Procedure Computation List

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        {
            "ob_id": 44533,
            "uuid": "61b36b80ecd64b9dad766c4ea9e81d9f",
            "title": "Computation for JNCC Sentinel-2 indices Analysis Ready Data (ARD) Enhanced Vegetation Index v2  (EVI)",
            "abstract": "EVI2 equation from Jiang et al. 2008 (link in Related Documents). Sentinel-2 surface reflectance products are obtained from Defra and JNCC Sentinel-2 ARD CEDA dataset. EVI2 index files are generated in R v4.4.1 (R Core Team, 2024) using the calc function of the R package raster v3.6-26 (Hijmans, 2023). Data 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\nContains modified Copernicus Sentinel data 2015-2025",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 44562,
            "uuid": "132af0ab09d34dd0bf918b6addd96c14",
            "title": "Computation for JNCC Sentinel-1 indices Analysis Ready Data (ARD) VH/VV Cross Ratio Index",
            "abstract": "Sentinel-1 dual polarisation data are obtained from the Defra and JNCC Sentinel-1 ARD CEDA dataset. The data are converted from decibel to linear scale prior to the VH/VV calculation and generation of the index files. This is performed in R v4.4.1 (R Core Team, 2024) using the calc function of the R package raster v3.6-26 (Hijmans, 2023). Data 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. Pixels with values of 50 or higher, or -50 or lower, were assigned the no-data value.\r\n\r\nContains modified Copernicus Sentinel data 2015-2025",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 44563,
            "uuid": "09344f0c880a4a368816d9cfd514fb50",
            "title": "Computation for ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Retrieval of sea-ice thickness from Sentinel-3a radar altimetry data",
            "abstract": "The method used to extract sea-ice thickness from radar altimetry data is based on the\r\npioneering work of Peacock and Laxon, 2004; Laxon et al., 2003 for the ERS-2 mission. The\r\nmethod involves separating the radar echoes returning from the ice floes from those\r\nreturning from the sea surface in the leads between the floes. This step of a surface-type\r\nclassification is crucial and allows for a separate determination of the ice floe and\r\nsea-surface heights. The freeboard that is the elevation of the ice upper side (or ice/snow\r\ninterface) above the sea level can then be computed by deducting the interpolated\r\nsea-surface height at the floe location from the height of the floe. Sea-ice thickness can then\r\nbe calculated from the sea-ice freeboard with the additional information of the snow load.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 44567,
            "uuid": "b2860073d21346c597c51a8da6b0005b",
            "title": "Computation for ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Retrieval of sea-ice thickness from Sentinel-3B radar altimetry data",
            "abstract": "The method used to extract sea-ice thickness from radar altimetry data is based on the\r\npioneering work of Peacock and Laxon, 2004; Laxon et al., 2003 for the ERS-2 mission. The\r\nmethod involves separating the radar echoes returning from the ice floes from those\r\nreturning from the sea surface in the leads between the floes. This step of a surface-type\r\nclassification is crucial and allows for a separate determination of the ice floe and\r\nsea-surface heights. The freeboard that is the elevation of the ice upper side (or ice/snow\r\ninterface) above the sea level can then be computed by deducting the interpolated\r\nsea-surface height at the floe location from the height of the floe. Sea-ice thickness can then\r\nbe calculated from the sea-ice freeboard with the additional information of the snow load.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 44588,
            "uuid": "9eb211acb6a64a6883baf8a4dc049fd7",
            "title": "Computation for IPCC AR7 figure 2.27",
            "abstract": "This figure can be reproduced using the following code on GitHub:\r\n[LINK TO GITHUB]\r\n\r\nInstructions on how to use the code are also provided in the GitHub repository.",
            "keywords": "",
            "inputDescription": 26,
            "outputDescription": null,
            "softwareReference": null,
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        },
        {
            "ob_id": 44591,
            "uuid": "f639b44f609b4d19b47547876d48fc6b",
            "title": "University of Leicester IASI retrieval scheme (ULIRS)",
            "abstract": "This computation involved: ULIRS deployed on JASMIN processing cluster. The ULIRS is an optimal estimation-based retrieval scheme, which utilises equidistant pressure levels and a floating pressure grid based on topography. ULIRS was developed at the University of Leicester through the National Centre for Earth Observation (NCEO).",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 44593,
            "uuid": "18235a17734541eb80960eeb7b086748",
            "title": "EOCIS: Land Vegetation Parameters, V1",
            "abstract": "The produced vegetation parameters datasets (April 2018 to December 2024) are generated by LEAF production toolbox. The algorithm applies the heterogenous 4SAIL2 model, together with a shoot clumping parameterization. In particular, the algorithm uses PROSPECT-D coupled with 4SAIL2 modelling of uncollided fluxes and single scattering using geometric optics, after modifying the latter to account for foliage clumping within shoots.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 44605,
            "uuid": "f31d788506b94aab8822d8c3c98a958f",
            "title": "Derivation of ESA Land Surface Temperature Climate Change Initiative (LST_cci): Multisensor Infra-Red (IR) Low Earth Orbit (LEO) Land surface temperature (LST) time series level 3 supercollated (L3S) global product (1995-2020), version 3.00, daily and monthly products",
            "abstract": "For more information on the retrieval algorithm used see the documentation on the LST CCI webpage.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 44629,
            "uuid": "54b3003288914bcabb4c56c0b7b313ad",
            "title": "FLEXPART",
            "abstract": "Ten-day particle dispersion calculations made using the FLEXPART model initiated from the ground site (67.07° N, 49.38° W 1073 m AMSL) in the ablation zone in southwest Greenland, approximately 35 km from the ice sheet margin.  \r\nThe outputs were collated into animations",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 44638,
            "uuid": "54dcc854130f4ef48cf74949aed6a616",
            "title": "Machine-Learning-Based Prediction of Air Pollution Estimates",
            "abstract": "The dataset was created using a supervised machine-learning pipeline. The pipeline generates air pollution concentration predictions across a global  0.25 x 0.25 degree grid.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 44683,
            "uuid": "43dba09585b84c8d9ca195191fe5f0dd",
            "title": "Derivation of the ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily land surface temperature from AVHRR/3 (Advanced Very High Resolution Radiometer /3) on NOAA-15, NOAA-16, NOAA-17, NOAA-18 and NOAA-19",
            "abstract": "The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using a two channel Generalised Split Window retrieval algorithm and data were processed in the UoL processing chain.\r\n\r\nFor more information on the retrieval algorithm used see the documentation on the LST CCI webpage:  https://climate.esa.int/en/projects/land-surface-temperature/",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 44758,
            "uuid": "8bb0551ac46242469f1f46180fe0edca",
            "title": "The NASA Goddard Space Flight Center two-dimensional (2D) chemistry-climate model (GSFC2D)",
            "abstract": "The NASA Goddard Space Flight Center two-dimensional (2D) chemistry-climate model (GSFC2D) has a domain extending from the surface to ∼92 km (0.002 hPa). The model has 76 levels, with 1 km vertical resolution from the surface to the lower mesosphere (60 km) and 2 km resolution above (60-92 km). The horizontal resolution is 4° latitude, and the model uses a 2D (latitude-altitude) finite volume dynamical core (Lin & Rood, 1996) for advective transport.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 44769,
            "uuid": "c116848868054e18a9f5a51d68c3ad21",
            "title": "Computation of Permafrost v5 datsets by the ESA Permafrost CCI",
            "abstract": "Complementing ground-based monitoring networks, the Permafrost CCI project is establishing Earth Observation (EO) based products for the permafrost ECV spanning the last two decades. Since ground temperature and thaw depth cannot be directly observed from space-borne sensors, a variety of satellite and reanalysis data are combined in a ground thermal model. The algorithm uses remotely sensed data sets of Land Surface Temperature (MODIS LST/ ESA LST CCI) and landcover (ESA Landcover CCI and ESA Permafrost CCI Circumarctic landcover units) to drive the transient permafrost model CryoGrid, which yields thaw depth and ground temperature at various depths, while ground temperature forms the basis for permafrost fraction. The Land Surface Temperature data sets are employed to determine the upper boundary condition of the differential equation, while its coefficients (e.g. heat capacity and thermal conductivity) are selected according to the landcover information (Westermann et al., 2017). With this, a spatial resolution of the final product of 1 km is possible, corresponding to “breakthrough” according to the WMO OSCAR database.\r\n\r\nInput data: MODIS Land surface temperature is used as the main input for the L4 production for 2003-2023 data. Sensors of auxiliary data are listed in the meta data.\r\n\r\nDownscaled and bias corrected ERA reanalyses data based on statistics of the overlap period between ERA reanalysis and MODIS LST are used for data before 2003. Sensors of auxiliary data are listed in the meta data.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 44772,
            "uuid": "0dff018a1b6f4d308c8fa5d922d0d00f",
            "title": "EC-Earth3-AerChem climate model",
            "abstract": "The EC-Earth3-AerChem climate model, released in 2019, includes the following components: // aerosol: TM5 (3 x 2 degrees; 120 x 90 longitude/latitude; 34 levels; top level: 0.1 hPa), atmos: IFS cy36r4 (TL255, linearly reduced Gaussian grid equivalent to 512 x 256 longitude/latitude; 91 levels; top level 0.01 hPa), atmosChem: TM5 (3 x 2 degrees; 120 x 90 longitude/latitude; 34 levels; top level: 0.1 hPa), land: HTESSEL (land surface scheme built in IFS), ocean: NEMO3.6 (ORCA1 tripolar primarily 1 degree with meridional refinement down to 1/3 degree in the tropics; 362 x 292 longitude/latitude; 75 levels; top grid cell 0-1 m), seaIce: LIM3. // For CMIP6, the model was run by the AEMET, Spain; BSC, Spain; CNR-ISAC, Italy; DMI, Denmark; ENEA, Italy; FMI, Finland; Geomar, Germany; ICHEC, Ireland; ICTP, Italy; IDL, Portugal; IMAU, The Netherlands; IPMA, Portugal; KIT, Karlsruhe, Germany; KNMI, The Netherlands; Lund University, Sweden; Met Eireann, Ireland; NLeSC, The Netherlands; NTNU, Norway; Oxford University, UK; surfSARA, The Netherlands; SMHI, Sweden; Stockholm University, Sweden; Unite ASTR, Belgium; University College Dublin, Ireland; University of Bergen, Norway; University of Copenhagen, Denmark; University of Helsinki, Finland; University of Santiago de Compostela, Spain; Uppsala University, Sweden; Utrecht University, The Netherlands; Vrije Universiteit Amsterdam, the Netherlands; Wageningen University, The Netherlands. Mailing address: EC-Earth consortium, Rossby Center, Swedish Meteorological and Hydrological Institute/SMHI, SE-601 76 Norrkoping, Sweden (EC-Earth-Consortium) in native nominal resolutions: aerosol: 250 km, atmos: 100 km, atmosChem: 250 km, land: 100 km, ocean: 100 km, seaIce: 100 km. For RAMIP, the model was run by FMI.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 44804,
            "uuid": "b06c8ef6f34f4cd687f14cdec8e6a214",
            "title": "GFDL-SPEAR_LO climate model",
            "abstract": "The GFDL-SPEAR_LO climate model, released in 2018, includes the following components: // aerosol: interactive, atmos: GFDL-AM4.0.1 (Cubed-sphere (c96) - 1 degree nominal horizontal resolution; 360 x 180 longitude/latitude; 33 levels; top level 1 hPa), atmosChem: fast chemistry, aerosol only, land: GFDL-LM4.0.1 (1 degree nominal horizontal resolution; 360 x 180 longitude/latitude; 20 levels; bottom level 10m); land-Veg:unnamed (dynamic vegetation, dynamic land use); land-Hydro:unnamed (soil water and ice, multi-layer snow, rivers and lakes), landIce: GFDL-LM4.0.1, ocean: GFDL-OM4p25 (GFDL-MOM6, tripolar - nominal 1.00 deg; 360 x 180 longitude/latitude; 75 levels; top grid cell 0-2 m), ocnBgchem: none seaIce: GFDL-SIM4p25 (GFDL-SIS2.0, tripolar - nominal 1.00 deg;  360 x 180 longitude/latitude; 5 layers; 5 thickness categories). // The model was run by the National Oceanic and Atmospheric Administration, Geophysical Fluid Dynamics Laboratory, Princeton, NJ 08540, USA (NOAA-GFDL) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, atmosChem: 100 km, land: 100 km, landIce: 100 km, ocean: 100 km,  seaIce: 100 km.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 44815,
            "uuid": "734f0774e7d9409789fb749c94ceb5d7",
            "title": "Optimal Retrieval of Aerosol and Cloud (ORAC)",
            "abstract": "Optimal Retrieval of Aerosol and Cloud (ORAC) - more details on request",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 44825,
            "uuid": "74c87adf0d5d43f2a5e9336754afcfe3",
            "title": "Computation for Deformation, Strains, and Velocities for the Tibetan Plateau",
            "abstract": "Approximately 44,500 Sentinel-1 SAR acquisitions were processed to generate around 341,400 interferograms at ~100 m resolution using the automated COMET-LiCSAR system. The Tibetan Plateau was divided into 127 ascending and 114 descending frames (each approximately 250 km wide), forming short-baseline interferometric networks with additional 6- and 12-month pairs to reduce phase bias.\r\n\r\nUsing LiCSBAS, line-of-sight displacement time series and average velocities at ~1 km resolution were estimated. Corrections were applied for tropospheric, ionospheric, and solid Earth tidal effects. Velocity uncertainties were flattened via semi-variogram analysis, and Eurasian plate motion was subtracted from all frames.\r\n\r\nA total of 18,203 GNSS velocities and 6,607 levelling rates were compiled from 131 studies published since 2013. The dataset was refined by removing outliers, aligning studies via Euler poles, eliminating outdated or duplicate entries, and merging collocated measurements.\r\n\r\nTo construct a unified coarse velocity model integrating GNSS, levelling, and InSAR data, the velmap approach was followed. This involved inverting for 3D velocities at each node of a triangular mesh spaced by ~0.2° in longitude and latitude, along with frame-specific reference frame adjustment parameters and linear-with-height atmospheric correction terms. The reference frame adjustment parameters consisted of a second-order polynomial surface, and regularization was applied using Laplacian smoothing.\r\n\r\nEast-west and vertical velocities were derived from georeferenced mosaics of ascending and descending line-of-sight velocities, using coarse north-south velocities as constraints. Horizontal strain and rotation rates were calculated from velocity gradients, with spherical corrections applied. A 150 km median filter was applied to east-west velocities to balance resolution and noise.\r\n\r\nFurther details are available in Wright et al. (2025, Science).",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
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        },
        {
            "ob_id": 44834,
            "uuid": "274c1d78e3f34dad8d84c8bcb6aa8d83",
            "title": "ESA Snow Climate Change Initiative: Derivation of SCFV AVHRR v4.0 product",
            "abstract": "The retrieval method of the snow_cci SCFV product from AVHRR data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre- and post-classification module. All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 0.63 µm and 1.61 µm (channel 3a or the reflective part of channel 3b (ref3b)), and an emissive band centred at about 10.8 µm. The snow_cci snow cover mapping algorithm is a three-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFV retrieval method is applied. Finally, a post-processing removes erroneous snow pixels caused either by falsely classified clouds in the tropics or by unreliable ref3b values at a global scale. The following auxiliary data set is used for product generation: ESA CCI Land Cover from 2000; water bodies and permanent snow and ice areas are masked based on this dataset. Both classes were separately aggregated to the pixel spacing of the SCF product. Water areas are masked if more than 50 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map. RMSE is retrieved from a statistical model and added as pixel-wise information.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 44835,
            "uuid": "fbd3aaf5620e49c796c72a99507186ef",
            "title": "ESA Snow Climate Change Initiative: Derivation of SCFG AVHRR v4.0 product",
            "abstract": "The retrieval method of the snow_cci SCFG product from AVHRR data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module. All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 0.63 µm and 1.61 µm (channel 3a or the reflective part of channel 3b), and an emissive band centred at about 10.8 µm. The snow_cci snow cover mapping algorithm is a three-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFG retrieval method is applied. Finally, a post-processing removes erroneous snow pixels caused either by falsely classified clouds in the tropics or by unreliable ref3b values at a global scale. The following auxiliary data sets are used for product generation: i) ESA CCI Land Cover from 2000; water bodies and permanent snow and ice areas are masked based on this dataset. Both classes were separately aggregated to the pixel spacing of the SCF product. Water areas are masked if more than 50 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map; ii) Forest canopy transmissivity map; this layer is based on the tree cover classes of the ESA CCI Land Cover 2000 data set and the tree cover density map from Landsat data for the year 2000 (Hansen et al., Science, 2013, DOI: 10.1126/science.1244693). This layer is used to apply a forest canopy correction and estimate in forested areas the fractional snow cover on ground. RMSE is retrieved from a statistical model and added as pixel-wise information.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
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        },
        {
            "ob_id": 44836,
            "uuid": "0b9ad9b988ad419fb2fab93ebc4f38c6",
            "title": "ESA Snow Climate Change Initiative (snow_cci): SWE, v4",
            "abstract": "The snow_cci SWE product has been based on the ESA GlobSnow SWE retrieval approach (Takala et al. 2011). The retrieval is based on passive microwave radiometer (PMR) data considering the change of brightness temperature due to different snow depth, snow density, grain size and more. The retrieval algorithm handles data from the sensors SMMR, SSM/I, SSMIS, AMSR-E and AMSR-2. The retrieval methodology combines the satellite passive microwave radiometer (PMR) measurements with ground-based synoptic weather station observations by Bayesian non-linear iterative assimilation. A background snow-depth field from re-gridded surface snow-depth observations and a passive microwave emission model are required components of the retrieval scheme. The GlobSnow algorithm implemented for snow_cci version 4 includes the utilisation of an advanced emission model with an improved forest transmissivity module and treatment of sub-grid lake ice. Because of the importance of the weather station snow-depth observations on the SWE retrieval, there is improved screening for consistency through the time series. Passive microwave radiometer data are obtained from the recalibrated enhanced resolution CETB ESDR dataset (MEaSUREs Calibrated Enhanced-Resolution Passive Microwave Daily EASE-Grid 2.0 Brightness Temperature (CETB) Earth System Data Record (ESDR) https://nsidc.org/pmesdr/data/) The retrieval algorithm has been modified relative to snow_cci v3.1 to prioritize morning overpass (descending) data over evening (ascending) data. This change affects the SWE retrieval for all years except 1988–1991. Data from those years is from the F08 satellite, which has a reversed orbit, and evening (descending) data is prioritized, as in earlier versions of the SWE retrieval. Snow masking in post-production now uses CryoClim SCE data for 35–40° latitude and −30–3° longitude. Elsewhere, the baseline snow mask and CryoClim are combined so that any pixel flagged by either is marked snow-covered, as in v3.1. The pixel-wise uncertainty model has been updated for North America using extensive snow course data. The time series has been extended from version 3.1 by one year, to 2023. SWE products are based on SMMR, SSM/I and SSMIS passive microwave radiometer data for non-alpine regions of the Northern Hemisphere.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 44869,
            "uuid": "ff0f37f9cbca448da41a6849935d10a9",
            "title": "Computation for HighResMIP model simulations for high-resolution hydrological simulations over Peninsular Malaysia: using HadGEM3-GC3.1-HM for the IMPRESS-Malaysia project.",
            "abstract": "Computation for HighResMIP model simulations for high-resolution hydrological simulations over Peninsular Malaysia: using HadGEM3-GC3.1-HM for the IMPRESS-Malaysia project. This used the HadGEM3-GC3.1-HM model.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 44894,
            "uuid": "2bb0fafcd143488497e6833baa416bbb",
            "title": "Windstorm Tracks",
            "abstract": "cf. Leckebusch et al., 2008",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 44906,
            "uuid": "7e7cc1392e694b149ae5b4858550a47d",
            "title": "Computation for the Copernicus Land Cover product",
            "abstract": "Pre-processing:\r\n\r\nDeep-learning (DL) based clouds detection: Land Occlusion Score (LOS) product\r\nLOS weighted compositing and timeseries interpolation\r\nLSF-ANNUAL-S2 and LSF-ANNUAL-S1 extraction\r\nAncillary data preparation: AgERA5 climatic regions embeddings processing\r\nModelling: The backbone to produce the LCM-10 layers is EvoNet, a novel algorithm that integrates the strengths of convolutional neural networks (CNNs) and pixel-based classifiers into a unified framework. EvoNet avoids the inefficiencies of conventional approaches that either rely on multiple regional models, requiring complex post-processing, or exclusively use CNNs or pixel classifiers, each of which has limitations. CNNs excel in generalization but struggle with fine spatial details, while pixel classifiers offer high spatial resolution but are prone to noise and overfitting. The core innovation of EvoNet lies in unifying these strengths with its dual architecture: a CNN-based spatial feature extractor and a multi-layer perceptron (MLP) pixel classifier. \r\n\r\nPost-processing: expert rules polishing and tiling of the final product.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
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        },
        {
            "ob_id": 44917,
            "uuid": "09f6377c9f344f1ba0e92ce082c69e42",
            "title": "UK Earth System Model (UKESM) version 1,  UKESM1-1-LL model run for GloSAT project.",
            "abstract": "The GloSAT model simulations were run on UK supercomputing platforms ARCHER2 and MONSooN using the UKESM1-1-LL Earth System Model described in Mulcahy, et al 2023 (https://doi.org/10.5194/gmd-16-1569-2023).\r\n\r\nThe experiments and model set up are all described in:\r\nBallinger, A., Schurer, A., Hegerl, G., Dittus, A., Hawkins, E., Cornes, R., Kent, E., Marshall, L., Morice, C., Osborn, T., and Rayner, N.A. Rumbold, S.:(2025) Importance of beginning industrial-era climate simulations in the eighteenth century, Environmental Research Letters,\r\n650, accepted.",
            "keywords": "CMIP6, WCRP, climate change, MOHC, UKESM1-1-LL, historical, AERmon, AERmonZ, Amon, CFmon, Emon, LImon, Lmon, Omon, SImon",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 44923,
            "uuid": "20bd7627fe2249aeb92620e2db5743d5",
            "title": "Simultaneous building Height And FootprinT extraction from Sentinel imagery (SHAFTS)",
            "abstract": "A multi-task deep-learning-based Python package SHAFTS (Simultaneous building Height And FootprinT extraction from Sentinel imagery) to extract the average building height and footprint from Sentinel imagery. Evaluation in 46 cities worldwide shows that SHAFTS achieves significant improvement over existing machine-learning-based methods.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 45091,
            "uuid": "3a36c1fe5a3c461497690d3451b862dc",
            "title": "Derivation of ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily Moderate resolution Infra-red Spectroradiometer (MODIS) on Terra level 3 collated (L3C) global product (2000-2021), version 4.00",
            "abstract": "For more information on the retrieval algorithm used see the documentation on the LST CCI webpage: \r\nhttps://climate.esa.int/en/projects/land-surface-temperature/",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 45094,
            "uuid": "4e2a942d249347f1bcac25bf1ba25fa4",
            "title": "Derivation of ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily Moderate resolution Infra-red Spectroradiometer (MODIS) on Aqua level 3 collated (L3C) global product (2002-2021), version 4.00",
            "abstract": "For more information on the retrieval algorithm used see the documentation on the LST CCI webpage: https://climate.esa.int/en/projects/land-surface-temperature/",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 45099,
            "uuid": "fa02152813cb4a988a2bfbbb9e37a2f3",
            "title": "Derivation of ESA Land Surface Temperature Climate Change Initiative: Daily land surface temperature from SLSTR (Sea and Land Surface Temperature Radiometer) on Sentinel 3A and Sentinel 3B, level 3 collated (L3C) global product, version 4.00",
            "abstract": "For more information on the retrieval algorithm used see the following paper: Ghent, D. J., Anand, J. S., Veal, K., Remedios, J. J. (2024). The operational and climate land surface temperature products from the Sea and Land Surface Temperature Radiometers on Sentinel-3A and 3B. Remote Sensing, 16, 3403. https://doi.org/10.3390/rs16183403",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 45108,
            "uuid": "def822b6166c4e5da4c732b86f72c5a5",
            "title": "ESA Snow Climate Change Initiative: Derivation of SCFV MODIS v4.0 product.",
            "abstract": "The SCFV product is based on Moderate resolution Imaging Spectroradiometer (MODIS) data on-board the Terra satellite.\r\nThe retrieval method of the snow_cci SCFV product from MODIS data has been developed and improved by ENVEO (ENVironmental Earth Observation IT GmbH) to provide consistent snow cover fraction estimations with the Snow Cover Fraction on Ground (SCFG) product (https://catalogue.ceda.ac.uk/uuid/375ffdb8f0a445e380b4b9548655f5f9/) which is based on an enhanced version of the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module developed by ENVEO. For the SCFV product generation from MODIS, multiple reflective and emissive spectral bands are used. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm (SCDA) (Metsämäki et al., 2015). For all remaining pixels, the snow_cci SCFV retrieval method is applied, using spectral bands centred at about 0.55 µm and 1.6 µm, and an emissive band centred at about 11 µm. The snow_cci snow cover mapping method is a two-step approach that first identifies pixels that are largely snow free, followed by SCFV retrieval for remaining pixels. \r\n\r\nSpatially variable background reflectance and forest reflectance maps and a constant value for the spectral reflectance of wet snow are used for the SCFV retrieval approach. In non-forested areas, the SCFV and SCFG estimations from MODIS data are the same. \r\n\r\nPermanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the pixel spacing of the SCFV product. Water areas are masked if more than 30 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map. Salt lakes are masked based on manual delineation from Terra MODIS data. \r\n\r\nThe product uncertainty for observed land pixels is provided as unbiased root mean square error (RMSE) per pixel in the ancillary variable. \r\n\r\nSCFV products and associated layers from individual MODIS tiles are merged into daily global SCFV products.\r\n\r\nEach daily product contains additionally the sensor zenith angle per pixel in degree, and the acquisition time per pixel referring to the scan line time of the MODIS granule used for the classification.\r\n\r\nInput description:\r\n•\tTerra MODIS Collection 6.1 MOD021KM (Level 1B Calibrated Radiances - 1km; DOI: 10.5067/MODIS/MOD021KM.061) and MOD03 (Geolocation - 1km; DOI: 10.5067/MODIS/MOD03.061) products\r\n•\tESA Land Cover CCI project team; Defourny, P. (2019): ESA Land Cover Climate Change Initiative (Land_Cover_cci): Global Land Cover Maps, Version 2.0.7. Centre for Environmental Data Analysis, 14.11.2025. https://catalogue.ceda.ac.uk/uuid/b382ebe6679d44b8b0e68ea4ef4b701c\r\n•\tHansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, and J. R. G. Townshend. 2013. “High-Resolution Global Maps of 21st-Century Forest Cover Change.” Science 342 (15 November): 850–53. Data available on-line from: http://earthenginepartners.appspot.com/science-2013-global-forest. \r\n•\tGlobal auxiliary layers prepared by ENVEO: \r\no\tpermanent snow and ice area and water mask based on Land Cover map v2.0.7 from 2000 and salt lake mask manually mapped from MODIS data (v2.0, 2025-04-03) \r\n•\tspectral reflectance layers for snow free ground (v4, 2025-05-24) and snow free forest (v4, 2025-05-24), \r\n•\tNormalized Difference Snow Index (NDSI) threshold map (v5.0, 2025-03-19),\r\n•\ttransmissivity map based on tree canopy cover v1.4 for year 2000 (Hansen et al., 2013) and Land Cover map v2.0.7 for year 2000 (v01, 2021-10-01)\r\n\r\nOutput description:\r\nDaily global Snow Cover Fraction products including uncertainty estimation, sensor zenith angle and acquisition time per pixel",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 45109,
            "uuid": "5406563f6d4844d991b5c46f28d3ca4b",
            "title": "ESA Snow Climate Change Initiative: Derivation of SCFG MODIS v4.0 product.",
            "abstract": "The SCFG product is based on Moderate resolution Imaging Spectroradiometer (MODIS) data on-board the Terra satellite.\r\nThe retrieval method of the snow_cci SCFG product from MODIS data has been further developed and improved by ENVEO (ENVironmental Earth Observation IT GmbH) based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module developed by ENVEO. For the SCFG product generation from MODIS, multiple reflective and emissive spectral bands are used. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm (SCDA) (Metsämäki et al., 2015). For all remaining pixels, the snow_cci SCFG retrieval method is applied, using spectral bands centred at about 0.55 µm and 1.6 µm, and an emissive band centred at about 11 µm. The snow_cci snow cover mapping method is a two-step approach that first identifies pixels that are largely snow free, followed by SCFG retrieval for remaining pixels. \r\n\r\nThe main differences of the snow_cci snow cover mapping algorithm compared to the GlobSnow algorithm described in Metsämäki et al. (2015) are (i) improvements of the cloud screening approach applicable on a global scale, (ii) the pre-classification of snow free areas on global land areas, (iii) the usage of spatially variable snow free ground reflectance and snow free forest reflectance maps instead of global constant values, (iv) the update of the constant value for wet snow based on analyses of spatially distributed reflectance time series of MODIS data, and (v) the usage of a global forest canopy transmissivity based on tree canopy cover of the year 2000 from Hansen et al. (2013) and forest type layers from Land Cover CCI (Defourny, 2019) of the year 2000. The retrieval approach ensures consistency between the SCFG CRDP v4.0 and the Snow Cover Fraction Viewable from above (SCFV) CRDP 4.0 from MODIS data (https://catalogue.ceda.ac.uk/uuid/ bc13bb02a958449aac139853c4638f32). In non-forested areas, the SCFG and SCFV estimations from MODIS data are the same.\r\n\r\nPermanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the pixel spacing of the SCFG product. Water areas are masked if more than 30 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map. Salt lakes are masked based on manual delineation from Terra MODIS data. \r\n\r\nThe product uncertainty for observed land pixels is provided as unbiased root mean square error (RMSE) per pixel in the ancillary variable. \r\n\r\nSCFG products and associated layers from individual MODIS tiles are merged into daily global SCFG products.\r\n\r\nEach daily product contains additionally the sensor zenith angle per pixel in degree, and the acquisition time per pixel referring to the scan line time of the MODIS granule used for the classification.\r\n\r\nInput description:\r\n•\tTerra MODIS Collection 6.1 MOD021KM (Level 1B Calibrated Radiances - 1km; DOI: 10.5067/MODIS/MOD021KM.061) and MOD03 (Geolocation - 1km; DOI: 10.5067/MODIS/MOD03.061) products\r\n•\tESA Land Cover CCI project team; Defourny, P. (2019): ESA Land Cover Climate Change Initiative (Land_Cover_cci): Global Land Cover Maps, Version 2.0.7. Centre for Environmental Data Analysis, 14.11.2025. https://catalogue.ceda.ac.uk/uuid/b382ebe6679d44b8b0e68ea4ef4b701c\r\n•\tHansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, and J. R. G. Townshend. 2013. “High-Resolution Global Maps of 21st-Century Forest Cover Change.” Science 342 (15 November): 850–53. Data available on-line from: http://earthenginepartners.appspot.com/science-2013-global-forest. \r\n•\tGlobal auxiliary layers prepared by ENVEO: \r\n•\tpermanent snow and ice area and water mask based on Land Cover map v2.0.7 from 2000 and salt lake mask manually mapped from MODIS data (v2.0, 2025-04-03) \r\n•\tspectral reflectance layers for snow free ground (v4, 2025-05-24) and snow free forest (v4, 2025-05-24), \r\n•\tNormalized Difference Snow Index (NDSI) threshold map (v5.0, 2025-03-19),\r\n•\ttransmissivity map based on tree canopy cover v1.4 for year 2000 (Hansen et al., 2013) and Land Cover map v2.0.7 for year 2000 (v01, 2021-10-01)\r\n\r\nOutput description:",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 45119,
            "uuid": "c7c98daad12c47e884ace9a30e433800",
            "title": "ESA Snow Climate Change Initiative: Derivation of SCFG SLSTR v1.0 product.",
            "abstract": "The SCFG product is based on Sea and Land Surface Temperature Radiometer (SLSTR) data on-board the Sentinel-3A and Sentinel-3B satellites.\r\n\r\nThe retrieval method of the snow_cci SCFG product from SLSTR data has been developed and improved by ENVEO (ENVironmental Earth Observation IT GmbH) based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module developed by ENVEO. For the SCFG product generation from SLSTR, multiple reflective and emissive spectral bands are used. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm (SCDA) (Metsämäki et al., 2015). For all remaining pixels, the snow_cci SCFG retrieval method is applied, using spectral bands centred at about 0.55 µm and 1.6 µm, and an emissive band centred at about 11 µm. The snow_cci snow cover mapping method is a two-step approach that first identifies pixels that are largely snow free, followed by SCFG retrieval for remaining pixels. \r\n\r\nThe main differences of the snow_cci snow cover mapping algorithm compared to the GlobSnow algorithm described in Metsämäki et al. (2015) are (i) improvements of the cloud screening approach applicable on a global scale, (ii) the pre-classification of snow free areas on global land areas, (iii) the usage of spatially variable snow free ground reflectance and snow free forest reflectance maps instead of global constant values, (iv) the update of the constant value for wet snow based on analyses of spatially distributed reflectance time series of MODIS data, and (v) the usage of a global forest canopy transmissivity based on tree canopy cover of the year 2000 from Hansen et al. (2013) and forest type layers from Land Cover CCI (Defourny, 2019) of the year 2000. \r\n\r\nThe retrieval approach ensures consistency between the SCFG CRDP v1.0 and the Snow Cover Fraction Viewable from above (SCFV) CRDP 1.0 from SLSTR data (https://catalogue.ceda.ac.uk/uuid/f5dce1f7bec2447093cf460a4d3ba2c2) In non-forested areas, the SCFG and SCFV estimations from SLSTR data are the same.\r\n\r\nPermanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the pixel spacing of the SCFG product. Water areas are masked if more than 30 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map. Salt lakes are masked based on manual delineation from Terra MODIS data. \r\n\r\nThe product uncertainty for observed land pixels is provided as unbiased root mean square error (RMSE) per pixel in the ancillary variable. \r\n\r\nSCFG products and associated layers from individual SLSTR frames from Sentinel-3A and Sentinel-3B satellites are merged into daily global SCFG products.\r\n\r\nEach daily product contains additionally the sensor zenith angle per pixel in degree, and the acquisition time per pixel referring to the scan line time of the SLSTR frame used for the classification.\r\n\r\nInput description:\r\n•\tSentinel-3A SLSTR L1B and Sentinel-3B SLSTR L1B data (SL_1_RBT), NTC products, baseline collection 4.\r\n•\tESA Land Cover CCI project team; Defourny, P. (2019): ESA Land Cover Climate Change Initiative (Land_Cover_cci): Global Land Cover Maps, Version 2.0.7. Centre for Environmental Data Analysis, 14.11.2025. https://catalogue.ceda.ac.uk/uuid/b382ebe6679d44b8b0e68ea4ef4b701c\r\n•\tHansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, and J. R. G. Townshend. 2013. “High-Resolution Global Maps of 21st-Century Forest Cover Change.” Science 342 (15 November): 850–53. Data available on-line from: http://earthenginepartners.appspot.com/science-2013-global-forest. \r\n•\tGlobal auxiliary layers prepared by ENVEO: \r\n•\tpermanent snow and ice area and water mask based on Land Cover map v2.0.7 from 2000 and salt lake mask manually mapped from MODIS data (v2.0, 2025-04-03) \r\n•\tspectral reflectance layers for snow free ground (v4, 2025-05-23) and snow free forest (v4, 2025-05-23), \r\n•\tNormalized Difference Snow Index (NDSI) threshold map (v5.0, 2025-03-19),\r\n•\ttransmissivity map based on tree canopy cover v1.4 for year 2000 (Hansen et al., 2013) and Land Cover map v2.0.7 for year 2000 (v01, 2021-10-01)\r\n\r\nOutput description:\r\nDaily global Snow Cover Fraction products including uncertainty estimation, sensor zenith angle and acquisition time per pixel",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 45120,
            "uuid": "1738cfe71a9d4e3e8d75d1cf943fa047",
            "title": "ESA Snow Climate Change Initiative: Derivation of SCFV SLSTR v1.0 product.",
            "abstract": "The SCFV product is based on Sea and Land Surface Temperature Radiometer (SLSTR) data on-board the Sentinel-3A and Sentinel-3B satellites.\r\n\r\nThe retrieval method of the snow_cci SCFV product from SLSTR data has been developed and improved by ENVEO (ENVironmental Earth Observation IT GmbH) to provide consistent snow cover fraction estimations with the Snow Cover Fraction on Ground (SCFG) product (https://catalogue.ceda.ac.uk/uuid/38a71d034b5c4097821de29ee3bc2498/) which is based on an enhanced version of the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module developed by ENVEO. For the SCFV product generation from SLSTR, multiple reflective and emissive spectral bands are used. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm (SCDA) (Metsämäki et al., 2015). For all remaining pixels, the snow_cci SCFV retrieval method is applied, using spectral bands centred at about 0.55 µm and 1.6 µm, and an emissive band centred at about 11 µm. The snow_cci snow cover mapping method is a two-step approach that first identifies pixels that are largely snow free, followed by SCFV retrieval for remaining pixels. \r\n\r\nSpatially variable background reflectance and forest reflectance maps and a constant value for the spectral reflectance of wet snow are used for the SCFV retrieval approach. In non-forested areas, the SCFV and SCFG estimations from SLSTR data are the same. \r\n\r\nPermanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the pixel spacing of the SCFV product. Water areas are masked if more than 30 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map. Salt lakes are masked based on manual delineation from Terra MODIS data. \r\n\r\nThe product uncertainty for observed land pixels is provided as unbiased root mean square error (RMSE) per pixel in the ancillary variable. \r\n\r\nSCFV products and associated layers from individual SLSTR frames from Sentinel-3A and Sentinel-3B satellites are merged into daily global SCFV products.\r\n\r\nEach daily product contains additionally the sensor zenith angle per pixel in degree, and the acquisition time per pixel referring to the scan line time of the SLSTR frame used for the classification.\r\n\r\nInput Description:\r\n•\tSentinel-3A SLSTR L1B and Sentinel-3B SLSTR L1B data (SL_1_RBT), NTC products, baseline collection 4.\r\n•\tESA Land Cover CCI project team; Defourny, P. (2019): ESA Land Cover Climate Change Initiative (Land_Cover_cci): Global Land Cover Maps, Version 2.0.7. Centre for Environmental Data Analysis, 14.11.2025. https://catalogue.ceda.ac.uk/uuid/b382ebe6679d44b8b0e68ea4ef4b701c\r\n•\tHansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, and J. R. G. Townshend. 2013. “High-Resolution Global Maps of 21st-Century Forest Cover Change.” Science 342 (15 November): 850–53. Data available on-line from: http://earthenginepartners.appspot.com/science-2013-global-forest. \r\n•\tGlobal auxiliary layers prepared by ENVEO: \r\n•\tpermanent snow and ice area and water mask based on Land Cover map v2.0.7 from 2000 and salt lake mask manually mapped from MODIS data (v2.0, 2025-04-03) \r\n•\tspectral reflectance layers for snow free ground (v4, 2025-05-23) and snow free forest (v4, 2025-05-23), \r\n•\tNormalized Difference Snow Index (NDSI) threshold map (v5.0, 2025-03-19),\r\n•\ttransmissivity map based on tree canopy cover v1.4 for year 2000 (Hansen et al., 2013) and Land Cover map v2.0.7 for year 2000 (v01, 2021-10-01)\r\n\r\nOutput Description:\r\nDaily global Snow Cover Fraction products including uncertainty estimation, sensor zenith angle and acquisition time per pixel",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 45146,
            "uuid": "4a53771be2af4e5e960e5942eb155bb6",
            "title": "Derivation of the ESA Climate Change Initiative River Discharge Water Level product, v2.0",
            "abstract": "For information on the derivation of the Water Level dataset see the project documentation \r\n(https://climate.esa.int/projects/river-discharge)",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 45155,
            "uuid": "3bc2778e31e245b599f3c4d208e1aae2",
            "title": "WM-Air model",
            "abstract": "ADMS-Urban is a quasi-Gaussian plume air dispersion model. ADMS-Urban explicitly represents the full range of source types occurring in an urban area at high resolution (industry, transport and diffuse sources); the model is able to account for the influence of complex urban morphology (building density, street canyons) on dispersion and generates street-scale resolution maps that highlight both pollution hotspots and areas of better air quality.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 45158,
            "uuid": "a210d4aa862246ec96773b0d04761da3",
            "title": "ESA Precursors for Aerosol and Ozone Climate Change Initiative: Derivation of Merged CO v1.0 product.",
            "abstract": "An 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 nighttime from January 2008 to December 2024. Surface altitude grids as well as data source flags grids are also provided.\r\n\r\nThe version number is 1.0. Data are available in NetCDF format.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 45170,
            "uuid": "6b1d65fa55984f8d8dea315c9e19bc9f",
            "title": "Derivation of the 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": "For more information on the retrieval algorithm used see the documentation on the LST CCI webpage:  https://climate.esa.int/en/projects/land-surface-temperature/",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 45172,
            "uuid": "58962da25319475cb57ad9641bb05ef2",
            "title": "Derivation of the 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 1.10",
            "abstract": "For more information on the retrieval algorithm used see the documentation on the LST CCI webpage:  https://climate.esa.int/en/projects/land-surface-temperature/",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 45212,
            "uuid": "f449d947b30e44319cac24b7f67565c5",
            "title": "Recent Heat Packs methodology",
            "abstract": "The Recent Heat Packs comprise a 2-page .pdf factsheet and 3 .csv files containing daily minimum/maximum temperatures and climate metrics, for each of the 393 UK local authorities. The Recent Heat Packs were built entirely from the Crowd-Grid gridded dataset. The metrics were calculated at 1km grid-box level and then averaged over the local authority and census areas shapes (MSOAs for England & Wales, IZ for Scotland, or DEA in NI). They are intended to make the information in the Crowd-Grid gridded dataset available in a more accessible form. These packs have been designed to complement the Met Office Local Authority Climate Service (https://climatedataportal.metoffice.gov.uk/pages/lacs) which provides considerable information about future climate.\r\n\r\nAn accompanying README describes the sources, processing, interpretation, and guidance for proper use of the Recent Heat Packs (doi:10.5281/zenodo.17787357)",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 45213,
            "uuid": "43a33d5a11c44c0ea744391c8a23ac41",
            "title": "Crowd-Grid gridded climate observations methodology",
            "abstract": "The gridded dataset uses observations from the Met Office's Weather Observation Website (WOW) and other sources, in addition to observations from official Met Office stations, to give a more detailed view of the temperatures people experience, including in built-up areas. Regression and inverse-distance weighted interpolation are used to generate values on a regular 1km grid from the irregularly spaced observations, taking into account factors such as latitude and longitude, altitude, coastal influence, and urban land use.\r\n\r\nThe methods used are an adaptation of the foundational methods described in Hollis et al. (2019) doi:10.1002/gdj3.78. For details on the modifications, refer to Mitchell & Fry (2024) doi:10.1002/joc.8390. An accompanying README describes the sources, processing, interpretation, and guidance for proper use of the grids (doi:10.5281/zenodo.17787357)",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 45227,
            "uuid": "7b19d7110d1344dea40af8fb6505320a",
            "title": "Derivation of the ESA Land Surface Temperature Climate Change Initiative (LST_cci): 3-Hourly Multisensor Infra-Red (IR) Low Earth Orbit (LEO) and Geostationary Earth Orbit (GEO) Land surface temperature (LST) level 3 supercollated (L3S) global product, version 3.00",
            "abstract": "For more information on the retrieval algorithm used see the documentation on the LST CCI webpages: https://climate.esa.int/en/projects/land-surface-temperature/",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 45234,
            "uuid": "5d30c46f88c740e6837ee30a7263d1b8",
            "title": "Mars Planetary Climate Model (Mars-PCM): Mars_PCM-UK-spectral version",
            "abstract": "The Mars Planetary Climate Model (Mars-PCM) is maintained by the Laboratoire de Météorologie Dynamique at Sorbonne Université in Paris, France. The version of the model used to produce the MACDA dataset in the CEDA archive is the UK spectral version, which is developed by the Oxford University and the Open University in the UK. The Analysis Correction scheme used to assimilate spacecraft observations in the Mars_PCM-UK-spectral model was originally developed at the UK Met Office and implemented in the model at the Oxford University and The Open University.",
            "keywords": "MACDA",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 45266,
            "uuid": "88d080fefc274ddf848320a1171e8419",
            "title": "Computation for the ESA Precursors for Aerosols and Ozone Climate Change Initiative TROPOMI monthly mean level 3 tropospheric nitrogen dioxide (NO2) version 1.0 May 2018- December 2021",
            "abstract": "TROPOspheric Monitoring Instrument (TROPOMI) global tropospheric nitrogen dioxide (NO2) spatiotemporally averaged per month over a standard grid from May 2018 to December 2021. The dataset is provided in four different spatial resolutions: 0.2x0.2 (900x1800 grid cells), 0.5x0.5 (360x720), 1x1 (180x360), 2x2.5 (91x144) and includes auxilliary variables (e.g. cloud and surface properties, propagated level 3 uncertainties, averaging kernels). The data are provided in monthly netCDF files for each spatial resolution.\r\nL3 data were generated by spatiotemporally averaging operational TROPOMI L2 NO2 v2.3.1 data as part of the ESA CCI precursors project.",
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            "inputDescription": null,
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            "identifier_set": []
        },
        {
            "ob_id": 45271,
            "uuid": "a078f93f707542f5b5833c54095a806d",
            "title": "Computation process of the gridded ammonia (NH3) monthly L3 data from IASI/Metop-A",
            "abstract": "This long-term dataset has been created from the ANNI version 4 NH3 L2 data retrieved from IASI/Metop-A data. Detailed information on the L2 algorithm can be found in Clarisse et al. (2023). Detailed information on the L3 algorithm can be found in the Algorithm Theoretical Basis Document.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 45274,
            "uuid": "d31f7097bd724286a3c170a13f46b234",
            "title": "Computation of the gridded ammonia (NH3) monthly L3 data from IASI/Metop-B.",
            "abstract": "This long-term dataset has been created from the ANNI version 4 NH3 L2 data retrieved from IASI/Metop-B data. Detailed information on the L2 algorithm can be found in Clarisse et al. (2023). Detailed information on the L3 algorithm can be found in the Algorithm Theoretical Basis Document.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 45277,
            "uuid": "edbf03e6afb74a9594ba91062b2eaaa6",
            "title": "Computation of the gridded ammonia (NH3) monthly L3 data from IASI/Metop-C.",
            "abstract": "This long-term dataset has been created from the ANNI version 4 NH3 L2 data retrieved from IASI/Metop-C data. Detailed information on the L2 algorithm can be found in Clarisse et al. (2023). Detailed information on the L3 algorithm can be found in the Algorithm Theoretical Basis Document.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 45280,
            "uuid": "a70005a91d5642a78a8342ca01b2ad9c",
            "title": "Computation of the gridded ammonia (NH3) monthly L3 data from IASI/Metop-A/B/C",
            "abstract": "This long-term dataset has been created from the ANNI version 4 NH3 L2 data retrieved from IASI/Metop-A/B/C data. Detailed information on the L2 algorithm can be found in Clarisse et al. (2023). Detailed information on the L3 algorithm can be found in the Algorithm Theoretical Basis Document.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 45284,
            "uuid": "10a684056adb42d8b3f8e04f310ba9db",
            "title": "Derivation of ESA Sea State Climate Change Initiative (Sea_State_cci): Global remote sensing multi-mission along-track significant wave height from altimetery,  version 4 datasets",
            "abstract": "For information on the derivation of the Sea_State_cci Global remote sensingmulti-mission along-track significant wave height from altimetery version 4 products please see the product user guide.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 45293,
            "uuid": "8a5d84c459c544189c12f7cb6d0fc0e5",
            "title": "Computation of ESA Precursors for Aerosols and Ozone Climate Change Initiative (Precursors_cci): monthly L3 HCHO from TROPOMI, version 2.0",
            "abstract": "The Formaldehyde(HCHO) Climate Data Record (CDR) product is a Level 3 HCHO product developed by using satellite data from the TROPOMI instrument (on S5P) as part of the ESA Climate Change Initiative (CCI) Precursors for Aerosols and Ozone project.\r\nThis dataset provides gridded HCHO tropospheric column densities of monthly 0.125°x0.125° resolution grids from May 2018 to December 2024.\r\n\r\nCompared to the operational TROPOMI product, the air mass factors have been reprocessed using an update albedo climatology, and the CAMS Reanalysis Model for the a priori vertical profiles. The background correction and the quality values have also been updated.\r\n\r\nIn addition to the main product results, such as HCHO slant column, vertical column and air mass factor, the Level 3 data files contain several additional parameters and diagnostic information such as uncertainties, a priori profiles and averaging kernels.",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        },
        {
            "ob_id": 45667,
            "uuid": "1d1aef5dbdf24499b20793f6464b9649",
            "title": "UoL_FP: University of Leicester Full-Physics retrieval algorithm for retrieval of SIF",
            "abstract": "",
            "keywords": "",
            "inputDescription": null,
            "outputDescription": null,
            "softwareReference": null,
            "identifier_set": []
        }
    ]
}