Observation List

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            "title": "Daily Mean, Minimum and Maximum Central England Temperature (HadCET) series v2.1.0.0",
            "abstract": "The Central England Temperature (HadCET) daily series start in 1772 for mean temperature and 1878 for minimum and maximum temperature.\r\n \r\nThese historical temperature series are representative of the Midlands region in England, UK (a roughly triangular area of the United Kingdom enclosed by Bristol, Lancashire and London).\r\n \r\nPrior to 1973, the daily mean temperature series is anchored to the mean temperature series constructed by Gordon Manley, with the daily minimum and maximum temperature series adjusted to the mean temperature series to ensure values are consistent.\r\n \r\nAlthough the station selection has changed through time, the series is homogenised and adjusted to ensure consistency with Manley's selection and for periods when only a single station value was used.\r\n \r\nStations used in the construction of the CET daily series between 1772 and 1852 include: Kennington, Crane Court, Lyndon Hall, Syon House, Somerset House, Greenwich Observatory, Chiswick\r\n \r\nStations used in the construction of the CET daily series from 1853 onwards include: Radcliffe (Oxford), Cambridge (legacy), Ross-on-Wye, Rothamsted, Malvern, Stonyhurst, Ringway, Squires Gate, Pershore College\r\n \r\nThe current station selection used is Rothamsted, Stonyhurst and Pershore College.\r\n \r\nFor more information on the change in station selection, please refer to the papers supplied with the data collection.\r\n \r\nThe dataset is compiled by the Met Office Hadley Centre.\r\n\r\nLatest provisional release data are available via the Hadley Centre Observations website (https://www.metoffice.gov.uk/hadobs/hadcet/data/download.html).\r\n\r\nThe version controlled CET series is updated annually (February-March), with the previous complete year’s values refreshed to ensure that data acquisition and quality control procedures have been completed and ensure the most accurate station temperature values are used. Each version of the dataset will include data up until the end of the previous complete year and an incremental version number will be updated.\r\n\r\nThe CET datasets employ the following version control protocol: \r\n\r\nVersion Vx.y.z.a:\r\n• x – major changes – e.g. change in scientific methodology\r\n• y – minor changes – e.g. small bug fixes or updates to diagnostics pages\r\n• z – incremental changes\r\n• a – reserved for use internally\r\n\r\nThe standard annual release cycle of CET will constitute an incremental release (z). However, if more substantial\r\nchanges have been made to the codebase, scientific methodology or source data values, then this may warrant a minor (y) or major (x) version release. (Note, these are applied to a cohort of datasets together - i.e. apply to the seasonal, monthly, daily and adjustment datasets as a coordinated version release).\r\n\r\nThis new version of the datasets supersedes the previous version.",
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            "title": "Seasonal Mean, Minimum and Maximum Central England Temperature (HadCET) series v2.1.0.0",
            "abstract": "The Central England Temperature (HadCET) seasonal series starts in 1659 for mean temperature and 1878 for minimum and maximum temperature.\r\n\r\nThese historical temperature series are representative of the Midlands region in England, UK (a roughly triangular area of the United Kingdom enclosed by Bristol, Lancashire and London).\r\n \r\nThe seasonal temperature series are derived as the mean of the monthly temperature series values.\r\n\r\nStations used in the construction of the CET daily series between 1772 and 1852 include: Kennington, Crane Court, Lyndon Hall, Syon House, Somerset House, Greenwich Observatory, Chiswick\r\n \r\nStations used in the construction of the CET daily series from 1853 onwards include: Radcliffe (Oxford), Cambridge (legacy), Ross-on-Wye, Rothamsted, Malvern, Stonyhurst, Ringway, Squires Gate, Pershore College\r\n \r\nThe current station selection used is Rothamsted, Stonyhurst and Pershore College.\r\n \r\nThe dataset is compiled by the Met Office Hadley Centre.\r\n\r\nLatest provisional release data are available via the Hadley Centre Observations website (https://www.metoffice.gov.uk/hadobs/hadcet/data/download.html).\r\n\r\nThe version controlled CET series is updated annually (February-March), with the previous complete year’s values refreshed to ensure that data acquisition and quality control procedures have been completed and ensure the most accurate station temperature values are used. Each version of the dataset will include data up until the end of the previous complete year and an incremental version number will be updated.\r\n\r\nThe CET datasets employ the following version control protocol: \r\n\r\nVersion Vx.y.z.a:\r\n• x – major changes – e.g. change in scientific methodology\r\n• y – minor changes – e.g. small bug fixes or updates to diagnostics pages\r\n• z – incremental changes\r\n• a – reserved for use internally\r\n\r\nThe standard annual release cycle of CET will constitute an incremental release (z). However, if more substantial\r\nchanges have been made to the codebase, scientific methodology or source data values, then this may warrant a minor (y) or major (x) version release. (Note, these are applied to a cohort of datasets together - i.e. apply to the seasonal, monthly, daily and adjustment datasets as a coordinated version release).\r\n\r\nThis new version of the datasets supersedes the previous version.",
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            "abstract": "NDWI is used to identify water bodies and detect changes in their extent. It is calculated as the ratio of the green and near-infrared (NIR) bands.  Positive index values generally indicate the presence of water, with higher values corresponding to water bodies.\r\n\r\nNDWI = (GREEN – NIR) / (GREEN + NIR)\r\n\r\nSentinel-2 NDWI (Defra/JNCC ARD bands) = (B02 – B07) / (B02 + B07)\r\n\r\nEquivalent ESA Sentinel-2 bands: B03, B08\r\n\r\nData are provided in EPSG: 27700 OSGB36 / British National Grid, with a pixel size of 10m, and data is pixel-aligned to the source ARD file. No-data pixels are set to a value of -9999.\r\n\r\nThese data have been created by the Joint Nature Conservation Committee (JNCC) as part of the “Earth observation-based habitat change detection” project. This project is funded by the Department for Environment, Food and Rural Affairs (Defra) as part of the Natural Capital and Ecosystem Assessment (NCEA) programme. The project seeks to facilitate the effective uptake and use of Earth Observation data by producing data and tools for investigating and detecting parcel-level change in habitats and habitat condition. \r\n\r\nThe dataset contains NDVI, NDMI, NDWI, NBR and EVI2 indices derived from Defra and JNCC Sentinel-2 ARD. Index files have been generated for Sentinel-2 granules covering England and Scotland for the period from 2015 to 2025. Note that new unmasked index files (v2) have superseded the previous masked index files (v1). Masked files will no longer be produced. Users can mask the new index files if required using the cloud and topographic shadow masks provided with the ARD, or masks of their choice. \r\n\r\nContains modified Copernicus Sentinel data 2015-2025",
            "keywords": "JNCC, ARD, NDWI, Sentinel 2, EO indices, Analysis Ready Data, Sentinel, Normalised Difference Water Index, NCEA",
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            "dataPublishedTime": "2025-07-09T07:25:42",
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                "storageStatus": "online",
                "volume": 16204585278099,
                "numberOfFiles": 60828,
                "fileFormat": "These data are provided in Cloud Optimized GeoTIFF (COG) format."
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            "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).\r\nContains modified Copernicus Sentinel data 2015-2025",
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            "language": "English",
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                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/212077/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/212078/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/212079/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/212080/?format=api"
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            "title": "3D ocean bottom node seismic data across the 13°30'N Oceanic Core Complex and the Semenov Hydrothermal Field region of the Mid-Atlantic Ridge (JC254, 2023).",
            "abstract": "The dataset contains 3D wide-angle seismic data from 18 ocean bottom nodes (OBXs) collected during RRS James Cook cruise JC254 in November 2023. The data were acquired at the 1330 Oceanic Core Complex (OCC) and the Semenov Hydrothermal Field region of the Mid-Atlantic Ridge at 13°30'N, and were shot in six sequences (SEM4A to SEM4F), along 15 profiles in total. Two GI-guns were used as seismic sources, each with a total volume of 355 cubic inches (250 cu. inch generator, 105 cu. inch injector chamber. The data were recorded at a sampling rate of 2000 Hz, with a shot interval of 20 s at a ship speed of 4 knots, resulting in shot interval of ~30 m, with no stop during profile turns. The data are provided in raw SEGY format, cut into four components (three geophone channels and one hydrophone channel). This dataset was collected to derive a seismic velocity structure of the 13°30'N OCC. The aim was to investigate the crustal or mantle lithologies in the 13°30'N OCC and to understand the interplay between tectonic faulting, magmatism, alteration and hydrothermal circulation at the ultramafic-hosted hydrothermal systems along slow-spreading ridges. The data were collected as part of a NERC-funded strategic research project called 'Ultramafic-hosted mineral Resource Assessment (ULTRA)', grant reference NE/S004068/1. The data were processed and interpreted as part of a PhD studentship hosted by the University of Southampton, awarded to Szu-Ying Lai, and funded by Equinor A.S, Norway.",
            "keywords": "",
            "publicationState": "citable",
            "dataPublishedTime": "2025-05-27T18:05:24",
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                "storageStatus": "online",
                "volume": 68248058190,
                "numberOfFiles": 435,
                "fileFormat": ".segy, .jpg, .txt"
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            "dataLineage": "The data are archived on the British Oceanographic Data Centre (BODC)'s space at the Centre for Environmental Data Analysis (CEDA) and assigned a DOI. No quality control procedures were applied by BODC.",
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        {
            "ob_id": 44345,
            "uuid": "f61458fc1bfa4d3982b34b702eb96689",
            "title": "Terrestrial LiDAR data for the multi-trophic impact of ash dieback project, Wytham Woods, July 2020 - July 2022",
            "abstract": "This dataset is comprised of raw data and rotation matrices captured with the NERC-funded Terrestrial Laser Scanner (TLS) instrument. Data was collected across 15 plots in Wytham Woods, UK, in leaf-on and -off conditions for 3 years. For 5 of the plots all ash trees were girdled to simulate death by the fungal pathogen Hymenoscyphus fraxineus ('ash dieback'), 5 of the plots were 'ash-control' where the dominant species were Fraxinus excelsior and the remaining 5 plots were non-ash dominant.\r\n\r\nTLS data was acquired on the four edges of the plot and the plot centre. For the summer 2020 and winter 2021 acquisitions the scanner was mounted on a pneumatic mast to capture data from different heights (2 m, 4 m, and 6 m), from summer 2021 onwards the scanner was mounted at a height of 2 m. The scanner was set to an angular step of 0.04 degrees.  In between each scan position a set of retro-reflective targets were positioned to be used as tie-points between scans. Scan data was coregistered using RiSCAN Pro, the 4x4 rotation transformation matrices to transform the point cloud data into a common reference coordinate system can be found in the \"matrix\" directory, all acquistions for the same plot were registered to the same coordinate system. For more information on TLS acquisition for this project see Wilkes et al. (2021) and more generally refer to Wilkes et al. (2017) which can be found in the documentation section.",
            "keywords": "Terrestrial Laser Scanning, Trees, LiDAR",
            "publicationState": "published",
            "dataPublishedTime": "2025-06-03T08:00:30",
            "doiPublishedTime": null,
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            "status": "completed",
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                "dataPath": "/neodc/tls/data/raw/uk/WHY/multitrophic_wytham",
                "oldDataPath": [],
                "storageLocation": "internal",
                "storageStatus": "online",
                "volume": 215835327818,
                "numberOfFiles": 6909,
                "fileFormat": "Point cloud data in RIEGL proprietry .rxp format, image data in .jpg format and matrix data in text file."
            },
            "timePeriod": "https://api.catalogue.ceda.ac.uk/api/v2/times/12400/?format=api",
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            "dataLineage": "Data were produced by the UCL project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).",
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                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/212102/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/212108/?format=api",
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                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/212111/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/212112/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/212113/?format=api"
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            "procedureAcquisition": "https://api.catalogue.ceda.ac.uk/api/v2/acquisitions/44346/?format=api",
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                    "accessCategory": "public",
                    "accessRoles": null,
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                    "ob_id": 1138,
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        {
            "ob_id": 44348,
            "uuid": "f244267a4b4f4edcb87e248a27214ca3",
            "title": "European Atlantic Offshore Wave Spectral Climatology under Historical and Future Climate Scenarios",
            "abstract": "This dataset provides 3-hourly ocean wave spectral data at 1,031 fixed offshore locations along the Atlantic coast of Europe (including the British Isles) spanning three 30-year periods: historical (1985–2014), and future projections (2030-2059) under two Shared Socioeconomic Pathways (SSPs). It also includes wave parametric data (significant height, peak period, direction...).\r\n\r\nThe wave spectra were generated using the Simulating WAves Nearshore (SWAN) model, forced with high-resolution winds obtained through downscaling global climate models using the Weather Research and Forecasting (WRF) model, and open boundary conditions from simulations carried out with the WaveWatch III wave propagation model.\r\n\r\nEach NetCDF-4 file includes eight 3-hourly spectral records per day, capturing wave energy distribution across 25 frequency bands and 36 directions for all 1,031 locations (spaced ~10 km apart, 50 km from the coast). \r\n\r\nThis spectral climatology is suitable for climate impact assessments, wave energy studies, and coastal hazard analysis.",
            "keywords": "Wave spectra,SWAN,Climate change scenarios,Atlantic Ocean,Downscaling,CMIP6,Ocean wave climatology",
            "publicationState": "removed",
            "dataPublishedTime": "2025-08-12T14:58:18",
            "doiPublishedTime": "2025-08-12T15:27:20",
            "updateFrequency": "notPlanned",
            "status": "superseded",
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                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/78330/?format=api",
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                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/78333/?format=api"
            ],
            "dataLineage": "This dataset was generated using a multi-stage modeling workflow. First, atmospheric variables from multiple CMIP6 climate models were bias-corrected and dynamically downscaled using the WRF model to produce high-resolution wind fields. These winds were then used to drive WaveWatch III simulations across the North Atlantic to provide offshore boundary conditions. Finally, the SWAN wave model was run at high resolution along the European Atlantic coast using WRF winds and WaveWatch III boundaries to compute detailed 3-hourly wave spectra at 1,031 offshore points. The entire workflow was repeated for three periods: historical (1985–2014) and future scenarios SSP2-4.5 and SSP5-8.5 (2030–2059). The output files were processed, verified, and stored in NetCDF-4 format following CF conventions before being transferred to the CEDA data centre.",
            "removedDataTime": "2026-01-12T18:01:14",
            "removedDataReason": "Dataset has been superseded before release from embargo by a new version.",
            "language": "English",
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            "uuid": "6b029e226c534b359e416d30dbdab5a1",
            "title": "Mantle Circulation Constrained (MC2) - Mantle Circulation Simulations",
            "abstract": "This dataset constains for models produced for the NERC Large Grant - Mantle Circulation Constrained (MC2; NERC grant award NE/T012633/1). Each model is produced using the 3D mantle circulation code, TERRA and deployed on the ARCHER2 UK National Supercomputing Service. Each folder represents a model which comprises output files (NetCDF and ParaView) between 1000-100 Ma (in 100 Myr intervals), 90-10 Ma (in 10 Myr intervals) and 1-0 Ma (in 1 Myr intervals). Additionally we provide the run files for each model, at various restart times which include a parameter file and a compiled executable. Additional output files are described in the README files.",
            "keywords": "Mantle Circulation,Geodynamics,Mantle Modelling,TERRA",
            "publicationState": "working",
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                "storageLocation": "internal",
                "storageStatus": "online",
                "volume": 7811338635657,
                "numberOfFiles": 51710,
                "fileFormat": "NetCDF and Paraview"
            },
            "timePeriod": "https://api.catalogue.ceda.ac.uk/api/v2/times/12480/?format=api",
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            "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA)",
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                    "accessRoles": "mc2_mantle_circulation_sims",
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            "title": "Methane doubly substituted (clumped) isotopologues database",
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            "title": "ESA Lakes Climate Change Initiative (Lakes_cci):  Lake products, Version 3.0",
            "abstract": "This dataset contains the Lakes Essential Climate Variable (ECV) Products, comprised of processed satellite observations at the global scale, over the period 1992-2022, for over 2000 inland water bodies. This dataset was produced by the European Space Agency (ESA) Lakes Climate Change Initiative (Lakes_cci) project. For more information about the Lakes_cci please visit the project website.\r\n\r\nThis is version 3.0.0 of the dataset which benefits from longer observation time series, improved spatial coverage, new variables and improved algorithms for certain variables.\r\n\r\nThe Essential Climate Variable Products included in this dataset are:\r\n•Lake Water Level (LWL), derived from satellite altimetry, fundamental to understand the balance between water inputs and water loss and their connection with regional and global climate change.\r\n•Lake Water Extent (LWE), modelled from the relation between LWL and high-resolution spatial extent observed at set time-points, describing the areal extent of the water body. This allows the observation of drought in arid environments, expansion in high Asia, or impact of large-scale atmospheric oscillations on lakes in tropical regions for example.\r\n•Lake Surface Water temperature (LSWT), derived from optical and thermal satellite observations, correlated with regional air temperatures and supporting analysis of vertical mixing regimes, biogeochemical cycling and seasonality.\r\n•Lake Ice Cover (LIC), determined from optical observations, describing the freeze-up in autumn and break-up of ice in spring, which are proxies for gradually changing climate patterns and seasonality.\r\n•Lake Water-Leaving Reflectance (LWLR), derived from optical satellite observations, and supporting interpretation of biogeochemical processes and habitats in the visible part of the water column (e.g. seasonal phytoplankton biomass fluctuations), and serving as an indicator of the frequency of extreme events (peak terrestrial run-off, changing mixing conditions).\r\n•Lake Ice Thickness (LIT), provided for 13 lakes from radar altimetry to study the integrity and velocity of lake ice formation. \r\n•Lake Storage Change (LSC), derived from Water extent and/or level, describing water volume evolution of water bodies over time to inform studies of water stress. \r\n\r\nData generated in the Lakes_cci are derived from over 35 satellite sensors. The following sensors are associated with each of the ECV Products:\r\n•LWL: Poseidon-1 (TOPEX/Poseidon), Poseidon-2 (Jason-1),  Poseidon-3 (Jason-2), Poseidon-3B (Jason-3), Poseidon-4 (Sentinel-6A), Radar Altimeter RA-2 (Envisat), AltiKa (SARAL), GFO, SAR Altimeter – SRAL (Sentinel-3A, Sentinel-3B), Radar Altimeter RA (ERS-1, ERS-2)\r\n•LWE: Landsat (4 TM, 5 TM, 7 ETM+, 8 OLI), Sentinel-1 C-band SAR, Sentinel-2 MSI, Sentinel-3A SRAL, Sentinel-3B SRAL, ERS-1 AMI, ERS-2 AMI\r\n•LSWT: Envisat AATSR, Terra/Aqua MODIS, Sentinel-3A ATTSR-2, Sentinel-3B, ERS-2 AVHRR, Metop-A/B\r\n•LIC: Terra/Aqua MODIS\r\n•LWLR: Envisat MERIS, Sentinel-3A OLCI A/B, Aqua MODIS\r\n•LIT: Jason1, Jason2, Jason3, POSEIDON-2, POSEIDON-3 and POSEIDON-3B\r\n•LSC: All sensors listed under LWL and LWE.\r\n\r\nDetailed information on the generation and validation of this dataset is available from the Lakes_cci documentation available on the project website, relating to this specific release. A further presentation of the dataset v2 can be found in Carrea, L., Crétaux, JF., Liu, X. et al. Satellite-derived multivariate world-wide lake physical variable timeseries for climate studies. Sci Data 10, 30 (2023). https://doi.org/10.1038/s41597-022-01889-z.",
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            "abstract": "This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from multiple Infra-Red (IR) instruments on Low Earth Orbiting (LEO) sun-synchronous (a.k.a. polar orbiting) satellites. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.\r\n\r\nDaytime and night-time temperatures are provided in separate files corresponding to 10:00 and 22:00 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.\r\n\r\nThe dataset is comprised of LSTs from a series of instruments with a common heritage: the Along-Track Scanning Radiometer 2 (ATSR-2); the Advanced Along-Track Scanning Radiometer (AATSR) and the Sea and Land Surface Temperature Radiometer on Sentinel 3B (SLSTRB); and data from the Moderate Imaging Spectroradiometer on Earth Observation System - Terra (MODIS Terra), to fill the gap between AATSR and SLSTR. So, the instruments contributing to the time series are: ATSR-2 from June 1995 to May 2002; AATSR from June 2002 to March 2012; MODIS Terra from April 2012 to November 2018; and SLSTRB from December 2018 to December 2024. Inter-instrument biases are accounted for by cross-calibration with the Infrared Atmospheric Sounding Interferometer (IASI) instruments on Meteorological Operational (METOP) satellites. For consistency, a common algorithm is used for LST retrieval for all instruments. Furthermore, an adjustment is made to the LSTs to account for the half-hour difference between satellite equator crossing times. For consistency through the time series, coverage is restricted to the narrowest instrument swath width.\r\n\r\nThe dataset coverage is near global over the land surface. During the period covered by ATSR-2, small regions were not covered due to downlinking constraints (most noticeably a track extending southwards across central Asia through India – further details can be found on the ATSR project webpages at https://artefacts.ceda.ac.uk/frozen_sites/www.atsr.rl.ac.uk/documentation/docs/userguide/index.shtml).\r\n\r\nLSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. Full Earth coverage is achieved in 3 days so the daily files have gaps where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.\r\n\r\nDataset coverage starts on 1st June 1995 and currently ends on 31st December 2024. There are two gaps of several months in the dataset: no data were acquired from ATSR-2 between 23 December 1995 and 27 June 1996 due to a scan mirror anomaly; and the ERS-2 gyro failed in January 2001, data quality was less good between 17th Jan 2001 and 5th July 2001 and are not used in this dataset. Also, there is a twelve day gap in the dataset due to Envisat mission extension orbital manoeuvres from 21st October 2010 to 1st November 2010. There are minor interruptions (1-10 days) during satellite/instrument maintenance periods or instrument anomalies.\r\n\r\nThis version of the dataset (Version 3.00) extends the temporal coverage by four years to the end of 2024.   This dataset provides a daily product, and a separate monthly averaged product also exists.  The temporal coverage of the monthly product will be further extended at 6 monthly intervals through the Copernicus Climate Change Service. Other changes in Version 3.00 include:  SLSTR on Sentinel 3A is no longer used, instead data from  SLSTR on Sentinel 3B is used from November 2018; the correction for time differences between the sensors is calculated in brightness temperature space using radiative transfer simulations; and the ATSR-2 and AATSR data are from the fourth reprocessing of these datasets.\r\n\r\nThe dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using a Generalised Split Window retrieval algorithm and data were processed in the UoL processing chain.\r\nThe dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.",
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            "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Monthly Multisensor Infra-Red (IR) Low Earth Orbit (LEO) land surface temperature (LST) time series level 3 supercollated (L3S) global product (1995-2024), version 3.00",
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            "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Enhanced Vegetation Index v2 (EVI2)",
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            "uuid": "03634cd8f10943b39d9982e6f9bbb402",
            "title": "Weighing trees with lasers: terrestrial laser scanner data; Danum Valley Conservation Area (Plot CBN-01), March 2017",
            "abstract": "This dataset is comprised of raw and proceesed  data from the NERC-funded, full waveform terrestrial laser scanner (TLS) deployed on a  plot site  situated in the Danum Valley Conservation Area. Danum Valley Conservation Area is a 438 square kilometres (169 sq mi) tract of relatively undisturbed lowland dipterocarp forest in Sabah, Malaysia. It has an extensive diversity of tropical flora and fauna.\r\n\r\nThere were no human settlements within the area before it became a conservation area, meaning that hunting, logging and other human interference were non-existent, making the area almost unique. It is managed by Yayasan Sabah for conservation, research, education, and habitat restoration training purposes. \r\n\r\nThe aim of the weighing trees with lasers project is to test if current allometric relationships are invariant across continents, or whether they differ significantly, and require continental level models; quantify the impact of assumptions of tree shape and wood density on tropical forest allometry; test hypotheses relating to pan-tropical differences in observed AGB (Above Ground Biomass) from satellite and field data. It also aims to apply new knowledge to assessing retrieval accuracy of forthcoming ESA BIOMASS and NASA GEDI (Global Ecosystem Dynamics Investigation Lidar) missions and providing calibration datasets; In addition to testing the capability of low-cost instruments to augment TLS data including: UAVs (unmanned aerial vehicle) for mapping cover and canopy height; low-cost lidar instruments to assess biomass rapidly, at lower accuracy.",
            "keywords": "Terrestrial Laser Scanner data, Malaysia Sabah Kabili-Sepilok Forest Reserve, Moist, Lowland, Terra Firme, Old-growth",
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            "uuid": "027bdbbbdb28400daa95e6c1cb54ecc9",
            "title": "[PREVIEW RECORD] Chapter 2 of the Working Group I Contribution to the IPCC Seventh Assessment Report - Input data for Figure 2.27 (v20211112)",
            "abstract": "This dataset contains the input data for Figure 2.27 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Seventh Assessment Report (AR7). Additional information, including how to cite this figure dataset, is provided in the README.txt alongside the data. \r\n\r\nFigure 2.27 caption: Changes in ocean salinity. \r\nEstimates of salinity trends using a total least absolute differences fitting method for (a) global near-surface salinity (SSS) changes and (b) global zonal mean subsurface salinity changes. Black contours show the associated climatological mean salinity (either near-surface (a) or subsurface (b)) for the analysis period (1950–2019). Both panels represent changes in Practical Salinity Scale 1978 [PSS-78], per decade. In both panels green denotes freshening regions and orange/brown denotes regions with enhanced salinities (‘×’ marks denote non-significant changes). Further details on data sources and processing are available in the chapter data table (Table 2.SM.1).\r\n\r\nLinks to the figure, report chapter and supplementary material are provided in the Details/Docs section of this catalogue record. Code to reproduce the figure which is hosted on GitHub is also linked, and described under the Process tab of this catalogue record.",
            "keywords": "IPCC-DDC, IPCC, AR7, WG1, WGI, Seventh Assessment Report, Working Group I, Physical Science Basis, Chapter 2, Changing state, Multi-millennial context, pre-industrial, Natural forcing, anthropogenic forcing, Radiative forcing, Large-scale indicators, observed changes, Modes of variability, Figure 2.27 Input, ocean salinity",
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            "title": "MIDAS Open: UK hourly solar radiation data, v202507",
            "abstract": "The UK hourly solar radiation data contain the amount of solar irradiance received during the hour ending at the specified time. All sites report 'global' radiation amounts. This is also known as 'total sky radiation' as it includes both direct solar irradiance and 'diffuse' irradiance as a result of light scattering. Some sites also provide separate diffuse and direct irradiation amounts, depending on the instrumentation at the site. For these the sun's path is tracked with two pyrometers - one where the path to the sun is blocked by a suitable disc to allow the scattered sunlight to be measured to give the diffuse measurement, while the other has a tube pointing at the sun to measure direct solar irradiance whilst blanking out scattered sun light. \r\n\r\nFor details about the different measurements made and the limited number of sites making them please see the MIDAS Solar Irradiance table linked to in the online resources section of this record.\r\n\r\nThis version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data. These include the addition of data for calendar year 2024.\r\n\r\nThe data were collected by observation stations operated by the Met Office across the UK and transmitted within the following message types: SYNOP, HCM, AWSHRLY, MODLERAD, ESAWRADT and DRADR35 messages. The data spans from 1947 to 2024.\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by the Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record.",
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            "title": "ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost Ground Temperature for the Northern Hemisphere, v5.0",
            "abstract": "This dataset contains permafrost ground temperature data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the second version of their Climate Research Data Package (CRDP v4). It is derived from a thermal model driven and constrained by satellite data. Grid products of CRDP v4 are released in annual files, covering the start to the end of the Julian year. This corresponds to average annual ground temperatures and is provided for specific depths (surface, 1m, 2m, 5m, 10m).\r\n \r\nCase A: It covers the Northern Hemisphere (north of 30°N) for the period 2003-2023 based on MODIS Land Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data.\r\ne.g. ESACCI-PERMAFROST-L4-GTD-MODISLST_CRYOGRID-AREA4_PP-****-fv05.0.nc\r\nCase B: It covers the Northern Hemisphere (north of 30°N) for the period 1997-2002 based on downscaled ERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for the overlap period 2003-2023 using a pixel-specific statistics for each day of the year.\r\ne.g. ESACCI-PERMAFROST-L4-GTD-ERA5_MODISLST_BIASCORRECTED-AREA4_PP-****-fv05.0.nc",
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            "title": "Wave projections from the North Western European shelf modelled from 1992-2093.",
            "abstract": "This dataset consists of outputs from a wave model for the North-West European Shelf. It contains simulation periods conducted over monthly time spans. These include: historic reanalysis periods: January 2017; June 2018 and climate projection periods: June 1992; January 1996; January 2050; June 2053; January 2090; June 2093. The outputs contain wave parameters integrated from the two-dimensional (frequency, direction) wave spectrum and describe wave height, period and directional characteristics for both the overall sea-state and wind-sea and swell components. The data are delivered on a regular grid at approximately 1.5km resolution, at an hourly frequency. The wave model is WAVEWATCH III and the North-West Shelf configuration is based on a rotated 1.5 km grid used for NORTHWESTSHELF_ANALYSIS_FORECAST_PHY_004_013. The model is forced by lateral boundary conditions from a consistent Global wave model. The reanalysis atmospheric forcing is given by the ECMWF ERA-5 Numerical Weather Prediction reanalysis. Future projections are forced by HadGEM3-GC3.05 PPE derived surface forcing and ocean lateral boundaries (Tinker et. al 2023). The outputs were generated under the ECOWind ACCELERATE project funded by the Natural Environment Research Council (NERC) under reference NE/X008886/1.",
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            "title": "Model outputs containing ocean physics projections from the North Western European shelf from 1992-2093.",
            "abstract": "This dataset consists of outputs from a baroclinic hydrodynamic model for the North-West European Shelf. It contains simulation periods conducted over monthly time spans. These include: historic reanalysis periods: January 2017; June 2018 and climate projection periods: June 1992; January 1996; January 2050; June 2053; January 2090; June 2093. The outputs contain sea surface height, bottom and top temperature, bottom and top salinity, and seabed stress diagnostics, bottom and top current speed, as well as barotropic (depth integrated) velocities. The data are delivered on a regular grid at approximately 1.5km resolution, at an hourly frequency. The baroclinic hydrodnamic model is NEMO (Nucleus for European Modelling of the Ocean) and the North-West Shelf configuration is based on a rotated 1.5 km grid used for NORTHWESTSHELF_ANALYSIS_FORECAST_PHY_004_013. The reanalysis atmospheric forcing is given by the ECMWF ERA-5 Numerical Weather Prediction reanalysis. Future projections are forced by HadGEM3-GC3.05 PPE derived surface forcing and ocean lateral boundaries (Tinker et. al 2023). To reduce spin up time, the simulations are restarted from conditions base on the consistent AMM7 simulations interpolated to the AMM15 grid. The North Western boundary of the AMM15 grid, not covered by AMM7, is constructed by integrating a set of consistent AMM15 boundaries provided by the Met Office (not publicly available) and interpolating over the missing region. The outputs were generated under the ECOWind ACCELERATE project funded by the Natural Environment Research Council (NERC) under reference NE/X008886/1.",
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            "title": "Model outputs containing seabed mobility projections from the North Western European shelf from 1992-2093.",
            "abstract": "This dataset consists of outputs from a hydrodynamic and wave model for the North-West European Shelf. It contains simulation periods conducted over monthly time spans. These include: historic reanalysis periods: January 2017; June 2018 and climate projection periods: June 1992; January 1996; January 2050; June 2053; January 2090; June 2093. The outputs contain hourly combined mean and maximum bed shear stresses (Tau), and monthly derived seabed motion, or 'capacity to move'. Wave and hydrodynamic seabed stresses are calculated following the approach of Soulsby (1997). This indicates what sediment grain size could be mobilised at the seabed as the given value of shear stress. The data are delivered on a regular grid at approximately 1.5km resolution. The hydrodynamic model is NEMO (Nucleus for European Modelling of the Ocean) and wave model is WAVEWATCH III and the North-West Shelf configuration is based on a rotated 1.5 km grid used for NORTHWESTSHELF_ANALYSIS_FORECAST_PHY_004_013. The model is forced by lateral boundary conditions from a consistent Global wave hindcast. The reanalysis atmospheric forcing is given by the ECMWF ERA-5 Numerical Weather Prediction reanalysis. Future projections are forced by HadGEM3-GC3.05 PPE derived surface forcing and ocean lateral boundaries (Tinker et. al 2023). The wave model is forced by lateral boundary conditions from a consistent Global wave simulation. The outputs were generated under the ECOWind ACCELERATE project funded by the Natural Environment Research Council (NERC) under reference NE/X008886/1.",
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        {
            "ob_id": 44618,
            "uuid": "7ce5eb9abfbd43bcb62f619864e2598a",
            "title": "Landslide inventories for Nepal: Database 2010-2021",
            "abstract": "A record of landslide inventories that cover areas of Nepal and were published between 2010 and 2021. Specifically, this includes an Excel table listing inventories published between 2010 and 2021 as well as several characteristics for each inventory, such as the area mapped, the number of landslides mapped, where known, and methods used. Also included is an ArcGIS shapefile dividing Nepal into 5km2 hexagons. Each hexagon contains the number of landslide inventories within each region and several metrics, such as mean susceptibility and the number of recorded landslides from the Government of Nepal's Bipad Portal (bipadportal.gov.np).",
            "keywords": "Nepal,landslides,inventories",
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            "dataLineage": "Data were compiled as part of the NERC-funded Sajag-Nepal project (NE_T01038X_1). Full details of how the inventory was compiled are provided in Harvey et al. (2024) Landslides, available at https://doi.org/10.1007/s11069-024-07013-1",
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            "uuid": "00927c853d2e4c3486abce95fe049511",
            "title": "Evolution of coseismic and post-seismic landsliding after the 2015 Mw 7.8 Gorkha earthquake, Nepal",
            "abstract": "Dataset comprises landslide area density rasters used in the analysis of the spatial evolution of landsliding following the 2015 Mw 7.8 Gorkha earthquake in Nepal. The data include 11 epochs of landslide area density, including bi-annual assessments between 2014 and 2018 (pre- and post-monsoon), and an additional coseismic assessment from 2015.\r\n\r\nThe dataset covers an area of 25,575 km2 covering the 14 administrative districts most intensively affected by the 2015 Gorkha earthquake. An AOI shapefile polygon is included within the dataset.",
            "keywords": "Nepal,landslides,inventory,coseismic landslides,post-seismic landslides",
            "publicationState": "working",
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            "phenomena": [],
            "dataLineage": "Data were mapped by researchers at Durham University and NSET-Nepal with support from the UKRI-DFID SHEAR program (201844-112). A full description of the methodology and dataset is provided in Kincey et al. (2021) Journal of Geophysical Research-Earth Surface, https://doi.org/10.1029/2020JF005803. The dataset is also available via Zenodo at https://doi.org/10.5281/zenodo.4274485",
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                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/213615/?format=api",
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                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/213618/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/213619/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/213620/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/213621/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/213622/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/213623/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/213624/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/213625/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/213626/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/213627/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/213628/?format=api",
                "https://api.catalogue.ceda.ac.uk/api/v2/rpis/213629/?format=api"
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        {
            "ob_id": 44621,
            "uuid": "526a1e026a99414aa86216ac0600fc51",
            "title": "National-Scale Rainfall-Triggered Landslide Susceptibility and Exposure in Nepal",
            "abstract": "This dataset is a 30 m resolution landslide susceptibility (hazard) model for Nepal. The dataset is derived from a fuzzy-logic model following the approach developed by Kritikos et al (2015), and is trained on a manually mapped landslide inventory (polygons of landslide locations and extents), originally produced by Durham University and NSET Nepal as part of the UKRI-DFID SHEAR program. The dataset shows the relative probability of a landslide occurring across the geographical extent of Nepal for rainfall triggered landslides only, on a scale of 1 (very highly anticipated) to 0 (very weakly anticipated). Further details and an online map viewer containing the data are available through the Government of Nepal's Bipad Portal (https://bipadportal.gov.np/risk-info/#/hazard)",
            "keywords": "Nepal,landslide susceptibility",
            "publicationState": "working",
            "dataPublishedTime": null,
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            "updateFrequency": "notPlanned",
            "status": "pending",
            "result_field": null,
            "timePeriod": "https://api.catalogue.ceda.ac.uk/api/v2/times/12542/?format=api",
            "geographicExtent": "https://api.catalogue.ceda.ac.uk/api/v2/bboxes/4812/?format=api",
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            "phenomena": [],
            "dataLineage": "The dataset was created by researchers from Durham University with support from the UKRI-DFID SHEAR program (201844-112), the EPSRC project ‘Risk at the Margins’ (EP/T024747/1), the EU ECHO HIP project ‘Risk informed landslide management in Nepal’s hill areas’ (ECHO/-XA/BUD/2020/91026), and by a grant from the Global Challenges Research Fund Multi-Hazard and Systemic Risk programme for the ‘Sajag-Nepal’ project (NE/T01038X/1).\r\n\r\nA full description of the methodology and dataset is provided by Kincey et al. (2024) Earth's Future, available at https://doi.org/10.1029/2023EF004102. The dataset is also available from Zenodo via https://doi.org/10.5281/zenodo.8307963.",
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        {
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            "uuid": "7f91b1326a324caa9e436b8fdef4a0d8",
            "title": "Machine Learning for Hourly Air Pollution Prediction – Global (ML-HAPPG)",
            "abstract": "This dataset contains estimates of air pollution levels across the globe for every hour of the year 2022. It covers five major air pollutants that can affect human health and the environment. The data cover major air pollutants, including Nitrogen Dioxide (NO2), Ozone (O3), Particulate Matter smaller than 10 micrometres (PM10) and smaller than 2.5 micrometres (PM2.5), and Sulphur Dioxide (SO2). Each air pollutant's concentrations are predicted not only as average (mean) values but also include estimates at lower (5th percentile), median (50th percentile), and upper (95th percentile) levels to highlight typical and potential extreme pollution scenarios. The spatial coverage of the dataset includes the entire globe, structured as an evenly spaced grid, with each grid square covering an area of 0.25 degrees (0.25 degrees x 0.25 degrees). Data points correspond to the centre of these grid squares. There is also training data used for the model from real-world monitoring stations.",
            "keywords": "Ambient Air Quality,Global,Nitrogen Dioxide,Ozone,Particulate Matter,Sulphur Dioxide,Machine Learning,AI",
            "publicationState": "citable",
            "dataPublishedTime": "2025-08-13T09:15:33",
            "doiPublishedTime": "2025-08-13T10:36:50.108412",
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            "status": "completed",
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                "dataPath": "/badc/deposited2025/ML-HAPPG/",
                "oldDataPath": [],
                "storageLocation": "internal",
                "storageStatus": "online",
                "volume": 383201486318,
                "numberOfFiles": 26397,
                "fileFormat": "NetCDF, txt, json"
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                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/84386/?format=api",
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                "https://api.catalogue.ceda.ac.uk/api/v2/phenomona/86839/?format=api"
            ],
            "dataLineage": "These pollution estimates were produced using a supervised machine learning method, which is a computational approach where algorithms are trained to identify patterns in historical data and apply these learned patterns to predict new data points. The predictions incorporated various environmental factors, including weather conditions (e.g., temperature, wind, precipitation), satellite measurements, and emission inventories (datasets detailing pollutants released into the atmosphere). Additionally, the dataset provides uncertainty intervals through percentile-based estimates, giving users insights into the reliability of the predictions.",
            "removedDataTime": null,
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            "language": "English",
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                "https://api.catalogue.ceda.ac.uk/api/v2/identifiers/13493/?format=api"
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                    "label": "public: None group",
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        {
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            "title": "Atmospheric aerosols and meteorological observations for the Black and Bloom project.",
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            "title": "ESA Precursors for Aerosols and Ozone Climate Change Initiative (Precursors_cci): Merged CO product, version 1.0",
            "abstract": "The carbon monoxide (CO) Climate Data Record (CDR) merged product is a new monthly Level 3 CO product developed by merging satellite data from the IASI instrument (on METOP-A, B, and C) and the MOPITT instrument (on TERRA) as part of the ESA Climate Change Initiative (CCI) Precursors for Aerosols and Ozone project.\r\n\r\nAn intermediate IASI L3 product was created averaging cloud-free Level 2 CO from the three METOP platforms (A, B and C) using the Cloud Detection Product of Whitburn et al. (2022). These data were then combined with MOPITT V9T L3 data using a weighted averaging approach. Weights were determined based on the MOPITT CO total column to prior ratio. \r\n\r\nThe merged dataset includes CO total column monthly 1°x1° resolution grids as well as uncertainty grids, for both daytime and night-time from January 2008 to December 2024. Surface altitude grids as well as data source flags grids are also provided.\r\n\r\nThe European Space Agency (ESA) Precursors for Aerosol and Ozone Climate Change Initiative (Precursors CCI) project is part of ESA's Climate Change Initiative (CCI) to produce long term datasets of Essential Climate Variables derived from global satellite data.\r\n\r\nThe version number is 1.0. Data are available in NetCDF format.",
            "keywords": "Carbon Monoxide, CO, Climate Data Record, CDR, IASI, MOPITT, CCI",
            "publicationState": "citable",
            "dataPublishedTime": "2026-01-12T12:24:16",
            "doiPublishedTime": "2026-01-13T10:59:42",
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            "status": "completed",
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                "storageStatus": "online",
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            "title": "Shipping Emissions in the Arctic and North Atlantic atmosphere (SEANA)",
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            "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily land surface temperature from  MODIS (Moderate resolution Infra-red Spectroradiometer) on Aqua, level 3 collated (L3C) global product (2002-2021), version 4.00",
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            "abstract": "This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from the Along-Track Scanning Radiometer (ATSR-2) on European Remote-sensing Satellite 2 (ERS-2). Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.\r\n\r\nDaytime and nighttime temperatures are provided in separate files corresponding to the morning and evening ERS-2 equator crossing times which are 10:30 and 22:30 local solar time.\r\n\r\nPer pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length.\r\n\r\nAlso provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.\r\n\r\nThe dataset coverage is near global over the land surface. Small regions were not covered due to downlinking constraints (most noticeably a track extending southwards across central Asia through India – further details can be found on the ATSR project webpages at https://artefacts.ceda.ac.uk/frozen_sites/www.atsr.rl.ac.uk/documentation/docs/userguide/index.shtml).\r\n\r\nLSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. ATSR-2 achieves full Earth coverage in 3 days so the daily files have gaps where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.\r\n\r\nDataset coverage starts on 1st June 1995 and ends on 22nd June 2003. There are two gaps of several months in the dataset: no data were acquired from ATSR-2 between 23 December 1995 and 30 June 1996 due to a scan mirror anomaly; and the ERS-2 gyro failed in January 2001, data quality was less good between 17th Jan 2001 and 5th July 2001 and are not used in this dataset. There are minor interruptions (1-2 days) during satellite/instrument maintenance periods.\r\n\r\nVersion 4.00 uses data from the 4th reprocessing of the ATSR L1B archive. The emissivities used in the retrieval come from the Combined ASTER and MODIS Emissivity over Land (CAMEL) Version 2 database; in Version 4.00 a climatology is used since there are temporal instabilities in the CAMEL time series.\r\n\r\nThe dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.\r\n\r\nThe dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.",
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            "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily land surface temperature from AATSR (Advanced Along-Track Scanning Radiometer), level 3 collated (L3C) global product (2002-2012), version 4.00",
            "abstract": "This dataset contains daily-averaged land surface temperatures (LSTs) and their uncertainty estimates from the Advanced Along-Track Scanning Radiometer (AATSR) on the Environmental Satellite (Envisat). Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.\r\n\r\nDaytime and night-time temperatures are provided in separate files corresponding to the morning and evening Envisat equator crossing times which are 10:00 and 22:00 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.\r\n\r\nThe dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. AATSR achieves full Earth coverage in 3 days so the daily files have gaps where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.\r\n\r\nDataset coverage starts on 20th May 2002 and ends on 8th April 2012. There is a twelve day gap in the dataset due to Envisat mission extension orbital manoeuvres from 21st October 2010 to 1st November 2010. There are minor interruptions (1-2 days) during satellite/instrument maintenance periods.\r\n\r\nVersion 4.00 uses data from the 4th reprocessing of the ATSR L1B archive. The emissivities used in the retrieval come from the Combined ASTER and MODIS Emissivity over Land (CAMEL) Version 2 database; in Version 4.00 a climatology is used since there are temporal instabilities in the CAMEL time series.\r\n\r\nThe dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.\r\n\r\nThe dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.",
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            "uuid": "c836ded4f8154c3ba86ee58ec0a7a7b1",
            "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily land surface temperature from the Metop-A AVHRR (Advanced Very High Resolution Radiometer) instrument, level 3 collated (L3C) global product, version 2.00",
            "abstract": "This dataset contains daily land surface temperatures (LSTs) and their uncertainty estimates from the Advanced Very High Resolution Radiometer 3 (AVHRR-3) on the Metop-A satellite. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.\r\n\r\nDaytime and night-time temperatures are provided in separate files corresponding to the morning and evening METOP-A equator crossing times which are 9.30 and 21:30 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.\r\n\r\nThe dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. The daily files have gaps where the surface is not covered by the satellite swath during day or night on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.\r\n\r\nDataset coverage starts on 1st March 2007 and ends on 31st December 2020. There are minor interruptions during satellite/instrument maintenance periods or instrument anomalies.\r\n\r\nThe emissivities used in the retrieval come from the Combined ASTER and MODIS Emissivity over Land (CAMEL) Version 2 database; in Version 4.00 a climatology is used since there are temporal instabilities in the CAMEL time series.\r\n\r\nThe dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.\r\n\r\nThe dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.",
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            "title": "Multi-channel seismic data recorded by Sercel SEAL streamer and recording system during RRS James Cook expedition JC228, Guatemala Basin (2022-2023)",
            "abstract": "Multi-channel seismic (MCS) data were recorded by Sercel SEAL streamer and recording system during RRS James Cook expedition JC228 (December 2022 to January 2023) in the Guatemala Basin region of the North Pacific Ocean. The aim was to sample fast-spreading ocean lithosphere of the northern Cocos Plate from its formation to subduction in the Mid-America Trench. MCS data were recorded along a linear transect with grids in three focus areas; two on the passively cooling oceanic plate, the third spanned the transition into the subduction zone. This work aimed to deliver key information on the evolution of oceanic lithosphere as well as provide essential site survey information for two IODP Proposals: the flagship MoHole to Mantle initiative (M2M; IODP-805MDP) and the Bend Fault Serpentinization experiment (approved IODP-876Pre). The work was undertaken by scientists at the University of Southampton, UK in collaboration with GEOMAR Helmholtz Centre for Ocean Research, Germany, funded as part of NERC grant 'From Ridge to Trench, MoHole to Bend-Faults', grant reference NE/M021246/1.",
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            "title": "The GEBCO_2025 Grid - a continuous terrain model for oceans and land at 15 arc-second intervals",
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            "uuid": "d24cd5dd66064d7b84350b2495227836",
            "title": "WesCon: Met Office surface to 16 m meteorology, radiation and subsoil measurements from   Airfield Camp, Netheravon, v1.0 (20230421-20230914)",
            "abstract": "A continuous hydrometeorological record of the temporary Met Office Observation-based research site at Netheravon, Wiltshire (51.241°N, -1.777°W, 120 m amsl) in southern England during the summer of 2023. Netheravon formed a supersite for the Wessex Summertime Convection Experiment (WesCon)/WesCon - Observing the Evolving Structures of Turbulence (WOEST) field campaign. This dataset contains recorded surface meteorology, radiation and subsoil measurements  from sensors mounted on a combination of 1.2 m, 2 m and 16 m masts at 1, 5, 10, and 30 minute averaging periods.\r\n\r\nInstruments mounted on 1.2m, 2 m and 16 m masts include:\r\n•\tVector Instruments T302 PRT temperature sensors.\r\n•\tScreened and aspirated HMP155s for measuring temperature and relative humidity.\r\n•\tGill HS50 3-D horizontally symmetric ultrasonic anemometers for winds and turbulent heat fluxes.\r\n•\tLicor Li-7500 high-speed open-path hygrometer was located at 16 m.\r\n•\tDelta-T BS5 measured barometric pressure at 1.5 m.\r\n\r\nSurface instrumentation includes:\r\n•\tRainfall is measured with a EML SB500 tipping-bucket gauge with a 0.2 mm accuracy.\r\n•\tScreened and aspirated Rotronics Hydroclip2 measured grass canopy (8 cm) air temperature and RH.\r\nRadiation instrumentation includes:\r\n•\tClear-domed Kipp and Zonen CMP21 pyranometers located at 2 m measured global downwelling, and upwelling components (of wavelength between 0.3-3 μm).\r\n•\tKipp and Zonen CG4 pyrgeometers located at 2 m measured the downwelling and upwelling longwave radiation (4.5–40 μm).\r\n•\tGrass and concrete skin temperatures were measured radiometrically with the Heitronics KT15 pyrometer.\r\n\r\nAerosol and visibility instrumentation includes:\r\n\r\n•\tA Campbell CS120 visiometer located at 2 m measured visual range through air (visibility)\r\nSubsoil instrumentation includes:\r\n•\tDelta-T ML3 theta probes measured volumetric soil moisture at the surface (0-5 cm) and 10 cm depths.\r\n•\tDelta-T ST1 thermistor probes measured soil temperature at 1, 4, 10, 17 cm depths.\r\n•\tHukseflux HFP01SC flux plate measured ground heat flux (2 cm depth).\r\n\r\n\r\nThese data conform to the NCAS data standards and are available under the UK Government Open Licence agreement. Acknowledgement of NCAS as the data provider is required whenever and wherever these data are used.",
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            "title": "WesCon: Vertical wind profiles and backscatter measurements from the Met Office Halo Doppler Lidar unit 35 at Airfield Camp, Netheravon, v1.0 (20230426-20230918)",
            "abstract": "Vertical wind profiles and backscatter measurements from the Met Office Halo Doppler Lidar unit 35 deployed at Netheravon, Wiltshire (51.241°N, -1.777°W, 120 m amsl) in southern England during the summer of 2023. Netheravon formed a supersite for the Wessex Summertime Convection Experiment (WesCon)/WesCon - Observing the Evolving Structures of Turbulence (WOEST) field campaign. \r\nThe Halo lidar is based on a 1565 nm laser emitting linearly polarized pulsed light through an 8 cm diameter lens with a heterodyne detector. Laser beam returns from the atmosphere are range-gated velocity and back-scattered power. The Halo is capable of full hemispheric scanning of the backscatter coefficient and radial velocity as a function of beam range. \r\nThe usual operation was vertical stares (zenith angle=0°) with periodic wind scans that invoked various options of off-axis views. DBS (Doppler beam swinging) scans used a tri-axis azimuthally orthogonal technique using the single lidar beam to retrieve horizontal mean wind components every 30 mins. Multi-axis VAD (velocity azimuth display) scans are a more involved version of the DBS scans and used 12 point off-zenith views performed every 60 mins. The vertical stares, DBS and VAD wind scans produced separate archived netCDF files. \r\nAlthough depolarisation capability was possible with unit 35, this was switched off during WesCon.\r\n\r\nTo ensure optimal traceability and transparency of data, comprehensive metadata is included.",
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            "title": "WesCon: near-surface meteorology from Met Office automatic weather stations at 11 locations for the WesCon/WOEST measurement campaign.  v1.0 (20230414-20231004)",
            "abstract": "This dataset contains a continuous record of temporary in-house designed Met Office automatic weather stations (AWS) deployed at 11 locations in southern England during the summer of 2023 as part of the Wessex Summertime Convection Experiment (WesCon)/WesCon - Observing the Evolving Structures of Turbulence (WOEST) field campaign. \r\n\r\nThe dataset contains recorded surface meteorology and (at six of the locations) subsoil sensors at 1 minute averaging period based around a 1.5 m mast.\r\nInstruments at 1.5 m height include: (Chilbolton, Clench Common, Spire View, Yatesbury Field, Lydeway Field, Breach Hill, Keevil,  Wadswick Farm, The Park Brown Shutters, Henstridge)\r\n•\tPRT screened temperature sensor.\r\n•\tRotronics Hygroclip2 for measuring screened temperature and relative humidity.\r\n•\tBosch Sensortec BMP280 barometric pressure sensor.\r\n•\tRainfall is measured with a Hobo RG3 tipping-bucket gauge with a 0.2 mm accuracy.\r\n•\tHorizontal wind speed and direction from a Gill WindSonic 2-D ultrasonic sensor.\r\nSubsoil instrumentation (at six sites: Lydeway, Chilbolton, Keevil, Henstridge, The Park, Clench Common) includes:\r\n•\tDelta-T PR2 probe measured volumetric soil moisture at various depths (10, 20, 30, 40 cm).\r\n•\tDelta-T ST2 thermistor probes measured soil temperature at 1 cm depth.\r\n•\tHukseflux HFP01SC flux plate measured ground heat flux (2 cm depth).\r\n\r\n\r\nTo ensure optimal traceability and transparency of data, comprehensive metadata is included.",
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            "title": "WesCon: Cloud height measurements from the Met Office Vaisala CT25K Laser Ceilometer at Airfield Camp, Netheravon, v1.0 (20230426-20230919)",
            "abstract": "Cloud height measurements from the Met Office Vaisala CT25K Laser Ceilometer deployed at Netheravon, Wiltshire (51.241°N, -1.777°W, 120 m amsl) in southern England during the summer of 2023. Netheravon formed a supersite for the Wessex Summertime Convection Experiment (WesCon)/WesCon - Observing the Evolving Structures of Turbulence (WOEST) field campaign. \r\n\r\nThe CT25K Laser ceilometer is a general-purpose cloud height sensor employing LIDAR technology for detection of clouds, precipitation and other obstructions to vision. The CT25K provides reliable determination of cloud height up to 25,000 feet (7.5 km) thus covering most heights where dense clouds appear. It is designed to detect up to three possible cloud layers.\r\nThe Vaisala CT25K laser ceilometer measures cloud height-or vertical visibility if the cloud base is obscured. The single-lens design also ensures reliable measurements in fog, rain, snow and haze.\r\n\r\nTo ensure optimal traceability and transparency of data, comprehensive metadata is included.",
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