Related Observation Info List
Get a list of RelatedObservationInfo objects.
GET /api/v3/relatedobservationinfos/?format=api&offset=500
{ "count": 1153, "next": "https://api.catalogue.ceda.ac.uk/api/v3/relatedobservationinfos/?format=api&limit=100&offset=600", "previous": "https://api.catalogue.ceda.ac.uk/api/v3/relatedobservationinfos/?format=api&limit=100&offset=400", "results": [ { "ob_id": 533, "relationType": "Continues", "subjectObservation": { "ob_id": 32789, "uuid": "ab33998624364d63be7471a30cee635b", "short_code": "ob", "title": "Sentinel 1A C-band Synthetic Aperture Radar (SAR): Wave (WV) mode Ocean (OCN) Level 2 data, Instrument Processing Facility (IPF) v3", "abstract": "This dataset contains Level-2, Wave mode (WV) Ocean (OCN) C-band Synthetic Aperture Radar (SAR) data from the European Space Agency (ESA) Sentinel 1A satellite. Level-2 data consists of geolocated geophysical products derived from Level-1. \r\n\r\nFrom WV modes, the OCN product will only contain Ocean Swell Spectra (OSW) and Surface Radial Velocity (RVL). \r\n\r\nThe OSW component is a two-dimensional ocean surface swell spectrum and includes an estimate of wind speed and direction per swell spectrum. The OSW component provides continuity measurement of SAR swell spectra at C-band. OSW is estimated from Sentinel-1 SLC images by inversion of the corresponding image cross-spectra.\r\n\r\nThe OSW is generated from Stripmap and Wave modes only and is not available from the TOPSAR IW and EW modes. For Stripmap mode, there are multiple spectra derived from the Level-1 SLC image. For Wave mode, there is one spectrum per vignette.\r\n\r\nOcean wave height spectra are provided in units of m4 and given on a polar grid of wavenumber in rad/m and direction in degrees with respect to North.\r\n\r\nThe OSW product also contains one estimate of the wind speed in m/s and direction in degrees (meteorological convention) per ocean wave spectrum, as well as parameters derived from the ocean wave spectra (integrated wave parameters) and from the imagette (image statistics).\r\n\r\nThe spatial coverage of the OSW product is equal to the spatial coverage of the corresponding Level-1 WV SLC or Level-1 SM SLC product, limited to ocean areas.\r\n\r\nThe RVL surface radial velocity component is a ground range gridded difference between the measured Level-2 Doppler grid and the Level-1 calculated geometrical Doppler. The RVL component provides continuity of the ASAR Doppler grid. The RVL estimates are produced on a ground-range grid.\r\n\r\nThe Level-2 Doppler is computed on a grid similar to the OWI component grid and provides an estimate of the Doppler frequency and the Doppler spectral width. For TOPS, one grid is provided by swath (additional dimension in the NetCDF). The uncertainties of the estimates are also provided for both the Doppler and radial velocity. The Doppler frequency and the Doppler spectral width are estimated based on fitting the azimuth spectral profile of the data to the antenna model taking into account additive noise, aliasing, and sideband effects. The Doppler frequency provided in the product is the pure Doppler frequency estimated from the SLC data without correcting for geometry and mispointing errors.\r\n\r\nSentinel 1A was launched on 3rd April 2014 and provides continuous all-weather, day and night imaging radar data. These data are available via CEDA to any registered CEDA user." }, "objectObservation": { "ob_id": 27450, "uuid": "fb629f940ef84efba012e7e29c831d66", "short_code": "ob", "title": "Sentinel 1A C-band Synthetic Aperture Radar: Wave (WV) mode Ocean (OCN) Level 2 data, Instrument Processing Facility (IPF) v2", "abstract": "This dataset contains Level-2, Wave mode (WV) Ocean (OCN) C-band Synthetic Aperture Radar (SAR) data from the European Space Agency (ESA) Sentinel 1A satellite. Level-2 data consists of geolocated geophysical products derived from Level-1. \r\n\r\nFrom WV modes, the OCN product will only contain Ocean Swell Spectra (OSW) and Surface Radial Velocity (RVL). \r\n\r\nThe OSW component is a two-dimensional ocean surface swell spectrum and includes an estimate of wind speed and direction per swell spectrum. The OSW component provides continuity measurement of SAR swell spectra at C-band. OSW is estimated from Sentinel-1 SLC images by inversion of the corresponding image cross-spectra.\r\n\r\nThe OSW is generated from Stripmap and Wave modes only and is not available from the TOPSAR IW and EW modes. For Stripmap mode, there are multiple spectra derived from the Level-1 SLC image. For Wave mode, there is one spectrum per vignette.\r\n\r\nOcean wave height spectra are provided in units of m4 and given on a polar grid of wavenumber in rad/m and direction in degrees with respect to North.\r\n\r\nThe OSW product also contains one estimate of the wind speed in m/s and direction in degrees (meteorological convention) per ocean wave spectrum, as well as parameters derived from the ocean wave spectra (integrated wave parameters) and from the imagette (image statistics).\r\n\r\nThe spatial coverage of the OSW product is equal to the spatial coverage of the corresponding Level-1 WV SLC or Level-1 SM SLC product, limited to ocean areas.\r\n\r\nThe RVL surface radial velocity component is a ground range gridded difference between the measured Level-2 Doppler grid and the Level-1 calculated geometrical Doppler. The RVL component provides continuity of the ASAR Doppler grid. The RVL estimates are produced on a ground-range grid.\r\n\r\nThe Level-2 Doppler is computed on a grid similar to the OWI component grid and provides an estimate of the Doppler frequency and the Doppler spectral width. For TOPS, one grid is provided by swath (additional dimension in the NetCDF). The uncertainties of the estimates are also provided for both the Doppler and radial velocity. The Doppler frequency and the Doppler spectral width are estimated based on fitting the azimuth spectral profile of the data to the antenna model taking into account additive noise, aliasing, and sideband effects. The Doppler frequency provided in the product is the pure Doppler frequency estimated from the SLC data without correcting for geometry and mispointing errors.\r\n\r\nSentinel 1A was launched on 3rd April 2014 and provides continuous all-weather, day and night imaging radar data. These data are available via CEDA to any registered CEDA user." } }, { "ob_id": 534, "relationType": "Continues", "subjectObservation": { "ob_id": 32798, "uuid": "4ecd5242cde24b2bb9c0572218da9861", "short_code": "ob", "title": "Sentinel 1B C-band Synthetic Aperture Radar (SAR): Wave (WV) mode Ocean (OCN) Level 2 data, Instrument Processing Facility (IPF) v3", "abstract": "This dataset contains Level-2, Wave mode (WV) Ocean (OCN) C-band Synthetic Aperture Radar (SAR) data from the European Space Agency (ESA) Sentinel 1B satellite. Level-2 data consists of geolocated geophysical products derived from Level-1. \r\n\r\nFrom WV modes, the OCN product will only contain Ocean Swell Spectra (OSW) and Surface Radial Velocity (RVL). \r\n\r\nThe OSW component is a two-dimensional ocean surface swell spectrum and includes an estimate of wind speed and direction per swell spectrum. The OSW component provides continuity measurement of SAR swell spectra at C-band. OSW is estimated from Sentinel-1 SLC images by inversion of the corresponding image cross-spectra.\r\n\r\nThe OSW is generated from Stripmap and Wave modes only and is not available from the TOPSAR IW and EW modes. For Stripmap mode, there are multiple spectra derived from the Level-1 SLC image. For Wave mode, there is one spectrum per vignette.\r\n\r\nOcean wave height spectra are provided in units of m4 and given on a polar grid of wavenumber in rad/m and direction in degrees with respect to North.\r\n\r\nThe OSW product also contains one estimate of the wind speed in m/s and direction in degrees (meteorological convention) per ocean wave spectrum, as well as parameters derived from the ocean wave spectra (integrated wave parameters) and from the imagette (image statistics).\r\n\r\nThe spatial coverage of the OSW product is equal to the spatial coverage of the corresponding Level-1 WV SLC or Level-1 SM SLC product, limited to ocean areas.\r\n\r\nThe RVL surface radial velocity component is a ground range gridded difference between the measured Level-2 Doppler grid and the Level-1 calculated geometrical Doppler. The RVL component provides continuity of the ASAR Doppler grid. The RVL estimates are produced on a ground-range grid.\r\n\r\nThe Level-2 Doppler is computed on a grid similar to the OWI component grid and provides an estimate of the Doppler frequency and the Doppler spectral width. For TOPS, one grid is provided by swath (additional dimension in the NetCDF). The uncertainties of the estimates are also provided for both the Doppler and radial velocity. The Doppler frequency and the Doppler spectral width are estimated based on fitting the azimuth spectral profile of the data to the antenna model taking into account additive noise, aliasing, and sideband effects. The Doppler frequency provided in the product is the pure Doppler frequency estimated from the SLC data without correcting for geometry and mispointing errors.\r\n\r\nSentinel 1A was launched on 25th April 2016 and provides continuous all-weather, day and night imaging radar data. These data are available via CEDA to any registered CEDA user." }, "objectObservation": { "ob_id": 32795, "uuid": "e9df102eeac54d04b24686fc026c63f9", "short_code": "ob", "title": "Sentinel 1B C-band Synthetic Aperture Radar (SAR): Wave (WV) mode Ocean (OCN) Level 2 data, Instrument Processing Facility (IPF) v2", "abstract": "This dataset contains Level-2, Wave mode (WV) Ocean (OCN) C-band Synthetic Aperture Radar (SAR) data from the European Space Agency (ESA) Sentinel 1B satellite. Level-2 data consists of geolocated geophysical products derived from Level-1. \r\n\r\nFrom WV modes, the OCN product will only contain Ocean Swell Spectra (OSW) and Surface Radial Velocity (RVL). \r\n\r\nThe OSW component is a two-dimensional ocean surface swell spectrum and includes an estimate of wind speed and direction per swell spectrum. The OSW component provides continuity measurement of SAR swell spectra at C-band. OSW is estimated from Sentinel-1 SLC images by inversion of the corresponding image cross-spectra.\r\n\r\nThe OSW is generated from Stripmap and Wave modes only and is not available from the TOPSAR IW and EW modes. For Stripmap mode, there are multiple spectra derived from the Level-1 SLC image. For Wave mode, there is one spectrum per vignette.\r\n\r\nOcean wave height spectra are provided in units of m4 and given on a polar grid of wavenumber in rad/m and direction in degrees with respect to North.\r\n\r\nThe OSW product also contains one estimate of the wind speed in m/s and direction in degrees (meteorological convention) per ocean wave spectrum, as well as parameters derived from the ocean wave spectra (integrated wave parameters) and from the imagette (image statistics).\r\n\r\nThe spatial coverage of the OSW product is equal to the spatial coverage of the corresponding Level-1 WV SLC or Level-1 SM SLC product, limited to ocean areas.\r\n\r\nThe RVL surface radial velocity component is a ground range gridded difference between the measured Level-2 Doppler grid and the Level-1 calculated geometrical Doppler. The RVL component provides continuity of the ASAR Doppler grid. The RVL estimates are produced on a ground-range grid.\r\n\r\nThe Level-2 Doppler is computed on a grid similar to the OWI component grid and provides an estimate of the Doppler frequency and the Doppler spectral width. For TOPS, one grid is provided by swath (additional dimension in the NetCDF). The uncertainties of the estimates are also provided for both the Doppler and radial velocity. The Doppler frequency and the Doppler spectral width are estimated based on fitting the azimuth spectral profile of the data to the antenna model taking into account additive noise, aliasing, and sideband effects. The Doppler frequency provided in the product is the pure Doppler frequency estimated from the SLC data without correcting for geometry and mispointing errors.\r\n\r\nSentinel 1B was launched on 25th April 2016 and provides continuous all-weather, day and night imaging radar data. These data are available via CEDA to any registered CEDA user." } }, { "ob_id": 535, "relationType": "Continues", "subjectObservation": { "ob_id": 32807, "uuid": "d69ef5ff221a45f38c35cd77c0ca9352", "short_code": "ob", "title": "Sentinel 1B C-band Synthetic Aperture Radar (SAR): Interferometric Wide (IW) mode Single Look Complex (SLC) Level 1 data, Instrument Processing Facility (IPF) v3", "abstract": "This dataset contains Interferometric Wide swath (IW) Single Look Complex (SLC) C-band Synthetic Aperture Radar (SAR) data from the European Space Agency (ESA) Sentinel 1B satellite. Sentinel 1B was lanched on 25th April 2016 and provides continuous all-weather, day and night imaging radar data. The IW mode is the main operational mode. The IW mode supports single (HH or VV) and dual (HH+HV or VV+VH) polarisation. \r\n\r\nThe IW SLC product contains one image per sub-swath, per polarisation channel, for a total of three or six images. Each sub-swath image consists of a series of bursts, where each burst was processed as a separate SLC image. The individually focused complex burst images are included, in azimuth-time order, into a single sub-swath image, with black-fill demarcation in between\r\n\r\nUnlike SM and WV SLC products, which are sampled at the natural pixel spacing, the images for all bursts in all sub-swaths of an IW SLC product are re-sampled to a common pixel spacing grid in range and azimuth. The resampling to a common grid eliminates the need for further interpolation in case, in later processing stages, the bursts are merged to create a contiguous ground range, detected image.\r\n\r\n\r\nThese data are available via CEDA to any registered user." }, "objectObservation": { "ob_id": 20015, "uuid": "96de05733b5d464885da0e1495626f7f", "short_code": "ob", "title": "Sentinel 1B C-band Synthetic Aperture Radar (SAR): Interferometric Wide (IW) mode Single Look Complex (SLC) Level 1 data, Instrument Processing Facility (IPF) v2", "abstract": "This dataset contains Interferometric Wide swath (IW) Single Look Complex (SLC) C-band Synthetic Aperture Radar (SAR) data from the European Space Agency (ESA) Sentinel 1B satellite. Sentinel 1B was lanched on 25th April 2016 and provides continuous all-weather, day and night imaging radar data. The IW mode is the main operational mode. The IW mode supports single (HH or VV) and dual (HH+HV or VV+VH) polarisation. \r\n\r\nThe IW SLC product contains one image per sub-swath, per polarisation channel, for a total of three or six images. Each sub-swath image consists of a series of bursts, where each burst was processed as a separate SLC image. The individually focused complex burst images are included, in azimuth-time order, into a single sub-swath image, with black-fill demarcation in between\r\n\r\nUnlike SM and WV SLC products, which are sampled at the natural pixel spacing, the images for all bursts in all sub-swaths of an IW SLC product are re-sampled to a common pixel spacing grid in range and azimuth. The resampling to a common grid eliminates the need for further interpolation in case, in later processing stages, the bursts are merged to create a contiguous ground range, detected image.\r\n\r\n\r\nThese data are available via CEDA to any registered user." } }, { "ob_id": 536, "relationType": "Continues", "subjectObservation": { "ob_id": 32814, "uuid": "171d5d0e6ef44fc3addad6ba45c8fb17", "short_code": "ob", "title": "Sentinel 1B C-band Synthetic Aperture Radar (SAR): SM mode SLC Level 1 data, Instrument Processing Facility (IPF) v3", "abstract": "This dataset contains Stripmap Mode (SM) C-band Synthetic Aperture Radar (SAR) Single Look Complex (SLC) data from the European Space Agency (ESA) Sentinel 1B satellite. Sentinel 1B was launched on 25th April 2016 and provides continuous all-weather, day and night imaging radar data. The SM mode is used only on special request for extraordinary events such as emergency management. The SM mode supports single (HH or VV) and dual (HH+HV or VV+VH) polarisation. \r\n\r\nStripmap SLCs contain one image per polarisation band from one of six overlapping beams. Each beam covers 80.1 km, covering a combined range of 375 km. Pixel spacing is determined, in azimuth by the pulse repetition frequency (PRF), and in range by the radar range sampling frequency, providing natural pixel spacing.\r\n\r\nThese data are available via CEDA to any registered user." }, "objectObservation": { "ob_id": 32801, "uuid": "8305ad215f0f48c994bdb37df1bcf773", "short_code": "ob", "title": "Sentinel 1B C-band Synthetic Aperture Radar (SAR): SM mode SLC Level 1 data, Instrument Processing Facility (IPF) v2", "abstract": "This dataset contains Stripmap Mode (SM) C-band Synthetic Aperture Radar (SAR) Single Look Complex (SLC) data from the European Space Agency (ESA) Sentinel 1B satellite. Sentinel 1B was launched on 25th April 2016 and provides continuous all-weather, day and night imaging radar data. The SM mode is used only on special request for extraordinary events such as emergency management. The SM mode supports single (HH or VV) and dual (HH+HV or VV+VH) polarisation. \r\n\r\nStripmap SLCs contain one image per polarisation band from one of six overlapping beams. Each beam covers 80.1 km, covering a combined range of 375 km. Pixel spacing is determined, in azimuth by the pulse repetition frequency (PRF), and in range by the radar range sampling frequency, providing natural pixel spacing.\r\n\r\nThese data are available via CEDA to any registered user." } }, { "ob_id": 537, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 32817, "uuid": "4bdf41fc10af4caaa489b14745c665a6", "short_code": "ob", "title": "CRU JRA v2.2: A forcings dataset of gridded land surface blend of Climatic Research Unit (CRU) and Japanese reanalysis (JRA) data; Jan.1901 - Dec.2020.", "abstract": "The CRU JRA V2.2 dataset is a 6-hourly, land surface, gridded time series of ten meteorological variables produced by the Climatic Research Unit (CRU) at the University of East Anglia (UEA), and is intended to be used to drive models. The variables are provided on a 0.5 deg latitude x 0.5 deg longitude grid, the grid is near global but excludes Antarctica (this is same as the CRU TS grid, though the set of variables is different). The data are available at a 6 hourly time-step from January 1901 to December 2020.\r\n\r\nThe dataset is constructed by regridding data from the Japanese Reanalysis data (JRA) produced by the Japanese Meteorological Agency (JMA), adjusting where possible to align with the CRU TS 4.05 data (see the Process section and the ReadMe file for full details).\r\n\r\nThe CRU JRA data consists of the following ten meteorological variables: 2-metre temperature, 2-metre maximum and minimum temperature, total precipitation, specific humidity, downward solar radiation flux, downward long wave radiation flux, pressure and the zonal and meridional components of wind speed (see the ReadMe file for further details).\r\n\r\nThe CRU JRA dataset is intended to be a replacement of the CRU NCEP forcing dataset. The CRU JRA dataset follows the style of Nicolas Viovy's original CRU NCEP dataset rather than that which is available from UCAR. A link to the CRU NCEP documentation for comparison is provided in the documentation section. \r\n\r\nIf this dataset is used in addition to citing the dataset as per the data citation string users must also cite the following:\r\n\r\nHarris, I., Osborn, T.J., Jones, P. et al. Version 4 of the CRU TS\r\nmonthly high-resolution gridded multivariate climate dataset.\r\nSci Data 7, 109 (2020). https://doi.org/10.1038/s41597-020-0453-3\r\n\r\nHarris, I., Jones, P.D., Osborn, T.J. and Lister, D.H. (2014), Updated\r\nhigh-resolution grids of monthly climatic observations - the CRU TS3.10\r\nDataset. International Journal of Climatology 34, 623-642.\r\n\r\nKobayashi, S., et. al., The JRA-55 Reanalysis: General Specifications and\r\nBasic Characteristics. J. Met. Soc. Jap., 93(1), 5-48\r\nhttps://dx.doi.org/10.2151/jmsj.2015-001" }, "objectObservation": { "ob_id": 31963, "uuid": "10d2c73e5a7d46f4ada08b0a26302ef7", "short_code": "ob", "title": "CRU JRA v2.1: A forcings dataset of gridded land surface blend of Climatic Research Unit (CRU) and Japanese reanalysis (JRA) data; Jan.1901 - Dec.2019.", "abstract": "The CRU JRA V2.1 dataset is a 6-hourly, land surface, gridded time series of ten meteorological variables produced by the Climatic Research Unit (CRU) at the University of East Anglia (UEA), and is intended to be used to drive models. The variables are provided on a 0.5 deg latitude x 0.5 deg longitude grid, the grid is near global but excludes Antarctica (this is same as the CRU TS grid, though the set of variables is different). The data are available at a 6 hourly time-step from January 1901 to December 2019.\r\n\r\nThe dataset is constructed by regridding data from the Japanese Reanalysis data (JRA) produced by the Japanese Meteorological Agency (JMA), adjusting where possible to align with the CRU TS 4.04 data (see the Process section and the ReadMe file for full details).\r\n\r\nThe CRU JRA data consists of the following ten meteorological variables: 2-metre temperature, 2-metre maximum and minimum temperature, total precipitation, specific humidity, downward solar radiation flux, downward long wave radiation flux, pressure and the zonal and meridional components of wind speed (see the ReadMe file for further details).\r\n\r\nThe CRU JRA dataset is intended to be a replacement of the CRU NCEP forcing dataset. The CRU JRA dataset follows the style of Nicolas Viovy's original CRU NCEP dataset rather than that which is available from UCAR. A link to the CRU NCEP documentation for comparison is provided in the documentation section. \r\n\r\nIf this dataset is used in addition to citing the dataset as per the data citation string users must also cite the following:\r\n\r\nHarris, I., Osborn, T.J., Jones, P. et al. Version 4 of the CRU TS\r\nmonthly high-resolution gridded multivariate climate dataset.\r\nSci Data 7, 109 (2020). https://doi.org/10.1038/s41597-020-0453-3\r\n\r\nHarris, I., Jones, P.D., Osborn, T.J. and Lister, D.H. (2014), Updated\r\nhigh-resolution grids of monthly climatic observations - the CRU TS3.10\r\nDataset. International Journal of Climatology 34, 623-642.\r\n\r\nKobayashi, S., et. al., The JRA-55 Reanalysis: General Specifications and\r\nBasic Characteristics. J. Met. Soc. Jap., 93(1), 5-48\r\nhttps://dx.doi.org/10.2151/jmsj.2015-001" } }, { "ob_id": 538, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 32803, "uuid": "7a5529a8758041eb83b9c32f8461e50d", "short_code": "ob", "title": "CRU CY4.05: Climatic Research Unit year-by-year variation of selected climate variables by country version 4.05 (Jan. 1901 - Dec. 2020)", "abstract": "The Climatic Research Unit (CRU) Country (CY) data version 4.05 dataset consists of ten climate variables for country averages at a monthly, seasonal and annual frequency: including cloud cover, diurnal temperature range, frost day frequency, precipitation, daily mean temperature, monthly average daily maximum and minimum temperature, vapour pressure, potential evapotranspiration and wet day frequency. This version uses the updated set of country definitions, please see the appropriate Release Notes.\r\n\r\nThis dataset was produced in 2021 by CRU at the University of East Anglia and extends the CRU CY4.04 data to include 2020. The data are available as text files with the extension '.per' and can be opened by most text editors.\r\n\r\nSpatial averages are calculated using area-weighted means. CRU CY4.05 is derived directly from the CRU time series (TS) 4.05 dataset. CRU CY version 4.05 spans the period 1901-2020 for 292 countries.\r\n\r\nTo understand the CRU CY4.05 dataset, it is important to understand the construction and limitations of the underlying dataset, CRU TS4.05. It is therefore recommended that all users read the Harris et al, 2020 paper and the CRU TS4.05 release notes listed in the online documentation on this record.\r\n\r\nCRU CY data are available for download to all CEDA users." }, "objectObservation": { "ob_id": 30262, "uuid": "58d87204bd974f04b14141eb275f4a1e", "short_code": "ob", "title": "CRU CY4.04: Climatic Research Unit year-by-year variation of selected climate variables by country version 4.04 (Jan. 1901 - Dec. 2019)", "abstract": "The Climatic Research Unit (CRU) Country (CY) data version 4.04 dataset consists of ten climate variables for country averages at a monthly, seasonal and annual frequency: including cloud cover, diurnal temperature range, frost day frequency, precipitation, daily mean temperature, monthly average daily maximum and minimum temperature, vapour pressure, potential evapotranspiration and wet day frequency. This version uses the updated set of country definitions, please see the appropriate Release Notes.\r\n\r\nThis dataset was produced in 2020 by CRU at the University of East Anglia and extends the CRU CY4.03 data to include 2019. The data are available as text files with the extension '.per' and can be opened by most text editors.\r\n\r\nSpatial averages are calculated using area-weighted means. CRU CY4.04 is derived directly from the CRU time series (TS) 4.04 dataset. CRU CY version 4.04 spans the period 1901-2019 for 292 countries.\r\n\r\nTo understand the CRU CY4.04 dataset, it is important to understand the construction and limitations of the underlying dataset, CRU TS4.04. It is therefore recommended that all users read the Harris et al, 2020 paper and the CRU TS4.04 release notes listed in the online documentation on this record.\r\n\r\nCRU CY data are available for download to all CEDA users." } }, { "ob_id": 539, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 32804, "uuid": "c26a65020a5e4b80b20018f148556681", "short_code": "ob", "title": "CRU TS4.05: Climatic Research Unit (CRU) Time-Series (TS) version 4.05 of high-resolution gridded data of month-by-month variation in climate (Jan. 1901- Dec. 2020)", "abstract": "The gridded Climatic Research Unit (CRU) Time-series (TS) data version 4.05 data are month-by-month variations in climate over the period 1901-2020, provided on high-resolution (0.5x0.5 degree) grids, produced by CRU at the University of East Anglia and funded by the UK National Centre for Atmospheric Science (NCAS), a NERC collaborative centre.\r\n\r\nThe CRU TS4.05 variables are cloud cover, diurnal temperature range, frost day frequency, wet day frequency, potential evapotranspiration (PET), precipitation, daily mean temperature, monthly average daily maximum and minimum temperature, and vapour pressure for the period January 1901 - December 2020.\r\n\r\nThe CRU TS4.05 data were produced using angular-distance weighting (ADW) interpolation. All versions prior to 4.00 used triangulation routines in IDL. Please see the release notes for full details of this version update. \r\n\r\nThe CRU TS4.05 data are monthly gridded fields based on monthly observational data calculated from daily or sub-daily data by National Meteorological Services and other external agents. The ASCII and NetCDF data files both contain monthly mean values for the various parameters. The NetCDF versions contain an additional integer variable, ’stn’, which provides, for each datum in the main variable, a count (between 0 and 8) of the number of stations used in that interpolation. The missing value code for 'stn' is -999.\r\n\r\nAll CRU TS output files are actual values - NOT anomalies." }, "objectObservation": { "ob_id": 30263, "uuid": "89e1e34ec3554dc98594a5732622bce9", "short_code": "ob", "title": "CRU TS4.04: Climatic Research Unit (CRU) Time-Series (TS) version 4.04 of high-resolution gridded data of month-by-month variation in climate (Jan. 1901- Dec. 2019)", "abstract": "The gridded Climatic Research Unit (CRU) Time-series (TS) data version 4.04 data are month-by-month variations in climate over the period 1901-2019, provided on high-resolution (0.5x0.5 degree) grids, produced by CRU at the University of East Anglia and funded by the UK National Centre for Atmospheric Science (NCAS), a NERC collaborative centre.\r\n\r\nThe CRU TS4.04 variables are cloud cover, diurnal temperature range, frost day frequency, wet day frequency, potential evapotranspiration (PET), precipitation, daily mean temperature, monthly average daily maximum and minimum temperature, and vapour pressure for the period January 1901 - December 2019.\r\n\r\nThe CRU TS4.04 data were produced using angular-distance weighting (ADW) interpolation. All versions prior to 4.00 used triangulation routines in IDL. Please see the release notes for full details of this version update. \r\n\r\nThe CRU TS4.04 data are monthly gridded fields based on monthly observational data calculated from daily or sub-daily data by National Meteorological Services and other external agents. The ASCII and NetCDF data files both contain monthly mean values for the various parameters. The NetCDF versions contain an additional integer variable, ’stn’, which provides, for each datum in the main variable, a count (between 0 and 8) of the number of stations used in that interpolation. The missing value code for 'stn' is -999.\r\n\r\nAll CRU TS output files are actual values - NOT anomalies." } }, { "ob_id": 540, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 32169, "uuid": "ef1627f523764eae8bbb6b81bf1f7a0a", "short_code": "ob", "title": "ESA Lakes Climate Change Initiative (Lakes_cci): Lake products, Version 1.1", "abstract": "This dataset contains various global lake products (1992-2019) produced by the European Space Agency (ESA) Lakes Climate Change Initiative (Lakes_cci) project. This is version 1.1 of the dataset.\r\n\r\nLakes are of significant interest to the scientific community, local to national governments, industries and the wider public. A range of scientific disciplines including hydrology, limnology, climatology, biogeochemistry and geodesy are interested in distribution and functioning of the millions of lakes (from small ponds to inland seas), from the local to the global scale. Remote sensing provides an opportunity to extend the spatio-temporal scale of lake observation. \r\n\r\nThe five thematic climate variables included in this dataset are:\r\n•\tLake Water Level (LWL): a proxy fundamental to understand the balance between water inputs and water loss and their connection with regional and global climate changes.\r\n•\tLake Water Extent (LWE): a proxy for change in glacial regions (lake expansion) and drought in many arid environments, water extent relates to local climate for the cooling effect that water bodies provide.\r\n•\tLake Surface Water temperature (LSWT): correlated with regional air temperatures and a proxy for mixing regimes, driving biogeochemical cycling and seasonality. \r\n•\tLake Ice Cover (LIC): freeze-up in autumn and advancing break-up in spring are proxies for gradually changing climate patterns and seasonality. \r\n•\tLake Water-Leaving Reflectance (LWLR): a direct indicator of biogeochemical processes and habitats in the visible part of the water column (e.g. seasonal phytoplankton biomass fluctuations), and an indicator of the frequency of extreme events (peak terrestrial run-off, changing mixing conditions).\r\n\r\nData generated in the Lakes_cci project are derived from data from multiple instruments and multiple satellites including; TOPEX/Poseidon, Jason, ENVISAT, SARAL, Sentinel, Landsat, ERS, Terra/Aqua, Suomi NPP, Metop and Orbview. For more information please see the product user guide in the documents." }, "objectObservation": { "ob_id": 30253, "uuid": "3c324bb4ee394d0d876fe2e1db217378", "short_code": "ob", "title": "ESA Lakes Climate Change Initiative (Lakes_cci): Lake products, Version 1.0", "abstract": "This dataset contains various global lake products (1992-2019) produced by the European Space Agency (ESA) Lakes Climate Change Initiative (Lakes_cci) project.\r\n\r\nLakes are of significant interest to the scientific community, local to national governments, industries and the wider public. A range of scientific disciplines including hydrology, limnology, climatology, biogeochemistry and geodesy are interested in distribution and functioning of the millions of lakes (from small ponds to inland seas), from the local to the global scale. Remote sensing provides an opportunity to extend the spatio-temporal scale of lake observation. \r\n\r\nThe five thematic climate variables included in this dataset are:\r\n•\tLake Water Level (LWL): a proxy fundamental to understand the balance between water inputs and water loss and their connection with regional and global climate changes.\r\n•\tLake Water Extent (LWE): a proxy for change in glacial regions (lake expansion) and drought in many arid environments, water extent relates to local climate for the cooling effect that water bodies provide.\r\n•\tLake Surface Water temperature (LSWT): correlated with regional air temperatures and a proxy for mixing regimes, driving biogeochemical cycling and seasonality. \r\n•\tLake Ice Cover (LIC): freeze-up in autumn and advancing break-up in spring are proxies for gradually changing climate patterns and seasonality. \r\n•\tLake Water-Leaving Reflectance (LWLR): a direct indicator of biogeochemical processes and habitats in the visible part of the water column (e.g. seasonal phytoplankton biomass fluctuations), and an indicator of the frequency of extreme events (peak terrestrial run-off, changing mixing conditions).\r\n\r\nData generated in the Lakes_cci project are derived from data from multiple instruments and multiple satellites including; TOPEX/Poseidon, Jason, ENVISAT, SARAL, Sentinel, Landsat, ERS, Terra/Aqua, Suomi NPP, Metop and Orbview. For more information please see the product user guide in the documents." } }, { "ob_id": 541, "relationType": "IsVariantFormOf", "subjectObservation": { "ob_id": 32607, "uuid": "9252ff9ddeb249a2bd8433e9ae9dfe13", "short_code": "ob", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged carbon dioxide from TANSAT, generated with the OCFP algorithm, for global land areas, version 1.0", "abstract": "This dataset contains column-average dry-air mole fractions of atmospheric carbon dioxide (CO2), derived from the TANSAT satellite, using the University of Leicester Full-Physics Retrieval Algorithm (UoL-FP, also known as OCFP). This dataset is also referred to as CO2_TAN_OCFP. This version of the dataset provides data globally over land. For further information on the dataset, please see the linked documentation.\r\n\r\nInitially this dataset contains two months of data (June and August 2017), delivered as part of the GHG_cci Climate Research Data Package 6. Additional time periods will be added in the future.\r\n\r\n\r\nThis data has been produced as part of the European Space Agency (ESA)'s Climate Change Initiative (CCI) programme, with support from the UK's National Centre for Earth Observation (NCEO)." }, "objectObservation": { "ob_id": 31872, "uuid": "2cc63301f1854239aa61c70e58c61207", "short_code": "ob", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged carbon dioxide from TANSAT, generated with the OCFP algorithm, for selected validation sites, version 1.0", "abstract": "This dataset contains column-average dry-air mole fractions of atmospheric carbon dioxide (CO2), derived from the TANSAT satellite, using the University of Leicester Full-Physics Retrieval Algorithm (UoL-FP, also known as OCFP). This dataset is also referred to as CO2_TAN_OCFP. The data covers the period from March 2017 to May 2018 and is provided for TCCON (Total Carbon Column Observing Network) validation sites only. A full global dataset is in production. For further information on the dataset, please see the linked documentation.\r\n\r\nThis data has been produced as part of the European Space Agency (ESA)'s Climate Change Initiative (CCI) programme, with support from the UK's National Centre for Earth Observation (NCEO)." } }, { "ob_id": 542, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 32744, "uuid": "1c9c816d0b8a4fbf878e7e0bfef5d79f", "short_code": "ob", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged methane from Sentinel-5P, generated with the WFM-DOAS algorithm, version 1.2, November 2017 - July 2020", "abstract": "This product is the column-average dry-air mole fraction of atmospheric methane, denoted XCH4. It has been retrieved from radiance measurements from the TROPOspheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor satellite in the 2.3 µm spectral range of the solar spectral range, using the Weighting Function Modified Differential Optical Absorption Spectroscopy (WFM-DOAS or WFMD) retrieval algorithm. This dataset is also referred to as CH4_S5P_WFMD. This version of the product is version 1.2, and covers the period from November 2017 - July 2020. \r\n\r\nThe WFMD algorithm is based on iteratively fitting a simulated radiance spectrum to the measured spectrum using a least-squares method. The algorithm is very fast as it is based on a radiative transfer model based look-up table scheme. The product is limited to cloud-free scenes on the Earth's day side.\r\n\r\nThese data were produced as part of the European Space Agency's (ESA) Greenhouse Gases (GHG) Climate Change Initiative (CCI) project.\r\n\r\nWhen citing this dataset, please also cite the following peer-reviewed publication: \r\nSchneising, O., Buchwitz, M., Reuter, M., Bovensmann, H., Burrows, J. P., Borsdorff, T., Deutscher, N. M., Feist, D. G., Griffith, D. W. T., Hase, F., Hermans, C., Iraci, L. T., Kivi, R., Landgraf, J., Morino, I., Notholt, J., Petri, C., Pollard, D. F., Roche, S., Shiomi, K., Strong, K., Sussmann, R., Velazco, V. A., Warneke, T., and Wunch, D.: A scientific algorithm to simultaneously retrieve carbon monoxide and methane from TROPOMI onboard Sentinel-5 Precursor, Atmos. Meas. Tech., 12, 6771–6802, https://doi.org/10.5194/amt-12-6771-2019, 2019." }, "objectObservation": { "ob_id": 30126, "uuid": "3534bbf43fa14e40bc61944eaf664511", "short_code": "ob", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged methane from Sentinel-5P, generated with the WFM-DOAS algorithm, version 1.2", "abstract": "This product is the column-average dry-air mole fraction of atmospheric methane, denoted XCH4. It has been retrieved from radiance measurements from the TROPOspheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor satellite in the 2.3 µm spectral range of the solar spectral range, using the Weighting Function Modified Differential Optical Absorption Spectroscopy (WFM-DOAS or WFMD) retrieval algorithm. This dataset is also referred to as CH4_S5P_WFMD.\r\n\r\nThe WFMD algorithm is based on iteratively fitting a simulated radiance spectrum to the measured spectrum using a least-squares method. The algorithm is very fast as it is based on a radiative transfer model based look-up table scheme. The product is limited to cloud-free scenes on the Earth's day side.\r\n\r\nThis data was produced as part of the European Space Agency's (ESA) Greenhouse Gases (GHG) Climate Change Initiative (CCI) project." } }, { "ob_id": 543, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 32938, "uuid": "a1c7ae7387af4f048346d05ba36babd8", "short_code": "ob", "title": "ESA Ozone Climate Change Initiative (Ozone_cci): ACE-FTS Level 3 monthly mean zonal mean ozone profiles on an altitude grid, v0001", "abstract": "The dataset provides monthly zonal mean (MZM) ozone profiles from the ACE-FTS (Atmospheric Chemistry Experiment Fourier Transform Spectrometer) instrument onboard SCISAT, created by the ESA Ozone Climate Change Initiative project. The data are provided as ozone molar concentration (mol m-3) in 10 degree latitude bands from 90S to 90N, in the altitude range from 6 to 94 km. \r\n\r\nThe ACE-FTS Level 2 profiles (Bernath et al., 2005; Bernath, 2017), which are used for the MZM data, are retrieved with the University of Toronto processor UoT v3.5/3.6 and included into the new version of the HARMonized datasets of OZone profiles (HARMOZ_ALT, Sofieva et al., 2013). A more detailed description of the MZM data processing and dataset parameters can be found in README and in (Sofieva et al., 2017)." }, "objectObservation": { "ob_id": 14157, "uuid": "ccbeb356a88847058159049678fe5c35", "short_code": "ob", "title": "ESA Ozone Climate Change Initiative (Ozone CCI): ACE Level 3 Limb Ozone Monthly Zonal Mean (MZM) Profiles, Version 1", "abstract": "This dataset comprises gridded limb ozone monthly zonal mean profiles from the ACE FTS instrument on the SCISAT satellite. \r\n\r\nThe data are zonal mean time series (10° latitude bin) and include uncertainty/variability of the Monthly Zonal Mean.\r\n\r\nThe monthly zonal mean (MZM) data set provides ozone profiles averaged in 10° latitude zones from 90°S to 90°N, for each month. The monthly zonal mean data are structured into yearly netcdf files, for each instrument separately. The filename indicates the instrument and the year. For example, the file “ESACCI-OZONE-L3-LP-ACE_FTS_SCISAT-MZM-2008-fv0001.nc” contains monthly zonal mean data for ACE in 2008." } }, { "ob_id": 544, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 32944, "uuid": "30eca31abb0244efa68e05dd28b2e1de", "short_code": "ob", "title": "ESA Ozone Climate Change Initiative (Ozone_cci): GOMOS Level 3 monthly mean zonal mean ozone profiles on an altitude grid, v0001", "abstract": "This dataset provides monthly zonal mean (MZM) ozone profiles from the GOMOS (Global Ozone Monitoring by Occultation of Stars) instrument onboard ENVISAT, created by the ESA Ozone Climate Change Initiative project. The data are provided as ozone molar concentration (mol m-3) in 10 degree latitude bands from 90S to 90N, in the altitude range from 10 to 105 km. \r\n\r\nThe GOMOS Level 2 profiles, which are used for the MZM data, are retrieved with the scientific ALGOM2s v1 processor (Sofieva et al., 2017a) and included into the new version of the HARMonized datasets of OZone profiles (HARMOZ_ALT, Sofieva et al., 2013). \r\n\r\nA more detailed description of the GOMOS Monthly zonal mean data can be found in the README and in (Sofieva et al., 2017b).\r\n\r\n\r\n\r\n\r\n." }, "objectObservation": { "ob_id": 14152, "uuid": "b431fbecf73c4442ad5d7bcf80929b03", "short_code": "ob", "title": "ESA Ozone Climate Change Initiative (Ozone CCI): GOMOS Level 3 Limb Ozone Monthly Zonal Mean (MZM) Profiles, Version 1", "abstract": "This dataset comprises gridded limb ozone monthly zonal mean profiles from the GOMOS instrument. \r\n\r\nThe data are zonal mean time series (10° latitude bin) and include uncertainty/variability of the Monthly Zonal Mean.\r\n\r\nThe monthly zonal mean (MZM) data set provides ozone profiles averaged in 10° latitude zones from 90°S to 90°N, for each month. The monthly zonal mean data are structured into yearly netcdf files, for each instrument separately. The filename indicates the instrument and the year. For example, the file “ESACCI-OZONE-L3-LP-GOMOS_ENVISAT-MZM-2008.nc” contains monthly zonal mean data for GOMOS in 2008." } }, { "ob_id": 545, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 32948, "uuid": "8e930bdbd3d34e91995ca631da982c52", "short_code": "ob", "title": "ESA Ozone Climate Change Initiative (Ozone_cci): MIPAS Level 3 monthly mean zonal mean ozone profiles on an altitude grid, v0001", "abstract": "This dataset provides monthly zonal mean (MZM) ozone profiles from the MIPAS (Michelson Interferometer for Passive Atmospheric Sounding) instrument onboard ENVISAT, created by the ESA Ozone Climate Change Initiative project. The data are provided as ozone molar concentration (mol m-3) in 10 degree latitude bands from 90S to 90N, in the altitude range from 6 to 70 km. \r\n\r\nThe MIPAS Level 2 profiles, which are used for the MZM data, are retrieved with the Karlsruhe Institute of Technology processor KIT/IAA V7R_O3_240 (von Clarmann et al., 2003; 2009) and included into the new version of the HARMonized datasets of Ozone profiles (HARMOZ_ALT, Sofieva et al., 2013). \r\n\r\nA more detailed description of the MIPAS Monthly zonal mean data can be found in the README and in (Sofieva et al., 2017)." }, "objectObservation": { "ob_id": 14153, "uuid": "4e106bb70a6b42d8a5a86c4635c855b9", "short_code": "ob", "title": "ESA Ozone Climate Change Initiative (Ozone CCI): MIPAS Level 3 Limb Ozone Monthly Zonal Mean (MZM) Profiles, Version 1", "abstract": "This dataset comprises gridded limb ozone monthly zonal mean profiles from the MIPAS instrument on the ENVISAT satellite. \r\n\r\nThe data are zonal mean time series (10° latitude bin) and include uncertainty/variability of the Monthly Zonal Mean.\r\n\r\nThe monthly zonal mean (MZM) data set provides ozone profiles averaged in 10° latitude zones from 90°S to 90°N, for each month. The monthly zonal mean data are structured into yearly netcdf files, for each instrument separately. The filename indicates the instrument and the year. For example, the file \"ESACCI-OZONE-L3-LP-MIPAS_ENVISAT-MZM-2008-fv0001.nc“ contains monthly zonal mean data for MIPAS in 2008." } }, { "ob_id": 546, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 32961, "uuid": "27d14e4b9ecb426bb22c3c8a93391d4e", "short_code": "ob", "title": "ESA Ozone Climate Change Initiative (Ozone_cci): OSIRIS Level 3 monthly mean zonal mean ozone profiles on an altitude grid, v0002", "abstract": "The dataset provides monthly zonal mean (MZM) ozone profiles from the OSIRIS (Optical Spectrograph and InfraRed Imaging System) instrument onboard Odin, created as part of the ESA Ozone Climate Change Initiative project. The data are provided as ozone molar concentration (mol m-3) in 10 degree latitude bands from 90S to 90N, in the altitude range from 10 to 59 km. \r\n\r\nThe OSIRIS Level 2 profiles, which are used for the MZM data, are retrieved with the University of Saskatchewan processor USask v.5.10 (Bourassa et al., 2017; Degenstein et al., 2009) and included into the new version of the HARMonized datasets of Ozone profiles (HARMOZ_ALT, Sofieva et al., 2013). \r\n\r\nA more detailed description of the data processing and dataset parameters can be found in the README and in (Sofieva et al., 2017)." }, "objectObservation": { "ob_id": 14154, "uuid": "f428fffb26cf4cd5b97dfb6381cb16bb", "short_code": "ob", "title": "ESA Ozone Climate Change Initiative (Ozone CCI): OSIRIS Level 3 Limb Ozone Monthly Zonal Mean (MZM) Profiles, Version 1", "abstract": "This dataset comprises gridded limb ozone monthly zonal mean profiles from the OSIRIS instrument on the ODIN satellite. \r\n\r\nThe data are zonal mean time series (10° latitude bin) and include uncertainty/variability of the Monthly Zonal Mean.\r\n\r\nThe monthly zonal mean (MZM) data set provides ozone profiles averaged in 10° latitude zones from 90°S to 90°N, for each month. The monthly zonal mean data are structured into yearly netcdf files, for each instrument separately. The filename indicates the instrument and the year. For example, the file “ESACCI-OZONE-L3-LP-OSIRIS_ODIN-MZM-2008-fv0001.nc” contains monthly zonal mean data for OSIRIS in 2008." } }, { "ob_id": 547, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 32964, "uuid": "876e3b41a2d6482db13887914b38a96c", "short_code": "ob", "title": "ESA Ozone Climate Change Initiative (Ozone_cci): SCIAMACHY Level 3 monthly mean zonal mean ozone profiles on an altitude grid, v0001", "abstract": "This dataset provides monthly zonal mean (MZM) ozone profiles from the SCIAMACHY (SCanning Imaging Spectrometer for Atmospheric CHartographY) instrument onboard ENVISAT, created as part of the ESA Ozone Climate Change Initiative project. The data are provided as ozone molar concentration (mol m-3) in 10 degree latitude bands from 90S to 90N, in the altitude range from 5 to 65 km. \r\n\r\nThe SCIAMACHY Level 2 profiles, which are used for the MZM data, are retrieved with the University of Bremen processor UBr v3.5 (Jia et al., 2015) and included into the new version of the HARMonized datasets of Ozone profiles (HARMOZ_ALT, Sofieva et al., 2013). \r\n\r\nA more detailed description of the data processing and dataset parameters can be found in the README and in (Sofieva et al., 2017)." }, "objectObservation": { "ob_id": 14151, "uuid": "cb54bd70826842a9acf658ebabe4a104", "short_code": "ob", "title": "ESA Ozone Climate Change Initiative (Ozone CCI): SCIAMACHY Level 3 Limb Ozone Monthly Zonal Mean (MZM) Profiles, Version 1", "abstract": "This dataset comprises gridded limb ozone monthly zonal mean profiles from the SCIAMACHY instrument on ENVISAT. \r\n\r\nThe data are zonal mean time series (10° latitude bin) and include uncertainty/variability of the Monthly Zonal Mean.\r\n\r\nThe monthly zonal mean (MZM) data set provides ozone profiles averaged in 10° latitude zones from 90°S to 90°N, for each month. The monthly zonal mean data are structured into yearly netcdf files, for each instrument separately. The filename indicates the instrument and the year. For example, the file “ESACCI-OZONE-L3-LP-SCIAMACHY_ENVISAT-MZM-2008-fv0001.nc” contains monthly zonal mean data for SCIAMACHY in 2008." } }, { "ob_id": 548, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 32975, "uuid": "625f5ea4ddac4578a2aacf47bcf39657", "short_code": "ob", "title": "MIDAS Open: UK hourly solar radiation data, v202107", "abstract": "The UK hourly solar radiation data contain the amount of solar irradiance received during the hour ending at the specified time. All sites report 'global' radiation amounts. This is also known as 'total sky radiation' as it includes both direct solar irradiance and 'diffuse' irradiance as a result of light scattering. Some sites also provide separate diffuse and direct irradiation amounts, depending on the instrumentation at the site. For these the sun's path is tracked with two pyrometers - one where the path to the sun is blocked by a suitable disc to allow the scattered sunlight to be measured to give the diffuse measurement, while the other has a tube pointing at the sun to measure direct solar irradiance whilst blanking out scattered sun light.\r\n\r\nThis version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data.\r\n\r\nThe data were collected by observation stations operated by the Met Office across the UK and transmitted within the following message types: SYNOP, HCM, AWSHRLY, MODLERAD, ESAWRADT and DRADR35 messages. The data spans from 1947 to 2020.\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by the Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record." }, "objectObservation": { "ob_id": 31890, "uuid": "1dc8578eb7434a7d8a661744d53eedf9", "short_code": "ob", "title": "MIDAS Open: UK hourly solar radiation data, v202007", "abstract": "The UK hourly solar radiation data contain the amount of solar irradiance received during the hour ending at the specified time. All sites report 'global' radiation amounts. This is also known as 'total sky radiation' as it includes both direct solar irradiance and 'diffuse' irradiance as a result of light scattering. Some sites also provide separate diffuse and direct irradiation amounts, depending on the instrumentation at the site. For these the sun's path is tracked with two pyrometers - one where the path to the sun is blocked by a suitable disc to allow the scattered sunlight to be measured to give the diffuse measurement, while the other has a tube pointing at the sun to measure direct solar irradiance whilst blanking out scattered sun light.\r\n\r\nThis version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data.\r\n\r\nThe data were collected by observation stations operated by the Met Office across the UK and transmitted within the following message types: SYNOP, HCM, AWSHRLY, MODLERAD, ESAWRADT and DRADR35 messages. The data spans from 1947 to 2019.\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by the Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record." } }, { "ob_id": 549, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 32968, "uuid": "92e823b277cc4f439803a87f5246db5f", "short_code": "ob", "title": "MIDAS Open: UK daily temperature data, v202107", "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 2020. 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. Of particular note, however, is that as well as including data for 2020, historical data recovery has added further data for Eskdalemuir (1915-1948) and Eastbourne (1887-1910).\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by the Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record. Currently this represents approximately 95% of available daily temperature observations within the full MIDAS collection." }, "objectObservation": { "ob_id": 31889, "uuid": "064f3a982cfc4b07bc5de627cd8676f1", "short_code": "ob", "title": "MIDAS Open: UK daily temperature data, v202007", "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 2019. 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. Of particular note, however, is that as well as including data for 2019, historical data recovery has added temperature and weather data for Bude (1937-1958), Teignmouth (1912-1930), and Eskdalemuir (1915-1948).\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": 550, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 32974, "uuid": "3bd7221d4844435dad2fa030f26ab5fd", "short_code": "ob", "title": "MIDAS Open: UK hourly weather observation data, v202107", "abstract": "The UK hourly weather observation data contain meteorological values measured on an hourly time scale. The measurements of the concrete state, wind speed and direction, cloud type and amount, visibility, and temperature were recorded by observation stations operated by the Met Office across the UK and transmitted within SYNOP, DLY3208, AWSHRLY and NCM messages. The sunshine duration measurements were transmitted in the HSUN3445 message. The data spans from 1875 to 2020.\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. Of particular note, however, is that as well as including data for 2020, historical data recovery has added further data for Eskdalemuir (1914-1944) and Eastbourne (1887-1910).\r\n\r\nFor details on observing practice see the message type information in the MIDAS User Guide linked from this record and relevant sections for parameter types.\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record. Note, METAR message types are not included in the Open version of this dataset. Those data may be accessed via the full MIDAS hourly weather data." }, "objectObservation": { "ob_id": 31883, "uuid": "8d85f664fc614ba0a28af3a2d7ef4533", "short_code": "ob", "title": "MIDAS Open: UK hourly weather observation data, v202007", "abstract": "The UK hourly weather observation data contain meteorological values measured on an hourly time scale. The measurements of the concrete state, wind speed and direction, cloud type and amount, visibility, and temperature were recorded by observation stations operated by the Met Office across the UK and transmitted within SYNOP, DLY3208, AWSHRLY and NCM messages. The sunshine duration measurements were transmitted in the HSUN3445 message. The data spans from 1875 to 2019.\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. Of particular note, however, is that as well as including data for 2019, historical data recovery has added temperature and weather data for Bude (1937-1958), Teignmouth (1912-1930), and Eskdalemuir (1915-1948).\r\n\r\nFor details on observing practice see the message type information in the MIDAS User Guide linked from this record and relevant sections for parameter types.\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record. Note, METAR message types are not included in the Open version of this dataset. Those data may be accessed via the full MIDAS hourly weather data." } }, { "ob_id": 551, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 32969, "uuid": "cabc37d867fa4f2a84302350df908693", "short_code": "ob", "title": "MIDAS Open: UK soil temperature data, v202107", "abstract": "The UK soil temperature data contain daily and hourly values of soil temperatures at depths of 5, 10, 20, 30, 50, and 100 centimetres. The measurements were recorded by observation stations operated by the Met Office across the UK and transmitted within NCM or DLY3208 messages. The data spans from 1900 to 2020.\r\n\r\nThis version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data.\r\n\r\nAt many stations temperatures below the surface are measured at various depths. The depths used today are 5, 10, 20, 30 and 100cm, although measurements are not necessarily made at all these depths at a station and exceptionally measurements may be made at other depths. When imperial units were in general use, typically before 1961, the normal depths of measurement were 4, 8, 12, 24 and 48 inches.\r\n\r\nLiquid-in-glass soil thermometers at a depth of 20 cm or less are unsheathed and have a bend in the stem between the bulb and the lowest graduation. At greater depths the thermometer is suspended in a steel tube and has its bulb encased in wax.\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by the Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record." }, "objectObservation": { "ob_id": 31899, "uuid": "85b9dad7af814bfa9047a525927257f4", "short_code": "ob", "title": "MIDAS Open: UK soil temperature data, v202007", "abstract": "The UK soil temperature data contain daily and hourly values of soil temperatures at depths of 5, 10, 20, 30, 50, and 100 centimetres. The measurements were recorded by observation stations operated by the Met Office across the UK and transmitted within NCM or DLY3208 messages. The data spans from 1900 to 2019.\r\n\r\nThis version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data.\r\n\r\nAt many stations temperatures below the surface are measured at various depths. The depths used today are 5, 10, 20, 30 and 100cm, although measurements are not necessarily made at all these depths at a station and exceptionally measurements may be made at other depths. When imperial units were in general use, typically before 1961, the normal depths of measurement were 4, 8, 12, 24 and 48 inches.\r\n\r\nLiquid-in-glass soil thermometers at a depth of 20 cm or less are unsheathed and have a bend in the stem between the bulb and the lowest graduation. At greater depths the thermometer is suspended in a steel tube and has its bulb encased in wax.\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by the Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record." } }, { "ob_id": 552, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 32972, "uuid": "4d48efaaeb7f47a7963df75d6d1dbdc5", "short_code": "ob", "title": "MIDAS Open: UK mean wind data, v202107", "abstract": "The UK mean wind data contain the mean wind speed and direction, and the direction, speed and time of the maximum gust, all during 1 or more hours, ending at the stated time and date. The data were collected by observation stations operated by the Met Office across the UK and transmitted within the following message types: SYNOP, HCM, AWSHRLY, DLY3208, HWNDAUTO and HWND6910. The data spans from 1949 to 2020.\r\n\r\nThis version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data.\r\n\r\nFor further details on observing practice, including measurement accuracies for the message types, see relevant sections of the MIDAS User Guide linked from this record (e.g. section 3.3 details the wind network in the UK, section 5.5 covers wind measurements in general and section 4 details message type information).\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by the Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record." }, "objectObservation": { "ob_id": 31885, "uuid": "f7e09e89de234c15964a4cc7a75f3f74", "short_code": "ob", "title": "MIDAS Open: UK mean wind data, v202007", "abstract": "The UK mean wind data contain the mean wind speed and direction, and the direction, speed and time of the maximum gust, all during 1 or more hours, ending at the stated time and date. The data were collected by observation stations operated by the Met Office across the UK and transmitted within the following message types: SYNOP, HCM, AWSHRLY, DLY3208, HWNDAUTO and HWND6910. The data spans from 1949 to 2019.\r\n\r\nThis version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data.\r\n\r\nFor further details on observing practice, including measurement accuracies for the message types, see relevant sections of the MIDAS User Guide linked from this record (e.g. section 3.3 details the wind network in the UK, section 5.5 covers wind measurements in general and section 4 details message type information).\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by the Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record." } }, { "ob_id": 553, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 32973, "uuid": "d399794d81fa41779a925b6d4758a5cd", "short_code": "ob", "title": "MIDAS Open: UK daily weather observation data, v202107", "abstract": "The UK daily weather observation data contain meteorological values measured on a 24 hour time scale. The measurements of sunshine duration, concrete state, snow depth, fresh snow depth, and days of snow, hail, thunder and gail were attained by observation stations operated by the Met Office across the UK operated and transmitted within DLY3208, NCM, AWSDLY and SYNOP messages. The data span from 1887 to 2020. For details of observations see the relevant sections of the MIDAS User Guide linked from this record for the various message types.\r\n\r\nThis version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data. Of particular note, however, is that as well as including data for 2020, historical data recovery has added further data for Eastbourne (1887-1910).\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by the Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record. Currently this represents approximately 95% of available daily weather observations within the full MIDAS collection." }, "objectObservation": { "ob_id": 31884, "uuid": "f8612c43a1244fda9463787313d3892a", "short_code": "ob", "title": "MIDAS Open: UK daily weather observation data, v202007", "abstract": "The UK daily weather observation data contain meteorological values measured on a 24 hour time scale. The measurements of sunshine duration, concrete state, snow depth, fresh snow depth, and days of snow, hail, thunder and gail were attained by observation stations operated by the Met Office across the UK operated and transmitted within DLY3208, NCM, AWSDLY and SYNOP messages. The data span from 1889 to 2019. For details of observations see the relevant sections of the MIDAS User Guide linked from this record for the various message types.\r\n\r\nThis version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data. Of particular note, however, is that as well as including data for 2019, historical data recovery has added temperature and weather data for Bude (1937-1958), Teignmouth (1912-1930), and Eskdalemuir (1915-1948).\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by the Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record. Currently this represents approximately 95% of available daily weather observations within the full MIDAS collection." } }, { "ob_id": 554, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 32971, "uuid": "d6bcf4171c2f4754a7455d00deda0f72", "short_code": "ob", "title": "MIDAS Open: UK daily rainfall data, v202107", "abstract": "The UK daily rainfall data contain rainfall accumulation and precipitation amounts over a 24 hour period. The data were collected by observation stations operated by the Met Office across the UK and transmitted within the following message types: NCM, AWSDLY, DLY3208 and SSER. The data spans from 1853 to 2020. Over time a range of rain gauges have been used - see section 5.6 and the relevant message type information in the linked MIDAS User Guide for further details.\r\n\r\nThis version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data.\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record. A large proportion of the UK raingauge observing network (associated with WAHRAIN, WADRAIN and WAMRAIN for hourly, daily and monthly rainfall measurements respectively) is operated by other agencies beyond the Met Office, and are consequently currently excluded from the Midas-open dataset. Currently this represents approximately 13% of available daily rainfall observations within the full MIDAS collection." }, "objectObservation": { "ob_id": 31887, "uuid": "ec9e894089434b03bd9532d7b343ec4b", "short_code": "ob", "title": "MIDAS Open: UK daily rainfall data, v202007", "abstract": "The UK daily rainfall data contain rainfall accumulation and precipitation amounts over a 24 hour period. The data were collected by observation stations operated by the Met Office across the UK and transmitted within the following message types: NCM, AWSDLY, DLY3208 and SSER. The data spans from 1853 to 2019. Over time a range of rain gauges have been used - see section 5.6 and the relevant message type information in the linked MIDAS User Guide for further details.\r\n\r\nThis version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data.\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record. A large proportion of the UK raingauge observing network (associated with WAHRAIN, WADRAIN and WAMRAIN for hourly, daily and monthly rainfall measurements respectively) is operated by other agencies beyond the Met Office, and are consequently currently excluded from the Midas-open dataset. Currently this represents approximately 13% of available daily rainfall observations within the full MIDAS collection." } }, { "ob_id": 555, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 32970, "uuid": "f7ae919f96b44a1c9695f40a9cf988dd", "short_code": "ob", "title": "MIDAS Open: UK hourly rainfall data, v202107", "abstract": "The UK hourly rainfall data contain the rainfall amount (and duration from tilting syphon gauges) during the hour (or hours) ending at the specified time. The data also contains precipitation amounts, however precipitation measured over 24 hours are not stored. Over time a range of rain gauges have been used - see the linked MIDAS User Guide for further details.\r\n\r\nThis version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data.\r\n\r\nThe data were collected by observation stations operated by the Met Office across the UK and transmitted within the following message types: NCM, AWSHRLY, DLY3208, SREW and SSER. The data spans from 1915 to 2020.\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by the Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record. A large proportion of the UK raingauge observing network (associated with WAHRAIN, WADRAIN and WAMRAIN for hourly, daily and monthly rainfall measurements respectively) is operated by other agencies beyond the Met Office, and are consequently currently excluded from the Midas-open dataset." }, "objectObservation": { "ob_id": 31888, "uuid": "77187ac1e0a341ca993c3366f8c59c3c", "short_code": "ob", "title": "MIDAS Open: UK hourly rainfall data, v202007", "abstract": "The UK hourly rainfall data contain the rainfall amount (and duration from tilting syphon gauges) during the hour (or hours) ending at the specified time. The data also contains precipitation amounts, however precipitation measured over 24 hours are not stored. Over time a range of rain gauges have been used - see the linked MIDAS User Guide for further details.\r\n\r\nThis version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data.\r\n\r\nThe data were collected by observation stations operated by the Met Office across the UK and transmitted within the following message types: NCM, AWSHRLY, DLY3208, SREW and SSER. The data spans from 1915 to 2019.\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by the Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record. A large proportion of the UK raingauge observing network (associated with WAHRAIN, WADRAIN and WAMRAIN for hourly, daily and monthly rainfall measurements respectively) is operated by other agencies beyond the Met Office, and are consequently currently excluded from the Midas-open dataset." } }, { "ob_id": 556, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 32984, "uuid": "0cb035c7598a4dcb8aecb6b6558c83e9", "short_code": "ob", "title": "HadUK-Grid Climate Observations by UK river basins, v1.0.3.0 (1862-2020)", "abstract": "HadUK-Grid is a collection of gridded climate variables derived from the network of UK land surface observations. The data have been interpolated from meteorological station data onto a uniform grid to provide complete and consistent coverage across the UK. These data at 1 km resolution have been averaged across a set of discrete geographies defining UK river basins consistent with data from UKCP18 climate projections. The dataset spans the period from 1862 to 2020, but the start time is dependent on climate variable and temporal resolution.\r\n\r\nThe gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.\r\n\r\nThis data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2018, see linked documentation).\r\n\r\nThis release includes data for the calendar year 2020. Ongoing quality checks and data recovery to historical data results in changes to around 0.01% of the observational station data used as input to produce the gridded dataset. A correction to _FillValue assignment in the metadata for seasonal and annual grids has also been applied to be consistent with the rest of the dataset.\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 data recovery activity to supplement 19th and early 20th Century data availability has also been funded by the Natural Environment Research Council (NERC grant ref: NE/L01016X/1) project \"Analysis of historic drought and water scarcity in the UK\". The dataset is provided under Open Government Licence." }, "objectObservation": { "ob_id": 31902, "uuid": "7d205e6cb7b4441eb3cdd5bbc4fd7829", "short_code": "ob", "title": "HadUK-Grid Climate Observations by UK river basins, v1.0.2.1 (1862-2019)", "abstract": "HadUK-Grid is a collection of gridded climate variables derived from the network of UK land surface observations. The data have been interpolated from meteorological station data onto a uniform grid to provide complete and consistent coverage across the UK. These data at 1 km resolution have been averaged across a set of discrete geographies defining UK river basins consistent with data from UKCP18 climate projections. The dataset spans the period from 1862 to 2019, but the start time is dependent on climate variable and temporal resolution.\r\n\r\nThe gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.\r\n\r\nThis data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2018, see linked documentation).\r\n\r\nFor this version of note is that historical data recovery has improved monthly rainfall 1862-1910, daily rainfall 1883-1910, monthly temperature 1900-1909, and additional sunshine grids for 1919-1928 have been added.\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 data recovery activity to supplement 19th and early 20th Century data availability has also been funded by the Natural Environment Research Council (NERC grant ref: NE/L01016X/1) project \"Analysis of historic drought and water scarcity in the UK\". The dataset is provided under Open Government Licence." } }, { "ob_id": 557, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 32985, "uuid": "489e22fb2961482bb76711cedbeecedd", "short_code": "ob", "title": "HadUK-Grid Climate Observations by UK countries, v1.0.3.0 (1862-2020)", "abstract": "HadUK-Grid is a collection of gridded climate variables derived from the network of UK land surface observations. The data have been interpolated from meteorological station data onto a uniform grid to provide complete and consistent coverage across the UK. These data at 1 km resolution have been averaged across a set of discrete geographies defining UK countries consistent with data from UKCP18 climate projections. The dataset spans the period from 1862 to 2020, but the start time is dependent on climate variable and temporal resolution.\r\n\r\nThe gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.\r\n\r\nThis data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2018, see linked documentation).\r\n\r\nThis release includes data for the calendar year 2020. Ongoing quality checks and data recovery to historical data results in changes to around 0.01% of the observational station data used as input to produce the gridded dataset. A correction to _FillValue assignment in the metadata for seasonal and annual grids has also been applied to be consistent with the rest of the dataset.\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 data recovery activity to supplement 19th and early 20th Century data availability has also been funded by the Natural Environment Research Council (NERC grant ref: NE/L01016X/1) project \"Analysis of historic drought and water scarcity in the UK\". The dataset is provided under Open Government Licence." }, "objectObservation": { "ob_id": 31901, "uuid": "4f165fc3b96b430fb6e35b859758c9ce", "short_code": "ob", "title": "HadUK-Grid Climate Observations by UK countries, v1.0.2.1 (1862-2019)", "abstract": "HadUK-Grid is a collection of gridded climate variables derived from the network of UK land surface observations. The data have been interpolated from meteorological station data onto a uniform grid to provide complete and consistent coverage across the UK. These data at 1 km resolution have been averaged across a set of discrete geographies defining UK countries consistent with data from UKCP18 climate projections. The dataset spans the period from 1862 to 2019, but the start time is dependent on climate variable and temporal resolution.\r\n\r\nThe gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.\r\n\r\nThis data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2018, see linked documentation).\r\n\r\nFor this version of note is that historical data recovery has improved monthly rainfall 1862-1910, daily rainfall 1883-1910, monthly temperature 1900-1909, and additional sunshine grids for 1919-1928 have been added.\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 data recovery activity to supplement 19th and early 20th Century data availability has also been funded by the Natural Environment Research Council (NERC grant ref: NE/L01016X/1) project \"Analysis of historic drought and water scarcity in the UK\". The dataset is provided under Open Government Licence." } }, { "ob_id": 558, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 32986, "uuid": "bc774f1b83524437a8046d8b9a9e3c6d", "short_code": "ob", "title": "HadUK-Grid Gridded Climate Observations on a 60km grid over the UK, v1.0.3.0 (1862-2020)", "abstract": "HadUK-Grid is a collection of gridded climate variables derived from the network of UK land surface observations. The data have been interpolated from meteorological station data onto a uniform grid to provide complete and consistent coverage across the UK. The dataset at 60 km resolution is derived from the associated 1 km x 1 km resolution to allow for comparison to data from UKCP18 climate projections. The dataset spans the period from 1862 to 2020, but the start time is dependent on climate variable and temporal resolution.\r\n\r\nThe gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.\r\n\r\nThis data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2018, see linked documentation).\r\n\r\nThis release includes data for the calendar year 2020. Ongoing quality checks and data recovery to historical data results in changes to around 0.01% of the observational station data used as input to produce the gridded dataset. A correction to _FillValue assignment in the metadata for seasonal and annual grids has also been applied to be consistent with the rest of the dataset.\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 data recovery activity to supplement 19th and early 20th Century data availability has also been funded by the Natural Environment Research Council (NERC grant ref: NE/L01016X/1) project \"Analysis of historic drought and water scarcity in the UK\". The dataset is provided under Open Government Licence." }, "objectObservation": { "ob_id": 31904, "uuid": "df33c78736a44019b8ceb20ab440cae1", "short_code": "ob", "title": "HadUK-Grid Gridded Climate Observations on a 60km grid over the UK, v1.0.2.1 (1862-2019)", "abstract": "HadUK-Grid is a collection of gridded climate variables derived from the network of UK land surface observations. The data have been interpolated from meteorological station data onto a uniform grid to provide complete and consistent coverage across the UK. The dataset at 60 km resolution is derived from the associated 1 km x 1 km resolution to allow for comparison to data from UKCP18 climate projections. The dataset spans the period from 1862 to 2019, but the start time is dependent on climate variable and temporal resolution.\r\n\r\nThe gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.\r\n\r\nThis data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2018, see linked documentation).\r\n\r\nFor this version of note is that historical data recovery has improved monthly rainfall 1862-1910, daily rainfall 1883-1910, monthly temperature 1900-1909, and additional sunshine grids for 1919-1928 have been added.\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 data recovery activity to supplement 19th and early 20th Century data availability has also been funded by the Natural Environment Research Council (NERC grant ref: NE/L01016X/1) project \"Analysis of historic drought and water scarcity in the UK\". The dataset is provided under Open Government Licence." } }, { "ob_id": 559, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 32987, "uuid": "616e6194a8c742d790f3b43bf66a534d", "short_code": "ob", "title": "HadUK-Grid Gridded Climate Observations on a 25km grid over the UK, v1.0.3.0 (1862-2020)", "abstract": "HadUK-Grid is a collection of gridded climate variables derived from the network of UK land surface observations. The data have been interpolated from meteorological station data onto a uniform grid to provide complete and consistent coverage across the UK. The dataset at 25 km resolution is derived from the associated 1 km x 1 km resolution to allow for comparison to data from UKCP18 climate projections. The dataset spans the period from 1862 to 2020, but the start time is dependent on climate variable and temporal resolution.\r\n\r\nThe gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.\r\n\r\nThis data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2018, see linked documentation).\r\n\r\nThis release includes data for the calendar year 2020. Ongoing quality checks and data recovery to historical data results in changes to around 0.01% of the observational station data used as input to produce the gridded dataset. A correction to _FillValue assignment in the metadata for seasonal and annual grids has also been applied to be consistent with the rest of the dataset.\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 data recovery activity to supplement 19th and early 20th Century data availability has also been funded by the Natural Environment Research Council (NERC grant ref: NE/L01016X/1) project \"Analysis of historic drought and water scarcity in the UK\". The dataset is provided under Open Government Licence." }, "objectObservation": { "ob_id": 31905, "uuid": "725e1339c06344cc813e4cb123c12f81", "short_code": "ob", "title": "HadUK-Grid Gridded Climate Observations on a 25km grid over the UK, v1.0.2.1 (1862-2019)", "abstract": "HadUK-Grid is a collection of gridded climate variables derived from the network of UK land surface observations. The data have been interpolated from meteorological station data onto a uniform grid to provide complete and consistent coverage across the UK. The dataset at 25 km resolution is derived from the associated 1 km x 1 km resolution to allow for comparison to data from UKCP18 climate projections. The dataset spans the period from 1862 to 2019, but the start time is dependent on climate variable and temporal resolution.\r\n\r\nThe gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.\r\n\r\nThis data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2018, see linked documentation).\r\n\r\nFor this version of note is that historical data recovery has improved monthly rainfall 1862-1910, daily rainfall 1883-1910, monthly temperature 1900-1909, and additional sunshine grids for 1919-1928 have been added.\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 data recovery activity to supplement 19th and early 20th Century data availability has also been funded by the Natural Environment Research Council (NERC grant ref: NE/L01016X/1) project \"Analysis of historic drought and water scarcity in the UK\". The dataset is provided under Open Government Licence." } }, { "ob_id": 560, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 32988, "uuid": "54a99222c1e741a4a70ef1caa8f10c7e", "short_code": "ob", "title": "HadUK-Grid Gridded Climate Observations on a 12km grid over the UK, v1.0.3.0 (1862-2020)", "abstract": "HadUK-Grid is a collection of gridded climate variables derived from the network of UK land surface observations. The data have been interpolated from meteorological station data onto a uniform grid to provide complete and consistent coverage across the UK. The dataset at 12 km resolution is derived from the associated 1 km x 1 km resolution to allow for comparison to data from climate projections. The dataset spans the period from 1862 to 2020, but the start time is dependent on climate variable and temporal resolution.\r\n\r\nThe gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.\r\n\r\nThis data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2018, see linked documentation). \r\n\r\nThis release includes data for the calendar year 2020. Ongoing quality checks and data recovery to historical data results in changes to around 0.01% of the observational station data used as input to produce the gridded dataset. A correction to _FillValue assignment in the metadata for seasonal and annual grids has also been applied to be consistent with the rest of the dataset.\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 data recovery activity to supplement 19th and early 20th Century data availability has also been funded by the Natural Environment Research Council (NERC grant ref: NE/L01016X/1) project \"Analysis of historic drought and water scarcity in the UK\". The dataset is provided under Open Government Licence." }, "objectObservation": { "ob_id": 31906, "uuid": "1bea761180674b9f9b1830f9aabfac15", "short_code": "ob", "title": "HadUK-Grid Gridded Climate Observations on a 12km grid over the UK, v1.0.2.1 (1862-2019)", "abstract": "HadUK-Grid is a collection of gridded climate variables derived from the network of UK land surface observations. The data have been interpolated from meteorological station data onto a uniform grid to provide complete and consistent coverage across the UK. The dataset at 12 km resolution is derived from the associated 1 km x 1 km resolution to allow for comparison to data from climate projections. The dataset spans the period from 1862 to 2019, but the start time is dependent on climate variable and temporal resolution.\r\n\r\nThe gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.\r\n\r\nThis data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2018, see linked documentation). \r\n\r\nFor this version of note is that historical data recovery has improved monthly rainfall 1862-1910, daily rainfall 1883-1910, monthly temperature 1900-1909, and additional sunshine grids for 1919-1928 have been added. Additionally, this version has corrected the grid definition used for the 12 km grid product to match UKCP18 climate model products.\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 data recovery activity to supplement 19th and early 20th Century data availability has also been funded by the Natural Environment Research Council (NERC grant ref: NE/L01016X/1) project \"Analysis of historic drought and water scarcity in the UK\". The dataset is provided under Open Government Licence." } }, { "ob_id": 561, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 32989, "uuid": "786b3ce6be54468496a3e11ce2f2669c", "short_code": "ob", "title": "HadUK-Grid Gridded Climate Observations on a 1km grid over the UK, v1.0.3.0 (1862-2020)", "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 1862 to 2020, but the start time is dependent on climate variable and temporal resolution. \r\n\r\nThe gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.\r\n\r\nThis data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2018, see linked documentation).\r\n\r\nThis release includes data for the calendar year 2020. Ongoing quality checks and data recovery to historical data results in changes to around 0.01% of the observational station data used as input to produce the gridded dataset. A correction to _FillValue assignment in the metadata for seasonal and annual grids has also been applied to be consistent with the rest of the dataset.\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 data recovery activity to supplement 19th and early 20th Century data availability has also been funded by the Natural Environment Research Council (NERC grant ref: NE/L01016X/1) project \"Analysis of historic drought and water scarcity in the UK\". The dataset is provided under Open Government Licence." }, "objectObservation": { "ob_id": 31907, "uuid": "89908dfcb97b4a28976df806b4818639", "short_code": "ob", "title": "HadUK-Grid Gridded Climate Observations on a 1km grid over the UK, v1.0.2.1 (1862-2019)", "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 1862 to 2019, but the start time is dependent on climate variable and temporal resolution. \r\n\r\nThe gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.\r\n\r\nThis data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2018, see linked documentation).\r\n\r\nFor this version of note is that historical data recovery has improved monthly rainfall 1862-1910, daily rainfall 1883-1910, monthly temperature 1900-1909, and additional sunshine grids for 1919-1928 have been added.\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 data recovery activity to supplement 19th and early 20th Century data availability has also been funded by the Natural Environment Research Council (NERC grant ref: NE/L01016X/1) project \"Analysis of historic drought and water scarcity in the UK\". The dataset is provided under Open Government Licence." } }, { "ob_id": 562, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 32990, "uuid": "f2da35c56afb4fa6aebf44094b65dff3", "short_code": "ob", "title": "HadUK-Grid Gridded Climate Observations on a 5km grid over the UK, v1.0.3.0 (1862-2020)", "abstract": "HadUK-Grid is a collection of gridded climate variables derived from the network of UK land surface observations. The data have been interpolated from meteorological station data onto a uniform grid to provide complete and consistent coverage across the UK. The dataset at 5 km resolution is derived from the associated 1 km x 1 km resolution to allow for comparison to data from UKCP18 climate projections. The dataset spans the period from 1862 to 2020, but the start time is dependent on climate variable and temporal resolution.\r\n\r\nThe gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.\r\n\r\nThis data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2018, see linked documentation).\r\n\r\nThis release includes data for the calendar year 2020. Ongoing quality checks and data recovery to historical data results in changes to around 0.01% of the observational station data used as input to produce the gridded dataset. A correction to _FillValue assignment in the metadata for seasonal and annual grids has also been applied to be consistent with the rest of the dataset.\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 data recovery activity to supplement 19th and early 20th Century data availability has also been funded by the Natural Environment Research Council (NERC grant ref: NE/L01016X/1) project \"Analysis of historic drought and water scarcity in the UK\". The dataset is provided under Open Government Licence." }, "objectObservation": { "ob_id": 31908, "uuid": "2fd7c824e7e549809c1bc6a128ad74db", "short_code": "ob", "title": "HadUK-Grid Gridded Climate Observations on a 5km grid over the UK, v1.0.2.1 (1862-2019)", "abstract": "HadUK-Grid is a collection of gridded climate variables derived from the network of UK land surface observations. The data have been interpolated from meteorological station data onto a uniform grid to provide complete and consistent coverage across the UK. The dataset at 5 km resolution is derived from the associated 1 km x 1 km resolution to allow for comparison to data from UKCP18 climate projections. The dataset spans the period from 1862 to 2019, but the start time is dependent on climate variable and temporal resolution.\r\n\r\nThe gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.\r\n\r\nThis data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2018, see linked documentation).\r\n\r\nFor this version of note is that historical data recovery has improved monthly rainfall 1862-1910, daily rainfall 1883-1910, monthly temperature 1900-1909, and additional sunshine grids for 1919-1928 have been added.\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 data recovery activity to supplement 19th and early 20th Century data availability has also been funded by the Natural Environment Research Council (NERC grant ref: NE/L01016X/1) project \"Analysis of historic drought and water scarcity in the UK\". The dataset is provided under Open Government Licence." } }, { "ob_id": 563, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 32991, "uuid": "97bc0b64bc354898a242a42238e1b45c", "short_code": "ob", "title": "HadUK-Grid Climate Observations by Administrative Regions over the UK, v1.0.3.0 (1862-2020)", "abstract": "HadUK-Grid is a collection of gridded climate variables derived from the network of UK land surface observations. The data have been interpolated from meteorological station data onto a uniform grid to provide complete and consistent coverage across the UK. These data at 1 km resolution have been averaged across a set of discrete geographies defining UK administrative regions consistent with data from UKCP18 climate projections. The dataset spans the period from 1862 to 2020, but the start time is dependent on climate variable and temporal resolution.\r\n\r\nThe gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.\r\n\r\nThis data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2018, see linked documentation).\r\n\r\nThis release includes data for the calendar year 2020. Ongoing quality checks and data recovery to historical data results in changes to around 0.01% of the observational station data used as input to produce the gridded dataset. A correction to _FillValue assignment in the metadata for seasonal and annual grids has also been applied to be consistent with the rest of the dataset.\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 data recovery activity to supplement 19th and early 20th Century data availability has also been funded by the Natural Environment Research Council (NERC grant ref: NE/L01016X/1) project \"Analysis of historic drought and water scarcity in the UK\". The dataset is provided under Open Government Licence." }, "objectObservation": { "ob_id": 31903, "uuid": "e091188f36ff41fcae8c30da1ae77ea0", "short_code": "ob", "title": "HadUK-Grid Climate Observations by Administrative Regions over the UK, v1.0.2.1 (1862-2019)", "abstract": "HadUK-Grid is a collection of gridded climate variables derived from the network of UK land surface observations. The data have been interpolated from meteorological station data onto a uniform grid to provide complete and consistent coverage across the UK. These data at 1 km resolution have been averaged across a set of discrete geographies defining UK administrative regions consistent with data from UKCP18 climate projections. The dataset spans the period from 1862 to 2019, but the start time is dependent on climate variable and temporal resolution.\r\n\r\nThe gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.\r\n\r\nThis data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2018, see linked documentation).\r\n\r\nFor this version of note is that historical data recovery has improved monthly rainfall 1862-1910, daily rainfall 1883-1910, monthly temperature 1900-1909, and additional sunshine grids for 1919-1928 have been added.\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 data recovery activity to supplement 19th and early 20th Century data availability has also been funded by the Natural Environment Research Council (NERC grant ref: NE/L01016X/1) project \"Analysis of historic drought and water scarcity in the UK\". The dataset is provided under Open Government Licence." } }, { "ob_id": 564, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 32609, "uuid": "b0de069568a141b0b074ca0f7cee004b", "short_code": "ob", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column averaged carbon dioxide from OCO-2 generated with the FOCAL algorithm, version 09", "abstract": "This dataset contains column-average dry-air mole fractions of atmospheric carbon dioxide (XCO2), using the fast atmospheric trace gas retrieval for OCO2 (FOCAL-OCO2). The FOCAL-OCO2 algorithm which has been setup to retrieve XCO2 by analysing hyper spectral solar backscattered radiance measurements from NASA's Orbiting Carbon Observatory 2 (OCO-2) satellite. FOCAL includes a radiative transfer model which has been developed to approximate light scattering effects by multiple scattering at an optically thin scattering layer. This reduces the computational costs by several orders of magnitude. FOCAL's radiative transfer model is utilised to simulate the radiance in all three OCO-2 spectral bands allowing the simultaneous retrieval of CO2, H2O, and solar induced chlorophyll fluorescence. The product is limited to cloud-free scenes on the Earth's day side. This dataset is also referred to as CO2_OC2_FOCA.\r\n\r\nThis version of the data (v09) was produced as part of the European Space Agency's (ESA) \r\nClimate Change Initiative (CCI) Greenhouse Gases (GHG) project (GHG-CCI+, http://cci.esa.int/ghg)\r\nand got co-funding from the Univ. Bremen and EU H2020 projects CHE (grant agreement no. 776186) and VERIFY (grant agreement no. 776810).\r\n\r\nWhen citing this data, please also cite the following peer-reviewed publications:\r\n\r\nM.Reuter, M.Buchwitz, O.Schneising, S.Noël, V.Rozanov, H.Bovensmann and J.P.Burrows: A Fast Atmospheric Trace Gas Retrieval for Hyperspectral Instruments Approximating Multiple Scattering - Part 1: Radiative Transfer and a Potential OCO-2 XCO2 Retrieval Setup, Remote Sensing, 9(11), 1159; doi:10.3390/rs9111159, 2017\r\n\r\nM.Reuter, M.Buchwitz, O.Schneising, S.Noël, H.Bovensmann and J.P.Burrows: A Fast Atmospheric Trace Gas Retrieval for Hyperspectral Instruments Approximating Multiple Scattering - Part 2: Application to XCO2 Retrievals from OCO-2, Remote Sensing, 9(11), 1102; doi:10.3390/rs9111102, 2017" }, "objectObservation": { "ob_id": 30178, "uuid": "b06213c3f3934a689f89ab22aa50e471", "short_code": "ob", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column averaged carbon dioxide from OCO-2 generated with the FOCAL algorithm, version 08", "abstract": "This dataset contains column-average dry-air mole fractions of atmospheric carbon dioxide (XCO2), using the fast atmospheric trace gas retrieval for OCO2 (FOCAL-OCO2). The FOCAL-OCO2 algorithm which has been setup to retrieve XCO2 by analysing hyper spectral solar backscattered radiance measurements from NASA's Orbiting Carbon Observatory 2 (OCO-2) satellite. FOCAL includes a radiative transfer model which has been developed to approximate light scattering effects by multiple scattering at an optically thin scattering layer. This reduces the computational costs by several orders of magnitude. FOCAL's radiative transfer model is utilised to simulate the radiance in all three OCO-2 spectral bands allowing the simultaneous retrieval of CO2, H2O, and solar induced chlorophyll fluorescence. The product is limited to cloud-free scenes on the Earth's day side. This dataset is also referred to as CO2_OC2_FOCA.\r\n\r\nThis version of the data was produced as part of the European Space Agency's (ESA) \r\nClimate Change Initiative (CCI) Greenhouse Gases (GHG) project (GHG-CCI+, http://cci.esa.int/ghg)\r\nand got co-funding from the Univ. Bremen and EU H2020 projects CHE (grant agreement no. 776186) and VERIFY (grant agreement no. 776810).\r\n\r\nWhen citing this dataset, please also cite the following peer-review publications:\r\n\r\nM.Reuter, M.Buchwitz, O.Schneising, S.Noël, V.Rozanov, H.Bovensmann and J.P.Burrows: A Fast Atmospheric Trace Gas Retrieval for Hyperspectral Instruments Approximating Multiple Scattering - Part 1: Radiative Transfer and a Potential OCO-2 XCO2 Retrieval Setup, Remote Sensing, 9(11), 1159; doi:10.3390/rs9111159, 2017\r\n\r\nM.Reuter, M.Buchwitz, O.Schneising, S.Noël, H.Bovensmann and J.P.Burrows: A Fast Atmospheric Trace Gas Retrieval for Hyperspectral Instruments Approximating Multiple Scattering - Part 2: Application to XCO2 Retrievals from OCO-2, Remote Sensing, 9(11), 1102; doi:10.3390/rs9111102, 2017" } }, { "ob_id": 565, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 31899, "uuid": "85b9dad7af814bfa9047a525927257f4", "short_code": "ob", "title": "MIDAS Open: UK soil temperature data, v202007", "abstract": "The UK soil temperature data contain daily and hourly values of soil temperatures at depths of 5, 10, 20, 30, 50, and 100 centimetres. The measurements were recorded by observation stations operated by the Met Office across the UK and transmitted within NCM or DLY3208 messages. The data spans from 1900 to 2019.\r\n\r\nThis version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data.\r\n\r\nAt many stations temperatures below the surface are measured at various depths. The depths used today are 5, 10, 20, 30 and 100cm, although measurements are not necessarily made at all these depths at a station and exceptionally measurements may be made at other depths. When imperial units were in general use, typically before 1961, the normal depths of measurement were 4, 8, 12, 24 and 48 inches.\r\n\r\nLiquid-in-glass soil thermometers at a depth of 20 cm or less are unsheathed and have a bend in the stem between the bulb and the lowest graduation. At greater depths the thermometer is suspended in a steel tube and has its bulb encased in wax.\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by the Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record." }, "objectObservation": { "ob_id": 27821, "uuid": "9972bc173ef94068b2070d4b26f849a7", "short_code": "ob", "title": "MIDAS Open: UK soil temperature data, v201908", "abstract": "The UK soil temperature data contain daily and hourly values of soil temperatures at depths of 5, 10, 20, 30, 50, and 100 centimetres. The measurements were recorded by observation stations operated by the Met Office across the UK and transmitted within NCM or DLY3208 messages. The data spans from 1900 to 2018.\r\n\r\nThis version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data.\r\n\r\nAt many stations temperatures below the surface are measured at various depths. The depths used today are 5, 10, 20, 30 and 100cm, although measurements are not necessarily made at all these depths at a station and exceptionally measurements may be made at other depths. When imperial units were in general use, typically before 1961, the normal depths of measurement were 4, 8, 12, 24 and 48 inches.\r\n\r\nLiquid-in-glass soil thermometers at a depth of 20 cm or less are unsheathed and have a bend in the stem between the bulb and the lowest graduation. At greater depths the thermometer is suspended in a steel tube and has its bulb encased in wax.\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by the Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record." } }, { "ob_id": 566, "relationType": "IsVariantFormOf", "subjectObservation": { "ob_id": 32738, "uuid": "5c2b70d069cb467ab73e80b84c3e395a", "short_code": "ob", "title": "Global ocean lagrangian trajectories based on AVISO velocities, v2.2", "abstract": "The National Centre for Earth Observation (NCEO) Long Term Science Single Centre (LTSS) Global Ocean Lagrangian Trajectories (OLTraj) provide 30-day forward and backward Lagrangian trajectories based on AVISO (Satellite Altimetry Data project) surface velocities. Each trajectory represents the path that a water mass would move along starting at a given pixel and a given day. OLTraj can be thus used to implement analyses of oceanic data in a Lagrangian framework. The purpose of OLTraj is to allow non-specialists to conduct Lagrangian analyses of surface ocean data.\r\n\r\nThe dataset has global coverage and spans 1998-2019 with a daily temporal resolution. The trajectories were generated starting from zonal and meridional model velocity fields that were integrated using the LAMTA (6-hour time step - part of ) as described in Nencioli et al., 2018 and SPASSO (Software package for and adaptive satellite-based sampling for ocean graphic cruises containing LAMTA) software user guide. Please see the documentation section below for further information.\r\n\r\nVersion 2.2 is a higher resolution version of V2.0 and also has double value for time variables to permit access via THREDDS" }, "objectObservation": { "ob_id": 32470, "uuid": "fe3cb5120fa74fa7974820c2e2a238a7", "short_code": "ob", "title": "Global Ocean Lagrangian Trajectories based on AVISO velocities, v2.0, 1998-2018", "abstract": "The National Centre for Earth Observation (NCEO) Long Term Science Single Centre (LTSS) Global Ocean Lagrangian Trajectories (OLTraj) provides 30-day forward and backward Lagrangian trajectories based on AVISO (Satellite Altimetry Data project) surface velocities. Each trajectory represents the path that a water mass would move along starting at a given pixel and a given day. OLTraj can be thus used to implement analyses of oceanic data in a Lagrangian framework. The purpose of OLTraj is to allow non-specialists to conduct Lagrangian analyses of surface ocean data.\r\n\r\nThe dataset has global coverage and spans 1998-2018 with a daily temporal resolution. The trajectories were generated starting from zonal and meridional model velocity fields that were integrated using the LAMTA (6-hour time step - part of ) as described in Nencioli et al., 2018 and SPASSO (Software package for and adaptive satellite-based sampling for ocean graphic cruises containing LAMTA) software user guide. Please see the documentation section below for further information." } }, { "ob_id": 567, "relationType": "IsVariantFormOf", "subjectObservation": { "ob_id": 32738, "uuid": "5c2b70d069cb467ab73e80b84c3e395a", "short_code": "ob", "title": "Global ocean lagrangian trajectories based on AVISO velocities, v2.2", "abstract": "The National Centre for Earth Observation (NCEO) Long Term Science Single Centre (LTSS) Global Ocean Lagrangian Trajectories (OLTraj) provide 30-day forward and backward Lagrangian trajectories based on AVISO (Satellite Altimetry Data project) surface velocities. Each trajectory represents the path that a water mass would move along starting at a given pixel and a given day. OLTraj can be thus used to implement analyses of oceanic data in a Lagrangian framework. The purpose of OLTraj is to allow non-specialists to conduct Lagrangian analyses of surface ocean data.\r\n\r\nThe dataset has global coverage and spans 1998-2019 with a daily temporal resolution. The trajectories were generated starting from zonal and meridional model velocity fields that were integrated using the LAMTA (6-hour time step - part of ) as described in Nencioli et al., 2018 and SPASSO (Software package for and adaptive satellite-based sampling for ocean graphic cruises containing LAMTA) software user guide. Please see the documentation section below for further information.\r\n\r\nVersion 2.2 is a higher resolution version of V2.0 and also has double value for time variables to permit access via THREDDS" }, "objectObservation": { "ob_id": 32702, "uuid": "12eae5e708e541f390898af4187a1c20", "short_code": "ob", "title": "Global Ocean Lagrangian Trajectories based on AVISO velocities, v2.1", "abstract": "The National Centre for Earth Observation (NCEO) Long Term Science Single Centre (LTSS) Global Ocean Lagrangian Trajectories (OLTraj) provides 30-day forward and backward Lagrangian trajectories based on AVISO (Satellite Altimetry Data project) surface velocities. Each trajectory represents the path that a water mass would move along starting at a given pixel and a given day. OLTraj can be thus used to implement analyses of oceanic data in a Lagrangian framework. The purpose of OLTraj is to allow non-specialists to conduct Lagrangian analyses of surface ocean data.\r\n\r\nThe dataset has global coverage and spans the year 2018 with a daily temporal resolution. The trajectories were generated starting from zonal and meridional model velocity fields that were integrated using the LAMTA (6-hour time step - part of ) as described in Nencioli et al., 2018 and SPASSO (Software package for and adaptive satellite-based sampling for ocean graphic cruises containing LAMTA) software user guide. Please see the documentation section below for further information.\r\n\r\nVersion 2.1 has the same resolution as version V2.0 but has double value for time variables to permit access via THREDDS" } }, { "ob_id": 568, "relationType": "IsDerivedFrom", "subjectObservation": { "ob_id": 12222, "uuid": "befddc832f8947e8834188df4661565d", "short_code": "ob", "title": "Wardon Hill C-band rain radar 5 km rainfall rate data", "abstract": "5 km resolution rain rate data from Met Office's Wardon Hill C-band rain radar, Dorset, England as part of the NIMROD, very short range forecasting system used by the Met Office. 5 km rain rate data are available from 2006 until present. Radar images from the C-band (5.3 cm wavelength) radar are received by the Nimrod system 5 minute intervals respectively.\r\n\r\nPlease note that this radar is operated for research purposes only and thus subject to changes in operation and data are subject to interruptions. \r\n\r\nData from the Cobbacombe Cross, Dean Hill or Jersey radars provide coverage for the same area covered by the Wardon Hill radar." }, "objectObservation": { "ob_id": 5719, "uuid": "910f888f47966082800a228a25ee76aa", "short_code": "ob", "title": "Wardon Hill C-band rain radar dual polar products", "abstract": "Dual-polar products from the Met Office's Wardon Hill C-band rain radar, Dorset, England. Data include augmented ldr (both long and short pulse), augmented zdr (both long and short pulse) and augmented refractivity scan data. The radar is a C-band (5.3 cm wavelength) radar and data are received by the Nimrod system at 5 minute intervals.\r\n\r\nPlease note that this radar is operated for research purposes only and thus subject to changes in operation and data are subject to interruptions. \r\n\r\nData from the Cobbacombe Cross, Dean Hill or Jersey radars provide coverage for the same area covered by the Wardon Hill radar." } }, { "ob_id": 569, "relationType": "IsDerivedFrom", "subjectObservation": { "ob_id": 5750, "uuid": "fec3e0939484ebe85d64c02790d1fc40", "short_code": "ob", "title": "Wardon Hill C-band rain radar 2 km rainfall rate data", "abstract": "2 km resolution rain rate data from Met Office's Wardon Hill C-band rain radar, Dorset, England as part of the NIMROD, very short range forecasting system used by the Met Office. 1 km rain rate data are available from 2006 until present. Radar images from the C-band (5.3 cm wavelength) radar are received by the Nimrod system 5 minute intervals respectively.\r\n\r\nPlease note that this radar is operated for research purposes only and thus subject to changes in operation and data are subject to interruptions. \r\n\r\nData from the Cobbacombe Cross, Dean Hill or Jersey radars provide coverage for the same area covered by the Wardon Hill radar." }, "objectObservation": { "ob_id": 5719, "uuid": "910f888f47966082800a228a25ee76aa", "short_code": "ob", "title": "Wardon Hill C-band rain radar dual polar products", "abstract": "Dual-polar products from the Met Office's Wardon Hill C-band rain radar, Dorset, England. Data include augmented ldr (both long and short pulse), augmented zdr (both long and short pulse) and augmented refractivity scan data. The radar is a C-band (5.3 cm wavelength) radar and data are received by the Nimrod system at 5 minute intervals.\r\n\r\nPlease note that this radar is operated for research purposes only and thus subject to changes in operation and data are subject to interruptions. \r\n\r\nData from the Cobbacombe Cross, Dean Hill or Jersey radars provide coverage for the same area covered by the Wardon Hill radar." } }, { "ob_id": 570, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 5750, "uuid": "fec3e0939484ebe85d64c02790d1fc40", "short_code": "ob", "title": "Wardon Hill C-band rain radar 2 km rainfall rate data", "abstract": "2 km resolution rain rate data from Met Office's Wardon Hill C-band rain radar, Dorset, England as part of the NIMROD, very short range forecasting system used by the Met Office. 1 km rain rate data are available from 2006 until present. Radar images from the C-band (5.3 cm wavelength) radar are received by the Nimrod system 5 minute intervals respectively.\r\n\r\nPlease note that this radar is operated for research purposes only and thus subject to changes in operation and data are subject to interruptions. \r\n\r\nData from the Cobbacombe Cross, Dean Hill or Jersey radars provide coverage for the same area covered by the Wardon Hill radar." }, "objectObservation": { "ob_id": 12180, "uuid": "4446c25cb42a446eb585fc876670beb2", "short_code": "ob", "title": "Cobbacombe Cross C-band rain radar 2 km rainfall rate data", "abstract": "2 km resolution rain rate data from Met Office's Cobbacombe Cross C-band rain radar, Devon, England as part of the NIMROD, very short range forecasting system used by the Met Office. 2 km rain rate data are available from April 2004 until present. Radar images from the C-band (5.3 cm wavelength) radar are received by the Nimrod system 5 minute intervals respectively." } }, { "ob_id": 571, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 5750, "uuid": "fec3e0939484ebe85d64c02790d1fc40", "short_code": "ob", "title": "Wardon Hill C-band rain radar 2 km rainfall rate data", "abstract": "2 km resolution rain rate data from Met Office's Wardon Hill C-band rain radar, Dorset, England as part of the NIMROD, very short range forecasting system used by the Met Office. 1 km rain rate data are available from 2006 until present. Radar images from the C-band (5.3 cm wavelength) radar are received by the Nimrod system 5 minute intervals respectively.\r\n\r\nPlease note that this radar is operated for research purposes only and thus subject to changes in operation and data are subject to interruptions. \r\n\r\nData from the Cobbacombe Cross, Dean Hill or Jersey radars provide coverage for the same area covered by the Wardon Hill radar." }, "objectObservation": { "ob_id": 5760, "uuid": "875eef122e8018e5d5b5a63a8b39dd23", "short_code": "ob", "title": "Jersey C-band rain radar 2 km rainfall rate data", "abstract": "2 km resolution data from the NIMROD system data describe rain-rate observations recorded by the Jersery rain radar, Channel Islands, by NIMROD, which is a very short range forecasting system used by the Met Office. 2 km rain rate data are available from 2004 until present. Radar images from the C-band (5.3 cm wavelength) radar are received by the Nimrod system 5 minute intervals." } }, { "ob_id": 572, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 12222, "uuid": "befddc832f8947e8834188df4661565d", "short_code": "ob", "title": "Wardon Hill C-band rain radar 5 km rainfall rate data", "abstract": "5 km resolution rain rate data from Met Office's Wardon Hill C-band rain radar, Dorset, England as part of the NIMROD, very short range forecasting system used by the Met Office. 5 km rain rate data are available from 2006 until present. Radar images from the C-band (5.3 cm wavelength) radar are received by the Nimrod system 5 minute intervals respectively.\r\n\r\nPlease note that this radar is operated for research purposes only and thus subject to changes in operation and data are subject to interruptions. \r\n\r\nData from the Cobbacombe Cross, Dean Hill or Jersey radars provide coverage for the same area covered by the Wardon Hill radar." }, "objectObservation": { "ob_id": 12182, "uuid": "a02c22c28c664aaa8ce4f43d503bddc4", "short_code": "ob", "title": "Cobbacombe Cross C-band rain radar 5 km rainfall rate data", "abstract": "5 km resolution rain rate data from Met Office's Cobbacombe Cross C-band rain radar, Devon, England as part of the NIMROD, very short range forecasting system used by the Met Office. 5 km rain rate data are available from April 2004 until present. Radar images from the C-band (5.3 cm wavelength) radar are received by the Nimrod system 5 minute intervals respectively." } }, { "ob_id": 573, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 12222, "uuid": "befddc832f8947e8834188df4661565d", "short_code": "ob", "title": "Wardon Hill C-band rain radar 5 km rainfall rate data", "abstract": "5 km resolution rain rate data from Met Office's Wardon Hill C-band rain radar, Dorset, England as part of the NIMROD, very short range forecasting system used by the Met Office. 5 km rain rate data are available from 2006 until present. Radar images from the C-band (5.3 cm wavelength) radar are received by the Nimrod system 5 minute intervals respectively.\r\n\r\nPlease note that this radar is operated for research purposes only and thus subject to changes in operation and data are subject to interruptions. \r\n\r\nData from the Cobbacombe Cross, Dean Hill or Jersey radars provide coverage for the same area covered by the Wardon Hill radar." }, "objectObservation": { "ob_id": 12212, "uuid": "1bc73bbf64b6427fb8652bdc3de0ecc1", "short_code": "ob", "title": "Jersey C-band rain radar 5 km rainfall rate data", "abstract": "5 km resolution data from the NIMROD system data describe rain-rate observations recorded by the Jersery rain radar, Channel Islands, by NIMROD, which is a very short range forecasting system used by the Met Office. 5 km rain rate data are available from 2004 until present. Radar images from the C-band (5.3 cm wavelength) radar are received by the Nimrod system 5 minute intervals." } }, { "ob_id": 574, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 5719, "uuid": "910f888f47966082800a228a25ee76aa", "short_code": "ob", "title": "Wardon Hill C-band rain radar dual polar products", "abstract": "Dual-polar products from the Met Office's Wardon Hill C-band rain radar, Dorset, England. Data include augmented ldr (both long and short pulse), augmented zdr (both long and short pulse) and augmented refractivity scan data. The radar is a C-band (5.3 cm wavelength) radar and data are received by the Nimrod system at 5 minute intervals.\r\n\r\nPlease note that this radar is operated for research purposes only and thus subject to changes in operation and data are subject to interruptions. \r\n\r\nData from the Cobbacombe Cross, Dean Hill or Jersey radars provide coverage for the same area covered by the Wardon Hill radar." }, "objectObservation": { "ob_id": 12209, "uuid": "c3e80be157644981a0b6badda8d2e454", "short_code": "ob", "title": "Cobbacombe Cross C-band rain radar dual polar products", "abstract": "Dual-polar products from the Met Office's Cobbacombe Cross C-band rain radar, Devon, England. Data include augmented ldr and zdr scan data (both long and short pulse). The radar is a C-band (5.3 cm wavelength) radar and data are received by the Nimrod system at 5 minute intervals." } }, { "ob_id": 575, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 5719, "uuid": "910f888f47966082800a228a25ee76aa", "short_code": "ob", "title": "Wardon Hill C-band rain radar dual polar products", "abstract": "Dual-polar products from the Met Office's Wardon Hill C-band rain radar, Dorset, England. Data include augmented ldr (both long and short pulse), augmented zdr (both long and short pulse) and augmented refractivity scan data. The radar is a C-band (5.3 cm wavelength) radar and data are received by the Nimrod system at 5 minute intervals.\r\n\r\nPlease note that this radar is operated for research purposes only and thus subject to changes in operation and data are subject to interruptions. \r\n\r\nData from the Cobbacombe Cross, Dean Hill or Jersey radars provide coverage for the same area covered by the Wardon Hill radar." }, "objectObservation": { "ob_id": 32605, "uuid": "5b22789f362c43f3b3d1c65bc30c30ee", "short_code": "ob", "title": "Deanhill C-band rain radar dual polar products", "abstract": "Dual-polar products from the Met Office's Deanhill C-band rain radar, Whiteparish, Wiltshire, England. Data include augmented ldr (linear depolarization ratio) and zdr (differential reflectivity) scan data (both long and short pulse). The radar is a C-band (5.3 cm wavelength) radar and data are received by the Nimrod system at 5 minute intervals." } }, { "ob_id": 576, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 5719, "uuid": "910f888f47966082800a228a25ee76aa", "short_code": "ob", "title": "Wardon Hill C-band rain radar dual polar products", "abstract": "Dual-polar products from the Met Office's Wardon Hill C-band rain radar, Dorset, England. Data include augmented ldr (both long and short pulse), augmented zdr (both long and short pulse) and augmented refractivity scan data. The radar is a C-band (5.3 cm wavelength) radar and data are received by the Nimrod system at 5 minute intervals.\r\n\r\nPlease note that this radar is operated for research purposes only and thus subject to changes in operation and data are subject to interruptions. \r\n\r\nData from the Cobbacombe Cross, Dean Hill or Jersey radars provide coverage for the same area covered by the Wardon Hill radar." }, "objectObservation": { "ob_id": 5725, "uuid": "2fa373b69f5015339df43709e5c8b656", "short_code": "ob", "title": "Jersey C-band rain radar single polar products", "abstract": "Single-polar products from the Met Office's Jersey C-band rain radar, Channel Islands. Data include reflectivity wand Doppler products from April 2012 and May 2014 respectively. The radar is a C-band (5.3 cm wavelength) radar and data are received by the Nimrod system at 5 minute intervals." } }, { "ob_id": 579, "relationType": "IsSupplementTo", "subjectObservation": { "ob_id": 33116, "uuid": "67ad0c489e2b4d18aa152e78f28ae0c0", "short_code": "ob", "title": "1/12 degree Nucleus for European Modelling of the Ocean (NEMO) model of the Southern Ocean: JRA55-do interannually-varying forced control run (1978 - 2017)", "abstract": "The dataset is a 40 year control run of a NEMO-based 1/12 degree grid spacing model of the Southern Ocean as part of the ORCHESTRA (Ocean Regulation of Climate by Heat and Carbon Sequestration and Transports) LTS-M project. It uses the NEMO \"extended\" grid, although ice cavities are closed. The model was run on Archer, the national HPC platform. The dataset covers the full length of the model run and includes regular (5 day mean) output of the model state, as well as more frequent (1 day mean) output of surface variables and fluxes and 1 month mean of more extensive transport diagnostics. This is the second of two control runs and was initialised from the end of the 30th year (nominally 1978) of CORE2NYF (Munday et al., 2021), a 3+37 year control run forced with CORE2 (corrected normal year forcing version 2.0) normal year forcing.\r\n\r\nForced by JRA55-do, an interannually-varying forcing set (Tsujino et al., 2018). With some additional forcing as supplied by the UK Met Office (freshwater runoff, tidal friction, geothermal heating) and additional freshwater runoff to suppress polynya formation." }, "objectObservation": { "ob_id": 32844, "uuid": "2e982e6692e3427dbe35e64ad9dee12d", "short_code": "ob", "title": "1/12 degree Nucleus for European Modelling of the Ocean (NEMO) model of the Southern Ocean: CORE2 normal year forced control run (1951-1987)", "abstract": "The dataset is a 37 year control run of a NEMO-based 1/12 degree grid spacing model of the Southern Ocean as part of the ORCHESTRA LTS-M project. It uses the NEMO \"extended\" grid, although ice cavities are closed. The model was run on Archer, the national HPC platform. The dataset covers the full length of the model run (excluding a three year spinup period) and includes regular (5 day mean) output of the model state, as well as more frequent (1 day mean) output of surface variables and fluxes and 1 month mean of more extensive transport diagnostics.\r\n\r\nForced by the GFDL (Geophysical Fluid Dynamics Laboratory) CORE2 (corrected normal year forcing version 2.0) normal year forcing. With some additional forcing as supplied by the UK Met Office (freshwater runoff, tidal friction, geothermal heating) and additional freshwater runoff to suppress polynya formation. Initialised from January of a climatology of ECCOv4r2 (Estimating the Circulation and Climate of the Ocean) in nominal year 1948." } }, { "ob_id": 580, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 13919, "uuid": "4aba1697ab3041148f1e5191679411dc", "short_code": "ob", "title": "HiTemp: High Density Temperature measurements within the Urban Birmingham Conurbation.", "abstract": "Temperature data from a high density network of meteorological sensors installed within the Birmingham conurbation: low-cost, battery-powered WiFi Aginova Sentinel Micro air temperature sensors were operated at 73 stations between 2012-14.\r\n\r\nThese measurements have been made by the Birmingham Urban Climate Laboratory (BUCL) for the HiTemp (High Density Measurements within the Urban Environment) project in order to study the Birmingham Urban Heat Island (UHI)" }, "objectObservation": { "ob_id": 33141, "uuid": "bfa7f9b9bde5414c96a281fa0a566e0c", "short_code": "ob", "title": "HiTemp: High density temperature and meteorological measurements within the Urban Birmingham conurbation. CSV version as supplied by project team.", "abstract": "This dataset contains temperature and meteorological data from a high density network of meteorological sensors installed within the Birmingham conurbation: This includes low-cost, battery-powered WiFi Aginova Sentinel Micro air temperature sensors, operated at 73 stations and 25 Vaisala WXT520 weather transmitters measuring temperature, precipitation, relative humidity, wind speed and direction, pressure, solar radiation and quality flags. \r\n\r\nThis data is the basic Comma Separated Variable (CSV) format version as supplied by the project team. Reformatted versions with inclusive metadata have superceded this data.\r\n\r\nThese measurements have been made by the Birmingham Urban Climate Laboratory (BUCL) for the HiTemp (High Density Measurements within the Urban Environment) project in order to study the Birmingham Urban Heat Island (UHI)" } }, { "ob_id": 581, "relationType": "IsSupplementTo", "subjectObservation": { "ob_id": 33115, "uuid": "aa4106a7a35246dfb84fb925a7d65650", "short_code": "ob", "title": "1/12 degree Nucleus for European Modelling of the Ocean (NEMO) model of the Southern Ocean: JRA55-do with absolute wind stress experiment (1988 - 2007)", "abstract": "The dataset is a 20 year experiment using a NEMO-based 1/12 degree grid spacing model of the Southern Ocean as part of the ORCHESTRA (Ocean Regulation of Climate by Heat and Carbon Sequestration and Transports) LTS-M project. It uses the NEMO \"extended\" grid, although ice cavities are closed. The model was run on Archer, the national HPC platform. The dataset covers the full length of the model run and includes regular (5 day mean) output of the model state, as well as more frequent (1 day mean) output of surface variables and fluxes and 1 month mean of more extensive transport diagnostics. The experiment neglects the ocean surface current in the bulk formula calculations for surface fluxes, so-called absolute wind stress. It starts from the end of 1987 of JRA55IAF (Munday et al., 2021).\r\n\r\nForced by JRA55-do, an interannually-varying forcing set (Tsujino et al., 2018). With some additional forcing as supplied by the UK Met Office (freshwater runoff, tidal friction, geothermal heating) and additional freshwater runoff to suppress polynya formation." }, "objectObservation": { "ob_id": 33116, "uuid": "67ad0c489e2b4d18aa152e78f28ae0c0", "short_code": "ob", "title": "1/12 degree Nucleus for European Modelling of the Ocean (NEMO) model of the Southern Ocean: JRA55-do interannually-varying forced control run (1978 - 2017)", "abstract": "The dataset is a 40 year control run of a NEMO-based 1/12 degree grid spacing model of the Southern Ocean as part of the ORCHESTRA (Ocean Regulation of Climate by Heat and Carbon Sequestration and Transports) LTS-M project. It uses the NEMO \"extended\" grid, although ice cavities are closed. The model was run on Archer, the national HPC platform. The dataset covers the full length of the model run and includes regular (5 day mean) output of the model state, as well as more frequent (1 day mean) output of surface variables and fluxes and 1 month mean of more extensive transport diagnostics. This is the second of two control runs and was initialised from the end of the 30th year (nominally 1978) of CORE2NYF (Munday et al., 2021), a 3+37 year control run forced with CORE2 (corrected normal year forcing version 2.0) normal year forcing.\r\n\r\nForced by JRA55-do, an interannually-varying forcing set (Tsujino et al., 2018). With some additional forcing as supplied by the UK Met Office (freshwater runoff, tidal friction, geothermal heating) and additional freshwater runoff to suppress polynya formation." } }, { "ob_id": 582, "relationType": "IsSupplementTo", "subjectObservation": { "ob_id": 32759, "uuid": "465d86a5750447128f24f79c4f2ecdd4", "short_code": "ob", "title": "BBUBL: 1 km gridded output from WRF v3.6.1 model runs for the Birmingham conurbation for 2015", "abstract": "This dataset contains a range of parameters from a 1 km gridded output from runs of version 3.6.1 of the Weather Research and Forecasting (WRF) model deployed on the ARCHER UK National Supercomputing Service. These runs were part of the NERC funded BBUBL project (Biotelemetry/Bio-aerial-platforms for the Urban Boundary Layer - also known as City Flocks, NERC grant award NE/N003195/1). The domain of the model runs was over the set over Birmingham conurbation for all of 2015. This geo-temporal domain encompasses measurements of the urban boundary layer obtained from instrumentation attached to birds flown around the area. See related dataset.\r\n\r\nThe WRF model set up followed that used by Heaviside et al. (2015) - see linked documentation for details - and was run on the ARCHER UK National Supercomputing Service. Meteorology data from the European Centre for Medium-range Weather Forecasts (ECMWF) ERA-interim reanalysis data for initial and lateral boundary conditions.\r\n\r\nThe WRF v3.6.1 model set up implemented in this study included four nested domains. The domains had grid resolutions of 36 km x 36 km, 12 km x 12 km, 3 km x 3 km and 1 km x 1 km. The finest domain covered the West Midlands, centering over Birmingham. The multi-layer building energy parametrization (BEP) scheme with three land-use types (low-intensity residential, high-intensity residential and industrial/commercial) was also used." }, "objectObservation": { "ob_id": 33121, "uuid": "f5a2bbcadec2428cab157653a7039919", "short_code": "ob", "title": "BBUBL: airborne meteorological measurements from various avian sensor packages for flights in 2018-19 over the Birmingham conurbation", "abstract": "This dataset contains data from Avian-Meteorology-Instrument Packages (AvMIPs) from a series of flights over Birmingham as part of the Biotelemetry/Bio-aerial-platforms for the Urban Boundary Layer (BBUBL) project (NERC grant: NE/N003195/1), also known as City Flocks. The flights took place in 2018 and 2019.\r\n\r\nThe BBUBL project utilised Biotelemetry/bio-aerial-platforms as a novel and practicable solution to the data paucity above urban rooftops in the Urban Boundary Layer. The project developed a suite of low-cost Avian-Meteorology-Instrument Packages (AvMIPs) for ensemble deployment in Birmingham as a suitably large and heterogeneous test case.\r\n\r\nA range of different sensor packages were used which were subsequently further characterised through air-tunnel tests. See Thomas et al. (2018) citation (DOI:10.1175/BAMS-D-16-0181.1) listed in the online resources section of this record for further details, including a precursor system flown on a larger bird species. In summary, temperature sensors were compared against U.K. Accreditation Service (UKAS)-accredited sensors in a controlled temperature chamber (WKL 34/40; Weiss Technik, Belgium and Germany) and in ambient conditions at the University of Birmingham weather station.\r\n\r\nAdditional material by Thomas et al. (2018) included in the online resource section of this record provide additional material regarding the project and instrumentation usage.\r\n\r\nNote, within the data there are a range of sensor packet and bird IDs used to denote the different bird-sensor package combinations used. A range of sensor packets were used, with one lost and others found unusable, resulting in the sensor packet numbers shown in the data. Bird ID were taken from the bird ring numbers unless clashes existed, in which case an alternative two digit number was used, therefore are not consecutive and no other data from other bird and sensor package combinations are available." } }, { "ob_id": 583, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 32759, "uuid": "465d86a5750447128f24f79c4f2ecdd4", "short_code": "ob", "title": "BBUBL: 1 km gridded output from WRF v3.6.1 model runs for the Birmingham conurbation for 2015", "abstract": "This dataset contains a range of parameters from a 1 km gridded output from runs of version 3.6.1 of the Weather Research and Forecasting (WRF) model deployed on the ARCHER UK National Supercomputing Service. These runs were part of the NERC funded BBUBL project (Biotelemetry/Bio-aerial-platforms for the Urban Boundary Layer - also known as City Flocks, NERC grant award NE/N003195/1). The domain of the model runs was over the set over Birmingham conurbation for all of 2015. This geo-temporal domain encompasses measurements of the urban boundary layer obtained from instrumentation attached to birds flown around the area. See related dataset.\r\n\r\nThe WRF model set up followed that used by Heaviside et al. (2015) - see linked documentation for details - and was run on the ARCHER UK National Supercomputing Service. Meteorology data from the European Centre for Medium-range Weather Forecasts (ECMWF) ERA-interim reanalysis data for initial and lateral boundary conditions.\r\n\r\nThe WRF v3.6.1 model set up implemented in this study included four nested domains. The domains had grid resolutions of 36 km x 36 km, 12 km x 12 km, 3 km x 3 km and 1 km x 1 km. The finest domain covered the West Midlands, centering over Birmingham. The multi-layer building energy parametrization (BEP) scheme with three land-use types (low-intensity residential, high-intensity residential and industrial/commercial) was also used." }, "objectObservation": { "ob_id": 33121, "uuid": "f5a2bbcadec2428cab157653a7039919", "short_code": "ob", "title": "BBUBL: airborne meteorological measurements from various avian sensor packages for flights in 2018-19 over the Birmingham conurbation", "abstract": "This dataset contains data from Avian-Meteorology-Instrument Packages (AvMIPs) from a series of flights over Birmingham as part of the Biotelemetry/Bio-aerial-platforms for the Urban Boundary Layer (BBUBL) project (NERC grant: NE/N003195/1), also known as City Flocks. The flights took place in 2018 and 2019.\r\n\r\nThe BBUBL project utilised Biotelemetry/bio-aerial-platforms as a novel and practicable solution to the data paucity above urban rooftops in the Urban Boundary Layer. The project developed a suite of low-cost Avian-Meteorology-Instrument Packages (AvMIPs) for ensemble deployment in Birmingham as a suitably large and heterogeneous test case.\r\n\r\nA range of different sensor packages were used which were subsequently further characterised through air-tunnel tests. See Thomas et al. (2018) citation (DOI:10.1175/BAMS-D-16-0181.1) listed in the online resources section of this record for further details, including a precursor system flown on a larger bird species. In summary, temperature sensors were compared against U.K. Accreditation Service (UKAS)-accredited sensors in a controlled temperature chamber (WKL 34/40; Weiss Technik, Belgium and Germany) and in ambient conditions at the University of Birmingham weather station.\r\n\r\nAdditional material by Thomas et al. (2018) included in the online resource section of this record provide additional material regarding the project and instrumentation usage.\r\n\r\nNote, within the data there are a range of sensor packet and bird IDs used to denote the different bird-sensor package combinations used. A range of sensor packets were used, with one lost and others found unusable, resulting in the sensor packet numbers shown in the data. Bird ID were taken from the bird ring numbers unless clashes existed, in which case an alternative two digit number was used, therefore are not consecutive and no other data from other bird and sensor package combinations are available." } }, { "ob_id": 584, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 32841, "uuid": "7da8723b16e94771be1a2717d8a6e2fe", "short_code": "ob", "title": "ESA Sea Surface Salinity Climate Change Initiative (Sea_Surface_Salinity_cci): Monthly sea surface salinity product, v03.21, for 2010 to 2020", "abstract": "The ESA Sea Surface Salinity Climate Change Initiative (CCI) consortium has produced global, level 4, multi-sensor Sea Surface Salinity maps covering the 2010-2020 period.\r\n\r\nThis dataset provides Sea Surface Salinity (SSS) data at a spatial resolution of 25 km and a time resolution of 1 month. This has been spatially sampled on a 25 km EASE (Equal Area Scalable Earth) grid and 15 days of time sampling. A weekly product is also available. In addition to salinity, information on errors are provided. For more information, see the user guide and other product documentation available from the linked Sea Surface Salinity CCI web page.\r\n\r\nCompared to the previous version of the data, version 3 SSS and associated uncertainties are more precise and cover a longer period (Jan 2010-sept 2020); version 3 SSS are provided closer to land than version 2 SSS, with a possible degraded quality. Users might remove these additional near land data by using the lsc_qc flag." }, "objectObservation": { "ob_id": 30645, "uuid": "7813eb75a131474a8d908f69c716b031", "short_code": "ob", "title": "ESA Sea Surface Salinity Climate Change Initiative (Sea_Surface_Salinity_cci): Monthly sea surface salinity product, v2.31, for 2010 to 2019", "abstract": "The ESA Sea Surface Salinity CCI consortium has produced global, level 4, multi-sensor Sea Surface Salinity maps covering the 2010-2019 period.\r\n\r\nThis dataset provides Sea Surface Salinity (SSS) data at a spatial resolution of 25 km and a time resolution of 1 month. This has been spatially sampled on a 25 km EASE (Equal Area Scalable Earth) grid and 15 days of time sampling. A weekly product is also available. In addition to salinity, information on errors are provided (see more in the user guide and product documentation available below and on the Sea Surface Salinity CCI web page).\r\n\r\nAn overview paper about CCI SSS is now published:\r\n\r\nBoutin, J., N. Reul, J. Koehler, A. Martin, R. Catany, S. Guimbard, F. Rouffi, et al. (2021), Satellite-Based Sea Surface Salinity Designed for Ocean and Climate Studies, Journal of Geophysical Research: Oceans, 126(11), e2021JC017676, doi:https://doi.org/10.1029/2021JC017676.\r\n\r\nAn updated version of CCI SSS (version 3.21) is now available on: https://catalogue.ceda.ac.uk/uuid/5920a2c77e3c45339477acd31ce62c3c ; version 3 SSS and associated uncertainties are more precise and cover a longer period (Jan 2010-sept 2020); version 3 SSS are provided closer to land than version 2 SSS, with a possible degraded quality. Users might remove these additional near land data by using the lsc_qc flag." } }, { "ob_id": 585, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 32842, "uuid": "fad2e982a59d44788eda09e3c67ed7d5", "short_code": "ob", "title": "ESA Sea Surface Salinity Climate Change Initiative (Sea_Surface_Salinity_cci): Weekly sea surface salinity product, v03.21, for 2010 to 2020", "abstract": "The ESA Sea Surface Salinity Climate Change Initiative (CCI) consortium has produced global, level 4, multi-sensor Sea Surface Salinity maps covering the 2010-2020 period.\r\n\r\nThis dataset contains Sea Surface Salinity (SSS) v03.21 data at a spatial resolution of 50 km and a time resolution of 1 week. It has been spatially sampled on a 25 km EASE (Equal Area Scalable Earth) grid and 1 day of time sampling. A monthly product is also available. In addition to salinity, information on errors are provided. For more information see the user guide and other product documentation available from the linked Sea Surface Salinity CCI web page).\r\n\r\nCompared to the previous version of the data, version 3 SSS and associated uncertainties are more precise and cover a longer period (Jan 2010-sept 2020); version 3 SSS are provided closer to land than version 2 SSS, with a possible degraded quality. Users might remove these additional near land data by using the lsc_qc flag." }, "objectObservation": { "ob_id": 30644, "uuid": "eacb7580e1b54afeaabb0fd2b0a53828", "short_code": "ob", "title": "ESA Sea Surface Salinity Climate Change Initiative (Sea_Surface_Salinity_cci): Weekly sea surface salinity product, v2.31, for 2010 to 2019", "abstract": "The ESA Sea Surface Salinity CCI consortium has produced global, level 4, multi-sensor Sea Surface Salinity maps covering the 2010-2019 period.\r\n\r\nThis dataset contains Sea Surface Salinity (SSS) v2.31 data at a spatial resolution of 50 km and a time resolution of 1 week. It has been spatially sampled on a 25 km EASE (Equal Area Scalable Earth) grid and 1 day of time sampling. A monthly product is also available. In addition to salinity, information on errors are provided (see more in the user guide and product documentation available below and on the Sea Surface Salinity CCI web page).\r\n\r\nAn overview paper about CCI SSS is now published:\r\n\r\nBoutin, J., N. Reul, J. Koehler, A. Martin, R. Catany, S. Guimbard, F. Rouffi, et al. (2021), Satellite-Based Sea Surface Salinity Designed for Ocean and Climate Studies, Journal of Geophysical Research: Oceans, 126(11), e2021JC017676, doi:https://doi.org/10.1029/2021JC017676.\r\n\r\nAn updated version of CCI SSS (version 3.21) is now available on: https://catalogue.ceda.ac.uk/uuid/5920a2c77e3c45339477acd31ce62c3c ; version 3 SSS and associated uncertainties are more precise and cover a longer period (Jan 2010-sept 2020); version 3 SSS are provided closer to land than version 2 SSS, with a possible degraded quality. Users might remove these additional near land data by using the lsc_qc flag." } }, { "ob_id": 586, "relationType": "IsDerivedFrom", "subjectObservation": { "ob_id": 8548, "uuid": "e1449eac35108b49d3a0af26bb6d2060", "short_code": "ob", "title": "UK building heights", "abstract": "A database of building heights for the main conurbations of the UK, acquired by the Landmap project from The GeoInformation Group's Cities Revealed project. The height information is derived principally from the Cities Revealed LiDAR or Modern aerial imagery (see linked datasets). Each building block has three values: base of building above sea level, top of the building above sea level and height of building above local ground level. Accurate to +/- 0.5m with 95% confidence limits.\r\n\r\nThe Joint Information Systems Committee (JISC) funded Landmap service which ran from 2001 to July 2014 collected and hosted a large amount of earth observation data for the majority of the UK, part of which was buildings data. After removal of JISC funding in 2013, the Landmap service is no longer operational, with the data now held at the NEODC.\r\n\r\nWhen using these data please also add the following copyright statement: Cities Revealed © The GeoInformation Group yyyy" }, "objectObservation": { "ob_id": 8053, "uuid": "acfa8955414aef710105ef640802b9aa", "short_code": "ob", "title": "1m resolution LiDAR-derived Digital Terrain/Surface Models (DTMs/DSMs) for cities of England and Scotland", "abstract": "Light Detection and Ranging (LiDAR) data was collected by The Geoinformation Group using LiDAR-equipped survey aircraft for the main urban conurbations of England and Wales (including London, Manchester, Birmingham, Liverpool, Newcastle, Edinburgh and Glasgow) as part of the Cities Revealed project, and made available through the Landmap service. The GeoInformation Group (TGG) has processed the data so that they are available as Digital Terrain Models (ground surface only) and Digital Surface/Elevation Models (the ground and all features on it), both geographic databases with height and surface measurement information in the form of regular grids with intervals of 1 or 2 m. In addition, some First Pass and Last Pass data are available. The First Pass data provides height values for the top of the canopy (i.e. buildings, trees etc.) while the Last Pulse data provides height values for the bottom of the canopy and provides information about the shape of the terrain. The data are available in img format. The Joint Information Systems Committee (JISC) funded Landmap service which ran from 2001 to July 2014 collected and hosted a large amount of earth observation data for the majority of the UK, part of which was elevation data. After removal of JISC funding in 2013, the Landmap service is no longer operational, with the data now held at the NEODC.\r\n\r\nWhen using the data please also add the following copyright statement: Cities Revealed © The GeoInformation Group yyyy" } }, { "ob_id": 587, "relationType": "IsDerivedFrom", "subjectObservation": { "ob_id": 8548, "uuid": "e1449eac35108b49d3a0af26bb6d2060", "short_code": "ob", "title": "UK building heights", "abstract": "A database of building heights for the main conurbations of the UK, acquired by the Landmap project from The GeoInformation Group's Cities Revealed project. The height information is derived principally from the Cities Revealed LiDAR or Modern aerial imagery (see linked datasets). Each building block has three values: base of building above sea level, top of the building above sea level and height of building above local ground level. Accurate to +/- 0.5m with 95% confidence limits.\r\n\r\nThe Joint Information Systems Committee (JISC) funded Landmap service which ran from 2001 to July 2014 collected and hosted a large amount of earth observation data for the majority of the UK, part of which was buildings data. After removal of JISC funding in 2013, the Landmap service is no longer operational, with the data now held at the NEODC.\r\n\r\nWhen using these data please also add the following copyright statement: Cities Revealed © The GeoInformation Group yyyy" }, "objectObservation": { "ob_id": 8239, "uuid": "1999cc89208158c8616845f01ac93100", "short_code": "ob", "title": "Modern UK Aerial Photography", "abstract": "Aerial photography obtained from The Geoinformation Group's (TGG) Cities Revealed project, acquired by the Landmap project, is available for over 65% of the UK's population, from 1969 to 2010. The imagery has a high resolution of 5-25cm. The Joint Information Systems Committee (JISC) funded Landmap service which ran from 2001 to July 2014 collected and hosted a large amount of earth observation data for the majority of the UK. After removal of JISC funding in 2013, the Landmap service is no longer operational, with the data now held at the NEODC.\r\n\r\nWhen using these data please also add the following copyright statement: Cities Revealed © The GeoInformation Group yyyy" } }, { "ob_id": 588, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 33218, "uuid": "5f331c418e9f4935b8eb1b836f8a91b8", "short_code": "ob", "title": "ESA Biomass Climate Change Initiative (Biomass_cci): Global datasets of forest above-ground biomass for the years 2010, 2017 and 2018, v3", "abstract": "This dataset comprises estimates of forest above-ground biomass for the years 2010, 2017 and 2018. They are derived from a combination of Earth observation data, depending on the year, from the Copernicus Sentinel-1 mission, Envisat’s ASAR instrument and JAXA’s Advanced Land Observing Satellite (ALOS-1 and ALOS-2), along with additional information from Earth observation sources. The data has been produced as part of the European Space Agency's (ESA's) Climate Change Initiative (CCI) programme by the Biomass CCI team. \r\n\r\nThis release of the data is version 3. Compared to version 2, this is a consolidated version of the Above Ground Biomass (AGB) maps. This version also includes a preliminary estimate of AGB changes for two epochs.\r\n\r\nThe data products consist of two (2) global layers that include estimates of:\r\n1) above ground biomass (AGB, unit: tons/ha i.e., Mg/ha) (raster dataset). This is defined as the mass, expressed as oven-dry weight of the woody parts (stem, bark, branches and twigs) of all living trees excluding stump and roots\r\n2) per-pixel estimates of above-ground biomass uncertainty expressed as the standard deviation in Mg/ha (raster dataset)\r\n\r\nIn addition, files describing the AGB change between 2018 and the other two years are provided (labelled as 2018_2010 and 2018_2017). These consist of two sets of maps: the standard deviation of the AGB change and a quality flag of the AGB change. Note that the change itself can be simply computed as the difference between two AGB maps, so is not provided directly.\r\n\r\nData are provided in both netcdf and geotiff format." }, "objectObservation": { "ob_id": 32065, "uuid": "84403d09cef3485883158f4df2989b0c", "short_code": "ob", "title": "ESA Biomass Climate Change Initiative (Biomass_cci): Global datasets of forest above-ground biomass for the years 2010, 2017 and 2018, v2", "abstract": "This dataset comprises estimates of forest above-ground biomass for the years 2010, 2017 and 2018. They are derived from a combination of Earth observation data, depending on the year, from the Copernicus Sentinel-1 mission, Envisat’s ASAR instrument and JAXA’s Advanced Land Observing Satellite (ALOS-1 and ALOS-2), along with additional information from Earth observation sources. The data has been produced as part of the European Space Agency's (ESA's) Climate Change Initiative (CCI) programme by the Biomass CCI team. \r\n\r\nThe data products consist of two (2) global layers that include estimates of:\r\n1) above ground biomass (AGB, unit: tons/ha i.e., Mg/ha) (raster dataset). This is defined as the mass, expressed as oven-dry weight of the woody parts (stem, bark, branches and twigs) of all living trees excluding stump and roots\r\n2) per-pixel estimates of above-ground biomass uncertainty expressed as the standard deviation in Mg/ha (raster dataset)\r\n\r\nThis release of the data is version 2, with data provided in both netcdf and geotiff format. The quantification of AGB changes by taking the difference of two maps is strongly discouraged due to local biases and uncertainties. Version 3 maps will ensure a more realistic representation of AGB changes." } }, { "ob_id": 589, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 33302, "uuid": "17c2ce31784048de93996275ee976fff", "short_code": "ob", "title": "ESA Sea Level Budget Closure Climate Change Initiative (SLBC_cci): Time series of global mean sea level budget and ocean mass budget elements (1993-2016, at monthly resolution), version 2.2", "abstract": "This dataset is a compilation of time series, together with uncertainties, of the following elements of the global mean sea level budget and ocean mass budget:\r\n(a) global mean sea level\r\n(b) the steric contribution to global mean sea level, that is, the effect of ocean water density change, which is dominated, on a global average, by thermal expansion\r\n(c) the mass contribution to global mean sea level\r\n(d) the global glaciers contribution (excluding Greenland and Antarctica)\r\n(e) the Greenland Ice Sheet and Greenland peripheral glaciers contribution\r\n(f) the Antarctic Ice Sheet contribution\r\n(g) the contribution from changes in land water storage (including snow cover).\r\n\r\nThe compilation is a result from the Sea-level Budget Closure (SLBC_cci) project conducted in the framework of ESA’s Climate Change Initiative (CCI). It provides assessments of the global mean sea level and ocean mass budgets. Assessment of the global mean sea level budget means to assess how well (a) agrees, within uncertainties, to the sum of (b) and (c) or to the sum of (b), (d), (e), (f) and (g). Assessment of the ocean mass budget means to assess how well (c) agrees to the sum (d), (e), (f) and (g).\r\n\r\nAll time series are expressed in terms of anomalies (in millimetres of equivalent global mean sea level) with respect to the mean value over the 10-year reference period 2006-2015. \r\nThe temporal resolution is monthly. The temporal range is from January 1993 to December 2016. Some time series do not cover this full temporal range. All time series are complete over the temporal range from January 2003 to August 2016.\r\n\r\nFor some elements, more than one time series are given, as a result of different assessments from different data sources and methods.\r\n\r\nData and methods underlying the time series are as follows:\r\n(a) satellite altimetry analysis by the Sea Level CCI project.\r\n(b) a new analysis of Argo drifter data with incorporation of sea surface temperature data; an alternative time series consists in an ensemble mean over previous global mean steric sea level anomaly time series.\r\n(c) analysis of monthly global gravity field solutions from the Gravity Recovery and Climate Experiment (GRACE) satellite gravimetry mission.\r\n(d) results from a global glacier model.\r\n(e) analysis of satellite radar altimetry over the Greenland Ice Sheet, amended by results from the global glacier model for the Greenland peripheral glaciers; an alternative time series consists of results from GRACE satellite gravimetry.\r\n(f) analysis of satellite radar altimetry over the Antarctic Ice Sheet; an alternative time series consists of results from GRACE satellite gravimetry.\r\n(g) results from the WaterGAP global hydrological model.\r\n\r\nVersion 2.2 is an update of the previous Version 2.1. The update concerns the estimates of ocean mass change from GRACE." }, "objectObservation": { "ob_id": 32239, "uuid": "1562578dd07844f19f01f0db9366106d", "short_code": "ob", "title": "ESA Sea Level Budget Closure Climate Change Initiative (SLBC_cci): Time series of global mean sea level budget and ocean mass budget elements (1993-2016, at monthly resolution), version 2.1", "abstract": "This dataset is a compilation of time series, together with uncertainties, of the following elements of the global mean sea level budget and ocean mass budget:\r\n(a) global mean sea level\r\n(b) the steric contribution to global mean sea level, that is, the effect of ocean water density change, which is dominated, on a global average, by thermal expansion\r\n(c) the mass contribution to global mean sea level\r\n(d) the global glaciers contribution (excluding Greenland and Antarctica)\r\n(e) the Greenland Ice Sheet and Greenland peripheral glaciers contribution\r\n(f) the Antarctic Ice Sheet contribution\r\n(g) the contribution from changes in land water storage (including snow cover).\r\n\r\nThe compilation is a result from the Sea-level Budget Closure (SLBC_cci) project conducted in the framework of ESA’s Climate Change Initiative (CCI). It provides assessments of the global mean sea level and ocean mass budgets. Assessment of the global mean sea level budget means to assess how well (a) agrees, within uncertainties, to the sum of (b) and (c) or to the sum of (b), (d), (e), (f) and (g). Assessment of the ocean mass budget means to assess how well (c) agrees to the sum (d), (e), (f) and (g).\r\n\r\nAll time series are expressed in terms of anomalies (in millimetres of equivalent global mean sea level) with respect to the mean value over the 10-year reference period 2006-2015. \r\nThe temporal resolution is monthly. The temporal range is from January 1993 to December 2016. Some time series do not cover this full temporal range. All time series are complete over the temporal range from January 2003 to August 2016.\r\n\r\nFor some elements, more than one time series are given, as a result of different assessments from different data sources and methods.\r\n\r\nData and methods underlying the time series are as follows:\r\n(a) satellite altimetry analysis by the Sea Level CCI project.\r\n(b) a new analysis of Argo drifter data with incorporation of sea surface temperature data; an alternative time series consists in an ensemble mean over previous global mean steric sea level anomaly time series.\r\n(c) analysis of monthly global gravity field solutions from the Gravity Recovery and Climate Experiment (GRACE) satellite gravimetry mission.\r\n(d) results from a global glacier model.\r\n(e) analysis of satellite radar altimetry over the Greenland Ice Sheet, amended by results from the global glacier model for the Greenland peripheral glaciers; an alternative time series consists of results from GRACE satellite gravimetry.\r\n(f) analysis of satellite radar altimetry over the Antarctic Ice Sheet; an alternative time series consists of results from GRACE satellite gravimetry.\r\n(g) results from the WaterGAP global hydrological model." } }, { "ob_id": 590, "relationType": "IsDerivedFrom", "subjectObservation": { "ob_id": 33275, "uuid": "6a6ccbb8ef2645308a60dc47e9b8b5fb", "short_code": "ob", "title": "BICEP / NCEO: Monthly global Phytoplankton Carbon, between 1998-2020 at 9 km resolution (derived from the Ocean Colour Climate Change Initiative v5.0 dataset)", "abstract": "This dataset contains monthly global carbon products for pico-, nano- and microphytoplankton (C_picophyto, C_nanophyto and C_microphyto, respectively, in mg C m-3) and the total phytoplankton community (C_phyto in mg C m-3) for the period of 1998 to 2020 at 9 km spatial resolution.\r\n\r\nA spectrally-resolved photoacclimation model was unified with a primary production model that simulated photosynthesis as a function of irradiance using a two-parameter photosynthesis versus irradiance (P-I) function to estimate the carbon content of marine phytoplankton based on ocean-colour remote sensing products (Sathyendranath et al. 2020 and references therein for details). The photoacclimation model contains a maximum chlorophyll-to-carbon ratio for three different phytoplankton size classes (pico-, nano- and microphytoplankton) that was inferred from field data, as in Sathyendranath et al. (2020). Chlorophyll-a products were obtained from the European Space Agency (ESA) Ocean Colour Climate Change Initiative (OC-CCI v5.0 dataset). Photosynthetic Active Radiation (PAR) products were obtained from the National Aeronautics and Space Administration (NASA) and were corrected for inter-sensor bias in products. Mixed Layer Depth (MLD) was obtained from the French Research Institute for Exploration of the Sea (Ifremer). In situ datasets P-I parameters were incorporated as described in Kulk et al. (2020). \r\n\r\nThe phytoplankton carbon products were generated as part of the ESA Biological Pump and Carbon Exchange Processes (BICEP) project. Support from the Simons Foundation grant ‘Computational Biogeochemical Modeling of Marine Ecosystems’ (CBIOMES, number 549947) and from the National Centre of Earth Observation (NCEO) is acknowledged.\r\n\r\nData are provided as netCDF files containing carbon products for pico-, nano- and microphytoplankton (C_picophyto, C_nanophyto and C_microphyto, respectively, in mg C m-3) and the total phytoplankton community (C_phyto in mg C m-3) for the period of 1998 to 2020 at 9 km spatial resolution. Additional variables that were used for the calculation of the phytoplankton carbon products are also provided, including chlorophyll-a (chl_a in mg m-3), photosynthetically activate radiation (par, in µmol photons m-2 d-1), mixed layer depth (mld in m) and the mean spectral nondimensional irradiance (mean_spectral_i_star).\r\n\r\nReferences:\r\n\r\nSathyendranath, S.; Platt, T.; Kovač, Ž.; Dingle, J.; Jackson, T.; Brewin, R.J.W.; Franks, P.; Marañón, E.; Kulk, G.; Bouman, H.A. Reconciling models of primary production and photoacclimation. Applies Optics, 2020, 59, C100. doi.org/10.1364/AO.386252\r\n\r\nKulk, G.; Platt, T.; Dingle, J.; Jackson, T.; Jönsson, B.F.; Bouman, H.A., Babin, M.; Doblin, M.; Estrada, M.; Figueiras, F.G.; Furuya, K.; González, N.; Gudfinnsson, H.G.; Gudmundsson, K.; Huang, B.; Isada, T.; Kovač, Ž.; Lutz, V.A.; Marañón, E.; Raman, M.; Richardson, K.; Rozema, P.D.; Van de Poll, W.H.; Segura, V.; Tilstone, G.H.; Uitz, J.; van Dongen-Vogels, V.; Yoshikawa, T.; Sathyendranath S. Primary production, an index of climate change in the ocean: Satellite-based estimates over two decades. Remote Sens. 2020, 12,826. doi:10.3390/rs12050826" }, "objectObservation": { "ob_id": 32140, "uuid": "66534da90ed44abebfc1b08adca4f9c3", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global chlorophyll-a data products gridded on a sinusoidal projection, Version 5.0", "abstract": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains their Version 5.0 chlorophyll-a product (in mg/m3) on a sinusoidal projection at 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites) covering the period 1997 - 2020. Note, the chlorophyll-a data are also included in the 'All Products' dataset. \r\n\r\nThis data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection.)\r\n\r\nPlease note, data from December 2020 onwards are affected by an anomaly discovered after production and resulting in a spurious jump in remote sensing reflectance. The anomaly has been corrected in the version 5.0.1 of the dataset available through the Copernicus Climate Change Service (https://doi.org/10.24381/cds.f85b319d)\r\n\r\nVersion 6.0 of this data is now also available here: https://doi.org/10.5285/5011d22aae5a4671b0cbc7d05c56c4f0" } }, { "ob_id": 591, "relationType": "IsDerivedFrom", "subjectObservation": { "ob_id": 31969, "uuid": "69b2c9c6c4714517ba10dab3515e4ee6", "short_code": "ob", "title": "BICEP / NCEO: Monthly global Marine Phytoplankton Primary Production, between 1998-2020 at 9 km resolution (derived from the Ocean Colour Climate Change Initiative v4.2 dataset)", "abstract": "This dataset contains global, monthly marine phytoplankton primary production products (in mg C m-2 d-1) for the period of 1998 to 2018 at 9 km spatial resolution. Data are provided in NetCDF format.\r\n\r\nPrimary production by marine phytoplankton was modelled using ocean-colour remote sensing products and a spectrally-resolved primary production model that incorporates the vertical structure of phytoplankton and simulates changes in photosynthesis as a function of irradiance using a two-parameter photosynthesis versus irradiance (P-I) function (see Kulk et al. 2020, Sathyendranath et al. 2020a, and references therein for details). Chlorophyll-a products were obtained from the European Space Agency (ESA) Ocean Colour Climate Change Initiative (OC-CCI v4.2 dataet). Photosynthetic Active Radiation (PAR) products were obtained from the National Aeronautics and Space Administration (NASA) and were corrected for inter-sensor bias in products. In situ datasets of chlorophyll-a profile parameters and P-I parameters were incorporated as described in Kulk et al. (2020). \r\n\r\nThe primary production products were generated as part of the ESA Living Planet Fellowship programme ‘Primary production, Index of Climate Change in the Ocean: Long-term Observations’\r\n(PICCOLO). Support from the Simons Foundation grant ‘Computational Biogeochemical Modeling of Marine Ecosystems’ (CBIOMES, number 549947), from the ESA Biological Pump and Carbon\r\nExchange Processes (BICEP) project and from the National Centre of Earth Observation (NCEO) is acknowledged.\r\n\r\nData are provided as netCDF files containing global, monthly marine phytoplankton primary production products (in mg C m-2 d-1) for the period of 1998 to 2020 at 9 km spatial resolution.\r\n\r\nReferences:\r\n\r\nKulk, G.; Platt, T.; Dingle, J.; Jackson, T.; Jönsson, B.F.; Bouman, H.A., Babin, M.; Doblin, M.; Estrada, M.; Figueiras, F.G.; Furuya, K.; González, N.; Gudfinnsson, H.G.; Gudmundsson, K.; Huang, B.; Isada, T.; Kovac, Z.; Lutz, V.A.; Marañón, E.; Raman, M.; Richardson, K.; Rozema, P.D.; Van de Poll, W.H.; Segura, V.; Tilstone, G.H.; Uitz, J.; van Dongen-Vogels, V.; Yoshikawa, T.; Sathyendranath S. Primary production, an index of climate change in the ocean: Satellite-based estimates over two decades. Remote Sens. 2020, 12, 826. doi:10.3390/rs12050826\r\n\r\nSathyendranath, S.; Platt, T.; Žarko K.; Dingle, J.; Jackson, T.; Brewin, R.J.W.; Franks, P.; Nón, E.M.; Kulk, G.; Bouman, H. Reconciling models of primary production and photoacclimation. Appl. Opt.\r\n2020a, 59, C100-C114. doi.org/10.1364/AO.386252." }, "objectObservation": { "ob_id": 30603, "uuid": "99348189bd33459cbd597a58c30d8d10", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global chlorophyll-a data products gridded on a sinusoidal projection, Version 4.2", "abstract": "The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains their Version 4.2 chlorophyll-a product (in mg/m3) on a sinusoidal projection at 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites). Note, the chlorophyll-a data are also included in the 'All Products' dataset. \r\n\r\nThis data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection.)\r\n\r\nPlease note, this dataset has been superseded. Later versions of the data are now available." } }, { "ob_id": 592, "relationType": "IsDerivedFrom", "subjectObservation": { "ob_id": 33374, "uuid": "46f5d503ce284114b5925709258bacc5", "short_code": "ob", "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Normalised Difference Moisture Index (NDMI) v1", "abstract": "Sentinel-Hub NDMI description: \r\n\r\nThe NDMI is a normalized difference moisture index, that uses NIR and SWIR bands to display moisture. The SWIR band reflects changes in both the vegetation water content and the spongy mesophyll structure in vegetation canopies, while the NIR reflectance is affected by leaf internal structure and leaf dry matter content but not by water content. The combination of the NIR with the SWIR removes variations induced by leaf internal structure and leaf dry matter content, improving the accuracy in retrieving the vegetation water content. The amount of water available in the internal leaf structure largely controls the spectral reflectance in the SWIR interval of the electromagnetic spectrum. SWIR reflectance is therefore negatively related to leaf water content. In short, NDMI is used to monitor changes in the water content of leaves and was proposed by Gao. NDWI is computed using the near-infrared (NIR) and the short wave infrared (SWIR) reflectances:\r\n\r\nNDMI = (NIR – SWIR) / (NIR + SWIR) \r\n\r\nSentinel-2 NDMI = (B08 - B11) / (B08 + B11)\r\n\r\nThese data have been created by the Joint Nature Conservation Committee (JNCC) as part of a Defra Natural Capital and Ecosystem Assessment (NCEA) project to produce a regional, and ultimately national, system for detecting a change in habitat conditions at a land parcel level. The first stage of the project is focused on Yorkshire, UK, and therefore the dataset includes granules and scenes covering Yorkshire and surrounding areas only. The dataset contains Normalised Difference Moisture Index data derived from Defra and JNCC Sentinel-2 Analysis Ready Data.\r\n\r\nNDMI files are generated for the following Sentinel-2 granules:\r\n•\tT30UWE\r\n•\tT30UXF\r\n•\tT30UWF\r\n•\tT30UXE\r\n•\tT31UCV \r\n•\tT30UYE\r\n•\tT31UCA\r\n\r\nAs the project continues, JNCC will expand the geographical coverage of this dataset and will provide continuous updates as ARD becomes available." }, "objectObservation": { "ob_id": 30199, "uuid": "bf9568b558204b81803eeebcc7f529ef", "short_code": "ob", "title": "Defra and JNCC Sentinel-2 Analysis Ready Data (ARD)", "abstract": "These data have been created by the Department for Environment, Food and Rural Affairs (Defra) and Joint Nature Conservation Committee (JNCC) in order to cost-effectively provide high quality, Analysis Ready Data (ARD) for a wide range of applications. The dataset contains modified Copernicus Sentinel-2 (Level 1C data processed into a surface reflectance product (Level 2)). Defra and JNCC data were processed on separate platforms using a common specification to produce complementary outputs up to and including the acquisition date 23/06/2023. Data acquired after that date were processed on a single platform to the same specification.\r\n\r\nThe majority of data captured between July 2015 and August 2017 was processed by Aberystwyth University for Defra and later updated by JNCC to the same specification as the rest of this dataset. Please see the image-level metadata for details of data lineage and processing.\r\n\r\nThe Sentinel-2 ARD filename format was changed in April 2023. Filenames of data acquired on or after 01/04/2023 include the timestamp of data generation and display image latitude and longitude to a consistent number of significant figures preceded by ‘n’ (North) and ‘e/w’ (East / West). Filenames of data acquired before this date do not include the data generation timestamp and display latitude and longitude to varying significant figures not preceded by ‘n’ and ‘e/w’." } }, { "ob_id": 593, "relationType": "IsDerivedFrom", "subjectObservation": { "ob_id": 33384, "uuid": "102909b1d169469f85c099a6f1686bad", "short_code": "ob", "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Enhanced Vegetation Index (EVI)", "abstract": "EVI is a development on Normalised Difference Vegetation Index (NDVI).\r\n\r\nSentinel-Hub EVI description: \r\nIn areas of dense canopy cover, where leaf area index (LAI) is high, the blue wavelengths can be used to improve the accuracy of NDVI, as it corrects for soil background signals and atmospheric influences. The range of values for EVI is -1 to 1, with healthy vegetation generally around 0.20 to 0.80.\r\n\r\nEVI is calculated: EVI = 2.5 * ((NIR – RED) / ((NIR + (6 * RED) – (7.5 * BLUE)) + 1))\r\n\r\nSentinel 2 EVI = 2.5 * ((B8 – B4) / ((B8 + (6 * B4) – (7.5 * B2)) + 1))\r\n\r\nThese data have been created by the Joint Nature Conservation Committee (JNCC) as part of a Defra Natural Capital and Ecosystem Assessment (NCEA) project to produce a regional, and ultimately national, system for detecting a change in habitat conditions at a land parcel level. The first stage of the project is focused on Yorkshire, UK, and therefore the dataset includes granules and scenes covering Yorkshire and surrounding areas only. The dataset contains Enhanced Vegetation Index data derived from Defra and JNCC Sentinel-2 Analysis Ready Data.\r\n\r\nEVI files are generated for the following Sentinel-2 granules:\r\n•\tT30UWE\r\n•\tT30UXF\r\n•\tT30UWF\r\n•\tT30UXE\r\n•\tT31UCV \r\n•\tT30UYE\r\n•\tT31UCA\r\n\r\nAs the project continues, JNCC will expand the geographical coverage of this dataset and will provide continuous updates as ARD becomes available." }, "objectObservation": { "ob_id": 30199, "uuid": "bf9568b558204b81803eeebcc7f529ef", "short_code": "ob", "title": "Defra and JNCC Sentinel-2 Analysis Ready Data (ARD)", "abstract": "These data have been created by the Department for Environment, Food and Rural Affairs (Defra) and Joint Nature Conservation Committee (JNCC) in order to cost-effectively provide high quality, Analysis Ready Data (ARD) for a wide range of applications. The dataset contains modified Copernicus Sentinel-2 (Level 1C data processed into a surface reflectance product (Level 2)). Defra and JNCC data were processed on separate platforms using a common specification to produce complementary outputs up to and including the acquisition date 23/06/2023. Data acquired after that date were processed on a single platform to the same specification.\r\n\r\nThe majority of data captured between July 2015 and August 2017 was processed by Aberystwyth University for Defra and later updated by JNCC to the same specification as the rest of this dataset. Please see the image-level metadata for details of data lineage and processing.\r\n\r\nThe Sentinel-2 ARD filename format was changed in April 2023. Filenames of data acquired on or after 01/04/2023 include the timestamp of data generation and display image latitude and longitude to a consistent number of significant figures preceded by ‘n’ (North) and ‘e/w’ (East / West). Filenames of data acquired before this date do not include the data generation timestamp and display latitude and longitude to varying significant figures not preceded by ‘n’ and ‘e/w’." } }, { "ob_id": 594, "relationType": "IsDerivedFrom", "subjectObservation": { "ob_id": 33339, "uuid": "51725f90c60a45e69f07a748a25b9729", "short_code": "ob", "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Normalised Difference Vegetation Index (NDVI) v1", "abstract": "Sentinel-Hub NDVI description: \r\nNDVI is a simple, but effective index for quantifying green vegetation. It normalizes green leaf scattering in Near Infra-red wavelengths with chlorophyll absorption in red wavelengths.\r\n\r\nThe value range of the NDVI is -1 to 1. Negative values of NDVI (values approaching -1) correspond to water. Values close to zero (-0.1 to 0.1) generally correspond to barren areas of rock, sand, or snow. Low, positive values represent shrub and grassland (approximately 0.2 to 0.4), while high values indicate temperate and tropical rainforests (values approaching 1). It is a good proxy for live green vegetation.\r\n\r\nNDVI = (NIR – Red) / (NIR + RED)\r\n\r\nSentinel-2 NDVI = (B8 - B4) / (B8 + B4)\r\n\r\nThese data have been created by the Joint Nature Conservation Committee (JNCC) as part of a Defra Natural Capital and Ecosystem Assessment (NCEA) project to produce a regional, and ultimately national, system for detecting a change in habitat conditions at a land parcel level. The first stage of the project is focused on Yorkshire, UK, and therefore the dataset includes granules and scenes covering Yorkshire and surrounding areas only. The dataset contains Normalised Difference Vegetation Index (NDVI) data derived from Defra and JNCC Sentinel-2 Analysis Ready Data. \r\n\r\nNDVI files are generated for the following Sentinel-2 granules:\r\n•\tT30UWE\r\n•\tT30UXF\r\n•\tT30UWF\r\n•\tT30UXE\r\n•\tT31UCV \r\n•\tT30UYE\r\n•\tT31UCA\r\n\r\nAs the project continues, JNCC will expand the geographical coverage of this dataset and will provide continuous updates as ARD becomes available.\r\n\r\nVersion 1 contains masked index files (using the Defra and JNCC ARD cloud and topographic shadow masks)." }, "objectObservation": { "ob_id": 30199, "uuid": "bf9568b558204b81803eeebcc7f529ef", "short_code": "ob", "title": "Defra and JNCC Sentinel-2 Analysis Ready Data (ARD)", "abstract": "These data have been created by the Department for Environment, Food and Rural Affairs (Defra) and Joint Nature Conservation Committee (JNCC) in order to cost-effectively provide high quality, Analysis Ready Data (ARD) for a wide range of applications. The dataset contains modified Copernicus Sentinel-2 (Level 1C data processed into a surface reflectance product (Level 2)). Defra and JNCC data were processed on separate platforms using a common specification to produce complementary outputs up to and including the acquisition date 23/06/2023. Data acquired after that date were processed on a single platform to the same specification.\r\n\r\nThe majority of data captured between July 2015 and August 2017 was processed by Aberystwyth University for Defra and later updated by JNCC to the same specification as the rest of this dataset. Please see the image-level metadata for details of data lineage and processing.\r\n\r\nThe Sentinel-2 ARD filename format was changed in April 2023. Filenames of data acquired on or after 01/04/2023 include the timestamp of data generation and display image latitude and longitude to a consistent number of significant figures preceded by ‘n’ (North) and ‘e/w’ (East / West). Filenames of data acquired before this date do not include the data generation timestamp and display latitude and longitude to varying significant figures not preceded by ‘n’ and ‘e/w’." } }, { "ob_id": 595, "relationType": "IsDerivedFrom", "subjectObservation": { "ob_id": 33376, "uuid": "b42f524bc9cd4dd6850b2399b616f5c4", "short_code": "ob", "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Normalised Difference Water Index (NDWI) v1", "abstract": "Sentinel-Hub NDWI description: The NDWI is used to monitor changes related to water content in water bodies. As water bodies strongly absorb light in visible to the infrared electromagnetic spectrum, NDWI uses green and near-infrared bands to highlight water bodies. It is sensitive to built-up land and can result in the over-estimation of water bodies. \r\nIndex values greater than 0.5 usually correspond to water bodies. Vegetation usually corresponds to much smaller values and built-up areas to values between zero and 0.2.\r\n\r\nNDWI = (GREEN – NIR) / (GREEN + NIR)\r\n\r\nSentinel-2 NDWI = (B03 - B08) / (B03 + B08)\r\n\r\nThese data have been created by the Joint Nature Conservation Committee (JNCC) as part of a Defra Natural Capital and Ecosystem Assessment (NCEA) project to produce a regional, and ultimately national, system for detecting a change in habitat conditions at a land parcel level. The first stage of the project is focused on Yorkshire, UK, and therefore the dataset includes granules and scenes covering Yorkshire and surrounding areas only. The dataset contains Normalised Difference Water Index (NDWI) data derived from Defra and JNCC Sentinel-2 Analysis Ready Data.\r\n\r\nNDWI files are generated for the following Sentinel-2 granules:\r\n•\tT30UWE\r\n•\tT30UXF\r\n•\tT30UWF\r\n•\tT30UXE\r\n•\tT31UCV \r\n•\tT30UYE\r\n•\tT31UCA\r\n\r\nAs the project continues, JNCC will expand the geographical coverage of this dataset and will provide continuous updates as ARD becomes available." }, "objectObservation": { "ob_id": 30199, "uuid": "bf9568b558204b81803eeebcc7f529ef", "short_code": "ob", "title": "Defra and JNCC Sentinel-2 Analysis Ready Data (ARD)", "abstract": "These data have been created by the Department for Environment, Food and Rural Affairs (Defra) and Joint Nature Conservation Committee (JNCC) in order to cost-effectively provide high quality, Analysis Ready Data (ARD) for a wide range of applications. The dataset contains modified Copernicus Sentinel-2 (Level 1C data processed into a surface reflectance product (Level 2)). Defra and JNCC data were processed on separate platforms using a common specification to produce complementary outputs up to and including the acquisition date 23/06/2023. Data acquired after that date were processed on a single platform to the same specification.\r\n\r\nThe majority of data captured between July 2015 and August 2017 was processed by Aberystwyth University for Defra and later updated by JNCC to the same specification as the rest of this dataset. Please see the image-level metadata for details of data lineage and processing.\r\n\r\nThe Sentinel-2 ARD filename format was changed in April 2023. Filenames of data acquired on or after 01/04/2023 include the timestamp of data generation and display image latitude and longitude to a consistent number of significant figures preceded by ‘n’ (North) and ‘e/w’ (East / West). Filenames of data acquired before this date do not include the data generation timestamp and display latitude and longitude to varying significant figures not preceded by ‘n’ and ‘e/w’." } }, { "ob_id": 596, "relationType": "IsDerivedFrom", "subjectObservation": { "ob_id": 33382, "uuid": "6df6b803c2784b8ab9e03834bf9a4337", "short_code": "ob", "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Normalised Burn Ratio (NBR) v1", "abstract": "Sentinel Hub NBR description: To detect burned areas, the NBR-RAW index is the most appropriate choice. Using bands 8 and 12 it highlights burnt areas in large fire zones greater than 500 acres. To observe burn severity, you may subtract the post-fire NBR image from the pre-fire NBR image. Darker pixels indicate burned areas.\r\n\r\nNBR = (NIR – SWIR) / (NIR + SWIR) \r\n\r\nSentinel-2 NBR = (B08 - B12) / (B08 + B12)\r\n\r\nThese data have been created by the Joint Nature Conservation Committee (JNCC) as part of a Defra Natural Capital & Ecosystem Assessment (NCEA) project to produce a regional, and ultimately national, system for detecting a change in habitat condition at a land parcel level. The first stage of the project is focused on Yorkshire, UK, and therefore the dataset includes granules and scenes covering Yorkshire and surrounding areas only. The dataset contains the following indices derived from Defra and JNCC Sentinel-2 Analysis Ready Data.\r\n\r\nNDVI, NDMI, NDWI, NBR, and EVI files are generated for the following Sentinel-2 granules:\r\n•\tT30UWE\r\n•\tT30UXF\r\n•\tT30UWF\r\n•\tT30UXE\r\n•\tT31UCV \r\n•\tT30UYE\r\n•\tT31UCA\r\n\r\nAs the project continues, JNCC will expand the geographical coverage of this dataset and will provide continuous updates as ARD becomes available." }, "objectObservation": { "ob_id": 30199, "uuid": "bf9568b558204b81803eeebcc7f529ef", "short_code": "ob", "title": "Defra and JNCC Sentinel-2 Analysis Ready Data (ARD)", "abstract": "These data have been created by the Department for Environment, Food and Rural Affairs (Defra) and Joint Nature Conservation Committee (JNCC) in order to cost-effectively provide high quality, Analysis Ready Data (ARD) for a wide range of applications. The dataset contains modified Copernicus Sentinel-2 (Level 1C data processed into a surface reflectance product (Level 2)). Defra and JNCC data were processed on separate platforms using a common specification to produce complementary outputs up to and including the acquisition date 23/06/2023. Data acquired after that date were processed on a single platform to the same specification.\r\n\r\nThe majority of data captured between July 2015 and August 2017 was processed by Aberystwyth University for Defra and later updated by JNCC to the same specification as the rest of this dataset. Please see the image-level metadata for details of data lineage and processing.\r\n\r\nThe Sentinel-2 ARD filename format was changed in April 2023. Filenames of data acquired on or after 01/04/2023 include the timestamp of data generation and display image latitude and longitude to a consistent number of significant figures preceded by ‘n’ (North) and ‘e/w’ (East / West). Filenames of data acquired before this date do not include the data generation timestamp and display latitude and longitude to varying significant figures not preceded by ‘n’ and ‘e/w’." } }, { "ob_id": 597, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 33374, "uuid": "46f5d503ce284114b5925709258bacc5", "short_code": "ob", "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Normalised Difference Moisture Index (NDMI) v1", "abstract": "Sentinel-Hub NDMI description: \r\n\r\nThe NDMI is a normalized difference moisture index, that uses NIR and SWIR bands to display moisture. The SWIR band reflects changes in both the vegetation water content and the spongy mesophyll structure in vegetation canopies, while the NIR reflectance is affected by leaf internal structure and leaf dry matter content but not by water content. The combination of the NIR with the SWIR removes variations induced by leaf internal structure and leaf dry matter content, improving the accuracy in retrieving the vegetation water content. The amount of water available in the internal leaf structure largely controls the spectral reflectance in the SWIR interval of the electromagnetic spectrum. SWIR reflectance is therefore negatively related to leaf water content. In short, NDMI is used to monitor changes in the water content of leaves and was proposed by Gao. NDWI is computed using the near-infrared (NIR) and the short wave infrared (SWIR) reflectances:\r\n\r\nNDMI = (NIR – SWIR) / (NIR + SWIR) \r\n\r\nSentinel-2 NDMI = (B08 - B11) / (B08 + B11)\r\n\r\nThese data have been created by the Joint Nature Conservation Committee (JNCC) as part of a Defra Natural Capital and Ecosystem Assessment (NCEA) project to produce a regional, and ultimately national, system for detecting a change in habitat conditions at a land parcel level. The first stage of the project is focused on Yorkshire, UK, and therefore the dataset includes granules and scenes covering Yorkshire and surrounding areas only. The dataset contains Normalised Difference Moisture Index data derived from Defra and JNCC Sentinel-2 Analysis Ready Data.\r\n\r\nNDMI files are generated for the following Sentinel-2 granules:\r\n•\tT30UWE\r\n•\tT30UXF\r\n•\tT30UWF\r\n•\tT30UXE\r\n•\tT31UCV \r\n•\tT30UYE\r\n•\tT31UCA\r\n\r\nAs the project continues, JNCC will expand the geographical coverage of this dataset and will provide continuous updates as ARD becomes available." }, "objectObservation": { "ob_id": 33384, "uuid": "102909b1d169469f85c099a6f1686bad", "short_code": "ob", "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Enhanced Vegetation Index (EVI)", "abstract": "EVI is a development on Normalised Difference Vegetation Index (NDVI).\r\n\r\nSentinel-Hub EVI description: \r\nIn areas of dense canopy cover, where leaf area index (LAI) is high, the blue wavelengths can be used to improve the accuracy of NDVI, as it corrects for soil background signals and atmospheric influences. The range of values for EVI is -1 to 1, with healthy vegetation generally around 0.20 to 0.80.\r\n\r\nEVI is calculated: EVI = 2.5 * ((NIR – RED) / ((NIR + (6 * RED) – (7.5 * BLUE)) + 1))\r\n\r\nSentinel 2 EVI = 2.5 * ((B8 – B4) / ((B8 + (6 * B4) – (7.5 * B2)) + 1))\r\n\r\nThese data have been created by the Joint Nature Conservation Committee (JNCC) as part of a Defra Natural Capital and Ecosystem Assessment (NCEA) project to produce a regional, and ultimately national, system for detecting a change in habitat conditions at a land parcel level. The first stage of the project is focused on Yorkshire, UK, and therefore the dataset includes granules and scenes covering Yorkshire and surrounding areas only. The dataset contains Enhanced Vegetation Index data derived from Defra and JNCC Sentinel-2 Analysis Ready Data.\r\n\r\nEVI files are generated for the following Sentinel-2 granules:\r\n•\tT30UWE\r\n•\tT30UXF\r\n•\tT30UWF\r\n•\tT30UXE\r\n•\tT31UCV \r\n•\tT30UYE\r\n•\tT31UCA\r\n\r\nAs the project continues, JNCC will expand the geographical coverage of this dataset and will provide continuous updates as ARD becomes available." } }, { "ob_id": 598, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 33374, "uuid": "46f5d503ce284114b5925709258bacc5", "short_code": "ob", "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Normalised Difference Moisture Index (NDMI) v1", "abstract": "Sentinel-Hub NDMI description: \r\n\r\nThe NDMI is a normalized difference moisture index, that uses NIR and SWIR bands to display moisture. The SWIR band reflects changes in both the vegetation water content and the spongy mesophyll structure in vegetation canopies, while the NIR reflectance is affected by leaf internal structure and leaf dry matter content but not by water content. The combination of the NIR with the SWIR removes variations induced by leaf internal structure and leaf dry matter content, improving the accuracy in retrieving the vegetation water content. The amount of water available in the internal leaf structure largely controls the spectral reflectance in the SWIR interval of the electromagnetic spectrum. SWIR reflectance is therefore negatively related to leaf water content. In short, NDMI is used to monitor changes in the water content of leaves and was proposed by Gao. NDWI is computed using the near-infrared (NIR) and the short wave infrared (SWIR) reflectances:\r\n\r\nNDMI = (NIR – SWIR) / (NIR + SWIR) \r\n\r\nSentinel-2 NDMI = (B08 - B11) / (B08 + B11)\r\n\r\nThese data have been created by the Joint Nature Conservation Committee (JNCC) as part of a Defra Natural Capital and Ecosystem Assessment (NCEA) project to produce a regional, and ultimately national, system for detecting a change in habitat conditions at a land parcel level. The first stage of the project is focused on Yorkshire, UK, and therefore the dataset includes granules and scenes covering Yorkshire and surrounding areas only. The dataset contains Normalised Difference Moisture Index data derived from Defra and JNCC Sentinel-2 Analysis Ready Data.\r\n\r\nNDMI files are generated for the following Sentinel-2 granules:\r\n•\tT30UWE\r\n•\tT30UXF\r\n•\tT30UWF\r\n•\tT30UXE\r\n•\tT31UCV \r\n•\tT30UYE\r\n•\tT31UCA\r\n\r\nAs the project continues, JNCC will expand the geographical coverage of this dataset and will provide continuous updates as ARD becomes available." }, "objectObservation": { "ob_id": 33382, "uuid": "6df6b803c2784b8ab9e03834bf9a4337", "short_code": "ob", "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Normalised Burn Ratio (NBR) v1", "abstract": "Sentinel Hub NBR description: To detect burned areas, the NBR-RAW index is the most appropriate choice. Using bands 8 and 12 it highlights burnt areas in large fire zones greater than 500 acres. To observe burn severity, you may subtract the post-fire NBR image from the pre-fire NBR image. Darker pixels indicate burned areas.\r\n\r\nNBR = (NIR – SWIR) / (NIR + SWIR) \r\n\r\nSentinel-2 NBR = (B08 - B12) / (B08 + B12)\r\n\r\nThese data have been created by the Joint Nature Conservation Committee (JNCC) as part of a Defra Natural Capital & Ecosystem Assessment (NCEA) project to produce a regional, and ultimately national, system for detecting a change in habitat condition at a land parcel level. The first stage of the project is focused on Yorkshire, UK, and therefore the dataset includes granules and scenes covering Yorkshire and surrounding areas only. The dataset contains the following indices derived from Defra and JNCC Sentinel-2 Analysis Ready Data.\r\n\r\nNDVI, NDMI, NDWI, NBR, and EVI files are generated for the following Sentinel-2 granules:\r\n•\tT30UWE\r\n•\tT30UXF\r\n•\tT30UWF\r\n•\tT30UXE\r\n•\tT31UCV \r\n•\tT30UYE\r\n•\tT31UCA\r\n\r\nAs the project continues, JNCC will expand the geographical coverage of this dataset and will provide continuous updates as ARD becomes available." } }, { "ob_id": 599, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 33374, "uuid": "46f5d503ce284114b5925709258bacc5", "short_code": "ob", "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Normalised Difference Moisture Index (NDMI) v1", "abstract": "Sentinel-Hub NDMI description: \r\n\r\nThe NDMI is a normalized difference moisture index, that uses NIR and SWIR bands to display moisture. The SWIR band reflects changes in both the vegetation water content and the spongy mesophyll structure in vegetation canopies, while the NIR reflectance is affected by leaf internal structure and leaf dry matter content but not by water content. The combination of the NIR with the SWIR removes variations induced by leaf internal structure and leaf dry matter content, improving the accuracy in retrieving the vegetation water content. The amount of water available in the internal leaf structure largely controls the spectral reflectance in the SWIR interval of the electromagnetic spectrum. SWIR reflectance is therefore negatively related to leaf water content. In short, NDMI is used to monitor changes in the water content of leaves and was proposed by Gao. NDWI is computed using the near-infrared (NIR) and the short wave infrared (SWIR) reflectances:\r\n\r\nNDMI = (NIR – SWIR) / (NIR + SWIR) \r\n\r\nSentinel-2 NDMI = (B08 - B11) / (B08 + B11)\r\n\r\nThese data have been created by the Joint Nature Conservation Committee (JNCC) as part of a Defra Natural Capital and Ecosystem Assessment (NCEA) project to produce a regional, and ultimately national, system for detecting a change in habitat conditions at a land parcel level. The first stage of the project is focused on Yorkshire, UK, and therefore the dataset includes granules and scenes covering Yorkshire and surrounding areas only. The dataset contains Normalised Difference Moisture Index data derived from Defra and JNCC Sentinel-2 Analysis Ready Data.\r\n\r\nNDMI files are generated for the following Sentinel-2 granules:\r\n•\tT30UWE\r\n•\tT30UXF\r\n•\tT30UWF\r\n•\tT30UXE\r\n•\tT31UCV \r\n•\tT30UYE\r\n•\tT31UCA\r\n\r\nAs the project continues, JNCC will expand the geographical coverage of this dataset and will provide continuous updates as ARD becomes available." }, "objectObservation": { "ob_id": 33376, "uuid": "b42f524bc9cd4dd6850b2399b616f5c4", "short_code": "ob", "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Normalised Difference Water Index (NDWI) v1", "abstract": "Sentinel-Hub NDWI description: The NDWI is used to monitor changes related to water content in water bodies. As water bodies strongly absorb light in visible to the infrared electromagnetic spectrum, NDWI uses green and near-infrared bands to highlight water bodies. It is sensitive to built-up land and can result in the over-estimation of water bodies. \r\nIndex values greater than 0.5 usually correspond to water bodies. Vegetation usually corresponds to much smaller values and built-up areas to values between zero and 0.2.\r\n\r\nNDWI = (GREEN – NIR) / (GREEN + NIR)\r\n\r\nSentinel-2 NDWI = (B03 - B08) / (B03 + B08)\r\n\r\nThese data have been created by the Joint Nature Conservation Committee (JNCC) as part of a Defra Natural Capital and Ecosystem Assessment (NCEA) project to produce a regional, and ultimately national, system for detecting a change in habitat conditions at a land parcel level. The first stage of the project is focused on Yorkshire, UK, and therefore the dataset includes granules and scenes covering Yorkshire and surrounding areas only. The dataset contains Normalised Difference Water Index (NDWI) data derived from Defra and JNCC Sentinel-2 Analysis Ready Data.\r\n\r\nNDWI files are generated for the following Sentinel-2 granules:\r\n•\tT30UWE\r\n•\tT30UXF\r\n•\tT30UWF\r\n•\tT30UXE\r\n•\tT31UCV \r\n•\tT30UYE\r\n•\tT31UCA\r\n\r\nAs the project continues, JNCC will expand the geographical coverage of this dataset and will provide continuous updates as ARD becomes available." } }, { "ob_id": 600, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 33374, "uuid": "46f5d503ce284114b5925709258bacc5", "short_code": "ob", "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Normalised Difference Moisture Index (NDMI) v1", "abstract": "Sentinel-Hub NDMI description: \r\n\r\nThe NDMI is a normalized difference moisture index, that uses NIR and SWIR bands to display moisture. The SWIR band reflects changes in both the vegetation water content and the spongy mesophyll structure in vegetation canopies, while the NIR reflectance is affected by leaf internal structure and leaf dry matter content but not by water content. The combination of the NIR with the SWIR removes variations induced by leaf internal structure and leaf dry matter content, improving the accuracy in retrieving the vegetation water content. The amount of water available in the internal leaf structure largely controls the spectral reflectance in the SWIR interval of the electromagnetic spectrum. SWIR reflectance is therefore negatively related to leaf water content. In short, NDMI is used to monitor changes in the water content of leaves and was proposed by Gao. NDWI is computed using the near-infrared (NIR) and the short wave infrared (SWIR) reflectances:\r\n\r\nNDMI = (NIR – SWIR) / (NIR + SWIR) \r\n\r\nSentinel-2 NDMI = (B08 - B11) / (B08 + B11)\r\n\r\nThese data have been created by the Joint Nature Conservation Committee (JNCC) as part of a Defra Natural Capital and Ecosystem Assessment (NCEA) project to produce a regional, and ultimately national, system for detecting a change in habitat conditions at a land parcel level. The first stage of the project is focused on Yorkshire, UK, and therefore the dataset includes granules and scenes covering Yorkshire and surrounding areas only. The dataset contains Normalised Difference Moisture Index data derived from Defra and JNCC Sentinel-2 Analysis Ready Data.\r\n\r\nNDMI files are generated for the following Sentinel-2 granules:\r\n•\tT30UWE\r\n•\tT30UXF\r\n•\tT30UWF\r\n•\tT30UXE\r\n•\tT31UCV \r\n•\tT30UYE\r\n•\tT31UCA\r\n\r\nAs the project continues, JNCC will expand the geographical coverage of this dataset and will provide continuous updates as ARD becomes available." }, "objectObservation": { "ob_id": 33339, "uuid": "51725f90c60a45e69f07a748a25b9729", "short_code": "ob", "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Normalised Difference Vegetation Index (NDVI) v1", "abstract": "Sentinel-Hub NDVI description: \r\nNDVI is a simple, but effective index for quantifying green vegetation. It normalizes green leaf scattering in Near Infra-red wavelengths with chlorophyll absorption in red wavelengths.\r\n\r\nThe value range of the NDVI is -1 to 1. Negative values of NDVI (values approaching -1) correspond to water. Values close to zero (-0.1 to 0.1) generally correspond to barren areas of rock, sand, or snow. Low, positive values represent shrub and grassland (approximately 0.2 to 0.4), while high values indicate temperate and tropical rainforests (values approaching 1). It is a good proxy for live green vegetation.\r\n\r\nNDVI = (NIR – Red) / (NIR + RED)\r\n\r\nSentinel-2 NDVI = (B8 - B4) / (B8 + B4)\r\n\r\nThese data have been created by the Joint Nature Conservation Committee (JNCC) as part of a Defra Natural Capital and Ecosystem Assessment (NCEA) project to produce a regional, and ultimately national, system for detecting a change in habitat conditions at a land parcel level. The first stage of the project is focused on Yorkshire, UK, and therefore the dataset includes granules and scenes covering Yorkshire and surrounding areas only. The dataset contains Normalised Difference Vegetation Index (NDVI) data derived from Defra and JNCC Sentinel-2 Analysis Ready Data. \r\n\r\nNDVI files are generated for the following Sentinel-2 granules:\r\n•\tT30UWE\r\n•\tT30UXF\r\n•\tT30UWF\r\n•\tT30UXE\r\n•\tT31UCV \r\n•\tT30UYE\r\n•\tT31UCA\r\n\r\nAs the project continues, JNCC will expand the geographical coverage of this dataset and will provide continuous updates as ARD becomes available.\r\n\r\nVersion 1 contains masked index files (using the Defra and JNCC ARD cloud and topographic shadow masks)." } }, { "ob_id": 601, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 33384, "uuid": "102909b1d169469f85c099a6f1686bad", "short_code": "ob", "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Enhanced Vegetation Index (EVI)", "abstract": "EVI is a development on Normalised Difference Vegetation Index (NDVI).\r\n\r\nSentinel-Hub EVI description: \r\nIn areas of dense canopy cover, where leaf area index (LAI) is high, the blue wavelengths can be used to improve the accuracy of NDVI, as it corrects for soil background signals and atmospheric influences. The range of values for EVI is -1 to 1, with healthy vegetation generally around 0.20 to 0.80.\r\n\r\nEVI is calculated: EVI = 2.5 * ((NIR – RED) / ((NIR + (6 * RED) – (7.5 * BLUE)) + 1))\r\n\r\nSentinel 2 EVI = 2.5 * ((B8 – B4) / ((B8 + (6 * B4) – (7.5 * B2)) + 1))\r\n\r\nThese data have been created by the Joint Nature Conservation Committee (JNCC) as part of a Defra Natural Capital and Ecosystem Assessment (NCEA) project to produce a regional, and ultimately national, system for detecting a change in habitat conditions at a land parcel level. The first stage of the project is focused on Yorkshire, UK, and therefore the dataset includes granules and scenes covering Yorkshire and surrounding areas only. The dataset contains Enhanced Vegetation Index data derived from Defra and JNCC Sentinel-2 Analysis Ready Data.\r\n\r\nEVI files are generated for the following Sentinel-2 granules:\r\n•\tT30UWE\r\n•\tT30UXF\r\n•\tT30UWF\r\n•\tT30UXE\r\n•\tT31UCV \r\n•\tT30UYE\r\n•\tT31UCA\r\n\r\nAs the project continues, JNCC will expand the geographical coverage of this dataset and will provide continuous updates as ARD becomes available." }, "objectObservation": { "ob_id": 33382, "uuid": "6df6b803c2784b8ab9e03834bf9a4337", "short_code": "ob", "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Normalised Burn Ratio (NBR) v1", "abstract": "Sentinel Hub NBR description: To detect burned areas, the NBR-RAW index is the most appropriate choice. Using bands 8 and 12 it highlights burnt areas in large fire zones greater than 500 acres. To observe burn severity, you may subtract the post-fire NBR image from the pre-fire NBR image. Darker pixels indicate burned areas.\r\n\r\nNBR = (NIR – SWIR) / (NIR + SWIR) \r\n\r\nSentinel-2 NBR = (B08 - B12) / (B08 + B12)\r\n\r\nThese data have been created by the Joint Nature Conservation Committee (JNCC) as part of a Defra Natural Capital & Ecosystem Assessment (NCEA) project to produce a regional, and ultimately national, system for detecting a change in habitat condition at a land parcel level. The first stage of the project is focused on Yorkshire, UK, and therefore the dataset includes granules and scenes covering Yorkshire and surrounding areas only. The dataset contains the following indices derived from Defra and JNCC Sentinel-2 Analysis Ready Data.\r\n\r\nNDVI, NDMI, NDWI, NBR, and EVI files are generated for the following Sentinel-2 granules:\r\n•\tT30UWE\r\n•\tT30UXF\r\n•\tT30UWF\r\n•\tT30UXE\r\n•\tT31UCV \r\n•\tT30UYE\r\n•\tT31UCA\r\n\r\nAs the project continues, JNCC will expand the geographical coverage of this dataset and will provide continuous updates as ARD becomes available." } }, { "ob_id": 602, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 33384, "uuid": "102909b1d169469f85c099a6f1686bad", "short_code": "ob", "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Enhanced Vegetation Index (EVI)", "abstract": "EVI is a development on Normalised Difference Vegetation Index (NDVI).\r\n\r\nSentinel-Hub EVI description: \r\nIn areas of dense canopy cover, where leaf area index (LAI) is high, the blue wavelengths can be used to improve the accuracy of NDVI, as it corrects for soil background signals and atmospheric influences. The range of values for EVI is -1 to 1, with healthy vegetation generally around 0.20 to 0.80.\r\n\r\nEVI is calculated: EVI = 2.5 * ((NIR – RED) / ((NIR + (6 * RED) – (7.5 * BLUE)) + 1))\r\n\r\nSentinel 2 EVI = 2.5 * ((B8 – B4) / ((B8 + (6 * B4) – (7.5 * B2)) + 1))\r\n\r\nThese data have been created by the Joint Nature Conservation Committee (JNCC) as part of a Defra Natural Capital and Ecosystem Assessment (NCEA) project to produce a regional, and ultimately national, system for detecting a change in habitat conditions at a land parcel level. The first stage of the project is focused on Yorkshire, UK, and therefore the dataset includes granules and scenes covering Yorkshire and surrounding areas only. The dataset contains Enhanced Vegetation Index data derived from Defra and JNCC Sentinel-2 Analysis Ready Data.\r\n\r\nEVI files are generated for the following Sentinel-2 granules:\r\n•\tT30UWE\r\n•\tT30UXF\r\n•\tT30UWF\r\n•\tT30UXE\r\n•\tT31UCV \r\n•\tT30UYE\r\n•\tT31UCA\r\n\r\nAs the project continues, JNCC will expand the geographical coverage of this dataset and will provide continuous updates as ARD becomes available." }, "objectObservation": { "ob_id": 33376, "uuid": "b42f524bc9cd4dd6850b2399b616f5c4", "short_code": "ob", "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Normalised Difference Water Index (NDWI) v1", "abstract": "Sentinel-Hub NDWI description: The NDWI is used to monitor changes related to water content in water bodies. As water bodies strongly absorb light in visible to the infrared electromagnetic spectrum, NDWI uses green and near-infrared bands to highlight water bodies. It is sensitive to built-up land and can result in the over-estimation of water bodies. \r\nIndex values greater than 0.5 usually correspond to water bodies. Vegetation usually corresponds to much smaller values and built-up areas to values between zero and 0.2.\r\n\r\nNDWI = (GREEN – NIR) / (GREEN + NIR)\r\n\r\nSentinel-2 NDWI = (B03 - B08) / (B03 + B08)\r\n\r\nThese data have been created by the Joint Nature Conservation Committee (JNCC) as part of a Defra Natural Capital and Ecosystem Assessment (NCEA) project to produce a regional, and ultimately national, system for detecting a change in habitat conditions at a land parcel level. The first stage of the project is focused on Yorkshire, UK, and therefore the dataset includes granules and scenes covering Yorkshire and surrounding areas only. The dataset contains Normalised Difference Water Index (NDWI) data derived from Defra and JNCC Sentinel-2 Analysis Ready Data.\r\n\r\nNDWI files are generated for the following Sentinel-2 granules:\r\n•\tT30UWE\r\n•\tT30UXF\r\n•\tT30UWF\r\n•\tT30UXE\r\n•\tT31UCV \r\n•\tT30UYE\r\n•\tT31UCA\r\n\r\nAs the project continues, JNCC will expand the geographical coverage of this dataset and will provide continuous updates as ARD becomes available." } }, { "ob_id": 603, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 33384, "uuid": "102909b1d169469f85c099a6f1686bad", "short_code": "ob", "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Enhanced Vegetation Index (EVI)", "abstract": "EVI is a development on Normalised Difference Vegetation Index (NDVI).\r\n\r\nSentinel-Hub EVI description: \r\nIn areas of dense canopy cover, where leaf area index (LAI) is high, the blue wavelengths can be used to improve the accuracy of NDVI, as it corrects for soil background signals and atmospheric influences. The range of values for EVI is -1 to 1, with healthy vegetation generally around 0.20 to 0.80.\r\n\r\nEVI is calculated: EVI = 2.5 * ((NIR – RED) / ((NIR + (6 * RED) – (7.5 * BLUE)) + 1))\r\n\r\nSentinel 2 EVI = 2.5 * ((B8 – B4) / ((B8 + (6 * B4) – (7.5 * B2)) + 1))\r\n\r\nThese data have been created by the Joint Nature Conservation Committee (JNCC) as part of a Defra Natural Capital and Ecosystem Assessment (NCEA) project to produce a regional, and ultimately national, system for detecting a change in habitat conditions at a land parcel level. The first stage of the project is focused on Yorkshire, UK, and therefore the dataset includes granules and scenes covering Yorkshire and surrounding areas only. The dataset contains Enhanced Vegetation Index data derived from Defra and JNCC Sentinel-2 Analysis Ready Data.\r\n\r\nEVI files are generated for the following Sentinel-2 granules:\r\n•\tT30UWE\r\n•\tT30UXF\r\n•\tT30UWF\r\n•\tT30UXE\r\n•\tT31UCV \r\n•\tT30UYE\r\n•\tT31UCA\r\n\r\nAs the project continues, JNCC will expand the geographical coverage of this dataset and will provide continuous updates as ARD becomes available." }, "objectObservation": { "ob_id": 33374, "uuid": "46f5d503ce284114b5925709258bacc5", "short_code": "ob", "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Normalised Difference Moisture Index (NDMI) v1", "abstract": "Sentinel-Hub NDMI description: \r\n\r\nThe NDMI is a normalized difference moisture index, that uses NIR and SWIR bands to display moisture. The SWIR band reflects changes in both the vegetation water content and the spongy mesophyll structure in vegetation canopies, while the NIR reflectance is affected by leaf internal structure and leaf dry matter content but not by water content. The combination of the NIR with the SWIR removes variations induced by leaf internal structure and leaf dry matter content, improving the accuracy in retrieving the vegetation water content. The amount of water available in the internal leaf structure largely controls the spectral reflectance in the SWIR interval of the electromagnetic spectrum. SWIR reflectance is therefore negatively related to leaf water content. In short, NDMI is used to monitor changes in the water content of leaves and was proposed by Gao. NDWI is computed using the near-infrared (NIR) and the short wave infrared (SWIR) reflectances:\r\n\r\nNDMI = (NIR – SWIR) / (NIR + SWIR) \r\n\r\nSentinel-2 NDMI = (B08 - B11) / (B08 + B11)\r\n\r\nThese data have been created by the Joint Nature Conservation Committee (JNCC) as part of a Defra Natural Capital and Ecosystem Assessment (NCEA) project to produce a regional, and ultimately national, system for detecting a change in habitat conditions at a land parcel level. The first stage of the project is focused on Yorkshire, UK, and therefore the dataset includes granules and scenes covering Yorkshire and surrounding areas only. The dataset contains Normalised Difference Moisture Index data derived from Defra and JNCC Sentinel-2 Analysis Ready Data.\r\n\r\nNDMI files are generated for the following Sentinel-2 granules:\r\n•\tT30UWE\r\n•\tT30UXF\r\n•\tT30UWF\r\n•\tT30UXE\r\n•\tT31UCV \r\n•\tT30UYE\r\n•\tT31UCA\r\n\r\nAs the project continues, JNCC will expand the geographical coverage of this dataset and will provide continuous updates as ARD becomes available." } }, { "ob_id": 604, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 33384, "uuid": "102909b1d169469f85c099a6f1686bad", "short_code": "ob", "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Enhanced Vegetation Index (EVI)", "abstract": "EVI is a development on Normalised Difference Vegetation Index (NDVI).\r\n\r\nSentinel-Hub EVI description: \r\nIn areas of dense canopy cover, where leaf area index (LAI) is high, the blue wavelengths can be used to improve the accuracy of NDVI, as it corrects for soil background signals and atmospheric influences. The range of values for EVI is -1 to 1, with healthy vegetation generally around 0.20 to 0.80.\r\n\r\nEVI is calculated: EVI = 2.5 * ((NIR – RED) / ((NIR + (6 * RED) – (7.5 * BLUE)) + 1))\r\n\r\nSentinel 2 EVI = 2.5 * ((B8 – B4) / ((B8 + (6 * B4) – (7.5 * B2)) + 1))\r\n\r\nThese data have been created by the Joint Nature Conservation Committee (JNCC) as part of a Defra Natural Capital and Ecosystem Assessment (NCEA) project to produce a regional, and ultimately national, system for detecting a change in habitat conditions at a land parcel level. The first stage of the project is focused on Yorkshire, UK, and therefore the dataset includes granules and scenes covering Yorkshire and surrounding areas only. The dataset contains Enhanced Vegetation Index data derived from Defra and JNCC Sentinel-2 Analysis Ready Data.\r\n\r\nEVI files are generated for the following Sentinel-2 granules:\r\n•\tT30UWE\r\n•\tT30UXF\r\n•\tT30UWF\r\n•\tT30UXE\r\n•\tT31UCV \r\n•\tT30UYE\r\n•\tT31UCA\r\n\r\nAs the project continues, JNCC will expand the geographical coverage of this dataset and will provide continuous updates as ARD becomes available." }, "objectObservation": { "ob_id": 33339, "uuid": "51725f90c60a45e69f07a748a25b9729", "short_code": "ob", "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Normalised Difference Vegetation Index (NDVI) v1", "abstract": "Sentinel-Hub NDVI description: \r\nNDVI is a simple, but effective index for quantifying green vegetation. It normalizes green leaf scattering in Near Infra-red wavelengths with chlorophyll absorption in red wavelengths.\r\n\r\nThe value range of the NDVI is -1 to 1. Negative values of NDVI (values approaching -1) correspond to water. Values close to zero (-0.1 to 0.1) generally correspond to barren areas of rock, sand, or snow. Low, positive values represent shrub and grassland (approximately 0.2 to 0.4), while high values indicate temperate and tropical rainforests (values approaching 1). It is a good proxy for live green vegetation.\r\n\r\nNDVI = (NIR – Red) / (NIR + RED)\r\n\r\nSentinel-2 NDVI = (B8 - B4) / (B8 + B4)\r\n\r\nThese data have been created by the Joint Nature Conservation Committee (JNCC) as part of a Defra Natural Capital and Ecosystem Assessment (NCEA) project to produce a regional, and ultimately national, system for detecting a change in habitat conditions at a land parcel level. The first stage of the project is focused on Yorkshire, UK, and therefore the dataset includes granules and scenes covering Yorkshire and surrounding areas only. The dataset contains Normalised Difference Vegetation Index (NDVI) data derived from Defra and JNCC Sentinel-2 Analysis Ready Data. \r\n\r\nNDVI files are generated for the following Sentinel-2 granules:\r\n•\tT30UWE\r\n•\tT30UXF\r\n•\tT30UWF\r\n•\tT30UXE\r\n•\tT31UCV \r\n•\tT30UYE\r\n•\tT31UCA\r\n\r\nAs the project continues, JNCC will expand the geographical coverage of this dataset and will provide continuous updates as ARD becomes available.\r\n\r\nVersion 1 contains masked index files (using the Defra and JNCC ARD cloud and topographic shadow masks)." } }, { "ob_id": 605, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 33382, "uuid": "6df6b803c2784b8ab9e03834bf9a4337", "short_code": "ob", "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Normalised Burn Ratio (NBR) v1", "abstract": "Sentinel Hub NBR description: To detect burned areas, the NBR-RAW index is the most appropriate choice. Using bands 8 and 12 it highlights burnt areas in large fire zones greater than 500 acres. To observe burn severity, you may subtract the post-fire NBR image from the pre-fire NBR image. Darker pixels indicate burned areas.\r\n\r\nNBR = (NIR – SWIR) / (NIR + SWIR) \r\n\r\nSentinel-2 NBR = (B08 - B12) / (B08 + B12)\r\n\r\nThese data have been created by the Joint Nature Conservation Committee (JNCC) as part of a Defra Natural Capital & Ecosystem Assessment (NCEA) project to produce a regional, and ultimately national, system for detecting a change in habitat condition at a land parcel level. The first stage of the project is focused on Yorkshire, UK, and therefore the dataset includes granules and scenes covering Yorkshire and surrounding areas only. The dataset contains the following indices derived from Defra and JNCC Sentinel-2 Analysis Ready Data.\r\n\r\nNDVI, NDMI, NDWI, NBR, and EVI files are generated for the following Sentinel-2 granules:\r\n•\tT30UWE\r\n•\tT30UXF\r\n•\tT30UWF\r\n•\tT30UXE\r\n•\tT31UCV \r\n•\tT30UYE\r\n•\tT31UCA\r\n\r\nAs the project continues, JNCC will expand the geographical coverage of this dataset and will provide continuous updates as ARD becomes available." }, "objectObservation": { "ob_id": 33384, "uuid": "102909b1d169469f85c099a6f1686bad", "short_code": "ob", "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Enhanced Vegetation Index (EVI)", "abstract": "EVI is a development on Normalised Difference Vegetation Index (NDVI).\r\n\r\nSentinel-Hub EVI description: \r\nIn areas of dense canopy cover, where leaf area index (LAI) is high, the blue wavelengths can be used to improve the accuracy of NDVI, as it corrects for soil background signals and atmospheric influences. The range of values for EVI is -1 to 1, with healthy vegetation generally around 0.20 to 0.80.\r\n\r\nEVI is calculated: EVI = 2.5 * ((NIR – RED) / ((NIR + (6 * RED) – (7.5 * BLUE)) + 1))\r\n\r\nSentinel 2 EVI = 2.5 * ((B8 – B4) / ((B8 + (6 * B4) – (7.5 * B2)) + 1))\r\n\r\nThese data have been created by the Joint Nature Conservation Committee (JNCC) as part of a Defra Natural Capital and Ecosystem Assessment (NCEA) project to produce a regional, and ultimately national, system for detecting a change in habitat conditions at a land parcel level. The first stage of the project is focused on Yorkshire, UK, and therefore the dataset includes granules and scenes covering Yorkshire and surrounding areas only. The dataset contains Enhanced Vegetation Index data derived from Defra and JNCC Sentinel-2 Analysis Ready Data.\r\n\r\nEVI files are generated for the following Sentinel-2 granules:\r\n•\tT30UWE\r\n•\tT30UXF\r\n•\tT30UWF\r\n•\tT30UXE\r\n•\tT31UCV \r\n•\tT30UYE\r\n•\tT31UCA\r\n\r\nAs the project continues, JNCC will expand the geographical coverage of this dataset and will provide continuous updates as ARD becomes available." } }, { "ob_id": 606, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 33382, "uuid": "6df6b803c2784b8ab9e03834bf9a4337", "short_code": "ob", "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Normalised Burn Ratio (NBR) v1", "abstract": "Sentinel Hub NBR description: To detect burned areas, the NBR-RAW index is the most appropriate choice. Using bands 8 and 12 it highlights burnt areas in large fire zones greater than 500 acres. To observe burn severity, you may subtract the post-fire NBR image from the pre-fire NBR image. Darker pixels indicate burned areas.\r\n\r\nNBR = (NIR – SWIR) / (NIR + SWIR) \r\n\r\nSentinel-2 NBR = (B08 - B12) / (B08 + B12)\r\n\r\nThese data have been created by the Joint Nature Conservation Committee (JNCC) as part of a Defra Natural Capital & Ecosystem Assessment (NCEA) project to produce a regional, and ultimately national, system for detecting a change in habitat condition at a land parcel level. The first stage of the project is focused on Yorkshire, UK, and therefore the dataset includes granules and scenes covering Yorkshire and surrounding areas only. The dataset contains the following indices derived from Defra and JNCC Sentinel-2 Analysis Ready Data.\r\n\r\nNDVI, NDMI, NDWI, NBR, and EVI files are generated for the following Sentinel-2 granules:\r\n•\tT30UWE\r\n•\tT30UXF\r\n•\tT30UWF\r\n•\tT30UXE\r\n•\tT31UCV \r\n•\tT30UYE\r\n•\tT31UCA\r\n\r\nAs the project continues, JNCC will expand the geographical coverage of this dataset and will provide continuous updates as ARD becomes available." }, "objectObservation": { "ob_id": 33376, "uuid": "b42f524bc9cd4dd6850b2399b616f5c4", "short_code": "ob", "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Normalised Difference Water Index (NDWI) v1", "abstract": "Sentinel-Hub NDWI description: The NDWI is used to monitor changes related to water content in water bodies. As water bodies strongly absorb light in visible to the infrared electromagnetic spectrum, NDWI uses green and near-infrared bands to highlight water bodies. It is sensitive to built-up land and can result in the over-estimation of water bodies. \r\nIndex values greater than 0.5 usually correspond to water bodies. Vegetation usually corresponds to much smaller values and built-up areas to values between zero and 0.2.\r\n\r\nNDWI = (GREEN – NIR) / (GREEN + NIR)\r\n\r\nSentinel-2 NDWI = (B03 - B08) / (B03 + B08)\r\n\r\nThese data have been created by the Joint Nature Conservation Committee (JNCC) as part of a Defra Natural Capital and Ecosystem Assessment (NCEA) project to produce a regional, and ultimately national, system for detecting a change in habitat conditions at a land parcel level. The first stage of the project is focused on Yorkshire, UK, and therefore the dataset includes granules and scenes covering Yorkshire and surrounding areas only. The dataset contains Normalised Difference Water Index (NDWI) data derived from Defra and JNCC Sentinel-2 Analysis Ready Data.\r\n\r\nNDWI files are generated for the following Sentinel-2 granules:\r\n•\tT30UWE\r\n•\tT30UXF\r\n•\tT30UWF\r\n•\tT30UXE\r\n•\tT31UCV \r\n•\tT30UYE\r\n•\tT31UCA\r\n\r\nAs the project continues, JNCC will expand the geographical coverage of this dataset and will provide continuous updates as ARD becomes available." } }, { "ob_id": 607, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 33382, "uuid": "6df6b803c2784b8ab9e03834bf9a4337", "short_code": "ob", "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Normalised Burn Ratio (NBR) v1", "abstract": "Sentinel Hub NBR description: To detect burned areas, the NBR-RAW index is the most appropriate choice. Using bands 8 and 12 it highlights burnt areas in large fire zones greater than 500 acres. To observe burn severity, you may subtract the post-fire NBR image from the pre-fire NBR image. Darker pixels indicate burned areas.\r\n\r\nNBR = (NIR – SWIR) / (NIR + SWIR) \r\n\r\nSentinel-2 NBR = (B08 - B12) / (B08 + B12)\r\n\r\nThese data have been created by the Joint Nature Conservation Committee (JNCC) as part of a Defra Natural Capital & Ecosystem Assessment (NCEA) project to produce a regional, and ultimately national, system for detecting a change in habitat condition at a land parcel level. The first stage of the project is focused on Yorkshire, UK, and therefore the dataset includes granules and scenes covering Yorkshire and surrounding areas only. The dataset contains the following indices derived from Defra and JNCC Sentinel-2 Analysis Ready Data.\r\n\r\nNDVI, NDMI, NDWI, NBR, and EVI files are generated for the following Sentinel-2 granules:\r\n•\tT30UWE\r\n•\tT30UXF\r\n•\tT30UWF\r\n•\tT30UXE\r\n•\tT31UCV \r\n•\tT30UYE\r\n•\tT31UCA\r\n\r\nAs the project continues, JNCC will expand the geographical coverage of this dataset and will provide continuous updates as ARD becomes available." }, "objectObservation": { "ob_id": 33374, "uuid": "46f5d503ce284114b5925709258bacc5", "short_code": "ob", "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Normalised Difference Moisture Index (NDMI) v1", "abstract": "Sentinel-Hub NDMI description: \r\n\r\nThe NDMI is a normalized difference moisture index, that uses NIR and SWIR bands to display moisture. The SWIR band reflects changes in both the vegetation water content and the spongy mesophyll structure in vegetation canopies, while the NIR reflectance is affected by leaf internal structure and leaf dry matter content but not by water content. The combination of the NIR with the SWIR removes variations induced by leaf internal structure and leaf dry matter content, improving the accuracy in retrieving the vegetation water content. The amount of water available in the internal leaf structure largely controls the spectral reflectance in the SWIR interval of the electromagnetic spectrum. SWIR reflectance is therefore negatively related to leaf water content. In short, NDMI is used to monitor changes in the water content of leaves and was proposed by Gao. NDWI is computed using the near-infrared (NIR) and the short wave infrared (SWIR) reflectances:\r\n\r\nNDMI = (NIR – SWIR) / (NIR + SWIR) \r\n\r\nSentinel-2 NDMI = (B08 - B11) / (B08 + B11)\r\n\r\nThese data have been created by the Joint Nature Conservation Committee (JNCC) as part of a Defra Natural Capital and Ecosystem Assessment (NCEA) project to produce a regional, and ultimately national, system for detecting a change in habitat conditions at a land parcel level. The first stage of the project is focused on Yorkshire, UK, and therefore the dataset includes granules and scenes covering Yorkshire and surrounding areas only. The dataset contains Normalised Difference Moisture Index data derived from Defra and JNCC Sentinel-2 Analysis Ready Data.\r\n\r\nNDMI files are generated for the following Sentinel-2 granules:\r\n•\tT30UWE\r\n•\tT30UXF\r\n•\tT30UWF\r\n•\tT30UXE\r\n•\tT31UCV \r\n•\tT30UYE\r\n•\tT31UCA\r\n\r\nAs the project continues, JNCC will expand the geographical coverage of this dataset and will provide continuous updates as ARD becomes available." } }, { "ob_id": 608, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 33382, "uuid": "6df6b803c2784b8ab9e03834bf9a4337", "short_code": "ob", "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Normalised Burn Ratio (NBR) v1", "abstract": "Sentinel Hub NBR description: To detect burned areas, the NBR-RAW index is the most appropriate choice. Using bands 8 and 12 it highlights burnt areas in large fire zones greater than 500 acres. To observe burn severity, you may subtract the post-fire NBR image from the pre-fire NBR image. Darker pixels indicate burned areas.\r\n\r\nNBR = (NIR – SWIR) / (NIR + SWIR) \r\n\r\nSentinel-2 NBR = (B08 - B12) / (B08 + B12)\r\n\r\nThese data have been created by the Joint Nature Conservation Committee (JNCC) as part of a Defra Natural Capital & Ecosystem Assessment (NCEA) project to produce a regional, and ultimately national, system for detecting a change in habitat condition at a land parcel level. The first stage of the project is focused on Yorkshire, UK, and therefore the dataset includes granules and scenes covering Yorkshire and surrounding areas only. The dataset contains the following indices derived from Defra and JNCC Sentinel-2 Analysis Ready Data.\r\n\r\nNDVI, NDMI, NDWI, NBR, and EVI files are generated for the following Sentinel-2 granules:\r\n•\tT30UWE\r\n•\tT30UXF\r\n•\tT30UWF\r\n•\tT30UXE\r\n•\tT31UCV \r\n•\tT30UYE\r\n•\tT31UCA\r\n\r\nAs the project continues, JNCC will expand the geographical coverage of this dataset and will provide continuous updates as ARD becomes available." }, "objectObservation": { "ob_id": 33339, "uuid": "51725f90c60a45e69f07a748a25b9729", "short_code": "ob", "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Normalised Difference Vegetation Index (NDVI) v1", "abstract": "Sentinel-Hub NDVI description: \r\nNDVI is a simple, but effective index for quantifying green vegetation. It normalizes green leaf scattering in Near Infra-red wavelengths with chlorophyll absorption in red wavelengths.\r\n\r\nThe value range of the NDVI is -1 to 1. Negative values of NDVI (values approaching -1) correspond to water. Values close to zero (-0.1 to 0.1) generally correspond to barren areas of rock, sand, or snow. Low, positive values represent shrub and grassland (approximately 0.2 to 0.4), while high values indicate temperate and tropical rainforests (values approaching 1). It is a good proxy for live green vegetation.\r\n\r\nNDVI = (NIR – Red) / (NIR + RED)\r\n\r\nSentinel-2 NDVI = (B8 - B4) / (B8 + B4)\r\n\r\nThese data have been created by the Joint Nature Conservation Committee (JNCC) as part of a Defra Natural Capital and Ecosystem Assessment (NCEA) project to produce a regional, and ultimately national, system for detecting a change in habitat conditions at a land parcel level. The first stage of the project is focused on Yorkshire, UK, and therefore the dataset includes granules and scenes covering Yorkshire and surrounding areas only. The dataset contains Normalised Difference Vegetation Index (NDVI) data derived from Defra and JNCC Sentinel-2 Analysis Ready Data. \r\n\r\nNDVI files are generated for the following Sentinel-2 granules:\r\n•\tT30UWE\r\n•\tT30UXF\r\n•\tT30UWF\r\n•\tT30UXE\r\n•\tT31UCV \r\n•\tT30UYE\r\n•\tT31UCA\r\n\r\nAs the project continues, JNCC will expand the geographical coverage of this dataset and will provide continuous updates as ARD becomes available.\r\n\r\nVersion 1 contains masked index files (using the Defra and JNCC ARD cloud and topographic shadow masks)." } }, { "ob_id": 609, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 33410, "uuid": "299b1bb28eaa440f9a36e9786adfe398", "short_code": "ob", "title": "BICEP/NCEO: Monthly global Particulate Organic Carbon (POC), between 1997-2020 at 4 km resolution (produced from the Ocean Colour Climate Change Initiative v4.2 dataset), version 2", "abstract": "The BICEP/NCEO: Monthly global Particulate Organic Carbon (POC) v4.2 datasets contain POC concentrations (mg m^-3) with per pixel uncertainties estimates gridded on both geographic and sinusoidal projections at 4 km spatial resolution for the period of 1997 to 2020. The POC products were generated as part of the European Space Agency (ESA) Biological Pump and Carbon Exchange Processes (BICEP) project with support from the National Centre of Earth Observation (NCEO). \r\n\r\nThe POC concentrations were estimated using an empirical Remote Sensing Reflectance (Rrs) band ratio algorithm by Stramski et al. (2008): 203.2*Rrs(443)/Rrs(555)^-1.034. This algorithm has shown a relatively good performance in the recent global inter-comparison study conducted by Evers-King et al. (2017). Additional variables that were used for the calculation of the POC products are also provided in the datasets, including the Rrs at 443 nm and 555 nm obtained from the ESA Ocean Colour Climate Change Initiative version 4.2 dataset (OC-CCI v4.2)(Sathyendranath et al., 2020). In addition to the papers by Stramski et al. (2008) and Evers-king et al. (2017), for more details on the algorithm and its validation, please see the BICEP Algorithm Theoretical Basis Document (ATBD) and validation report (https://bicep-project.org/Home) \r\n\r\nThis version of the dataset is an updated version of the previous 'NCEO: Monthly global Particulate Organic Carbon (POC) (produced from the Ocean Colour Climate Change Initiative, Version 4.2 dataset)'.\r\n\r\nA related product based on the Ocean Colour Climate Change Initiative v5.0 data is also available (see the link in the related records section)." }, "objectObservation": { "ob_id": 31913, "uuid": "ef09d81517a84979ac60329e4859f449", "short_code": "ob", "title": "NCEO: Monthly global Particulate Organic Carbon (POC) (produced from the Ocean Colour Climate Change Initiative, Version 4.2 dataset)", "abstract": "The National Centre for Earth Observation (NCEO): Monthly global Particulate Organic Carbon (POC) dataset contains POC concentrations gridded on both sinusoidal (SIN) and geographic (GEO) grid projections at 4 km spatial resolution for 1997-2020. The POC dataset has been produced using the Ocean Colour Climate Change Initiative Remote Sensing Reflectance (Rrs) products, Version 4.2. The dataset includes the Rrs at 443 nm and 555 nm with pixel-by-pixel uncertainty estimates for each wavelength.\r\n\r\nFor more details on the algorithm and its validation, please see papers by Stramski et al. (2008) and Evers-King et al. (2017). Please note that the validation of the POC algorithm is a continuing process. To increase the accuracy of POC algorithms, further in situ POC data need to be collected with high spatial and temporal resolution." } }, { "ob_id": 610, "relationType": "IsDerivedFrom", "subjectObservation": { "ob_id": 33410, "uuid": "299b1bb28eaa440f9a36e9786adfe398", "short_code": "ob", "title": "BICEP/NCEO: Monthly global Particulate Organic Carbon (POC), between 1997-2020 at 4 km resolution (produced from the Ocean Colour Climate Change Initiative v4.2 dataset), version 2", "abstract": "The BICEP/NCEO: Monthly global Particulate Organic Carbon (POC) v4.2 datasets contain POC concentrations (mg m^-3) with per pixel uncertainties estimates gridded on both geographic and sinusoidal projections at 4 km spatial resolution for the period of 1997 to 2020. The POC products were generated as part of the European Space Agency (ESA) Biological Pump and Carbon Exchange Processes (BICEP) project with support from the National Centre of Earth Observation (NCEO). \r\n\r\nThe POC concentrations were estimated using an empirical Remote Sensing Reflectance (Rrs) band ratio algorithm by Stramski et al. (2008): 203.2*Rrs(443)/Rrs(555)^-1.034. This algorithm has shown a relatively good performance in the recent global inter-comparison study conducted by Evers-King et al. (2017). Additional variables that were used for the calculation of the POC products are also provided in the datasets, including the Rrs at 443 nm and 555 nm obtained from the ESA Ocean Colour Climate Change Initiative version 4.2 dataset (OC-CCI v4.2)(Sathyendranath et al., 2020). In addition to the papers by Stramski et al. (2008) and Evers-king et al. (2017), for more details on the algorithm and its validation, please see the BICEP Algorithm Theoretical Basis Document (ATBD) and validation report (https://bicep-project.org/Home) \r\n\r\nThis version of the dataset is an updated version of the previous 'NCEO: Monthly global Particulate Organic Carbon (POC) (produced from the Ocean Colour Climate Change Initiative, Version 4.2 dataset)'.\r\n\r\nA related product based on the Ocean Colour Climate Change Initiative v5.0 data is also available (see the link in the related records section)." }, "objectObservation": { "ob_id": 30602, "uuid": "d6d0d7b4cf3540448b4ddcaed2f54b81", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global remote sensing reflectance gridded on a sinusoidal projection, Version 4.2", "abstract": "The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains the Version 4.2 Remote Sensing Reflectance product on a sinusoidal projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites). Values for remote sensing reflectance at the sea surface are provided for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm) with pixel-by-pixel uncertainty estimates for each wavelength. These are merged products based on SeaWiFS, MERIS and Aqua-MODIS data. Note, these data are also contained within the 'All Products' dataset. \r\n\r\nThis data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection).\r\n\r\nPlease note, this dataset has been superseded. Later versions of the data are now available." } }, { "ob_id": 611, "relationType": "IsDerivedFrom", "subjectObservation": { "ob_id": 33412, "uuid": "5006f2c553cd4f26a6af0af2ee6d7c94", "short_code": "ob", "title": "BICEP/NCEO: Monthly global Particulate Organic Carbon (POC), between 1997-2020 at 4 km resolution (produced from the Ocean Colour Climate Change Initiative v5.0 dataset)", "abstract": "The BICEP/NCEO: Monthly global Particulate Organic Carbon (POC) v5 datasets contain POC concentrations (mg m^-3) with per pixel uncertainties estimates gridded on both geographic and sinusoidal projections at 4 km spatial resolution for the period of 1997 to 2020. The POC products were generated as part of the European Space Agency (ESA) Biological Pump and Carbon Exchange Processes (BICEP) project with support from the National Centre of Earth Observation (NCEO). \r\n\r\nThe POC datasets have been produced by using a modified empirical band ratio algorithm by Stramski et al. (2008): 292*Rrs(490)/Rrs(560)^-1.49. Additional variables that were used for the calculation of the POC products are also provided in the datasets, including the Remote Sensing Reflectance (Rrs) at 490 nm and 560 nm obtained from the ESA Ocean Colour Climate Change Initiative version 5 dataset (OC-CCI v5). For more details on the algorithm and its validation, please see the BICEP Algorithm Theoretical Basis Document (ATBD) and validation report (https://bicep-project.org/Home).\r\n\r\nA related dataset based on the ESA Ocean Colour Climate Change Initiative v4.2 data is also available (see link in the related records section)." }, "objectObservation": { "ob_id": 32141, "uuid": "f30495d4425f46c489765a2f84dd6862", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global remote sensing reflectance gridded on a sinusoidal projection, Version 5.0", "abstract": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains the Version 5.0 Remote Sensing Reflectance product on a sinusoidal projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites) covering the period 1997 - 2020. Values for remote sensing reflectance at the sea surface are provided for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm) with pixel-by-pixel uncertainty estimates for each wavelength. These are merged products based on SeaWiFS, MERIS and Aqua-MODIS data. Note, these data are also contained within the 'All Products' dataset. \r\n\r\nThis data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection).\r\n\r\nPlease note, data from December 2020 onwards are affected by an anomaly discovered after production and resulting in a spurious jump in remote sensing reflectance. The anomaly has been corrected in the version 5.0.1 of the dataset available through the Copernicus Climate Change Service (https://doi.org/10.24381/cds.f85b319d)\r\n\r\nVersion 6.0 of this data is now also available here: https://doi.org/10.5285/5011d22aae5a4671b0cbc7d05c56c4f0" } }, { "ob_id": 612, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 33339, "uuid": "51725f90c60a45e69f07a748a25b9729", "short_code": "ob", "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Normalised Difference Vegetation Index (NDVI) v1", "abstract": "Sentinel-Hub NDVI description: \r\nNDVI is a simple, but effective index for quantifying green vegetation. It normalizes green leaf scattering in Near Infra-red wavelengths with chlorophyll absorption in red wavelengths.\r\n\r\nThe value range of the NDVI is -1 to 1. Negative values of NDVI (values approaching -1) correspond to water. Values close to zero (-0.1 to 0.1) generally correspond to barren areas of rock, sand, or snow. Low, positive values represent shrub and grassland (approximately 0.2 to 0.4), while high values indicate temperate and tropical rainforests (values approaching 1). It is a good proxy for live green vegetation.\r\n\r\nNDVI = (NIR – Red) / (NIR + RED)\r\n\r\nSentinel-2 NDVI = (B8 - B4) / (B8 + B4)\r\n\r\nThese data have been created by the Joint Nature Conservation Committee (JNCC) as part of a Defra Natural Capital and Ecosystem Assessment (NCEA) project to produce a regional, and ultimately national, system for detecting a change in habitat conditions at a land parcel level. The first stage of the project is focused on Yorkshire, UK, and therefore the dataset includes granules and scenes covering Yorkshire and surrounding areas only. The dataset contains Normalised Difference Vegetation Index (NDVI) data derived from Defra and JNCC Sentinel-2 Analysis Ready Data. \r\n\r\nNDVI files are generated for the following Sentinel-2 granules:\r\n•\tT30UWE\r\n•\tT30UXF\r\n•\tT30UWF\r\n•\tT30UXE\r\n•\tT31UCV \r\n•\tT30UYE\r\n•\tT31UCA\r\n\r\nAs the project continues, JNCC will expand the geographical coverage of this dataset and will provide continuous updates as ARD becomes available.\r\n\r\nVersion 1 contains masked index files (using the Defra and JNCC ARD cloud and topographic shadow masks)." }, "objectObservation": { "ob_id": 33384, "uuid": "102909b1d169469f85c099a6f1686bad", "short_code": "ob", "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Enhanced Vegetation Index (EVI)", "abstract": "EVI is a development on Normalised Difference Vegetation Index (NDVI).\r\n\r\nSentinel-Hub EVI description: \r\nIn areas of dense canopy cover, where leaf area index (LAI) is high, the blue wavelengths can be used to improve the accuracy of NDVI, as it corrects for soil background signals and atmospheric influences. The range of values for EVI is -1 to 1, with healthy vegetation generally around 0.20 to 0.80.\r\n\r\nEVI is calculated: EVI = 2.5 * ((NIR – RED) / ((NIR + (6 * RED) – (7.5 * BLUE)) + 1))\r\n\r\nSentinel 2 EVI = 2.5 * ((B8 – B4) / ((B8 + (6 * B4) – (7.5 * B2)) + 1))\r\n\r\nThese data have been created by the Joint Nature Conservation Committee (JNCC) as part of a Defra Natural Capital and Ecosystem Assessment (NCEA) project to produce a regional, and ultimately national, system for detecting a change in habitat conditions at a land parcel level. The first stage of the project is focused on Yorkshire, UK, and therefore the dataset includes granules and scenes covering Yorkshire and surrounding areas only. The dataset contains Enhanced Vegetation Index data derived from Defra and JNCC Sentinel-2 Analysis Ready Data.\r\n\r\nEVI files are generated for the following Sentinel-2 granules:\r\n•\tT30UWE\r\n•\tT30UXF\r\n•\tT30UWF\r\n•\tT30UXE\r\n•\tT31UCV \r\n•\tT30UYE\r\n•\tT31UCA\r\n\r\nAs the project continues, JNCC will expand the geographical coverage of this dataset and will provide continuous updates as ARD becomes available." } }, { "ob_id": 613, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 33339, "uuid": "51725f90c60a45e69f07a748a25b9729", "short_code": "ob", "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Normalised Difference Vegetation Index (NDVI) v1", "abstract": "Sentinel-Hub NDVI description: \r\nNDVI is a simple, but effective index for quantifying green vegetation. It normalizes green leaf scattering in Near Infra-red wavelengths with chlorophyll absorption in red wavelengths.\r\n\r\nThe value range of the NDVI is -1 to 1. Negative values of NDVI (values approaching -1) correspond to water. Values close to zero (-0.1 to 0.1) generally correspond to barren areas of rock, sand, or snow. Low, positive values represent shrub and grassland (approximately 0.2 to 0.4), while high values indicate temperate and tropical rainforests (values approaching 1). It is a good proxy for live green vegetation.\r\n\r\nNDVI = (NIR – Red) / (NIR + RED)\r\n\r\nSentinel-2 NDVI = (B8 - B4) / (B8 + B4)\r\n\r\nThese data have been created by the Joint Nature Conservation Committee (JNCC) as part of a Defra Natural Capital and Ecosystem Assessment (NCEA) project to produce a regional, and ultimately national, system for detecting a change in habitat conditions at a land parcel level. The first stage of the project is focused on Yorkshire, UK, and therefore the dataset includes granules and scenes covering Yorkshire and surrounding areas only. The dataset contains Normalised Difference Vegetation Index (NDVI) data derived from Defra and JNCC Sentinel-2 Analysis Ready Data. \r\n\r\nNDVI files are generated for the following Sentinel-2 granules:\r\n•\tT30UWE\r\n•\tT30UXF\r\n•\tT30UWF\r\n•\tT30UXE\r\n•\tT31UCV \r\n•\tT30UYE\r\n•\tT31UCA\r\n\r\nAs the project continues, JNCC will expand the geographical coverage of this dataset and will provide continuous updates as ARD becomes available.\r\n\r\nVersion 1 contains masked index files (using the Defra and JNCC ARD cloud and topographic shadow masks)." }, "objectObservation": { "ob_id": 33382, "uuid": "6df6b803c2784b8ab9e03834bf9a4337", "short_code": "ob", "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Normalised Burn Ratio (NBR) v1", "abstract": "Sentinel Hub NBR description: To detect burned areas, the NBR-RAW index is the most appropriate choice. Using bands 8 and 12 it highlights burnt areas in large fire zones greater than 500 acres. To observe burn severity, you may subtract the post-fire NBR image from the pre-fire NBR image. Darker pixels indicate burned areas.\r\n\r\nNBR = (NIR – SWIR) / (NIR + SWIR) \r\n\r\nSentinel-2 NBR = (B08 - B12) / (B08 + B12)\r\n\r\nThese data have been created by the Joint Nature Conservation Committee (JNCC) as part of a Defra Natural Capital & Ecosystem Assessment (NCEA) project to produce a regional, and ultimately national, system for detecting a change in habitat condition at a land parcel level. The first stage of the project is focused on Yorkshire, UK, and therefore the dataset includes granules and scenes covering Yorkshire and surrounding areas only. The dataset contains the following indices derived from Defra and JNCC Sentinel-2 Analysis Ready Data.\r\n\r\nNDVI, NDMI, NDWI, NBR, and EVI files are generated for the following Sentinel-2 granules:\r\n•\tT30UWE\r\n•\tT30UXF\r\n•\tT30UWF\r\n•\tT30UXE\r\n•\tT31UCV \r\n•\tT30UYE\r\n•\tT31UCA\r\n\r\nAs the project continues, JNCC will expand the geographical coverage of this dataset and will provide continuous updates as ARD becomes available." } }, { "ob_id": 614, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 33339, "uuid": "51725f90c60a45e69f07a748a25b9729", "short_code": "ob", "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Normalised Difference Vegetation Index (NDVI) v1", "abstract": "Sentinel-Hub NDVI description: \r\nNDVI is a simple, but effective index for quantifying green vegetation. It normalizes green leaf scattering in Near Infra-red wavelengths with chlorophyll absorption in red wavelengths.\r\n\r\nThe value range of the NDVI is -1 to 1. Negative values of NDVI (values approaching -1) correspond to water. Values close to zero (-0.1 to 0.1) generally correspond to barren areas of rock, sand, or snow. Low, positive values represent shrub and grassland (approximately 0.2 to 0.4), while high values indicate temperate and tropical rainforests (values approaching 1). It is a good proxy for live green vegetation.\r\n\r\nNDVI = (NIR – Red) / (NIR + RED)\r\n\r\nSentinel-2 NDVI = (B8 - B4) / (B8 + B4)\r\n\r\nThese data have been created by the Joint Nature Conservation Committee (JNCC) as part of a Defra Natural Capital and Ecosystem Assessment (NCEA) project to produce a regional, and ultimately national, system for detecting a change in habitat conditions at a land parcel level. The first stage of the project is focused on Yorkshire, UK, and therefore the dataset includes granules and scenes covering Yorkshire and surrounding areas only. The dataset contains Normalised Difference Vegetation Index (NDVI) data derived from Defra and JNCC Sentinel-2 Analysis Ready Data. \r\n\r\nNDVI files are generated for the following Sentinel-2 granules:\r\n•\tT30UWE\r\n•\tT30UXF\r\n•\tT30UWF\r\n•\tT30UXE\r\n•\tT31UCV \r\n•\tT30UYE\r\n•\tT31UCA\r\n\r\nAs the project continues, JNCC will expand the geographical coverage of this dataset and will provide continuous updates as ARD becomes available.\r\n\r\nVersion 1 contains masked index files (using the Defra and JNCC ARD cloud and topographic shadow masks)." }, "objectObservation": { "ob_id": 33376, "uuid": "b42f524bc9cd4dd6850b2399b616f5c4", "short_code": "ob", "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Normalised Difference Water Index (NDWI) v1", "abstract": "Sentinel-Hub NDWI description: The NDWI is used to monitor changes related to water content in water bodies. As water bodies strongly absorb light in visible to the infrared electromagnetic spectrum, NDWI uses green and near-infrared bands to highlight water bodies. It is sensitive to built-up land and can result in the over-estimation of water bodies. \r\nIndex values greater than 0.5 usually correspond to water bodies. Vegetation usually corresponds to much smaller values and built-up areas to values between zero and 0.2.\r\n\r\nNDWI = (GREEN – NIR) / (GREEN + NIR)\r\n\r\nSentinel-2 NDWI = (B03 - B08) / (B03 + B08)\r\n\r\nThese data have been created by the Joint Nature Conservation Committee (JNCC) as part of a Defra Natural Capital and Ecosystem Assessment (NCEA) project to produce a regional, and ultimately national, system for detecting a change in habitat conditions at a land parcel level. The first stage of the project is focused on Yorkshire, UK, and therefore the dataset includes granules and scenes covering Yorkshire and surrounding areas only. The dataset contains Normalised Difference Water Index (NDWI) data derived from Defra and JNCC Sentinel-2 Analysis Ready Data.\r\n\r\nNDWI files are generated for the following Sentinel-2 granules:\r\n•\tT30UWE\r\n•\tT30UXF\r\n•\tT30UWF\r\n•\tT30UXE\r\n•\tT31UCV \r\n•\tT30UYE\r\n•\tT31UCA\r\n\r\nAs the project continues, JNCC will expand the geographical coverage of this dataset and will provide continuous updates as ARD becomes available." } }, { "ob_id": 615, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 33339, "uuid": "51725f90c60a45e69f07a748a25b9729", "short_code": "ob", "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Normalised Difference Vegetation Index (NDVI) v1", "abstract": "Sentinel-Hub NDVI description: \r\nNDVI is a simple, but effective index for quantifying green vegetation. It normalizes green leaf scattering in Near Infra-red wavelengths with chlorophyll absorption in red wavelengths.\r\n\r\nThe value range of the NDVI is -1 to 1. Negative values of NDVI (values approaching -1) correspond to water. Values close to zero (-0.1 to 0.1) generally correspond to barren areas of rock, sand, or snow. Low, positive values represent shrub and grassland (approximately 0.2 to 0.4), while high values indicate temperate and tropical rainforests (values approaching 1). It is a good proxy for live green vegetation.\r\n\r\nNDVI = (NIR – Red) / (NIR + RED)\r\n\r\nSentinel-2 NDVI = (B8 - B4) / (B8 + B4)\r\n\r\nThese data have been created by the Joint Nature Conservation Committee (JNCC) as part of a Defra Natural Capital and Ecosystem Assessment (NCEA) project to produce a regional, and ultimately national, system for detecting a change in habitat conditions at a land parcel level. The first stage of the project is focused on Yorkshire, UK, and therefore the dataset includes granules and scenes covering Yorkshire and surrounding areas only. The dataset contains Normalised Difference Vegetation Index (NDVI) data derived from Defra and JNCC Sentinel-2 Analysis Ready Data. \r\n\r\nNDVI files are generated for the following Sentinel-2 granules:\r\n•\tT30UWE\r\n•\tT30UXF\r\n•\tT30UWF\r\n•\tT30UXE\r\n•\tT31UCV \r\n•\tT30UYE\r\n•\tT31UCA\r\n\r\nAs the project continues, JNCC will expand the geographical coverage of this dataset and will provide continuous updates as ARD becomes available.\r\n\r\nVersion 1 contains masked index files (using the Defra and JNCC ARD cloud and topographic shadow masks)." }, "objectObservation": { "ob_id": 33374, "uuid": "46f5d503ce284114b5925709258bacc5", "short_code": "ob", "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Normalised Difference Moisture Index (NDMI) v1", "abstract": "Sentinel-Hub NDMI description: \r\n\r\nThe NDMI is a normalized difference moisture index, that uses NIR and SWIR bands to display moisture. The SWIR band reflects changes in both the vegetation water content and the spongy mesophyll structure in vegetation canopies, while the NIR reflectance is affected by leaf internal structure and leaf dry matter content but not by water content. The combination of the NIR with the SWIR removes variations induced by leaf internal structure and leaf dry matter content, improving the accuracy in retrieving the vegetation water content. The amount of water available in the internal leaf structure largely controls the spectral reflectance in the SWIR interval of the electromagnetic spectrum. SWIR reflectance is therefore negatively related to leaf water content. In short, NDMI is used to monitor changes in the water content of leaves and was proposed by Gao. NDWI is computed using the near-infrared (NIR) and the short wave infrared (SWIR) reflectances:\r\n\r\nNDMI = (NIR – SWIR) / (NIR + SWIR) \r\n\r\nSentinel-2 NDMI = (B08 - B11) / (B08 + B11)\r\n\r\nThese data have been created by the Joint Nature Conservation Committee (JNCC) as part of a Defra Natural Capital and Ecosystem Assessment (NCEA) project to produce a regional, and ultimately national, system for detecting a change in habitat conditions at a land parcel level. The first stage of the project is focused on Yorkshire, UK, and therefore the dataset includes granules and scenes covering Yorkshire and surrounding areas only. The dataset contains Normalised Difference Moisture Index data derived from Defra and JNCC Sentinel-2 Analysis Ready Data.\r\n\r\nNDMI files are generated for the following Sentinel-2 granules:\r\n•\tT30UWE\r\n•\tT30UXF\r\n•\tT30UWF\r\n•\tT30UXE\r\n•\tT31UCV \r\n•\tT30UYE\r\n•\tT31UCA\r\n\r\nAs the project continues, JNCC will expand the geographical coverage of this dataset and will provide continuous updates as ARD becomes available." } }, { "ob_id": 616, "relationType": "IsDerivedFrom", "subjectObservation": { "ob_id": 33410, "uuid": "299b1bb28eaa440f9a36e9786adfe398", "short_code": "ob", "title": "BICEP/NCEO: Monthly global Particulate Organic Carbon (POC), between 1997-2020 at 4 km resolution (produced from the Ocean Colour Climate Change Initiative v4.2 dataset), version 2", "abstract": "The BICEP/NCEO: Monthly global Particulate Organic Carbon (POC) v4.2 datasets contain POC concentrations (mg m^-3) with per pixel uncertainties estimates gridded on both geographic and sinusoidal projections at 4 km spatial resolution for the period of 1997 to 2020. The POC products were generated as part of the European Space Agency (ESA) Biological Pump and Carbon Exchange Processes (BICEP) project with support from the National Centre of Earth Observation (NCEO). \r\n\r\nThe POC concentrations were estimated using an empirical Remote Sensing Reflectance (Rrs) band ratio algorithm by Stramski et al. (2008): 203.2*Rrs(443)/Rrs(555)^-1.034. This algorithm has shown a relatively good performance in the recent global inter-comparison study conducted by Evers-King et al. (2017). Additional variables that were used for the calculation of the POC products are also provided in the datasets, including the Rrs at 443 nm and 555 nm obtained from the ESA Ocean Colour Climate Change Initiative version 4.2 dataset (OC-CCI v4.2)(Sathyendranath et al., 2020). In addition to the papers by Stramski et al. (2008) and Evers-king et al. (2017), for more details on the algorithm and its validation, please see the BICEP Algorithm Theoretical Basis Document (ATBD) and validation report (https://bicep-project.org/Home) \r\n\r\nThis version of the dataset is an updated version of the previous 'NCEO: Monthly global Particulate Organic Carbon (POC) (produced from the Ocean Colour Climate Change Initiative, Version 4.2 dataset)'.\r\n\r\nA related product based on the Ocean Colour Climate Change Initiative v5.0 data is also available (see the link in the related records section)." }, "objectObservation": { "ob_id": 30590, "uuid": "51fc11a9438b466db2ec8bd098efe7d5", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global remote sensing reflectance gridded on a geographic projection, Version 4.2", "abstract": "The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains the Version 4.2 Remote Sensing Reflectance product on a geographic projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites). Values for remote sensing reflectance at the sea surface are provided for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm) with pixel-by-pixel uncertainty estimates for each wavelength. These are merged products based on SeaWiFS, MERIS and Aqua-MODIS data. Note, this dataset is also contained within the 'All Products' dataset. \r\n\r\nThis data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection).\r\n\r\nPlease note, this dataset has been superseded. Later versions of the data are now available." } }, { "ob_id": 617, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 33376, "uuid": "b42f524bc9cd4dd6850b2399b616f5c4", "short_code": "ob", "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Normalised Difference Water Index (NDWI) v1", "abstract": "Sentinel-Hub NDWI description: The NDWI is used to monitor changes related to water content in water bodies. As water bodies strongly absorb light in visible to the infrared electromagnetic spectrum, NDWI uses green and near-infrared bands to highlight water bodies. It is sensitive to built-up land and can result in the over-estimation of water bodies. \r\nIndex values greater than 0.5 usually correspond to water bodies. Vegetation usually corresponds to much smaller values and built-up areas to values between zero and 0.2.\r\n\r\nNDWI = (GREEN – NIR) / (GREEN + NIR)\r\n\r\nSentinel-2 NDWI = (B03 - B08) / (B03 + B08)\r\n\r\nThese data have been created by the Joint Nature Conservation Committee (JNCC) as part of a Defra Natural Capital and Ecosystem Assessment (NCEA) project to produce a regional, and ultimately national, system for detecting a change in habitat conditions at a land parcel level. The first stage of the project is focused on Yorkshire, UK, and therefore the dataset includes granules and scenes covering Yorkshire and surrounding areas only. The dataset contains Normalised Difference Water Index (NDWI) data derived from Defra and JNCC Sentinel-2 Analysis Ready Data.\r\n\r\nNDWI files are generated for the following Sentinel-2 granules:\r\n•\tT30UWE\r\n•\tT30UXF\r\n•\tT30UWF\r\n•\tT30UXE\r\n•\tT31UCV \r\n•\tT30UYE\r\n•\tT31UCA\r\n\r\nAs the project continues, JNCC will expand the geographical coverage of this dataset and will provide continuous updates as ARD becomes available." }, "objectObservation": { "ob_id": 33384, "uuid": "102909b1d169469f85c099a6f1686bad", "short_code": "ob", "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Enhanced Vegetation Index (EVI)", "abstract": "EVI is a development on Normalised Difference Vegetation Index (NDVI).\r\n\r\nSentinel-Hub EVI description: \r\nIn areas of dense canopy cover, where leaf area index (LAI) is high, the blue wavelengths can be used to improve the accuracy of NDVI, as it corrects for soil background signals and atmospheric influences. The range of values for EVI is -1 to 1, with healthy vegetation generally around 0.20 to 0.80.\r\n\r\nEVI is calculated: EVI = 2.5 * ((NIR – RED) / ((NIR + (6 * RED) – (7.5 * BLUE)) + 1))\r\n\r\nSentinel 2 EVI = 2.5 * ((B8 – B4) / ((B8 + (6 * B4) – (7.5 * B2)) + 1))\r\n\r\nThese data have been created by the Joint Nature Conservation Committee (JNCC) as part of a Defra Natural Capital and Ecosystem Assessment (NCEA) project to produce a regional, and ultimately national, system for detecting a change in habitat conditions at a land parcel level. The first stage of the project is focused on Yorkshire, UK, and therefore the dataset includes granules and scenes covering Yorkshire and surrounding areas only. The dataset contains Enhanced Vegetation Index data derived from Defra and JNCC Sentinel-2 Analysis Ready Data.\r\n\r\nEVI files are generated for the following Sentinel-2 granules:\r\n•\tT30UWE\r\n•\tT30UXF\r\n•\tT30UWF\r\n•\tT30UXE\r\n•\tT31UCV \r\n•\tT30UYE\r\n•\tT31UCA\r\n\r\nAs the project continues, JNCC will expand the geographical coverage of this dataset and will provide continuous updates as ARD becomes available." } }, { "ob_id": 618, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 33376, "uuid": "b42f524bc9cd4dd6850b2399b616f5c4", "short_code": "ob", "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Normalised Difference Water Index (NDWI) v1", "abstract": "Sentinel-Hub NDWI description: The NDWI is used to monitor changes related to water content in water bodies. As water bodies strongly absorb light in visible to the infrared electromagnetic spectrum, NDWI uses green and near-infrared bands to highlight water bodies. It is sensitive to built-up land and can result in the over-estimation of water bodies. \r\nIndex values greater than 0.5 usually correspond to water bodies. Vegetation usually corresponds to much smaller values and built-up areas to values between zero and 0.2.\r\n\r\nNDWI = (GREEN – NIR) / (GREEN + NIR)\r\n\r\nSentinel-2 NDWI = (B03 - B08) / (B03 + B08)\r\n\r\nThese data have been created by the Joint Nature Conservation Committee (JNCC) as part of a Defra Natural Capital and Ecosystem Assessment (NCEA) project to produce a regional, and ultimately national, system for detecting a change in habitat conditions at a land parcel level. The first stage of the project is focused on Yorkshire, UK, and therefore the dataset includes granules and scenes covering Yorkshire and surrounding areas only. The dataset contains Normalised Difference Water Index (NDWI) data derived from Defra and JNCC Sentinel-2 Analysis Ready Data.\r\n\r\nNDWI files are generated for the following Sentinel-2 granules:\r\n•\tT30UWE\r\n•\tT30UXF\r\n•\tT30UWF\r\n•\tT30UXE\r\n•\tT31UCV \r\n•\tT30UYE\r\n•\tT31UCA\r\n\r\nAs the project continues, JNCC will expand the geographical coverage of this dataset and will provide continuous updates as ARD becomes available." }, "objectObservation": { "ob_id": 33382, "uuid": "6df6b803c2784b8ab9e03834bf9a4337", "short_code": "ob", "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Normalised Burn Ratio (NBR) v1", "abstract": "Sentinel Hub NBR description: To detect burned areas, the NBR-RAW index is the most appropriate choice. Using bands 8 and 12 it highlights burnt areas in large fire zones greater than 500 acres. To observe burn severity, you may subtract the post-fire NBR image from the pre-fire NBR image. Darker pixels indicate burned areas.\r\n\r\nNBR = (NIR – SWIR) / (NIR + SWIR) \r\n\r\nSentinel-2 NBR = (B08 - B12) / (B08 + B12)\r\n\r\nThese data have been created by the Joint Nature Conservation Committee (JNCC) as part of a Defra Natural Capital & Ecosystem Assessment (NCEA) project to produce a regional, and ultimately national, system for detecting a change in habitat condition at a land parcel level. The first stage of the project is focused on Yorkshire, UK, and therefore the dataset includes granules and scenes covering Yorkshire and surrounding areas only. The dataset contains the following indices derived from Defra and JNCC Sentinel-2 Analysis Ready Data.\r\n\r\nNDVI, NDMI, NDWI, NBR, and EVI files are generated for the following Sentinel-2 granules:\r\n•\tT30UWE\r\n•\tT30UXF\r\n•\tT30UWF\r\n•\tT30UXE\r\n•\tT31UCV \r\n•\tT30UYE\r\n•\tT31UCA\r\n\r\nAs the project continues, JNCC will expand the geographical coverage of this dataset and will provide continuous updates as ARD becomes available." } }, { "ob_id": 619, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 33376, "uuid": "b42f524bc9cd4dd6850b2399b616f5c4", "short_code": "ob", "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Normalised Difference Water Index (NDWI) v1", "abstract": "Sentinel-Hub NDWI description: The NDWI is used to monitor changes related to water content in water bodies. As water bodies strongly absorb light in visible to the infrared electromagnetic spectrum, NDWI uses green and near-infrared bands to highlight water bodies. It is sensitive to built-up land and can result in the over-estimation of water bodies. \r\nIndex values greater than 0.5 usually correspond to water bodies. Vegetation usually corresponds to much smaller values and built-up areas to values between zero and 0.2.\r\n\r\nNDWI = (GREEN – NIR) / (GREEN + NIR)\r\n\r\nSentinel-2 NDWI = (B03 - B08) / (B03 + B08)\r\n\r\nThese data have been created by the Joint Nature Conservation Committee (JNCC) as part of a Defra Natural Capital and Ecosystem Assessment (NCEA) project to produce a regional, and ultimately national, system for detecting a change in habitat conditions at a land parcel level. The first stage of the project is focused on Yorkshire, UK, and therefore the dataset includes granules and scenes covering Yorkshire and surrounding areas only. The dataset contains Normalised Difference Water Index (NDWI) data derived from Defra and JNCC Sentinel-2 Analysis Ready Data.\r\n\r\nNDWI files are generated for the following Sentinel-2 granules:\r\n•\tT30UWE\r\n•\tT30UXF\r\n•\tT30UWF\r\n•\tT30UXE\r\n•\tT31UCV \r\n•\tT30UYE\r\n•\tT31UCA\r\n\r\nAs the project continues, JNCC will expand the geographical coverage of this dataset and will provide continuous updates as ARD becomes available." }, "objectObservation": { "ob_id": 33374, "uuid": "46f5d503ce284114b5925709258bacc5", "short_code": "ob", "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Normalised Difference Moisture Index (NDMI) v1", "abstract": "Sentinel-Hub NDMI description: \r\n\r\nThe NDMI is a normalized difference moisture index, that uses NIR and SWIR bands to display moisture. The SWIR band reflects changes in both the vegetation water content and the spongy mesophyll structure in vegetation canopies, while the NIR reflectance is affected by leaf internal structure and leaf dry matter content but not by water content. The combination of the NIR with the SWIR removes variations induced by leaf internal structure and leaf dry matter content, improving the accuracy in retrieving the vegetation water content. The amount of water available in the internal leaf structure largely controls the spectral reflectance in the SWIR interval of the electromagnetic spectrum. SWIR reflectance is therefore negatively related to leaf water content. In short, NDMI is used to monitor changes in the water content of leaves and was proposed by Gao. NDWI is computed using the near-infrared (NIR) and the short wave infrared (SWIR) reflectances:\r\n\r\nNDMI = (NIR – SWIR) / (NIR + SWIR) \r\n\r\nSentinel-2 NDMI = (B08 - B11) / (B08 + B11)\r\n\r\nThese data have been created by the Joint Nature Conservation Committee (JNCC) as part of a Defra Natural Capital and Ecosystem Assessment (NCEA) project to produce a regional, and ultimately national, system for detecting a change in habitat conditions at a land parcel level. The first stage of the project is focused on Yorkshire, UK, and therefore the dataset includes granules and scenes covering Yorkshire and surrounding areas only. The dataset contains Normalised Difference Moisture Index data derived from Defra and JNCC Sentinel-2 Analysis Ready Data.\r\n\r\nNDMI files are generated for the following Sentinel-2 granules:\r\n•\tT30UWE\r\n•\tT30UXF\r\n•\tT30UWF\r\n•\tT30UXE\r\n•\tT31UCV \r\n•\tT30UYE\r\n•\tT31UCA\r\n\r\nAs the project continues, JNCC will expand the geographical coverage of this dataset and will provide continuous updates as ARD becomes available." } }, { "ob_id": 620, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 33376, "uuid": "b42f524bc9cd4dd6850b2399b616f5c4", "short_code": "ob", "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Normalised Difference Water Index (NDWI) v1", "abstract": "Sentinel-Hub NDWI description: The NDWI is used to monitor changes related to water content in water bodies. As water bodies strongly absorb light in visible to the infrared electromagnetic spectrum, NDWI uses green and near-infrared bands to highlight water bodies. It is sensitive to built-up land and can result in the over-estimation of water bodies. \r\nIndex values greater than 0.5 usually correspond to water bodies. Vegetation usually corresponds to much smaller values and built-up areas to values between zero and 0.2.\r\n\r\nNDWI = (GREEN – NIR) / (GREEN + NIR)\r\n\r\nSentinel-2 NDWI = (B03 - B08) / (B03 + B08)\r\n\r\nThese data have been created by the Joint Nature Conservation Committee (JNCC) as part of a Defra Natural Capital and Ecosystem Assessment (NCEA) project to produce a regional, and ultimately national, system for detecting a change in habitat conditions at a land parcel level. The first stage of the project is focused on Yorkshire, UK, and therefore the dataset includes granules and scenes covering Yorkshire and surrounding areas only. The dataset contains Normalised Difference Water Index (NDWI) data derived from Defra and JNCC Sentinel-2 Analysis Ready Data.\r\n\r\nNDWI files are generated for the following Sentinel-2 granules:\r\n•\tT30UWE\r\n•\tT30UXF\r\n•\tT30UWF\r\n•\tT30UXE\r\n•\tT31UCV \r\n•\tT30UYE\r\n•\tT31UCA\r\n\r\nAs the project continues, JNCC will expand the geographical coverage of this dataset and will provide continuous updates as ARD becomes available." }, "objectObservation": { "ob_id": 33339, "uuid": "51725f90c60a45e69f07a748a25b9729", "short_code": "ob", "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Normalised Difference Vegetation Index (NDVI) v1", "abstract": "Sentinel-Hub NDVI description: \r\nNDVI is a simple, but effective index for quantifying green vegetation. It normalizes green leaf scattering in Near Infra-red wavelengths with chlorophyll absorption in red wavelengths.\r\n\r\nThe value range of the NDVI is -1 to 1. Negative values of NDVI (values approaching -1) correspond to water. Values close to zero (-0.1 to 0.1) generally correspond to barren areas of rock, sand, or snow. Low, positive values represent shrub and grassland (approximately 0.2 to 0.4), while high values indicate temperate and tropical rainforests (values approaching 1). It is a good proxy for live green vegetation.\r\n\r\nNDVI = (NIR – Red) / (NIR + RED)\r\n\r\nSentinel-2 NDVI = (B8 - B4) / (B8 + B4)\r\n\r\nThese data have been created by the Joint Nature Conservation Committee (JNCC) as part of a Defra Natural Capital and Ecosystem Assessment (NCEA) project to produce a regional, and ultimately national, system for detecting a change in habitat conditions at a land parcel level. The first stage of the project is focused on Yorkshire, UK, and therefore the dataset includes granules and scenes covering Yorkshire and surrounding areas only. The dataset contains Normalised Difference Vegetation Index (NDVI) data derived from Defra and JNCC Sentinel-2 Analysis Ready Data. \r\n\r\nNDVI files are generated for the following Sentinel-2 granules:\r\n•\tT30UWE\r\n•\tT30UXF\r\n•\tT30UWF\r\n•\tT30UXE\r\n•\tT31UCV \r\n•\tT30UYE\r\n•\tT31UCA\r\n\r\nAs the project continues, JNCC will expand the geographical coverage of this dataset and will provide continuous updates as ARD becomes available.\r\n\r\nVersion 1 contains masked index files (using the Defra and JNCC ARD cloud and topographic shadow masks)." } }, { "ob_id": 621, "relationType": "IsDerivedFrom", "subjectObservation": { "ob_id": 33412, "uuid": "5006f2c553cd4f26a6af0af2ee6d7c94", "short_code": "ob", "title": "BICEP/NCEO: Monthly global Particulate Organic Carbon (POC), between 1997-2020 at 4 km resolution (produced from the Ocean Colour Climate Change Initiative v5.0 dataset)", "abstract": "The BICEP/NCEO: Monthly global Particulate Organic Carbon (POC) v5 datasets contain POC concentrations (mg m^-3) with per pixel uncertainties estimates gridded on both geographic and sinusoidal projections at 4 km spatial resolution for the period of 1997 to 2020. The POC products were generated as part of the European Space Agency (ESA) Biological Pump and Carbon Exchange Processes (BICEP) project with support from the National Centre of Earth Observation (NCEO). \r\n\r\nThe POC datasets have been produced by using a modified empirical band ratio algorithm by Stramski et al. (2008): 292*Rrs(490)/Rrs(560)^-1.49. Additional variables that were used for the calculation of the POC products are also provided in the datasets, including the Remote Sensing Reflectance (Rrs) at 490 nm and 560 nm obtained from the ESA Ocean Colour Climate Change Initiative version 5 dataset (OC-CCI v5). For more details on the algorithm and its validation, please see the BICEP Algorithm Theoretical Basis Document (ATBD) and validation report (https://bicep-project.org/Home).\r\n\r\nA related dataset based on the ESA Ocean Colour Climate Change Initiative v4.2 data is also available (see link in the related records section)." }, "objectObservation": { "ob_id": 32137, "uuid": "5ab5267b17254152bcdbc055747faa02", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global remote sensing reflectance gridded on a geographic projection, Version 5.0", "abstract": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains the Version 5.0 Remote Sensing Reflectance product on a geographic projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites) covering the period 1997 - 2020. Values for remote sensing reflectance at the sea surface are provided for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm) with pixel-by-pixel uncertainty estimates for each wavelength. These are merged products based on SeaWiFS, MERIS and Aqua-MODIS data. Note, this dataset is also contained within the 'All Products' dataset. \r\n\r\nThis data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection).\r\n\r\nPlease note, data from December 2020 onwards are affected by an anomaly discovered after production and resulting in a spurious jump in remote sensing reflectance. The anomaly has been corrected in the version 5.0.1 of the dataset available through the Copernicus Climate Change Service (https://doi.org/10.24381/cds.f85b319d)\r\n\r\nVersion 6.0 of this data is now also available here: https://doi.org/10.5285/5011d22aae5a4671b0cbc7d05c56c4f0" } }, { "ob_id": 622, "relationType": "IsDerivedFrom", "subjectObservation": { "ob_id": 33414, "uuid": "a6fc730d88fd4935b59d64903715d891", "short_code": "ob", "title": "BICEP / NCEO: Monthly global Oceanic Export Production, between 1998-2019 at 9 km resolution (derived from the Ocean Colour Climate Change Initiative v4.2 dataset)", "abstract": "This dataset contains monthly global data for Oceanic Export Production as part of the BICEP project. Data is provided between 1998-2019 at 9 km resolution. It has been derived from the Ocean Colour Climate Change Initiative v4.2 dataset.\r\n\r\nExport production can be defined as steady-state Net Community Production (NCP) with all temporal lags accounted for and with a well defined depth horizon over which the community production is integrated over. (Laws 1991). This is the net amount of carbon assimilated in the euphotic zone that will be exported to deeper waters. Export Production can by definition only vary on timescales significantly longer that any processes directly controlling production and respiration as to not violate the steady state assumption." }, "objectObservation": { "ob_id": 30596, "uuid": "db32212d86f9431dae67076dd122565e", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global attenuation coefficient for downwelling irradiance (Kd490) gridded on a geographic projection, Version 4.2", "abstract": "The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains the Version 4.2 Kd490 attenuation coefficient (m-1) for downwelling irradiance product on a geographic projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites). It is computed from the Ocean Colour CCI Version 4.2 inherent optical properties dataset at 490 nm and the solar zenith angle. Note, these data are also contained within the 'All Products' dataset.\r\n\r\nThis data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection).\r\n\r\nPlease note, this dataset has been superseded. Later versions of the data are now available." } }, { "ob_id": 623, "relationType": "IsDerivedFrom", "subjectObservation": { "ob_id": 33414, "uuid": "a6fc730d88fd4935b59d64903715d891", "short_code": "ob", "title": "BICEP / NCEO: Monthly global Oceanic Export Production, between 1998-2019 at 9 km resolution (derived from the Ocean Colour Climate Change Initiative v4.2 dataset)", "abstract": "This dataset contains monthly global data for Oceanic Export Production as part of the BICEP project. Data is provided between 1998-2019 at 9 km resolution. It has been derived from the Ocean Colour Climate Change Initiative v4.2 dataset.\r\n\r\nExport production can be defined as steady-state Net Community Production (NCP) with all temporal lags accounted for and with a well defined depth horizon over which the community production is integrated over. (Laws 1991). This is the net amount of carbon assimilated in the euphotic zone that will be exported to deeper waters. Export Production can by definition only vary on timescales significantly longer that any processes directly controlling production and respiration as to not violate the steady state assumption." }, "objectObservation": { "ob_id": 30594, "uuid": "5400de38636d43de9808bfc0b500e863", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global chlorophyll-a data products gridded on a geographic projection, Version 4.2", "abstract": "The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains their Version 4.2 chlorophyll-a product (in mg/m3) on a geographic projection at 4 km spatial resolution and at number of time resolutions (daily, 5day, 8day and monthly composites). Note, this chlor_a data is also included in the 'All Products' dataset. \r\n\r\nThis data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection.)\r\n\r\nPlease note, this dataset has been superseded. Later versions of the data are now available." } }, { "ob_id": 624, "relationType": "IsDerivedFrom", "subjectObservation": { "ob_id": 33414, "uuid": "a6fc730d88fd4935b59d64903715d891", "short_code": "ob", "title": "BICEP / NCEO: Monthly global Oceanic Export Production, between 1998-2019 at 9 km resolution (derived from the Ocean Colour Climate Change Initiative v4.2 dataset)", "abstract": "This dataset contains monthly global data for Oceanic Export Production as part of the BICEP project. Data is provided between 1998-2019 at 9 km resolution. It has been derived from the Ocean Colour Climate Change Initiative v4.2 dataset.\r\n\r\nExport production can be defined as steady-state Net Community Production (NCP) with all temporal lags accounted for and with a well defined depth horizon over which the community production is integrated over. (Laws 1991). This is the net amount of carbon assimilated in the euphotic zone that will be exported to deeper waters. Export Production can by definition only vary on timescales significantly longer that any processes directly controlling production and respiration as to not violate the steady state assumption." }, "objectObservation": { "ob_id": 31969, "uuid": "69b2c9c6c4714517ba10dab3515e4ee6", "short_code": "ob", "title": "BICEP / NCEO: Monthly global Marine Phytoplankton Primary Production, between 1998-2020 at 9 km resolution (derived from the Ocean Colour Climate Change Initiative v4.2 dataset)", "abstract": "This dataset contains global, monthly marine phytoplankton primary production products (in mg C m-2 d-1) for the period of 1998 to 2018 at 9 km spatial resolution. Data are provided in NetCDF format.\r\n\r\nPrimary production by marine phytoplankton was modelled using ocean-colour remote sensing products and a spectrally-resolved primary production model that incorporates the vertical structure of phytoplankton and simulates changes in photosynthesis as a function of irradiance using a two-parameter photosynthesis versus irradiance (P-I) function (see Kulk et al. 2020, Sathyendranath et al. 2020a, and references therein for details). Chlorophyll-a products were obtained from the European Space Agency (ESA) Ocean Colour Climate Change Initiative (OC-CCI v4.2 dataet). Photosynthetic Active Radiation (PAR) products were obtained from the National Aeronautics and Space Administration (NASA) and were corrected for inter-sensor bias in products. In situ datasets of chlorophyll-a profile parameters and P-I parameters were incorporated as described in Kulk et al. (2020). \r\n\r\nThe primary production products were generated as part of the ESA Living Planet Fellowship programme ‘Primary production, Index of Climate Change in the Ocean: Long-term Observations’\r\n(PICCOLO). Support from the Simons Foundation grant ‘Computational Biogeochemical Modeling of Marine Ecosystems’ (CBIOMES, number 549947), from the ESA Biological Pump and Carbon\r\nExchange Processes (BICEP) project and from the National Centre of Earth Observation (NCEO) is acknowledged.\r\n\r\nData are provided as netCDF files containing global, monthly marine phytoplankton primary production products (in mg C m-2 d-1) for the period of 1998 to 2020 at 9 km spatial resolution.\r\n\r\nReferences:\r\n\r\nKulk, G.; Platt, T.; Dingle, J.; Jackson, T.; Jönsson, B.F.; Bouman, H.A., Babin, M.; Doblin, M.; Estrada, M.; Figueiras, F.G.; Furuya, K.; González, N.; Gudfinnsson, H.G.; Gudmundsson, K.; Huang, B.; Isada, T.; Kovac, Z.; Lutz, V.A.; Marañón, E.; Raman, M.; Richardson, K.; Rozema, P.D.; Van de Poll, W.H.; Segura, V.; Tilstone, G.H.; Uitz, J.; van Dongen-Vogels, V.; Yoshikawa, T.; Sathyendranath S. Primary production, an index of climate change in the ocean: Satellite-based estimates over two decades. Remote Sens. 2020, 12, 826. doi:10.3390/rs12050826\r\n\r\nSathyendranath, S.; Platt, T.; Žarko K.; Dingle, J.; Jackson, T.; Brewin, R.J.W.; Franks, P.; Nón, E.M.; Kulk, G.; Bouman, H. Reconciling models of primary production and photoacclimation. Appl. Opt.\r\n2020a, 59, C100-C114. doi.org/10.1364/AO.386252." } }, { "ob_id": 625, "relationType": "IsDerivedFrom", "subjectObservation": { "ob_id": 33416, "uuid": "22ae54ba3ab14ce8aa6a5271dfddaeb3", "short_code": "ob", "title": "JNCC Sentinel-1 indices Analysis Ready Data (ARD) Radar Vegetation Index (RVI)", "abstract": "These data have been created by the Joint Nature Conservation Committee (JNCC) as part of a Defra NCEA project to produce a regional, and ultimately national, system for detecting a change in habitat conditions at a land parcel level. The first stage of the project is focused on Yorkshire, UK, and therefore the dataset includes granules and scenes covering Yorkshire and surrounding areas only. The dataset contains the following indices derived from Defra and JNCC Sentinel-1 Analysis Ready Data.\r\n\r\nRVI and RVIv files are generated for Sentinel-1 orbit 132 (ascending) every 12 days. \r\n\r\nIndices have been generated using the Defra and JNCC Sentinel-1 and Sentinel-2 ARD for the granules and scenes described above. As the project continues, JNCC will expand the geographical coverage of this dataset and will provide continuous updates as ARD becomes available." }, "objectObservation": { "ob_id": 30193, "uuid": "05cea0662aa54aa2b7e2c5811e09431f", "short_code": "ob", "title": "Defra and JNCC Sentinel-1 Analysis Ready Data (ARD)", "abstract": "These data have been created by the Department for Environment, Food and Rural Affairs (Defra) and Joint Nature Conservation Committee (JNCC) in order to cost effectively provide high quality, Analysis Ready Data (ARD) for a wide range of applications. The dataset contains modified Copernicus Sentinel-1 data processed into a normalised radar backscatter product on a linear scale in dB. Products acquired from ESA are Ground-Range Detected (GRD) Interferometric Wide-swath (IW) in the dual VV+VH polarisation (DV) mode, where both VV and VH polarisations are collected. Defra and JNCC data were processed on separate platforms using a common specification to produce complementary outputs up to and including the acquisition date 23/06/2023. Data acquired after that date were processed on a single platform to the same specification.\r\n\r\nSentinel-1 scenes processed before July 2021 have had a strip of data clipped from their northern edge to remove an artefact caused by a deprecated processing method. Details can be found in the lineage statement of the metadata for all affected scenes." } }, { "ob_id": 626, "relationType": "IsDerivedFrom", "subjectObservation": { "ob_id": 33431, "uuid": "eac7485cce194194b6731cb41ae463b5", "short_code": "ob", "title": "JNCC Sentinel-1 indices Analysis Ready Data (ARD) Radar Vegetation Index (RVIv)", "abstract": "These data have been created by the Joint Nature Conservation Committee (JNCC) as part of a Defra NCEA project to produce a regional, and ultimately national, system for detecting a change in habitat conditions at a land parcel level. The first stage of the project is focused on Yorkshire, UK, and therefore the dataset includes granules and scenes covering Yorkshire and surrounding areas only. The dataset contains the following indices derived from Defra and JNCC Sentinel-1 Analysis Ready Data.\r\n\r\nRVI and RVIv files are generated for Sentinel-1 orbit 132 (ascending) every 12 days. \r\n\r\nIndices have been generated using the Defra and JNCC Sentinel-1 and Sentinel-2 ARD for the granules and scenes described above. As the project continues, JNCC will expand the geographical coverage of this dataset and will provide continuous updates as ARD becomes available." }, "objectObservation": { "ob_id": 30193, "uuid": "05cea0662aa54aa2b7e2c5811e09431f", "short_code": "ob", "title": "Defra and JNCC Sentinel-1 Analysis Ready Data (ARD)", "abstract": "These data have been created by the Department for Environment, Food and Rural Affairs (Defra) and Joint Nature Conservation Committee (JNCC) in order to cost effectively provide high quality, Analysis Ready Data (ARD) for a wide range of applications. The dataset contains modified Copernicus Sentinel-1 data processed into a normalised radar backscatter product on a linear scale in dB. Products acquired from ESA are Ground-Range Detected (GRD) Interferometric Wide-swath (IW) in the dual VV+VH polarisation (DV) mode, where both VV and VH polarisations are collected. Defra and JNCC data were processed on separate platforms using a common specification to produce complementary outputs up to and including the acquisition date 23/06/2023. Data acquired after that date were processed on a single platform to the same specification.\r\n\r\nSentinel-1 scenes processed before July 2021 have had a strip of data clipped from their northern edge to remove an artefact caused by a deprecated processing method. Details can be found in the lineage statement of the metadata for all affected scenes." } }, { "ob_id": 627, "relationType": "IsSupplementTo", "subjectObservation": { "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." }, "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": 628, "relationType": "IsSupplementedBy", "subjectObservation": { "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." }, "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": 629, "relationType": "IsVariantFormOf", "subjectObservation": { "ob_id": 33412, "uuid": "5006f2c553cd4f26a6af0af2ee6d7c94", "short_code": "ob", "title": "BICEP/NCEO: Monthly global Particulate Organic Carbon (POC), between 1997-2020 at 4 km resolution (produced from the Ocean Colour Climate Change Initiative v5.0 dataset)", "abstract": "The BICEP/NCEO: Monthly global Particulate Organic Carbon (POC) v5 datasets contain POC concentrations (mg m^-3) with per pixel uncertainties estimates gridded on both geographic and sinusoidal projections at 4 km spatial resolution for the period of 1997 to 2020. The POC products were generated as part of the European Space Agency (ESA) Biological Pump and Carbon Exchange Processes (BICEP) project with support from the National Centre of Earth Observation (NCEO). \r\n\r\nThe POC datasets have been produced by using a modified empirical band ratio algorithm by Stramski et al. (2008): 292*Rrs(490)/Rrs(560)^-1.49. Additional variables that were used for the calculation of the POC products are also provided in the datasets, including the Remote Sensing Reflectance (Rrs) at 490 nm and 560 nm obtained from the ESA Ocean Colour Climate Change Initiative version 5 dataset (OC-CCI v5). For more details on the algorithm and its validation, please see the BICEP Algorithm Theoretical Basis Document (ATBD) and validation report (https://bicep-project.org/Home).\r\n\r\nA related dataset based on the ESA Ocean Colour Climate Change Initiative v4.2 data is also available (see link in the related records section)." }, "objectObservation": { "ob_id": 33410, "uuid": "299b1bb28eaa440f9a36e9786adfe398", "short_code": "ob", "title": "BICEP/NCEO: Monthly global Particulate Organic Carbon (POC), between 1997-2020 at 4 km resolution (produced from the Ocean Colour Climate Change Initiative v4.2 dataset), version 2", "abstract": "The BICEP/NCEO: Monthly global Particulate Organic Carbon (POC) v4.2 datasets contain POC concentrations (mg m^-3) with per pixel uncertainties estimates gridded on both geographic and sinusoidal projections at 4 km spatial resolution for the period of 1997 to 2020. The POC products were generated as part of the European Space Agency (ESA) Biological Pump and Carbon Exchange Processes (BICEP) project with support from the National Centre of Earth Observation (NCEO). \r\n\r\nThe POC concentrations were estimated using an empirical Remote Sensing Reflectance (Rrs) band ratio algorithm by Stramski et al. (2008): 203.2*Rrs(443)/Rrs(555)^-1.034. This algorithm has shown a relatively good performance in the recent global inter-comparison study conducted by Evers-King et al. (2017). Additional variables that were used for the calculation of the POC products are also provided in the datasets, including the Rrs at 443 nm and 555 nm obtained from the ESA Ocean Colour Climate Change Initiative version 4.2 dataset (OC-CCI v4.2)(Sathyendranath et al., 2020). In addition to the papers by Stramski et al. (2008) and Evers-king et al. (2017), for more details on the algorithm and its validation, please see the BICEP Algorithm Theoretical Basis Document (ATBD) and validation report (https://bicep-project.org/Home) \r\n\r\nThis version of the dataset is an updated version of the previous 'NCEO: Monthly global Particulate Organic Carbon (POC) (produced from the Ocean Colour Climate Change Initiative, Version 4.2 dataset)'.\r\n\r\nA related product based on the Ocean Colour Climate Change Initiative v5.0 data is also available (see the link in the related records section)." } }, { "ob_id": 630, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 34557, "uuid": "dc6c126c95b1445d8e66d6b9f62054d4", "short_code": "ob", "title": "Summary for Policymakers of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure SPM.3 (v20221116)", "abstract": "Data for Figure SPM.3 from the Summary for Policymakers (SPM) of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure SPM.3 shows the synthesis of assessed observed and attributable regional changes in hot extremes, heavy precipitation and agricultural and ecological droughts and confidence in human contribution to the observed changes in the world’s regions.\r\n---------------------------------------------------\r\nHow to cite this dataset\r\n---------------------------------------------------\r\nIPCC, 2021: Summary for Policymakers. 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. 3−32, doi:10.1017/9781009157896.001.\r\n\r\n---------------------------------------------------\r\nFigure subpanels\r\n---------------------------------------------------\r\nThe figure has three panels, with data provided for all panels in subdirectories named panel_a, panel_b and panel_c.\r\n---------------------------------------------------\r\nList of data provided\r\n---------------------------------------------------\r\nPanel a: Synthesis of assessment of observed change in hot extremes and confidence in human contribution to the observed changes in the AR6 land-regions, excluding Antarctica.\r\n\r\nPanel b: Synthesis of assessment of observed change in heavy precipitation and confidence in human contribution to the observed changes in the AR6 land-regions, excluding Antarctica.\r\n\r\nPanel c: Synthesis of assessment of observed change in agricultural and ecological drought and confidence in human contribution to the observed changes in the AR6 land-regions, excluding Antarctica.\r\n---------------------------------------------------\r\nData provided in relation to figure\r\n---------------------------------------------------\r\n\r\n·\tData file: 'SPM3_panel_a.csv' (AR6 world regions, observed change in hot extremes, confidence in human contribution); middle entry relates to the colour of the map, showing [increase] (red), [decrease](blue),[low agreement in type of change](white/grey),[limited data and/or literature](grey) .\r\n\r\n·\tData file: 'SPM3_panel_b.csv' (AR6 world regions, observed change in heavy precipitation, confidence in human contribution); middle entry relates to the colour of the map, showing [increase] (green), [decrease](yellow),[low agreement in type of change](white/grey),[limited data and/or literature](grey) .\r\n\r\n·\tData file: 'SPM3_panel_c.csv' (AR6 world regions, observed change in agricultural and ecological drought, confidence in human contribution); middle entry relates to the colour of the map, showing [increase] (yellow), [decrease](green),[low agreement in type of change](white/grey),[limited data and/or literature](grey) \r\n\r\n---------------------------------------------------\r\nSources of additional information\r\n---------------------------------------------------\r\nThe data in the files is an assessment of section 11.9 in chapter 11 that is provided in the second first two columns of the tables in that section.\r\n\r\n- Link to the figure on the IPCC AR6 website\r\n- Link to the report component containing the figure (IPCC Report SPM)\r\n- Link to related publication for input data\r\n- Link to the webpage of the WGI report" }, "objectObservation": { "ob_id": 33236, "uuid": "118104a74a5e460b8cea189c67558e0b", "short_code": "ob", "title": "Summary for Policymakers of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure SPM.3 (v20210809)", "abstract": "Data for Figure SPM.3 from the Summary for Policymakers (SPM) of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\nFigure SPM.3 shows the synthesis of assessed observed and attributable regional changes in hot extremes, heavy precipitation and agricultural and ecological droughts and confidence in human contribution to the observed changes in the world’s regions.\r\n---------------------------------------------------\r\nHow to cite this dataset\r\n---------------------------------------------------\r\nIPCC, 2021: Summary for Policymakers. 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. 3−32, doi:10.1017/9781009157896.001.\r\n\r\n---------------------------------------------------\r\nFigure subpanels\r\n---------------------------------------------------\r\nThe figure has three panels, with data provided for all panels in subdirectories named panel_a, panel_b and panel_c.\r\n---------------------------------------------------\r\nList of data provided\r\n---------------------------------------------------\r\nPanel a: Synthesis of assessment of observed change in hot extremes and confidence in human contribution to the observed changes in the AR6 land-regions, excluding Antarctica.\r\n\r\nPanel b: Synthesis of assessment of observed change in heavy precipitation and confidence in human contribution to the observed changes in the AR6 land-regions, excluding Antarctica.\r\n\r\nPanel c: Synthesis of assessment of observed change in agricultural and ecological drought and confidence in human contribution to the observed changes in the AR6 land-regions, excluding Antarctica.\r\n---------------------------------------------------\r\nData provided in relation to figure\r\n---------------------------------------------------\r\n\r\n·\tData file: panel_a/SPM3_panel_a.csv (AR6 world regions, observed change in hot extremes, confidence in human contribution); middle entry relates to the colour of the map, showing [increase] (red), [decrease](blue),[low agreement in type of change](white/grey),[limited data and/or literature](grey) .\r\n·\tData file: panel_b/SPM3_panel_b.csv (AR6 world regions, observed change in heavy precipitation, confidence in human contribution); middle entry relates to the colour of the map, showing [increase] (green), [decrease](yellow),[low agreement in type of change](white/grey),[limited data and/or literature](grey) .\r\n\r\n·\tData file: panel_c/SPM3_panel_c.csv (AR6 world regions, observed change in agricultural and ecological drought, confidence in human contribution); middle entry relates to the colour of the map, showing [increase] (yellow), [decrease](green),[low agreement in type of change](white/grey),[limited data and/or literature](grey) \r\n\r\n---------------------------------------------------\r\nSources of additional information\r\n---------------------------------------------------\r\nThe data in the files is an assessment of section 11.9 in chapter 11 that is provided in the second first two columns of the tables in that section." } }, { "ob_id": 631, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 34591, "uuid": "e1ff6e07cd624c59a7e7983ce60add44", "short_code": "ob", "title": "Summary for Policymakers of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure SPM.9 (v20220105)", "abstract": "Data for Figure SPM.9 from the Summary for Policymakers (SPM) of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure SPM.9 provides a synthesis of the number of AR6 WGI reference regions where climatic impact-drivers are projected to change.\r\n\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\n\r\nIPCC, 2021: Summary for Policymakers. 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. 3−32, doi:10.1017/9781009157896.001.\r\n\r\n\r\n---------------------------------------------------\r\nTemporal range\r\n---------------------------------------------------\r\n\r\nNumber of land & coastal regions and open-ocean regions where each Climatic Impact-Drivers (CID) is projected to increase or decrease with high confidence or medium confidence. Changes refer to a 20–30 year period centred around 2050 and/or consistent with 2°C global warming compared to a similar period within 1960-2014, except for hydrological drought and agricultural and ecological drought which is compared to 1850-1900. \r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has two panels, with data provided for all panels in a single file named consolidated_data_figure_SPM9.csv\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\nThis dataset contains the number of AR6 WGI regions where climatic impact-drivers are projected to change if a global warming level of 2°C is reached compared to a climatological reference period included within 1960-2014.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\nData file: consolidated_data_figure_SPM.9.csv (count of regions with increasing or decreasing changes in climatic impact-drivers); relates to panel (a) and panel (b) and it's shown by the bars in the figure. The first row of data relates to the darker purple bars, the second row to the lighter purple bars, the third row to the lighter brown bars and the fourth row to the darker brown bars. Row 5 represents the maximum number of regions for which each climatic impact-driver is relevant. It is shown on the figure as the lighter-shaded ‘envelope’.\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\r\n - Link to origin of figure (IPCC WG1 Summary for Policy Makers)\r\n - Link to the report webpage, which includes the report component containing the figure (Summary for Policymakers)\r\n - Link to the Interactive Atlas webpage\r\n - Link to the figure on the IPCC AR6 website" }, "objectObservation": { "ob_id": 32913, "uuid": "35a7ee81a50c4b95ab59f9bd128f9b63", "short_code": "ob", "title": "Summary for Policymakers of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure SPM.9 (v20210809)", "abstract": "Data for Figure SPM.9 from the Summary for Policymakers (SPM) of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure SPM.9 provides a synthesis of the number of AR6 WGI reference regions where climatic impact-drivers are projected to change.\r\n\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\n\r\nIPCC, 2021: Summary for Policymakers. 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. 3−32, doi:10.1017/9781009157896.001.\r\n\r\n\r\n---------------------------------------------------\r\nTemporal range\r\n---------------------------------------------------\r\nChanges refer to a 20–30 year period centred around 2050 and/or consistent with 2°C global warming compared to a similar period within 1960-2014 or 1850-1900.\r\n\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has two panels, with data provided for all panels in a single file named consolidated_data_figure_SPM9.csv\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\nThis dataset contains the number of AR6 WGI regions where climatic impact-drivers are projected to change if a global warming level of 2°C is reached compared to a climatological reference period included within 1960-2014.\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\nData file: consolidated_data_figure_SPM.9.csv (count of regions with increasing or decreasing changes in climatic impact-drivers); relates to panel (a) and panel (b) and it's shown by the bars in the figure. The first row of data relates to the darker purple bars, the second row to the lighter purple bars, the third row to the lighter brown bars and the fourth row to the darker brown bars. Row 5 represents the maximum number of regions for which each climatic impact-driver is relevant. It is shown on the figure as the lighter-shaded ‘envelope’.\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\r\n - Link to the report webpage, which includes the report component containing the figure (Summary for Policymakers)" } }, { "ob_id": 632, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 34603, "uuid": "d1c2018e17d54df384fdbf562d2a9e8b", "short_code": "ob", "title": "ESA Water Vapour Climate Change Initiative (Water_Vapour_cci): Total Column Water Vapour monthly gridded data over land at 0.05 degree resolution, version 3.2", "abstract": "This dataset consists of monthly averaged total column water vapour (TCWV) over land, at a 0.05 degree resolution, observed by various satellite instruments. It has been produced by the European Space Agency Water Vapour Climate Change Initiative (Water_Vapour_cci), and forms part of their TCVW over land Climate Data Record -1 (TCWV-land (CDR-1).\r\n\r\nThis version of the data is v3.2. This is an updated dataset, which fixes an issue with the filtering of the v3.1 data." }, "objectObservation": { "ob_id": 32071, "uuid": "14e296d401f94ece8a2f143a7ddb0069", "short_code": "ob", "title": "ESA Water Vapour Climate Change Initiative (Water_Vapour_cci): Total Column Water Vapour monthly gridded data over land at 0.05 degree resolution, version 3.1", "abstract": "This dataset consists of monthly averaged total column water vapour (TCWV) over land, at a 0.05 degree resolution, observed by various satellite instruments. It has been produced by the European Space Agency Water Vapour Climate Change Initiative (Water_Vapour_cci), and forms part of their TCVW over land Climate Data Record -1 (TCWV-land (CDR-1).\r\n\r\nThis version of the data is v3.1." } }, { "ob_id": 633, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 34604, "uuid": "80ad33b237084a8dbfb81ec5414e68dd", "short_code": "ob", "title": "ESA Water Vapour Climate Change Initiative (Water_Vapour_cci): Total Column Water Vapour monthly gridded data over land at 0.5 degree resolution, version 3.2", "abstract": "This dataset consists of monthly averaged total column water vapour (TCWV) over land, at a 0.5 degree resolution, observed by various satellite instruments. It has been produced by the European Space Agency Water Vapour Climate Change Initiative (Water_Vapour_cci), and forms part of their TCVW over land Climate Data Record -1 (TCWV-land (CDR-1).\r\n\r\nThis version of the data is v3.2. This is an updated dataset, which fixes an issue with the filtering of the v3.1 data." }, "objectObservation": { "ob_id": 32070, "uuid": "008b306d2cda435cb7ea9eb44bc07c73", "short_code": "ob", "title": "ESA Water Vapour Climate Change Initiative (Water_Vapour_cci): Total Column Water Vapour monthly gridded data over land at 0.5 degree resolution, version 3.1", "abstract": "This dataset consists of monthly averaged total column water vapour (TCWV) over land, at a 0.5 degree resolution, observed by various satellite instruments. It has been produced by the European Space Agency Water Vapour Climate Change Initiative (Water_Vapour_cci), and forms part of their TCVW over land Climate Data Record -1 (TCWV-land (CDR-1).\r\n\r\nThis version of the data is v3.1." } }, { "ob_id": 634, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 34605, "uuid": "5bd687c7ea624e3aa68c51e242ae828a", "short_code": "ob", "title": "ESA Water Vapour Climate Change Initiative (Water_Vapour_cci): Total Column Water Vapour daily gridded data over land at 0.5 degree resolution, version 3.2", "abstract": "This dataset consists of daily total column water vapour (TCWV) over land, at a 0.5 degree resolution, observed by various satellite instruments. It has been produced by the European Space Agency Water Vapour Climate Change Initiative (Water_Vapour_cci), and forms part of their TCVW over land Climate Data Record -1 (TCWV-land (CDR-1).\r\n\r\nThis version of the data is v3.2. This is an updated dataset, which fixes an issue with the filtering of the v3.1 data." }, "objectObservation": { "ob_id": 32069, "uuid": "97d8054089a04235ab55a419622b4f1b", "short_code": "ob", "title": "ESA Water Vapour Climate Change Initiative (Water_Vapour_cci): Total Column Water Vapour daily gridded data over land at 0.5 degree resolution, version 3.1", "abstract": "This dataset consists of daily total column water vapour (TCWV) over land, at a 0.5 degree resolution, observed by various satellite instruments. It has been produced by the European Space Agency Water Vapour Climate Change Initiative (Water_Vapour_cci), and forms part of their TCVW over land Climate Data Record -1 (TCWV-land (CDR-1).\r\n\r\nThis version of the data is v3.1." } } ] }