Observation List

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            "title": "CRU JRA v2.5: A forcings dataset of gridded land surface blend of Climatic Research Unit (CRU) and Japanese reanalysis (JRA) data; Jan.1901 - Dec.2023.",
            "abstract": "The CRU JRA V2.5 dataset is a 6-hourly, land surface, gridded time series of ten meteorological variables produced by the Climatic Research Unit (CRU) at the University of East Anglia (UEA), and is intended to be used to drive models. The variables are provided on a 0.5 degree latitude x 0.5 degree longitude grid, the grid is near global but excludes Antarctica (this is the same as the CRU TS grid, though the set of variables is different). The data are available at a 6 hourly time-step from January 1901 to December 2023.\r\n\r\nThe dataset is constructed by regridding data from the Japanese Reanalysis data (JRA) produced by the Japanese Meteorological Agency (JMA), adjusting where possible to align with the CRU TS 4.08 data (see the Process section and the ReadMe file for full details).\r\n\r\nThe CRU JRA data consists of the following ten meteorological variables: 2-metre temperature, 2-metre maximum and minimum temperature, total precipitation, specific humidity, downward solar radiation flux, downward long wave radiation flux, pressure and the zonal and meridional components of wind speed (see the ReadMe file for further details).\r\n\r\nThe CRU JRA dataset is intended to be a replacement of the CRU NCEP forcing dataset. The CRU JRA dataset follows the style of Nicolas Viovy's original CRU NCEP dataset rather than that which is available from UCAR. A link to the CRU NCEP documentation for comparison is provided in the documentation section. \r\nThis version of CRUJRA, v2.5 (1901-2023) is, where possible, adjusted to align with CRU TS monthly means or totals. A consequence of this is that, if CRU TS changes, then CRUJRA changes.\r\n\r\nFor this version, and version 4.07 of CRU TS, the CLD (cloud cover, %) variable is now actualised (converted from gridded anomalies) using the original CLD climatology and not the revised climatology introduced last year. This change/reversion is summarised here: https://crudata.uea.ac.uk/cru/data/hrg/cru_cl_1.1/Read_Me_CRU_CL_CLD_Reversion.txt\r\n\r\nSince CLD is used to align DSWRF, CRUJRA Downward Short Wave Radiation Flux (DSWRF) will now be 'closer to' version 2.2 and earlier and should be used in preference to v2.3.\r\n\r\nIf this dataset is used in addition to citing the dataset as per the data citation string users must also cite the following:\r\n\r\nHarris, I., Osborn, T.J., Jones, P. et al. Version 4 of the CRU TS\r\nmonthly high-resolution gridded multivariate climate dataset.\r\nSci Data 7, 109 (2020). https://doi.org/10.1038/s41597-020-0453-3\r\n\r\nHarris, I., Jones, P.D., Osborn, T.J. and Lister, D.H. (2014), Updated\r\nhigh-resolution grids of monthly climatic observations - the CRU TS3.10\r\nDataset. International Journal of Climatology 34, 623-642.\r\n\r\nKobayashi, S., et. al., The JRA-55 Reanalysis: General Specifications and\r\nBasic Characteristics. J. Met. Soc. Jap., 93(1), 5-48\r\nhttps://dx.doi.org/10.2151/jmsj.2015-001",
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                "startTime": "1901-01-01T00:00:00",
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                "title": "Climatic Research Unit (CRU) procedure to produce the CRU JRA v2.5 data.",
                "abstract": "The CRU JRA (Japanese reanalysis) data is a replacement to the CRU NCEP dataset, CRU JRA data follows the style of Nicolas Viovy's original dataset rather than that which is available from UCAR.\r\n\r\nThe CRU JRA dataset is based on the JRA-55 reanalysis dataset and aligned where appropriate with the CRU TS dataset version 4.08 (1901-2023).\r\n\r\nAll JRA variables are regridded from their native TL319 Gaussian grid to the CRU regular 0.5° x 0.5° grid, using the g2fsh spherical harmonics routine from NCL (NCAR Command Language), based on the 'Spherepack' code. The exception is precipitation, which is regridded using ESMF 'nearest neighbour': all other algorithms tried exhibited unwanted artifacts.\r\n\r\nThe JRA-55 reanalysis dataset starts in 1958. The years 1901-1957 are constructed using randomly-selected years between 1958 and 1967. Where alignment with CRU TS occurs, the relevant CRU TS data is used.\r\n\r\nOf the ten variables listed above, the last four do not have analogs in the CRU TS dataset. These are simply regridded, masked for land only, and output as CRUJRA. The other six are aligned with CRU TS as follows:\r\n\r\nTMP is aligned with CRU TS TMP. A monthly mean for the JRA data is\r\ncalculated and compared with the equivalent CRU TS mean. The difference\r\nbetween the means is added to every JRA value.\r\n\r\n---\r\n\r\nTMAX and TMIN are aligned with CRUJRA TMP and CRU TS DTR. Firstly, at\r\neach time step, the TMAX-TMP-TMIN triplets are checked and adjusted so\r\nthat TMAX is always >= TMP, and TMIN is always <= TMP. This triplet\r\nalignment is prioritised above DTR alignment. Secondly, monthly JRA DTR\r\nis calculated by first establishing the daily maxima and minima (max and\r\nmin of the subdaily values in TMAX and TMIN respectively), then monthly\r\nmaxima and minima, (means of the daily DTR values), giving JRA monthly\r\nDTR. This is compared with CRU TS DTR and the fractional difference\r\n(factor) calculated as (CRU TS DTR) / (JRA monthly DTR). This factor is\r\nthen used to adjust the DTR of each pair of subdaily TMAX and TMIN\r\nvalues, though not if the triplet alignment would be broken.\r\n\r\n---\r\n\r\nPRE is aligned with CRU TS PRE and WET (rain day counts). Firstly, the\r\nmonthly total precipitation is calculated for JRA and compared to CRU TS\r\nPRE; an adjustment factor is acquired (crupre/jrapre) and all values\r\nadjusted. Precipitation amounts are now aligned at a monthly level, and\r\nthis alignment is prioritised above WET alignment. Secondly, the number\r\nof rain days is calculated for JRA: a day is declared wet if the total\r\nprecipitation is equal to, or exceeds, 0.1mm (the same threshold as CRU\r\nTS WET). If JRA has more wet days than CRU TS, then the driest of those\r\nare reduced to a random amount below 0.1 (an adjustment factor is\r\ncalculated and applied to each time step, to preserve the subdaily\r\ndistribution). If JRA has fewer wet days than CRU TS, then sufficient\r\ndry days are set to a random amount equal to or closely above 0.1mm,\r\nagain using an adjustment factor to preserve the subdaily distribution. \r\nWhere wet day alignment threatens precipitation alignment, the process\r\nis abandoned and the cell/month reverts to the previously-aligned\r\nprecip version. Exception handling is very complicated and cannot be\r\nsummarised here.\r\n\r\n---\r\n\r\nSPFH is aligned with CRU TS VAP. VAP is converted to SPFH, and JRA mean\r\nmonthly SPFH is calculated. The fractional difference (factor) is\r\ncalculated as (CRU TS SPFH) / (JRA monthly SPFH), this factor is then\r\napplied to the JRA subdaily humidity values.\r\n\r\n---\r\n\r\nDSWRF is aligned with CRU TS CLD. CLD is converted to shortwave\r\nradiation, and JRA mean monthly DSWRF is calculated. The fractional\r\ndifference (factor) is calculated as (CRU TS SWR) / (JRA monthly DSWRF),\r\nthis factor is then applied to the JRA subdaily radiation values.\r\n\r\n---\r\n\r\nWhere appropriate, CRUJRA values are kept within physically-appropriate\r\nconstraints (such as negative precipitation), which could result from\r\nregridding as well as adjustments."
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                    "title": "CRU JRA: Collection of CRU JRA forcing datasets of gridded land surface blend of Climatic Research Unit (CRU) and Japanese reanalysis (JRA) data.",
                    "abstract": "This is a collection of the University of East Anglia Climatic Research Unit (CRU) Japanese Reanalysis (JRA) data. The CRU JRA data are 6-hourly, land surface, gridded time series of ten meteorological variables produced by the Climatic Research Unit (CRU) at the University of East Anglia (UEA), and is intended to be used to drive models.\r\n\r\nThe dataset is constructed by combining data from the Japanese Reanalysis data produced by the Japanese Meteorological Agency (JMA) and adjusted where possible to align with the CRU TS data (these 'ten meteorological variables' are not the same ten available from CRU TS).\r\n\r\nThe CRU JRA dataset is intended to be a replacement of the CRUNCEP forcing dataset. The CRU JRA dataset follows the style of Nicolas Viovy's original CRUNCEP dataset rather than that which is available from UCAR."
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            "title": "FAFMIP_HadCM3_HadGEM2-ES",
            "abstract": "Data from HadCM3 and HadGEM2 in supporting the Thresholds for the future of the Greenland ice-sheet project (grant NE/P014976/1)",
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            "title": "CRU TS4.08: Climatic Research Unit (CRU) Time-Series (TS) version 4.08 of high-resolution gridded data of month-by-month variation in climate (Jan. 1901- Dec. 2023)",
            "abstract": "The gridded Climatic Research Unit (CRU) Time-series (TS) data version 4.08 data are month-by-month variations in climate over the period 1901-2023, provided on high-resolution (0.5x0.5 degree) grids, produced by CRU at the University of East Anglia and funded by the UK National Centre for Atmospheric Science (NCAS), a NERC collaborative centre.\r\n\r\nThe CRU TS4.08 variables are cloud cover, diurnal temperature range, frost day frequency, wet day frequency, potential evapotranspiration (PET), precipitation, daily mean temperature, monthly average daily maximum and minimum temperature, and vapour pressure for the period January 1901 - December 2023.\r\n\r\nThe CRU TS4.08 data were produced using angular-distance weighting (ADW) interpolation. All versions prior to 4.00 used triangulation routines in IDL. Please see the release notes for full details of this version update. \r\n\r\nThe CRU TS4.08 data are monthly gridded fields based on monthly observational data calculated from daily or sub-daily data by National Meteorological Services and other external agents. The ASCII and NetCDF data files both contain monthly mean values for the various parameters. The NetCDF versions contain an additional integer variable, ’stn’, which provides, for each datum in the main variable, a count (between 0 and 8) of the number of stations used in that interpolation. The missing value code for 'stn' is -999.\r\n\r\nAll CRU TS output files are actual values - NOT anomalies.",
            "creationDate": "2022-07-22T09:15:57.183554",
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            "dataLineage": "The CRU TS data are produced by the Climatic Research Unit (CRU) at the University of East Anglia and are passed to the Centre for Environmental Data Analysis (CEDA) for long-term archival and distribution. Previous releases of the CRU TS data include:\r\nCRU TS 4.08 was provided to CEDA for archival in July 2024.\r\n\r\nCRU TS 4.07 was provided to CEDA for archival in June 2023.\r\n\r\nCRU TS 4.06 was provided to CEDA for archival in May 2022.\r\n\r\nCRU TS 4.05 was provided to CEDA for archival in June 2021.\r\n\r\nCRU TS 4.04 was provided to CEDA for archival in April 2020.\r\n\r\nCRU TS 4.03 was provided to CEDA for archival in May 2019. \r\n\r\nCRU TS 4.02 was provided to CEDA for archival in December 2018. \r\n\r\nCRU TS 4.01 was provided to CEDA for archival in September 2017. \r\n\r\nCRU TS 4.00 was provided to CEDA for archival in March 2017. \r\n\r\nCRU TS 3.24.01  was provided to CEDA for archival in January 2017. This is the latest version available and is a replacement for the withdrawn dataset 3.24, it supersedes all previous data versions (which are available to allow user comparisons)\r\n\r\nCRU TS 3.24 was provided to CEDA for archival in July 2016. This is the latest version available, superseding all previous data versions (which are available to allow user comparisons), v3.24 has been withdrawn.\r\n\r\nCRU TS 3.23 was provided to CEDA in October 2015 by CRU. This is the latest version available, superseding all previous data versions (which are available to allow user comparisons).\r\n\r\nCRU TS 3.22 was provided to CEDA for archival in July 2014 by CRU.\r\n\r\nCRU TS 3.21 was provided to CEDA for archival in July 2013 by CRU.\r\n\r\nCRU TS 3.20 was produced in December 2012.\r\nIn March 2013, CRU TS observation databases for TMP and PRE variables were provided by CRU. Others are in preparation. In July 2013, two errors were found in the PRE and WET variables of CRU TS v3.20. These have been repaired in CRU TS v3.21. Details of the errors found are available in the Release Notes in the archive.\r\n\r\nCRU TS 3.10.01 In July 2012, systematic errors were discovered in the CRUTS v3.10 process. The effect was, in some cases, to reduce the gridded values for PRE and therefore WET. Values of FRS were found to be unrealistic in some areas due to the algorithms used for synthetic generation. The files (pre, frs and wet) were immediately removed from BADC. The corrected run for precipitation, based on the v3.10 precipitation station data, was generated as a direct replacement and given the version number 3.10.01. There were no corrected runs produced for wet and frs.\r\n\r\nCRU TS 3.00 data files acquired directly from CRU in 2007. CRU provided the BADC with software to generate the CRU datasets in 2010, and this was used to produce CRU TS 3.10 at the BADC in early 2011.",
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            "abstract": "Manx shearwaters' foraging movements were tracked during the breeding seasons of 2022 and 2023 from two colonies: Bardsey (North Wales) and Copeland (Northern Ireland). 247 tracking sessions were recorded in total, with 57 and 40 shearwaters tracked from Bardsey and Copeland respectively in 2022, and 63 and 62 shearwaters tracked from Bardsey and Copeland respectively in 2023. Tracks were recorded using AxyTrek tags produced by TechnoSmart. Tags recorded location (latitude/longitude), tri-axial acceleration, and pressure, which has been converted to depth (in metres). Data were generated by converting files downloaded from the loggers using the tag manufacturer's software. Data were collected between May and August each year (during the breeding season for Manx shearwaters).  Breeding Manx shearwaters were captured at their nests and equipped with Axy-Trek loggers, attached to birds' back feathers using TESA tape.",
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                "abstract": "The precipitation estimates from the various precipitation-relevant satellite passive microwave (PMW) sensors comprising the GPM constellation are computed using the 2017 version of the Goddard Profiling Algorithm (GPROF2017), then gridded, intercalibrated to the GPM Combined Ku Radar-Radiometer Algorithm (CORRA) product, and merged into half-hourly 0.1°x0.1° (roughly 10x10 km) fields.\r\n\r\nVersion 07B is the current version of the IMERG data sets. Older versions will no longer be available and have been superseded by Version 07. The Integrated Multi-satellitE Retrievals for GPM (IMERG) is the unified U.S. algorithm that provides the multi-satellite precipitation product for the U.S. GPM team. The precipitation estimates from the various precipitation-relevant satellite passive microwave (PMW) sensors comprising the GPM constellation are computed using the 2021 version of the Goddard Profiling Algorithm (GPROF2021), then gridded, intercalibrated to the GPM Combined Ku Radar-Radiometer Algorithm (CORRA) product, and merged into half-hourly 0.1°x0.1° (roughly 10x10 km) fields. Note that CORRA is adjusted to the monthly Global Precipitation Climatology Project (GPCP) Satellite-Gauge (SG) product over high-latitude ocean to correct known biases. The half-hourly intercalibrated merged PMW estimates are then input to both a Morphing-Kalman Filter (KF) Lagrangian time interpolation scheme based on work by the Climate Prediction Center (CPC) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) Dynamic Infrared–Rain Rate (PDIR) re-calibration scheme. In parallel, CPC assembles the zenith-angle-corrected, intercalibrated merged geo-IR fields and forwards them to PPS for input to the PERSIANN-CCS algorithm (supported by an asynchronous re-calibration cycle) which are then input to the KF morphing (quasi-Lagrangian time interpolation) scheme. The KF morphing (supported by an asynchronous KF weights updating cycle) uses the PMW and IR estimates to create half-hourly estimates. Motion vectors for the morphing are computed by maximizing the pattern correlation of successive hours within each of the precipitation (PRECTOT), total precipitable liquid water (TQL), and vertically integrated vapor (TQV) data fields provided by the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) and Goddard Earth Observing System model Version 5 (GEOS-5) Forward Processing (FP) for the post-real-time (Final) Run and the near-real-time (Early and Late) Runs, respectively. The vectors from PRECTOT are chosen if available, else from TQL, if available, else from TQV. The KF uses the morphed data as the “forecast” and the IR estimates as the “observations”, with weighting that depends on the time interval(s) away from the microwave overpass time. The IR becomes important after about ±90 minutes away from the overpass time. Variable averaging in the KF is accounted for in a routine (Scheme for Histogram Adjustment with Ranked Precipitation Estimates in the Neighborhood, or SHARPEN) that compares the local histogram of KF morphed precipitation to the local histogram of forward- and backward-morphed microwave data and the IR. The IMERG system is run twice in near-real time: \"Early\" multi-satellite product ~4 hr after observation time using only forward morphing and \"Late\" multi-satellite product ~14 hr after observation time, using both forward and backward morphing and once after the monthly gauge analysis is received: \"Final\", satellite-gauge product ~4 months after the observation month, using both forward and backward morphing and including monthly gauge analyses. In V07, the near-real-time Early and Late half-hourly estimates have a monthly climatological concluding calibration based on averaging the concluding calibrations computed in the Final, while in the post-real-time Final Run the multi-satellite half-hourly estimates are adjusted so that they sum to the Final Run monthly satellite-gauge combination. In all cases the output contains multiple fields that provide information on the input data, selected intermediate fields, and estimation quality. In general, the complete calibrated precipitation, precipitation, is the data field of choice for most users. Briefly describing the Final Run, the input precipitation estimates computed from the various satellite passive microwave sensors are intercalibrated to the CORRA product (because it is presumed to be the best snapshot TRMM/GPM estimate after adjustment to the monthly GPCP SG), then \"forward/backward morphed\" and combined with microwave precipitation-calibrated geo-IR fields, and adjusted with seasonal GPCP SG surface precipitation data to provide half-hourly and monthly precipitation estimates on a 0.1°x0.1° (roughly 10x10 km) grid over the globe. Precipitation phase is a diagnostic variable computed using analyses of surface temperature, humidity, and pressure. The current period of record is June 2000 to the present (delayed by about 4 months). The Integrated Multi-Satellite Retrievals for GPM (IMERG) algorithm is designed to leverage the international constellation of precipitation-relevant satellites to create a long record of uniformly time/space gridded precipitation estimates for the globe. The algorithm is focused on creating the best estimate at each time step, meaning that it is not a Climate Data Record, although the ideal is as homogenous a record as possible"
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            "title": "ESA Fire Climate Change Initiative (Fire_cci): Long-term Small Fire Dataset (SFDL) Burned Area pixel product for Test Sites: Amazonia, Africa and Siberia, version 1.0",
            "abstract": "The ESA Fire Disturbance Climate Change Initiative (Fire_cci) project aims to generate burned area developed from satellite observations. The Long-Term Small Fire Dataset (SFDL) pixel products have been obtained using spectral information from Landsat sensors for three study areas located in different parts of the world (Amazon, Sahel and Siberia), and coinciding with the ESA CCI High Resolution Land Cover product.\r\n\r\nThe dataset uses surface reflectance information from the Landsat-4 and Landsat-5 TM, Landsat-7 ETM+ and Landsat-8 OLI sensors, and covers the period 1990 to 2019, with a spatial resolution of 0.00025 degrees (approximately 30 m at the Equator).",
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                    "abstract": "The European Space Agency (ESA)  Fire Climate Change Initiative (Fire CCI) project, led by University of Alcala (Spain), 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 Fire CCI focuses on the following issues relating to Fire Disturbance:  Analysis and specification of scientific requirements relating to climate; Development and improvement of pre-processing and burned area algorithms; Inter-comparison and selection of burned area algorithms; System prototyping and production of burned area datasets; Product validation and product assessment\r\n"
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                    "abstract": "The ESA Fire Climate Change Initiative (Fire_cci) project is producing long-term datasets of burned area information from satellites, as part of the ESA Climate Change Initiative. The data is of use for those interested in historical burned patterns, fire management and emissions analysis and climate change research, by providing a consistent burned area time series.   \r\n\r\nCurrent datasets consist of maps of global burned area for the years 1982 to 2019.  Products are available at different spatial resolutions: the Pixel product (at the original resolution of the sensor data) and the Grid product (0.25 degrees resolution), the latter of which is produced from the Pixel product.  They are based upon spectral information from different sensors, and in many cases also thermal information from active fires.\r\n\r\nGlobal products: \r\n\r\nFireCCI41: Medium Resolution Imaging Spectrometer (MERIS) reflectance, on board the ENVISAT ESA satellite, 300m spatial resolution, and MODIS active fires. Temporal resolution: 2005 – 2011.\r\nFireCCI50: Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance and active fires, on board the TERRA satellite, 250m spatial resolution,  temporal resolution: 2001 – 2016.\r\nFireCCI51: Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance and active fires, on board the TERRA satellite, 250m spatial resolution,  temporal resolution: 2001 – 2019.\r\n\r\nFireCCILT10 (beta product): Advanced Very High Resolution Radiometer (AVHRR) Land Long Term Data Record (LTDR) reflectance. Provided only as grid product. Temporal resolution: 1982-2017.\r\n\r\nContinental products:\r\n\r\nFireCCISFD11: Multispectral Instrument (MSI) reflectance, on board the Sentinel-2A satellite, 20 spatial resolution, and MODIS active fires. Temporal resolution: 2016, spatial coverage: Sub-Saharan Africa."
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            "title": "AATSR: Multimission land and sea surface temperature data, 4th Reprocessing (v4) AT_1_RBT",
            "abstract": "Advanced Along-Track Scanning Radiometer (AATSR) mission was funded jointly by the UK Department of Energy and Climate Change External Link (DECC) and the Australian Department of Innovation, Industry, Science and Research External Link (DIISR).\r\n\r\nThe Envisat AATSR Level 1B Brightness Temperature/Radiance product (RBT) contains top of atmosphere (TOA) brightness temperature (BT) values for the infra-red channels and radiance values for the visible channels, on a 1-km pixel grid. Values for each channel and for the nadir and oblique views occupy separate NetCDF files within the Sentinel-SAFE format, along with associated uncertainty estimates. Additional files contain cloud flags, land and water masks, and confidence flags for each image pixel, as well as instrument and ancillary meteorological information.\r\n\r\nThis AATSR product (AT_1_RBT) in NetCDF format stemming from the 4th AATSR reprocessing, is a continuation of ERS ATSR data and a precursor of Sentinel-3 SLSTR data. It has replaced the former L1B product [ATS_TOA_1P] in Envisat format from the 3rd reprocessing. Users with Envisat-format products are recommended to move to the new Sentinel-SAFE like/NetCDF format products. \r\n\r\nThe 4th reprocessing of Envisat AATSR data was completed in 2022.",
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                    "abstract": "The (A)ATSR (Advanced Along-Track Scanning Radiometer) mission programme was funded jointly by the UK Department of Energy and Climate Change External Link (DECC) and the Australian Department of Innovation, Industry, Science and Research External Link (DIISR).\r\n\r\nThe instrument was built by Astrium, subsequently calibrated and characterised at the Rutherford Appleton Laboratory, and continues the ATSR-1 and ATSR-2 mission data sets of precise sea surface temperature (SST), thereby ensuring the production of a 17 year near-continuous data set from the ERS-1, ERS-2 and ENVISAT missions at the levels of accuracy of 0.3 K or better for climate research.\r\n\r\nThe instrument uses thermal channels at 3.7, 10.8, and 12 microns wavelength; and reflected visible/near infra-red channels at 0.555, 0.659, 0.865, and 1.61 microns wavelength. Level 1b products contain gridded brightness temperature and reflectance. Level 2 products contain land and sea-surface temperature, and NDVI at a range of spatial resolutions. The third reprocessing was done to implement updated algorithms, processors, and auxiliary files. The data were acquired by the European Space Agency's (ESA) Envisat satellite, and the NERC Earth Observation Data Centre (NEODC) mirrors the data for UK users."
                }
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                    "abstract": "Advanced Along-Track Scanning Radiometer (AATSR) mission was funded jointly by the UK Department of Energy and Climate Change  External Link (DECC) and the Australian Department of Innovation, Industry, Science and Research External Link (DIISR).  This dataset collection contains data from the 4th reprocessing of the AATSR data.  This was done to make the data format consistent with that of the later Copernicus Sentinel data.\r\n\r\nThe Envisat AATSR Level 1B Brightness Temperature/Radiance product (AT_1_RBT) contains top of atmosphere (TOA) brightness temperature (BT) values for the infra-red channels and radiance values for the visible channels, on a 1-km pixel grid. Values for each channel and for the nadir and oblique views occupy separate NetCDF files within the Sentinel-SAFE format, along with associated uncertainty estimates. Additional files contain cloud flags, land and water masks, and confidence flags for each image pixel, as well as instrument and ancillary meteorological information.\r\n\r\nThis AATSR product [ENV_AT_1_RBT] in NetCDF format stemming from the 4th AATSR reprocessing, is a continuation of ERS ATSR data and a precursor of Sentinel-3 SLSTR data.  It has replaced the former L1B product [ATS_TOA_1P] in Envisat format from the 3rd reprocessing.  Users with Envisat-format products are recommended to move to the new Sentinel-SAFE like/NetCDF format products.  The 4th reprocessing of Envisat AATSR data was completed in 2022.\r\n\r\nThe instrument uses thermal channels at 3.7, 10.8, and 12 microns wavelength; and reflected visible/near infra-red channels at 0.555, 0.659, 0.865, and 1.61 microns wavelength.  Data is available from 2002 to 2012.\r\n\r\nThe data were acquired by the European Space Agency's (ESA) Envisat satellite, and the Centre for Environmental Data Analysis (CEDA) mirrors the data and allows for access via JASMIN."
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            "abstract": "This dataset contains v4.0 of the Daily Snow Water Equivalent (SWE) product from the ESA Climate Change Initiative (CCI) Snow project, at 0.1 degree resolution.\r\n\r\nSnow water equivalent (SWE) is the depth of liquid water that would result if the of snow cover melted completely, which equates to the snow cover mass per unit area. The SWE product covers the Northern Hemisphere from 1979/01 to 2023/12 with complex terrain, land ice, and large lakes masked. The dataset covers the Northern Hemisphere winter season (October – May; occasional data produced during June and September) at a daily frequency starting in October 1987 and every second day from 1979 to May 1987. Retrievals are not produced for coastal regions of Greenland.\r\n\r\nThe product combines passive microwave data with ground-based snow depth measurements, via Bayesian non-linear iterative assimilation, to estimate SWE. It is based on data 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/) resampled to the 12.5km EASE-Grid 2.0.\r\n\r\nA background snow-depth field, derived from re-gridded snow-depth observations made at synoptic weather stations, and a passive microwave emission model are the key components of the retrieval scheme. Snow density, which varies in both time and space, is parameterized from interpolated in situ observations from snow courses and snow pillows equipped with co-located snow depth sensors.\r\nThe dataset is aimed to serve the needs of users working on climate research and monitoring activities, including the detection of variability and trends, climate modelling, and aspects of hydrology and meteorology.\r\n\r\nThe Finnish Meteorological Institute (FMI) is responsible for the SWE product generation. The SWE development is carried out in collaboration by FMI and Environment and Climate Change Canada (ECCC).\r\n\r\nChanges from v3.1 \r\n\r\nThe time series has been extended from version 3.1 by one year, to 2023. The retrieval algorithm has been modified 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.",
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            "abstract": "This dataset contains Daily Snow Cover Fraction (snow on ground) from AVHRR, produced by the Snow project of the ESA Climate Change Initiative programme. \r\n\r\nSnow cover fraction on ground (SCFG) indicates the area of snow observed from space over land surfaces, in forested areas corrected for the transmissivity of the forest canopy. The SCFG is given in percentage (%) per pixel. \r\n\r\nThe global SCFG product is available at about 5 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets. The coastal zones of Greenland are included.\r\n\r\nThe SCFG time series provides daily products for the period 1979-2023.\r\n \r\nThe product V4.0 is based on EUMETSAT Fundamental Data Record (FDR) medium resolution optical satellite data from the Advanced Very High Resolution Radiometer (AVHRR). Clouds are masked using the CLARA-A3 cloud product. \r\n\r\nThe retrieval method of the snow_cci SCFG product from AVHRR data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module. All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 0.63 µm and 1.61 µm (channel 3a or the reflective part of channel 3b (ref3b)), and an emissive band centred at about 10.8 µm. The snow_cci snow cover mapping algorithm is a three-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFG retrieval method is applied using dynamic reference reflectance values (snow, forest, ground) temporally and spatially adapted to consider angle dependencies (sun, view). Finally, a post-processing removes erroneous snow pixels caused either by falsely classified clouds in the tropics or by unreliable ref3b values at a global scale. \r\n\r\nThe following auxiliary data sets are used for product generation: i) ESA CCI Land Cover from 2000; water bodies and permanent snow and ice areas are masked based on this dataset. Both classes were separately aggregated to the pixel spacing of the SCF product. Water areas are masked if more than 50 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map; ii) Forest canopy transmissivity map; this layer is based on the tree cover classes of the ESA CCI Land Cover 2000 data set and the tree cover density map from Landsat data for the year 2000 (Hansen et al., Science, 2013, DOI: 10.1126/science.1244693). This layer is used to apply a forest canopy correction and estimate in forested areas the fractional snow cover on ground. RMSE is retrieved from a statistical model and added as pixel-wise information. \r\n\r\nThe SCFG product is aimed to serve the needs of users working in cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.\r\n\r\nThe Remote Sensing Research Group of the University of Bern, in cooperation with Gamma Remote Sensing is responsible for the SCFG product development and generation. ENVEO (ENVironmental Earth Observation IT GmbH) developed and prepared all auxiliary data sets used for the product generation.\r\n\r\nThe SCFG AVHRR product comprises a few data gaps in 1979 – 1986 (1979: 22.-24.Feb.; 01.-07.Oct.; 03.-04.Nov.; 07.Nov.; 17.-18.Nov.; 1980: 22.-27.Feb.; 01.March; 03.March; 15.-20.March; 30.March – 02.April; 26.-29.June; 12.-19.July; 12.-18.Dec.; 1981: 09.-11.May; 01.-03.Aug.; 14.-23.Aug.; 1982: 28.- 31.May; 25.-26. Oct.; 1983: 27.- 31. July; 01.- 02. and 06. Aug.; 1984: 14.-15.Jan.; 06. Dec.; 1985: 01.- 24.Feb; 1986: 15. March), resulting in a 99% data coverage over the entire study period of 43 years.",
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            "abstract": "Terrestrial laser scanning (TLS) was conducted at three ForestScan 1ha (100m x 100m) Forest Biomass Reference Measurement Site (FBRMS) plots in Malaysia during March 2017 by Mathias Disney using a Riegl VZ-400 scanner. Data collection assistance was provided by postdocs Dr Phil Wilkes, Dr Andy Burt and Dr Toby Jackson and a local team of field assistants. Data processing was performed by Dr Cecilia Chavana-Bryant with assistance provided by Mr Peter Vines. This data collection was part of the European Space Agency (ESA) funded ForestScan project designed to improve the use of new Earth Observation (EO) estimates of above ground biomass (AGB) by providing TLS-, unmanned airborne vehicles (UAV-LS)- and airborne (ALS) LiDAR scanning-derived AGB and tree census data to compare to allometric and EO-derived estimates.\r\n\r\nScans were acquired using chain sampling at 121 locations along a 10m Cartesian grid to ensure sufficient data overlap to produce high-quality point clouds for all ForestScan 1ha FBRMS plots. Due to the scanner's 100° field of view, capturing a complete sample of the scene at each scan location required two scans -an upright scan and a tilt scan. Upright scans are odd-numbered while tilt scans are even-numbered. The first scan at each plot is collected at the southwest corner, i.e. scan position 0,0 (unless something impedes it, e.g. stream, large tree fall, etc. or if the plot is oriented differently). To facilitate scan registration, five retro-reflective targets were located between scan positions with all tilt scans along the first sampling line were oriented towards the same sampling position along the next sampling line and tilt scans at the ends of sampling lines (i.e. tilt scans along plot edges) were oriented towards the inside of the plot. This aids scan registration as it allows tilt scans to capture the previous scan location within its field of view. A total of 242 scans were collected at each plot.\r\n\r\nThe Riegl operating and processing software RiSCAN PRO version 2.14.1 was used to generate a plot-level point cloud, scans were coarse registered using the shared retro-reflective targets located between consecutive scan positions. Coarse registration was then fine-tuned using Multi Station Adjustment 2 (MSA2). \r\n\r\nData for each of the three FBRMS plots is found within plot directories: SEP-11, SEP-12 and SEP-30. Plot directories contain a main project directory (named using the starting date of data collection, e.g. 2017-03-20.001.riproject) with nine data subdirectories and a tile_index.dat file as shown in the archived document /neodc/forestscan/data/malaysia/SEP-30/ForestScan_example_data_directory_structure.pdf which details the data structure shared by all FBRSM plot TLS datasets.",
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                "title": "FORESTSCAN: Terrestrial Laser Scan (TLS) of Kabili-Sepilok, Malaysian Borneo  1 ha plot SEP-11, March 2017",
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                    "title": "ForestScan Project: a unique multiscale dataset of tropical forest structure across 3 continents including terrestrial, UAV and airborne LiDAR and in-situ forest census data",
                    "abstract": "The ForestScan project was conceived to evaluate new technologies for characterizing forest structure and biomass at Forest Biomass Reference Measurement Sites (FBRMS). It is closely aligned with other international initiatives, particularly the Committee on Earth Observation Satellites (CEOS) Working Group on Calibration & Validation (WGCV) AGB cal/val protocols, and is part of GEO-TREES, an international consortium dedicated to establishing a global network of Forest Biomass Reference Measurement Sites (FBRMS) to support EO and encourage investment in relevant field-based observations and science. ForestScan is the first demonstration of what can be achieved more broadly under GEO-TREES, which would significantly expand and enhance the use of E-derived AGB estimates.\r\n\r\nWe present data from the ForestScan project, a unique multiscale dataset of tropical forest 3D structural measurements, including terrestrial laser scanning (TLS), unpiloted aerial vehicle LiDAR scanning (UAV-LS), airborne LiDAR scanning (ALS), and in-situ tree census and ancillary data.  These data are critical for the calibration and validation of earth observation (EO) estimates of forest biomass, as well as providing broader insights into tropical forest structure.\r\n\r\nData are presented for three FBRMS: FBRMS-01: Paracou, French Guiana; FBRMS-02: Lopé, Gabon; and FBRMS-03: Kabili-Sepilok, Malaysia. Field data for each site include new 3D LiDAR measurements combined with plot tree census and ancillary data, at a multi-hectare scale. Not all data types were collected at all sites, reflecting the practical challenges of field data collection. We also provide detailed data collection protocols and recommendations for TLS, UAV-LS, and plot census measurements for each site, along with requirements for ancillary data to enable integration with ALS data (where possible) and upscaling to EO estimates. We outline the requirements and challenges for field data collection for each data type and discuss the practical considerations for establishing new FBRMS or upgrading existing sites to FBRMS standard, including insights into the associated costs and benefits.\r\n\r\nThe ForestScan Project was funded under ESA contract: 4000126857/20/NL/AI"
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                    "abstract": "This collection is part of the European Space Agency (ESA) funded ForestScan project designed to improve the use of new Earth Observation (EO) estimates of above ground biomass (AGB) by providing TLS-, unmanned airborne vehicles (UAV-LS)- and airborne (ALS) LiDAR scanning-derived AGB and tree census data to compare to allometric and EO-derived estimates. The collection contains a multiscale dataset of tropical forest 3D structural measurements, including terrestrial laser scanning (TLS), unoccupied aerial vehicle LiDAR scanning (UAV-LS), airborne laser scanning (ALS), and in-situ tree census and ancillary data. ForestScan was conceived to evaluate new technologies for characterising forest structure and biomass at Forest Biomass Reference Measurement Sites (FBRMS). These data are critical for the calibration and validation of earth observation (EO) estimates of forest biomass, as well as providing broader insights into tropical forest structure.\r\n\r\nData are presented for the first three Forest Biomass Research Monitoring Sites in Paracou Research Station in French Guiana; Station d'Etudes des Gorilles et Chimpanzes, Lopé National Park in Gabon; and Kabili-Sepilok Forest Reserve, Malaysia. Field data for each site include new 3D LiDAR measurements combined with plot tree census and ancillary data, unmanned aerial vehicle-based laser scanning (UAV-LS) and airborne laser scanning (ALS) where possible, at a multi-hectare scale. Not all data types were collected at all sites, reflecting the practical challenges of field data collection.\r\n\r\nWe also provide detailed field data collection protocols for TLS, UAV-LS, and ALS measurements for each site, along with requirements for ancillary data to enable integration with ALS data (where possible) and upscaling to EO estimates.\r\n\r\nThe ForestScan project is closely aligned with other international initiatives, particularly the Committee on Earth Observation Satellites (CEOS) Working Group on Calibration & Validation (WGCV) AGB cal/val protocols, and GEO-TREES, a new Group on Earth Observations (GEO) initiative aimed at establishing a network of FBRM sites. ForestScan is the first demonstration of what could be achieved more broadly under GEO-TREES, which would significantly expand and enhance the use of EO-derived AGB estimates"
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                    "abstract": "The National Centre for Earth Observation (NCEO) has a proud tradition of being involved with some of the most successful international collaborations in the Earth observation. This Collection contains dataset generated and/or archived with the support of NCEO resource or scientific expertise. Some notable collaboration which generated data within this collection are as follows:\r\n\r\nThe European Space Agency (ESA)'s Climate Change Initiative (CCI) program. The program goal is to provide stable, long-term, satellite-based Essential Climate Variable (ECV) data products for climate modelers and researchers.\r\n\r\nThe EUSTACE (EU Surface Temperature for All Corners of Earth) project is produced publicly available daily estimates of surface air temperature since 1850 across the globe for the first time by combining surface and satellite data using novel statistical techniques.\r\n\r\nFIDUCEO has created new climate datasets from Earth Observations with a rigorous treatment of uncertainty informed by the discipline of metrology. This response to the need for enhanced credibility for climate data, to support rigorous science, decision-making and climate services. The project approach was to develop methodologies for generating Fundamental Climate Data Records (FCDRs) and Climate Data Records (CDRs) that are widely applicable and metrologically rigorous. \r\n\r\nThe “BACI” project translates satellite data streams into novel “essential biodiversity variables” by integrating ground-based observations. The trans-disciplinary project offers new insights into the functioning and state of ecosystems and biodiversity. BACI enables the user community to detect abrupt and transient changes of ecosystems and quantify the implications for regional biodiversity.\r\n\r\nThe UK Natural Environment Research Council has established a knowledge transfer network called NCAVEO (Network for Calibration and Validation of EO data - NCAVEO) which has as its aim the promotion and support of methodologies based upon quantitative, traceable measurements in Earth observation. \r\n\r\nThe Geostationary Earth Radiation Budget 1 & 2 instruments (GERB-1 and GERB-2) make accurate measurements of the Earth Radiation Budget. They are specifically designed to be mounted on a geostationary satellite and are carried onboard the Meteosat Second Generation satellites operated by EUMETSAT. They were produced by a European consortium led by the UK (NERC) together with Belgium, Italy, and EUMETSAT, with funding from national agencies.\r\n\r\nGloboLakes analysed 20 years of data from more than 1000 large lakes across the globe to determine 'what controls the differential sensitivity of lakes to environmental perturbation'. This was an ambitious project that was only possible by bringing together a consortium of scientists with complementary skills. These include expertise in remote sensing of freshwaters and processing large volumes of satellite images, collation and analysis of large-scale environmental data, environmental statistics and the assessment of data uncertainty, freshwater ecology and mechanisms of environmental change and the ability to produce lake models to forecast future lake conditions.\r\n\r\nThis SPEI collaboration consists of high spatial resolution Standardized Precipitation-Evapotranspiration Index (SPEI) drought dataset over the whole of Africa at different time scales from 1 month to 48 months. It is calculated based on precipitation estimates from the satellite-based Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and potential evaporation estimates by the Global Land Evaporation Amsterdam Model (GLEAM)."
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