers the period between 08/1981 and 12/2016. This L3U product provides these SST data on a 0.05 regular latitude-longitude grid with with a single orbit per file.\r\n\r\nThe dataset has been produced as part of the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project(ESA SST_cci). The data products from SST_cci accurately map the surface temperature of the global oceans over the period 1981 to 2016 using observations from many satellites. The data provide independently quantified SSTs to a quality suitable for climate research.\r\n\r\nThis CDR Version 2.1 product supercedes the CDR Version 2.0 product. Data are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ .\r\n\r\nWhen citing this dataset please also cite the associated data paper: Merchant, C.J., Embury, O., Bulgin, C.E., Block T., Corlett, G.K., Fiedler, E., Good, S.A., Mittaz, J., Rayner, N.A., Berry, D., Eastwood, S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R., Donlon, C. Satellite-based time-series of sea-surface temperature since 1981 for climate applications, Scientific Data 6:223 (2019). http://doi.org/10.1038/s41597-019-0236-x" } }, { "ob_id": 924, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 40860, "uuid": "ec659b31a8ca40918e58ec6d03af07a6", "short_code": "ob", "title": "ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Advanced Very High Resolution Radiometer (AVHRR) Level 2 Pre-processed (L2P) product, version 3.0", "abstract": "This dataset provides global sea surface temperatures (SST) from Advanced Very High Resolution Radiometers (AVHRR), presented on the native geometry of observation, and spanning 1980 to 2021. \r\n\r\nThe SST CCI AVHRR product contains two different SST estimates. The first is the skin temperature of the water at the time it was observed. The second is an estimate of the temperature at 20 cm depth at either 1030h or 2230h local time, which closely approximates the daily mean SST. Each SST value has an associated total uncertainty estimate, and uncertainty estimates for various contributions to that total. \r\n\r\nThe dataset has been produced as part of the version 3 Climate Data Record (CDR) produced by the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project (ESA SST_cci). The CDR accurately maps the surface temperature of the global oceans over the period 1980 to 2021 using observations from many satellites, with a high degree of independence from in situ measurements. The data provide independently quantified SSTs to a quality suitable for climate research. \r\n\r\nThis CDR Version 3.0 product supersedes the CDR v2.1 product. Compared to the previous version the major changes are: \r\n\r\n* Longer time series: 1980 to 2021 (previous CDR was Sept 1981 to 2016) \r\n\r\n* Improved retrieval to reduce systematic biases using bias-aware optimal methods (for single view sensors) \r\n\r\n* Improved retrieval with respect to desert-dust aerosols \r\n\r\n* Addition of early AVHRR/1 data in 1980s, and improved AVHRR processing to reduce data gaps in 1980s \r\n\r\n* Use of full-resolution MetOp AVHRR data (previously used ‘global area coverage’ Level 1 data) \r\n\r\nData are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ \r\n\r\nWhen citing this dataset please also cite the associated data paper: \r\n\r\nEmbury, O., Merchant, C.J., Good, S.A., Rayner, N.A., Høyer, J.L., Atkinson, C., Block, T., Alerskans, E., Pearson, K.J., Worsfold, M., McCarroll, N., Donlon, C. Satellite-based time-series of sea-surface temperature since 1980 for climate applications. Scientific Data 11, 326 (2024). https://doi.org/10.1038/s41597-024-03147-w" }, "objectObservation": { "ob_id": 27526, "uuid": "373638ed9c434e78b521cbe01ace5ef7", "short_code": "ob", "title": "ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Advanced Very High Resolution Radiometer (AVHRR) Level 2 Preprocessed (L2P) Climate Data Record, version 2.1", "abstract": "This v2.1 SST_cci Advanced Very High Resolution Radiometer (AVHRR) Level 2 Preprocessed (L2P) Climate Data Record (CDR) consists of stable, low-bias sea surface temperature (SST) data from the AVHRR series of satellite instruments. It covers the period between 08/1981 and 12/2016. This L2P product provides these SST data on the original satellite swath with a single orbit of data per file.\r\n\r\nThe dataset has been produced as part of the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project(ESA SST_cci). The data products from SST_cci accurately map the surface temperature of the global oceans over the period 1981 to 2016 using observations from many satellites. The data provide independently quantified SSTs to a quality suitable for climate research.\r\n\r\nThis CDR Version 2.1 product supercedes the CDR Version 2.0 product. Data are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ .\r\n\r\nWhen citing this dataset please also cite the associated data paper: Merchant, C.J., Embury, O., Bulgin, C.E., Block T., Corlett, G.K., Fiedler, E., Good, S.A., Mittaz, J., Rayner, N.A., Berry, D., Eastwood, S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R., Donlon, C. Satellite-based time-series of sea-surface temperature since 1981 for climate applications, Scientific Data 6:223 (2019). http://doi.org/10.1038/s41597-019-0236-x" } }, { "ob_id": 925, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 40082, "uuid": "ac43da11867243a1bb414e1637802dec", "short_code": "ob", "title": "Hydro-JULES: Global high-resolution drought datasets from 1981-2022", "abstract": "These are global scale high-resolution drought indices developed from a combination of precipitation and potential evapotranspiration datasets for the Hydro-JULES project. Climate Hazards group InfraRed Precipitation with Station data (CHIRPS), Multi-Source Weighted-Ensemble Precipitation (MSWEP) precipitation estimates, Global Land Evaporation Amsterdam Model (GLEAM) and Bristol Hourly potential evapotranspiration (hPET) estimates were used. The drought index is developed using the Standardized Precipitation-Evapotranspiration Index (SPEI). These high-resolution global scale drought indices are available from 1981-2022 at a monthly and 5km spatial resolution. The SPEI indices are available from 1-48 months. The datasets provide valuable information for the study and analysis of droughts at much higher resolution from global to local scale. \r\nThese data were produced for Hydro-Jules (NE/S017380/1) and REACH (Foreign, Commonwealth and Development Office): Programme Code 201880." }, "objectObservation": { "ob_id": 41500, "uuid": "e652f0109f21401680bc3c0ac834a96e", "short_code": "ob", "title": "Hydro-JULES: Global high-resolution drought datasets from 1981-2022 - Amendment to CHIRPS_GLEAM subset", "abstract": "This is a revised version of the Climate Hazards group InfraRed Precipitation with Station data (CHIRPS) Global Land Evaporation Amsterdam Model (GLEAM) data from the Hydro-JULES: Global high-resolution drought dataset (doi:10.5285/ac43da11867243a1bb414e1637802dec).\r\n\r\nThis version corrects some errors found in the previous version due to the model run errors in some areas. The model has been re-run to accurately produce the dataset. \r\n\r\nThese are global scale high-resolution drought indices developed from a combination of precipitation and potential evapotranspiration datasets for the Hydro-JULES project. Climate Hazards group InfraRed Precipitation with Station data (CHIRPS), Multi-Source Weighted-Ensemble Precipitation (MSWEP) precipitation estimates, Global Land Evaporation Amsterdam Model (GLEAM) and Bristol Hourly potential evapotranspiration (hPET) estimates were used. The drought index is developed using the Standardized Precipitation-Evapotranspiration Index (SPEI). These high-resolution global scale drought indices are available from 1981-2022 at a monthly and 5km spatial resolution. The SPEI indices are available from 1-48 months. The datasets provide valuable information for the study and analysis of droughts at much higher resolution from global to local scale. \r\n\r\nThese data were produced for Hydro-Jules (NE/S017380/1) and REACH (Foreign, Commonwealth and Development Office): Programme Code 201880." } }, { "ob_id": 926, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 41766, "uuid": "1de7b50d827b4b4f966bd4e3ec5516ea", "short_code": "ob", "title": "HadISDH.blend: gridded global monthly land and ocean surface humidity data version 1.5.0.2023f", "abstract": "This is the HadISDH.blend 1.5.0.2023f version of the Met Office Hadley Centre Integrated Surface Dataset of Humidity (HadISDH). HadISDH.blend is a near-global gridded monthly mean surface humidity climate monitoring product. It is created from in situ observations of air temperature and dew point temperature from ships and weather stations. The observations have been quality controlled and homogenised / bias adjusted. Uncertainty estimates for observation issues and gridbox sampling are provided (see data quality statement section below). These data are provided by the Met Office Hadley Centre. This version spans 1/1/1973 to 31/12/2023.\r\n\r\nThe data are monthly gridded (5 degree by 5 degree) fields. Products are available for temperature and six humidity variables: specific humidity (q), relative humidity (RH), dew point temperature (Td), wet bulb temperature (Tw), vapour pressure (e), dew point depression (DPD).\r\n\r\nThis version extends the previous version to the end of 2023. It combines the latest version of HadISDH.land and HadISDH.marine and therefore their respective update notes. Users are advised to read the update documents in the Docs section for full details.\r\n\r\nTo keep informed about updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS.\r\n\r\nFor more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISDH blog: http://hadisdh.blogspot.co.uk/\r\n\r\nReferences:\r\n\r\nWhen using the dataset in a paper please cite the following papers (see Docs for link\r\nto the publications) and this dataset (using the \"citable as\" reference):\r\n\r\nWillett, K. M., Dunn, R. J. H., Kennedy, J. J. and Berry, D. I., 2020: Development of\r\nthe HadISDH marine humidity climate monitoring dataset. Earth System Sciences Data,\r\n12, 2853-2880, https://doi.org/10.5194/essd-12-2853-2020\r\n\r\nFreeman, E., Woodruff, S. D., Worley, S. J., Lubker, S. J., Kent, E. C., Angel, W. E.,\r\nBerry, D. I., Brohan, P., Eastman, R., Gates, L., Gloeden, W., Ji, Z., Lawrimore, J.,\r\nRayner, N. A., Rosenhagen, G. and Smith, S. R., ICOADS Release 3.0: A major update to\r\nthe historical marine climate record. International Journal of Climatology.\r\ndoi:10.1002/joc.4775.\r\n\r\nWillett, K. M., Dunn, R. J. H., Thorne, P. W., Bell, S., de Podesta, M., Parker, D. E.,\r\nJones, P. D., and Williams Jr., C. N.: HadISDH land surface multi-variable humidity and\r\ntemperature record for climate monitoring, Clim. Past, 10, 1983-2006,\r\ndoi:10.5194/cp-10-1983-2014, 2014.\r\n\r\nDunn, R. J. H., et al. 2016: Expanding HadISD: quality-controlled, sub-daily station\r\ndata from 1931, Geoscientific Instrumentation, Methods and Data Systems, 5, 473-491.\r\n\r\nSmith, A., N. Lott, and R. Vose, 2011: The Integrated Surface Database: Recent\r\nDevelopments and Partnerships. Bulletin of the American Meteorological Society, 92,\r\n704-708, doi:10.1175/2011BAMS3015.1\r\n\r\nWe strongly recommend that you read these papers before making use of the data, more\r\ndetail on the dataset can be found in an earlier publication:\r\n\r\nWillett, K. M., Williams Jr., C. N., Dunn, R. J. H., Thorne, P. W., Bell, S., de\r\nPodesta, M., Jones, P. D., and Parker D. E., 2013: HadISDH: An updated land surface\r\nspecific humidity product for climate monitoring. Climate of the Past, 9, 657-677,\r\ndoi:10.5194/cp-9-657-2013." }, "objectObservation": { "ob_id": 40621, "uuid": "c3c1526fba8f4a5382d2f9fb86966d82", "short_code": "ob", "title": "HadISDH.blend: gridded global monthly land and ocean surface humidity data version 1.4.1.2022f", "abstract": "This is the HadISDH.blend 1.4.1.2022f version of the Met Office Hadley Centre Integrated Surface Dataset of Humidity (HadISDH). HadISDH.blend is a near-global gridded monthly mean surface humidity climate monitoring product. It is created from in situ observations of air temperature and dew point temperature from ships and weather stations. The observations have been quality controlled and homogenised / bias adjusted. Uncertainty estimates for observation issues and gridbox sampling are provided (see data quality statement section below). These data are provided by the Met Office Hadley Centre. This version spans 1/1/1973 to 31/12/2022.\r\n\r\nThe data are monthly gridded (5 degree by 5 degree) fields. Products are available for temperature and six humidity variables: specific humidity (q), relative humidity (RH), dew point temperature (Td), wet bulb temperature (Tw), vapour pressure (e), dew point depression (DPD).\r\n\r\nThis version extends the previous version to the end of 2022. It combines the latest version of HadISDH.land and HadISDH.marine. and therefore their respective update notes. Users are advised to read the update documents in the Docs section for full details.\r\n\r\nTo keep informed about updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS.\r\n\r\nFor more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISDH blog: http://hadisdh.blogspot.co.uk/\r\n\r\nReferences:\r\n\r\nWhen using the dataset in a paper please cite the following papers (see Docs for link\r\nto the publications) and this dataset (using the \"citable as\" reference):\r\n\r\nWillett, K. M., Dunn, R. J. H., Kennedy, J. J. and Berry, D. I., 2020: Development of\r\nthe HadISDH marine humidity climate monitoring dataset. Earth System Sciences Data,\r\n12, 2853-2880, https://doi.org/10.5194/essd-12-2853-2020\r\n\r\nFreeman, E., Woodruff, S. D., Worley, S. J., Lubker, S. J., Kent, E. C., Angel, W. E.,\r\nBerry, D. I., Brohan, P., Eastman, R., Gates, L., Gloeden, W., Ji, Z., Lawrimore, J.,\r\nRayner, N. A., Rosenhagen, G. and Smith, S. R., ICOADS Release 3.0: A major update to\r\nthe historical marine climate record. International Journal of Climatology.\r\ndoi:10.1002/joc.4775.\r\n\r\nWillett, K. M., Dunn, R. J. H., Thorne, P. W., Bell, S., de Podesta, M., Parker, D. E.,\r\nJones, P. D., and Williams Jr., C. N.: HadISDH land surface multi-variable humidity and\r\ntemperature record for climate monitoring, Clim. Past, 10, 1983-2006,\r\ndoi:10.5194/cp-10-1983-2014, 2014.\r\n\r\nDunn, R. J. H., et al. 2016: Expanding HadISD: quality-controlled, sub-daily station\r\ndata from 1931, Geoscientific Instrumentation, Methods and Data Systems, 5, 473-491.\r\n\r\nSmith, A., N. Lott, and R. Vose, 2011: The Integrated Surface Database: Recent\r\nDevelopments and Partnerships. Bulletin of the American Meteorological Society, 92,\r\n704-708, doi:10.1175/2011BAMS3015.1\r\n\r\nWe strongly recommend that you read these papers before making use of the data, more\r\ndetail on the dataset can be found in an earlier publication:\r\n\r\nWillett, K. M., Williams Jr., C. N., Dunn, R. J. H., Thorne, P. W., Bell, S., de\r\nPodesta, M., Jones, P. D., and Parker D. E., 2013: HadISDH: An updated land surface\r\nspecific humidity product for climate monitoring. Climate of the Past, 9, 657-677,\r\ndoi:10.5194/cp-9-657-2013." } }, { "ob_id": 927, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 41769, "uuid": "2fdc37a517b54376ba19d5c7432457d5", "short_code": "ob", "title": "HadISDH.marine: gridded global monthly ocean surface humidity data version 1.6.0.2023f", "abstract": "This is the HadISDH.marine 1.6.0.2023f version of the Met Office Hadley Centre Integrated Surface Dataset of Humidity (HadISDH). HadISDH.marine is a near-global gridded monthly mean marine surface humidity climate monitoring product. It is created from in situ observations of air temperature and dew point temperature from ships. The observations have been quality controlled and bias-adjusted. Uncertainty estimates for observation issues and gridbox sampling are provided (see data quality statement section below). The data are provided by the Met Office Hadley Centre and this version spans 1/1/1973 to 31/12/2023.\r\n\r\nThe data are monthly gridded (5 degree by 5 degree) fields. Products are available for temperature and six humidity variables: specific humidity (q), relative humidity (RH), dew point temperature (Td), wet bulb temperature (Tw), vapour pressure (e), dew point depression (DPD).\r\n\r\nThis version extends the previous version to the end of 2023. Users are advised to read the update document in the Docs section for full details on all changes from the previous release.\r\n\r\nTo keep informed about updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS.\r\n\r\nFor more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISDH blog: http://hadisdh.blogspot.co.uk/\r\n\r\nReferences:\r\n\r\nWhen using the dataset in a paper please cite the following papers (see Docs for link\r\nto the publications) and this dataset (using the \"citable as\" reference):\r\n\r\nWillett, K. M., Dunn, R. J. H., Kennedy, J. J. and Berry, D. I., 2020: Development of\r\nthe HadISDH marine humidity climate monitoring dataset. Earth System Sciences Data,\r\n12, 2853-2880, https://doi.org/10.5194/essd-12-2853-2020\r\n\r\nFreeman, E., Woodruff, S. D., Worley, S. J., Lubker, S. J., Kent, E. C., Angel, W. E.,\r\nBerry, D. I., Brohan, P., Eastman, R., Gates, L., Gloeden, W., Ji, Z., Lawrimore, J.,\r\nRayner, N. A., Rosenhagen, G. and Smith, S. R., ICOADS Release 3.0: A major update to\r\nthe historical marine climate record. International Journal of Climatology.\r\ndoi:10.1002/joc.4775." }, "objectObservation": { "ob_id": 40618, "uuid": "755bc61b5524498db67f9468a92d8cfc", "short_code": "ob", "title": "HadISDH.marine: gridded global monthly ocean surface humidity data version 1.4.1.2022f", "abstract": "This is the HadISDH.marine 1.4.1.2022f version of the Met Office Hadley Centre Integrated Surface Dataset of Humidity (HadISDH). HadISDH.marine is a near-global gridded monthly mean marine surface humidity climate monitoring product. It is created from in situ observations of air temperature and dew point temperature from ships. The observations have been quality controlled and bias-adjusted. Uncertainty estimates for observation issues and gridbox sampling are provided (see data quality statement section below). The data are provided by the Met Office Hadley Centre and this version spans 1/1/1973 to 31/12/2022.\r\n\r\nThe data are monthly gridded (5 degree by 5 degree) fields. Products are available for temperature and six humidity variables: specific humidity (q), relative humidity (RH), dew point temperature (Td), wet bulb temperature (Tw), vapour pressure (e), dew point depression (DPD).\r\n\r\nThis version extends the previous version to the end of 2022. Users are advised to read the update document in the Docs section for full details on all changes from the previous release.\r\n\r\nTo keep informed about updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS.\r\n\r\nFor more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISDH blog: http://hadisdh.blogspot.co.uk/\r\n\r\nReferences:\r\n\r\nWhen using the dataset in a paper please cite the following papers (see Docs for link\r\nto the publications) and this dataset (using the \"citable as\" reference):\r\n\r\nWillett, K. M., Dunn, R. J. H., Kennedy, J. J. and Berry, D. I., 2020: Development of\r\nthe HadISDH marine humidity climate monitoring dataset. Earth System Sciences Data,\r\n12, 2853-2880, https://doi.org/10.5194/essd-12-2853-2020\r\n\r\nFreeman, E., Woodruff, S. D., Worley, S. J., Lubker, S. J., Kent, E. C., Angel, W. E.,\r\nBerry, D. I., Brohan, P., Eastman, R., Gates, L., Gloeden, W., Ji, Z., Lawrimore, J.,\r\nRayner, N. A., Rosenhagen, G. and Smith, S. R., ICOADS Release 3.0: A major update to\r\nthe historical marine climate record. International Journal of Climatology.\r\ndoi:10.1002/joc.4775." } }, { "ob_id": 928, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 41767, "uuid": "0a36ca390a5844578905780ed4c78ded", "short_code": "ob", "title": "HadISDH.extremes: gridded global monthly land surface wet bulb and dry bulb temperature extremes index data version 1.1.0.2023f", "abstract": "This is the HadISDH.extremes 1.1.0.2023f version of the Met Office Hadley Centre Integrated Surface Dataset of Humidity (HadISDH). HadISDH.extremes is a near-global gridded monthly land surface extremes index climate monitoring product. It is created from in situ sub-daily observations of wet bulb (converted from dew point temperature) and dry bulb temperature from weather stations. The observations have been quality controlled at the hourly level with strict temporal completeness thresholds applied at daily, monthly, annual, climatological and whole period scales to minimise biases. Gridbox months are assessed for inhomogeneity and scores provided (see Homogeneity Score Document in Docs). The data are provided by the Met Office Hadley Centre and this version spans 1/1/1973 to 31/12/2023.\r\n\r\nThe data are monthly gridded (5 degree by 5 degree) fields. Products are available for 27 different heat extremes indices based on the ET-SCI (Expert Team on Sector-Specific Climate Indices) framework. These indices capture a range of moderate to severe extremes. They utilise the daily maximum and minimum values of sub-daily dry bulb and wet bulb temperature observations. Note that these will most likely underestimate the true extremes even when hourly data are available. The data are designed for assessing large scale features over long time scales, ideally using the anomaly fields as these are less affected by sampling biases. Users are advised to cross-compare with national datasets other supporting evidence when assessing small scale localised features.\r\n\r\nThis version is the first with annual updates envisaged. An update record will be maintained in the Docs section.\r\n\r\nHadISD.3.4.0.2023f is the basis of HadISDH.extremes.\r\n\r\nTo keep informed about updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS.\r\n\r\nFor more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISDH blog: http://hadisdh.blogspot.co.uk/\r\n\r\nReferences:\r\n\r\nWhen using the dataset in a paper please cite the following papers (see Docs for link\r\nto the publications) and this dataset (using the \"citable as\" reference):\r\n\r\nWillett, K, 2023: HadISDH.extremes Part 1: a gridded wet bulb temperature extremes index product for climate monitoring. Advances in Atmospheric Sciences, 40, 1952–1967, doi: 10.1007/s00376-023-2347-8. https://link.springer.com/article/10.1007/s00376-023-2347-8\r\n\r\nWillett, K. 2023: HadISDH.extremes Part 2: exploring humid heat extremes using wet bulb temperature indices. Advances in Atmospheric Sciences, 40, 1968–1985, doi: 10.1007/s00376-023-2348-7. https://link.springer.com/article/10.1007/s00376-023-2348-7\r\n\r\n\r\nDunn, R. J. H., et al. 2016: Expanding HadISD: quality-controlled, sub-daily station\r\ndata from 1931, Geoscientific Instrumentation, Methods and Data Systems, 5, 473-491.\r\nSmith, A., N. Lott, and R. Vose, 2011: The Integrated Surface Database: Recent\r\nDevelopments and Partnerships. Bulletin of the American Meteorological Society, 92,\r\n704-708, doi:10.1175/2011BAMS3015.1" }, "objectObservation": { "ob_id": 40168, "uuid": "2d1613955e1b4cd1b156e5f3edbd7e66", "short_code": "ob", "title": "HadISDH.extremes: gridded global monthly land surface wet bulb and dry bulb temperature extremes index data version 1.0.0.2022f", "abstract": "This is the HadISDH.extremes 1.0.0.2022f version of the Met Office Hadley Centre Integrated Surface Dataset of Humidity (HadISDH). HadISDH.extremes is a near-global gridded monthly land surface extremes index climate monitoring product. It is created from in situ sub-daily observations of wet bulb (converted from dew point temperature) and dry bulb temperature from weather stations. The observations have been quality controlled at the hourly level with strict temporal completeness thresholds applied at daily, monthly, annual, climatological and whole period scales to minimise biases. Gridbox months are assessed for inhomogeneity and scores provided (see Homogeneity Score Document in Docs). The data are provided by the Met Office Hadley Centre and this version spans 1/1/1973 to 31/12/2022.\r\n\r\nThe data are monthly gridded (5 degree by 5 degree) fields. Products are available for 27 different heat extremes indices based on the ET-SCI (Expert Team on Sector-Specific Climate Indices; https://public.wmo.int/en/events/meetings/expert-team-sector-specific-climate-indices-et-sci) framework. These indices capture a range of moderate to severe extremes. They utilise the daily maximum and minimum values of sub-daily dry bulb and wet bulb temperature observations. Note that these will most likely underestimate the true extremes even when hourly data are available. The data are designed for assessing large scale features over long time scales, ideally using the anomaly fields as these are less affected by sampling biases. Users are advised to cross-compare with national datasets other supporting evidence when assessing small scale localised features.\r\n\r\nThis version is the first with annual updates envisaged. An update record will be maintained in the Docs section.\r\n\r\nHadISD.3.3.0.2022f is the basis of HadISDH.extremes.\r\n\r\nTo keep informed about updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS.\r\n\r\nFor more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISDH blog: http://hadisdh.blogspot.co.uk/\r\n\r\nReferences:\r\n\r\nWhen using the dataset in a paper please cite the following papers (see Docs for link\r\nto the publications) and this dataset (using the \"citable as\" reference):\r\n\r\nWillett, K, in press: HadISDH.extremes Part 1: a gridded wet bulb temperature extremes index product for climate monitoring. Advances in Atmospheric Sciences, doi: 10.1007/s00376-023-2347-8. http://www.iapjournals.ac.cn/aas/en/article/doi/10.1007/s00376-023-2347-8\r\n\r\nWillett, K. in press: HadISDH.extremes Part 2: exploring humid heat extremes using wet bulb temperature indices. Advances in Atmospheric Sciences, doi: 10.1007/s00376-023-2348-7. http://www.iapjournals.ac.cn/aas/en/article/doi/10.1007/s00376-023-2348-7\r\n\r\nDunn, R. J. H., et al. 2016: Expanding HadISD: quality-controlled, sub-daily station\r\ndata from 1931, Geoscientific Instrumentation, Methods and Data Systems, 5, 473-491.\r\nSmith, A., N. Lott, and R. Vose, 2011: The Integrated Surface Database: Recent\r\nDevelopments and Partnerships. Bulletin of the American Meteorological Society, 92,\r\n704-708, doi:10.1175/2011BAMS3015.1" } }, { "ob_id": 929, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 41768, "uuid": "c844fc58615a422aa2e7d2fc8bd8cccf", "short_code": "ob", "title": "HadISDH.land: gridded global monthly land surface humidity data version 4.6.0.2023f", "abstract": "This is the HadISDH.land 4.6.0.2023f version of the Met Office Hadley Centre Integrated Surface Dataset of Humidity (HadISDH). HadISDH.land is a near-global gridded monthly mean land surface humidity climate monitoring product. It is created from in situ observations of air temperature and dew point temperature from weather stations. The observations have been quality controlled and homogenised. Uncertainty estimates for observation issues and gridbox sampling are provided (see data quality statement section below). The data are provided by the Met Office Hadley Centre and this version spans 1/1/1973 to 31/12/2023. \r\n\r\nThe data are monthly gridded (5 degree by 5 degree) fields. Products are available for temperature and six humidity variables: specific humidity (q), relative humidity (RH), dew point temperature (Td), wet bulb temperature (Tw), vapour pressure (e), dew point depression (DPD).\r\n\r\nThis version extends the previous version to the end of 2023. Users are advised to read the update document in the Docs section for full details on all changes from the previous release.\r\n\r\nAs in previous years, the annual scrape of NOAAs Integrated Surface Dataset for HadISD.3.4.0.2023f, which is the basis of HadISDH.land, has pulled through some historical changes to stations. This, and the additional year of data, results in small changes to station selection. The homogeneity adjustments differ slightly due to sensitivity to the addition and loss of stations, historical changes to stations previously included and the additional 12 months of data.\r\n\r\nTo keep informed about updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS.\r\n\r\nFor more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISDH blog: http://hadisdh.blogspot.co.uk/\r\n\r\nReferences:\r\n\r\nWhen using the dataset in a paper please cite the following papers (see Docs for link\r\nto the publications) and this dataset (using the \"citable as\" reference):\r\n\r\nWillett, K. M., Dunn, R. J. H., Thorne, P. W., Bell, S., de Podesta, M., Parker, D. E.,\r\nJones, P. D., and Williams Jr., C. N.: HadISDH land surface multi-variable humidity and\r\ntemperature record for climate monitoring, Clim. Past, 10, 1983-2006,\r\ndoi:10.5194/cp-10-1983-2014, 2014.\r\n\r\nDunn, R. J. H., et al. 2016: Expanding HadISD: quality-controlled, sub-daily station\r\ndata from 1931, Geoscientific Instrumentation, Methods and Data Systems, 5, 473-491.\r\n\r\nSmith, A., N. Lott, and R. Vose, 2011: The Integrated Surface Database: Recent\r\nDevelopments and Partnerships. Bulletin of the American Meteorological Society, 92,\r\n704-708, doi:10.1175/2011BAMS3015.1\r\n\r\nWe strongly recommend that you read these papers before making use of the data, more\r\ndetail on the dataset can be found in an earlier publication:\r\n\r\nWillett, K. M., Williams Jr., C. N., Dunn, R. J. H., Thorne, P. W., Bell, S., de\r\nPodesta, M., Jones, P. D., and Parker D. E., 2013: HadISDH: An updated land surface\r\nspecific humidity product for climate monitoring. Climate of the Past, 9, 657-677,\r\ndoi:10.5194/cp-9-657-2013." }, "objectObservation": { "ob_id": 40166, "uuid": "8956cf9e31334914ab4991796f0f645a", "short_code": "ob", "title": "HadISDH.land: gridded global monthly land surface humidity data version 4.5.1.2022f", "abstract": "This is the HadISDH.land 4.5.1.2022f version of the Met Office Hadley Centre Integrated Surface Dataset of Humidity (HadISDH). HadISDH.land is a near-global gridded monthly mean land surface humidity climate monitoring product. It is created from in situ observations of air temperature and dew point temperature from weather stations. The observations have been quality controlled and homogenised. Uncertainty estimates for observation issues and gridbox sampling are provided (see data quality statement section below). The data are provided by the Met Office Hadley Centre and this version spans 1/1/1973 to 31/12/2022. \r\n\r\nThe data are monthly gridded (5 degree by 5 degree) fields. Products are available for temperature and six humidity variables: specific humidity (q), relative humidity (RH), dew point temperature (Td), wet bulb temperature (Tw), vapour pressure (e), dew point depression (DPD).\r\n\r\nThis version extends the previous version to the end of 2022. Users are advised to read the update document in the Docs section for full details on all changes from the previous release.\r\n\r\nAs in previous years, the annual scrape of NOAAs Integrated Surface Dataset for HadISD.3.3.0.2022f, which is the basis of HadISDH.land, has pulled through some historical changes to stations. This, and the additional year of data, results in small changes to station selection. The homogeneity adjustments differ slightly due to sensitivity to the addition and loss of stations, historical changes to stations previously included and the additional 12 months of data.\r\n\r\nTo keep informed about updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS.\r\n\r\nFor more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISDH blog: http://hadisdh.blogspot.co.uk/\r\n\r\nReferences:\r\n\r\nWhen using the dataset in a paper please cite the following papers (see Docs for link\r\nto the publications) and this dataset (using the \"citable as\" reference):\r\n\r\nWillett, K. M., Dunn, R. J. H., Thorne, P. W., Bell, S., de Podesta, M., Parker, D. E.,\r\nJones, P. D., and Williams Jr., C. N.: HadISDH land surface multi-variable humidity and\r\ntemperature record for climate monitoring, Clim. Past, 10, 1983-2006,\r\ndoi:10.5194/cp-10-1983-2014, 2014.\r\n\r\nDunn, R. J. H., et al. 2016: Expanding HadISD: quality-controlled, sub-daily station\r\ndata from 1931, Geoscientific Instrumentation, Methods and Data Systems, 5, 473-491.\r\n\r\nSmith, A., N. Lott, and R. Vose, 2011: The Integrated Surface Database: Recent\r\nDevelopments and Partnerships. Bulletin of the American Meteorological Society, 92,\r\n704-708, doi:10.1175/2011BAMS3015.1\r\n\r\nWe strongly recommend that you read these papers before making use of the data, more\r\ndetail on the dataset can be found in an earlier publication:\r\n\r\nWillett, K. M., Williams Jr., C. N., Dunn, R. J. H., Thorne, P. W., Bell, S., de\r\nPodesta, M., Jones, P. D., and Parker D. E., 2013: HadISDH: An updated land surface\r\nspecific humidity product for climate monitoring. Climate of the Past, 9, 657-677,\r\ndoi:10.5194/cp-9-657-2013." } }, { "ob_id": 930, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 41620, "uuid": "02e1b18071ad45a19b4d3e8adafa2817", "short_code": "ob", "title": "ESA Biomass Climate Change Initiative (Biomass_cci): Global datasets of forest above-ground biomass for the years 2010, 2015, 2016, 2017, 2018, 2019, 2020 and 2021, v5", "abstract": "This dataset comprises estimates of forest above-ground biomass for the years 2010, 2015, 2016, 2017, 2018, 2019, 2020 and 2021. They are derived from a combination of Earth observation data, depending on the year, from the Copernicus Sentinel-1 mission, Envisat’s ASAR (Advanced Synthetic Aperture Radar) instrument and JAXA’s (Japan Aerospace Exploration Agency) Advanced Land Observing Satellite (ALOS-1 and ALOS-2), along with additional information from Earth observation sources. The data has been produced as part of the European Space Agency's (ESA's) Climate Change Initiative (CCI) programme by the Biomass CCI team. \r\n\r\nThis release of the data is version 5. Compared to version 4, version 5 consists of an update of the three maps of AGB (aboveground biomass) for the years 2010, 2017, 2018, 2019, 2020 and new AGB maps for 2015, 2016 and 2021. New AGB change maps have been created for consecutive years (2015-2016, 2016-2017 and 2020-2021), alongside an update of change maps for years 2010-2020, 2017-2018, 2018-2019 and 2019-2020, and for a decadal interval (2020-2010). The pool of remote sensing data now includes multi-temporal observations at L-band for all biomes and for all years. The AGB maps rely on revised allometries which are now based on a longer record of spaceborne LiDAR data from the GEDI and ICESat-2 missions. Temporal information is now implemented in the retrieval algorithm to preserve biomass dynamics as expressed in the remote sensing data. Biases between 2010 and more recent years have been reduced.\r\n\r\nThe data products consist of two (2) global layers that include estimates of:\r\n1) above ground biomass (AGB, unit: tons/ha i.e., Mg/ha) (raster dataset). This is defined as the mass, expressed as oven-dry weight of the woody parts (stem, bark, branches and twigs) of all living trees excluding stump and roots\r\n2) per-pixel estimates of above-ground biomass uncertainty expressed as the standard deviation in Mg/ha (raster dataset)\r\n\r\nAdditionally provided in this version release are new aggregated data products. These aggregated products of the AGB and AGB change data layers are available at coarser resolutions (1, 10, 25 and 50km).\r\n\r\nIn addition, files describing the AGB change between two consecutive years (i.e., 2015-2016, 2016-2017, 2018-2017, 2019-2018, 2019-2020, 2020-2021) and over a decade (2020-2010) are provided (labelled as 2015_2016, 2016_2017, 2017_2018, 2018_2019, 2019_2020 and 2020_2010). Each AGB change product consists of two sets of maps: the standard deviation of the AGB change and a quality flag of the AGB change. Note that the change itself can be simply computed as the difference between two AGB maps, so is not provided directly.\r\n\r\n\r\nData are provided in both netcdf and geotiff format." }, "objectObservation": { "ob_id": 39899, "uuid": "af60720c1e404a9e9d2c145d2b2ead4e", "short_code": "ob", "title": "ESA Biomass Climate Change Initiative (Biomass_cci): Global datasets of forest above-ground biomass for the years 2010, 2017, 2018, 2019 and 2020, v4", "abstract": "This dataset comprises estimates of forest above-ground biomass for the years 2010, 2017, 2018, 2019 and 2020. They are derived from a combination of Earth observation data, depending on the year, from the Copernicus Sentinel-1 mission, Envisat’s ASAR instrument and JAXA’s Advanced Land Observing Satellite (ALOS-1 and ALOS-2), along with additional information from Earth observation sources. The data has been produced as part of the European Space Agency's (ESA's) Climate Change Initiative (CCI) programme by the Biomass CCI team. \r\n\r\nThis release of the data is version 4. Compared to version 3, version 4 consists of an update of the three maps of AGB for the years 2010, 2017 and 2018 and new AGB maps for 2019 and 2020. New AGB change maps have been created for consecutive years (2018-2017, 2019-2018 and 2020-2019) and for a decadal interval (2020-2010). The pool of remote sensing data now includes multi-temporal observations at L-band for all biomes and for all years. The AGB maps rely on revised allometries which are now based on a longer record of spaceborne LiDAR data from the GEDI and ICESat-2 missions. Temporal information is now implemented in the retrieval algorithm to preserve biomass dynamics as expressed in the remote sensing data. Biases between 2010 and more recent years have been reduced.\r\n\r\n\r\n\r\nThe data products consist of two (2) global layers that include estimates of:\r\n1) above ground biomass (AGB, unit: tons/ha i.e., Mg/ha) (raster dataset). This is defined as the mass, expressed as oven-dry weight of the woody parts (stem, bark, branches and twigs) of all living trees excluding stump and roots\r\n2) per-pixel estimates of above-ground biomass uncertainty expressed as the standard deviation in Mg/ha (raster dataset)\r\n\r\nIn addition, files describing the AGB change between two consecutive years (i.e., 2018-2017, 2019-2018 and 2020-2010) and over a decade (2020-2010) are provided (labelled as 2018_2017, 2019_2018, 2020_2019 and 2020_2010). Each AGB change product consists of two sets of maps: the standard deviation of the AGB change and a quality flag of the AGB change. Note that the change itself can be simply computed as the difference between two AGB maps, so is not provided directly.\r\n\r\n\r\nData are provided in both netcdf and geotiff format." } }, { "ob_id": 931, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 42321, "uuid": "c22d0b462321447882d2d1367cc77d3c", "short_code": "ob", "title": "HadUK-Grid Gridded Climate Observations on a 60km grid over the UK, v1.3.0.ceda (1836-2023)", "abstract": "HadUK-Grid is a collection of gridded 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 dataset at 60 km resolution is derived from the associated 1 km x 1 km resolution to allow for comparison to data from UKCP18 climate projections. The dataset spans the period from 1836 to 2023, but the start time is dependent on climate variable and temporal resolution.\r\n\r\nThe gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.\r\n\r\nThis data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2019, see linked documentation).\r\n\r\nThe changes for v1.3.0.ceda HadUK-Grid datasets are as follows:\r\n \r\n* Added data for calendar year 2023\r\n \r\n* Added newly digitised data for daily rainfall (62 Scottish stations for 1945-1960)\r\n \r\n* Daily rainfall data for Bolton, 1916-1919 have been corrected (previous values were corrupted and needed redigitising)\r\n \r\n* Daily rainfall data for Buxton, 1960 have been corrected (conversion from inches to mm had been applied incorrectly)\r\n \r\n* Rainfall data from EA and SEPA APIs are included for the last three months of the dataset (Oct-Dec 2023) (for all earlier months the rainfall data from partner agencies is obtained from the Met Office's MIDAS database)\r\n \r\n* The number of stations used for groundfrost, sunshine and windspeed have reduced at different points in the historical series when comparing v1.3.0.ceda to the previous version v1.2.0.ceda. These reductions in station numbers have been caused by changes made in the data processing steps upstream of the gridding process.\r\n \r\n* For groundfrost this reduction has been caused by an automated quality control process flagging the historical data which have been removed as suspect (mostly affecting data from 1961 to 1970).\r\n \r\n* For sunshine the small reduction in the 1960s has been caused by the removal of digitized monthly sunshine data through this period where we wish to reverify the data source.\r\n \r\n* For windspeed the reduction from 1969 to 2010 has been caused by changes to rules applied relating to data completeness when compiling daily mean windspeeds, which in turn have followed through to monthly statistics.\r\n \r\n* We plan to carry out a review of the data which have been excluded from this version. Some of it may be reintroduced in a future release.\r\n \r\n* Net changes to the input station data:\r\n \r\n- Total of 126970983 observations\r\n- 125384735 (98.75%) unchanged\r\n- 28487 (0.02%) modified for this version\r\n- 1557761 (1.23%) added in this version\r\n- 188522 (0.15%) deleted from this version\r\n \r\n\r\nThe primary purpose of these data are to facilitate monitoring of UK climate and research into climate change, impacts and adaptation. The datasets have been created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project. The output from a number of data recovery activities relating to 19th and early 20th Century data have been used in the creation of this dataset, these activities were supported by: the Met Office Hadley Centre Climate Programme; the Natural Environment Research Council project \"Analysis of historic drought and water scarcity in the UK\"; the UK Research & Innovation (UKRI) Strategic Priorities Fund UK Climate Resilience programme; The UK Natural Environment Research Council (NERC) Public Engagement programme; the National Centre for Atmospheric Science; National Centre for Atmospheric Science and the NERC GloSAT project; and the contribution of many thousands of public volunteers. The dataset is provided under Open Government Licence." }, "objectObservation": { "ob_id": 40132, "uuid": "22df6602b5064b1686dda7e9455f86fc", "short_code": "ob", "title": "HadUK-Grid Gridded Climate Observations on a 60km grid over the UK, v1.2.0.ceda (1836-2022)", "abstract": "HadUK-Grid is a collection of gridded 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 dataset at 60 km resolution is derived from the associated 1 km x 1 km resolution to allow for comparison to data from UKCP18 climate projections. The dataset spans the period from 1836 to 2022, but the start time is dependent on climate variable and temporal resolution.\r\n\r\nThe gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.\r\n\r\nThis data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2018, see linked documentation).\r\n\r\nThe changes for v1.2.0.ceda HadUK-Grid datasets are as follows:\r\n\r\n * Added data for calendar year 2022\r\n \r\n* Added newly digitised data for monthly sunshine 1910-1918\r\n\r\n * Added Rainfall Rescue version 2 doi:10.5281/zenodo.7554242\r\n\r\n * Updated shapefiles used for production of area average statistics https://github.com/ukcp-data/ukcp-spatial-files\r\n\r\n\r\n * Updated controlled vocabulary for metadata assignment https://github.com/ukcp-data/UKCP18_CVs\r\n\r\n * Updated assignment of timepoint for some periods so that the datetime is the middle of the period (e.g. season) rather than a fixed offset from the period start.\r\n\r\n * Updated ordering of regions within regional values files. Alphabetical ordering.\r\n\r\n * Files use netcdf level 4 compression using gzip https://www.unidata.ucar.edu/blogs/developer/entry/netcdf_compression\r\n\r\n* Net changes to the input station data used to generate this dataset:\r\n\r\n- Total of 125601744 observations\r\n\r\n- 122621050 (97.6%) unchanged\r\n\r\n- 26700 (0.02%) modified for this version\r\n\r\n- 2953994 (2.35%) added in this version\r\n\r\n- 16315 (0.01%) deleted from this version\r\n\r\n* Changes to monthly rainfall 1836-1960\r\n\r\n- Total of 4823973 observations\r\n\r\n- 3315657 (68.7%) unchanged\r\n\r\n- 21029 (0.4%) modified for this version\r\n\r\n- 1487287 (30.8%) added in this version\r\n\r\n- 11155 (0.2%) deleted from this version\r\n\r\nThe primary purpose of these data are to facilitate monitoring of UK climate and research into climate change, impacts and adaptation. The datasets have been created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project. The output from a number of data recovery activities relating to 19th and early 20th Century data have been used in the creation of this dataset, these activities were supported by: the Met Office Hadley Centre Climate Programme; the Natural Environment Research Council project \"Analysis of historic drought and water scarcity in the UK\"; the UK Research & Innovation (UKRI) Strategic Priorities Fund UK Climate Resilience programme; The UK Natural Environment Research Council (NERC) Public Engagement programme; the National Centre for Atmospheric Science; National Centre for Atmospheric Science and the NERC GloSAT project; and the contribution of many thousands of public volunteers. The dataset is provided under Open Government Licence." } }, { "ob_id": 932, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 42322, "uuid": "5ba67d62cdc249a3bc5b1c38b339beb3", "short_code": "ob", "title": "HadUK-Grid Gridded Climate Observations on a 5km grid over the UK, v1.3.0.ceda (1836-2023)", "abstract": "HadUK-Grid is a collection of gridded 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 dataset at 5 km resolution is derived from the associated 1 km x 1 km resolution to allow for comparison to data from UKCP18 climate projections. The dataset spans the period from 1836 to 2023, but the start time is dependent on climate variable and temporal resolution.\r\n\r\nThe gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.\r\n\r\nThis data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2019, see linked documentation).\r\n\r\nThe changes for v1.3.0.ceda HadUK-Grid datasets are as follows:\r\n \r\n* Added data for calendar year 2023\r\n \r\n* Added newly digitised data for daily rainfall (62 Scottish stations for 1945-1960)\r\n \r\n* Daily rainfall data for Bolton, 1916-1919 have been corrected (previous values were corrupted and needed redigitising)\r\n \r\n* Daily rainfall data for Buxton, 1960 have been corrected (conversion from inches to mm had been applied incorrectly)\r\n \r\n* Rainfall data from EA and SEPA APIs are included for the last three months of the dataset (Oct-Dec 2023) (for all earlier months the rainfall data from partner agencies is obtained from the Met Office's MIDAS database)\r\n \r\n* The number of stations used for groundfrost, sunshine and windspeed have reduced at different points in the historical series when comparing v1.3.0.ceda to the previous version v1.2.0.ceda. These reductions in station numbers have been caused by changes made in the data processing steps upstream of the gridding process.\r\n \r\n* For groundfrost this reduction has been caused by an automated quality control process flagging the historical data which have been removed as suspect (mostly affecting data from 1961 to 1970).\r\n \r\n* For sunshine the small reduction in the 1960s has been caused by the removal of digitized monthly sunshine data through this period where we wish to reverify the data source.\r\n \r\n* For windspeed the reduction from 1969 to 2010 has been caused by changes to rules applied relating to data completeness when compiling daily mean windspeeds, which in turn have followed through to monthly statistics.\r\n \r\n* We plan to carry out a review of the data which have been excluded from this version. Some of it may be reintroduced in a future release.\r\n \r\n* Net changes to the input station data:\r\n \r\n- Total of 126970983 observations\r\n- 125384735 (98.75%) unchanged\r\n- 28487 (0.02%) modified for this version\r\n- 1557761 (1.23%) added in this version\r\n- 188522 (0.15%) deleted from this version\r\n \r\nThe primary purpose of these data are to facilitate monitoring of UK climate and research into climate change, impacts and adaptation. The datasets have been created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project. The output from a number of data recovery activities relating to 19th and early 20th Century data have been used in the creation of this dataset, these activities were supported by: the Met Office Hadley Centre Climate Programme; the Natural Environment Research Council project \"Analysis of historic drought and water scarcity in the UK\"; the UK Research & Innovation (UKRI) Strategic Priorities Fund UK Climate Resilience programme; The UK Natural Environment Research Council (NERC) Public Engagement programme; the National Centre for Atmospheric Science; National Centre for Atmospheric Science and the NERC GloSAT project; and the contribution of many thousands of public volunteers. The dataset is provided under Open Government Licence." }, "objectObservation": { "ob_id": 40131, "uuid": "adf1a6cf830b4f5385c5d73609df8423", "short_code": "ob", "title": "HadUK-Grid Gridded Climate Observations on a 5km grid over the UK, v1.2.0.ceda (1836-2022)", "abstract": "HadUK-Grid is a collection of gridded 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 dataset at 5 km resolution is derived from the associated 1 km x 1 km resolution to allow for comparison to data from UKCP18 climate projections. The dataset spans the period from 1836 to 2022, but the start time is dependent on climate variable and temporal resolution.\r\n\r\nThe gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.\r\n\r\nThis data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2018, see linked documentation).\r\n\r\nThe changes for v1.2.0.ceda HadUK-Grid datasets are as follows:\r\n\r\n * Added data for calendar year 2022\r\n \r\n* Added newly digitised data for monthly sunshine 1910-1918\r\n\r\n * Added Rainfall Rescue version 2 doi:10.5281/zenodo.7554242\r\n\r\n * Updated shapefiles used for production of area average statistics https://github.com/ukcp-data/ukcp-spatial-files\r\n\r\n\r\n * Updated controlled vocabulary for metadata assignment https://github.com/ukcp-data/UKCP18_CVs\r\n\r\n * Updated assignment of timepoint for some periods so that the datetime is the middle of the period (e.g. season) rather than a fixed offset from the period start.\r\n\r\n * Updated ordering of regions within regional values files. Alphabetical ordering.\r\n\r\n * Files use netcdf level 4 compression using gzip https://www.unidata.ucar.edu/blogs/developer/entry/netcdf_compression\r\n\r\n* Net changes to the input station data used to generate this dataset:\r\n\r\n- Total of 125601744 observations\r\n\r\n- 122621050 (97.6%) unchanged\r\n\r\n- 26700 (0.02%) modified for this version\r\n\r\n- 2953994 (2.35%) added in this version\r\n\r\n- 16315 (0.01%) deleted from this version\r\n\r\n* Changes to monthly rainfall 1836-1960\r\n\r\n- Total of 4823973 observations\r\n\r\n- 3315657 (68.7%) unchanged\r\n\r\n- 21029 (0.4%) modified for this version\r\n\r\n- 1487287 (30.8%) added in this version\r\n\r\n- 11155 (0.2%) deleted from this version\r\n\r\nThe primary purpose of these data are to facilitate monitoring of UK climate and research into climate change, impacts and adaptation. The datasets have been created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project. The output from a number of data recovery activities relating to 19th and early 20th Century data have been used in the creation of this dataset, these activities were supported by: the Met Office Hadley Centre Climate Programme; the Natural Environment Research Council project \"Analysis of historic drought and water scarcity in the UK\"; the UK Research & Innovation (UKRI) Strategic Priorities Fund UK Climate Resilience programme; The UK Natural Environment Research Council (NERC) Public Engagement programme; the National Centre for Atmospheric Science; National Centre for Atmospheric Science and the NERC GloSAT project; and the contribution of many thousands of public volunteers. The dataset is provided under Open Government Licence." } }, { "ob_id": 933, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 42323, "uuid": "18ddbb686be549bfadfecbe0c673f405", "short_code": "ob", "title": "HadUK-Grid Gridded Climate Observations on a 25km grid over the UK, v1.3.0.ceda (1836-2023)", "abstract": "HadUK-Grid is a collection of gridded 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 dataset at 25 km resolution is derived from the associated 1 km x 1 km resolution to allow for comparison to data from UKCP18 climate projections. The dataset spans the period from 1836 to 2023, but the start time is dependent on climate variable and temporal resolution.\r\n\r\nThe gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.\r\n\r\nThis data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2019, see linked documentation).\r\n\r\nThe changes for v1.3.0.ceda HadUK-Grid datasets are as follows:\r\n \r\n* Added data for calendar year 2023\r\n \r\n* Added newly digitised data for daily rainfall (62 Scottish stations for 1945-1960)\r\n \r\n* Daily rainfall data for Bolton, 1916-1919 have been corrected (previous values were corrupted and needed redigitising)\r\n \r\n* Daily rainfall data for Buxton, 1960 have been corrected (conversion from inches to mm had been applied incorrectly)\r\n \r\n* Rainfall data from EA and SEPA APIs are included for the last three months of the dataset (Oct-Dec 2023) (for all earlier months the rainfall data from partner agencies is obtained from the Met Office's MIDAS database)\r\n \r\n* The number of stations used for groundfrost, sunshine and windspeed have reduced at different points in the historical series when comparing v1.3.0.ceda to the previous version v1.2.0.ceda. These reductions in station numbers have been caused by changes made in the data processing steps upstream of the gridding process.\r\n \r\n* For groundfrost this reduction has been caused by an automated quality control process flagging the historical data which have been removed as suspect (mostly affecting data from 1961 to 1970).\r\n \r\n* For sunshine the small reduction in the 1960s has been caused by the removal of digitized monthly sunshine data through this period where we wish to reverify the data source.\r\n \r\n* For windspeed the reduction from 1969 to 2010 has been caused by changes to rules applied relating to data completeness when compiling daily mean windspeeds, which in turn have followed through to monthly statistics.\r\n \r\n* We plan to carry out a review of the data which have been excluded from this version. Some of it may be reintroduced in a future release.\r\n \r\n* Net changes to the input station data:\r\n \r\n- Total of 126970983 observations\r\n- 125384735 (98.75%) unchanged\r\n- 28487 (0.02%) modified for this version\r\n- 1557761 (1.23%) added in this version\r\n- 188522 (0.15%) deleted from this version\r\n \r\nThe primary purpose of these data are to facilitate monitoring of UK climate and research into climate change, impacts and adaptation. The datasets have been created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project. The output from a number of data recovery activities relating to 19th and early 20th Century data have been used in the creation of this dataset, these activities were supported by: the Met Office Hadley Centre Climate Programme; the Natural Environment Research Council project \"Analysis of historic drought and water scarcity in the UK\"; the UK Research & Innovation (UKRI) Strategic Priorities Fund UK Climate Resilience programme; The UK Natural Environment Research Council (NERC) Public Engagement programme; the National Centre for Atmospheric Science; National Centre for Atmospheric Science and the NERC GloSAT project; and the contribution of many thousands of public volunteers. The dataset is provided under Open Government Licence." }, "objectObservation": { "ob_id": 40130, "uuid": "0545f37fb7124df381d42468eb63c144", "short_code": "ob", "title": "HadUK-Grid Gridded Climate Observations on a 25km grid over the UK, v1.2.0.ceda (1836-2022)", "abstract": "HadUK-Grid is a collection of gridded 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 dataset at 25 km resolution is derived from the associated 1 km x 1 km resolution to allow for comparison to data from UKCP18 climate projections. The dataset spans the period from 1836 to 2022, but the start time is dependent on climate variable and temporal resolution.\r\n\r\nThe gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.\r\n\r\nThis data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2018, see linked documentation).\r\n\r\nThe changes for v1.2.0.ceda HadUK-Grid datasets are as follows:\r\n\r\n * Added data for calendar year 2022\r\n \r\n* Added newly digitised data for monthly sunshine 1910-1918\r\n\r\n * Added Rainfall Rescue version 2 doi:10.5281/zenodo.7554242\r\n\r\n * Updated shapefiles used for production of area average statistics https://github.com/ukcp-data/ukcp-spatial-files\r\n\r\n * Updated controlled vocabulary for metadata assignment https://github.com/ukcp-data/UKCP18_CVs\r\n\r\n * Updated assignment of timepoint for some periods so that the datetime is the middle of the period (e.g. season) rather than a fixed offset from the period start.\r\n\r\n * Updated ordering of regions within regional values files. Alphabetical ordering.\r\n\r\n * Files use netcdf level 4 compression using gzip https://www.unidata.ucar.edu/blogs/developer/entry/netcdf_compression\r\n\r\n* Net changes to the input station data used to generate this dataset:\r\n\r\n- Total of 125601744 observations\r\n\r\n- 122621050 (97.6%) unchanged\r\n\r\n- 26700 (0.02%) modified for this version\r\n\r\n- 2953994 (2.35%) added in this version\r\n\r\n- 16315 (0.01%) deleted from this version\r\n\r\n* Changes to monthly rainfall 1836-1960\r\n\r\n- Total of 4823973 observations\r\n\r\n- 3315657 (68.7%) unchanged\r\n\r\n- 21029 (0.4%) modified for this version\r\n\r\n- 1487287 (30.8%) added in this version\r\n\r\n- 11155 (0.2%) deleted from this version\r\n\r\nThe primary purpose of these data are to facilitate monitoring of UK climate and research into climate change, impacts and adaptation. The datasets have been created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project. The output from a number of data recovery activities relating to 19th and early 20th Century data have been used in the creation of this dataset, these activities were supported by: the Met Office Hadley Centre Climate Programme; the Natural Environment Research Council project \"Analysis of historic drought and water scarcity in the UK\"; the UK Research & Innovation (UKRI) Strategic Priorities Fund UK Climate Resilience programme; The UK Natural Environment Research Council (NERC) Public Engagement programme; the National Centre for Atmospheric Science; National Centre for Atmospheric Science and the NERC GloSAT project; and the contribution of many thousands of public volunteers. The dataset is provided under Open Government Licence." } }, { "ob_id": 934, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 42324, "uuid": "b963ead70580451aa7455782224479d5", "short_code": "ob", "title": "HadUK-Grid Gridded Climate Observations on a 1km grid over the UK, v1.3.0.ceda (1836-2023)", "abstract": "HadUK-Grid is a collection of gridded 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 datasets cover the UK at 1 km x 1 km resolution. These 1 km x 1 km data have been used to provide a range of other resolutions and across countries, administrative regions and river basins to allow for comparison to data from UKCP18 climate projections. The dataset spans the period from 1836 to 2023, but the start time is dependent on climate variable and temporal resolution. \r\n\r\nThe gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.\r\n\r\nThis data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2019, see linked documentation).\r\n\r\nThe changes for v1.3.0.ceda HadUK-Grid datasets are as follows:\r\n \r\n* Added data for calendar year 2023\r\n \r\n* Added newly digitised data for daily rainfall (62 Scottish stations for 1945-1960)\r\n \r\n* Daily rainfall data for Bolton, 1916-1919 have been corrected (previous values were corrupted and needed redigitising)\r\n \r\n* Daily rainfall data for Buxton, 1960 have been corrected (conversion from inches to mm had been applied incorrectly)\r\n \r\n* Rainfall data from EA and SEPA APIs are included for the last three months of the dataset (Oct-Dec 2023) (for all earlier months the rainfall data from partner agencies is obtained from the Met Office's MIDAS database)\r\n \r\n* The number of stations used for groundfrost, sunshine and windspeed have reduced at different points in the historical series when comparing v1.3.0.ceda to the previous version v1.2.0.ceda. These reductions in station numbers have been caused by changes made in the data processing steps upstream of the gridding process.\r\n \r\n* For groundfrost this reduction has been caused by an automated quality control process flagging the historical data which have been removed as suspect (mostly affecting data from 1961 to 1970).\r\n \r\n* For sunshine the small reduction in the 1960s has been caused by the removal of digitized monthly sunshine data through this period where we wish to reverify the data source.\r\n \r\n* For windspeed the reduction from 1969 to 2010 has been caused by changes to rules applied relating to data completeness when compiling daily mean windspeeds, which in turn have followed through to monthly statistics.\r\n \r\n* We plan to carry out a review of the data which have been excluded from this version. Some of it may be reintroduced in a future release.\r\n \r\n* Net changes to the input station data:\r\n \r\n- Total of 126970983 observations\r\n- 125384735 (98.75%) unchanged\r\n- 28487 (0.02%) modified for this version\r\n- 1557761 (1.23%) added in this version\r\n- 188522 (0.15%) deleted from this version\r\n \r\nThe primary purpose of these data are to facilitate monitoring of UK climate and research into climate change, impacts and adaptation. The datasets have been created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project. The output from a number of data recovery activities relating to 19th and early 20th Century data have been used in the creation of this dataset, these activities were supported by: the Met Office Hadley Centre Climate Programme; the Natural Environment Research Council project \"Analysis of historic drought and water scarcity in the UK\"; the UK Research & Innovation (UKRI) Strategic Priorities Fund UK Climate Resilience programme; The UK Natural Environment Research Council (NERC) Public Engagement programme; the National Centre for Atmospheric Science; National Centre for Atmospheric Science and the NERC GloSAT project; and the contribution of many thousands of public volunteers. The dataset is provided under Open Government Licence." }, "objectObservation": { "ob_id": 40129, "uuid": "46f8c1377f8849eeb8570b8ac9b26d86", "short_code": "ob", "title": "HadUK-Grid Gridded Climate Observations on a 1km grid over the UK, v1.2.0.ceda (1836-2022)", "abstract": "HadUK-Grid is a collection of gridded 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 datasets cover the UK at 1 km x 1 km resolution. These 1 km x 1 km data have been used to provide a range of other resolutions and across countries, administrative regions and river basins to allow for comparison to data from UKCP18 climate projections. The dataset spans the period from 1836 to 2022, but the start time is dependent on climate variable and temporal resolution. \r\n\r\nThe gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.\r\n\r\nThis data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2018, see linked documentation).\r\n\r\nThe changes for v1.2.0.ceda HadUK-Grid datasets are as follows:\r\n\r\n * Added data for calendar year 2022\r\n \r\n* Added newly digitised data for monthly sunshine 1910-1918\r\n\r\n * Added Rainfall Rescue version 2 doi:10.5281/zenodo.7554242\r\n\r\n * Updated shapefiles used for production of area average statistics https://github.com/ukcp-data/ukcp-spatial-files\r\n\r\n * Updated controlled vocabulary for metadata assignment https://github.com/ukcp-data/UKCP18_CVs\r\n\r\n * Updated assignment of timepoint for some periods so that the datetime is the middle of the period (e.g. season) rather than a fixed offset from the period start.\r\n\r\n * Updated ordering of regions within regional values files. Alphabetical ordering.\r\n\r\n * Files use netcdf level 4 compression using gzip https://www.unidata.ucar.edu/blogs/developer/entry/netcdf_compression\r\n\r\n* Net changes to the input station data used to generate this dataset:\r\n\r\n- Total of 125601744 observations\r\n\r\n- 122621050 (97.6%) unchanged\r\n\r\n- 26700 (0.02%) modified for this version\r\n\r\n- 2953994 (2.35%) added in this version\r\n\r\n- 16315 (0.01%) deleted from this version\r\n\r\n* Changes to monthly rainfall 1836-1960\r\n\r\n- Total of 4823973 observations\r\n\r\n- 3315657 (68.7%) unchanged\r\n\r\n- 21029 (0.4%) modified for this version\r\n\r\n- 1487287 (30.8%) added in this version\r\n\r\n- 11155 (0.2%) deleted from this version\r\n\r\nThe primary purpose of these data are to facilitate monitoring of UK climate and research into climate change, impacts and adaptation. The datasets have been created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project. The output from a number of data recovery activities relating to 19th and early 20th Century data have been used in the creation of this dataset, these activities were supported by: the Met Office Hadley Centre Climate Programme; the Natural Environment Research Council project \"Analysis of historic drought and water scarcity in the UK\"; the UK Research & Innovation (UKRI) Strategic Priorities Fund UK Climate Resilience programme; The UK Natural Environment Research Council (NERC) Public Engagement programme; the National Centre for Atmospheric Science; National Centre for Atmospheric Science and the NERC GloSAT project; and the contribution of many thousands of public volunteers. The dataset is provided under Open Government Licence." } }, { "ob_id": 935, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 42325, "uuid": "5a248096468640a6bfb0dfda8b018ac5", "short_code": "ob", "title": "HadUK-Grid Gridded Climate Observations on a 12km grid over the UK, v1.3.0.ceda (1836-2023)", "abstract": "HadUK-Grid is a collection of gridded 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 dataset at 12 km resolution is derived from the associated 1 km x 1 km resolution to allow for comparison to data from climate projections. The dataset spans the period from 1836 to 2023, but the start time is dependent on climate variable and temporal resolution.\r\n\r\nThe gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.\r\n\r\nThis data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2019, see linked documentation). \r\n\r\nThe changes for v1.3.0.ceda HadUK-Grid datasets are as follows:\r\n \r\n* Added data for calendar year 2023\r\n \r\n* Added newly digitised data for daily rainfall (62 Scottish stations for 1945-1960)\r\n \r\n* Daily rainfall data for Bolton, 1916-1919 have been corrected (previous values were corrupted and needed redigitising)\r\n \r\n* Daily rainfall data for Buxton, 1960 have been corrected (conversion from inches to mm had been applied incorrectly)\r\n \r\n* Rainfall data from EA and SEPA APIs are included for the last three months of the dataset (Oct-Dec 2023) (for all earlier months the rainfall data from partner agencies is obtained from the Met Office's MIDAS database)\r\n \r\n* The number of stations used for groundfrost, sunshine and windspeed have reduced at different points in the historical series when comparing v1.3.0.ceda to the previous version v1.2.0.ceda. These reductions in station numbers have been caused by changes made in the data processing steps upstream of the gridding process.\r\n \r\n* For groundfrost this reduction has been caused by an automated quality control process flagging the historical data which have been removed as suspect (mostly affecting data from 1961 to 1970).\r\n \r\n* For sunshine the small reduction in the 1960s has been caused by the removal of digitized monthly sunshine data through this period where we wish to reverify the data source.\r\n \r\n* For windspeed the reduction from 1969 to 2010 has been caused by changes to rules applied relating to data completeness when compiling daily mean windspeeds, which in turn have followed through to monthly statistics.\r\n \r\n* We plan to carry out a review of the data which have been excluded from this version. Some of it may be reintroduced in a future release.\r\n \r\n* Net changes to the input station data:\r\n \r\n- Total of 126970983 observations\r\n- 125384735 (98.75%) unchanged\r\n- 28487 (0.02%) modified for this version\r\n- 1557761 (1.23%) added in this version\r\n- 188522 (0.15%) deleted from this version\r\n\r\nThe primary purpose of these data are to facilitate monitoring of UK climate and research into climate change, impacts and adaptation. The datasets have been created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project. The output from a number of data recovery activities relating to 19th and early 20th Century data have been used in the creation of this dataset, these activities were supported by: the Met Office Hadley Centre Climate Programme; the Natural Environment Research Council project \"Analysis of historic drought and water scarcity in the UK\"; the UK Research & Innovation (UKRI) Strategic Priorities Fund UK Climate Resilience programme; The UK Natural Environment Research Council (NERC) Public Engagement programme; the National Centre for Atmospheric Science; National Centre for Atmospheric Science and the NERC GloSAT project; and the contribution of many thousands of public volunteers. The dataset is provided under Open Government Licence." }, "objectObservation": { "ob_id": 40128, "uuid": "640d33e0cf99477990f7fee35a101850", "short_code": "ob", "title": "HadUK-Grid Gridded Climate Observations on a 12km grid over the UK, v1.2.0.ceda (1836-2022)", "abstract": "HadUK-Grid is a collection of gridded 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 dataset at 12 km resolution is derived from the associated 1 km x 1 km resolution to allow for comparison to data from climate projections. The dataset spans the period from 1836 to 2022, but the start time is dependent on climate variable and temporal resolution.\r\n\r\nThe gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.\r\n\r\nThis data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2018, see linked documentation). \r\n\r\nThe changes for v1.2.0.ceda HadUK-Grid datasets are as follows:\r\n\r\n * Added data for calendar year 2022\r\n \r\n* Added newly digitised data for monthly sunshine 1910-1918\r\n\r\n * Added Rainfall Rescue version 2 doi:10.5281/zenodo.7554242\r\n\r\n * Updated shapefiles used for production of area average statistics https://github.com/ukcp-data/ukcp- spatial-files\r\n\r\n * Updated controlled vocabulary for metadata assignment https://github.com/ukcp-data/UKCP18_CVs\r\n\r\n * Updated assignment of timepoint for some periods so that the datetime is the middle of the period (e.g. season) rather than a fixed offset from the period start.\r\n\r\n * Updated ordering of regions within regional values files. Alphabetical ordering.\r\n\r\n * Files use netcdf level 4 compression using gzip https://www.unidata.ucar.edu/blogs/developer/entry/netcdf_compression\r\n\r\n* Net changes to the input station data used to generate this dataset:\r\n\r\n- Total of 125601744 observations\r\n\r\n- 122621050 (97.6%) unchanged\r\n\r\n- 26700 (0.02%) modified for this version\r\n\r\n- 2953994 (2.35%) added in this version\r\n\r\n- 16315 (0.01%) deleted from this version\r\n\r\n* Changes to monthly rainfall 1836-1960\r\n\r\n- Total of 4823973 observations\r\n\r\n- 3315657 (68.7%) unchanged\r\n\r\n- 21029 (0.4%) modified for this version\r\n\r\n- 1487287 (30.8%) added in this version\r\n\r\n- 11155 (0.2%) deleted from this version\r\n\r\nThe primary purpose of these data are to facilitate monitoring of UK climate and research into climate change, impacts and adaptation. The datasets have been created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project. The output from a number of data recovery activities relating to 19th and early 20th Century data have been used in the creation of this dataset, these activities were supported by: the Met Office Hadley Centre Climate Programme; the Natural Environment Research Council project \"Analysis of historic drought and water scarcity in the UK\"; the UK Research & Innovation (UKRI) Strategic Priorities Fund UK Climate Resilience programme; The UK Natural Environment Research Council (NERC) Public Engagement programme; the National Centre for Atmospheric Science; National Centre for Atmospheric Science and the NERC GloSAT project; and the contribution of many thousands of public volunteers. The dataset is provided under Open Government Licence." } }, { "ob_id": 936, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 42326, "uuid": "b1282951f38947da93c0b0db31bb8419", "short_code": "ob", "title": "HadUK-Grid Climate Observations by UK river basins, v1.3.0.ceda (1836-2023)", "abstract": "HadUK-Grid is a collection of gridded 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. These data at 1 km resolution have been averaged across a set of discrete geographies defining UK river basins consistent with data from UKCP18 climate projections. The dataset spans the period from 1836 to 2023, but the start time is dependent on climate variable and temporal resolution.\r\n\r\nThe gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.\r\n\r\nThis data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2019, see linked documentation).\r\n\r\nThe changes for v1.3.0.ceda HadUK-Grid datasets are as follows:\r\n \r\n* Added data for calendar year 2023\r\n \r\n* Added newly digitised data for daily rainfall (62 Scottish stations for 1945-1960)\r\n \r\n* Daily rainfall data for Bolton, 1916-1919 have been corrected (previous values were corrupted and needed redigitising)\r\n \r\n* Daily rainfall data for Buxton, 1960 have been corrected (conversion from inches to mm had been applied incorrectly)\r\n \r\n* Rainfall data from EA and SEPA APIs are included for the last three months of the dataset (Oct-Dec 2023) (for all earlier months the rainfall data from partner agencies is obtained from the Met Office's MIDAS database)\r\n \r\n* The number of stations used for groundfrost, sunshine and windspeed have reduced at different points in the historical series when comparing v1.3.0.ceda to the previous version v1.2.0.ceda. These reductions in station numbers have been caused by changes made in the data processing steps upstream of the gridding process.\r\n \r\n* For groundfrost this reduction has been caused by an automated quality control process flagging the historical data which have been removed as suspect (mostly affecting data from 1961 to 1970).\r\n \r\n* For sunshine the small reduction in the 1960s has been caused by the removal of digitized monthly sunshine data through this period where we wish to reverify the data source.\r\n \r\n* For windspeed the reduction from 1969 to 2010 has been caused by changes to rules applied relating to data completeness when compiling daily mean windspeeds, which in turn have followed through to monthly statistics.\r\n \r\n* We plan to carry out a review of the data which have been excluded from this version. Some of it may be reintroduced in a future release.\r\n \r\n* Net changes to the input station data:\r\n \r\n- Total of 126970983 observations\r\n- 125384735 (98.75%) unchanged\r\n- 28487 (0.02%) modified for this version\r\n- 1557761 (1.23%) added in this version\r\n- 188522 (0.15%) deleted from this version\r\n \r\nThe primary purpose of these data are to facilitate monitoring of UK climate and research into climate change, impacts and adaptation. The datasets have been created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project. The output from a number of data recovery activities relating to 19th and early 20th Century data have been used in the creation of this dataset, these activities were supported by: the Met Office Hadley Centre Climate Programme; the Natural Environment Research Council project \"Analysis of historic drought and water scarcity in the UK\"; the UK Research & Innovation (UKRI) Strategic Priorities Fund UK Climate Resilience programme; The UK Natural Environment Research Council (NERC) Public Engagement programme; the National Centre for Atmospheric Science; National Centre for Atmospheric Science and the NERC GloSAT project; and the contribution of many thousands of public volunteers. The dataset is provided under Open Government Licence." }, "objectObservation": { "ob_id": 40127, "uuid": "e6822428e4124c5986b689a37fda10bc", "short_code": "ob", "title": "HadUK-Grid Climate Observations by UK river basins, v1.2.0.ceda (1836-2022)", "abstract": "HadUK-Grid is a collection of gridded 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. These data at 1 km resolution have been averaged across a set of discrete geographies defining UK river basins consistent with data from UKCP18 climate projections. The dataset spans the period from 1836 to 2022, but the start time is dependent on climate variable and temporal resolution.\r\n\r\nThe gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.\r\n\r\nThis data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2018, see linked documentation).\r\n\r\nThe changes for v1.2.0.ceda HadUK-Grid datasets are as follows:\r\n\r\n * Added data for calendar year 2022\r\n \r\n* Added newly digitised data for monthly sunshine 1910-1918\r\n\r\n * Added Rainfall Rescue version 2 doi:10.5281/zenodo.7554242\r\n\r\n * Updated shapefiles used for production of area average statistics https://github.com/ukcp-data/ukcp-spatial-files\r\n\r\n * Updated controlled vocabulary for metadata assignment https://github.com/ukcp-data/UKCP18_CVs\r\n\r\n * Updated assignment of timepoint for some periods so that the datetime is the middle of the period (e.g. season) rather than a fixed offset from the period start.\r\n\r\n * Updated ordering of regions within regional values files. Alphabetical ordering.\r\n\r\n * Files use netcdf level 4 compression using gzip https://www.unidata.ucar.edu/blogs/developer/entry/netcdf_compression\r\n\r\n* Net changes to the input station data used to generate this dataset:\r\n\r\n- Total of 125601744 observations\r\n\r\n- 122621050 (97.6%) unchanged\r\n\r\n- 26700 (0.02%) modified for this version\r\n\r\n- 2953994 (2.35%) added in this version\r\n\r\n- 16315 (0.01%) deleted from this version\r\n\r\n* Changes to monthly rainfall 1836-1960\r\n\r\n- Total of 4823973 observations\r\n\r\n- 3315657 (68.7%) unchanged\r\n\r\n- 21029 (0.4%) modified for this version\r\n\r\n- 1487287 (30.8%) added in this version\r\n\r\n- 11155 (0.2%) deleted from this version\r\n\r\n\r\nThe primary purpose of these data are to facilitate monitoring of UK climate and research into climate change, impacts and adaptation. The datasets have been created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project. The output from a number of data recovery activities relating to 19th and early 20th Century data have been used in the creation of this dataset, these activities were supported by: the Met Office Hadley Centre Climate Programme; the Natural Environment Research Council project \"Analysis of historic drought and water scarcity in the UK\"; the UK Research & Innovation (UKRI) Strategic Priorities Fund UK Climate Resilience programme; The UK Natural Environment Research Council (NERC) Public Engagement programme; the National Centre for Atmospheric Science; National Centre for Atmospheric Science and the NERC GloSAT project; and the contribution of many thousands of public volunteers. The dataset is provided under Open Government Licence." } }, { "ob_id": 937, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 42327, "uuid": "a508838f92c74005a26b9277eae59a7c", "short_code": "ob", "title": "HadUK-Grid Climate Observations by UK countries, v1.3.0.ceda (1836-2023)", "abstract": "HadUK-Grid is a collection of gridded 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. These data at 1 km resolution have been averaged across a set of discrete geographies defining UK countries consistent with data from UKCP18 climate projections. The dataset spans the period from 1836 to 2023, but the start time is dependent on climate variable and temporal resolution.\r\n\r\nThe gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.\r\n\r\nThis data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2019, see linked documentation).\r\n\r\nThe changes for v1.3.0.ceda HadUK-Grid datasets are as follows:\r\n\r\n * Added data for calendar year 2023\r\n \r\n* Added newly digitised data for monthly sunshine 1910-1918\r\n\r\n * Added Rainfall Rescue version 2 doi:10.5281/zenodo.7554242\r\n\r\n * Updated shapefiles used for production of area average statistics https://github.com/ukcp-data/ukcp-spatial-files\r\n\r\n * Updated controlled vocabulary for metadata assignment https://github.com/ukcp-data/UKCP18_CVs\r\n\r\n * Updated assignment of timepoint for some periods so that the datetime is the middle of the period (e.g. season) rather than a fixed offset from the period start.\r\n\r\n * Updated ordering of regions within regional values files. Alphabetical ordering.\r\n\r\n * Files use netcdf level 4 compression using gzip https://www.unidata.ucar.edu/blogs/developer/entry/netcdf_compression\r\n\r\n* Net changes to the input station data used to generate this dataset:\r\n\r\n- Total of 125601744 observations\r\n\r\n- 122621050 (97.6%) unchanged\r\n\r\n- 26700 (0.02%) modified for this version\r\n\r\n- 2953994 (2.35%) added in this version\r\n\r\n- 16315 (0.01%) deleted from this version\r\n\r\n* Changes to monthly rainfall 1836-1960\r\n\r\n- Total of 4823973 observations\r\n\r\n- 3315657 (68.7%) unchanged\r\n\r\n- 21029 (0.4%) modified for this version\r\n\r\n- 1487287 (30.8%) added in this version\r\n\r\n- 11155 (0.2%) deleted from this version\r\n\r\nThe primary purpose of these data are to facilitate monitoring of UK climate and research into climate change, impacts and adaptation. The datasets have been created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project. The output from a number of data recovery activities relating to 19th and early 20th Century data have been used in the creation of this dataset, these activities were supported by: the Met Office Hadley Centre Climate Programme; the Natural Environment Research Council project \"Analysis of historic drought and water scarcity in the UK\"; the UK Research & Innovation (UKRI) Strategic Priorities Fund UK Climate Resilience programme; The UK Natural Environment Research Council (NERC) Public Engagement programme; the National Centre for Atmospheric Science; National Centre for Atmospheric Science and the NERC GloSAT project; and the contribution of many thousands of public volunteers. The dataset is provided under Open Government Licence." }, "objectObservation": { "ob_id": 40126, "uuid": "3d30627eee5a48be844c32723b7b6be8", "short_code": "ob", "title": "HadUK-Grid Climate Observations by UK countries, v1.2.0.ceda (1836-2022)", "abstract": "HadUK-Grid is a collection of gridded 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. These data at 1 km resolution have been averaged across a set of discrete geographies defining UK countries consistent with data from UKCP18 climate projections. The dataset spans the period from 1836 to 2022, but the start time is dependent on climate variable and temporal resolution.\r\n\r\nThe gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.\r\n\r\nThis data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2018, see linked documentation).\r\n\r\nThe changes for v1.2.0.ceda HadUK-Grid datasets are as follows:\r\n\r\n * Added data for calendar year 2022\r\n \r\n* Added newly digitised data for monthly sunshine 1910-1918\r\n\r\n * Added Rainfall Rescue version 2 doi:10.5281/zenodo.7554242\r\n\r\n * Updated shapefiles used for production of area average statistics https://github.com/ukcp-data/ukcp-spatial-files\r\n\r\n * Updated controlled vocabulary for metadata assignment https://github.com/ukcp-data/UKCP18_CVs\r\n\r\n * Updated assignment of timepoint for some periods so that the datetime is the middle of the period (e.g. season) rather than a fixed offset from the period start.\r\n\r\n * Updated ordering of regions within regional values files. Alphabetical ordering.\r\n\r\n * Files use netcdf level 4 compression using gzip https://www.unidata.ucar.edu/blogs/developer/entry/netcdf_compression\r\n\r\n* Net changes to the input station data used to generate this dataset:\r\n\r\n- Total of 125601744 observations\r\n\r\n- 122621050 (97.6%) unchanged\r\n\r\n- 26700 (0.02%) modified for this version\r\n\r\n- 2953994 (2.35%) added in this version\r\n\r\n- 16315 (0.01%) deleted from this version\r\n\r\n* Changes to monthly rainfall 1836-1960\r\n\r\n- Total of 4823973 observations\r\n\r\n- 3315657 (68.7%) unchanged\r\n\r\n- 21029 (0.4%) modified for this version\r\n\r\n- 1487287 (30.8%) added in this version\r\n\r\n- 11155 (0.2%) deleted from this version\r\n\r\nThe primary purpose of these data are to facilitate monitoring of UK climate and research into climate change, impacts and adaptation. The datasets have been created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project. The output from a number of data recovery activities relating to 19th and early 20th Century data have been used in the creation of this dataset, these activities were supported by: the Met Office Hadley Centre Climate Programme; the Natural Environment Research Council project \"Analysis of historic drought and water scarcity in the UK\"; the UK Research & Innovation (UKRI) Strategic Priorities Fund UK Climate Resilience programme; The UK Natural Environment Research Council (NERC) Public Engagement programme; the National Centre for Atmospheric Science; National Centre for Atmospheric Science and the NERC GloSAT project; and the contribution of many thousands of public volunteers. The dataset is provided under Open Government Licence." } }, { "ob_id": 938, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 42328, "uuid": "8a51496be92b4e9488954c7c0199f3f9", "short_code": "ob", "title": "HadUK-Grid Climate Observations by Administrative Regions over the UK, v1.3.0.ceda (1836-2023)", "abstract": "HadUK-Grid is a collection of gridded 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. These data at 1 km resolution have been averaged across a set of discrete geographies defining UK administrative regions consistent with data from UKCP18 climate projections. The dataset spans the period from 1836 to 2023 but the start time is dependent on climate variable and temporal resolution.\r\n\r\nThe gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.\r\n\r\nThis data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2019, see linked documentation).\r\n\r\nThe changes for v1.3.0.ceda HadUK-Grid datasets are as follows:\r\n \r\n* Added data for calendar year 2023\r\n \r\n* Added newly digitised data for daily rainfall (62 Scottish stations for 1945-1960)\r\n \r\n* Daily rainfall data for Bolton, 1916-1919 have been corrected (previous values were corrupted and needed redigitising)\r\n \r\n* Daily rainfall data for Buxton, 1960 have been corrected (conversion from inches to mm had been applied incorrectly)\r\n \r\n* Rainfall data from EA and SEPA APIs are included for the last three months of the dataset (Oct-Dec 2023) (for all earlier months the rainfall data from partner agencies is obtained from the Met Office's MIDAS database)\r\n \r\n* The number of stations used for groundfrost, sunshine and windspeed have reduced at different points in the historical series when comparing v1.3.0.ceda to the previous version v1.2.0.ceda. These reductions in station numbers have been caused by changes made in the data processing steps upstream of the gridding process.\r\n \r\n* For groundfrost this reduction has been caused by an automated quality control process flagging the historical data which have been removed as suspect (mostly affecting data from 1961 to 1970).\r\n \r\n* For sunshine the small reduction in the 1960s has been caused by the removal of digitized monthly sunshine data through this period where we wish to reverify the data source.\r\n \r\n* For windspeed the reduction from 1969 to 2010 has been caused by changes to rules applied relating to data completeness when compiling daily mean windspeeds, which in turn have followed through to monthly statistics.\r\n \r\n* We plan to carry out a review of the data which have been excluded from this version. Some of it may be reintroduced in a future release.\r\n \r\n* Net changes to the input station data:\r\n \r\n- Total of 126970983 observations\r\n- 125384735 (98.75%) unchanged\r\n- 28487 (0.02%) modified for this version\r\n- 1557761 (1.23%) added in this version\r\n- 188522 (0.15%) deleted from this version\r\n \r\nThe primary purpose of these data are to facilitate monitoring of UK climate and research into climate change, impacts and adaptation. The datasets have been created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project. The output from a number of data recovery activities relating to 19th and early 20th Century data have been used in the creation of this dataset, these activities were supported by: the Met Office Hadley Centre Climate Programme; the Natural Environment Research Council project \"Analysis of historic drought and water scarcity in the UK\"; the UK Research & Innovation (UKRI) Strategic Priorities Fund UK Climate Resilience programme; The UK Natural Environment Research Council (NERC) Public Engagement programme; the National Centre for Atmospheric Science; National Centre for Atmospheric Science and the NERC GloSAT project; and the contribution of many thousands of public volunteers. The dataset is provided under Open Government Licence." }, "objectObservation": { "ob_id": 40125, "uuid": "b39898e76ab7434a9a20a6dc4ab721f0", "short_code": "ob", "title": "HadUK-Grid Climate Observations by Administrative Regions over the UK, v1.2.0.ceda (1836-2022)", "abstract": "HadUK-Grid is a collection of gridded 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. These data at 1 km resolution have been averaged across a set of discrete geographies defining UK administrative regions consistent with data from UKCP18 climate projections. The dataset spans the period from 1836 to 2022 but the start time is dependent on climate variable and temporal resolution.\r\n\r\nThe gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.\r\n\r\nThis data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2018, see linked documentation).\r\n\r\nThe changes for v1.2.0.ceda HadUK-Grid datasets are as follows:\r\n\r\n * Added data for calendar year 2022\r\n \r\n* Added newly digitised data for monthly sunshine 1910-1918\r\n\r\n * Added Rainfall Rescue version 2 doi:10.5281/zenodo.7554242\r\n\r\n * Updated shapefiles used for production of area average statistics https://github.com/ukcp-data/ukcp-spatial-files\r\n\r\n * Updated controlled vocabulary for metadata assignment https://github.com/ukcp-data/UKCP18_CVs\r\n\r\n * Updated assignment of timepoint for some periods so that the datetime is the middle of the period (e.g. season) rather than a fixed offset from the period start.\r\n\r\n * Updated ordering of regions within regional values files. Alphabetical ordering.\r\n\r\n * Files use netcdf level 4 compression using gzip https://www.unidata.ucar.edu/blogs/developer/entry/netcdf_compression\r\n\r\n* Net changes to the input station data used to generate this dataset:\r\n\r\n- Total of 125601744 observations\r\n\r\n- 122621050 (97.6%) unchanged\r\n\r\n- 26700 (0.02%) modified for this version\r\n\r\n- 2953994 (2.35%) added in this version\r\n\r\n- 16315 (0.01%) deleted from this version\r\n\r\n* Changes to monthly rainfall 1836-1960\r\n\r\n- Total of 4823973 observations\r\n\r\n- 3315657 (68.7%) unchanged\r\n\r\n- 21029 (0.4%) modified for this version\r\n\r\n- 1487287 (30.8%) added in this version\r\n\r\n- 11155 (0.2%) deleted from this version\r\n\r\nThe primary purpose of these data are to facilitate monitoring of UK climate and research into climate change, impacts and adaptation. The datasets have been created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project. The output from a number of data recovery activities relating to 19th and early 20th Century data have been used in the creation of this dataset, these activities were supported by: the Met Office Hadley Centre Climate Programme; the Natural Environment Research Council project \"Analysis of historic drought and water scarcity in the UK\"; the UK Research & Innovation (UKRI) Strategic Priorities Fund UK Climate Resilience programme; The UK Natural Environment Research Council (NERC) Public Engagement programme; the National Centre for Atmospheric Science; National Centre for Atmospheric Science and the NERC GloSAT project; and the contribution of many thousands of public volunteers. The dataset is provided under Open Government Licence." } }, { "ob_id": 939, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 42330, "uuid": "a6bb3e8def544b5790d4b05a6f37f901", "short_code": "ob", "title": "MIDAS Open: UK soil temperature data, v202407", "abstract": "The UK soil temperature data contain daily and hourly values of soil temperatures at depths of 5, 10, 20, 30, 50, and 100 centimetres. The measurements were recorded by observation stations operated by the Met Office across the UK and transmitted within NCM or DLY3208 messages. The data spans from 1900 to 2023.\r\n\r\nThis version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data. These include the addition of data for calendar year 2023.\r\n\r\nAt many stations temperatures below the surface are measured at various depths. The depths used today are 5, 10, 20, 30 and 100cm, although measurements are not necessarily made at all these depths at a station and exceptionally measurements may be made at other depths. When imperial units were in general use, typically before 1961, the normal depths of measurement were 4, 8, 12, 24 and 48 inches.\r\n\r\nLiquid-in-glass soil thermometers at a depth of 20 cm or less are unsheathed and have a bend in the stem between the bulb and the lowest graduation. At greater depths the thermometer is suspended in a steel tube and has its bulb encased in wax.\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by the Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record." }, "objectObservation": { "ob_id": 40653, "uuid": "85596b72ff024837a64bf22a8d1a72be", "short_code": "ob", "title": "MIDAS Open: UK soil temperature data, v202308", "abstract": "The UK soil temperature data contain daily and hourly values of soil temperatures at depths of 5, 10, 20, 30, 50, and 100 centimetres. The measurements were recorded by observation stations operated by the Met Office across the UK and transmitted within NCM or DLY3208 messages. The data spans from 1900 to 2022.\r\n\r\nThis version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data. These include the addition of data for calendar year 2022.\r\n\r\nAt many stations temperatures below the surface are measured at various depths. The depths used today are 5, 10, 20, 30 and 100cm, although measurements are not necessarily made at all these depths at a station and exceptionally measurements may be made at other depths. When imperial units were in general use, typically before 1961, the normal depths of measurement were 4, 8, 12, 24 and 48 inches.\r\n\r\nLiquid-in-glass soil thermometers at a depth of 20 cm or less are unsheathed and have a bend in the stem between the bulb and the lowest graduation. At greater depths the thermometer is suspended in a steel tube and has its bulb encased in wax.\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by the Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record." } }, { "ob_id": 940, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 42331, "uuid": "91cb9985a6c2453d99084bde4ff5f314", "short_code": "ob", "title": "MIDAS Open: UK mean wind data, v202407", "abstract": "The UK mean wind data contain the mean wind speed and direction, and the direction, speed and time of the maximum gust, all during 1 or more hours, ending at the stated time and date. The data were collected by observation stations operated by the Met Office across the UK and transmitted within the following message types: SYNOP, HCM, AWSHRLY, DLY3208, HWNDAUTO and HWND6910. The data spans from 1949 to 2023.\r\n\r\nThis version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data. These include the addition of data for calendar year 2023.\r\n\r\nFor further details on observing practice, including measurement accuracies for the message types, see relevant sections of the MIDAS User Guide linked from this record (e.g. section 3.3 details the wind network in the UK, section 5.5 covers wind measurements in general and section 4 details message type information).\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by the Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record." }, "objectObservation": { "ob_id": 40656, "uuid": "68920a29caf44f21be6371d9f87f578b", "short_code": "ob", "title": "MIDAS Open: UK mean wind data, v202308", "abstract": "The UK mean wind data contain the mean wind speed and direction, and the direction, speed and time of the maximum gust, all during 1 or more hours, ending at the stated time and date. The data were collected by observation stations operated by the Met Office across the UK and transmitted within the following message types: SYNOP, HCM, AWSHRLY, DLY3208, HWNDAUTO and HWND6910. The data spans from 1949 to 2022.\r\n\r\nThis version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data. These include the addition of data for calendar year 2022.\r\n\r\nFor further details on observing practice, including measurement accuracies for the message types, see relevant sections of the MIDAS User Guide linked from this record (e.g. section 3.3 details the wind network in the UK, section 5.5 covers wind measurements in general and section 4 details message type information).\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by the Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record." } }, { "ob_id": 941, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 42332, "uuid": "c50776e4903942cdb329589da70b83fe", "short_code": "ob", "title": "MIDAS Open: UK hourly weather observation data, v202407", "abstract": "The UK hourly weather observation data contain meteorological values measured on an hourly time scale. The measurements of the concrete state, wind speed and direction, cloud type and amount, visibility, and temperature were recorded by observation stations operated by the Met Office across the UK and transmitted within SYNOP, DLY3208, AWSHRLY and NCM messages. The sunshine duration measurements were transmitted in the HSUN3445 message. The data spans from 1875 to 2023.\r\n\r\nThis version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data. These include the addition of data for calendar year 2023.\r\n\r\nFor details on observing practice see the message type information in the MIDAS User Guide linked from this record and relevant sections for parameter types.\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record. Note, METAR message types are not included in the Open version of this dataset. Those data may be accessed via the full MIDAS hourly weather data." }, "objectObservation": { "ob_id": 40655, "uuid": "c9663d0c525f4b0698f1ec4beae3688e", "short_code": "ob", "title": "MIDAS Open: UK hourly weather observation data, v202308", "abstract": "The UK hourly weather observation data contain meteorological values measured on an hourly time scale. The measurements of the concrete state, wind speed and direction, cloud type and amount, visibility, and temperature were recorded by observation stations operated by the Met Office across the UK and transmitted within SYNOP, DLY3208, AWSHRLY and NCM messages. The sunshine duration measurements were transmitted in the HSUN3445 message. The data spans from 1875 to 2022.\r\n\r\nThis version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data. These include the addition of data for calendar year 2022.\r\n\r\nFor details on observing practice see the message type information in the MIDAS User Guide linked from this record and relevant sections for parameter types.\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record. Note, METAR message types are not included in the Open version of this dataset. Those data may be accessed via the full MIDAS hourly weather data." } }, { "ob_id": 942, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 42333, "uuid": "0afba628c2f4462da68b0a81ebf1ff4c", "short_code": "ob", "title": "MIDAS Open: UK hourly solar radiation data, v202407", "abstract": "The UK hourly solar radiation data contain the amount of solar irradiance received during the hour ending at the specified time. All sites report 'global' radiation amounts. This is also known as 'total sky radiation' as it includes both direct solar irradiance and 'diffuse' irradiance as a result of light scattering. Some sites also provide separate diffuse and direct irradiation amounts, depending on the instrumentation at the site. For these the sun's path is tracked with two pyrometers - one where the path to the sun is blocked by a suitable disc to allow the scattered sunlight to be measured to give the diffuse measurement, while the other has a tube pointing at the sun to measure direct solar irradiance whilst blanking out scattered sun light. \r\n\r\nFor details about the different measurements made and the limited number of sites making them please see the MIDAS Solar Irradiance table linked to in the online resources section of this record.\r\n\r\nThis version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data. These include the addition of data for calendar year 2023.\r\n\r\nThe data were collected by observation stations operated by the Met Office across the UK and transmitted within the following message types: SYNOP, HCM, AWSHRLY, MODLERAD, ESAWRADT and DRADR35 messages. The data spans from 1947 to 2023.\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by the Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record." }, "objectObservation": { "ob_id": 40654, "uuid": "87eb67c08f5c4518a3723d0ca2d9b4b1", "short_code": "ob", "title": "MIDAS Open: UK hourly solar radiation data, v202308", "abstract": "The UK hourly solar radiation data contain the amount of solar irradiance received during the hour ending at the specified time. All sites report 'global' radiation amounts. This is also known as 'total sky radiation' as it includes both direct solar irradiance and 'diffuse' irradiance as a result of light scattering. Some sites also provide separate diffuse and direct irradiation amounts, depending on the instrumentation at the site. For these the sun's path is tracked with two pyrometers - one where the path to the sun is blocked by a suitable disc to allow the scattered sunlight to be measured to give the diffuse measurement, while the other has a tube pointing at the sun to measure direct solar irradiance whilst blanking out scattered sun light. \r\n\r\nFor details about the different measurements made and the limited number of sites making them please see the MIDAS Solar Irradiance table linked to in the online resources section of this record.\r\n\r\nThis version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data. These include the addition of data for calendar year 2022.\r\n\r\nThe data were collected by observation stations operated by the Met Office across the UK and transmitted within the following message types: SYNOP, HCM, AWSHRLY, MODLERAD, ESAWRADT and DRADR35 messages. The data spans from 1947 to 2022.\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by the Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record." } }, { "ob_id": 943, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 42334, "uuid": "6c619c67138843b8839a5788ac749e12", "short_code": "ob", "title": "MIDAS Open: UK hourly rainfall data, v202407", "abstract": "The UK hourly rainfall data contain the rainfall amount (and duration from tilting syphon gauges) during the hour (or hours) ending at the specified time. The data also contains precipitation amounts, however precipitation measured over 24 hours are not stored. Over time a range of rain gauges have been used - see the linked MIDAS User Guide for further details.\r\n\r\nThis version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data.\r\n\r\nThe data were collected by observation stations operated by the Met Office across the UK and transmitted within the following message types: NCM, AWSHRLY, DLY3208, SREW and SSER. The data spans from 1915 to 2023.\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by the Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record. A large proportion of the UK raingauge observing network (associated with WAHRAIN, WADRAIN and WAMRAIN for hourly, daily and monthly rainfall measurements respectively) is operated by other agencies beyond the Met Office, and are consequently currently excluded from the Midas-open dataset." }, "objectObservation": { "ob_id": 40649, "uuid": "c21639861fb54623a749e502ebac74ed", "short_code": "ob", "title": "MIDAS Open: UK hourly rainfall data, v202308", "abstract": "The UK hourly rainfall data contain the rainfall amount (and duration from tilting syphon gauges) during the hour (or hours) ending at the specified time. The data also contains precipitation amounts, however precipitation measured over 24 hours are not stored. Over time a range of rain gauges have been used - see the linked MIDAS User Guide for further details.\r\n\r\nThis version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data.\r\n\r\nThe data were collected by observation stations operated by the Met Office across the UK and transmitted within the following message types: NCM, AWSHRLY, DLY3208, SREW and SSER. The data spans from 1915 to 2022.\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by the Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record. A large proportion of the UK raingauge observing network (associated with WAHRAIN, WADRAIN and WAMRAIN for hourly, daily and monthly rainfall measurements respectively) is operated by other agencies beyond the Met Office, and are consequently currently excluded from the Midas-open dataset." } }, { "ob_id": 944, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 42335, "uuid": "8070d47e1b7340468fa7cf654dee938b", "short_code": "ob", "title": "MIDAS Open: UK daily weather observation data, v202407", "abstract": "The UK daily weather observation data contain meteorological values measured on a 24 hour time scale. The measurements of sunshine duration, concrete state, snow depth, fresh snow depth, and days of snow, hail, thunder and gail were attained by observation stations operated by the Met Office across the UK operated and transmitted within DLY3208, NCM, AWSDLY and SYNOP messages. The data span from 1887 to 2023. For details of observations see the relevant sections of the MIDAS User Guide linked from this record for the various message types.\r\n\r\nThis version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data. These include the addition of data for calendar year 2023.\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by the Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record. Currently this represents approximately 95% of available daily weather observations within the full MIDAS collection." }, "objectObservation": { "ob_id": 40652, "uuid": "1ce37461affc43bbbd78beaaacf5911d", "short_code": "ob", "title": "MIDAS Open: UK daily weather observation data, v202308", "abstract": "The UK daily weather observation data contain meteorological values measured on a 24 hour time scale. The measurements of sunshine duration, concrete state, snow depth, fresh snow depth, and days of snow, hail, thunder and gail were attained by observation stations operated by the Met Office across the UK operated and transmitted within DLY3208, NCM, AWSDLY and SYNOP messages. The data span from 1887 to 2022. For details of observations see the relevant sections of the MIDAS User Guide linked from this record for the various message types.\r\n\r\nThis version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data. These include the addition of data for calendar year 2022.\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by the Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record. Currently this represents approximately 95% of available daily weather observations within the full MIDAS collection." } }, { "ob_id": 945, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 42336, "uuid": "b7c6295b72c54fa9bcd8308fea2727e7", "short_code": "ob", "title": "MIDAS Open: UK daily temperature data, v202407", "abstract": "The UK daily temperature data contain maximum and minimum temperatures (air, grass and concrete slab) measured over a period of up to 24 hours. The measurements were recorded by observation stations operated by the Met Office across the UK and transmitted within NCM, DLY3208 or AWSDLY messages. The data span from 1853 to 2023. For details on measurement techniques, including calibration information and changes in measurements, see section 5.2 of the MIDAS User Guide linked to from this record. Soil temperature data may be found in the UK soil temperature datasets linked from this record.\r\n\r\nThis version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data. These include the addition of data for calendar year 2023.\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by the Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record. Currently this represents approximately 95% of available daily temperature observations within the full MIDAS collection." }, "objectObservation": { "ob_id": 40651, "uuid": "220b9b8ffbed43fcbbd323e739118f6c", "short_code": "ob", "title": "MIDAS Open: UK daily temperature data, v202308", "abstract": "The UK daily temperature data contain maximum and minimum temperatures (air, grass and concrete slab) measured over a period of up to 24 hours. The measurements were recorded by observation stations operated by the Met Office across the UK and transmitted within NCM, DLY3208 or AWSDLY messages. The data span from 1853 to 2022. For details on measurement techniques, including calibration information and changes in measurements, see section 5.2 of the MIDAS User Guide linked to from this record. Soil temperature data may be found in the UK soil temperature datasets linked from this record.\r\n\r\nThis version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data. These include the addition of data for calendar year 2022.\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by the Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record. Currently this represents approximately 95% of available daily temperature observations within the full MIDAS collection." } }, { "ob_id": 946, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 42337, "uuid": "8606115371e44b079e25d479cfec465c", "short_code": "ob", "title": "MIDAS Open: UK daily rainfall data, v202407", "abstract": "The UK daily rainfall data contain rainfall accumulation and precipitation amounts over a 24 hour period. The data were collected by observation stations operated by the Met Office across the UK and transmitted within the following message types: NCM, AWSDLY, DLY3208 and SSER. The data spans from 1853 to 2023. Over time a range of rain gauges have been used - see section 5.6 and the relevant message type information in the linked MIDAS User Guide for further details.\r\n\r\nThis version supersedes the previous version (202308) of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data. These include the addition of data for calendar year 2023.\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record. A large proportion of the UK raingauge observing network (associated with WAHRAIN, WADRAIN and WAMRAIN for hourly, daily and monthly rainfall measurements respectively) is operated by other agencies beyond the Met Office, and are consequently currently excluded from the Midas-open dataset. Currently this represents approximately 13% of available daily rainfall observations within the full MIDAS collection." }, "objectObservation": { "ob_id": 40650, "uuid": "3f3809143a224c84962f52757d668f77", "short_code": "ob", "title": "MIDAS Open: UK daily rainfall data, v202308", "abstract": "The UK daily rainfall data contain rainfall accumulation and precipitation amounts over a 24 hour period. The data were collected by observation stations operated by the Met Office across the UK and transmitted within the following message types: NCM, AWSDLY, DLY3208 and SSER. The data spans from 1853 to 2022. Over time a range of rain gauges have been used - see section 5.6 and the relevant message type information in the linked MIDAS User Guide for further details.\r\n\r\nThis version supersedes the previous version (202207) of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data. These include the addition of data for calendar year 2022.\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record. A large proportion of the UK raingauge observing network (associated with WAHRAIN, WADRAIN and WAMRAIN for hourly, daily and monthly rainfall measurements respectively) is operated by other agencies beyond the Met Office, and are consequently currently excluded from the Midas-open dataset. Currently this represents approximately 13% of available daily rainfall observations within the full MIDAS collection." } }, { "ob_id": 947, "relationType": "IsDerivedFrom", "subjectObservation": { "ob_id": 41458, "uuid": "44c930e1388f40728884fbdf7e28c109", "short_code": "ob", "title": "ESA River Discharge Climate Change Initiative (RD_cci): Altimetry-based River Discharge product, v1.0", "abstract": "This dataset comprises the altimetry-based river discharge (RD-ALTI) Climate Research Data Package (CRDP), derived from nadir radar altimeter missions by the ESA CCI River Discharge precursor project (RD_cci). \r\n\r\nIt provides long-term satellite river discharge (RD) time series at specified locations (defined in the \"Selection of river basins\" document, available at https://climate.esa.int/documents/2189/D2_CCI-Discharge-0004-RP_WP2_v1-1.pdf) River discharge (in m3/s) corresponds to the water volume passing through the river cross-section per unit of time. In this dataset, it is computed from a rating curve applied to long-term satellite altimeter water surface elevation (WSE) from https://catalogue.ceda.ac.uk/uuid/c5f0aa806ec444b4a4209b49efc4bb65. The rating curve is obtained by fitting the relationship between in-situ discharge and altimeter WSE with a power law following a Bayesian approach." }, "objectObservation": { "ob_id": 41205, "uuid": "c5f0aa806ec444b4a4209b49efc4bb65", "short_code": "ob", "title": "ESA River Discharge Climate Change Initiative (RD_cci): Nadir radar altimeters Water Level product, v1.1", "abstract": "This dataset contains water level (WL) data from the ESA Climate Change Initiative River Discharge project (RD_cci). Water level in this context corresponds to the distance between river surface water and a reference surface (the WGS84 ellipsoid). This physical variable might also be referred to as Water Surface Elevation (WSE) in other dataset or publications.\r\n\r\n These river water level time series have been computed in at 54 locations (within 18 river basins). The data has been derived from nadir viewing satellite radar altimeter missions (ERS-2, Envisat, Saral, Topex-Poseidon, Jason-1, Jason-2, Jason-3, Sentinel-3A/B and Sentinel 6). At each location, time series are provided for each available single nadir radar altimetry mission. Based on these single mission time series, merged multi-missions WL time series have also been produced." } }, { "ob_id": 948, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 43090, "uuid": "bf535053562141c6bb7ad831f5998d77", "short_code": "ob", "title": "ESA Biomass Climate Change Initiative (Biomass_cci): Global datasets of forest above-ground biomass for the years 2010, 2015, 2016, 2017, 2018, 2019, 2020 and 2021, v5.01", "abstract": "This dataset comprises estimates of forest above-ground biomass for the years 2010, 2015, 2016, 2017, 2018, 2019, 2020 and 2021. They are derived from a combination of Earth observation data, depending on the year, from the Copernicus Sentinel-1 mission, Envisat’s ASAR (Advanced Synthetic Aperture Radar) instrument and JAXA’s (Japan Aerospace Exploration Agency) Advanced Land Observing Satellite (ALOS-1 and ALOS-2), along with additional information from Earth observation sources. The data has been produced as part of the European Space Agency's (ESA's) Climate Change Initiative (CCI) programme by the Biomass CCI team. \r\n\r\nThis release of the data is version 5. Compared to version 4, version 5 consists of an update of the three maps of AGB (aboveground biomass) for the years 2010, 2017, 2018, 2019, 2020 and new AGB maps for 2015, 2016 and 2021. New AGB change maps have been created for consecutive years (2015-2016, 2016-2017 and 2020-2021), alongside an update of change maps for years 2010-2020, 2017-2018, 2018-2019 and 2019-2020, and for a decadal interval (2020-2010). The pool of remote sensing data now includes multi-temporal observations at L-band for all biomes and for all years. The AGB maps rely on revised allometries which are now based on a longer record of spaceborne LiDAR data from the GEDI and ICESat-2 missions. Temporal information is now implemented in the retrieval algorithm to preserve biomass dynamics as expressed in the remote sensing data. Biases between 2010 and more recent years have been reduced.\r\n\r\nThe data products consist of two (2) global layers that include estimates of:\r\n1) above ground biomass (AGB, unit: tons/ha i.e., Mg/ha) (raster dataset). This is defined as the mass, expressed as oven-dry weight of the woody parts (stem, bark, branches and twigs) of all living trees excluding stump and roots\r\n2) per-pixel estimates of above-ground biomass uncertainty expressed as the standard deviation in Mg/ha (raster dataset)\r\n\r\nAdditionally provided in this version release are new aggregated data products. These aggregated products of the AGB and AGB change data layers are available at coarser resolutions (1, 10, 25 and 50km).\r\n\r\nIn addition, files describing the AGB change between two consecutive years (i.e., 2015-2016, 2016-2017, 2018-2017, 2019-2018, 2019-2020, 2020-2021) and over a decade (2020-2010) are provided (labelled as 2015_2016, 2016_2017, 2017_2018, 2018_2019, 2019_2020 and 2020_2010). Each AGB change product consists of two sets of maps: the standard deviation of the AGB change and a quality flag of the AGB change. Note that the change itself can be simply computed as the difference between two AGB maps, so is not provided directly.\r\n\r\n\r\nData are provided in both netcdf and geotiff format.\r\n\r\nThis version represents an update of v5.0 which was missing a number of tiles covering islands on the Pacific and Indian Ocean and one tile covering Scandinavia north of 70 deg latitude." }, "objectObservation": { "ob_id": 41620, "uuid": "02e1b18071ad45a19b4d3e8adafa2817", "short_code": "ob", "title": "ESA Biomass Climate Change Initiative (Biomass_cci): Global datasets of forest above-ground biomass for the years 2010, 2015, 2016, 2017, 2018, 2019, 2020 and 2021, v5", "abstract": "This dataset comprises estimates of forest above-ground biomass for the years 2010, 2015, 2016, 2017, 2018, 2019, 2020 and 2021. They are derived from a combination of Earth observation data, depending on the year, from the Copernicus Sentinel-1 mission, Envisat’s ASAR (Advanced Synthetic Aperture Radar) instrument and JAXA’s (Japan Aerospace Exploration Agency) Advanced Land Observing Satellite (ALOS-1 and ALOS-2), along with additional information from Earth observation sources. The data has been produced as part of the European Space Agency's (ESA's) Climate Change Initiative (CCI) programme by the Biomass CCI team. \r\n\r\nThis release of the data is version 5. Compared to version 4, version 5 consists of an update of the three maps of AGB (aboveground biomass) for the years 2010, 2017, 2018, 2019, 2020 and new AGB maps for 2015, 2016 and 2021. New AGB change maps have been created for consecutive years (2015-2016, 2016-2017 and 2020-2021), alongside an update of change maps for years 2010-2020, 2017-2018, 2018-2019 and 2019-2020, and for a decadal interval (2020-2010). The pool of remote sensing data now includes multi-temporal observations at L-band for all biomes and for all years. The AGB maps rely on revised allometries which are now based on a longer record of spaceborne LiDAR data from the GEDI and ICESat-2 missions. Temporal information is now implemented in the retrieval algorithm to preserve biomass dynamics as expressed in the remote sensing data. Biases between 2010 and more recent years have been reduced.\r\n\r\nThe data products consist of two (2) global layers that include estimates of:\r\n1) above ground biomass (AGB, unit: tons/ha i.e., Mg/ha) (raster dataset). This is defined as the mass, expressed as oven-dry weight of the woody parts (stem, bark, branches and twigs) of all living trees excluding stump and roots\r\n2) per-pixel estimates of above-ground biomass uncertainty expressed as the standard deviation in Mg/ha (raster dataset)\r\n\r\nAdditionally provided in this version release are new aggregated data products. These aggregated products of the AGB and AGB change data layers are available at coarser resolutions (1, 10, 25 and 50km).\r\n\r\nIn addition, files describing the AGB change between two consecutive years (i.e., 2015-2016, 2016-2017, 2018-2017, 2019-2018, 2019-2020, 2020-2021) and over a decade (2020-2010) are provided (labelled as 2015_2016, 2016_2017, 2017_2018, 2018_2019, 2019_2020 and 2020_2010). Each AGB change product consists of two sets of maps: the standard deviation of the AGB change and a quality flag of the AGB change. Note that the change itself can be simply computed as the difference between two AGB maps, so is not provided directly.\r\n\r\n\r\nData are provided in both netcdf and geotiff format." } }, { "ob_id": 949, "relationType": "IsSupplementTo", "subjectObservation": { "ob_id": 41672, "uuid": "a2e1601a29004c13849be5e84594f37a", "short_code": "ob", "title": "OpenCLIM: Catchment Discharges", "abstract": "This dataset contains simulated river flow discharges from the SHETRAN and HBV hydrological models used in the OpenCLIM (Open CLimate Impacts Modelling framework) project. All values are daily flows (i.e. flow at midnight on the given day) in cumecs. Flows are given for 698 catchments in the UK at catchment outlets/gauge locations: https://nrfa.ceh.ac.uk/data/search. These data can be used for the continued analysis of climate impacts and for comparison with future studies.\r\n\r\nHistorical simulations were driven by CHESS/GEAR (Great Britain) and HADUK-Grid (Northern Ireland) datasets from 01/01/1980 to 31/12/2010 (described below).\r\nClimate change simulations were driven by bias corrected UKCP18 data from from 01/12/1980 to 30/11/2080. \r\n\r\nFile naming convention: Project_Data_Model_ClimateDriver_ScenarioNumber_ScenarioNote_RCMNumber.csv\r\n\r\nScenario notes:\r\nSc01: Land use was taken from baseline Urban Development Model (UDM) setups with no Natural Flood Management (NFM) applied.\r\nSc02 & Sc06: Land use was taken from baseline UDM setups with maximum/moderate NFM were applied (increased woodland and storage). \r\nSc03: Land use was taken from storylined UDM setups (2050 and 2080) for SSPs 2 and 4. No NFM adaptations were applied. Only those (GB) catchments that have UDM changes relative to the baseline simulations or the previous UDM year are simulated.\r\n'RCM' refers to the UKCP18 Regional Climate Model time series that was used in the simulation.\r\n\r\nFurther information, including descriptions of the urban development model (UDM) and natural flood management (NFM) setups, is provided in the supplementary README.txt alongside the data.\r\n\r\nInput datasets:\r\nCHESS - The Climate hydrology and ecology research support system (CHESS) provides high-resolution gridded datasets for environmental research. These datasets include data for driving land surface models and hydrological models, as well as model output.\r\nGEAR - The Centre for Ecology & Hydrology – Gridded Estimates of Areal Rainfall (CEH-GEAR) data set was developed to provide reliable 1 km gridded estimates of daily and monthly rainfall for Great Britain (GB) and Northern Ireland (NI) from 1890 onwards. The data set was primarily required to support hydrological modelling.\r\nHadUK-Grid - HadUK-Grid is a collection of gridded climate variables derived from the network of UK land surface observations by the Met Office. \r\nUKCP18 - UK Climate Projections 2018 (UKCP18) is a climate analysis tool that forms part of the Met Office Hadley Centre Climate Programme." }, "objectObservation": { "ob_id": 41670, "uuid": "81567bfb789e4ec4ae30cdd3772f8242", "short_code": "ob", "title": "OpenCLIM - Catchment Flow Metrics", "abstract": "This dataset contains flood, drought, and flow metrics calculated as part of the OpenCLIM (Open CLimate Impacts Modelling framework) project. All metrics have been calculated for 698 UK catchments from timeseries of daily river flow simulated by the hydrological models SHETRAN and HBV by teams from Newcastle University and the University of East Anglia respectively for historical and future periods. All analysis code is available on GitHub (https://github.com/OpenCLIM/OpenCLIM-SHETRAN_Flow_Analysis).\r\n\r\nClimate change simulations were driven by bias corrected UKCP18 data from 01/12/1980 to 30/11/2080. Datasets from all 12 regional climate models (RCMs) were used and are presented separately. Flow metrics are given for 698 catchments in the UK at catchment outlets/gauge locations: https://nrfa.ceh.ac.uk/data/search. These data can be used for the continued analysis of climate impacts and for comparison with future studies.\r\n\r\nFile naming convention: Project_Data_Model_ClimateDriver_ScenarioNumber_ScenarioNote_Metric.csv\r\n\r\nScenario notes:\r\nSc01: Land use was taken from baseline Urban Development Model (UDM) setups with no Natural Flood Management (NFM) adaptations applied. \r\nSc02 & Sc06: Land use was taken from baseline UDM setups with NFM max/moderate adaptations applied. \r\nSc03: Land use was taken from storylined UDM setups (2050 and 2080) for SSPs 2 and 4. No NFM adaptations were applied. For SHETRAN, only catchments with UDM changes relative to the baseline simulations or the previous UDM year were simulated.\r\n\r\nFurther information, including descriptions of the urban development model (UDM) and natural flood management (NFM) setups, is provided in the supplementary README.txt alongside the data.\r\n\r\nUKCP18 - UK Climate Projections 2018 (UKCP18) is a climate analysis tool that forms part of the Met Office Hadley Centre Climate Programme." } }, { "ob_id": 950, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 43093, "uuid": "43ce517d74624a5ebf6eec5330cd18d5", "short_code": "ob", "title": "CRU JRA v2.5: A forcings dataset of gridded land surface blend of Climatic Research Unit (CRU) and Japanese reanalysis (JRA) data; Jan.1901 - Dec.2023.", "abstract": "The CRU JRA V2.5 dataset is a 6-hourly, land surface, gridded time series of ten meteorological variables produced by the Climatic Research Unit (CRU) at the University of East Anglia (UEA), and is intended to be used to drive models. The variables are provided on a 0.5 degree latitude x 0.5 degree longitude grid, the grid is near global but excludes Antarctica (this is the same as the CRU TS grid, though the set of variables is different). The data are available at a 6 hourly time-step from January 1901 to December 2023.\r\n\r\nThe dataset is constructed by regridding data from the Japanese Reanalysis data (JRA) produced by the Japanese Meteorological Agency (JMA), adjusting where possible to align with the CRU TS 4.08 data (see the Process section and the ReadMe file for full details).\r\n\r\nThe CRU JRA data consists of the following ten meteorological variables: 2-metre temperature, 2-metre maximum and minimum temperature, total precipitation, specific humidity, downward solar radiation flux, downward long wave radiation flux, pressure and the zonal and meridional components of wind speed (see the ReadMe file for further details).\r\n\r\nThe CRU JRA dataset is intended to be a replacement of the CRU NCEP forcing dataset. The CRU JRA dataset follows the style of Nicolas Viovy's original CRU NCEP dataset rather than that which is available from UCAR. A link to the CRU NCEP documentation for comparison is provided in the documentation section. \r\nThis version of CRUJRA, v2.5 (1901-2023) is, where possible, adjusted to align with CRU TS monthly means or totals. A consequence of this is that, if CRU TS changes, then CRUJRA changes.\r\n\r\nFor this version, and version 4.07 of CRU TS, the CLD (cloud cover, %) variable is now actualised (converted from gridded anomalies) using the original CLD climatology and not the revised climatology introduced last year. This change/reversion is summarised here: https://crudata.uea.ac.uk/cru/data/hrg/cru_cl_1.1/Read_Me_CRU_CL_CLD_Reversion.txt\r\n\r\nSince CLD is used to align DSWRF, CRUJRA Downward Short Wave Radiation Flux (DSWRF) will now be 'closer to' version 2.2 and earlier and should be used in preference to v2.3.\r\n\r\nIf this dataset is used in addition to citing the dataset as per the data citation string users must also cite the following:\r\n\r\nHarris, I., Osborn, T.J., Jones, P. et al. Version 4 of the CRU TS\r\nmonthly high-resolution gridded multivariate climate dataset.\r\nSci Data 7, 109 (2020). https://doi.org/10.1038/s41597-020-0453-3\r\n\r\nHarris, I., Jones, P.D., Osborn, T.J. and Lister, D.H. (2014), Updated\r\nhigh-resolution grids of monthly climatic observations - the CRU TS3.10\r\nDataset. International Journal of Climatology 34, 623-642.\r\n\r\nKobayashi, S., et. al., The JRA-55 Reanalysis: General Specifications and\r\nBasic Characteristics. J. Met. Soc. Jap., 93(1), 5-48\r\nhttps://dx.doi.org/10.2151/jmsj.2015-001" }, "objectObservation": { "ob_id": 40271, "uuid": "aed8e269513f446fb1b5d2512bb387ad", "short_code": "ob", "title": "CRU JRA v2.4: A forcings dataset of gridded land surface blend of Climatic Research Unit (CRU) and Japanese reanalysis (JRA) data; Jan.1901 - Dec.2022.", "abstract": "The CRU JRA V2.4 dataset is a 6-hourly, land surface, gridded time series of ten meteorological variables produced by the Climatic Research Unit (CRU) at the University of East Anglia (UEA), and is intended to be used to drive models. The variables are provided on a 0.5 degree latitude x 0.5 degree longitude grid, the grid is near global but excludes Antarctica (this is the same as the CRU TS grid, though the set of variables is different). The data are available at a 6 hourly time-step from January 1901 to December 2022.\r\n\r\nThe dataset is constructed by regridding data from the Japanese Reanalysis data (JRA) produced by the Japanese Meteorological Agency (JMA), adjusting where possible to align with the CRU TS 4.07 data (see the Process section and the ReadMe file for full details).\r\n\r\nThe CRU JRA data consists of the following ten meteorological variables: 2-metre temperature, 2-metre maximum and minimum temperature, total precipitation, specific humidity, downward solar radiation flux, downward long wave radiation flux, pressure and the zonal and meridional components of wind speed (see the ReadMe file for further details).\r\n\r\nThe CRU JRA dataset is intended to be a replacement of the CRU NCEP forcing dataset. The CRU JRA dataset follows the style of Nicolas Viovy's original CRU NCEP dataset rather than that which is available from UCAR. A link to the CRU NCEP documentation for comparison is provided in the documentation section. \r\nThis version of CRUJRA, v2.4 (1901-2022) is, where possible, adjusted to align with CRU TS monthly means or totals. A consequence of this is that, if CRU TS changes, then CRUJRA changes.\r\n\r\nFor this version, and version 4.07 of CRU TS, the CLD (cloud cover, %) variable is now actualised (converted from gridded anomalies) using the original CLD climatology and not the revised climatology introduced last year. This change/reversion is summarised here: https://crudata.uea.ac.uk/cru/data/hrg/cru_cl_1.1/Read_Me_CRU_CL_CLD_Reversion.txt\r\n\r\nSince CLD is used to align DSWRF, CRUJRA DSWRF will now be 'closer to' version 2.2 and earlier and should be used in preference to v2.3.\r\n\r\nIf this dataset is used in addition to citing the dataset as per the data citation string users must also cite the following:\r\n\r\nHarris, I., Osborn, T.J., Jones, P. et al. Version 4 of the CRU TS\r\nmonthly high-resolution gridded multivariate climate dataset.\r\nSci Data 7, 109 (2020). https://doi.org/10.1038/s41597-020-0453-3\r\n\r\nHarris, I., Jones, P.D., Osborn, T.J. and Lister, D.H. (2014), Updated\r\nhigh-resolution grids of monthly climatic observations - the CRU TS3.10\r\nDataset. International Journal of Climatology 34, 623-642.\r\n\r\nKobayashi, S., et. al., The JRA-55 Reanalysis: General Specifications and\r\nBasic Characteristics. J. Met. Soc. Jap., 93(1), 5-48\r\nhttps://dx.doi.org/10.2151/jmsj.2015-001" } }, { "ob_id": 951, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 43100, "uuid": "715abce1604a42f396f81db83aeb2a4b", "short_code": "ob", "title": "CRU TS4.08: Climatic Research Unit (CRU) Time-Series (TS) version 4.08 of high-resolution gridded data of month-by-month variation in climate (Jan. 1901- Dec. 2023)", "abstract": "The gridded Climatic Research Unit (CRU) Time-series (TS) data version 4.08 data are month-by-month variations in climate over the period 1901-2023, provided on high-resolution (0.5x0.5 degree) grids, produced by CRU at the University of East Anglia and funded by the UK National Centre for Atmospheric Science (NCAS), a NERC collaborative centre.\r\n\r\nThe CRU TS4.08 variables are cloud cover, diurnal temperature range, frost day frequency, wet day frequency, potential evapotranspiration (PET), precipitation, daily mean temperature, monthly average daily maximum and minimum temperature, and vapour pressure for the period January 1901 - December 2023.\r\n\r\nThe CRU TS4.08 data were produced using angular-distance weighting (ADW) interpolation. All versions prior to 4.00 used triangulation routines in IDL. Please see the release notes for full details of this version update. \r\n\r\nThe CRU TS4.08 data are monthly gridded fields based on monthly observational data calculated from daily or sub-daily data by National Meteorological Services and other external agents. The ASCII and NetCDF data files both contain monthly mean values for the various parameters. The NetCDF versions contain an additional integer variable, ’stn’, which provides, for each datum in the main variable, a count (between 0 and 8) of the number of stations used in that interpolation. The missing value code for 'stn' is -999.\r\n\r\nAll CRU TS output files are actual values - NOT anomalies." }, "objectObservation": { "ob_id": 40300, "uuid": "5fda109ab71947b6b7724077bf7eb753", "short_code": "ob", "title": "CRU TS4.07: Climatic Research Unit (CRU) Time-Series (TS) version 4.07 of high-resolution gridded data of month-by-month variation in climate (Jan. 1901- Dec. 2022)", "abstract": "The gridded Climatic Research Unit (CRU) Time-series (TS) data version 4.07 data are month-by-month variations in climate over the period 1901-2022, provided on high-resolution (0.5x0.5 degree) grids, produced by CRU at the University of East Anglia and funded by the UK National Centre for Atmospheric Science (NCAS), a NERC collaborative centre.\r\n\r\nThe CRU TS4.07 variables are cloud cover, diurnal temperature range, frost day frequency, wet day frequency, potential evapotranspiration (PET), precipitation, daily mean temperature, monthly average daily maximum and minimum temperature, and vapour pressure for the period January 1901 - December 2022.\r\n\r\nThe CRU TS4.07 data were produced using angular-distance weighting (ADW) interpolation. All versions prior to 4.00 used triangulation routines in IDL. Please see the release notes for full details of this version update. \r\n\r\nThe CRU TS4.07 data are monthly gridded fields based on monthly observational data calculated from daily or sub-daily data by National Meteorological Services and other external agents. The ASCII and NetCDF data files both contain monthly mean values for the various parameters. The NetCDF versions contain an additional integer variable, ’stn’, which provides, for each datum in the main variable, a count (between 0 and 8) of the number of stations used in that interpolation. The missing value code for 'stn' is -999.\r\n\r\nAll CRU TS output files are actual values - NOT anomalies." } }, { "ob_id": 952, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 43120, "uuid": "3b7f475a30a642e9af5323cef748bb00", "short_code": "ob", "title": "CRU CY4.08: Climatic Research Unit year-by-year variation of selected climate variables by country version 4.08 (Jan. 1901 - Dec. 2023)", "abstract": "The Climatic Research Unit (CRU) Country (CY) data version 4.08 dataset consists of ten climate variables for country averages at a monthly, seasonal and annual frequency: including cloud cover, diurnal temperature range, frost day frequency, precipitation, daily mean temperature, monthly average daily maximum and minimum temperature, vapour pressure, potential evapotranspiration and wet day frequency. This version uses the updated set of country definitions, please see the appropriate Release Notes.\r\n\r\nThis dataset was produced in 2024 by CRU at the University of East Anglia and extends the CRU CY4.07 data to include 2023. The data are available as text files with the extension '.per' and can be opened by most text editors.\r\n\r\nSpatial averages are calculated using area-weighted means. CRU CY4.08 is derived directly from the CRU time series (TS) 4.07 dataset. CRU CY version 4.08 spans the period 1901-2023 for 292 countries.\r\n\r\nTo understand the CRU CY4.08 dataset, it is important to understand the construction and limitations of the underlying dataset, CRU TS4.07. It is therefore recommended that all users read the Harris et al, 2020 paper and the CRU TS4.08 release notes listed in the online documentation on this record.\r\n\r\nCRU CY data are available for download to all CEDA users." }, "objectObservation": { "ob_id": 40346, "uuid": "def64ef885684e199f03a4c50bc2f8dc", "short_code": "ob", "title": "CRU CY4.07: Climatic Research Unit year-by-year variation of selected climate variables by country version 4.07 (Jan. 1901 - Dec. 2022)", "abstract": "The Climatic Research Unit (CRU) Country (CY) data version 4.07 dataset consists of ten climate variables for country averages at a monthly, seasonal and annual frequency: including cloud cover, diurnal temperature range, frost day frequency, precipitation, daily mean temperature, monthly average daily maximum and minimum temperature, vapour pressure, potential evapotranspiration and wet day frequency. This version uses the updated set of country definitions, please see the appropriate Release Notes.\r\n\r\nThis dataset was produced in 2023 by CRU at the University of East Anglia and extends the CRU CY4.06 data to include 2022. The data are available as text files with the extension '.per' and can be opened by most text editors.\r\n\r\nSpatial averages are calculated using area-weighted means. CRU CY4.07 is derived directly from the CRU time series (TS) 4.06 dataset. CRU CY version 4.07 spans the period 1901-2022 for 292 countries.\r\n\r\nTo understand the CRU CY4.07 dataset, it is important to understand the construction and limitations of the underlying dataset, CRU TS4.06. It is therefore recommended that all users read the Harris et al, 2020 paper and the CRU TS4.07 release notes listed in the online documentation on this record.\r\n\r\nCRU CY data are available for download to all CEDA users." } }, { "ob_id": 953, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 40346, "uuid": "def64ef885684e199f03a4c50bc2f8dc", "short_code": "ob", "title": "CRU CY4.07: Climatic Research Unit year-by-year variation of selected climate variables by country version 4.07 (Jan. 1901 - Dec. 2022)", "abstract": "The Climatic Research Unit (CRU) Country (CY) data version 4.07 dataset consists of ten climate variables for country averages at a monthly, seasonal and annual frequency: including cloud cover, diurnal temperature range, frost day frequency, precipitation, daily mean temperature, monthly average daily maximum and minimum temperature, vapour pressure, potential evapotranspiration and wet day frequency. This version uses the updated set of country definitions, please see the appropriate Release Notes.\r\n\r\nThis dataset was produced in 2023 by CRU at the University of East Anglia and extends the CRU CY4.06 data to include 2022. The data are available as text files with the extension '.per' and can be opened by most text editors.\r\n\r\nSpatial averages are calculated using area-weighted means. CRU CY4.07 is derived directly from the CRU time series (TS) 4.06 dataset. CRU CY version 4.07 spans the period 1901-2022 for 292 countries.\r\n\r\nTo understand the CRU CY4.07 dataset, it is important to understand the construction and limitations of the underlying dataset, CRU TS4.06. It is therefore recommended that all users read the Harris et al, 2020 paper and the CRU TS4.07 release notes listed in the online documentation on this record.\r\n\r\nCRU CY data are available for download to all CEDA users." }, "objectObservation": { "ob_id": 38216, "uuid": "99120ddac5004caa85358f5250e2eece", "short_code": "ob", "title": "CRU CY4.06: Climatic Research Unit year-by-year variation of selected climate variables by country version 4.06 (Jan. 1901 - Dec. 2021)", "abstract": "The Climatic Research Unit (CRU) Country (CY) data version 4.06 dataset consists of ten climate variables for country averages at a monthly, seasonal and annual frequency: including cloud cover, diurnal temperature range, frost day frequency, precipitation, daily mean temperature, monthly average daily maximum and minimum temperature, vapour pressure, potential evapotranspiration and wet day frequency. This version uses the updated set of country definitions, please see the appropriate Release Notes.\r\n\r\nThis dataset was produced in 2022 by CRU at the University of East Anglia and extends the CRU CY4.06 data to include 2021. The data are available as text files with the extension '.per' and can be opened by most text editors.\r\n\r\nSpatial averages are calculated using area-weighted means. CRU CY4.06 is derived directly from the CRU time series (TS) 4.06 dataset. CRU CY version 4.06 spans the period 1901-2021 for 292 countries.\r\n\r\nTo understand the CRU CY4.06 dataset, it is important to understand the construction and limitations of the underlying dataset, CRU TS4.06. It is therefore recommended that all users read the Harris et al, 2020 paper and the CRU TS4.06 release notes listed in the online documentation on this record.\r\n\r\nCRU CY data are available for download to all CEDA users." } }, { "ob_id": 954, "relationType": "IsSupplementTo", "subjectObservation": { "ob_id": 43130, "uuid": "0c18a36ee02a4598963c1f7f97acd201", "short_code": "ob", "title": "ICECAPS-ACE: radiosonde measurements from the University of Leeds Windsond unit 5094 deployed by helikite above Summit Station, Greenland, July-August 2023", "abstract": "This dataset contains meteorology measurements (air pressure, temperature, and relative humidity) from the University of Leeds windsond unit 5094 deployed by tethered balloon above the Summit Station field site, Greenland.\r\n\r\nPost-processing of the radiosonde data revealed unrealistic temperature increases when the measurement platform was stationary, these are indicated by a quality control flag. \r\n\r\nThese data were collected as part of the joint Natural Environmental Research Council (NERC) and US National Science Foundation (NSF) -funded Integrated Characterisation of Energy, Clouds, Atmospheric state, and Precipitation at Summit - Aerosol Cloud Experiment (ICECAPS-ACE) project." }, "objectObservation": { "ob_id": 43128, "uuid": "6b68b5e1ffd2467886386eaf0dfafd24", "short_code": "ob", "title": "ICECAPS-ACE: Vertical aerosol particle size distributions from the University of Leeds POPS 0307 instrument collected via Helikite balloon above Summit Station, Greenland, July-August 2023", "abstract": "This dataset contains vertically resolved aerosol particle size distribution measurements collected using a tethered balloon platform at Summit Station, Greenland, in July and August 2023.\r\n\r\nAerosol particle size distributions were measured by a Handix Portable Optical Particle Spectrometer (POPS 1120, S/N: 0307). The POPS was placed in a lightweight insulating foam box, and a coarse mesh filter was placed over the inlet to prevent the growth of rime ice. The POPS was secured to the kite wing on the tethered balloon such that the inlet was always oriented into the wind.\r\n\r\nThese data were collected as part of the joint Natural Environmental Research Council (NERC) and US National Science Foundation (NSF) -funded Integrated Characterisation of Energy, Clouds, Atmospheric state, and Precipitation at Summit - Aerosol Cloud Experiment (ICECAPS-ACE) project." } }, { "ob_id": 955, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 43128, "uuid": "6b68b5e1ffd2467886386eaf0dfafd24", "short_code": "ob", "title": "ICECAPS-ACE: Vertical aerosol particle size distributions from the University of Leeds POPS 0307 instrument collected via Helikite balloon above Summit Station, Greenland, July-August 2023", "abstract": "This dataset contains vertically resolved aerosol particle size distribution measurements collected using a tethered balloon platform at Summit Station, Greenland, in July and August 2023.\r\n\r\nAerosol particle size distributions were measured by a Handix Portable Optical Particle Spectrometer (POPS 1120, S/N: 0307). The POPS was placed in a lightweight insulating foam box, and a coarse mesh filter was placed over the inlet to prevent the growth of rime ice. The POPS was secured to the kite wing on the tethered balloon such that the inlet was always oriented into the wind.\r\n\r\nThese data were collected as part of the joint Natural Environmental Research Council (NERC) and US National Science Foundation (NSF) -funded Integrated Characterisation of Energy, Clouds, Atmospheric state, and Precipitation at Summit - Aerosol Cloud Experiment (ICECAPS-ACE) project." }, "objectObservation": { "ob_id": 43130, "uuid": "0c18a36ee02a4598963c1f7f97acd201", "short_code": "ob", "title": "ICECAPS-ACE: radiosonde measurements from the University of Leeds Windsond unit 5094 deployed by helikite above Summit Station, Greenland, July-August 2023", "abstract": "This dataset contains meteorology measurements (air pressure, temperature, and relative humidity) from the University of Leeds windsond unit 5094 deployed by tethered balloon above the Summit Station field site, Greenland.\r\n\r\nPost-processing of the radiosonde data revealed unrealistic temperature increases when the measurement platform was stationary, these are indicated by a quality control flag. \r\n\r\nThese data were collected as part of the joint Natural Environmental Research Council (NERC) and US National Science Foundation (NSF) -funded Integrated Characterisation of Energy, Clouds, Atmospheric state, and Precipitation at Summit - Aerosol Cloud Experiment (ICECAPS-ACE) project." } }, { "ob_id": 956, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 43128, "uuid": "6b68b5e1ffd2467886386eaf0dfafd24", "short_code": "ob", "title": "ICECAPS-ACE: Vertical aerosol particle size distributions from the University of Leeds POPS 0307 instrument collected via Helikite balloon above Summit Station, Greenland, July-August 2023", "abstract": "This dataset contains vertically resolved aerosol particle size distribution measurements collected using a tethered balloon platform at Summit Station, Greenland, in July and August 2023.\r\n\r\nAerosol particle size distributions were measured by a Handix Portable Optical Particle Spectrometer (POPS 1120, S/N: 0307). The POPS was placed in a lightweight insulating foam box, and a coarse mesh filter was placed over the inlet to prevent the growth of rime ice. The POPS was secured to the kite wing on the tethered balloon such that the inlet was always oriented into the wind.\r\n\r\nThese data were collected as part of the joint Natural Environmental Research Council (NERC) and US National Science Foundation (NSF) -funded Integrated Characterisation of Energy, Clouds, Atmospheric state, and Precipitation at Summit - Aerosol Cloud Experiment (ICECAPS-ACE) project." }, "objectObservation": { "ob_id": 43129, "uuid": "ceaded7386ab4fb781e5344cb94db57d", "short_code": "ob", "title": "ICECAPS-ACE: surface aerosol particle size distributions from the University of Leeds POPS 0288 instrument at Summit Station, Greenland, July-August 2023", "abstract": "This dataset contains surface aerosol particle size distribution measurements from Summit Station Greenland measured by a Handix Portable Optical Particle Spectrometer (POPS 1120, S/N: 0288). The POPS was connected to an omnidirectional total air inlet and installed on the roof of the Atmospheric Watch Observatory building at Summit Station.\r\n\r\nThese data were collected as part of the joint Natural Environmental Research Council (NERC) and US National Science Foundation (NSF) -funded Integrated Characterisation of Energy, Clouds, Atmospheric state, and Precipitation at Summit - Aerosol Cloud Experiment (ICECAPS-ACE) project." } }, { "ob_id": 957, "relationType": "IsSupplementTo", "subjectObservation": { "ob_id": 43129, "uuid": "ceaded7386ab4fb781e5344cb94db57d", "short_code": "ob", "title": "ICECAPS-ACE: surface aerosol particle size distributions from the University of Leeds POPS 0288 instrument at Summit Station, Greenland, July-August 2023", "abstract": "This dataset contains surface aerosol particle size distribution measurements from Summit Station Greenland measured by a Handix Portable Optical Particle Spectrometer (POPS 1120, S/N: 0288). The POPS was connected to an omnidirectional total air inlet and installed on the roof of the Atmospheric Watch Observatory building at Summit Station.\r\n\r\nThese data were collected as part of the joint Natural Environmental Research Council (NERC) and US National Science Foundation (NSF) -funded Integrated Characterisation of Energy, Clouds, Atmospheric state, and Precipitation at Summit - Aerosol Cloud Experiment (ICECAPS-ACE) project." }, "objectObservation": { "ob_id": 43128, "uuid": "6b68b5e1ffd2467886386eaf0dfafd24", "short_code": "ob", "title": "ICECAPS-ACE: Vertical aerosol particle size distributions from the University of Leeds POPS 0307 instrument collected via Helikite balloon above Summit Station, Greenland, July-August 2023", "abstract": "This dataset contains vertically resolved aerosol particle size distribution measurements collected using a tethered balloon platform at Summit Station, Greenland, in July and August 2023.\r\n\r\nAerosol particle size distributions were measured by a Handix Portable Optical Particle Spectrometer (POPS 1120, S/N: 0307). The POPS was placed in a lightweight insulating foam box, and a coarse mesh filter was placed over the inlet to prevent the growth of rime ice. The POPS was secured to the kite wing on the tethered balloon such that the inlet was always oriented into the wind.\r\n\r\nThese data were collected as part of the joint Natural Environmental Research Council (NERC) and US National Science Foundation (NSF) -funded Integrated Characterisation of Energy, Clouds, Atmospheric state, and Precipitation at Summit - Aerosol Cloud Experiment (ICECAPS-ACE) project." } }, { "ob_id": 958, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 41959, "uuid": "89c654c2e4a74ce5a494b69753d8291e", "short_code": "ob", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Mass flow rate ice discharge (MFID) for Greenland from CCI IV, CCI SEC, and BedMachine v2.0", "abstract": "Mass flow rate ice discharge (MFID) for Greenland ice sheet sectors. This data set is part of the ESA Greenland Ice sheet CCI project. \r\n\r\nIt provides the following CSV files: \r\n- Mass flow rate ice discharge. Units are Gt yr^{-1}.\r\n- Mass flow rate ice discharge uncertainty. Units are Gt yr^{-1}.\r\n- Coverage for each sector at each timestamp. Unitless [0 to 1].\r\n\r\nIce discharge is calculated from the CCI Ice Velocity (IV) product, the CCI Surface Elevation Change (SEC) product (where it overlaps with the ice discharge gates), and ice thickness from BedMachine. Ice discharge gates are placed 10 km upstream from all marine terminating glacier termini that have baseline velocities of more than 150 m/yr. Results are summed by Zwally et al. (2012) sectors.\r\n\r\nThe methods, including description of \"coverage\", are described in Mankoff et al. 2020." }, "objectObservation": { "ob_id": 39550, "uuid": "dde78ab3388a4452b43ffe3e69e91fce", "short_code": "ob", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Mass flow rate ice discharge (MFID) for Greenland from CCI IV, CCI SEC, and BedMachine v1.0", "abstract": "Mass flow rate ice discharge (MFID) for Greenland ice sheet sectors. This data set is part of the ESA Greenland Ice sheet CCI project. \r\n\r\nIt provides the following CSV files: \r\n- Mass flow rate ice discharge. Units are Gt yr^{-1}.\r\n- Mass flow rate ice discharge uncertainty. Units are Gt yr^{-1}.\r\n- Coverage for each sector at each timestamp. Unitless [0 to 1].\r\n\r\nIce discharge is calculated from the CCI Ice Velocity (IV) product, the CCI Surface Elevation Change (SEC) product (where it overlaps with the ice discharge gates), and ice thickness from BedMachine. Ice discharge gates are placed 10 km upstream from all marine terminating glacier termini that have baseline velocities of more than 150 m/yr. Results are summed by Zwally et al. (2012) sectors.\r\n\r\nThe methods, including description of \"coverage\", are described in Mankoff et al. 2020." } }, { "ob_id": 959, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 43147, "uuid": "11163154cef4496988d45658c9cfbabf", "short_code": "ob", "title": "Atmospheric trace gas observations from the UK Deriving Emissions linked to Climate Change (DECC) Network and associated data - Version 24.01", "abstract": "This version 24.01 dataset consists of atmospheric trace gas observations made as part of the UK Deriving Emissions linked to Climate Change (DECC) Network. It includes core DECC Network measurements, funded by the UK Government Department for Energy Security and Net Zero (TRN: 5488/11/2021) and through the National Measurement System at the National Physical Laboratory, supplemented by observations funded through other associated projects. \r\nThe core DECC network consists of five sites in the UK and Ireland measuring greenhouse and ozone-depleting gases. The four UK-based sites (Ridge Hill, Herefordshire; Tacolneston, Norfolk; Bilsdale, North Yorkshire; and Heathfield, East Sussex) sample air from elevated inlets on tall telecommunications towers. Mace Head, situated on the west coast of Ireland, samples from an inlet 10 metres above ground level and is ideally situated to intercept baseline air from the North Atlantic Ocean. The measurement site at Weybourne, Norfolk, funded by the National Centre for Atmospheric Science (NCAS) and operated by the University of East Anglia, is also affiliated with the network. Mace Head and Weybourne data are archived separately - see links in documentation. Data from the UK DECC network are used to assess atmospheric trends and quantify UK emissions, and feed into other international research programs, including the Integrated Carbon Observation System (ICOS) and Advanced Global Atmospheric Gases Experiment (AGAGE) networks." }, "objectObservation": { "ob_id": 41180, "uuid": "bc5b7568ef2a467fa97642910eb45aa7", "short_code": "ob", "title": "Atmospheric trace gas observations from the UK Deriving Emissions linked to Climate Change (DECC) Network and associated data - Version 23.08", "abstract": "This version 23.08 dataset consists of atmospheric trace gas observations made as part of the UK Deriving Emissions linked to Climate Change (DECC) Network. It includes core DECC Network measurements, funded by the UK Government Department for Energy Security and Net Zero (TRN: 54\r\n88/11/2021) and through the National Measurement System at the National Physical Laboratory, supplemented by observations funded through other associated projects. The core DECC network\r\n consists of five sites in the UK and Ireland measuring greenhouse and ozone-depleting gases. The four UK-based sites (Ridge Hill, Herefordshire; Tacolneston, Norfolk; Bilsdale, North Yorkshire; and Heathfield, East Sussex) sample air from elevated inlets on tall telecommunications towers. Mace Head, situated on the west coast of Ireland, samples from an inlet 10 metres above ground level and is ideally situated to intercept baseline air from the North Atlantic Ocean. The measurement site at Weybourne, Norfolk, funded by the National Centre for Atmospheric Science (NCAS) and operated by the University of East Anglia, is also affiliated with the network. Mace Head and Weybourne data are archived separately - see links in documentation. Data from the UK DECC network are used to\r\n assess atmospheric trends and quantify UK emissions, and feed into other international research programs, including the Integrated Carbon Observation System (ICOS) and Advanced Global Atmospheric Gases Experiment (AGAGE) networks." } }, { "ob_id": 960, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 43176, "uuid": "6ae3dc8d92444b2bb954173fe98559b6", "short_code": "ob", "title": "Global Precipitation Measurements (GPM) Integrated Multi-satellitE Retrievals (IMERG) L3 half-hourly 0.1 degree x 0.1 degree v7", "abstract": "This dataset contains Global Precipitation Measurements (GPM) Integrated Multi-satellitE Retrievals (IMERG) v7. NASA’s Integrated Multi-satellitE Retrievals for GPM (IMERG) algorithm combines information from the GPM satellite constellation to estimate precipitation over most of the Earth's surface. IMERG is particularly valuable over areas of Earth's surface that lack ground-based precipitation-measuring instruments, including oceans and remote areas. \r\n\r\nIMERG fuses precipitation estimates collected during the TRMM satellite’s operation (2000 - 2015) with recent precipitation estimates collected by the GPM mission (2014 - present) creating a continuous precipitation dataset spanning over two decades. This extended record allows scientists to compare past and present precipitation trends, enabling more accurate climate and weather models and a better understanding of Earth’s water cycle and extreme precipitation events. IMERG is available in near real-time with estimates of Earth’s precipitation updated every half-hour, enabling a wide range of applications to help communities around the world make informed decisions for disasters, disease, resource management, energy production, food security, and more.\r\n\r\nThe precipitation estimates from the various precipitation-relevant satellite passive microwave (PMW) sensors comprising the GPM constellation are computed using the 2017 version of the Goddard Profiling Algorithm (GPROF2017), then gridded, intercalibrated to the GPM Combined Ku Radar-Radiometer Algorithm (CORRA) product, and merged into half-hourly 0.1°x0.1° (roughly 10x10 km) fields. Level 3 data are averaged global gridded products, screened for bad data points\r\n\r\nThe Global Precipitation Measurement (GPM) mission is an international network of satellites that provide the next-generation global observations of rain and snow." }, "objectObservation": { "ob_id": 29978, "uuid": "47c32530265d4d6e8fdb6c08b2330371", "short_code": "ob", "title": "Global Precipitation Measurements (GPM) Integrated Multi-satellitE Retrievals (IMERG) L3 Half Hourly 0.1 degree x 0.1 degree v6", "abstract": "This dataset contains Global Precipitation Measurements (GPM) Integrated Multi-satellitE Retrievals (IMERG) v6. The precipitation estimates from the various precipitation-relevant satellite passive microwave (PMW) sensors comprising the GPM constellation are computed using the 2017 version of the Goddard Profiling Algorithm (GPROF2017), then gridded, intercalibrated to the GPM Combined Ku Radar-Radiometer Algorithm (CORRA) product, and merged into half-hourly 0.1°x0.1° (roughly 10x10 km) fields. Level 3 data are averaged global gridded products, screened for bad data points\r\n\r\nThe Global Precipitation Measurement (GPM) mission is an international network of satellites that provide the next-generation global observations of rain and snow." } }, { "ob_id": 961, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 43187, "uuid": "bd7164851bcc491b912f9d650fcf7981", "short_code": "ob", "title": "Atmospheric trace gas observations from the UK Deriving Emissions linked to Climate Change (DECC) Network and associated data - Version 24.09", "abstract": "This version 24.09 dataset consists of atmospheric trace gas observations made as part of the UK Deriving Emissions linked to Climate Change (DECC) Network. It includes core DECC Network measurements, funded by the UK Government Department for Energy Security and Net Zero (TRN: 5488/11/2021) and through the National Measurement System at the National Physical Laboratory, supplemented by observations funded through other associated projects. \r\nThe core DECC network consists of five sites in the UK and Ireland measuring greenhouse and ozone-depleting gases. The four UK-based sites (Ridge Hill, Herefordshire; Tacolneston, Norfolk; Bilsdale, North Yorkshire; and Heathfield, East Sussex) sample air from elevated inlets on tall telecommunications towers. Mace Head, situated on the west coast of Ireland, samples from an inlet 10 metres above ground level and is ideally situated to intercept baseline air from the North Atlantic Ocean. The measurement site at Weybourne, Norfolk, funded by the National Centre for Atmospheric Science (NCAS) and operated by the University of East Anglia, is also affiliated with the network. Mace Head and Weybourne data are archived separately - see links in documentation. Data from the UK DECC network are used to assess atmospheric trends and quantify UK emissions, and feed into other international research programs, including the Integrated Carbon Observation System (ICOS) and Advanced Global Atmospheric Gases Experiment (AGAGE) networks." }, "objectObservation": { "ob_id": 43147, "uuid": "11163154cef4496988d45658c9cfbabf", "short_code": "ob", "title": "Atmospheric trace gas observations from the UK Deriving Emissions linked to Climate Change (DECC) Network and associated data - Version 24.01", "abstract": "This version 24.01 dataset consists of atmospheric trace gas observations made as part of the UK Deriving Emissions linked to Climate Change (DECC) Network. It includes core DECC Network measurements, funded by the UK Government Department for Energy Security and Net Zero (TRN: 5488/11/2021) and through the National Measurement System at the National Physical Laboratory, supplemented by observations funded through other associated projects. \r\nThe core DECC network consists of five sites in the UK and Ireland measuring greenhouse and ozone-depleting gases. The four UK-based sites (Ridge Hill, Herefordshire; Tacolneston, Norfolk; Bilsdale, North Yorkshire; and Heathfield, East Sussex) sample air from elevated inlets on tall telecommunications towers. Mace Head, situated on the west coast of Ireland, samples from an inlet 10 metres above ground level and is ideally situated to intercept baseline air from the North Atlantic Ocean. The measurement site at Weybourne, Norfolk, funded by the National Centre for Atmospheric Science (NCAS) and operated by the University of East Anglia, is also affiliated with the network. Mace Head and Weybourne data are archived separately - see links in documentation. Data from the UK DECC network are used to assess atmospheric trends and quantify UK emissions, and feed into other international research programs, including the Integrated Carbon Observation System (ICOS) and Advanced Global Atmospheric Gases Experiment (AGAGE) networks." } } ] }