Related Observation Info List
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{ "count": 1153, "next": null, "previous": "https://api.catalogue.ceda.ac.uk/api/v3/relatedobservationinfos/?format=api&limit=100&offset=1000", "results": [ { "ob_id": 1211, "relationType": "Continues", "subjectObservation": { "ob_id": 41588, "uuid": "a1e5dd132fad4e129669e71adeed1ab1", "short_code": "ob", "title": "UK 1.5km NWP meteorological data for Met Office NAME dispersion model (Mk4: Jul 2017 - current)", "abstract": "This dataset contains Numerical Weather Prediction (NWP) meteorological data produced by the operational UKV (United Kingdom Variable-resolution) configuration of the Met Office Unified Model. The files in this dataset have been processed into a form suitable for use in the Met Office NAME (Numerical Atmospheric-dispersion Modelling Environment) dispersion model. NAME uses the Met Office Numerical Weather Prediction model outputs as its source for weather data to be able to predict movement of atmospheric parcels forwards and backwards in time.\r\n\r\nThe files contain a basic collection of model-level fields (3-d winds, temperature, etc.) and a selection of single-level fields including mean sea level pressure, cloud and precipitation from the inner, fixed-resolution domain of the UKV model (this covers the UK area at a spatial resolution of 1.5 km). The UKV model uses a rotated-pole coordinate system. Fields are split into various geographical regions (referred to as \"parts\" or \"PTs\" in NAME) with separate files for each \"part\". Data are provided at hourly resolution for the period Feb 2015 - Jul 2017. All files are in packed PP format.\r\n\r\nThe NWP data used by NAME is different from other forms of Met Office NWP as follows:\r\n- It has been split into spatial partitions (i.e. different parts of the world/domain are in different files)\r\n- It has been reformatted into PP format\r\n\r\nHowever, from the perspective of the raw data, this dataset of UK gridded NWP meteorological data is generically useful for a whole range of scientific research and applications." }, "objectObservation": { "ob_id": 43199, "uuid": "6d490accd64a4290b9413d5ec94200f9", "short_code": "ob", "title": "UK 1.5km NWP meteorological data for Met Office NAME dispersion model (Mk3: Feb 2015 - Jul 2017)", "abstract": "This dataset contains Numerical Weather Prediction (NWP) meteorological data produced by the operational UKV (United Kingdom Variable-resolution) configuration of the Met Office Unified Model. The files in this dataset have been processed into a form suitable for use in the Met Office NAME (Numerical Atmospheric-dispersion Modelling Environment) dispersion model. NAME uses the Met Office Numerical Weather Prediction model outputs as its source for weather data to be able to predict movement of atmospheric parcels forwards and backwards in time.\r\n\r\nThe files contain a basic collection of model-level fields (3-d winds, temperature, etc.) and a selection of single-level fields including mean sea level pressure, cloud and precipitation from the inner, fixed-resolution domain of the UKV model (this covers the UK area at a spatial resolution of 1.5 km). The UKV model uses a rotated-pole coordinate system. Fields are split into various geographical regions (referred to as \"parts\" or \"PTs\" in NAME) with separate files for each \"part\". Data are provided at hourly resolution for the period Feb 2015 - Jul 2017. All files are in packed PP format.\r\n\r\nThe NWP data used by NAME is different from other forms of Met Office NWP as follows:\r\n- It has been split into spatial partitions (i.e. different parts of the world/domain are in different files)\r\n- It has been reformatted into PP format\r\n\r\nHowever, from the perspective of the raw data, this dataset of UK gridded NWP meteorological data is generically useful for a whole range of scientific research and applications." } }, { "ob_id": 1212, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 41588, "uuid": "a1e5dd132fad4e129669e71adeed1ab1", "short_code": "ob", "title": "UK 1.5km NWP meteorological data for Met Office NAME dispersion model (Mk4: Jul 2017 - current)", "abstract": "This dataset contains Numerical Weather Prediction (NWP) meteorological data produced by the operational UKV (United Kingdom Variable-resolution) configuration of the Met Office Unified Model. The files in this dataset have been processed into a form suitable for use in the Met Office NAME (Numerical Atmospheric-dispersion Modelling Environment) dispersion model. NAME uses the Met Office Numerical Weather Prediction model outputs as its source for weather data to be able to predict movement of atmospheric parcels forwards and backwards in time.\r\n\r\nThe files contain a basic collection of model-level fields (3-d winds, temperature, etc.) and a selection of single-level fields including mean sea level pressure, cloud and precipitation from the inner, fixed-resolution domain of the UKV model (this covers the UK area at a spatial resolution of 1.5 km). The UKV model uses a rotated-pole coordinate system. Fields are split into various geographical regions (referred to as \"parts\" or \"PTs\" in NAME) with separate files for each \"part\". Data are provided at hourly resolution for the period Feb 2015 - Jul 2017. All files are in packed PP format.\r\n\r\nThe NWP data used by NAME is different from other forms of Met Office NWP as follows:\r\n- It has been split into spatial partitions (i.e. different parts of the world/domain are in different files)\r\n- It has been reformatted into PP format\r\n\r\nHowever, from the perspective of the raw data, this dataset of UK gridded NWP meteorological data is generically useful for a whole range of scientific research and applications." }, "objectObservation": { "ob_id": 43199, "uuid": "6d490accd64a4290b9413d5ec94200f9", "short_code": "ob", "title": "UK 1.5km NWP meteorological data for Met Office NAME dispersion model (Mk3: Feb 2015 - Jul 2017)", "abstract": "This dataset contains Numerical Weather Prediction (NWP) meteorological data produced by the operational UKV (United Kingdom Variable-resolution) configuration of the Met Office Unified Model. The files in this dataset have been processed into a form suitable for use in the Met Office NAME (Numerical Atmospheric-dispersion Modelling Environment) dispersion model. NAME uses the Met Office Numerical Weather Prediction model outputs as its source for weather data to be able to predict movement of atmospheric parcels forwards and backwards in time.\r\n\r\nThe files contain a basic collection of model-level fields (3-d winds, temperature, etc.) and a selection of single-level fields including mean sea level pressure, cloud and precipitation from the inner, fixed-resolution domain of the UKV model (this covers the UK area at a spatial resolution of 1.5 km). The UKV model uses a rotated-pole coordinate system. Fields are split into various geographical regions (referred to as \"parts\" or \"PTs\" in NAME) with separate files for each \"part\". Data are provided at hourly resolution for the period Feb 2015 - Jul 2017. All files are in packed PP format.\r\n\r\nThe NWP data used by NAME is different from other forms of Met Office NWP as follows:\r\n- It has been split into spatial partitions (i.e. different parts of the world/domain are in different files)\r\n- It has been reformatted into PP format\r\n\r\nHowever, from the perspective of the raw data, this dataset of UK gridded NWP meteorological data is generically useful for a whole range of scientific research and applications." } }, { "ob_id": 1213, "relationType": "Continues", "subjectObservation": { "ob_id": 41592, "uuid": "45cb520616fc499c80aefd0b356a81f5", "short_code": "ob", "title": "Global NWP meteorological data for Met Office NAME dispersion model (Mk11: Apr 2022 - current)", "abstract": "This dataset contains Numerical Weather Prediction (NWP) global meteorological data produced by the Met Office Unified Model. The files in the dataset have been processed into a form suitable for use in the Met Office NAME (Numerical Atmospheric-dispersion Modelling Environment) dispersion model. NAME uses the Met Office Numerical Weather Prediction model outputs as its source for weather data to be able to predict movement of atmospheric parcels forwards and backwards in time.\r\n\r\nThe files contain a basic collection of model-level fields (3-d winds, temperature, etc.) and a selection of single-level fields including mean sea level pressure, cloud and precipitation. Fields are split into various geographical regions (referred to as \"parts\" or \"PTs\" in NAME) with separate files for each \"part\". Data are provided at 3-hourly resolution. All files are in packed PP format.\r\n\r\nThe NWP data used by NAME is different from other forms of Met Office NWP as follows:\r\n- It has been split into spatial partitions (i.e. different parts of the world/domain are in different files)\r\n- It has been reformatted into PP format\r\n\r\nHowever, from the perspective of the raw data, this dataset of global gridded NWP meteorological data is generically useful for a whole range of scientific research and applications." }, "objectObservation": { "ob_id": 41598, "uuid": "7d0fff9f59b94a3da347e3ae10bd8fc1", "short_code": "ob", "title": "Global NWP meteorological data for Met Office NAME dispersion model (Mk10: June 2017 - May 2022)", "abstract": "This dataset contains Numerical Weather Prediction (NWP) global meteorological data produced by the Met Office Unified Model. The files in the dataset have been processed into a form suitable for use in the Met Office NAME (Numerical Atmospheric-dispersion Modelling Environment) dispersion model. NAME uses the Met Office Numerical Weather Prediction model outputs as its source for weather data to be able to predict movement of atmospheric parcels forwards and backwards in time.\r\n\r\nThe files contain a basic collection of model-level fields (3-d winds, temperature, etc.) and a selection of single-level fields including mean sea level pressure, cloud and precipitation. Fields are split into various geographical regions (referred to as \"parts\" or \"PTs\" in NAME) with separate files for each \"part\". Data are provided at 3-hourly resolution. All files are in packed PP format.\r\n\r\nThe NWP data used by NAME is different from other forms of Met Office NWP as follows:\r\n- It has been split into spatial partitions (i.e. different parts of the world/domain are in different files)\r\n- It has been reformatted into PP format\r\n\r\nHowever, from the perspective of the raw data, this dataset of global gridded NWP meteorological data is generically useful for a whole range of scientific research and applications." } }, { "ob_id": 1214, "relationType": "Continues", "subjectObservation": { "ob_id": 41598, "uuid": "7d0fff9f59b94a3da347e3ae10bd8fc1", "short_code": "ob", "title": "Global NWP meteorological data for Met Office NAME dispersion model (Mk10: June 2017 - May 2022)", "abstract": "This dataset contains Numerical Weather Prediction (NWP) global meteorological data produced by the Met Office Unified Model. The files in the dataset have been processed into a form suitable for use in the Met Office NAME (Numerical Atmospheric-dispersion Modelling Environment) dispersion model. NAME uses the Met Office Numerical Weather Prediction model outputs as its source for weather data to be able to predict movement of atmospheric parcels forwards and backwards in time.\r\n\r\nThe files contain a basic collection of model-level fields (3-d winds, temperature, etc.) and a selection of single-level fields including mean sea level pressure, cloud and precipitation. Fields are split into various geographical regions (referred to as \"parts\" or \"PTs\" in NAME) with separate files for each \"part\". Data are provided at 3-hourly resolution. All files are in packed PP format.\r\n\r\nThe NWP data used by NAME is different from other forms of Met Office NWP as follows:\r\n- It has been split into spatial partitions (i.e. different parts of the world/domain are in different files)\r\n- It has been reformatted into PP format\r\n\r\nHowever, from the perspective of the raw data, this dataset of global gridded NWP meteorological data is generically useful for a whole range of scientific research and applications." }, "objectObservation": { "ob_id": 43389, "uuid": "2bb4d76ed2fa4fc2af3fbbca6eb80965", "short_code": "ob", "title": "Global NWP meteorological data for Met Office NAME dispersion model (Mk9: July 2015 - 2017)", "abstract": "This dataset contains Numerical Weather Prediction (NWP) global meteorological data produced by the Met Office Unified Model. The files in the dataset have been processed into a form suitable for use in the Met Office NAME (Numerical Atmospheric-dispersion Modelling Environment) dispersion model. NAME uses the Met Office Numerical Weather Prediction model outputs as its source for weather data to be able to predict movement of atmospheric parcels forwards and backwards in time.\r\n\r\nThe files contain a basic collection of model-level fields (3-d winds, temperature, etc.) and a selection of single-level fields including mean sea level pressure, cloud and precipitation. Fields are split into various geographical regions (referred to as \"parts\" or \"PTs\" in NAME) with separate files for each \"part\". Data are provided at 3-hourly resolution. All files are in packed PP format.\r\n\r\nThe NWP data used by NAME is different from other forms of Met Office NWP as follows:\r\n- It has been split into spatial partitions (i.e. different parts of the world/domain are in different files)\r\n- It has been reformatted into PP format\r\n\r\nHowever, from the perspective of the raw data, this dataset of global gridded NWP meteorological data is generically useful for a whole range of scientific research and applications." } }, { "ob_id": 1215, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 44871, "uuid": "4fcad04a4a224601875df64ceef48e26", "short_code": "ob", "title": "Auchencorth Moss Atmospheric Observatory (AU): Annual half-hourly meteorology since 1995, Near Edinburgh, UK - Version 2", "abstract": "This version 2 dataset includes annual files of half-hourly meteorological observations made at Auchencorth Moss Atmospheric Observatory, near Edinburgh, UK. \r\n\r\nThe site was setup in 1995 to measure meteorology, trace gases, aerosols and their fluxes. It is (55ᵒ47’36” N, 3°14’41” W) an ombrotrophic peatland with an extensive fetch at an elevation of 270 m, lying 18 km SSW of Edinburgh, and can be categorised as a transitional lowland raised bog. The site is grazed with < 1 sheep per hectare.\r\nDuring 2000s the site activity has increased and was established in 2006 as EMEP (European Monitoring and Evaluation Program, Level 2/3) supersite for the UK. Long term monitoring is led by NERC CEH with contributions from other organisations/research institutes including Ricardo AEA, BureauVeritas, NPL, the University of Birmingham and University of Edinburgh. In April 2014 the site was awarded WMO GAW regional station (World Meteorological Orgamisation Global Atmospheric Watch). In 2017 the site joined the ICOS network (Integrated Carbon Observation System).\r\nThe meteorological measurements were initially made to assist with interpretation of the fluxes and as such weren't installed with the intention of providing WMO standard measurements but since 2014 we have been moving towards these standards as well as enhancing instrumentation.\r\nFrom 1995 to 2023 the dataset includes the following parameters at half hourly intervals, although not every variable is available for the whole period:\r\n-T_upper_Avg - initially used to estimate senisble heat fluxes, fine wire type-E thermocouple.\r\n-T_lower_Avg - initially used to estimate senisble heat fluxes, fine wire type-E thermocouple.\r\n-T_RHT_Avg - Temperature measured by a Vailsala relative humidity and temperature probe.\r\n-RH_RHT_Avg - Relative humidity measured by a Vailsala relative humidity and temperature probe.\r\n-P_Avg - atmospheric pressure at the sites elevation.\r\n-Tot_Solar_Avg - Total solar radiation measured by a Skye SKS1110.\r\n-PAR_Avg - Photosynthetically Averaged Radiation measured by a Skye SKP215.\r\n-NetRad_Avg - Net radiation, Kipp & Zonen NrLite.\r\n-Rainfall - tipping bucket rainfall.\r\n-SoilTavg - Average soil temperature from four type-E thermocouple probes.\r\n-Soil Heat Flux - calculated from two measurements of soil heat flux with Hukseflux HFP01 plates, corrected to surface flux using the standard formula.\r\n-Cs = Bd(Cd+fSWC.Cw)\r\n-SC = DTs.Cs.d/Dtime\r\n-SHF = Plate Average + SC\r\n-DTs = change in average soil temperature from start to end of measurement period (first and last two minutes); d = plate depth 0.2 m; Bd = soil bulk density, 100 kg m-3; cd = Specific Heat Dry Soil, 840J kg-1 K-1; fSWC = fractional soil water content, measured or 0.9; cw = Specific Capacilty Heat of Water, 4190 J kg-1 K-1; Dtime = measurement period, 1800 s\r\n-Soil Moisture - soil water content measured with TDR probes, campbell CS616\r\n-WindSpd (measured) - measured by a Gill R3 then Windmaster sonic anemometer at 3.6 m\r\n-WindSpd 10 m - for most of the time period this is estimated from the turbulence measurements and 3.6 m windspeed but from 22/06/2016 a Gill Windsonic 2D anemeometer measures at 10 m\r\n-Wind Dir - measured by the sonic anemometer at 3.6 m\r\n-snow_depth_Avg - Campbell Scientific SR50A-L Sonic Ranging Sensor\r\n-Present Weather - Vaisala FD12P Present Weather Sensor\r\n-1 hr Past Weather - Vaisala FD12P Present Weather Sensor\r\n-Visibility - Vaisala FD12P Present Weather Sensor\r\n-Evaporation - to be estimated from the water-vapour flux measurements\r\n\r\nIn September 2025 the 1995-2023 data were revised to correct some errors in the original submission. These revised data are labelled version 2 and supercede the previous data.\r\n The key changes are:\r\n-\tSimplified data set with no fully gap-filled time series or modelled evaporation\r\n-\tSoil heat flux changed to measured average without storage correction (this is very uncertain on peat and caused spurious noise in the data)\r\n-\tWater table depth now reported in m, below the surface to match ICOS standards. Positive values indicate depth below the surface while negative values are above the surface\r\n\r\nIn November 2021 the site was officially labelled as an ICOS ecosystem station (https://meta.icos-cp.eu/resources/stations/ES_UK-AMo) as part of this process the meteorological instruments and their data processing system were modified to comply with ICOS requirements. From 2024 the data submitted to CEDA is modified to align with this system. For continuity data from the same sensors are submitted but there are some modifications:\r\n\r\n•\tAir Temperature average (TA_4_1_1, oC); from Rotronic HC2S3 temperature/humidity probe in unventilated shield \r\n•\tRelative Humidity average (RH_4_1_1, %); from Rotronic HC2S3 temperature/humidity probe in unventilated shield \r\n•\tPressure average (PA_4_1_1, kPa); barometric pressure at the site's altitude from Vaisala PTB110\r\n•\tTotal incident solar radiation average (RG_4_1_0, Wm-2); Skye SKS1110 thermopile pyranometer\r\n•\tPhotosynthetically Active Radiation average (PPFD_IN_4_1_1, umolm-2s-1); Skye SKP215 thermopile sensor\r\n•\tNet radiation (RN_5_1_1, Wm-2); Kipp & Zonen NRLite2, compact sensor gives single output\r\n•\tRainfall (P_13_1_1, mm); ARG314 Tipping bucket measurement\r\n•\tSoil temperature average, (TS, oC), Campbell Scientific Type-E thermocouple averaging probes or average of readings from Campbell Scientific CS616 soil moisture sensors\r\n•\tSoil Heat Flux (G, Wm-2), average of flux monitoring plates at several locations, Hukseflux HFP01-SC\r\n•\tSoil water content (same as soil moisture) (SWC, %); average of TDR probes, Campbell CS616 at several locations\r\n•\tWind speed 10 m (WS_6_1_1, ms-1); Gill Windsonic 2D sonic anemometer installed at 10 m\r\n•\tWind direction (WD_6_1_1, oN); Gill Windsonic 2D sonic anemometer installed at 10 m\r\n•\tSnow depth (D_SNOW, m) sonic snow depth sensor, Campbell Scientifc SR50AH\r\n•\twater table depth, (WTD, m); Druck PCR 1830, distance to water table from the average surface level (negative above the surface)\r\n•\tPresent_Weather (WMO4680 Code – Biral VPF50 gapfilled with Vaisala FD12P PWS\r\n•\t1 hour Past Weather (1_hr_Past_Weather, WMO4680 Code) - Biral VPF50 gapfilled with Vaisala FD12P PWS\r\n•\tVisibility (Visibility, m) Optical measurement from Biral VPF50 gapfilled with Vaisala FD12P PWS scaled so to match the maximum from the Biral sensor" }, "objectObservation": { "ob_id": 25086, "uuid": "8e6cbb111cfd41a19c92aadcb2d040fd", "short_code": "ob", "title": "Auchencorth Moss Atmospheric Observatory (AU): Annual half-hourly meteorology since 1995, Near Edinburgh, UK - Version 1", "abstract": "The site was setup in 1995 to measure meteorology, trace gases, aerosols and their fluxes. It is (55ᵒ47’36” N, 3°14’41” W) an ombrotrophic peatland with an extensive fetch at an elevation of 270 m, lying 18 km SSW of Edinburgh, and can be categorised as a transitional lowland raised bog. The site is grazed with < 1 sheep ha^-1.\r\n\r\nDuring 2000s the site activity has increased and was established in 2006 as EMEP (European Monitoring and Evaluation Program, Level 2/3) supersite for the UK. Long term monitoring is led by NERC CEH with contributions from other organisations/research institutes including Ricardo AEA, BureauVeritas, NPL, the University of Birmingham and University of Edinburgh. In April 2014 the site was awarded WMO GAW regional station (World Meteorological Orgamisation Global Atmospheric Watch). In 2017 the site joined the ICOS network (Integrated Carbon Observation System).\r\n\r\nThe meteorological measurements were initially made to assist with interpretation of the fluxes and as such weren't installed with the intention of providing WMO standard measurements but since 2014 we have been moving towards these standards as well as enhancing instrumentation.\r\n\r\nThe dataset includes the following parameters at half hourly intervals, although not every variable is available from 1995 to 2016:\r\n-T_upper_Avg - initially used to estimate senisble heat fluxes, fine wire type-E thermocouple.\r\n-T_lower_Avg - initially used to estimate senisble heat fluxes, fine wire type-E thermocouple.\r\n-T_RHT_Avg - Temperature measured by a Vailsala relative humidity and temperature probe.\r\n-RH_RHT_Avg - Relative humidity measured by a Vailsala relative humidity and temperature probe.\r\n-P_Avg - atmospheric pressure at the sites elevation.\r\n-Tot_Solar_Avg - Total solar radiation measured by a Skye SKS1110.\r\n-PAR_Avg - Photosynthetically Averaged Radiation measured by a Skye SKP215.\r\n-NetRad_Avg - Net radiation, Kipp & Zonen NrLite.\r\n-Rainfall - tipping bucket rainfall.\r\n-SoilTavg - Average soil temperature from four type-E thermocouple probes.\r\n-Soil Heat Flux - calculated from two measurements of soil heat flux with Hukseflux HFP01 plates, corrected to surface flux using the standard formula.\r\n-Cs = Bd(Cd+fSWC.Cw)\r\n-SC = DTs.Cs.d/Dtime\r\n-SHF = Plate Average + SC\r\n-DTs = change in average soil temperature from start to end of measurement period (first and last two minutes); d = plate depth 0.2 m; Bd = soil bulk density, 100 kg m-3; cd = Specific Heat Dry Soil, 840J kg-1 K-1; fSWC = fractional soil water content, measured or 0.9; cw = Specific Capacilty Heat of Water, 4190 J kg-1 K-1; Dtime = measurement period, 1800 s\r\n-Soil Moisture - soil water content measured with TDR probes, campbell CS616\r\n-WindSpd (measured) - measured by a Gill R3 then Windmaster sonic anemometer at 3.6 m\r\n-WindSpd 10 m - for most of the time period this is estimated from the turbulence measurements and 3.6 m windspeed but from 22/06/2016 a Gill Windsonic 2D anemeometer measures at 10 m\r\n-Wind Dir - measured by the sonic anemometer at 3.6 m\r\n-snow_depth_Avg - Campbell Scientific SR50A-L Sonic Ranging Sensor\r\n-Present Weather - Vaisala FD12P Present Weather Sensor\r\n-1 hr Past Weather - Vaisala FD12P Present Weather Sensor\r\n-Visibility - Vaisala FD12P Present Weather Sensor\r\n-Evaporation - to be estimated from the water-vapout flux measurements\r\n\r\nFor modelling purposes gapfilled (variables with _gf suffixes) times series will be included, they are created by linearly initially interpolating across upto an hours missing data, filling with colocated measurements (adjusted by linear interpolation with the core data), filling with measurements from nearby sites (adjusted by linear interpolation with the core data).\r\nTa_gf\r\nP_gf\r\nRH_gf\r\nTotal_Solar_gf\r\nRainfall_gf\r\nWindspd 10m_gf\r\nWind Dir_gf" } }, { "ob_id": 1216, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 44300, "uuid": "313854aedcb04a5eb56f711401a87396", "short_code": "ob", "title": "ESA Land Cover Climate Change Initiative (Land_Cover_cci): Global Plant Functional Types (PFT) Dataset, v2.0.81", "abstract": "This dataset contains Global Plant Functional Types (PFT) data, from the ESA Medium Resolution Land Cover (MRLC) Climate Change Initiative project. The data provides yearly data, and initially covers the time period from 1992 to 2020. It is anticipated that the dataset will be updated annually going forward.\r\n\r\nThis version of the data is v2.0.81, which corrects an issue found with a file in v2.0.8.\r\n\r\nThe PFT v2.0.81 global dataset has 14 layers, each describing the percentage cover (0-100%) of a plant functional type at a spatial resolution of 300 m: broadleaved evergreen trees, broadleaved deciduous trees, needleleaved evergreen trees, needleleaved deciduous trees, broadleaved evergreen shrubs, broadleaved deciduous shrubs, needleleaved evergreen shrubs, needleleaved deciduous shrubs, natural grasses, herbaceous cropland (i.e., managed grasses), built, water, bare areas, and snow and ice.\r\n\r\n\"Plant Functional Types” (PFTs) refer to globally representative and similarly behaving plant types. PFTs can be related to physiognomy and phenology, climate (which defines the geographical ranges in which a plant type can grow and reproduce under natural conditions, and physiological activity (e.g., C3/C4 photosynthetic pathways).\r\n\r\nAll terrestrial zones of the Earth between the parallels 90°N and 90°S are covered. The PFT dataset has a regular latitude-longitude grid with a grid spacing of 0.002777777777778°, corresponding to ~300 m at the equator and ~200 m in the midlatitudes. The Coordinate Reference System used for the global land cover database is a geographic coordinate system (GCS) based on the World Geodetic System 84 (WGS84) reference ellipsoid.\r\n\r\nThe plant functional type (PFT) distribution was created by combining auxiliary data products with the CCI MRLC map series. The LC classification provides the broad characteristics of the 300 m pixel, including the expected vegetation form(s) (tree, shrub, grass) and/or abiotic land type(s) (water, bare area, snow and ice, built-up) in the pixel. For some classes, the class legend specifies an expected range for the fractional covers of the contributing PFTs and broadly differentiates between natural and cultivated vegetation. We used a quantitative, globally consistent method that fuses the 300-metre MRLC product with a suite of existing high-resolution datasets to develop spatially explicit annual maps of PFT fractional composition at 300 metres. The new PFT product exhibits intraclass spatial variability in PFT fractional cover at the 300-metre pixel level and is complementary to the MRLC maps since the derived PFT fractions maintain consistency with the original LC class legend. \r\n\r\nThis dataset was generated to reduce the cross-walking component of uncertainty by adding spatial variability to the PFT composition within a LC class. This work moved beyond fine-tuning the cross-walking approach for specific LC classes or regions and, instead, separately quantifies the PFT fractional composition for each 300 m pixel globally. The result is a dataset representing the cover fractions of 14 PFTs at 300 m for each year within the time range, consistent with the CCI MRLC LC maps for the corresponding year.\r\n\r\nThis study was carried out with the continued support of the European Space Agency Climate Change Initiative under the contract ESA/No.4000126564 Land_Cover_cci." }, "objectObservation": { "ob_id": 37340, "uuid": "26a0f46c95ee4c29b5c650b129aab788", "short_code": "ob", "title": "ESA Land Cover Climate Change Initiative (Land_Cover_cci): Global Plant Functional Types (PFT) Dataset, v2.0.8", "abstract": "This dataset contains Global Plant Functional Types (PFT) data, from the ESA Medium Resolution Land Cover (MRLC) Climate Change Initiative project. The data provides yearly data, and initially covers the time period from 1992 to 2020. It is anticipated that the dataset will be updated annually going forward.\r\n\r\nThe PFT v2.0.8 global dataset has 14 layers, each describing the percentage cover (0-100%) of a plant functional type at a spatial resolution of 300 m: broadleaved evergreen trees, broadleaved deciduous trees, needleleaved evergreen trees, needleleaved deciduous trees, broadleaved evergreen shrubs, broadleaved deciduous shrubs, needleleaved evergreen shrubs, needleleaved deciduous shrubs, natural grasses, herbaceous cropland (i.e., managed grasses), built, water, bare areas, and snow and ice.\r\n\r\n\"Plant Functional Types” (PFTs) refer to globally representative and similarly behaving plant types. PFTs can be related to physiognomy and phenology, climate (which defines the geographical ranges in which a plant type can grow and reproduce under natural conditions, and physiological activity (e.g., C3/C4 photosynthetic pathways).\r\n\r\nAll terrestrial zones of the Earth between the parallels 90°N and 90°S are covered. The PFT dataset has a regular latitude-longitude grid with a grid spacing of 0.002777777777778°, corresponding to ~300 m at the equator and ~200 m in the midlatitudes. The Coordinate Reference System used for the global land cover database is a geographic coordinate system (GCS) based on the World Geodetic System 84 (WGS84) reference ellipsoid.\r\n\r\nThe plant functional type (PFT) distribution was created by combining auxiliary data products with the CCI MRLC map series. The LC classification provides the broad characteristics of the 300 m pixel, including the expected vegetation form(s) (tree, shrub, grass) and/or abiotic land type(s) (water, bare area, snow and ice, built-up) in the pixel. For some classes, the class legend specifies an expected range for the fractional covers of the contributing PFTs and broadly differentiates between natural and cultivated vegetation. We used a quantitative, globally consistent method that fuses the 300-metre MRLC product with a suite of existing high-resolution datasets to develop spatially explicit annual maps of PFT fractional composition at 300 metres. The new PFT product exhibits intraclass spatial variability in PFT fractional cover at the 300-metre pixel level and is complementary to the MRLC maps since the derived PFT fractions maintain consistency with the original LC class legend. \r\n\r\nThis dataset was generated to reduce the cross-walking component of uncertainty by adding spatial variability to the PFT composition within a LC class. This work moved beyond fine-tuning the cross-walking approach for specific LC classes or regions and, instead, separately quantifies the PFT fractional composition for each 300 m pixel globally. The result is a dataset representing the cover fractions of 14 PFTs at 300 m for each year within the time range, consistent with the CCI MRLC LC maps for the corresponding year.\r\n\r\nThis study was carried out with the continued support of the European Space Agency Climate Change Initiative under the contract ESA/No.4000126564 Land_Cover_cci." } }, { "ob_id": 1217, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 44877, "uuid": "a79fcb9ab0c443fc86d453cc064759b1", "short_code": "ob", "title": "ForestScan project: Unpiloted Aerial Vehicle LiDAR Scanning (UAV-LS) data of FBRMS-02: Station d’Etudes des Gorilles et Chimpanzés, Lopé National Park, Gabon, June 2022, Version 2.0", "abstract": "This dataset contains point cloud data (a set of data points in a 3D coordinate system) which were collected using a RIEGL miniVUX1-DL LiDAR scanner mounted on a DELAIR DT26X Unpiloted Aerial Vehicle (UAV). The data was collected in June 2022 as part of the ForestScan project. The person responsible for the data collection was Dr. Iain McNicol from the University of Edinburgh, who collected and processed the data." }, "objectObservation": { "ob_id": 40868, "uuid": "7a4649cabd3e4afb8cd31cfd7d95ac8e", "short_code": "ob", "title": "ForestScan project: Unpiloted Aerial Vehicle LiDAR Scanning (UAV-LS) data of FBRMS-02: Station d’Etudes des Gorilles et Chimpanzés, Lopé National Park, Gabon, June 2022", "abstract": "This dataset contains point cloud data (a set of data points in a 3D coordinate system) which were collected using a RIEGL miniVUX1-DL LiDAR scanner mounted on a DELAIR DT26X Unpiloted Aerial Vehicle (UAV). The data was collected in June 2022 as part of the ForestScan project. The person responsible for the data collection was Dr. Iain McNicol from the University of Edinburgh, who collected and processed the data." } }, { "ob_id": 1218, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 43386, "uuid": "80d96e3a14854420b6f742d70877c431", "short_code": "ob", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - snow on ground (SCFG) from AVHRR (1979 - 2023), version 4.0", "abstract": "This dataset contains Daily Snow Cover Fraction (snow on ground) from AVHRR, produced by the Snow project of the ESA Climate Change Initiative programme. \r\n\r\nSnow cover fraction on ground (SCFG) indicates the area of snow observed from space over land surfaces, in forested areas corrected for the transmissivity of the forest canopy. The SCFG is given in percentage (%) per pixel. \r\n\r\nThe global SCFG product is available at about 5 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets. The coastal zones of Greenland are included.\r\n\r\nThe SCFG time series provides daily products for the period 1979-2023.\r\n \r\nThe product V4.0 is based on EUMETSAT Fundamental Data Record (FDR) medium resolution optical satellite data from the Advanced Very High Resolution Radiometer (AVHRR). Clouds are masked using the CLARA-A3 cloud product. \r\n\r\nThe retrieval method of the snow_cci SCFG product from AVHRR data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module. All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 0.63 µm and 1.61 µm (channel 3a or the reflective part of channel 3b (ref3b)), and an emissive band centred at about 10.8 µm. The snow_cci snow cover mapping algorithm is a three-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFG retrieval method is applied using dynamic reference reflectance values (snow, forest, ground) temporally and spatially adapted to consider angle dependencies (sun, view). Finally, a post-processing removes erroneous snow pixels caused either by falsely classified clouds in the tropics or by unreliable ref3b values at a global scale. \r\n\r\nThe following auxiliary data sets are used for product generation: i) ESA CCI Land Cover from 2000; water bodies and permanent snow and ice areas are masked based on this dataset. Both classes were separately aggregated to the pixel spacing of the SCF product. Water areas are masked if more than 50 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map; ii) Forest canopy transmissivity map; this layer is based on the tree cover classes of the ESA CCI Land Cover 2000 data set and the tree cover density map from Landsat data for the year 2000 (Hansen et al., Science, 2013, DOI: 10.1126/science.1244693). This layer is used to apply a forest canopy correction and estimate in forested areas the fractional snow cover on ground. RMSE is retrieved from a statistical model and added as pixel-wise information. \r\n\r\nThe SCFG product is aimed to serve the needs of users working in cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.\r\n\r\nThe Remote Sensing Research Group of the University of Bern, in cooperation with Gamma Remote Sensing is responsible for the SCFG product development and generation. ENVEO (ENVironmental Earth Observation IT GmbH) developed and prepared all auxiliary data sets used for the product generation.\r\n\r\nThe SCFG AVHRR product comprises a few data gaps in 1979 – 1986 (1979: 22.-24.Feb.; 01.-07.Oct.; 03.-04.Nov.; 07.Nov.; 17.-18.Nov.; 1980: 22.-27.Feb.; 01.March; 03.March; 15.-20.March; 30.March – 02.April; 26.-29.June; 12.-19.July; 12.-18.Dec.; 1981: 09.-11.May; 01.-03.Aug.; 14.-23.Aug.; 1982: 28.- 31.May; 25.-26. Oct.; 1983: 27.- 31. July; 01.- 02. and 06. Aug.; 1984: 14.-15.Jan.; 06. Dec.; 1985: 01.- 24.Feb; 1986: 15. March), resulting in a 99% data coverage over the entire study period of 43 years." }, "objectObservation": { "ob_id": 40356, "uuid": "56ff07acabab42888afe2d20b488ec49", "short_code": "ob", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - snow on ground (SCFG) from AVHRR (1979 - 2022), version 3.0", "abstract": "This dataset contains Daily Snow Cover Fraction (snow on ground) from AVHRR, produced by the Snow project of the ESA Climate Change Initiative programme. \r\n\r\nSnow cover fraction on ground (SCFG) indicates the area of snow observed from space over land surfaces, in forested areas corrected for the transmissivity of the forest canopy. The SCFG is given in percentage (%) per pixel. \r\n\r\nThe global SCFG product is available at about 5 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets. The coastal zones of Greenland are included.\r\n\r\nThe SCFG time series provides daily products for the period 1979-2022. \r\n\r\nThe product is based on medium resolution optical satellite data from the Advanced Very High Resolution Radiometer (AVHRR). Clouds are masked using the CLARA-A3 cloud product. \r\n\r\nThe retrieval method of the snow_cci SCFG product from AVHRR data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module. All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 0.63 µm and 1.61 µm (channel 3a or the reflective part of channel 3b (ref3b)), and an emissive band centred at about 10.8 µm. The snow_cci snow cover mapping algorithm is a three-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFG retrieval method is applied. Finally, a post-processing removes erroneous snow pixels caused either by falsely classified clouds in the tropics or by unreliable ref3b values at a global scale. \r\n\r\nThe following auxiliary data sets are used for product generation: i) ESA CCI Land Cover from 2000; water bodies and permanent snow and ice areas are masked based on this dataset. Both classes were separately aggregated to the pixel spacing of the SCF product. Water areas are masked if more than 50 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map; ii) Forest canopy transmissivity map; this layer is based on the tree cover classes of the ESA CCI Land Cover 2000 data set and the tree cover density map from Landsat data for the year 2000 (Hansen et al., Science, 2013, DOI: 10.1126/science.1244693). This layer is used to apply a forest canopy correction and estimate in forested areas the fractional snow cover on ground.\r\n\r\nThe SCFG product is aimed to serve the needs of users working in cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.\r\n\r\nThe Remote Sensing Research Group of the University of Bern, in cooperation with Gamma Remote Sensing is responsible for the SCFG product development and generation. ENVEO (ENVironmental Earth Observation IT GmbH) developed and prepared all auxiliary data sets used for the product generation.\r\n\r\nThe SCFG AVHRR product comprises a few data gaps in 1979 – 1986 (1979: 22.-24.Feb.; 01.-07.Oct.; 03.-04.Nov.; 07.Nov.; 17.-18.Nov.; 1980: 22.-27.Feb.; 01.March; 03.March; 15.-20.March; 30.March – 02.April; 26.-29.June; 12.-19.July; 12.-18.Dec.; 1981: 09.-11.May; 01.-03.Aug.; 14.-23.Aug.; 1982: 28.- 31.May; 25.-26. Oct.; 1983: 27.- 31. July; 01.- 02. and 06. Aug.; 1984: 14.-15.Jan.; 06. Dec.; 1985: 01.- 24.Feb; 1986: 15. March), resulting in a 99% data coverage over the entire study period of 43 years." } }, { "ob_id": 1219, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 43385, "uuid": "3c71c04cf08a410fac2c680cbf88cfd7", "short_code": "ob", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - viewable (SCFV) from AVHRR (1979 - 2023), version 4.0", "abstract": "This dataset contains Daily Snow Cover Fraction of viewable snow from AVHRR, produced by the Snow project of the ESA Climate Change Initiative programme. \r\n\r\nSnow cover fraction viewable (SCFV) indicates the area of snow viewable from space over land surfaces. In forested areas this refers to snow viewable on top of the forest canopy. The SCFV is given in percentage (%) per pixel. \r\n\r\nThe global SCFV product is available at about 5 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets. The coastal zones of Greenland are included.\r\n\r\nThe SCFV time series provides daily products for the period 1979-2023. \r\n\r\nThe product V4.0 is based on EUMETSAT Fundamental Data Record (FDR) medium resolution optical satellite data from the Advanced Very High Resolution Radiometer (AVHRR). Clouds are masked using the CLARA-A3 cloud product. \r\n\r\nThe retrieval method of the snow_cci SCFV product from AVHRR data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre- and post-classification module. All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 0.63 µm and 1.61 µm (channel 3a or the reflective part of channel 3b (ref3b)), and an emissive band centred at about 10.8 µm. The snow_cci snow cover mapping algorithm is a three-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFV retrieval method is applied. Finally, a post-processing removes erroneous snow pixels caused either by falsely classified clouds in the tropics or by unreliable ref3b values at a global scale. \r\n\r\nThe following auxiliary data set is used for product generation: ESA CCI Land Cover from 2000; water bodies and permanent snow and ice areas are masked based on this dataset. Both classes were separately aggregated to the pixel spacing of the SCF product. Water areas are masked if more than 50 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map. RMSE is retrieved from a statistical model and added as pixel-wise information.\r\n\r\nThe SCFV product is aimed to serve the needs for users working in the cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology and biology.\r\n\r\nThe Remote Sensing Research Group of the University of Bern, in cooperation with Gamma Remote Sensing is responsible for the SCFV product development and generation. ENVEO (ENVironmental Earth Observation IT GmbH) developed and prepared all auxiliary data sets used for the product generation. \r\n\r\nThe SCFV AVHRR product comprises a few data gaps in 1979 – 1986 (1979: 22.-24.Feb.; 01.-07.Oct.; 03.-04.Nov.; 07.Nov.; 17.-18.Nov.; 1980: 22.-27.Feb.; 01.March; 03.March; 15.-20.March; 30.March – 02.April; 26.-29.June; 12.-19.July; 12.-18.Dec.; 1981: 09.-11.May; 01.-03.Aug.; 14.-23.Aug.; 1982: 28.- 31.May; 25.-26. Oct.; 1983: 27.- 31. July; 01.- 02. and 06. Aug.; 1984: 14.-15.Jan.; 06. Dec.; 1985: 01.- 24.Feb; 1986: 15. March), resulting in a 99% data coverage over the entire study period of 43 years." }, "objectObservation": { "ob_id": 40357, "uuid": "7491427f8c3442ce825ba5472c224322", "short_code": "ob", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - viewable (SCFV) from AVHRR (1979 - 2022), version 3.0", "abstract": "This dataset contains Daily Snow Cover Fraction of viewable snow from AVHRR, produced by the Snow project of the ESA Climate Change Initiative programme. \r\n\r\nSnow cover fraction viewable (SCFV) indicates the area of snow viewable from space over land surfaces. In forested areas this refers to snow viewable on top of the forest canopy. The SCFV is given in percentage (%) per pixel. \r\n\r\nThe global SCFV product is available at about 5 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets. The coastal zones of Greenland are included.\r\n\r\nThe SCFV time series provides daily products for the period 1979-2022. \r\n\r\nThe product is based on medium resolution optical satellite data from the Advanced Very High Resolution Radiometer (AVHRR). Clouds are masked using the CLARA-A3 cloud product. \r\n\r\nThe retrieval method of the snow_cci SCFV product from AVHRR data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre- and post-classification module. All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 0.630 µm and 1.61 µm (channel 3a or the reflective part of channel 3b (ref3b)), and an emissive band centred at about 10.8 µm. The snow_cci snow cover mapping algorithm is a three-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFV retrieval method is applied. Finally, a post-processing removes erroneous snow pixels caused either by falsely classified clouds in the tropics or by unreliable ref3b values at a global scale. \r\n\r\nThe following auxiliary data set is used for product generation: ESA CCI Land Cover from 2000; water bodies and permanent snow and ice areas are masked based on this dataset. Both classes were separately aggregated to the pixel spacing of the SCF product. Water areas are masked if more than 50 percent of the pixel is classified as water; permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map.\r\n\r\nThe SCFV product is aimed to serve the needs for users working in the cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology and biology.\r\n\r\nThe Remote Sensing Research Group of the University of Bern, in cooperation with Gamma Remote Sensing is responsible for the SCFV product development and generation. ENVEO (ENVironmental Earth Observation IT GmbH) developed and prepared all auxiliary data sets used for the product generation. \r\n\r\nThe SCFV AVHRR product comprises a few data gaps in 1979 – 1986 (1979: 22.-24.Feb.; 01.-07.Oct.; 03.-04.Nov.; 07.Nov.; 17.-18.Nov.; 1980: 22.-27.Feb.; 01.March; 03.March; 15.-20.March; 30.March – 02.April; 26.-29.June; 12.-19.July; 12.-18.Dec.; 1981: 09.-11.May; 01.-03.Aug.; 14.-23.Aug.; 1982: 28.- 31.May; 25.-26. Oct.; 1983: 27.- 31. July; 01.- 02. and 06. Aug.; 1984: 14.-15.Jan.; 06. Dec.; 1985: 01.- 24.Feb; 1986: 15. March), resulting in a 99% data coverage over the entire study period of 43 years." } }, { "ob_id": 1220, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 44902, "uuid": "f60cd72eae8b409fbfe4aa84aa04e97b", "short_code": "ob", "title": "HadISD: Global sub-daily, surface meteorological station data, 1931-2025, v3.4.3.2025f", "abstract": "This is the final version, v3.4.3.2025f, of Met Office Hadley Centre's Integrated Surface Database, HadISD. These data are global sub-daily surface meteorological data. The parent dataset of HadISD, the Integrated Surface Database at NOAA, stopped being updated on 29th August 2025. Therefore there will be no further updates to this dataset, and the final year will remain incomplete, going up to 29th August 2025 only\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_19310101-20250829_v3-4-3-2025f. 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": 43669, "uuid": "2a01faf75de64308b2bf4c7b43d393ef", "short_code": "ob", "title": "HadISD: Global sub-daily, surface meteorological station data, 1931-2024, v3.4.1.2024f", "abstract": "This is version v3.4.1.2024f 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_19310101-20250101_v3.4.1.2024f.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": 1222, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 44643, "uuid": "d56a6215ce394ddd8dff6bea5dbb0780", "short_code": "ob", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily land surface temperature from MODIS (Moderate resolution Infra-red Spectroradiometer) on Terra, level 3 collated (L3C) global product (2000-2021), version 4.00", "abstract": "This dataset contains daily land surface temperatures (LSTs) and their uncertainty estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS) on Earth Observing System – Terra (Terra). Satellite land surface temperatures are skin temperatures which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.\r\n\r\nDaytime and night-time temperatures are provided in separate files corresponding to the morning and evening Terra equator crossing times which are 10:30 and 22:30 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.\r\n\r\nThe dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. MODIS achieves full Earth coverage nearly twice per day so the daily files have small gaps primarily close to the equator where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.\r\n\r\nThe daily dataset starts from 24th February 2000 and ends 31st December 2021. There are minor interruptions (1-2 days) during satellite/instrument maintenance periods.\r\n\r\nIn Version 4.00 the time series has been extended to 2021. The emissivities used in the retrieval come from the Combined ASTER and MODIS Emissivity over Land (CAMEL) Version 2 database; in Version 4.00 a climatology is used since there are temporal instabilities in the CAMEL time series.\r\n\r\nThe dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using a generalised split window retrieval algorithm and data were processed in the UoL processing chain.\r\n\r\nThe dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards." }, "objectObservation": { "ob_id": 33368, "uuid": "58a01734f841466daa1837353aee5ff8", "short_code": "ob", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Land Surface Temperature from MODIS (Moderate resolution Infra-red Spectroradiometer) on Terra, level 3 collated (L3C) global product (2000-2018), version 3.00", "abstract": "This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS) on Earth Observing System – Terra (Terra). Satellite land surface temperatures are skin temperatures which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.\r\n\r\nDaytime and night-time temperatures are provided in separate files corresponding to the morning and evening Terra equator crossing times which are 10:30 and 22:30 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.\r\n\r\nThe dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. MODIS achieves full Earth coverage nearly twice per day so the daily files have small gaps primarily close to the equator where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.\r\n\r\nDataset coverage starts on 24th February 2000 and ends on 31st December 2018. There are minor interruptions (1-2 days) during satellite/instrument maintenance periods.\r\n\r\nThe dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using a generalised split window retrieval algorithm and data were processed in the UoL processing chain.\r\n\r\nThe dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards." } }, { "ob_id": 1223, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 44644, "uuid": "a92c495827ef4c81a901f878adb6ef70", "short_code": "ob", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily land surface temperature from MODIS (Moderate resolution Infra-red Spectroradiometer) on Aqua, level 3 collated (L3C) global product (2002-2021), version 4.00", "abstract": "This dataset contains daily-averaged land surface temperatures (LSTs) and their uncertainty estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS) on Earth Observing System – Aqua (Aqua). Satellite land surface temperatures are skin temperatures which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.\r\n\r\nDaytime and night-time temperatures are provided in separate files corresponding to the daytime and night-time Aqua equator crossing times which are 13:30 and 01:30 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.\r\n\r\nThe dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. MODIS achieves full Earth coverage nearly twice per day so the daily files have small gaps primarily close to the equator where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.\r\n\r\nDataset coverage starts on 4th July 2002 and ends on 31st December 2021. There are minor interruptions (1-2 days) during satellite/instrument maintenance periods.\r\n\r\nIn Version 4.00 the time series has been extended to 2021. The emissivities used in the retrieval come from the Combined ASTER and MODIS Emissivity over Land (CAMEL) Version 2 database; in Version 4.00 a climatology is used since there are temporal instabilities in the CAMEL time series.\r\n\r\nThe dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using a generalised split window retrieval algorithm and data were processed in the UoL processing chain.\r\nThe dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards." }, "objectObservation": { "ob_id": 33363, "uuid": "6babb8d9a8d247bcb3da6aed42f4b59a", "short_code": "ob", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Land surface temperature from MODIS (Moderate resolution Infra-red Spectroradiometer) on Aqua, level 3 collated (L3C) global product (2002-2018), version 3.00", "abstract": "This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS) on Earth Observing System – Aqua (Aqua). Satellite land surface temperatures are skin temperatures which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.\r\n\r\nDaytime and night-time temperatures are provided in separate files corresponding to the daytime and night-time Aqua equator crossing times which are 13:30 and 01:30 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.\r\n\r\nThe dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. MODIS achieves full Earth coverage nearly twice per day so the daily files have small gaps primarily close to the equator where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.\r\n\r\nDataset coverage starts on 4th July 2002 and ends on 31st December 2018. There are minor interruptions (1-2 days) during satellite/instrument maintenance periods.\r\n\r\nThe dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using a generalised split window retrieval algorithm and data were processed in the UoL processing chain.\r\n\r\nThe dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards." } }, { "ob_id": 1224, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 44644, "uuid": "a92c495827ef4c81a901f878adb6ef70", "short_code": "ob", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily land surface temperature from MODIS (Moderate resolution Infra-red Spectroradiometer) on Aqua, level 3 collated (L3C) global product (2002-2021), version 4.00", "abstract": "This dataset contains daily-averaged land surface temperatures (LSTs) and their uncertainty estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS) on Earth Observing System – Aqua (Aqua). Satellite land surface temperatures are skin temperatures which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.\r\n\r\nDaytime and night-time temperatures are provided in separate files corresponding to the daytime and night-time Aqua equator crossing times which are 13:30 and 01:30 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.\r\n\r\nThe dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. MODIS achieves full Earth coverage nearly twice per day so the daily files have small gaps primarily close to the equator where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.\r\n\r\nDataset coverage starts on 4th July 2002 and ends on 31st December 2021. There are minor interruptions (1-2 days) during satellite/instrument maintenance periods.\r\n\r\nIn Version 4.00 the time series has been extended to 2021. The emissivities used in the retrieval come from the Combined ASTER and MODIS Emissivity over Land (CAMEL) Version 2 database; in Version 4.00 a climatology is used since there are temporal instabilities in the CAMEL time series.\r\n\r\nThe dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using a generalised split window retrieval algorithm and data were processed in the UoL processing chain.\r\nThe dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards." }, "objectObservation": { "ob_id": 33363, "uuid": "6babb8d9a8d247bcb3da6aed42f4b59a", "short_code": "ob", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Land surface temperature from MODIS (Moderate resolution Infra-red Spectroradiometer) on Aqua, level 3 collated (L3C) global product (2002-2018), version 3.00", "abstract": "This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS) on Earth Observing System – Aqua (Aqua). Satellite land surface temperatures are skin temperatures which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.\r\n\r\nDaytime and night-time temperatures are provided in separate files corresponding to the daytime and night-time Aqua equator crossing times which are 13:30 and 01:30 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.\r\n\r\nThe dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. MODIS achieves full Earth coverage nearly twice per day so the daily files have small gaps primarily close to the equator where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.\r\n\r\nDataset coverage starts on 4th July 2002 and ends on 31st December 2018. There are minor interruptions (1-2 days) during satellite/instrument maintenance periods.\r\n\r\nThe dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using a generalised split window retrieval algorithm and data were processed in the UoL processing chain.\r\n\r\nThe dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards." } }, { "ob_id": 1225, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 44673, "uuid": "4a40c6fe12cc4c0786608065da06d287", "short_code": "ob", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily land surface temperature from SLSTR (Sea and Land Surface Temperature Radiometer) on Sentinel 3A, level 3 collated (L3C) global product (2016-2023), version 4.00", "abstract": "This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from the Sea and Land Surface Temperature Radiometer (SLSTR) on Sentinel 3A. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.\r\n\r\nDaytime and night-time temperatures are provided in separate files corresponding to the morning and evening Sentinel-3A equator crossing times which are 10:00 and 22:00 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.\r\n\r\nThe dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. SLSTRA achieves full Earth coverage in 1 day so the daily files have gaps where the surface is not covered by the satellite swath during day or night on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.\r\n\r\nDataset coverage starts on 1st May 2016 and ends on 31st December 2023. There are minor interruptions (1-10 days) during satellite/instrument maintenance periods or instrument anomalies.\r\n\r\nIn Version 4.00 the temporal coverage is extended to 31st December 2023. The emissivities used in the retrieval come from the Combined ASTER and MODIS Emissivity over Land (CAMEL) Version 2 database; in Version 4.00 a climatology is used since there are temporal instabilities in the CAMEL time series.\r\n\r\nThe dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.\r\n\r\nThe dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.\r\n\r\nAn extended version of this dataset is also provided through the EOCIS project." }, "objectObservation": { "ob_id": 33366, "uuid": "330b7c922a37420fabb3425671d7d7c6", "short_code": "ob", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Land surface temperature from SLSTR (Sea and Land Surface Temperature Radiometer) on Sentinel 3A, level 3 collated (L3C) global product (2016-2020), version 3.00", "abstract": "This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from the Sea and Land Surface Temperature Radiometer (SLSTR) on Sentinel 3A. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.\r\n\r\nDaytime and night-time temperatures are provided in separate files corresponding to the morning and evening Sentinel-3A equator crossing times which are 10:00 and 22:00 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.\r\n\r\nThe dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. SLSTRA achieves full Earth coverage in 1 day so the daily files have gaps where the surface is not covered by the satellite swath during day or night on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.\r\n\r\nDataset coverage starts on 1st May 2016 and ends on 31st December 2020. There are minor interruptions (1-10 days) during satellite/instrument maintenance periods or instrument anomalies.\r\n\r\nThe dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.\r\n\r\nThe dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards." } }, { "ob_id": 1226, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 44674, "uuid": "49b4836314db4fec8d05d14b85a8614d", "short_code": "ob", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily land Surface Temperature from SLSTR (Sea and Land Surface Temperature Radiometer) on Sentinel 3B, level 3 collated (L3C) global product (2018-2023), version 4.00", "abstract": "This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from the Sea and Land Surface Temperature Radiometer (SLSTR) on Sentinel 3B. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.\r\n\r\nDaytime and night-time temperatures are provided in separate files corresponding to the morning and evening Sentinel 3B equator crossing times which are 10:00 and 22:00 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.\r\n\r\nThe dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. SLSTRB achieves full Earth coverage in 1 day so the daily files have gaps where the surface is not covered by the satellite swath during day or night on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.\r\n\r\nDataset coverage starts on 17th November 2018 and ends on 31st December 2023. There are minor interruptions (1-10 days) during satellite/instrument maintenance periods or instrument anomalies.\r\n\r\nIn Version 4.00 the temporal coverage is extended to 31st December 2023. The emissivities used in the retrieval come from the Combined ASTER and MODIS Emissivity over Land (CAMEL) Version 2 database; in Version 4.00 a climatology is used since there are temporal instabilities in the CAMEL time series.\r\n\r\nThe dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.\r\n\r\nThe dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.\r\n\r\nAn extended version of this dataset is also provided through the EOCIS project." }, "objectObservation": { "ob_id": 33367, "uuid": "5f66a881adf846bfaad58b0e6068f0ea", "short_code": "ob", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Land Surface Temperature from SLSTR (Sea and Land Surface Temperature Radiometer) on Sentinel 3B, level 3 collated (L3C) global product (2018-2020), version 3.00", "abstract": "This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from the Sea and Land Surface Temperature Radiometer (SLSTR) on Sentinel 3B. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.\r\n\r\nDaytime and night-time temperatures are provided in separate files corresponding to the morning and evening Sentinel 3B equator crossing times which are 10:00 and 22:00 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.\r\n\r\nThe dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. SLSTRB achieves full Earth coverage in 1 day so the daily files have gaps where the surface is not covered by the satellite swath during day or night on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.\r\n\r\nDataset coverage starts on 17th November 2018 and ends on 31st December 2020. There are minor interruptions (1-10 days) during satellite/instrument maintenance periods or instrument anomalies.\r\n\r\nThe dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.\r\n\r\nThe dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards." } }, { "ob_id": 1227, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 44273, "uuid": "5e58f03c4d664b2b9e4e16f2d12e0c4f", "short_code": "ob", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Northern hemisphere sea ice thickness from Envisat on the satellite swath (L2P), v4.0", "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. 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 for the winter months of October to April annually on the satellite measurement grid (Level 2P) at the full sensor resolution for the period October 2002 to March 2012." }, "objectObservation": { "ob_id": 41406, "uuid": "92eb2ba942074bec804af6a8b5436bee", "short_code": "ob", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Northern hemisphere sea ice thickness from Envisat on the satellite swath (L2P), 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. 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 for the winter months of October to April annually on the satellite measurement grid (Level 2P) at the full sensor resolution for the period October 2002 to March 2012." } }, { "ob_id": 1228, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 44274, "uuid": "1ba530ada3bc4d61a2141e6fa68315f1", "short_code": "ob", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Southern hemisphere sea ice thickness from Envisat on the satellite swath (L2P), v4.0", "abstract": "This dataset provides a Climate Data Record of Sea Ice Thickness for the southern hemisphere polar region, derived from the RA-2 (Radar Altimeter -2) instrument on the Envisat satellite. 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 on the satellite measurement grid (Level 2P) at the full sensor resolution for the period October 2002 to March 2012. Note, the southern hemisphere sea ice thickness dataset is an experimental climate data record, as the algorithm does not properly considers the impact of the complex snow morphology in the freeboard retrieval. Sea ice thickness is provided for all months but needs to be considered biased high in areas with high snow depth and during the southern summer months. Please consult the Product User Guide (PUG) for more information." }, "objectObservation": { "ob_id": 41408, "uuid": "af96a1ec493f49caa39dc912d15f2b17", "short_code": "ob", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Southern hemisphere sea ice thickness from Envisat on the satellite swath (L2P), v3.0", "abstract": "This dataset provides a Climate Data Record of Sea Ice Thickness for the southern hemisphere polar region, derived from the RA-2 (Radar Altimeter -2) instrument on the Envisat satellite. 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 on the satellite measurement grid (Level 2P) at the full sensor resolution for the period October 2002 to March 2012. Note, the southern hemisphere sea ice thickness dataset is an experimental climate data record, as the algorithm does not properly considers the impact of the complex snow morphology in the freeboard retrieval. Sea ice thickness is provided for all months but needs to be considered biased high in areas with high snow depth and during the southern summer months. Please consult the Product User Guide (PUG) for more information." } }, { "ob_id": 1229, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 44275, "uuid": "5487186f657644f798a1e6828d8bed3c", "short_code": "ob", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Northern hemisphere sea ice thickness from CryoSat-2 on the satellite swath (L2P), v4.0", "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. 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 for the months October to April annually on the satellite measurement grid (Level 2P) at the full sensor resolution for the period October 2010 to April 2024." }, "objectObservation": { "ob_id": 41405, "uuid": "c6504378f78c4ecd9f839b0434023eff", "short_code": "ob", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Northern hemisphere sea ice thickness from CryoSat-2 on the satellite swath (L2P), v3.0", "abstract": "This dataset provides a Climate Data Record of Sea Ice Thickness for the NH polar region, derived from the SIRAL (SAR Interferometer Radar ALtimeter) instrument on the CryoSat-2 satellite. 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 for the months October to April annually on the satellite measurement grid (Level 2P) at the full sensor resolution for the period November 2010 to April 2020." } }, { "ob_id": 1230, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 44276, "uuid": "edfda165965749cbb8ba7c2e0d30a6b4", "short_code": "ob", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Southern hemisphere sea ice thickness from CryoSat-2 on the satellite swath (L2P), v4.0", "abstract": "This dataset provides a Climate Data Record of Sea Ice Thickness for the southern hemisphere polar region, derived from the SIRAL (SAR Interferometer Radar Altimeter) instrument on the CryoSat-2 satellite. 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 on the satellite measurement grid (Level 2P) at the full sensor resolution for the period November 2010 to April 2024. Note, the southern hemisphere sea ice thickness dataset is an experimental climate data record, as the algorithm does not properly consider the impact of the complex snow morphology in the freeboard retrieval. Sea ice thickness is provided for all months but needs to be considered biased high in areas with high snow depth and during the southern summer months. Please consult the Product User Guide (PUG) for more information." }, "objectObservation": { "ob_id": 41407, "uuid": "861ad3c7f3a34ebd8be6f618a92bd8e3", "short_code": "ob", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Southern hemisphere sea ice thickness from CryoSat-2 on the satellite swath (L2P), 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. 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 on the satellite measurement grid (Level 2P) at the full sensor resolution for the period November 2010 to April 2020. Note, the southern hemisphere sea ice thickness dataset is an experimental climate data record, as the algorithm does not properly consider the impact of the complex snow morphology in the freeboard retrieval. Sea ice thickness is provided for all months but needs to be considered biased high in areas with high snow depth and during the southern summer months. Please consult the Product User Guide (PUG) for more information." } }, { "ob_id": 1231, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 44282, "uuid": "a0120f31bcd94f8da74ddfebe658773d", "short_code": "ob", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Northern hemisphere sea ice thickness from Envisat on a monthly grid (L3C), v4.0", "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. 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 sea ice thickness data for the winter months of October to March annually on the satellite measurement grid (Level 3C) at the full sensor resolution for the period October 2002 to March 2012." }, "objectObservation": { "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." } }, { "ob_id": 1232, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 44283, "uuid": "f4631b76dbae432ead809836805944d7", "short_code": "ob", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Southern hemisphere sea ice thickness from Envisat on a monthly grid (L3C), v4.0", "abstract": "This dataset provides a Climate Data Record of Sea Ice Thickness for the southern hemisphere polar region, derived from the RA-2 (Radar Altimeter -2) instrument on the Envisat satellite. 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 sea ice thickness data annually on the satellite measurement grid (Level 3C) at the full sensor resolution for the period October 2002 to March 2012." }, "objectObservation": { "ob_id": 41404, "uuid": "ab6a05baacce4c848d137a0bc9921e6e", "short_code": "ob", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Southern 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 southern 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 Projection for the period October 2002 to March 2012. Note, the southern hemisphere sea ice thickness dataset is an experimental climate data record, as the algorithm does not properly consider the impact of the complex snow morphology in the freeboard retrieval. Sea ice thickness is provided for all months but needs to be considered biased high in areas with high snow depth and during the southern summer months. Please consult the Product User Guide (PUG) for more information." } }, { "ob_id": 1233, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 44284, "uuid": "887ebbe3aa4345f3bbcbe2dd1834d955", "short_code": "ob", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Northern hemisphere sea ice thickness from CryoSat-2 on a monthly grid (L3C), v4.0", "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. 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 sea ice thickness data for the winter months of October to March annually on the satellite measurement grid (Level 3C) at the full sensor resolution for the period October 2010 to April 2024." }, "objectObservation": { "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." } }, { "ob_id": 1234, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 44285, "uuid": "5cd23b8adfe149e59f218e9b1c9364b3", "short_code": "ob", "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Southern hemisphere sea ice thickness from CryoSat-2 on a monthly grid (L3C), v4.0", "abstract": "This dataset provides a Climate Data Record of Sea Ice Thickness for the southern hemisphere polar region, derived from the SIRAL (SAR Interferometer Radar Altimeter) instrument on the CryoSat-2 satellite. 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 sea ice thickness data annually on the satellite measurement grid (Level 3C) at the full sensor resolution for the period November 2010 to April 2024." }, "objectObservation": { "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. Note, the southern hemisphere sea ice thickness dataset is an experimental climate data record, as the algorithm does not properly considers the impact of the complex snow morphology in the freeboard retrieval. Sea ice thickness is provided for all months but needs to be considered biased high in areas with high snow depth and during the southern summer months. Please consult the Product User Guide (PUG) for more information." } }, { "ob_id": 1235, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 45114, "uuid": "f5f541ce05c44062a39d9a0e9e9463bc", "short_code": "ob", "title": "Chapter 9 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 9.13 (v20251121)", "abstract": "Data for Figure 9.13 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 9.13 shows Arctic sea ice historical records and CMIP6 projections. \r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nFox-Kemper, B., H.T. Hewitt, C. Xiao, G. Aðalgeirsdóttir, S.S. Drijfhout, T.L. Edwards, N.R. Golledge, M. Hemer, R.E. Kopp, G. Krinner, A. Mix, D. Notz, S. Nowicki, I.S. Nurhati, L. Ruiz, J.-B. Sallée, A.B.A. Slangen, and Y. Yu, 2021: Ocean, Cryosphere and Sea Level Change. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1211–1362, doi:10.1017/9781009157896.011.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has 2 subpanels, with data provided for both panels.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n- (Left panel) Absolute anomaly of monthly-mean Arctic sea ice area during the period 1979 to 2019 relative to the average monthly-mean Arctic sea ice area during the period 1979 to 2008. \r\n- (Right panel) Sea ice concentration in the Arctic for March and September, which usually are the months of maximum and minimum sea ice area, respectively. \r\n\r\nFirst column: Satellite-retrieved mean sea ice concentration during the decade 1979–1988. Second column: Satellite-retrieved mean sea ice concentration during the decade 2010-2019. \r\nThird column: Absolute change in sea ice concentration between these two decades, with grid lines indicating non-significant differences. \r\nFourth column: Number of available CMIP6 models that simulate a mean sea ice concentration above 15 % for the decade 2045–2054. \r\n\r\nThe average observational record of sea ice area is derived from the UHH sea ice area product (Doerr et al., 2021), based on the average sea ice concentration of OSISAF/CCI (OSI-450 for 1979–2015, OSI-430b for 2016–2019) (Lavergne et al., 2019), NASA Team (version 1, 1979–2019) (Cavalieri et al., 1996) and Bootstrap (version 3, 1979–2019) (Comiso, 2017) that is also used for the figure panels showing observed sea ice concentration. \r\n\r\nFurther details on data sources and processing are available in the chapter data table (Table 9.SM.9)\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 9.13\r\n \r\n - Data file: NSIDC_polehole_big.nc\r\n - Data file: NSIDC_polehole_small.nc\r\n - Data file: SeaIceArea__NorthernHemisphere__monthly__UHH__v2019_fv0.01.nc\r\n - Data file: SeaIceArea__SouthernHemisphere__monthly__UHH__v2019_fv0.01.nc\r\n - Data file: cryo_div.txt\r\n - Data file: cryo_seq.txt\r\n\r\nExtracted from 'mapplot_data.npz':\r\n - Data file: nmodels_ssp245_nh_nhsummer.nc\r\n - Data file: nmodels_ssp245_nh_nhwinter.nc\r\n - Data file: nmodels_ssp245_sh_nhsummer.nc\r\n - Data file: nmodels_ssp245_sh_nhwinter.nc\r\n - Data file: sic_obs_nh_nhsummer.nc\r\n - Data file: sic_obs_nh_nhwinter.nc\r\n - Data file: sic_obs_sh_nhsummer.nc\r\n - Data file: sic_obs_sh_nhwinter.nc\r\n\r\nData from the datafile 'mapplot_data.npz' included in the 'Plotted Data' folder of the dedicated GitHub repository has been extracted in NetCDF format for archival by the authors. The original .npz file is not archived here but on Zenodo at the link provided in the Related Documents section of this catalogue record.\r\n\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\nNSIDC is the National Snow and Ice Data Center.\r\nUHH is the University of Hamburg (Universtität Hamburg).\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nBoth panels were plotted using standard matplotlib software - code is available via the link in the documentation.\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 9)\r\n - Link to the Supplementary Material for Chapter 9, which contains details on the input data used in Table 9.SM.9\r\n- Link to the data and code used to produce this figure and others in Chapter 9, archived on Zenodo.\r\n- Link to the output data and scripts for this figure, contained in a dedicated GitHub repository." }, "objectObservation": { "ob_id": 37726, "uuid": "6f6697fff85e42fdb87156ad34e4a24e", "short_code": "ob", "title": "Chapter 9 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 9.13 (v20220721)", "abstract": "Data for Figure 9.13 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure 9.13 shows Arctic sea ice historical records and CMIP6 projections. \r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nFox-Kemper, B., H.T. Hewitt, C. Xiao, G. Aðalgeirsdóttir, S.S. Drijfhout, T.L. Edwards, N.R. Golledge, M. Hemer, R.E. Kopp, G. Krinner, A. Mix, D. Notz, S. Nowicki, I.S. Nurhati, L. Ruiz, J.-B. Sallée, A.B.A. Slangen, and Y. Yu, 2021: Ocean, Cryosphere and Sea Level Change. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1211–1362, doi:10.1017/9781009157896.011.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has 2 subpanels, with data provided for both panels.\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n This dataset contains:\r\n- (Left panel) Absolute anomaly of monthly-mean Arctic sea ice area during the period 1979 to 2019 relative to the average monthly-mean Arctic sea ice area during the period 1979 to 2008. \r\n- (Right panel) Sea ice concentration in the Arctic for March and September, which usually are the months of maximum and minimum sea ice area, respectively. \r\n\r\nFirst column: Satellite-retrieved mean sea ice concentration during the decade 1979–1988. Second column: Satellite-retrieved mean sea ice concentration during the decade 2010-2019. \r\nThird column: Absolute change in sea ice concentration between these two decades, with grid lines indicating non-significant differences. \r\nFourth column: Number of available CMIP6 models that simulate a mean sea ice concentration above 15 % for the decade 2045–2054. \r\n\r\nThe average observational record of sea ice area is derived from the UHH sea ice area product (Doerr et al., 2021), based on the average sea ice concentration of OSISAF/CCI (OSI-450 for 1979–2015, OSI-430b for 2016–2019) (Lavergne et al., 2019), NASA Team (version 1, 1979–2019) (Cavalieri et al., 1996) and Bootstrap (version 3, 1979–2019) (Comiso, 2017) that is also used for the figure panels showing observed sea ice concentration. \r\n\r\nFurther details on data sources and processing are available in the chapter data table (Table 9.SM.9)\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Data provided in relation to Figure 9.13\r\n \r\n - Data file: NSIDC_polehole_big.nc\r\n - Data file: NSIDC_polehole_small.nc\r\n - Data file: SeaIceArea__NorthernHemisphere__monthly__UHH__v2019_fv0.01.nc\r\n - Data file: SeaIceArea__SouthernHemisphere__monthly__UHH__v2019_fv0.01.nc\r\n - Data file: cryo_div.txt\r\n - Data file: cryo_seq.txt\r\n\r\nDatafile 'mapplot_data.npz' included in the 'Plotted Data' folder of the dedicated GitHub repository is not archived here but on Zenodo at the link provided in the Related Documents section of this catalogue record.\r\n\r\nCMIP6 is the sixth phase of the Coupled Model Intercomparison Project.\r\nNSIDC is the National Snow and Ice Data Center.\r\nUHH is the University of Hamburg (Universtität Hamburg).\r\n\r\n ---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\nBoth panels were plotted using standard matplotlib software - code is available via the link in the documentation.\r\n\r\n ---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 9)\r\n - Link to the Supplementary Material for Chapter 9, which contains details on the input data used in Table 9.SM.9\r\n- Link to the data and code used to produce this figure and others in Chapter 9, archived on Zenodo.\r\n- Link to the output data and scripts for this figure, contained in a dedicated GitHub repository." } }, { "ob_id": 1236, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 44642, "uuid": "c5e585820d1240e89eea85ff2c9b4569", "short_code": "ob", "title": "ESA River Discharge Climate Change Initiative (RD_cci): Nadir radar altimeters Water Level product, v2.0", "abstract": "This dataset contains water level (WL) data from the ESA Climate Change Initiative River Discharge project (RD_cci). Water level in this context corresponds to the distance between river surface water and a reference surface (the WGS84 ellipsoid). This physical variable might also be referred to as Water Surface Elevation (WSE) in other dataset or publications.\r\n\r\nThis version of the dataset is v2.0\r\n\r\nThese river water level time series have been computed in at 54 locations (within 18 river basins). The data has been derived from nadir viewing satellite radar altimeter missions (ERS-2, Envisat, Saral, Topex-Poseidon, Jason-1, Jason-2, Jason-3, Sentinel-3A/B and Sentinel 6A). At each location, time series are provided for each available single nadir radar altimetry mission. Based on these single mission time series, merged multi-missions WL time series (with two different methodologies for some basins) have also been produced." }, "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": 1237, "relationType": "IsVariantFormOf", "subjectObservation": { "ob_id": 43087, "uuid": "a784eeb9287b43bcb63ccae59e6af82e", "short_code": "ob", "title": "EOCIS: Daily land surface temperature from SLSTR (Sea and Land Surface Temperature Radiometer) on Sentinel 3A, level 3 collated (L3C) global product, version 4.00", "abstract": "This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from the Sea and Land Surface Temperature Radiometer (SLSTR) on Sentinel 3A. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.\r\n\r\nDaytime and night-time temperatures are provided in separate files corresponding to the morning and evening Sentinel-3A equator crossing times which are 10:00 and 22:00 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.\r\n\r\nThe dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. SLSTRA achieves full Earth coverage in 1 day so the daily files have gaps where the surface is not covered by the satellite swath during day or night on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.\r\n\r\nDataset coverage starts on 1st May 2016 and continues until 31st December 2024. There are minor interruptions (1-10 days) during satellite/instrument maintenance periods or instrument anomalies.\r\n\r\nThe dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.\r\n\r\nThe dataset was produced as part of the UK Earth Observation Climate Information Service (EOCIS) and is based on development funded under ESA CCI with additional funding from NCEO. The EOCIS dataset includes and continues the CCI v4 CDR (currently under development)." }, "objectObservation": { "ob_id": 44673, "uuid": "4a40c6fe12cc4c0786608065da06d287", "short_code": "ob", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily land surface temperature from SLSTR (Sea and Land Surface Temperature Radiometer) on Sentinel 3A, level 3 collated (L3C) global product (2016-2023), version 4.00", "abstract": "This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from the Sea and Land Surface Temperature Radiometer (SLSTR) on Sentinel 3A. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.\r\n\r\nDaytime and night-time temperatures are provided in separate files corresponding to the morning and evening Sentinel-3A equator crossing times which are 10:00 and 22:00 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.\r\n\r\nThe dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. SLSTRA achieves full Earth coverage in 1 day so the daily files have gaps where the surface is not covered by the satellite swath during day or night on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.\r\n\r\nDataset coverage starts on 1st May 2016 and ends on 31st December 2023. There are minor interruptions (1-10 days) during satellite/instrument maintenance periods or instrument anomalies.\r\n\r\nIn Version 4.00 the temporal coverage is extended to 31st December 2023. The emissivities used in the retrieval come from the Combined ASTER and MODIS Emissivity over Land (CAMEL) Version 2 database; in Version 4.00 a climatology is used since there are temporal instabilities in the CAMEL time series.\r\n\r\nThe dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.\r\n\r\nThe dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.\r\n\r\nAn extended version of this dataset is also provided through the EOCIS project." } }, { "ob_id": 1239, "relationType": "IsVariantFormOf", "subjectObservation": { "ob_id": 44674, "uuid": "49b4836314db4fec8d05d14b85a8614d", "short_code": "ob", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily land Surface Temperature from SLSTR (Sea and Land Surface Temperature Radiometer) on Sentinel 3B, level 3 collated (L3C) global product (2018-2023), version 4.00", "abstract": "This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from the Sea and Land Surface Temperature Radiometer (SLSTR) on Sentinel 3B. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.\r\n\r\nDaytime and night-time temperatures are provided in separate files corresponding to the morning and evening Sentinel 3B equator crossing times which are 10:00 and 22:00 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.\r\n\r\nThe dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. SLSTRB achieves full Earth coverage in 1 day so the daily files have gaps where the surface is not covered by the satellite swath during day or night on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.\r\n\r\nDataset coverage starts on 17th November 2018 and ends on 31st December 2023. There are minor interruptions (1-10 days) during satellite/instrument maintenance periods or instrument anomalies.\r\n\r\nIn Version 4.00 the temporal coverage is extended to 31st December 2023. The emissivities used in the retrieval come from the Combined ASTER and MODIS Emissivity over Land (CAMEL) Version 2 database; in Version 4.00 a climatology is used since there are temporal instabilities in the CAMEL time series.\r\n\r\nThe dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.\r\n\r\nThe dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.\r\n\r\nAn extended version of this dataset is also provided through the EOCIS project." }, "objectObservation": { "ob_id": 43088, "uuid": "fc0bc3d5887d441296091a8025f8f45d", "short_code": "ob", "title": "EOCIS: Daily land Surface Temperature from SLSTR (Sea and Land Surface Temperature Radiometer) on Sentinel 3B, level 3 collated (L3C) global product , version 4.00", "abstract": "This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from the Sea and Land Surface Temperature Radiometer (SLSTR) on Sentinel 3B. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.\r\n\r\nDaytime and night-time temperatures are provided in separate files corresponding to the morning and evening Sentinel 3B equator crossing times which are 10:00 and 22:00 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.\r\n\r\nThe dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. SLSTRB achieves full Earth coverage in 1 day so the daily files have gaps where the surface is not covered by the satellite swath during day or night on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.\r\n\r\nDataset coverage starts on 17th November 2018 and continues until 31st December 2024. There are minor interruptions (1-10 days) during satellite/instrument maintenance periods or instrument anomalies.\r\n\r\nThe dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.\r\n\r\nThe dataset was produced as part of the UK Earth Observation Climate Information Service (EOCIS) and is based on development funded under ESA CCI with additional funding from NCEO. The EOCIS dataset includes and continues the CCI v4 CDR (currently under development)." } }, { "ob_id": 1240, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 45085, "uuid": "2600f842cfeb481bb21f032a5741c353", "short_code": "ob", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): ACTIVE product, Version 09.2", "abstract": "The Soil Moisture CCI ACTIVE dataset is one of the three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The ACTIVE product has been created by fusing scatterometer soil moisture products, derived from the active remote sensing instruments AMI-WS and ASCAT. PASSIVE and COMBINED products have also been created.\r\n\r\nThe v09.2 ACTIVE product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees and is an extension in time of the v09.1 ACTIVE product. It is provided in percent of saturation [%] and covers the period (yyyy-mm-dd) 1991-08-05 to 2024-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.\r\n\r\nThe data set should be cited using the following references:\r\n\r\n1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-2019\r\n\r\n2. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001\r\n\r\n3. Preimesberger, W., Scanlon, T., Su, C. -H., Gruber, A. and Dorigo, W., \"Homogenization of Structural Breaks in the Global ESA CCI Soil Moisture Multisatellite Climate Data Record,\" in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 4, pp. 2845-2862, April 2021, doi: 10.1109/TGRS.2020.3012896." }, "objectObservation": { "ob_id": 41610, "uuid": "5b1caf9095d7412282f5ba6b558034e3", "short_code": "ob", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): ACTIVE product, Version 09.1", "abstract": "The Soil Moisture CCI ACTIVE dataset is one of the three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The ACTIVE product has been created by fusing scatterometer soil moisture products, derived from the active remote sensing instruments AMI-WS and ASCAT. PASSIVE and COMBINED products have also been created.\r\n\r\nThe v09.1 ACTIVE product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in percent of saturation [%] and covers the period (yyyy-mm-dd) 1991-08-05 to 2023-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.\r\n\r\nThe data set should be cited using the following references:\r\n\r\n1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-2019\r\n\r\n2. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001\r\n\r\n3. Preimesberger, W., Scanlon, T., Su, C. -H., Gruber, A. and Dorigo, W., \"Homogenization of Structural Breaks in the Global ESA CCI Soil Moisture Multisatellite Climate Data Record,\" in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 4, pp. 2845-2862, April 2021, doi: 10.1109/TGRS.2020.3012896." } }, { "ob_id": 1241, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 45087, "uuid": "c93c99588d5848b1ac7833e3bc6d5c2d", "short_code": "ob", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): PASSIVE product, Version 09.2", "abstract": "The Soil Moisture CCI PASSIVE dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The PASSIVE product has been created by merging data from the SMMR, SSM/I, TMI, AMSR-E, WindSat, FY-3B, FY-3C, FY3D, AMSR2, SMOS, GPM and SMAP passive remote sensing satellite instruments. ACTIVE and COMBINED products have also been created.\r\n\r\nThe v09.2 PASSIVE product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees and is an extension in time of the v09.1 PASSIVE product. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2024-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.\r\n\r\nThe data set should be cited using the following references:\r\n\r\n1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-2019\r\n\r\n2. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001\r\n\r\n3. Preimesberger, W., Scanlon, T., Su, C. -H., Gruber, A. and Dorigo, W., \"Homogenization of Structural Breaks in the Global ESA CCI Soil Moisture Multisatellite Climate Data Record,\" in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 4, pp. 2845-2862, April 2021, doi: 10.1109/TGRS.2020.3012896." }, "objectObservation": { "ob_id": 41611, "uuid": "ca55ac11fc814b0d95e68a34a10539c1", "short_code": "ob", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): PASSIVE product, Version 09.1", "abstract": "The Soil Moisture CCI PASSIVE dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The PASSIVE product has been created by merging data from the SMMR, SSM/I, TMI, AMSR-E, WindSat, FY-3B, FY-3C, FY3D, AMSR2, SMOS, GPM and SMAP passive remote sensing satellite instruments. ACTIVE and COMBINED products have also been created.\r\n\r\nThe v09.1 PASSIVE product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2023-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.\r\n\r\nThe data set should be cited using the following references:\r\n\r\n1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-2019\r\n\r\n2. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001\r\n\r\n3. Preimesberger, W., Scanlon, T., Su, C. -H., Gruber, A. and Dorigo, W., \"Homogenization of Structural Breaks in the Global ESA CCI Soil Moisture Multisatellite Climate Data Record,\" in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 4, pp. 2845-2862, April 2021, doi: 10.1109/TGRS.2020.3012896." } }, { "ob_id": 1242, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 45086, "uuid": "d4e66299f5054129b8076fb7502949e1", "short_code": "ob", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): COMBINED product, Version 09.2", "abstract": "The Soil Moisture CCI COMBINED dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The COMBINED product has been created by directly merging Level 2 scatterometer ('active' remote sensing) and radiometer ('passive' remote sensing) soil moisture products derived from the AMI-WS, ASCAT, SMMR, SSM/I, TMI, AMSR-E, WindSat, FY-3B, FY-3C, FY3D, AMSR2, SMOS, GPM and SMAP satellite instruments. PASSIVE and ACTIVE products have also been created.\r\n\r\nThe v09.2 COMBINED product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees and is an extension in time of the v09.1 COMBINED product. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2024-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.\r\n\r\nThe data set should be cited using the following references:\r\n\r\n1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-2019\r\n\r\n2. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001\r\n\r\n3. Preimesberger, W., Scanlon, T., Su, C. -H., Gruber, A. and Dorigo, W., \"Homogenization of Structural Breaks in the Global ESA CCI Soil Moisture Multisatellite Climate Data Record,\" in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 4, pp. 2845-2862, April 2021, doi: 10.1109/TGRS.2020.3012896." }, "objectObservation": { "ob_id": 41612, "uuid": "0e346e1e1e164ac99c60098848537a29", "short_code": "ob", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): COMBINED product, Version 09.1", "abstract": "The Soil Moisture CCI COMBINED dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The COMBINED product has been created by directly merging Level 2 scatterometer ('active' remote sensing) and radiometer ('passive' remote sensing) soil moisture products derived from the AMI-WS, ASCAT, SMMR, SSM/I, TMI, AMSR-E, WindSat, FY-3B, FY-3C, FY3D, AMSR2, SMOS, GPM and SMAP satellite instruments. PASSIVE and ACTIVE products have also been created.\r\n\r\nThe v09.1 COMBINED product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2023-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.\r\n\r\nThe data set should be cited using the following references:\r\n\r\n1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-2019\r\n\r\n2. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001\r\n\r\n3. Preimesberger, W., Scanlon, T., Su, C. -H., Gruber, A. and Dorigo, W., \"Homogenization of Structural Breaks in the Global ESA CCI Soil Moisture Multisatellite Climate Data Record,\" in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 4, pp. 2845-2862, April 2021, doi: 10.1109/TGRS.2020.3012896." } }, { "ob_id": 1243, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 43388, "uuid": "375ffdb8f0a445e380b4b9548655f5f9", "short_code": "ob", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - snow on ground (SCFG) from MODIS (2000-2023), version 4.0", "abstract": "This dataset provides daily Snow Cover Fraction on Ground (SCFG) derived from Terra MODIS observations, produced within the ESA Climate Change Initiative Snow project.\r\n\r\nSCFG expresses the proportion of land area within each about 1 km x 1 km pixel that is covered by snow. In forested areas, the masking effect of the forest canopy is corrected to estimate the SCFG. The SCFG is given in percentage (%) per pixel.\r\n\r\nThe SCFG product is available at about 1 km pixel size for global land areas except the Antarctica and Greenland ice sheets and permanent snow and ice areas. The coastal zones of Greenland are included. The SCFG time series spans 24 February 2000 to 31 December 2023.\r\n\r\nThe SCFG product is based on Moderate resolution Imaging Spectroradiometer (MODIS) data on-board the Terra satellite. For the SCFG product generation from MODIS, multiple reflective and emissive spectral bands are used. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm (SCDA) (Metsämäki et al., 2015). For all remaining pixels, the snow_cci SCFG retrieval method is applied, using spectral bands centred at about 0.55 µm and 1.6 µm, and an emissive band centred at about 11 µm. The snow_cci snow cover mapping algorithm is a two-step approach that first identifies pixels which are assessed as snow free, followed by SCFG retrieval for remaining pixels. \r\nPermanent snow/ice and water bodies are masked using the Land Cover CCI 2000 dataset, supplemented by a manually mapped salt-lake mask. Per-pixel uncertainty is provided in the ancillary variable as an unbiased Root Mean Square Error (RMSE) for all observed land pixels.\r\n\r\nCompared with SCFG CRDP v3.0 (https://catalogue.ceda.ac.uk/uuid/80567d38de3f4b038ee6e6e53ed1af8a/) the SCFG CRDP v4.0 includes the following improvements: \r\n•\tmore permissive pre-classification allowing more pixels to enter the SCFG retrieval; \r\n•\tcorrection function applied to spectral reflectance for improved SCFG retrieval at low solar illumination conditions;\r\n•\tupdated spectral reflectance layers for snow free ground and snow free forest to improve SCFG retrieval;\r\n•\tupdated uncertainty estimation to account for the changes in the SCFG retrieval;\r\n•\timproved merging method for generating daily global SCFG products;\r\n•\tupdated salt lake mask;\r\n•\textended time series, to December 2023.\r\n\r\nThere are several days with no MODIS acquisitions in the years 2000, 2001, 2002, 2003, 2008, 2016 and 2022. In addition, on multiple days between 2000 and 2006 and in 2023, as well as on single days in 2012, 2015 and 2016, 2018, and 2020, the available MODIS data exhibit either limited spatial coverage, or corruption during data download. SCFG products are provided for all of these days, but they contain data gaps.\r\n\r\nThe SCFG product is aimed to support cryosphere and climate research applications, including variability and trend analyses, climate modelling and studies in hydrology, meteorology, and ecology.\r\nENVEO leads the SCFG product development and product generation from MODIS data, with contributions on the product development from Syke." }, "objectObservation": { "ob_id": 40354, "uuid": "80567d38de3f4b038ee6e6e53ed1af8a", "short_code": "ob", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - snow on ground (SCFG) from MODIS (2000-2022), version 3.0", "abstract": "This dataset contains Daily Snow Cover Fraction (snow on ground) from MODIS, produced by the Snow project of the ESA Climate Change Initiative programme.\r\n\r\nSnow cover fraction on ground (SCFG) indicates the area of snow observed from space on land surfaces, in forested areas corrected for the masking effect of the forest canopy. The SCFG is given in percentage (%) per pixel. \r\n\r\nThe global SCFG product is available at about 1 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets and permanent snow and ice areas. The coastal zones of Greenland are included. \r\n\r\nThe SCFG time series provides daily products for the period 2000 – 2022. \r\n\r\nThe SCFG product is based on Moderate resolution Imaging Spectroradiometer (MODIS) data on-board the Terra satellite. \r\n\r\nThe retrieval method of the snow_cci SCFG product from MODIS data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module developed by ENVEO (ENVironmental Earth Observation IT GmbH). For the SCFG product generation from MODIS, multiple reflective and emissive spectral bands are used. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Metsämäki et al., 2015). All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 0.55 µm and 1.6 µm, and an emissive band centred at about 11 µm. The Snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFG retrieval method is applied. \r\n\r\nThe main differences of the snow_cci snow cover mapping algorithm compared to the GlobSnow algorithm described in Metsämäki et al. (2015) are (i) improvements of the cloud screening approach applicable on a global scale, (ii) the pre-classification of snow free areas on global land areas, (iii) the usage of spatially variable background reflectance and forest reflectance maps instead of global constant values for snow free land and forest, (iv) the update of the constant value for wet snow based on analyses of spatially distributed reflectance time series of MODIS data, and (v) the update of the global forest canopy transmissivity based on forest density from Hansen et al. (2013) and forest type layers from Land Cover CCI (Defourny, 2019) to assure in forested areas consistency of the SCFG and the SCFV CRDP v3.0 from MODIS data (https://catalogue.ceda.ac.uk/uuid/e955813b0e1a4eb7af971f923010b4a3) using the same retrieval approach.\r\n\r\nPermanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the pixel spacing of the SCFG product. Water areas are masked if more than 30 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map. Salt lakes are masked based on a manual delineation from MODIS data. The product uncertainty for observed land pixels is provided as unbiased root mean square error (RMSE) per pixel in the ancillary variable.\r\n\r\nCompared to the SCFG CRDP v2.0 (https://catalogue.ceda.ac.uk/uuid/8847a05eeda646a29da58b42bdf2a87c/) the following improvements were applied for the generation of the SCFG CRDP v3.0: \r\n1) the pre-classification module to identify snow free areas has been relaxed to consider more pixels for the SCFG retrieval; \r\n2) the SCFG retrieval has been improved adapting the spectral reflectance value for wet snow;\r\n3) the uncertainty estimation of the SCFG has been updated to account for the changes in the retrieval algorithm;\r\n4) salt lakes retrieved by manual delineation from Terra MODIS data are masked in the SCFG CRDP v3.0 and a new class for salt lakes is added in the coding;\r\n5) the time series, starting in February 2000, was extended from December 2020 to December 2022;\r\n6) two additional layers are provided for each daily product: \r\n•\tthe sensor zenith angle in degree per pixel;\r\n\tthe image acquisition time per pixel referring to the scanline time of the MODIS granule used for the classification of the pixel. \r\n\r\nThe SCFG product is aimed to serve the needs for users working in the cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.\r\nENVEO is responsible for the SCFG product development and generation from MODIS data, SYKE supported the development.\r\n\r\nThere are a few days without any MODIS acquisitions in the years 2000, 2001, 2002, 2003, 2008, 2016 and 2022. On several days in the years 2000 to 2006, and on a few days in the years 2012, 2015 and 2016, the acquired MODIS data have either only limited coverage, or some of the MODIS data were corrupted during the download process. For these days, the SCFG products are available but have data gaps." } }, { "ob_id": 1244, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 43387, "uuid": "bc13bb02a958449aac139853c4638f32", "short_code": "ob", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - viewable snow (SCFV) from MODIS (2000 - 2023), version 4.0", "abstract": "This dataset provides daily Snow Cover Fraction Viewable from above (SCFV) derived from Terra MODIS observations, produced within the ESA Climate Change Initiative Snow project.\r\n\r\nSCFV expresses the proportion of land area within each about 1 km x 1 km pixel that is covered by snow. SCFV represents snow viewable from above, whether on the forest canopy or on the ground in clear-cut or non-forested areas. The SCFV is given in percentage (%) per pixel.\r\n\r\nThis SCFV product is available at about 1 km pixel size for global land areas except the Antarctica and Greenland ice sheets and permanent snow and ice areas. The coastal zones of Greenland are included. The SCFV time series spans 24 February 2000 to 31 December 2023.\r\n\r\nThis SCFV product is based on Moderate resolution Imaging Spectroradiometer (MODIS) data on-board the Terra satellite. For the SCFV product generation from MODIS, multiple reflective and emissive spectral bands are used. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm (SCDA) (Metsämäki et al., 2015). For all remaining pixels, the snow_cci SCFV retrieval method is applied, using spectral bands centred at about 0.55 µm and 1.6 µm, and an emissive band centred at about 11 µm. The snow_cci snow cover mapping algorithm is a two-step approach that first identifies pixels which are assessed as snow free, followed by SCFV retrieval for remaining pixels. \r\nPermanent snow/ice and water bodies are masked using the Land Cover CCI 2000 dataset, supplemented by a manually mapped salt-lake mask. Per-pixel uncertainty is provided in the ancillary variable as an unbiased Root Mean Square Error (RMSE) for all observed land pixels.\r\n\r\nCompared with SCFV CRDP v3.0 (https://catalogue.ceda.ac.uk/uuid/e955813b0e1a4eb7af971f923010b4a3/) the SCFV CRDP v4.0 includes the following improvements: \r\n•\tmore permissive pre-classification allowing more pixels to enter the SCFV retrieval; \r\n•\tcorrection function applied to spectral reflectance for improved SCFV retrieval at low solar illumination conditions;\r\n•\tupdated spectral reflectance layers for snow free ground and snow free forest to improve SCFV retrieval;\r\n•\tupdated uncertainty estimation to account for the changes in the SCFV retrieval;\r\n•\timproved merging method for generating daily global SCFV products;\r\n•\tupdated salt lake mask;\r\n•\textended time series, to December 2023.\r\n\r\nThere are several days with no MODIS acquisitions in the years 2000, 2001, 2002, 2003, 2008, 2016 and 2022. In addition, on multiple days between 2000 and 2006 and in 2023, as well as on single days in 2012, 2015 and 2016, 2018, and 2020, the available MODIS data exhibit either limited spatial coverage, or corruption during data download. SCFV products are provided for all of these days, but they contain data gaps.\r\n\r\nThe SCFV product is aimed to support cryosphere and climate research applications, including variability and trend analyses, climate modelling and studies in hydrology, meteorology, and ecology.\r\nENVEO leads the SCFV product development and product generation from MODIS data, with contributions on the product development from Syke." }, "objectObservation": { "ob_id": 40355, "uuid": "e955813b0e1a4eb7af971f923010b4a3", "short_code": "ob", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - viewable snow (SCFV) from MODIS (2000 - 2022), version 3.0", "abstract": "This dataset contains Daily Snow Cover Fraction of viewable snow from the MODIS satellite instruments, produced by the Snow project of the ESA Climate Change Initiative programme. \r\n\r\nSnow cover fraction viewable (SCFV) indicates the area of snow viewable from space over all land surfaces. In forested areas this refers to snow viewable on top of the forest canopy. The SCFV is given in percentage (%) per pixel. \r\n\r\nThe global SCFV product is available at about 1 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets and permanent snow and ice areas. The coastal zones of Greenland are included. \r\n\r\nThe SCFV time series provides daily products for the period 2000 – 2022. \r\n\r\nThe SCFV product is based on Moderate resolution Imaging Spectroradiometer (MODIS) data on-board the Terra satellite. \r\n\r\nThe retrieval method of the Snow_cci SCFV product from MODIS data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module developed by ENVEO (ENVironmental Earth Observation IT GmbH). For the SCFV product generation from MODIS, multiple reflective and emissive spectral bands are used. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Metsämäki et al., 2015). All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 0.55 µm and 1.6 µm, and an emissive band centred at about 11 µm. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the Snow_cci SCFV retrieval method is applied. \r\n\r\nThe main differences of the Snow_cci snow cover mapping algorithm compared to the GlobSnow algorithm described in Metsämäki et al. (2015) are (i) improvements of the cloud screening approach applicable on a global scale, (ii) the pre-classification of snow free areas on global land areas, (iii) the adaptation of the retrieval method using of a spatially variable ground reflectance instead of global constant values for snow free land, (iv) the update of the constant value for wet snow based on analyses of spatially distributed reflectance time series of MODIS data to assure in forested areas consistency of the SCFV and the SCFG CRDP v3.0 from MODIS data (https://catalogue.ceda.ac.uk/uuid/80567d38de3f4b038ee6e6e53ed1af8a) using the same retrieval approach.\r\n\r\nPermanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the pixel spacing of the SCFV product. Water areas are masked if more than 30 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map. Salt lakes are masked based on a manual delineation from MODIS data. The product uncertainty for observed land pixels is provided as unbiased root mean square error (RMSE) per pixel in the ancillary variable.\r\n\r\nCompared to the SCFV CRDP v2.0 (https://catalogue.ceda.ac.uk/uuid/ebe625b6f77945a68bda0ab7c78dd76b/) the following improvements were applied for the generation of the SCFV CRDP v3.0: \r\n1) the pre-classification module to identify snow free areas has been relaxed to consider more pixels for the SCFG retrieval; \r\n2) the SCFG retrieval has been improved adapting the spectral reflectance value for wet snow;\r\n3) the uncertainty estimation of the SCFG has been updated to account for the changes in the retrieval algorithm;\r\n4) salt lakes retrieved by manual delineation from Terra MODIS data are masked in the SCFG CRDP v3.0 and a new class for salt lakes is added in the coding;\r\n5) the time series, starting in February 2000, was extended from December 2020 to December 2022;\r\n6) two additional layers are provided for each daily product: \r\n•\tthe sensor zenith angle in degree per pixel;\r\n•\tthe image acquisition time per pixel referring to the scanline time of the MODIS granule used for the classification of the pixel.\r\n\r\nThe SCFV product is aimed to serve the needs for users working in the cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.\r\n\r\nENVEO is responsible for the SCFV product development and generation from MODIS data, SYKE supported the development.\r\n\r\nThere are a few days without any MODIS acquisitions in the years 2000, 2001, 2002, 2003, 2008, 2016 and 2022. On several days in the years 2000 to 2006, and on a few days in the years 2012, 2015 and 2016, the acquired MODIS data have either only limited coverage, or some of the MODIS data were corrupted during the download process. For these days, the SCFV products are available but have data gaps." } }, { "ob_id": 1245, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 43156, "uuid": "64c373057a5f4cb7afc24a579a1e55d9", "short_code": "ob", "title": "EuroCORDEX-UK: Regional climate projections for the UK by Administrative Regions for 1980-2080 (v20240104)", "abstract": "Regional climate model projections produced by the CoOrdinated Regional Downscaling EXperiment (CORDEX) and complementary to that produced by the UK Climate Projection 2018 (UKCP18) project. The data provides information on changes in climate for the UK until 2080, downscaled to a high resolution (12km), helping to inform adaptation to a changing climate. \r\n\r\nThe projections cover the UK for a 100 year period, 1981-2080, for a high emissions scenario, RCP8.5. Each projection provides an example of climate variability in a changing climate, which is consistent across climate variables at different times and spatial locations.\r\n\r\nThis dataset contains average values of indices for administrative regions of the UK.\r\n\r\nDataset development was funded under the UK Climate Resilience programme, which is supported by the UKRI Strategic Priorities Fund. The programme is co-delivered by the Met Office and NERC on behalf of UKRI partners AHRC, EPSRC, and ESRC." }, "objectObservation": { "ob_id": 39541, "uuid": "3da1c0a9458e48bfbc20622f4df7d15b", "short_code": "ob", "title": "EuroCORDEX-UK: Regional climate projections for the UK by Administrative Regions for 1980-2080", "abstract": "Regional climate model projections produced by the CoOrdinated Regional Downscaling EXperiment (CORDEX) and complementary to that produced by the UK Climate Projection 2018 (UKCP18) project. The data provides information on changes in climate for the UK until 2080, downscaled to a high resolution (12km), helping to inform adaptation to a changing climate. \r\n\r\nThe projections cover the UK for a 100 year period, 1981-2080, for a high emissions scenario, RCP8.5. Each projection provides an example of climate variability in a changing climate, which is consistent across climate variables at different times and spatial locations.\r\n\r\nThis dataset contains average values of indices for administrative regions of the UK.\r\n\r\nDataset development was funded under the UK Climate Resilience programme, which is supported by the UKRI Strategic Priorities Fund. The programme is co-delivered by the Met Office and NERC on behalf of UKRI partners AHRC, EPSRC, and ESRC." } }, { "ob_id": 1246, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 43152, "uuid": "4b9a4bb5d29e4380b525e5579a277d45", "short_code": "ob", "title": "EuroCORDEX-UK: Regional climate projections for the UK by Countries for 1980-2080 (v20240104)", "abstract": "Regional climate model projections produced by the CoOrdinated Regional Downscaling EXperiment (CORDEX) and complementary to that produced by the UK Climate Projection 2018 (UKCP18) project. The data provides information on changes in climate for the UK until 2080, downscaled to a high resolution (12km), helping to inform adaptation to a changing climate. \r\n\r\nThe projections cover the UK for a 100 year period, 1981-2080, for a high emissions scenario, RCP8.5. Each projection provides an example of climate variability in a changing climate, which is consistent across climate variables at different times and spatial locations.\r\n\r\nThis dataset contains average values of indices for the countries of the UK.\r\n\r\nDataset development was funded under the UK Climate Resilience programme, which is supported by the UKRI Strategic Priorities Fund. The programme is co-delivered by the Met Office and NERC on behalf of UKRI partners AHRC, EPSRC, and ESRC." }, "objectObservation": { "ob_id": 39542, "uuid": "84548d27aac94c32bf94c4d9e7fd5323", "short_code": "ob", "title": "EuroCORDEX-UK: Regional climate projections for the UK by Countries for 1980-2080", "abstract": "Regional climate model projections produced by the CoOrdinated Regional Downscaling EXperiment (CORDEX) and complementary to that produced by the UK Climate Projection 2018 (UKCP18) project. The data provides information on changes in climate for the UK until 2080, downscaled to a high resolution (12km), helping to inform adaptation to a changing climate. \r\n\r\nThe projections cover the UK for a 100 year period, 1981-2080, for a high emissions scenario, RCP8.5. Each projection provides an example of climate variability in a changing climate, which is consistent across climate variables at different times and spatial locations.\r\n\r\nThis dataset contains average values of indices for the countries of the UK.\r\n\r\nDataset development was funded under the UK Climate Resilience programme, which is supported by the UKRI Strategic Priorities Fund. The programme is co-delivered by the Met Office and NERC on behalf of UKRI partners AHRC, EPSRC, and ESRC." } }, { "ob_id": 1247, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 43158, "uuid": "28f7e3c0f738453c9b945ef1b1bd3262", "short_code": "ob", "title": "EuroCORDEX-UK: Regional climate projections for the UK by River Basins for 1980-2080 (v20240104)", "abstract": "Regional climate model projections produced by the CoOrdinated Regional Downscaling EXperiment (CORDEX) and complementary to that produced by the UK Climate Projection 2018 (UKCP18) project. The data provides information on changes in climate for the UK until 2080, downscaled to a high resolution (12 km), helping to inform adaptation to a changing climate. \r\n\r\nThe projections cover the UK for a 100 year period, 1981-2080, for a high emissions scenario, RCP8.5. Each projection provides an example of climate variability in a changing climate, which is consistent across climate variables at different times and spatial locations.\r\n\r\nThis dataset contains average values of indices for major UK river basins.\r\n\r\nDataset development was funded under the UK Climate Resilience programme, which is supported by the UKRI Strategic Priorities Fund. The programme is co-delivered by the Met Office and NERC on behalf of UKRI partners AHRC, EPSRC, and ESRC." }, "objectObservation": { "ob_id": 39543, "uuid": "072fe59012d24ccaaa5b889a8b7a42b1", "short_code": "ob", "title": "EuroCORDEX-UK: Regional climate projections for the UK by River Basins for 1980-2080", "abstract": "Regional climate model projections produced by the CoOrdinated Regional Downscaling EXperiment (CORDEX) and complementary to that produced by the UK Climate Projection 2018 (UKCP18) project. The data provides information on changes in climate for the UK until 2080, downscaled to a high resolution (12 km), helping to inform adaptation to a changing climate. \r\n\r\nThe projections cover the UK for a 100 year period, 1981-2080, for a high emissions scenario, RCP8.5. Each projection provides an example of climate variability in a changing climate, which is consistent across climate variables at different times and spatial locations.\r\n\r\nThis dataset contains average values of indices for major UK river basins.\r\n\r\nDataset development was funded under the UK Climate Resilience programme, which is supported by the UKRI Strategic Priorities Fund. The programme is co-delivered by the Met Office and NERC on behalf of UKRI partners AHRC, EPSRC, and ESRC." } }, { "ob_id": 1248, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 43154, "uuid": "9e7ff32e78194ded8ec5df683e30303a", "short_code": "ob", "title": "EuroCORDEX-UK: Regional climate projections for the UK domain at 12 km Resolution for 1980-2080 (v20240104)", "abstract": "Regional climate model projections produced by the CoOrdinated Regional Downscaling EXperiment (CORDEX) and complementary to that produced by the UK Climate Projection 2018 (UKCP18) project. The data provides information on changes in climate for the UK until 2080, downscaled to a high resolution (12 km), helping to inform adaptation to a changing climate. \r\n\r\nThe projections cover the UK for a 100 year period, 1981-2080, for a high emissions scenario, RCP8.5. Each projection provides an example of climate variability in a changing climate, which is consistent across climate variables at different times and spatial locations.\r\n\r\nThis dataset contains 12 km data for the UK on the OSGB (WGS84) grid.\r\n\r\nDataset development was funded under the UK Climate Resilience programme, which is supported by the UKRI Strategic Priorities Fund. The programme is co-delivered by the Met Office and NERC on behalf of UKRI partners AHRC, EPSRC, and ESRC." }, "objectObservation": { "ob_id": 39539, "uuid": "b109bd69e1af425aa0f661b01c40dc51", "short_code": "ob", "title": "EuroCORDEX-UK: Regional climate projections for the UK domain at 12 km Resolution for 1980-2080", "abstract": "Regional climate model projections produced by the CoOrdinated Regional Downscaling EXperiment (CORDEX) and complementary to that produced by the UK Climate Projection 2018 (UKCP18) project. The data provides information on changes in climate for the UK until 2080, downscaled to a high resolution (12 km), helping to inform adaptation to a changing climate. \r\n\r\nThe projections cover the UK for a 100 year period, 1981-2080, for a high emissions scenario, RCP8.5. Each projection provides an example of climate variability in a changing climate, which is consistent across climate variables at different times and spatial locations.\r\n\r\nThis dataset contains 12 km data for the UK on the OSGB (WGS84) grid.\r\n\r\nDataset development was funded under the UK Climate Resilience programme, which is supported by the UKRI Strategic Priorities Fund. The programme is co-delivered by the Met Office and NERC on behalf of UKRI partners AHRC, EPSRC, and ESRC." } }, { "ob_id": 1252, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 44645, "uuid": "030157c0dda24ae28fc0f56d39cf1ed1", "short_code": "ob", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily land surface temperature from ATSR-2 (Along-Track Scanning Radiometer 2), level 3 collated (L3C) global product (1995-2003), version 4.00", "abstract": "This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from the Along-Track Scanning Radiometer (ATSR-2) on European Remote-sensing Satellite 2 (ERS-2). Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.\r\n\r\nDaytime and nighttime temperatures are provided in separate files corresponding to the morning and evening ERS-2 equator crossing times which are 10:30 and 22:30 local solar time.\r\n\r\nPer pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length.\r\n\r\nAlso provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.\r\n\r\nThe dataset coverage is near global over the land surface. Small regions were not covered due to downlinking constraints (most noticeably a track extending southwards across central Asia through India – further details can be found on the ATSR project webpages at https://artefacts.ceda.ac.uk/frozen_sites/www.atsr.rl.ac.uk/documentation/docs/userguide/index.shtml).\r\n\r\nLSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. ATSR-2 achieves full Earth coverage in 3 days so the daily files have gaps where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.\r\n\r\nDataset coverage starts on 1st June 1995 and ends on 22nd June 2003. There are two gaps of several months in the dataset: no data were acquired from ATSR-2 between 23 December 1995 and 30 June 1996 due to a scan mirror anomaly; and the ERS-2 gyro failed in January 2001, data quality was less good between 17th Jan 2001 and 5th July 2001 and are not used in this dataset. There are minor interruptions (1-2 days) during satellite/instrument maintenance periods.\r\n\r\nVersion 4.00 uses data from the 4th reprocessing of the ATSR L1B archive. The emissivities used in the retrieval come from the Combined ASTER and MODIS Emissivity over Land (CAMEL) Version 2 database; in Version 4.00 a climatology is used since there are temporal instabilities in the CAMEL time series.\r\n\r\nThe dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.\r\n\r\nThe dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards." }, "objectObservation": { "ob_id": 33365, "uuid": "f1445bde2f1249c99bb5a59b71e9a9d7", "short_code": "ob", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Land surface temperature from ATSR-2 (Along-Track Scanning Radiometer 2), level 3 collated (L3C) global product (1995-2013), version 3.00", "abstract": "This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from the Along-Track Scanning Radiometer (ATSR-2) on European Remote-sensing Satellite 2 (ERS-2). Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.\r\n\r\nDaytime and nighttime temperatures are provided in separate files corresponding to the morning and evening ERS-2 equator crossing times which are 10:30 and 22:30 local solar time. \r\n\r\nPer pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length.\r\n\r\nAlso provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.\r\n\r\nThe dataset coverage is near global over the land surface. Small regions were not covered due to downlinking constraints (most noticeably a track extending southwards across central Asia through India – further details can be found on the ATSR project webpages at http://www.atsr.rl.ac.uk/dataproducts/availability/coverage/atsr-2/index.shtml.\r\n\r\nLSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. ATSR-2 achieves full Earth coverage in 3 days so the daily files have gaps where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.\r\n\r\nDataset coverage starts on 1st August 1995 and ends on 22nd June 2003. There are two gaps of several months in the dataset: no data were acquired from ATSR-2 between 23 December 1995 and 30 June 1996 due to a scan mirror anomaly; and the ERS-2 gyro failed in January 2001, data quality was less good between 17th Jan 2001 and 5th July 2001 and are not used in this dataset. There are minor interruptions (1-2 days) during satellite/instrument maintenance periods.\r\n\r\nThe dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.\r\n\r\nThe dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards." } }, { "ob_id": 1253, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 44646, "uuid": "3c05b3af81f540718faeb6fa1b2046e6", "short_code": "ob", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily land surface temperature from AATSR (Advanced Along-Track Scanning Radiometer), level 3 collated (L3C) global product (2002-2012), version 4.00", "abstract": "This dataset contains daily-averaged land surface temperatures (LSTs) and their uncertainty estimates from the Advanced Along-Track Scanning Radiometer (AATSR) on the Environmental Satellite (Envisat). Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.\r\n\r\nDaytime and night-time temperatures are provided in separate files corresponding to the morning and evening Envisat equator crossing times which are 10:00 and 22:00 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.\r\n\r\nThe dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. AATSR achieves full Earth coverage in 3 days so the daily files have gaps where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.\r\n\r\nDataset coverage starts on 20th May 2002 and ends on 8th April 2012. There is a twelve day gap in the dataset due to Envisat mission extension orbital manoeuvres from 21st October 2010 to 1st November 2010. There are minor interruptions (1-2 days) during satellite/instrument maintenance periods.\r\n\r\nVersion 4.00 uses data from the 4th reprocessing of the ATSR L1B archive. The emissivities used in the retrieval come from the Combined ASTER and MODIS Emissivity over Land (CAMEL) Version 2 database; in Version 4.00 a climatology is used since there are temporal instabilities in the CAMEL time series.\r\n\r\nThe dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.\r\n\r\nThe dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards." }, "objectObservation": { "ob_id": 33364, "uuid": "1115d8946ba74c7f8a9fc3bfee5513a0", "short_code": "ob", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Land surface temperature from AATSR (Advanced Along-Track Scanning Radiometer), level 3 collated (L3C) global product (2002-2012), version 3.00", "abstract": "This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from the Advanced Along-Track Scanning Radiometer (AATSR) on Environmental Satellite (Envisat). Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.\r\n\r\nDaytime and night-time temperatures are provided in separate files corresponding to the morning and evening Envisat equator crossing times which are 10:00 and 22:00 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.\r\n\r\nThe dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. AATSR achieves full Earth coverage in 3 days so the daily files have gaps where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.\r\n\r\nDataset coverage starts on 25th July 2002 and ends on 8th April 2012. There is a twelve day gap in the dataset due to Envisat mission extension orbital manoeuvres from 21st October 2010 to 1st November 2010. There are minor interruptions (1-2 days) during satellite/instrument maintenance periods.\r\n\r\nThe dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.\r\n\r\nThe dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards." } }, { "ob_id": 1254, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 44647, "uuid": "c836ded4f8154c3ba86ee58ec0a7a7b1", "short_code": "ob", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily land surface temperature from the Metop-A AVHRR (Advanced Very High Resolution Radiometer) instrument, level 3 collated (L3C) global product, version 2.00", "abstract": "This dataset contains daily land surface temperatures (LSTs) and their uncertainty estimates from the Advanced Very High Resolution Radiometer 3 (AVHRR-3) on the Metop-A satellite. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.\r\n\r\nDaytime and night-time temperatures are provided in separate files corresponding to the morning and evening METOP-A equator crossing times which are 9.30 and 21:30 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.\r\n\r\nThe dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. The daily files have gaps where the surface is not covered by the satellite swath during day or night on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.\r\n\r\nDataset coverage starts on 1st March 2007 and ends on 31st December 2020. There are minor interruptions during satellite/instrument maintenance periods or instrument anomalies.\r\n\r\nThe emissivities used in the retrieval come from the Combined ASTER and MODIS Emissivity over Land (CAMEL) Version 2 database; in Version 4.00 a climatology is used since there are temporal instabilities in the CAMEL time series.\r\n\r\nThe dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.\r\n\r\nThe dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards." }, "objectObservation": { "ob_id": 41207, "uuid": "b94cbe2ae4bf45cfa8dc58e98170c07c", "short_code": "ob", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Land surface temperature from the Metop-A AVHRR (Advanced Very High Resolution Radiometer) instrument, level 3 collated (L3C) global product, version 1.10", "abstract": "This dataset contains daily land surface temperatures (LSTs) and their uncertainty estimates from the Advanced Very High Resolution Radiometer 3 (AVHRR-3) on the Metop-A satellite. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.\r\n\r\nDaytime and night-time temperatures are provided in separate files corresponding to the morning and evening METOP-A equator crossing times which are 9.30 and 21:30 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.\r\n\r\nThe dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. The daily files have gaps where the surface is not covered by the satellite swath during day or night on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.\r\n\r\nDataset coverage starts on 1st March 2007 and ends on 15th November 2021. There are minor interruptions during satellite/instrument maintenance periods or instrument anomalies.\r\n\r\nThe dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.\r\n\r\nThe dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards." } }, { "ob_id": 1255, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 45177, "uuid": "4be5091f70f04e50b1c1da79d2d89a97", "short_code": "ob", "title": "ESA Sea Level Climate Change Initiative (Sea_Level_cci): New network of virtual altimetry stations for measuring sea level along the world coastlines from 2002 to 2021, v3.0", "abstract": "This dataset contains a 19.5-year-long (January 2002 to June 2021), high-resolution (20 Hz), along-track sea level dataset in most of the world coastal zones, including tropical islands. It has been developed within the sea level project of the European Space Agency (ESA) Climate Change Initiative (SL_cci). \r\n\r\nThe main objective of this dataset is to analyze the sea level trends as well as the inter-annual variability at local scale at an average of less than 2.5km from the coastline. It provides essential information in areas devoid of other sources of measurements, and it also allows filling the gaps in existing timeseries of tide gauges located nearby the stations.\r\n\r\nThis dataset of coastal sea level anomalies is based on the reprocessing of raw radar altimetry waveforms from the Jason-1, Jason-2 and Jason-3 satellite missions to derive satellite-sea surface ranges as close as possible to the coast (a process called ‘retracking’) and optimization of the geophysical corrections applied to the range measurements to produce sea level time series.\r\n\r\nThis large amount of coastal sea level estimates has been further analysed to produce the present dataset: a total of 1634 altimetry-based virtual coastal stations have been selected and sea level anomalies time series together with associated coastal sea level trends have been computed over the study time span.\r\n\r\nThe new updated version (v3.0; June 2025) of along-track coastal sea level time series and associated trends from January 2002 to June 2021 differs from the previous v2.4 product (released in November 2024) by a spatial extension. It also uses the new improved FES22 ocean tide model instead of FES14 model in the previous versions. The data editing (outlier removal) has slightly evolved, and a new variable has been added (sla_mean_10pts_filt). We strongly recommend the users to use this latest v3.0 product.\r\n\r\nFor the latest version of the documentation, see the 'Technical Coastal Sea Level' Key Documents section of the project's website (https://climate.esa.int/en/projects/sea-level/).\r\nThis dataset is v3.0 of the data and is a copy of the v3.0 data published on the SEANOE (SEA scieNtific Open data Edition) website (https://doi.org/10.17882/74354#122284).\r\n\r\nThe dataset should be cited as: Cazenave Anny, Gouzenes Yvan, Leclercq Lancelot, Birol Florence, Legér Fabien, Passaro Marcello, Calafat Francisco M, Shaw Andrew, Niño Fernando, Legeais Jean François, Oelsmann Julius, Benveniste Jérôme, Connors Sarah (2025). New network of virtual altimetry stations for measuring sea level along the world coastlines. SEANOE. https://doi.org/10.17882/74354\r\n\r\nIn addition, it would be appreciated that the following work(s) be cited too, when using this dataset in a publication :\r\n\r\n- Cazenave Anny, Gouzenes Yvan, Birol Florence, Leger Fabien, Passaro Marcello, Calafat Francisco M., Shaw Andrew, Nino Fernando, Legeais Jean François, Oelsmann Julius, Restano Marco, Benveniste Jérôme (2022). Sea level along the world’s coastlines can be measured by a network of virtual altimetry stations. Communications Earth & Environment, 3 (1). https://doi.org/10.1038/s43247-022-00448-z\r\n\r\n- Benveniste Jérôme, Birol Florence, Calafat Francisco, Cazenave Anny, Dieng Habib, Gouzenes Yvan, Legeais Jean François, Léger Fabien, Niño Fernando, Passaro Marcello, Schwatke Christian, Shaw Andrew (2020). Coastal sea level anomalies and associated trends from Jason satellite altimetry over 2002–2018. Scientific Data, 7 (1). https://doi.org/10.1038/s41597-020-00694-w" }, "objectObservation": { "ob_id": 39803, "uuid": "90049a6555d1480bb5ce9637051dede8", "short_code": "ob", "title": "ESA Sea Level Climate Change Initiative (Sea_Level_cci): New network of virtual altimetry stations for measuring sea level along the world coastlines from 2002 to 2019, v2.2", "abstract": "This dataset contains a 17-year-long (January 2002 to December 2019 ), high-resolution (20 Hz), along-track sea level dataset in coastal zones of: Northeast Atlantic, Mediterranean Sea, whole African continent, North Indian Ocean, Southeast Asia, Australia and North and South America. Up to now, satellite altimetry has provided global gridded sea level time series up to 10-15 km from the coast only, preventing the estimation of how sea level changes very close to the coast on interannual to decadal time scales. \r\n\r\nThis dataset has been derived from a new version of the ESA SL_cci+ dataset of coastal sea level anomalies which is based on the reprocessing of raw radar altimetry waveforms from the Jason-1, Jason-2 and Jason-3 satellite missions to derive satellite-sea surface ranges as close as possible to the coast (a process called ‘retracking’) and optimization of the geophysical corrections applied to the range measurements to produce sea level time series.\r\n\r\nThis large amount of coastal sea level estimates has been further analysed to produce the present dataset: a total of 756 altimetry-based virtual coastal stations have been selected and sea level anomalies time series together with associated coastal sea level trends have been computed over the study time span. \r\n\r\nThe main objective of this dataset is to analyze the sea level trends close to the coast and compare them with the sea level trends observed in the open ocean and to determine the causes of the potential differences.\r\n\r\nThe product has been developed within the sea level project of the extension phase of the European Space Agency (ESA) Climate Change Initiative (SL_cci+). See 'The Climate Change Coastal Sea Level Team (2020). Sea level anomalies and associated trends estimated from altimetry from 2002 to 2018 at selected coastal sites. Scientific Data (Nature), in press'.\r\n\r\nThis dataset is v2.2 of the data and is a copy of the v2.2 data published on the SEANOE (SEA scieNtific Open data Edition) website (https://doi.org/10.17882/74354#98856) \r\n\r\nThe dataset should be cited as: \tCazenave Anny, Gouzenes Yvan, Birol Florence, Legér Fabien, Passaro Marcello, Calafat Francisco M, Shaw Andrew, Niño Fernando, Legeais Jean François, Oelsmann Julius, Benveniste Jérôme (2022). New network of virtual altimetry stations for measuring sea level along the world coastlines. SEANOE. https://doi.org/10.17882/74354\r\n\r\nIn addition,it would be appreciated that the following work(s) be cited too, when using this dataset in a publication :\r\n\r\n - Cazenave Anny, Gouzenes Yvan, Birol Florence, Leger Fabien, Passaro Marcello, Calafat Francisco M., Shaw Andrew, Nino Fernando, Legeais Jean François, Oelsmann Julius, Restano Marco, Benveniste Jérôme (2022). Sea level along the world’s coastlines can be measured by a network of virtual altimetry stations. Communications Earth & Environment, 3 (1). https://doi.org/10.1038/s43247-022-00448-z\r\n\r\n - Benveniste Jérôme, Birol Florence, Calafat Francisco, Cazenave Anny, Dieng Habib, Gouzenes Yvan, Legeais Jean François, Léger Fabien, Niño Fernando, Passaro Marcello, Schwatke Christian, Shaw Andrew (2020). Coastal sea level anomalies and associated trends from Jason satellite altimetry over 2002–2018. Scientific Data, 7 (1). https://doi.org/10.1038/s41597-020-00694-w" } }, { "ob_id": 1256, "relationType": "IsSupplementTo", "subjectObservation": { "ob_id": 45178, "uuid": "1ee84eb83cf8406e8ec86f914aaf172d", "short_code": "ob", "title": "Crowd-Grid Gridded Climate Observations on a 1km grid over the UK (2013-2024, v1.0, prototype)", "abstract": "The Crowd-Grid dataset comprises daily maximum/minimum temperature grids spanning the period 01/01/2013 to 31/12/2024 at 1km resolution for the UK. Crowd-Grid uses crowdsourced observations from WOW (the UK Met Office Weather Observation Website) and other sources to give a more detailed view of the temperatures people experience, including in built-up areas.\r\n\r\nThis dataset differs from and complements the \"standard\" gridded dataset, HadUK-Grid, the Met Office’s official climate record. HadUK-Grid uses the Met Office's network of calibrated instruments to give the UK's official record of temperature and is typically representative of grassy fields and parks. Crowd-Grid adds crowdsourced observations to give a more detailed view of the temperatures people experience, including in built-up areas.\r\n\r\nFor further details on the dataset and its interpretation, refer to the provided README (doi:10.5281/zenodo.17787357)." }, "objectObservation": { "ob_id": 44207, "uuid": "f02cc6ddd92f45b18b9ab6ab544df7d9", "short_code": "ob", "title": "HadUK-Grid Gridded Climate Observations on a 1km grid over the UK, v1.3.1.ceda (1836-2024)", "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 2024, 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 v1.3.1.ceda HadUK-Grid datasets are as follows:\r\n \r\nChanges to the dataset\r\n* Added data for calendar year 2024\r\n* Extended the daily temperature grids back to 1931\r\n\r\nChanges to the input data\r\n* Incorporated additional daily rainfall data for 60 sites in Scotland, 1922-45\r\n* Incorporated additional monthly rainfall data for two sites - Westonbirt (1880-1951) & Ackworth School (1852-53)\r\n* Fixed a 1-day offset for sunshine duration values for six stations between 1971 and 1993\r\n* Corrected the daily rainfall data for Macclesfield, 1958-60 (the values had been stored in the wrong units)\r\n* Improved the quality control of the most recent three months of rainfall data (Oct-Dec 2024)\r\n* Removed Corpach from the wind speed grids (the station is poorly modelled - this only affects 14 months)\r\n* Reviewed the quality control flags that had been applied automatically to historical air and grass minimum temperature data. In many cases it was possible to remove the flags and this has allowed us to incorporate additional data into the grids for 1961-1997 for these variables.\r\n* Improved the business logic relating to data completeness. This affects monthly wind speed and has allowed us to re-introduce some of the data that were excluded in the previous release.\r\n\r\n* Net changes to the input station data:\r\n - Total of 131314637 observations\r\n - 126821432 (96.6%) unchanged \r\n - 105327 (0.08%) modified for this version\r\n - 4387878 (3.34%) added in this version\r\n - 44224 (0.03%) 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": 1258, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 45178, "uuid": "1ee84eb83cf8406e8ec86f914aaf172d", "short_code": "ob", "title": "Crowd-Grid Gridded Climate Observations on a 1km grid over the UK (2013-2024, v1.0, prototype)", "abstract": "The Crowd-Grid dataset comprises daily maximum/minimum temperature grids spanning the period 01/01/2013 to 31/12/2024 at 1km resolution for the UK. Crowd-Grid uses crowdsourced observations from WOW (the UK Met Office Weather Observation Website) and other sources to give a more detailed view of the temperatures people experience, including in built-up areas.\r\n\r\nThis dataset differs from and complements the \"standard\" gridded dataset, HadUK-Grid, the Met Office’s official climate record. HadUK-Grid uses the Met Office's network of calibrated instruments to give the UK's official record of temperature and is typically representative of grassy fields and parks. Crowd-Grid adds crowdsourced observations to give a more detailed view of the temperatures people experience, including in built-up areas.\r\n\r\nFor further details on the dataset and its interpretation, refer to the provided README (doi:10.5281/zenodo.17787357)." }, "objectObservation": { "ob_id": 44207, "uuid": "f02cc6ddd92f45b18b9ab6ab544df7d9", "short_code": "ob", "title": "HadUK-Grid Gridded Climate Observations on a 1km grid over the UK, v1.3.1.ceda (1836-2024)", "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 2024, 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 v1.3.1.ceda HadUK-Grid datasets are as follows:\r\n \r\nChanges to the dataset\r\n* Added data for calendar year 2024\r\n* Extended the daily temperature grids back to 1931\r\n\r\nChanges to the input data\r\n* Incorporated additional daily rainfall data for 60 sites in Scotland, 1922-45\r\n* Incorporated additional monthly rainfall data for two sites - Westonbirt (1880-1951) & Ackworth School (1852-53)\r\n* Fixed a 1-day offset for sunshine duration values for six stations between 1971 and 1993\r\n* Corrected the daily rainfall data for Macclesfield, 1958-60 (the values had been stored in the wrong units)\r\n* Improved the quality control of the most recent three months of rainfall data (Oct-Dec 2024)\r\n* Removed Corpach from the wind speed grids (the station is poorly modelled - this only affects 14 months)\r\n* Reviewed the quality control flags that had been applied automatically to historical air and grass minimum temperature data. In many cases it was possible to remove the flags and this has allowed us to incorporate additional data into the grids for 1961-1997 for these variables.\r\n* Improved the business logic relating to data completeness. This affects monthly wind speed and has allowed us to re-introduce some of the data that were excluded in the previous release.\r\n\r\n* Net changes to the input station data:\r\n - Total of 131314637 observations\r\n - 126821432 (96.6%) unchanged \r\n - 105327 (0.08%) modified for this version\r\n - 4387878 (3.34%) added in this version\r\n - 44224 (0.03%) 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": 1259, "relationType": "IsDerivedFrom", "subjectObservation": { "ob_id": 45178, "uuid": "1ee84eb83cf8406e8ec86f914aaf172d", "short_code": "ob", "title": "Crowd-Grid Gridded Climate Observations on a 1km grid over the UK (2013-2024, v1.0, prototype)", "abstract": "The Crowd-Grid dataset comprises daily maximum/minimum temperature grids spanning the period 01/01/2013 to 31/12/2024 at 1km resolution for the UK. Crowd-Grid uses crowdsourced observations from WOW (the UK Met Office Weather Observation Website) and other sources to give a more detailed view of the temperatures people experience, including in built-up areas.\r\n\r\nThis dataset differs from and complements the \"standard\" gridded dataset, HadUK-Grid, the Met Office’s official climate record. HadUK-Grid uses the Met Office's network of calibrated instruments to give the UK's official record of temperature and is typically representative of grassy fields and parks. Crowd-Grid adds crowdsourced observations to give a more detailed view of the temperatures people experience, including in built-up areas.\r\n\r\nFor further details on the dataset and its interpretation, refer to the provided README (doi:10.5281/zenodo.17787357)." }, "objectObservation": { "ob_id": 44582, "uuid": "9244f715ecfd4e74b0b6200de55e1b1a", "short_code": "ob", "title": "MIDAS Open: UK daily temperature data, v202507", "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 2024. 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 2024.\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by the Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record. Currently this represents approximately 95% of available daily temperature observations within the full MIDAS collection." } }, { "ob_id": 1260, "relationType": "IsDerivedFrom", "subjectObservation": { "ob_id": 45183, "uuid": "9f45ae9217c44dea82be3ba4ef0fa30f", "short_code": "ob", "title": "Recent Heat Packs for local areas in the UK (v1.0, prototype)", "abstract": "This dataset contains Recent Heat Packs derived from the Crowd-Grid dataset. A Recent Heat Pack is a set of .csv files and a factsheet that provide information on temperatures and climate metrics from recent years (2013-2024). Created for each UK local authority, they are designed to equip users to fit extreme events into the context of recent climate. The Recent Heat Packs may be downloaded and used to understand how climate varies locally, to support decisions on climate adaptation\r\n\r\nCrowd-Grid is a new dataset of daily maximum/minimum temperatures grids spanning the period 01/01/2013 to 31/12/2024 at 1km resolution for the UK. Crowd-Grid uses crowdsourced observations from WOW and other sources to give a more detailed view of the temperatures people experience, including in built-up areas. It differs from and complements the standard dataset, HadUK-Grid - the Met Office’s official climate record.\r\n\r\nA set of Recent Heat Packs have been developed that provide for each of the 393 UK local authorities:\r\n–\ta 2-page Recent Heat Factsheet, which complements the 9-page Climate Report in the Met Office's Local Authority Climate Service (LACS)\r\n–\tthree .csv files that provide the underlying data for the local authority and its constituent census areas: MSOAs (Middle Layer Super Output Areas) in England and Wales, Intermediate Zones in Scotland, and District Electoral Areas in Northern Ireland\r\n\r\nFor further details on the dataset and its interpretation, refer to the README (doi:10.5281/zenodo.17787357)." }, "objectObservation": { "ob_id": 45178, "uuid": "1ee84eb83cf8406e8ec86f914aaf172d", "short_code": "ob", "title": "Crowd-Grid Gridded Climate Observations on a 1km grid over the UK (2013-2024, v1.0, prototype)", "abstract": "The Crowd-Grid dataset comprises daily maximum/minimum temperature grids spanning the period 01/01/2013 to 31/12/2024 at 1km resolution for the UK. Crowd-Grid uses crowdsourced observations from WOW (the UK Met Office Weather Observation Website) and other sources to give a more detailed view of the temperatures people experience, including in built-up areas.\r\n\r\nThis dataset differs from and complements the \"standard\" gridded dataset, HadUK-Grid, the Met Office’s official climate record. HadUK-Grid uses the Met Office's network of calibrated instruments to give the UK's official record of temperature and is typically representative of grassy fields and parks. Crowd-Grid adds crowdsourced observations to give a more detailed view of the temperatures people experience, including in built-up areas.\r\n\r\nFor further details on the dataset and its interpretation, refer to the provided README (doi:10.5281/zenodo.17787357)." } }, { "ob_id": 1261, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 45184, "uuid": "2f1d9204781e471cb76c2c4fdf3a0383", "short_code": "ob", "title": "European Atlantic Wave Data under Historical and Future Climate Scenarios (EAWAVES-CLIM)", "abstract": "This dataset provides 3-hourly ocean wave data for the European Atlantic domain spanning three 30-year periods: a historical simulation (1985–2014) and future projections (2030–2059) under two Shared Socioeconomic Pathways (SSP2-4.5 and SSP5-8.5). It includes directional wave spectral data at 1,031 fixed offshore locations along the Atlantic coast of Europe, as well as gridded wave parameters covering the full North Atlantic simulation domain.\r\n\r\nThe wave data were generated using the Simulating WAves Nearshore (SWAN) model, forced with high-resolution winds obtained through dynamical downscaling of global climate models using the Weather Research and Forecasting (WRF) model, and open boundary conditions derived from WaveWatch III simulations.\r\n\r\nThe spectral component consists of NetCDF-4 files describing wave energy distribution across 25 frequency bands and 36 directions at offshore locations spaced approximately 10 km alongshore and 50 km from the coast." }, "objectObservation": { "ob_id": 44348, "uuid": "f244267a4b4f4edcb87e248a27214ca3", "short_code": "ob", "title": "European Atlantic Offshore Wave Spectral Climatology under Historical and Future Climate Scenarios", "abstract": "This dataset provides 3-hourly ocean wave spectral data at 1,031 fixed offshore locations along the Atlantic coast of Europe (including the British Isles) spanning three 30-year periods: historical (1985–2014), and future projections (2030-2059) under two Shared Socioeconomic Pathways (SSPs). It also includes wave parametric data (significant height, peak period, direction...).\r\n\r\nThe wave spectra were generated using the Simulating WAves Nearshore (SWAN) model, forced with high-resolution winds obtained through downscaling global climate models using the Weather Research and Forecasting (WRF) model, and open boundary conditions from simulations carried out with the WaveWatch III wave propagation model.\r\n\r\nEach NetCDF-4 file includes eight 3-hourly spectral records per day, capturing wave energy distribution across 25 frequency bands and 36 directions for all 1,031 locations (spaced ~10 km apart, 50 km from the coast). \r\n\r\nThis spectral climatology is suitable for climate impact assessments, wave energy studies, and coastal hazard analysis." } }, { "ob_id": 1267, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 45233, "uuid": "cd037a9ea387438fabf4d674dbe53088", "short_code": "ob", "title": "Mars Analysis Correction Data Assimilation (MACDA): Atmospheric and surface fields produced from assimilated MGS/TES, ODY/THEMIS, and MRO/MCS observations, v2.0", "abstract": "This dataset contains 268 files of basic gridded atmospheric and surface data for the planet Mars, each covering a period of 30 sols (Martian mean solar days). The fields are produced by assimilating retrieved atmospheric quantities using the Analysis Correction (AC) scheme in the UK spectral version (Oxford University and The Open University) of the Mars Planetary Climate Model (Mars-PCM), which is maintained by the Laboratoire de Météorologie Dynamique at Sorbonne Université in Paris, France. \r\n\r\nObservations are taken from the Mars Global Surveyor/Thermal Emission Spectrometer (MGS/TES), the Mars Odyssey/Thermal Emission Imaging System (ODY/THEMIS), and the Mars Reconnaissance Orbiter/Mars Climate Sounder (MRO/MCS) instruments. Assimilated MGS/TES retrievals include nadir thermal profiles below approximately 40 km altitude and column dust optical depth values. Assimilated ODY/THEMIS retrievals include column dust optical depth values. Assimilated MRO/MCS retrievals include limb thermal profiles and dust extinction profiles below approximately 80 km altitude, as well as estimates of column dust optical depth values.\r\n\r\nThe dataset covers 12 Martian years (MY), from MY 24 through MY 35 (i.e. from July 1998 through February 2021). Each file contains 30 sols, and each sol contains variables at 12 times. TES, THEMIS, and MCS retrievals are assimilated separately, with no overlap. Several 30-sol periods at the beginning of MY 24 contain no available observations; in these cases the model is run with no assimilation (free run).\r\n\r\nThe file name and the \"assimilation_run_type\" global attribute indicate which observing system is assimilated in each file: MGS/TES, ODY/THEMIS, MRO/MCS, MRO/MCS_nodust (only thermal profiles, no dust extinction profiles nor column dust optical depth values), MRO/MCS_nodustprof (only thermal profiles and column dust optical depth values, no dust extinction profiles), or free-run (i.e. no assimilated observations because retrievals are not available)." }, "objectObservation": { "ob_id": 11019, "uuid": "c69013e492b4412380630ed77bee9543", "short_code": "ob", "title": "Mars Analysis Correction Data Assimilation (MACDA): MGS/TES v1.0", "abstract": "This dataset contains basic gridded atmospheric and surface variables for the planet Mars over three martian years (a martian year is 1.88 terrestrial years), as produced by data assimilation of spacecraft observations. Each file in the dataset spans 30 martian mean solar days (sols) during the science mapping phase of the National Aeronautics and Space Administrations's (NASA) Mars Global Surveyor (MGS) spacecraft, between May 1999 and August 2004. The dataset is produced by the re-analysis of Thermal Emission Spectrometer (TES) retrievals of nadir thermal profiles and total dust opacities, using the Mars Analysis Correction Data Assimilation (MACDA) scheme in a Mars global circulation model (MGCM). The MGCM used is the UK spectral version of the model developed by the Laboratoire de Meteorologie Dynamique in Paris, France. MACDA is a collaboration between the University of Oxford and The Open University in the UK." } }, { "ob_id": 1269, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 44926, "uuid": "f4b182204c2646cc94a44145f9f1d5a8", "short_code": "ob", "title": "ARA Grob Egrett T520 Aircraft measurements for the CWVC EMERALD-1 Campaign 2001.", "abstract": "In-situ and remote measurements made on the Airborne Research Australia (ARA) Grob Egrett T520 Aircraft for the first CWVC Egrett Microphysics Experiment, with Radiation, Lidar and Dynamics (EMERALD-1) Campaign based in Adelaide, Australia, during September 2001.\r\n\r\nThe dataset contains aircraft position information plus Cloud Particle Imager and Forward Scattering Spectrometer Probe (CPI-FSSP)(University of Manchester Institute of Science and Technology -UMIST) to measure cloud microphysical properties, turbulence and temperature probes (Aberystwyth/ARA), Tropospheric Airborne Fourier Transform Spectrometer (TAFTS) (Imperial College) which is a far infra-red spectrometer, an ozone monitor and frost point hygrometer (Aberystwyth/DLR-Deutsches Zentrum für Luft- und Raumfahrt - the German Aerospace Center) and a water vapour Tunable Diode Laser analyzer (TDL)(Aberystwyth).\r\n\r\nThe EMERALD projects were airborne measurement campaigns designed to study dynamical, microphysical and infra-red radiative properties of cirrus clouds, using both in-situ and remote measurement techniques. The ARA Grob Egrett T520 aircraft which flew above the cirrus clouds looking down in conjunction with the ARA King Air aircraft below looking up. \r\n\r\nThese data are part of the NERC Clouds, Water Vapour and Climate (CWVC) programme." }, "objectObservation": { "ob_id": 2795, "uuid": "3f19f0cea1588c25c948db5effc68893", "short_code": "ob", "title": "ARA Grob G520T Egrett aircraft flight summary for the CWVC EMERALD campaign", "abstract": "The EMERALD projects were airborne measurement campaigns designed to study dynamical, microphysical and infra-red radiative properties of cirrus clouds, using both in-situ and remote measurement techniques. The dataset contains static air temperature, static air pressure, relative humidity, water vapour mixing ratio, and ozone mixing ratio. These data are part of the NERC Clouds, Water Vapour and Climate (CWVC) programme." } }, { "ob_id": 1270, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 44677, "uuid": "6ab837fc79e4487a9930a221b294df01", "short_code": "ob", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): 3-Hourly Multisensor Infra-Red (IR) Low Earth Orbit (LEO) and Geostationary Earth Orbit (GEO) land surface temperature (LST) level 3 supercollated (L3S) global product, version 3.00", "abstract": "This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from multiple Infra-Red (IR) instruments on satellites in Geostationary Earth Orbit (GEO) and Low Earth Orbiting (LEO) sun-synchronous (a.k.a. polar orbiting) satellites. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.\r\n\r\nLST fields are provided at 3 hourly intervals each day (00:00 UTC, 03:00 UTC, 06:00 UTC, 09:00 UTC, 12:00 UTC, 15:00 UTC, 18:00 UTC and 21:00 UTC). Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and the solar geometry angles.\r\n\r\nThe product is based on merging of available GEO data and infilling with available LEO data outside of the GEO discs. Inter-instrument biases are accounted for by cross-calibration with the IASI instruments on METOP and LSTs are retrieved using a Generalised Split Window algorithm from all instruments. As data towards the edge of the GEO disc is known to have greater uncertainty, any datum with a satellite zenith angle of more than 60 degrees is discarded. All LSTs included have an observation time that lies within +/- 30 minutes of the file nominal Universal Time.\r\n\r\nData from the following instruments is included in the dataset: geostationary, Imagers on Geostationary Operational Environmental Satellite (GOES) 12 and GOES 13, Advanced Baseline Imager (ABI) on GOES 16, Japanese Advanced Meteorological Imager (JAMI) on Multifunctional Transport Satellite MTSAT) 1 and MTSAT 2, Advanced Himawari Imager (AHI) on Himawari 8 and Himawari 9 ; and polar, Moderate-resolution Imaging Spectroradiometer (MODIS) on Earth Observation System (EOS) - Aqua and EOS - Terra, Along-Track Scanning Radiometer 2 (ATSR-2) on European Remote-sensing Satellite 2 (ERS-2), Advanced Along-Track Scanning Radiometer (AATSR) on Environmental Satellite (Envisat), Sea and Land Surface Temperature Radiometer (SLSTR) on Sentinel-3A and Sentinel-3B, Advanced Very High Resolution Radiometer (AVHRR) on Metop-A, and Visible Infra-red Imaging Radiometer Suite(VIIRS) on Suomi National Polar-orbiting Partnership (Suomi NPP) . However, it should be noted that which instruments contribute to a particular product file depends on depends on mission start and end dates and instrument downtimes.\r\n\r\nDataset coverage starts on 24th January 2004 and ends on 31st December 2023.\r\n\r\nLSTs are provided on a global equal angle grid at a resolution of 0.05° longitude and 0.05° latitude. The dataset coverage is nominally global over the land surface but varies depending on satellite and instrument availability and coverage. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.\r\n\r\nThis version of the dataset (Version 3.00) extends the temporal coverage to the end of 2023. An extension of the dataset to the end of 2024 is planned in the future.\r\n\r\nThe dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using a Generalised Split Window retrieval algorithm and data were processed in the UoL processing chain.\r\n\r\nThe dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards." }, "objectObservation": { "ob_id": 33369, "uuid": "6775e27575124407afeebb4bb1dfaaf5", "short_code": "ob", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Multisensor Infra-Red (IR) Low Earth Orbit (LEO) and Geostationary Earth Orbit (GEO) land surface temperature (LST) level 3 supercollated (L3S) global product (2009-2020), version 1.00", "abstract": "This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from multiple Infra-Red (IR) instruments on satellites in Geostationary Earth Orbit (GEO) and Low Earth Orbiting (LEO) sun-synchronous (a.k.a. polar orbiting) satellites. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.\r\n\r\nLST fields are provided at 3 hourly intervals each day (00:00 UTC, 03:00 UTC, 06:00 UTC, 09:00 UTC, 12:00 UTC, 15:00 UTC, 18:00 UTC and 21:00 UTC). Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and the solar geometry angles.\r\n\r\nThe product is based on merging of available GEO data and infilling with available LEO data outside of the GEO discs. Inter-instrument biases are accounted for by cross-calibration with the IASI instruments on METOP and LSTs are retrieved using a Generalised Split Window algorithm from all instruments. As data towards the edge of the GEO disc is known to have greater uncertainty, any datum with a satellite zenith angle of more than 60 degrees is discarded. All LSTs included have an observation time that lies within +/- 30 minutes of the file nominal Universal Time.\r\n\r\nData from the following instruments is included in the dataset: geostationary, Imagers on Geostationary Operational Environmental Satellite (GOES) 12 and GOES 13, Advanced Baseline Imager (ABI) on GOES 16, Spinning Enhanced Visible Infra-Red Imager (SEVIRI) on Meteosat Second Generation (MSG) 1, MSG 2, MSG 3, and MSG 4, Japanese Advanced Meteorological Imager (JAMI) on Multifunctional Transport Satellite MTSAT) 1, and MTSAT 2; and polar, Advanced Along-Track Scanning Radiometer (AATSR) on Environmental Satellite (Envisat), Moderate-resolution Imaging Spectroradiometer (MODIS) on Earth Observation System (EOS) - Aqua and EOS - Terra, Sea and Land Surface Temperature Radiometer SLSTR on Sentinel-3A and Sentinel-3B. However, it should be noted that which instruments contribute to a particular product file depends on depends on mission start and end dates and instrument downtimes.\r\n\r\nDataset coverage starts on 1st January 2009 and ends on 31st December 2020. \r\n\r\nLSTs are provided on a global equal angle grid at a resolution of 0.05° longitude and 0.05° latitude. The dataset coverage is nominally global over the land surface but varies depending on satellite and instrument availability and coverage. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.\r\n\r\nThe dataset was produced by the University of Leicester (UoL) and data were processed in the UoL processing chain. The Geostationary data were produced by the Instituto Português do Mar e da Atmosfera (IPMA) before being merged into the final dataset.\r\n\r\nThe dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards." } }, { "ob_id": 1271, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 45233, "uuid": "cd037a9ea387438fabf4d674dbe53088", "short_code": "ob", "title": "Mars Analysis Correction Data Assimilation (MACDA): Atmospheric and surface fields produced from assimilated MGS/TES, ODY/THEMIS, and MRO/MCS observations, v2.0", "abstract": "This dataset contains 268 files of basic gridded atmospheric and surface data for the planet Mars, each covering a period of 30 sols (Martian mean solar days). The fields are produced by assimilating retrieved atmospheric quantities using the Analysis Correction (AC) scheme in the UK spectral version (Oxford University and The Open University) of the Mars Planetary Climate Model (Mars-PCM), which is maintained by the Laboratoire de Météorologie Dynamique at Sorbonne Université in Paris, France. \r\n\r\nObservations are taken from the Mars Global Surveyor/Thermal Emission Spectrometer (MGS/TES), the Mars Odyssey/Thermal Emission Imaging System (ODY/THEMIS), and the Mars Reconnaissance Orbiter/Mars Climate Sounder (MRO/MCS) instruments. Assimilated MGS/TES retrievals include nadir thermal profiles below approximately 40 km altitude and column dust optical depth values. Assimilated ODY/THEMIS retrievals include column dust optical depth values. Assimilated MRO/MCS retrievals include limb thermal profiles and dust extinction profiles below approximately 80 km altitude, as well as estimates of column dust optical depth values.\r\n\r\nThe dataset covers 12 Martian years (MY), from MY 24 through MY 35 (i.e. from July 1998 through February 2021). Each file contains 30 sols, and each sol contains variables at 12 times. TES, THEMIS, and MCS retrievals are assimilated separately, with no overlap. Several 30-sol periods at the beginning of MY 24 contain no available observations; in these cases the model is run with no assimilation (free run).\r\n\r\nThe file name and the \"assimilation_run_type\" global attribute indicate which observing system is assimilated in each file: MGS/TES, ODY/THEMIS, MRO/MCS, MRO/MCS_nodust (only thermal profiles, no dust extinction profiles nor column dust optical depth values), MRO/MCS_nodustprof (only thermal profiles and column dust optical depth values, no dust extinction profiles), or free-run (i.e. no assimilated observations because retrievals are not available)." }, "objectObservation": { "ob_id": 33462, "uuid": "acdfa050673c46d49d6a35bfa482762b", "short_code": "ob", "title": "Mars Analysis Correction Data Assimilation (MACDA): MGS/TES v1.0 Reference Run Data", "abstract": "This dataset contains basic gridded atmospheric and surface variables for the planet Mars over three martian years (a martian year is 1.88 terrestrial years), produced as a reference run in association with the Mars Analysis Correction Data Assimilation (MACDA) v1.0 re-analysis. Each file in the dataset spans 30 martian mean solar days (sols) during the science mapping phase of the National Aeronautics and Space Administration's (NASA) Mars Global Surveyor (MGS) spacecraft, between May 1999 and August 2004.\r\n\r\nThis dataset is a reference run produced by re-analysis of Thermal Emission Spectrometer (TES) retrievals of only total dust opacities, using the MACDA scheme in a Mars global circulation model (MGCM). This reference dataset, therefore, should be used in association with the full re-analysis of TES retrievals of nadir thermal profiles and total dust opacities - see linked dataset.\r\n\r\nThe MGCM used is the UK spectral version of the model developed by the Laboratoire de Météorologie Dynamique in Paris, France.\r\n\r\nMACDA is a collaboration between the University of Oxford and The Open University in the UK." } }, { "ob_id": 1272, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 45381, "uuid": "31819552d6764b58871507bc20f6b198", "short_code": "ob", "title": "Daily Mean, Minimum and Maximum Central England Temperature (HadCET) series v2.1.1.0", "abstract": "The Central England Temperature (HadCET) daily series start in 1772 for mean temperature and 1878 for minimum and maximum temperature.\r\n \r\nThese historical temperature series are representative of the Midlands region in England, UK (a roughly triangular area of the United Kingdom enclosed by Bristol, Lancashire and London).\r\n \r\nPrior to 1973, the daily mean temperature series is anchored to the mean temperature series constructed by Gordon Manley, with the daily minimum and maximum temperature series adjusted to the mean temperature series to ensure values are consistent.\r\n \r\nAlthough the station selection has changed through time, the series is homogenised and adjusted to ensure consistency with Manley's selection and for periods when only a single station value was used.\r\n \r\nStations used in the construction of the CET daily series between 1772 and 1852 include: Kennington, Crane Court, Lyndon Hall, Syon House, Somerset House, Greenwich Observatory, Chiswick\r\n \r\nStations used in the construction of the CET daily series from 1853 onwards include: Radcliffe (Oxford), Cambridge (legacy), Ross-on-Wye, Rothamsted, Malvern, Stonyhurst, Ringway, Squires Gate, Pershore College\r\n \r\nThe current station selection used is Rothamsted, Stonyhurst and Pershore College.\r\n \r\nFor more information on the change in station selection, please refer to the papers supplied with the data collection.\r\n \r\nThe dataset is compiled by the Met Office Hadley Centre.\r\n\r\nLatest provisional release data are available via the Hadley Centre Observations website (https://www.metoffice.gov.uk/hadobs/hadcet/data/download.html).\r\n\r\nThe version controlled CET series is updated annually (February-March), with the previous complete year’s values refreshed to ensure that data acquisition and quality control procedures have been completed and ensure the most accurate station temperature values are used. Each version of the dataset will include data up until the end of the previous complete year and an incremental version number will be updated.\r\n\r\nThe CET datasets employ the following version control protocol: \r\n\r\nVersion Vx.y.z.a:\r\n• x – major changes – e.g. change in scientific methodology\r\n• y – minor changes – e.g. small bug fixes or updates to diagnostics pages\r\n• z – incremental changes\r\n• a – reserved for use internally\r\n\r\nThe standard annual release cycle of CET will constitute an incremental release (z). However, if more substantial\r\nchanges have been made to the codebase, scientific methodology or source data values, then this may warrant a minor (y) or major (x) version release. (Note, these are applied to a cohort of datasets together - i.e. apply to the seasonal, monthly, daily and adjustment datasets as a coordinated version release).\r\n\r\nThis new version of the datasets supersedes the previous version." }, "objectObservation": { "ob_id": 44322, "uuid": "fe998c05ca854715b48bac53dc0e9998", "short_code": "ob", "title": "Daily Mean, Minimum and Maximum Central England Temperature (HadCET) series v2.1.0.0", "abstract": "The Central England Temperature (HadCET) daily series start in 1772 for mean temperature and 1878 for minimum and maximum temperature.\r\n \r\nThese historical temperature series are representative of the Midlands region in England, UK (a roughly triangular area of the United Kingdom enclosed by Bristol, Lancashire and London).\r\n \r\nPrior to 1973, the daily mean temperature series is anchored to the mean temperature series constructed by Gordon Manley, with the daily minimum and maximum temperature series adjusted to the mean temperature series to ensure values are consistent.\r\n \r\nAlthough the station selection has changed through time, the series is homogenised and adjusted to ensure consistency with Manley's selection and for periods when only a single station value was used.\r\n \r\nStations used in the construction of the CET daily series between 1772 and 1852 include: Kennington, Crane Court, Lyndon Hall, Syon House, Somerset House, Greenwich Observatory, Chiswick\r\n \r\nStations used in the construction of the CET daily series from 1853 onwards include: Radcliffe (Oxford), Cambridge (legacy), Ross-on-Wye, Rothamsted, Malvern, Stonyhurst, Ringway, Squires Gate, Pershore College\r\n \r\nThe current station selection used is Rothamsted, Stonyhurst and Pershore College.\r\n \r\nFor more information on the change in station selection, please refer to the papers supplied with the data collection.\r\n \r\nThe dataset is compiled by the Met Office Hadley Centre.\r\n\r\nLatest provisional release data are available via the Hadley Centre Observations website (https://www.metoffice.gov.uk/hadobs/hadcet/data/download.html).\r\n\r\nThe version controlled CET series is updated annually (February-March), with the previous complete year’s values refreshed to ensure that data acquisition and quality control procedures have been completed and ensure the most accurate station temperature values are used. Each version of the dataset will include data up until the end of the previous complete year and an incremental version number will be updated.\r\n\r\nThe CET datasets employ the following version control protocol: \r\n\r\nVersion Vx.y.z.a:\r\n• x – major changes – e.g. change in scientific methodology\r\n• y – minor changes – e.g. small bug fixes or updates to diagnostics pages\r\n• z – incremental changes\r\n• a – reserved for use internally\r\n\r\nThe standard annual release cycle of CET will constitute an incremental release (z). However, if more substantial\r\nchanges have been made to the codebase, scientific methodology or source data values, then this may warrant a minor (y) or major (x) version release. (Note, these are applied to a cohort of datasets together - i.e. apply to the seasonal, monthly, daily and adjustment datasets as a coordinated version release).\r\n\r\nThis new version of the datasets supersedes the previous version." } }, { "ob_id": 1273, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 45382, "uuid": "f157c171195f41b390a9795bd7323299", "short_code": "ob", "title": "Mean, Minimum and Maximum Central England Temperature (HadCET) series post 1973 static adjustments, v2.1.1.0", "abstract": "The Central England Temperature (HadCET) daily mean series is anchored to Gordon Manley’s original temperature record prior to 1973. Between 1848 and 1878, adjustments are applied to account for periods when only a single station was in use.\r\n\r\nThese historical temperature series are representative of the Midlands region in England, UK (a roughly triangular area of the United Kingdom enclosed by Bristol, Lancashire and London).\r\n \r\nFrom 1973 onwards, multiple adjustments ensure continuity with Manley’s series, homogenise the current station selection with Manley’s original dataset, and correct for the effects of increasing urbanisation.\r\n \r\nThese static adjustments are calculated on a monthly basis and are applied uniformly to all daily values within each month from 1973 to the present. \r\n \r\nUrbanisation adjustments remain static from November 2004 onward, while adjustments between 1974 and October 2004 are graded to reflect a progressive increase in urbanisation effects over time.\r\n \r\nThis dataset contains the post-Manley extended adjustments, station homogenisation adjustments, and static urban corrections.\r\n\r\nStations used in the construction of the CET daily series between 1772 and 1852 include: Kennington, Crane Court, Lyndon Hall, Syon House, Somerset House, Greenwich Observatory, Chiswick\r\n \r\nStations used in the construction of the CET daily series from 1853 onwards include: Radcliffe (Oxford), Cambridge (legacy), Ross-on-Wye, Rothamsted, Malvern, Stonyhurst, Ringway, Squires Gate, Pershore College\r\n \r\nThe current station selection used is Rothamsted, Stonyhurst and Pershore College.\r\n\r\nThe dataset is compiled by the Met Office Hadley Centre.\r\n\r\nLatest provisional release data are available via the Hadley Centre Observations website (https://www.metoffice.gov.uk/hadobs/hadcet/data/download.html).\r\n\r\nThe version controlled CET series is updated annually (February-March), with the previous complete year’s values refreshed to ensure that data acquisition and quality control procedures have been completed and ensure the most accurate station temperature values are used. Each version of the dataset will include data up until the end of the previous complete year and an incremental version number will be updated.\r\n\r\nThe CET datasets employ the following version control protocol: \r\n\r\nVersion Vx.y.z.a:\r\n• x – major changes – e.g. change in scientific methodology\r\n• y – minor changes – e.g. small bug fixes or updates to diagnostics pages\r\n• z – incremental changes\r\n• a – reserved for use internally\r\n\r\nThe standard annual release cycle of CET will constitute an incremental release (z). However, if more substantial\r\nchanges have been made to the codebase, scientific methodology or source data values, then this may warrant a minor (y) or major (x) version release. (Note, these are applied to a cohort of datasets together - i.e. apply to the seasonal, monthly, daily and adjustment datasets as a coordinated version release).\r\n\r\nThis new version of the datasets supersedes the previous version." }, "objectObservation": { "ob_id": 44321, "uuid": "2c9baaf3b032435980cdbd1b23038aa1", "short_code": "ob", "title": "Mean, Minimum and Maximum Central England Temperature (HadCET) series post 1973 static adjustments, v2.1.0.0", "abstract": "The Central England Temperature (HadCET) daily mean series is anchored to Gordon Manley’s original temperature record prior to 1973. Between 1848 and 1878, adjustments are applied to account for periods when only a single station was in use.\r\n\r\nThese historical temperature series are representative of the Midlands region in England, UK (a roughly triangular area of the United Kingdom enclosed by Bristol, Lancashire and London).\r\n \r\nFrom 1973 onwards, multiple adjustments ensure continuity with Manley’s series, homogenise the current station selection with Manley’s original dataset, and correct for the effects of increasing urbanisation.\r\n \r\nThese static adjustments are calculated on a monthly basis and are applied uniformly to all daily values within each month from 1973 to the present. \r\n \r\nUrbanisation adjustments remain static from November 2004 onward, while adjustments between 1974 and October 2004 are graded to reflect a progressive increase in urbanisation effects over time.\r\n \r\nThis dataset contains the post-Manley extended adjustments, station homogenisation adjustments, and static urban corrections.\r\n\r\nStations used in the construction of the CET daily series between 1772 and 1852 include: Kennington, Crane Court, Lyndon Hall, Syon House, Somerset House, Greenwich Observatory, Chiswick\r\n \r\nStations used in the construction of the CET daily series from 1853 onwards include: Radcliffe (Oxford), Cambridge (legacy), Ross-on-Wye, Rothamsted, Malvern, Stonyhurst, Ringway, Squires Gate, Pershore College\r\n \r\nThe current station selection used is Rothamsted, Stonyhurst and Pershore College.\r\n\r\nThe dataset is compiled by the Met Office Hadley Centre.\r\n\r\nLatest provisional release data are available via the Hadley Centre Observations website (https://www.metoffice.gov.uk/hadobs/hadcet/data/download.html).\r\n\r\nThe version controlled CET series is updated annually (February-March), with the previous complete year’s values refreshed to ensure that data acquisition and quality control procedures have been completed and ensure the most accurate station temperature values are used. Each version of the dataset will include data up until the end of the previous complete year and an incremental version number will be updated.\r\n\r\nThe CET datasets employ the following version control protocol: \r\n\r\nVersion Vx.y.z.a:\r\n• x – major changes – e.g. change in scientific methodology\r\n• y – minor changes – e.g. small bug fixes or updates to diagnostics pages\r\n• z – incremental changes\r\n• a – reserved for use internally\r\n\r\nThe standard annual release cycle of CET will constitute an incremental release (z). However, if more substantial\r\nchanges have been made to the codebase, scientific methodology or source data values, then this may warrant a minor (y) or major (x) version release. (Note, these are applied to a cohort of datasets together - i.e. apply to the seasonal, monthly, daily and adjustment datasets as a coordinated version release).\r\n\r\nThis new version of the datasets supersedes the previous version." } }, { "ob_id": 1274, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 45383, "uuid": "0b15342a451947ff884f5b52ec926c1a", "short_code": "ob", "title": "Monthly Mean, Minimum and Maximum Central England Temperature (HadCET) series v2.1.1.0", "abstract": "The Central England Temperature (HadCET) monthly series start in 1659 for mean temperature and 1878 for minimum and maximum temperature.\r\n\r\nThese historical temperature series are representative of the Midlands region in England, UK (a roughly triangular area of the United Kingdom enclosed by Bristol, Lancashire and London).\r\n \r\nThe monthly temperature series are derived as the mean of the daily temperature series values.\r\n \r\nFor mean temperature, the monthly values from 1659 to 1771 are derived directly from Gordon Manley's monthly mean values.\r\n\r\nStations used in the construction of the CET daily series between 1772 and 1852 include: Kennington, Crane Court, Lyndon Hall, Syon House, Somerset House, Greenwich Observatory, Chiswick\r\n \r\nStations used in the construction of the CET daily series from 1853 onwards include: Radcliffe (Oxford), Cambridge (legacy), Ross-on-Wye, Rothamsted, Malvern, Stonyhurst, Ringway, Squires Gate, Pershore College\r\n \r\nThe current station selection used is Rothamsted, Stonyhurst and Pershore College.\r\n \r\nThe dataset is compiled by the Met Office Hadley Centre.\r\n\r\nLatest provisional release data are available via the Hadley Centre Observations website (https://www.metoffice.gov.uk/hadobs/hadcet/data/download.html).\r\n\r\nThe version controlled CET series is updated annually (February-March), with the previous complete year’s values refreshed to ensure that data acquisition and quality control procedures have been completed and ensure the most accurate station temperature values are used. Each version of the dataset will include data up until the end of the previous complete year and an incremental version number will be updated.\r\n\r\nThe CET datasets employ the following version control protocol: \r\n\r\nVersion Vx.y.z.a:\r\n• x – major changes – e.g. change in scientific methodology\r\n• y – minor changes – e.g. small bug fixes or updates to diagnostics pages\r\n• z – incremental changes\r\n• a – reserved for use internally\r\n\r\nThe standard annual release cycle of CET will constitute an incremental release (z). However, if more substantial\r\nchanges have been made to the codebase, scientific methodology or source data values, then this may warrant a minor (y) or major (x) version release. (Note, these are applied to a cohort of datasets together - i.e. apply to the seasonal, monthly, daily and adjustment datasets as a coordinated version release).\r\n\r\nThis new version of the datasets supersedes the previous version." }, "objectObservation": { "ob_id": 44320, "uuid": "35fb8318798e437ba5b108e5eca6e92d", "short_code": "ob", "title": "Monthly Mean, Minimum and Maximum Central England Temperature (HadCET) series v2.1.0.0", "abstract": "The Central England Temperature (HadCET) monthly series start in 1659 for mean temperature and 1878 for minimum and maximum temperature.\r\n\r\nThese historical temperature series are representative of the Midlands region in England, UK (a roughly triangular area of the United Kingdom enclosed by Bristol, Lancashire and London).\r\n \r\nThe monthly temperature series are derived as the mean of the daily temperature series values.\r\n \r\nFor mean temperature, the monthly values from 1659 to 1771 are derived directly from Gordon Manley's monthly mean values.\r\n\r\nStations used in the construction of the CET daily series between 1772 and 1852 include: Kennington, Crane Court, Lyndon Hall, Syon House, Somerset House, Greenwich Observatory, Chiswick\r\n \r\nStations used in the construction of the CET daily series from 1853 onwards include: Radcliffe (Oxford), Cambridge (legacy), Ross-on-Wye, Rothamsted, Malvern, Stonyhurst, Ringway, Squires Gate, Pershore College\r\n \r\nThe current station selection used is Rothamsted, Stonyhurst and Pershore College.\r\n \r\nThe dataset is compiled by the Met Office Hadley Centre.\r\n\r\nLatest provisional release data are available via the Hadley Centre Observations website (https://www.metoffice.gov.uk/hadobs/hadcet/data/download.html).\r\n\r\nThe version controlled CET series is updated annually (February-March), with the previous complete year’s values refreshed to ensure that data acquisition and quality control procedures have been completed and ensure the most accurate station temperature values are used. Each version of the dataset will include data up until the end of the previous complete year and an incremental version number will be updated.\r\n\r\nThe CET datasets employ the following version control protocol: \r\n\r\nVersion Vx.y.z.a:\r\n• x – major changes – e.g. change in scientific methodology\r\n• y – minor changes – e.g. small bug fixes or updates to diagnostics pages\r\n• z – incremental changes\r\n• a – reserved for use internally\r\n\r\nThe standard annual release cycle of CET will constitute an incremental release (z). However, if more substantial\r\nchanges have been made to the codebase, scientific methodology or source data values, then this may warrant a minor (y) or major (x) version release. (Note, these are applied to a cohort of datasets together - i.e. apply to the seasonal, monthly, daily and adjustment datasets as a coordinated version release).\r\n\r\nThis new version of the datasets supersedes the previous version." } }, { "ob_id": 1275, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 45384, "uuid": "865a70100ed04f6394993349daec7fc5", "short_code": "ob", "title": "Seasonal Mean, Minimum and Maximum Central England Temperature (HadCET) series v2.1.1.0", "abstract": "The Central England Temperature (HadCET) seasonal series starts in 1659 for mean temperature and 1878 for minimum and maximum temperature.\r\n\r\nThese historical temperature series are representative of the Midlands region in England, UK (a roughly triangular area of the United Kingdom enclosed by Bristol, Lancashire and London).\r\n \r\nThe seasonal temperature series are derived as the mean of the monthly temperature series values.\r\n\r\nStations used in the construction of the CET daily series between 1772 and 1852 include: Kennington, Crane Court, Lyndon Hall, Syon House, Somerset House, Greenwich Observatory, Chiswick\r\n \r\nStations used in the construction of the CET daily series from 1853 onwards include: Radcliffe (Oxford), Cambridge (legacy), Ross-on-Wye, Rothamsted, Malvern, Stonyhurst, Ringway, Squires Gate, Pershore College\r\n \r\nThe current station selection used is Rothamsted, Stonyhurst and Pershore College.\r\n \r\nThe dataset is compiled by the Met Office Hadley Centre.\r\n\r\nLatest provisional release data are available via the Hadley Centre Observations website (https://www.metoffice.gov.uk/hadobs/hadcet/data/download.html).\r\n\r\nThe version controlled CET series is updated annually (February-March), with the previous complete year’s values refreshed to ensure that data acquisition and quality control procedures have been completed and ensure the most accurate station temperature values are used. Each version of the dataset will include data up until the end of the previous complete year and an incremental version number will be updated.\r\n\r\nThe CET datasets employ the following version control protocol: \r\n\r\nVersion Vx.y.z.a:\r\n• x – major changes – e.g. change in scientific methodology\r\n• y – minor changes – e.g. small bug fixes or updates to diagnostics pages\r\n• z – incremental changes\r\n• a – reserved for use internally\r\n\r\nThe standard annual release cycle of CET will constitute an incremental release (z). However, if more substantial\r\nchanges have been made to the codebase, scientific methodology or source data values, then this may warrant a minor (y) or major (x) version release. (Note, these are applied to a cohort of datasets together - i.e. apply to the seasonal, monthly, daily and adjustment datasets as a coordinated version release).\r\n\r\nThis new version of the datasets supersedes the previous version." }, "objectObservation": { "ob_id": 44323, "uuid": "ca43505702fa4eeeba4b65f1ce2c1e6a", "short_code": "ob", "title": "Seasonal Mean, Minimum and Maximum Central England Temperature (HadCET) series v2.1.0.0", "abstract": "The Central England Temperature (HadCET) seasonal series starts in 1659 for mean temperature and 1878 for minimum and maximum temperature.\r\n\r\nThese historical temperature series are representative of the Midlands region in England, UK (a roughly triangular area of the United Kingdom enclosed by Bristol, Lancashire and London).\r\n \r\nThe seasonal temperature series are derived as the mean of the monthly temperature series values.\r\n\r\nStations used in the construction of the CET daily series between 1772 and 1852 include: Kennington, Crane Court, Lyndon Hall, Syon House, Somerset House, Greenwich Observatory, Chiswick\r\n \r\nStations used in the construction of the CET daily series from 1853 onwards include: Radcliffe (Oxford), Cambridge (legacy), Ross-on-Wye, Rothamsted, Malvern, Stonyhurst, Ringway, Squires Gate, Pershore College\r\n \r\nThe current station selection used is Rothamsted, Stonyhurst and Pershore College.\r\n \r\nThe dataset is compiled by the Met Office Hadley Centre.\r\n\r\nLatest provisional release data are available via the Hadley Centre Observations website (https://www.metoffice.gov.uk/hadobs/hadcet/data/download.html).\r\n\r\nThe version controlled CET series is updated annually (February-March), with the previous complete year’s values refreshed to ensure that data acquisition and quality control procedures have been completed and ensure the most accurate station temperature values are used. Each version of the dataset will include data up until the end of the previous complete year and an incremental version number will be updated.\r\n\r\nThe CET datasets employ the following version control protocol: \r\n\r\nVersion Vx.y.z.a:\r\n• x – major changes – e.g. change in scientific methodology\r\n• y – minor changes – e.g. small bug fixes or updates to diagnostics pages\r\n• z – incremental changes\r\n• a – reserved for use internally\r\n\r\nThe standard annual release cycle of CET will constitute an incremental release (z). However, if more substantial\r\nchanges have been made to the codebase, scientific methodology or source data values, then this may warrant a minor (y) or major (x) version release. (Note, these are applied to a cohort of datasets together - i.e. apply to the seasonal, monthly, daily and adjustment datasets as a coordinated version release).\r\n\r\nThis new version of the datasets supersedes the previous version." } } ] }