Procedure Composite Process List

HTTP 200 OK
Allow: GET, POST, HEAD, OPTIONS
Content-Type: application/json
Vary: Accept

{
    "count": 662,
    "next": "https://api.catalogue.ceda.ac.uk/api/v3/composites/?format=api&limit=100&offset=500",
    "previous": "https://api.catalogue.ceda.ac.uk/api/v3/composites/?format=api&limit=100&offset=300",
    "results": [
        {
            "ob_id": 32014,
            "uuid": "aec6d7a8f57f495b85bd68c0d9c961fd",
            "title": "ESA Soil Moisture Climate Change Initiative:  Retrieval of Soil Moisture using combined active and passive sensors for version 05.2 data.",
            "abstract": "The ESA Soil Moisture Climate Change Initiative is using Active and Passive Sensors to derive information on soil moisture.   The combined product uses information from both active and passive sensors.",
            "computationComponent": [
                {
                    "ob_id": 32010,
                    "uuid": "5f1b78c424904c359c1c907b2bb176c2",
                    "short_code": "comp",
                    "title": "Algorithm for the  ESA Soil Moisture Climate Change Initiative, v05.2",
                    "abstract": "The ESA Soil Moisture Climate Change Initiative is deriving information on soil moisture from active and passive satellite sensors.   For information on the algorithm see the Algorithm Theoretical Baseline Document."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 32013,
                    "uuid": "99251b53c2e84901b4bf6ec6fd78ad51",
                    "short_code": "acq",
                    "title": "Acquistion process for the ESA Soil Moisture Climate Change Initiative Combined product, v5.2",
                    "abstract": "The ESA Climate Change Initiative Combined product has been derived from data from both active (AMI-SCAT, ASCAT) and passive satellite instruments  (SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, MIRAS (SMOS) and SMAP)"
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                141787,
                141788
            ]
        },
        {
            "ob_id": 32015,
            "uuid": "57f39d1021ee49ffa57820838e7d4b0a",
            "title": "ESA Soil Moisture Climate Change Initiative:  Retrieval of Soil Moisture using Passive sensors for version 05.2 data.",
            "abstract": "The ESA Soil Moisture Climate Change Initiative is using Active and Passive Sensors to derive information on soil moisture.   The passive product has been created by fusing radiometer soil moisture products, merging data from the SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMOS and SMAP satellite instruments.",
            "computationComponent": [
                {
                    "ob_id": 32010,
                    "uuid": "5f1b78c424904c359c1c907b2bb176c2",
                    "short_code": "comp",
                    "title": "Algorithm for the  ESA Soil Moisture Climate Change Initiative, v05.2",
                    "abstract": "The ESA Soil Moisture Climate Change Initiative is deriving information on soil moisture from active and passive satellite sensors.   For information on the algorithm see the Algorithm Theoretical Baseline Document."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 32012,
                    "uuid": "8e5d8bc49927483286faa5929e012d72",
                    "short_code": "acq",
                    "title": "Acquistion process for the ESA Soil Moisture Climate Change Initiative Passive product, v05.2",
                    "abstract": "The ESA Climate Change Initiative Passive product has been derived from data from the SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2 , MIRAS (SMOS) and SMAP satellite instruments."
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                141790,
                141789
            ]
        },
        {
            "ob_id": 32126,
            "uuid": "5ed1319721da44d7b1c26f2f606e6028",
            "title": "Composite Process for Level 1C data products from the IASI (Infrared Atmospheric Sounding Interferometer) instrument onboard the Eumetsat EPS MetOp - C satellite.",
            "abstract": "This process is comprised of multiple procedures: 1. Acquisition: Acquisition Process for Level 1C data products from the IASI (Infrared Atmospheric Sounding Interferometer) instrument onboard the Eumetsat EPS MetOp-A satellite.; \r\n2. Computation for IASI instrument deployed on Metop-C; for the following versions of data\r\nThis data set contains both the original processed data and reprocessed archive. in the following directories based on processing algorithm\r\nv8-0N: Original processing years 2019\r\nv8-2N: Original processing years 2019 - being acquired on an ongoing basis",
            "computationComponent": [
                {
                    "ob_id": 32127,
                    "uuid": "e19d06e0fbcd4e2fa396ac94f12fe5f7",
                    "short_code": "comp",
                    "title": "Computation for data from the  IASI instrument deployed on Metop-C",
                    "abstract": "This data set contains both the original processed data and reprocessed archive. in the following directories based on processing algorithm.  The version number indicates the computation/processor used by EUMETSAT to produce the data.  For further information on the version processor please see documentation on the EUMETSAT website\r\nv8-0N: Original processing years 2019\r\nv8-2N: Original processing years 2019 - being acquired on an ongoing basis"
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 32128,
                    "uuid": "22d0f387ba5748c794006d18d94c2f57",
                    "short_code": "acq",
                    "title": "Acquisition Process for Level 1C data from the IASI (Infrared Atmospheric Sounding Interferometer) instrument onboard the MetOp-C satellite.",
                    "abstract": "This acquisition is comprised of the following: INSTRUMENTS: IASI; PLATFORMS: Metop-C"
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                142487
            ]
        },
        {
            "ob_id": 32161,
            "uuid": "496e5d861ec54195980450641694df78",
            "title": "Composite Process for IGP MRR precipitation on board Alliance",
            "abstract": "Measurement data was further processed  using post processing software (IMProToo)",
            "computationComponent": [
                {
                    "ob_id": 32162,
                    "uuid": "e560269d82274ed4a096af819206fe37",
                    "short_code": "comp",
                    "title": "Computation and post processing of IGP MRR data using IMProToo software",
                    "abstract": "During the campaign, both ProcessedData (.pro) and RawSpectra (.raw)\r\nwere saved in daily files from 3 February to 22 March 2018. No averaged data files (.ave)\r\n were saved. Short breaks in data acquisition lead to several data gaps during the campaign, which are apparent as missing data in the data files.\r\nThe ProcessedData files were converted to compressed netCDF format, using a modified version of mrr2c V1.0.2 (c) 2017-2020 by Peter Kuma (https://github.com/peterkuma/mrr2c) Output from this conversion is stored as daily datafiles (naming: bergen-mrr2_yyyymmdd_processed-v1.nc; format: netcdf) in directory: MRR_Alliance_Pro_v1/\r\nIn addition, data files were processed with the tool IMProToo v0.101\r\n (https://github.com/maahn/IMProToo) based on the *.raw data files. Output of this processing is stored as daily datafiles (naming: bergen-mrr2_alliance_yyyymmdd_IMProToo-v0.nc; format: netcdf) in directory: MRR_Alliance_IMProToo_v0/\r\nPost processing was done by Harald Sodemann (UiB), who also acts as data contact."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 32160,
                    "uuid": "dd5ad889071847649a9fd58ad09fb359",
                    "short_code": "acq",
                    "title": "Iceland Greenland seas Project (IGP): Micro Rain Radar",
                    "abstract": "Iceland Greenland seas Project (IGP): Micro Rain Radar on board Alliance"
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                142994
            ]
        },
        {
            "ob_id": 32164,
            "uuid": "f9772a2a290444a595e971c5f6cf53ee",
            "title": "ESA Glaciers Climate Change Initiative:  Inventory of Ice-Marginal Lakes in Greenland",
            "abstract": "This describes the method of production of the ESA Glaciers Climate Change Project's Inventory of Ice-Marginal Lakes in Greenland product.",
            "computationComponent": [
                {
                    "ob_id": 32165,
                    "uuid": "54e1d465d6154a2a82bd76f4d82f89c8",
                    "short_code": "comp",
                    "title": "Derivation of the Glaciers_cci Inventory of Ice-Marginal Lakes in Greenland dataset",
                    "abstract": "Ice marginal lakes were identified using three independent remote sensing methods: \r\n1) multi-temporal backscatter classification from Sentinel-1 synthetic aperture radar imagery;\r\n2) multi-spectral indices classification from Sentinel-2 optical imagery; \r\nand 3) sink detection from the ArcticDEM (v3).  (The ArcticDEM is an NGA-NSF public-private initiative to automatically produce a high-resolution, high quality, digital surface model (DSM) of the Arctic using optical stereo imagery, high-performance computing, and open source photogrammetry software.)\r\n\r\nAll data were compiled and filtered in a semi-automated approach, using a modified version of the MEaSUREs GIMP ice mask (https://nsidc.org/data/NSIDC-0714/versions/1) to clip the dataset to within 1 km of the ice margin. Each detected lake was then verified manually."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 32166,
                    "uuid": "ffb464db0cd64ad0984d750d68563bcb",
                    "short_code": "acq",
                    "title": "Aquisition for the ESA Glaciers Climate Change Initiative Inventory of Ice-Marginal Lakes in Greenland project.",
                    "abstract": "Ice marginal lakes were identified using three independent remote sensing methods: 1) multi-temporal backscatter classification from Sentinel-1 synthetic aperture radar imagery; 2) multi-spectral indices classification from Sentinel-2 optical imagery; and 3) sink detection from the ArcticDEM (v3). All data were compiled and filtered in a semi-automated approach, using a modified ice mask to clip the dataset to within 1 km of the ice margin, and then verifying each detected lake manually.\r\n\r\nSentinel 1 Synthetic Aperture Radar (SAR) - The C-band Synthetic Aperture Radar (SAR) flown on the Sentinel 1 series of satellites is an instrument providing high resolution all-weather day and night radar coverage of the Earth's surface. Sentinel 1A was launched on 3rd April 2014 and Sentinel 1B was launched on 25th April 2016. This instrument has four acquisition modes; Stripmap (SM), Interferometric Wide Swath (IW), Extra Wide Swath (EW), and Wave (WV).\r\n\r\nSentinel 2 Multispectral Instrument (MSI) - Data from the Multispectral Instrument (MSI) on the Sentinel 2 series. Sentinel 2A was launched on 23rd June 2015 and Sentinel 2B was launch in March 2017. The instrument provides high-resolution optical imaging data of the Earth's surface.\r\n\r\nArcticDEM - ArcticDEM is an NGA-NSF public-private initiative to automatically produce a high-resolution, high quality, digital surface model (DSM) of the Arctic using optical stereo imagery, high-performance computing, and open source photogrammetry software."
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                143013
            ]
        },
        {
            "ob_id": 32225,
            "uuid": "2a08fad34170409f9e84ac219f93c242",
            "title": "ESA Soil Moisture Climate Change Initiative:  Retrieval of Soil Moisture using Passive sensors for version 05.3 data.",
            "abstract": "The ESA Soil Moisture Climate Change Initiative is using Active and Passive Sensors to derive information on soil moisture.   The passive product has been created by fusing radiometer soil moisture products, merging data from the SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMOS and SMAP satellite instruments.",
            "computationComponent": [
                {
                    "ob_id": 32226,
                    "uuid": "28a7188563934ccb9e44eb0fd534069f",
                    "short_code": "comp",
                    "title": "Algorithm for the  ESA Soil Moisture Climate Change Initiative, v06.1",
                    "abstract": "The ESA Soil Moisture Climate Change Initiative is deriving information on soil moisture from active and passive satellite sensors.   For information on the algorithm see the Algorithm Theoretical Baseline Document."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 32227,
                    "uuid": "c58157eefd194b0c993d2dc767e6e846",
                    "short_code": "acq",
                    "title": "Acquistion process for the ESA Soil Moisture Climate Change Initiative Passive product, v05.3",
                    "abstract": "The ESA Climate Change Initiative Passive product has been derived from data from the SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2 , MIRAS (SMOS) and SMAP satellite instruments."
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                143319,
                143320
            ]
        },
        {
            "ob_id": 32230,
            "uuid": "ce4ee1f9a40942edb4efc417716f5cd5",
            "title": "ESA Soil Moisture Climate Change Initiative:  Retrieval of Soil Moisture using combined active and passive sensors for version 05.3 data.",
            "abstract": "The ESA Soil Moisture Climate Change Initiative is using Active and Passive Sensors to derive information on soil moisture.   The combined product uses information from both active and passive sensors.",
            "computationComponent": [
                {
                    "ob_id": 32226,
                    "uuid": "28a7188563934ccb9e44eb0fd534069f",
                    "short_code": "comp",
                    "title": "Algorithm for the  ESA Soil Moisture Climate Change Initiative, v06.1",
                    "abstract": "The ESA Soil Moisture Climate Change Initiative is deriving information on soil moisture from active and passive satellite sensors.   For information on the algorithm see the Algorithm Theoretical Baseline Document."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 32228,
                    "uuid": "65efe382488a493fa196c0313d42649f",
                    "short_code": "acq",
                    "title": "Acquistion process for the ESA Soil Moisture Climate Change Initiative Combined product, v5.3",
                    "abstract": "The ESA Climate Change Initiative Combined product has been derived from data from both active (AMI-SCAT, ASCAT) and passive satellite instruments  (SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, MIRAS (SMOS) and SMAP)"
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                143329,
                143330
            ]
        },
        {
            "ob_id": 32231,
            "uuid": "f80cedbbe8c94b34bcacba59b4267203",
            "title": "ESA Soil Moisture Climate Change Initiative:  Retrieval of Soil Moisture using Active sensors for version 05.3 data.",
            "abstract": "The ESA Soil Moisture Climate Change Initiative is using Active and Passive Sensors to derive information on soil moisture.   The ACTIVE product has been created by fusing scatterometer soil moisture products, derived from the instruments AMI-WS and ASCAT.",
            "computationComponent": [
                {
                    "ob_id": 32226,
                    "uuid": "28a7188563934ccb9e44eb0fd534069f",
                    "short_code": "comp",
                    "title": "Algorithm for the  ESA Soil Moisture Climate Change Initiative, v06.1",
                    "abstract": "The ESA Soil Moisture Climate Change Initiative is deriving information on soil moisture from active and passive satellite sensors.   For information on the algorithm see the Algorithm Theoretical Baseline Document."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 32229,
                    "uuid": "a1bf27d8c7324fd89e2a5ae24d92aa20",
                    "short_code": "acq",
                    "title": "Acquistion process for the ESA Soil Moisture Climate Change Initiative Active product, v05.3",
                    "abstract": "The ESA Climate Change Initiative Active product has been derived from data from the AMI-WS and ASCAT satellite instruments."
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                143331,
                143332
            ]
        },
        {
            "ob_id": 32286,
            "uuid": "eba971fa045f46f99dd082dc1c103de8",
            "title": "ESA Cloud Climate Change Initiative ATSR2-AATSR version 3 data",
            "abstract": "The ATSR2-AATSR datasets from the ESA Cloud Climate Change Initiative project have been derived from the ATSR-2 (Along Track Scanning Radiometer-2) instrument on the ERS-2 satellite and the AATSR (Advanced Along Track Scanning Radiometer) on the ENVISAT satellite.   They were derived using the Community Cloud retrieval for Climate (CC4CL; Sus et al., 2018; McGarragh et al., 2018) retrieval framework.",
            "computationComponent": [
                {
                    "ob_id": 32285,
                    "uuid": "94870c9edbe1431994001d40f20e6779",
                    "short_code": "comp",
                    "title": "ESA Cloud Climate Change Initiative ATSR2-AATSR v3 data",
                    "abstract": "The ATSR2-AATSR v3 dataset has been derived using the Community Cloud retrieval for Climate (CC4CL; Sus et al., 2018; McGarragh et al., 2018) retrieval framework.  For further details, see the documentation at https://climate.esa.int/en/projects/cloud/key-documents/"
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 32284,
                    "uuid": "d1f8c4c3ea4747d79ad949afbf2a24d1",
                    "short_code": "acq",
                    "title": "Acquisition for the ESA Cloud Climate Change Initiative ATSR2-AATSR datasets",
                    "abstract": "The ESA Cloud Climate Change Initiative ATSR2-AATSR datasets are based on data from the Along Track Scanning Radiometer - 2 (ATSR-2) and Advanced Along Track Scanning Radiometer (AATSR)."
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                143708,
                143709
            ]
        },
        {
            "ob_id": 32293,
            "uuid": "56de9d2ddc9146b59fc7a4aa516fa803",
            "title": "ESA Cloud Climate Change Initiative AVHRR-AM version 3 data",
            "abstract": "The AVHRR-AM datasets from the ESA Cloud Climate Change Initiative project have been derived from the form the Advanced Very High Resolution Radiometer (AVHRR) sensors on-board the NOAA prime morning (AM) satellite NOAA-12,-15,-17, and the EUMETSAT Metop-A satellite. They were derived using the Community Cloud retrieval for Climate (CC4CL; Sus et al., 2018; McGarragh et al., 2018) retrieval framework.",
            "computationComponent": [
                {
                    "ob_id": 32287,
                    "uuid": "04f7fe1327cd4b92a3ac92712dc5027b",
                    "short_code": "comp",
                    "title": "ESA Cloud Climate Change Initiative AVHRR-AM v3 data",
                    "abstract": "The AVHRR-AM dataset has been derived using the Community Cloud retrieval for Climate (CC4CL; Sus et al., 2018; McGarragh et al., 2018) retrieval framework.  For further details, see the documentation at https://climate.esa.int/en/projects/cloud/key-documents/"
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 32290,
                    "uuid": "90a4f8cf26f24106a92d88db9f21a7e8",
                    "short_code": "acq",
                    "title": "Acquisition for the ESA Cloud Climate Change Initiative AVHRR-AM datasets",
                    "abstract": "The ESA Cloud Climate Change Initiative AVHRR-AM datasets are based on intercalibrated measurements form the Advanced Very High Resolution Radiometer (AVHRR) sensors on-board the NOAA prime morning (AM) satellite NOAA-12,-15,-17, and the EUMETSAT Metop-A satellite."
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                143720,
                143721
            ]
        },
        {
            "ob_id": 32294,
            "uuid": "548d2866e4db4b7cad1817c295bdb6ea",
            "title": "ESA Cloud Climate Change Initiative AVHRR-PM version 3 data",
            "abstract": "The AVHRR-PM datasets from the ESA Cloud Climate Change Initiative project have been derived from the Advanced Very High Resolution Radiometer (AVHRR) sensors on-board the NOAA prime afternoon (PM) satellite NOAA-7,-9,11,-14,-16,-18,-19 satellites. They were derived using the Community Cloud retrieval for Climate (CC4CL; Sus et al., 2018; McGarragh et al., 2018) retrieval framework.",
            "computationComponent": [
                {
                    "ob_id": 32289,
                    "uuid": "7c2ab8316ba14b66b6ae1fe35cf74c3d",
                    "short_code": "comp",
                    "title": "ESA Cloud Climate Change Initiative AVHRR-PM v3 data",
                    "abstract": "The AVHRR-PM dataset has been derived using the Community Cloud retrieval for Climate (CC4CL; Sus et al., 2018; McGarragh et al., 2018) retrieval framework.  For further details, see the documentation at https://climate.esa.int/en/projects/cloud/key-documents/"
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 32291,
                    "uuid": "350dffedb58a4592bb2ea1b374e53d37",
                    "short_code": "acq",
                    "title": "Acquisition for the ESA Cloud Climate Change Initiative AVHRR-PM datasets",
                    "abstract": "The ESA Cloud Climate Change Initiative AVHRR-AM datasets are based on intercalibrated measurements form the Advanced Very High Resolution Radiometer (AVHRR) sensors on-board the NOAA prime afternoon (PM) satellite NOAA-7,-9,11,-14,-16,-18,-19 satellites."
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                143722,
                143723
            ]
        },
        {
            "ob_id": 32300,
            "uuid": "d53529490ad14a5880add371873b79f7",
            "title": "ESA Soil Moisture Climate Change Initiative:  Retrieval of Soil Moisture using Active sensors for version 06.1 data.",
            "abstract": "The ESA Soil Moisture Climate Change Initiative is using Active and Passive Sensors to derive information on soil moisture.   The ACTIVE product has been created by fusing scatterometer soil moisture products, derived from the instruments AMI-WS and ASCAT.",
            "computationComponent": [
                {
                    "ob_id": 32226,
                    "uuid": "28a7188563934ccb9e44eb0fd534069f",
                    "short_code": "comp",
                    "title": "Algorithm for the  ESA Soil Moisture Climate Change Initiative, v06.1",
                    "abstract": "The ESA Soil Moisture Climate Change Initiative is deriving information on soil moisture from active and passive satellite sensors.   For information on the algorithm see the Algorithm Theoretical Baseline Document."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 32301,
                    "uuid": "9bd10428b3374f4497416b651ea9e61c",
                    "short_code": "acq",
                    "title": "Acquistion process for the ESA Soil Moisture Climate Change Initiative Active product, v06.1",
                    "abstract": "The ESA Climate Change Initiative Active product has been derived from data from the AMI-WS and ASCAT satellite instruments."
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                143824,
                143825
            ]
        },
        {
            "ob_id": 32307,
            "uuid": "80e3ac78b8e34eb994762120a2a73ca4",
            "title": "ESA Soil Moisture Climate Change Initiative:  Retrieval of Soil Moisture using Passive sensors for version 06.1 data.",
            "abstract": "The ESA Soil Moisture Climate Change Initiative is using Active and Passive Sensors to derive information on soil moisture.   The passive product has been created by fusing radiometer soil moisture products, merging data from the SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMOS, SMAP, FY-3B and GPM satellite instruments.",
            "computationComponent": [
                {
                    "ob_id": 32226,
                    "uuid": "28a7188563934ccb9e44eb0fd534069f",
                    "short_code": "comp",
                    "title": "Algorithm for the  ESA Soil Moisture Climate Change Initiative, v06.1",
                    "abstract": "The ESA Soil Moisture Climate Change Initiative is deriving information on soil moisture from active and passive satellite sensors.   For information on the algorithm see the Algorithm Theoretical Baseline Document."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 32302,
                    "uuid": "caf40bff974144e08c815200db700f48",
                    "short_code": "acq",
                    "title": "Acquistion process for the ESA Soil Moisture Climate Change Initiative Passive product, v06.1",
                    "abstract": "The ESA Climate Change Initiative Passive product has been derived from data from the SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2 , MIRAS (SMOS), SMAP,  FY-3B, and GPM satellite instruments."
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                143835,
                143836
            ]
        },
        {
            "ob_id": 32309,
            "uuid": "7c60f5393c5e4578a7e851de79b311e5",
            "title": "ESA Soil Moisture Climate Change Initiative:  Retrieval of Soil Moisture using combined active and passive sensors for version 06.1 data.",
            "abstract": "The ESA Soil Moisture Climate Change Initiative is using Active and Passive Sensors to derive information on soil moisture.   The combined product uses information from both active and passive sensors.",
            "computationComponent": [
                {
                    "ob_id": 32226,
                    "uuid": "28a7188563934ccb9e44eb0fd534069f",
                    "short_code": "comp",
                    "title": "Algorithm for the  ESA Soil Moisture Climate Change Initiative, v06.1",
                    "abstract": "The ESA Soil Moisture Climate Change Initiative is deriving information on soil moisture from active and passive satellite sensors.   For information on the algorithm see the Algorithm Theoretical Baseline Document."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 32308,
                    "uuid": "ca2e0dad967e49fbaf4c5ec4cf314583",
                    "short_code": "acq",
                    "title": "Acquistion process for the ESA Soil Moisture Climate Change Initiative Combined product, v6.1",
                    "abstract": "The ESA Climate Change Initiative Combined product has been derived from data from both active (AMI-SCAT, ASCAT) and passive satellite instruments  (SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, MIRAS (SMOS), SMAP, FY-3B, GPM)"
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                143839,
                143840
            ]
        },
        {
            "ob_id": 32515,
            "uuid": "782d062911e9431d84396119b2ef4ee3",
            "title": "Composite process for the ESA Snow Climate Change Initiative SCFG MODIS v1.0 product",
            "abstract": "The snow_cci SCFG products from MODIS are based on the MODIS/Terra Calibrated Radiances 5-Min L1B Swath 1km (MOD021KM) and the MODIS/Terra Geolocation Fields 5-Min L1A Swath 1km (MOD03) Collection 6.1 data sets, provided by NASA.\r\n\r\nThe retrieval method of the snow_cci SCFG product from MODIS data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module developed by ENVEO. For the SCFG product generation from MODIS, multiple reflective and emissive spectral bands are used. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Metsämäki et al., 2015). All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 550 nm and 1.6 µm, and an emissive band centred at about 11 µm. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFG retrieval method is applied. Improvements to the GlobSnow algorithm implemented for snow_cci version 1 include (i) the utilisation of background and forest reflectance maps derived from statistical analyses of MODIS time series replacing the constant values for snow free ground and snow free forest used in the GlobSnow approach, and (ii) the usage of a global forest transmissivity map developed and created within snow_cci based on forest density from Hansen et al. (2013) and forest type layers from Land Cover CCI (Defourny, 2019). The forest transmissivity map is used to account for the shading effects of the forest canopy and estimate also in forested areas the fractional snow cover on ground.\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. The product uncertainty for observed land pixels is provided as unbiased root mean square error (RMSE) per pixel in the ancillary variable.",
            "computationComponent": [
                {
                    "ob_id": 32514,
                    "uuid": "b69f14b6767e444d93c02a6e0cecf2ae",
                    "short_code": "comp",
                    "title": "ESA Snow Climate Change Initiative: Derivation of SCFG MODIS v1 product.",
                    "abstract": "The retrieval method of the snow_cci SCFG product from MODIS data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module developed by ENVEO. For the SCFG product generation from MODIS, multiple reflective and emissive spectral bands are used. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Metsämäki et al., 2015). All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 550 nm and 1.6 µm, and an emissive band centred at about 11 µm. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFG retrieval method is applied. Improvements to the GlobSnow algorithm implemented for snow_cci version 1 include (i) the utilisation of background and forest reflectance maps derived from statistical analyses of MODIS time series replacing the constant values for snow free ground and snow free forest used in the GlobSnow approach, and (ii) the usage of a global forest transmissivity map developed and created within snow_cci based on forest density from Hansen et al. (2013) and forest type layers from Land Cover CCI (Defourny, 2019). The forest transmissivity map is used to account for the shading effects of the forest canopy and estimate also in forested areas the fractional snow cover on ground.\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. The product uncertainty for observed land pixels is provided as unbiased root mean square error (RMSE) per pixel in the ancillary variable."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 32512,
                    "uuid": "b7f993e0c3e745dc9975da8aa580a654",
                    "short_code": "acq",
                    "title": "Aquisition for: ESA Snow Climate Change Initiative (snow_cci) Snow Cover Fraction from MODIS, v1.0",
                    "abstract": "The snow_cci SCFG and SCFV products from MODIS are based on the MODIS/Terra Calibrated Radiances 5-Min L1B Swath 1km (MOD021KM) and the MODIS/Terra Geolocation Fields 5-Min L1A Swath 1km (MOD03) Collection 6.1 data sets, provided by NASA.\r\n\r\nThe snow_cci SCF processing chain for MODIS includes the masking of clouds, the identification of certainly snow free areas, and the classification of snow cover fraction per pixel for all remaining observed pixels. Finally, permanent snow and ice areas as well as water bodies are masked in the SCFG products using the corresponding classes from the Land Cover CCI map of the year 2000 as auxiliary layers. All SCFG products are prepared according to the CCI data standards.\r\n\r\nAn automated and a manual quality check was performed on the full time series."
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                144760
            ]
        },
        {
            "ob_id": 32517,
            "uuid": "0ec32fe6a1544f149628790a7292cc46",
            "title": "Composite process for the ESA Snow Climate Change Initiative SCFV MODIS v1.0 product",
            "abstract": "The snow_cci SCFV products from MODIS are based on the MODIS/Terra Calibrated Radiances 5-Min L1B Swath 1km (MOD021KM) and the MODIS/Terra Geolocation Fields 5-Min L1A Swath 1km (MOD03) Collection 6.1 data sets, provided by NASA.\r\n\r\nThe retrieval method of the snow_cci SCFV product from MODIS data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module developed by ENVEO. For the SCFV product generation from MODIS, multiple reflective and emissive spectral bands are used. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Metsämäki et al., 2015). All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 550 nm and 1.6 µm, and an emissive band centred at about 11 µm. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFV retrieval method is applied. Improvements to the GlobSnow algorithm implemented for snow_cci version 1 include (i) the utilisation of a background reflectance map derived from statistical analyses of MODIS time series replacing the constant values for snow free ground used in the GlobSnow approach, and (ii) the adaptation of the retrieval method for mapping in forested areas the SCFV. \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. The product uncertainty for observed land pixels is provided as unbiased root mean square error (RMSE) per pixel in the ancillary variable.",
            "computationComponent": [
                {
                    "ob_id": 32516,
                    "uuid": "209e3c585d8b4d5b8f36f6c59687e1e9",
                    "short_code": "comp",
                    "title": "ESA Snow Climate Change Initiative: Derivation of SCFV MODIS v1 product.",
                    "abstract": "The retrieval method of the snow_cci SCFV product from MODIS data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module developed by ENVEO. For the SCFV product generation from MODIS, multiple reflective and emissive spectral bands are used. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Metsämäki et al., 2015). All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 550 nm and 1.6 µm, and an emissive band centred at about 11 µm. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFV retrieval method is applied. Improvements to the GlobSnow algorithm implemented for snow_cci version 1 include (i) the utilisation of a background reflectance map derived from statistical analyses of MODIS time series replacing the constant values for snow free ground used in the GlobSnow approach, and (ii) the adaptation of the retrieval method for mapping in forested areas the SCFV. \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. The product uncertainty for observed land pixels is provided as unbiased root mean square error (RMSE) per pixel in the ancillary variable."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 32512,
                    "uuid": "b7f993e0c3e745dc9975da8aa580a654",
                    "short_code": "acq",
                    "title": "Aquisition for: ESA Snow Climate Change Initiative (snow_cci) Snow Cover Fraction from MODIS, v1.0",
                    "abstract": "The snow_cci SCFG and SCFV products from MODIS are based on the MODIS/Terra Calibrated Radiances 5-Min L1B Swath 1km (MOD021KM) and the MODIS/Terra Geolocation Fields 5-Min L1A Swath 1km (MOD03) Collection 6.1 data sets, provided by NASA.\r\n\r\nThe snow_cci SCF processing chain for MODIS includes the masking of clouds, the identification of certainly snow free areas, and the classification of snow cover fraction per pixel for all remaining observed pixels. Finally, permanent snow and ice areas as well as water bodies are masked in the SCFG products using the corresponding classes from the Land Cover CCI map of the year 2000 as auxiliary layers. All SCFG products are prepared according to the CCI data standards.\r\n\r\nAn automated and a manual quality check was performed on the full time series."
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                144763
            ]
        },
        {
            "ob_id": 32520,
            "uuid": "f22556a6664647c29393fefcf1f0abad",
            "title": "Composite process for the ESA Snow Climate Change Initiative SCFG AVHRR v1.0 product",
            "abstract": "The ESA Snow Climate Change Initiative SCFG AVHRR v1.0 product is based on an AVHRR baseline FCDR pre-processed using pyGAC and pySTAT in the frame of the ESA CCI Cloud project.\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 630 nm 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 two-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFG retrieval method is applied. \r\n\r\nThe 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.",
            "computationComponent": [
                {
                    "ob_id": 32519,
                    "uuid": "0d8dfce486af4455abb556666416887b",
                    "short_code": "comp",
                    "title": "ESA Snow Climate Change Initiative: Derivation of SCFG AVHRR v1 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 630 nm 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 two-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFG retrieval method is applied. \r\n\r\nThe 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."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 32518,
                    "uuid": "fe25ba369f6e4247aba9650253ef9f6a",
                    "short_code": "acq",
                    "title": "Aquisition for: ESA Snow Climate Change Initiative (snow_cci) Snow Cover Fraction from AVHRR, v1.0",
                    "abstract": "The snow_cci SCFG and SCFV products from AVHRR are based on the AVHRR baseline FCDR that was pre-processed using pyGAC and pySTAT in the frame of the ESA CCI Cloud project."
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                144770
            ]
        },
        {
            "ob_id": 32525,
            "uuid": "1c8d6b2cfe3b40d2b999d5bbaaff6cd7",
            "title": "Composite process for the ESA Snow Climate Change Initiative SCFV AVHRR v1.0 product",
            "abstract": "The ESA Snow Climate Change Initiative SCFG AVHRR v1.0 product is based on an AVHRR baseline FCDR pre-processed using pyGAC and pySTAT in the frame of the ESA CCI Cloud project.\r\n\r\nThe retrieval method of the snow_cci SCFV product from AVHRR data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module. All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 630 nm 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 two-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFV retrieval method is applied. \r\n\r\nThe 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.",
            "computationComponent": [
                {
                    "ob_id": 32524,
                    "uuid": "d60e0d8070734e39951a356adbb8b497",
                    "short_code": "comp",
                    "title": "ESA Snow Climate Change Initiative: Derivation of SCFV AVHRR v1 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-classification module. All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 630 nm 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 two-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFV retrieval method is applied. \r\n\r\nThe 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."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 32518,
                    "uuid": "fe25ba369f6e4247aba9650253ef9f6a",
                    "short_code": "acq",
                    "title": "Aquisition for: ESA Snow Climate Change Initiative (snow_cci) Snow Cover Fraction from AVHRR, v1.0",
                    "abstract": "The snow_cci SCFG and SCFV products from AVHRR are based on the AVHRR baseline FCDR that was pre-processed using pyGAC and pySTAT in the frame of the ESA CCI Cloud project."
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                144801
            ]
        },
        {
            "ob_id": 32596,
            "uuid": "dfee2403b85d4aad8bc655aec772be07",
            "title": "NCEO Aboveground Biomass Map v21 2015",
            "abstract": "The map was generated by combining field inventory plots (KFS) with L-band SAR (JAXA ALOS-2 PALSAR-2) and multispectral optical data (NASA Landsat 8), by means of a Random Forests algorithm within a k-Fold calibration / validation framework.",
            "computationComponent": [
                {
                    "ob_id": 32595,
                    "uuid": "6802c93ca9d64942bd1cdb88e23b3546",
                    "short_code": "comp",
                    "title": "NCEO Aboveground Biomass Map v21 2015",
                    "abstract": "Algorithm / method The map shows aboveground woody biomass (AGB) in Kenyan forests. The map was generated by combining field inventory plots (KFS) with L-band SAR (JAXA ALOS-2 PALSAR-2) and multispectral optical data (NASA Landsat 8), by means of a Random Forests algorithm within a k-Fold calibration / validation framework. \r\n\r\nTraining dataset Forest inventory dataset collected consisting of 30 m diameter plots gathered in 4-plot clusters. The AGB pools measured were trees, bamboos and lianas. Pantropical allometries (1) were used to estimate AGB. A few plots with extremely large AGB (potentially due to the small plot size) were excluded (>98 percentile) \r\n\r\nSpatial data inputs ALOS-2 PALSAR-2 dual-polarization (2015)  and  Landsat 8 Surface Reflectance (2015±1)"
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 32594,
                    "uuid": "2b5e6abb44844bbfa34220b674d23461",
                    "short_code": "acq",
                    "title": "NCEO Aboveground Biomass Map V21 2015",
                    "abstract": "The map was generated by combining field inventory plots (KFS) with L-band SAR (JAXA ALOS-2 PALSAR-2)"
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                145131
            ]
        },
        {
            "ob_id": 32678,
            "uuid": "be7021278e334bd4865ae4ebcb79b53c",
            "title": "Composite process for the ESA Antarctic Ice Sheets CCI: Grounding line location for key glaciers, v2.0",
            "abstract": "-",
            "computationComponent": [
                {
                    "ob_id": 32677,
                    "uuid": "c763ca9f2a874b8ca75d9576efe6673b",
                    "short_code": "comp",
                    "title": "Derivation of Grounding Line Location data from the ESA Antarctic Ice Sheet Climate Change Initiative project",
                    "abstract": "For information on the derivation of the Grounding Line location dataset see the documentation in https://climate.esa.int/projects/ice-sheets-antarctic/key-documents/"
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 32676,
                    "uuid": "1555372bc28140aca7c86ae502bc8d5e",
                    "short_code": "acq",
                    "title": "Aquisition for: ESA Antarctic Ice Sheets CCI ground line location for key glaciers",
                    "abstract": "Data have been derived from satellite observations from the ERS-1/2, TerraSAR-X and Copernicus Sentinel-1 satellites, acquired between 1994 and 2020."
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                145711
            ]
        },
        {
            "ob_id": 32698,
            "uuid": "6a84d7e2a1c0408aae6e75dd67bcd89c",
            "title": "Composite process for the Antarctic Ice Sheet CCI Gravimetric Mass Balance products (v3.0)",
            "abstract": "Composite process for the Antarctic Ice Sheet CCI Gravimetric Mass Balance products (v3.0)",
            "computationComponent": [
                {
                    "ob_id": 32697,
                    "uuid": "29ac5e1fed1243ee8ad453ba74c5d6a4",
                    "short_code": "comp",
                    "title": "Derivation of Gravimetric Mass Balance products from the Antarctic Ice Sheet CCI  (v3.0)",
                    "abstract": "TU Dresden has generated a Gravimetric Mass Balance (GMB) product for the Antarctic Ice Sheet (AIS) based on monthly snapshots of the Earth’s gravity field provided by the Gravity Recovery and Climate Experiment (GRACE) and its follow-on satellite mission (GRACE-FO). The product relies on monthly gravity field solutions (L2) of release 06 generated at the Center for Space Research (University of Texas at Austin) and spans the period from April 2002 through July 2020. The GMB product covers the full GRACE mission period (April 2002 - June 2017) and is extended by means of GRACE-FO data starting from June 2018, thus including 187 monthly solutions. The mass change estimation is based on the tailored sensitivity kernel approach developed at TU Dresden.\r\n\r\nThe methodology is described in: Groh, A. & Horwath, M. (2021). Antarctic Ice Mass Change Products from GRACE/GRACE-FO Using Tailored Sensitivity Kernels. Remote Sens., 13(9), 1736. doi:10.3390/rs13091736"
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 32696,
                    "uuid": "8a71f0acff9e45199e34e879671f2b29",
                    "short_code": "acq",
                    "title": "Acquisition for ESA Antarctic Ice Sheet Climate Change Initiative (Antarctic_Ice_Sheet_cci): Antarctic Ice Sheet monthly Gravimetric Mass Balance Product, v3.0",
                    "abstract": "The Antarctic Ice Sheet Gravimetric Mass Balance (GMB) v3.0 product is based on monthly snapshots of the Earth’s gravity field provided by the Gravity Recovery and Climate Experiment (GRACE) and its follow-on satellite mission (GRACE-FO). The product relies on monthly gravity field solutions (L2) of release 06 generated at the Center for Space Research (University of Texas at Austin) and spans the period from April 2002 through July 2020."
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                145783
            ]
        },
        {
            "ob_id": 32701,
            "uuid": "426d8d365d97418087cde06c426c4afd",
            "title": "Composite process for the ESA Antarctic Ice Sheet Ice velocity from Sentinel-1, v1 dataset",
            "abstract": "-",
            "computationComponent": [
                {
                    "ob_id": 32700,
                    "uuid": "2b2b435fec204442a44ff2bf173d78b1",
                    "short_code": "comp",
                    "title": "Derivation of the Antarctic Ice Sheet CCI Ice Velocity v1 dataset from Sentinel-1",
                    "abstract": "The surface velocity is derived by applying feature tracking techniques using Sentinel-1 synthetic aperture radar (SAR) data acquired in the Interferometric Wide (IW) swath mode. Ice velocity is provided at 200m grid spacing in Polar Stereographic projection (EPSG: 3031). The horizontal velocity components are provided in true meters per day, towards easting and northing direction of the grid. The vertical displacement is derived from a digital elevation model. Provided is a NetCDF file with the velocity components: vx, vy, vz, along with maps showing the magnitude of the horizontal components, the valid pixel count and uncertainty. The product combines all ice velocity maps, based on 6- and 12-day repeats, acquired within a single month in a monthly averaged product."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 32699,
                    "uuid": "8ee58d233916481db9f0b25c30ae9ad1",
                    "short_code": "acq",
                    "title": "Aquistion for the ESA Antarctic Ice Sheet Climate Change Initiative (Antarctic_Ice_Sheet_cci): Antarctic Ice Sheet monthly velocity from 2017 to 2020, derived from Sentinel-1, v1.3",
                    "abstract": "Data were derived from Sentinel-1"
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                145786
            ]
        },
        {
            "ob_id": 32709,
            "uuid": "c66532fda9d148af98984dbab9f2f1ac",
            "title": "Composite process for level 1 Landsat 5 data.",
            "abstract": "Composite process for Level 1 data from the Landsat 5 Thematic Mapper (TM) instrument deployed on Landsat 5. This consists of the Acquisition process for raw imaging data from the Landsat 5 TM and the computation component to produce processed Level 1 data.",
            "computationComponent": [
                {
                    "ob_id": 32710,
                    "uuid": "dcb605e6db9b41c3959153f5686d6302",
                    "short_code": "comp",
                    "title": "Level 1 processing algorithm applied to Landsat 5 data",
                    "abstract": "Landsat Collection 1 Level-1 data products are produced by the Landsat Product Generation System (LPGS). LPGS also generates the 16-bit Quality Assessment Band (QA) and an angle coefficient file that are included in the Level-1 product, as well as a Full Resolution Browse (FRB) and an 8-bit Quality Image. The system then delivers the data products and images to the online cache for distribution. The Landsat 5 TM data is processed to full Precision Terrain correction."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 27068,
                    "uuid": "812b78639c2740f98090d73e5a8f938e",
                    "short_code": "acq",
                    "title": "Aquisition process for: LANDSAT 5 Thematic Mapper",
                    "abstract": "The Thematic Mapper (TM) instrument on the Landsat 5 satellite collected data between 1984 and 2011"
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                145830
            ]
        },
        {
            "ob_id": 32714,
            "uuid": "210f660a04024c3ebf247f9d1f8f4657",
            "title": "Composite Process for: Level 2 data from the Sentinel 5P TROPOspheric Monitoring Instrument (TROPOMI) for Sulphur Dioxide (SO2) total column data.",
            "abstract": "Composite process for Level 2 data from the TROPOspheric Monitoring Instrument (TROPOMI) deployed on Sentinel 5P. This consists of the acquisition process for raw imaging data from the Sentinel 5P TROPOMI and the computation component to produce processed Level 2 Sulphur Dioxide (SO2) total column data.",
            "computationComponent": [
                {
                    "ob_id": 32713,
                    "uuid": "c8de973897f042e29caf9edd71d5d705",
                    "short_code": "comp",
                    "title": "Level 2 Sulphur Dioxide (SO2) total column processing algorithm applied to Sentinel 5P TROPOspheric Monitoring Instrument (TROPOMI) raw data",
                    "abstract": "The baseline operation flow of the scheme is based on a DOAS retrieval algorithm and is identical to that implemented in the retrieval algorithm for HCHO (also developed by BIRA-IASB, see S5P HCHO ATBD [RD12]). The main output of the algorithm are SO2 vertical column density, slant column density, air mass factor, Averaging Kernels (AK), and error estimates. Here, we will first briefly discuss the principle of the DOAS VCD retrieval before discussing the separate steps of the process in more detail.\r\n\r\nFirst, the radiance and irradiance data are read from an S5P L1b file, along with geolocation data such as pixel coordinates and observation geometry (sun and viewing angles). At this stage also cloud cover information is obtained from the S5P cloud L2 data, as required for the calculation of the AMF, later in the scheme. Then relevant absorption cross-section data (SO2), as well as characteristics of the instrument (e.g., slit functions) are used as input for the SO2 slant column density determination. As a baseline, the slant column fit is done in a sensitive window from 312 to 326 nm. For pixels with a strong SO2 signal, results from alternative windows, where the SO2 absorption is weaker, can be used instead. An empirical offset correction (dependent on the fitting window used) is then applied to the SCD. The latter correction accounts for systematic biases in the SCDs. Following the SCD determination, the AMF is estimated. For computational efficiency, the algorithm makes no ‘on the fly’ calculation but uses a pre-calculated box air mass factor look-up table (LUT). This lookup-table is generated using the LIDORT radiative transfer code and has several entries: cloud cover data, topographic information, observation geometry, surface albedo, effective wavelength (representative of the fitting window used), total ozone column, and the shape of the vertical SO2 profile. The algorithm also includes an error calculation and retrieval characterization module that computes the so-called DOAS-type averaging kernels (Eskes & Boersma, 2003), which characterize the vertical sensitivity of the measurement and which are required for comparison with other types of data (Veefkind et al., 2012). For more information please look at the ATBD document on the TROPOMI  website."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 26443,
                    "uuid": "929d929b043242e69de7b5373acfb611",
                    "short_code": "acq",
                    "title": "Acquisition Process for: Sentinel 5P TROPOspheric Monitoring Instrument (TROPOMI)",
                    "abstract": "The acquisition process for the collection of data from the European Space Agency (ESA) Sentinel 5P TROPOspheric Monitoring Instrument (TROPOMI)."
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                145849,
                145850
            ]
        },
        {
            "ob_id": 32755,
            "uuid": "123da90e83e04211ad73c6cf7f91f531",
            "title": "Composite Process for: Level 2 data from the Sentinel 1A C-band Synthetic Aperture Radar (SAR), Instrument Processing Facility (IPF) v2",
            "abstract": "Composite process for Level 2 data from the C-band Synthetic Aperture Radar (SAR) deployed on Sentinel 1. This consists of the Acquisition process for raw radar data from the Sentinel 1 SAR and the computation component to produce processed Level 2 radar data.",
            "computationComponent": [
                {
                    "ob_id": 32756,
                    "uuid": "1d05ff37e7124eaa8dbc7a3aedbba431",
                    "short_code": "comp",
                    "title": "Level 2 processing algorithm applied to Sentinel 1 raw data, Instrument Processing Facility (IPF) version 2",
                    "abstract": "Level-2 consists of geo-located geophysical products derived from Level-1. Level-2 Ocean (OCN) products for wind, wave and currents applications may contain the following geophysical components derived from the SAR data:\r\n- Ocean Wind field (OWI)\r\n- Ocean Swell spectra (OSW)\r\n- Surface Radial Velocity (RVL)\r\nThe availability of components depends on the acquisition mode. The OSW component cannot be generated from IW and EW mode, since individual looks with sufficient time separation are required. The obtained inter look time separation within one burst is too short due to the progressive scanning (i.e. short dwell time).\r\n\r\nThe metadata referring to OWI are derived from an internally processed GRD product. The metadata referring to RVL (and OSW, for SM and WV mode) are derived from an internally processed SLC product.\r\n\r\nFor more information on the changes for this processing version please see the Sentinel 1 document libary under the docs tab."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 12318,
                    "uuid": "f95b77f14a554727a1975802b25ad8a7",
                    "short_code": "acq",
                    "title": "Acquisition Process for: Sentinel 1A C-band Synthetic Aperture Radar (SAR), Interferometric Wide (IW) mode.",
                    "abstract": "The acquisition process for the collection of raw radar data from the European Space Agency (ESA) Sentinel 1A C-band Synthetic Aperture Radar (SAR) in Interferometric Wide (IW) mode."
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                146169,
                146170
            ]
        },
        {
            "ob_id": 32764,
            "uuid": "0cea6c149f7842bca07e2b290a1b8e87",
            "title": "Composite Process for: Level 2 data from the Sentinel 1B C-band Synthetic Aperture Radar (SAR), Instrument Processing Facility (IPF) v3",
            "abstract": "Composite process for Level 2 data from the C-band Synthetic Aperture Radar (SAR) deployed on Sentinel 1. This consists of the Acquisition process for raw radar data from the Sentinel 1 SAR and the computation component to produce processed Level 2 radar data.",
            "computationComponent": [
                {
                    "ob_id": 26519,
                    "uuid": "179a8c9a1cd2402cb3d753071b0606a7",
                    "short_code": "comp",
                    "title": "Level 2 processing algorithm applied to Sentinel 1 raw data, Instrument Processing Facility (IPF) version 3",
                    "abstract": "Level-2 consists of geo-located geophysical products derived from Level-1. Level-2 Ocean (OCN) products for wind, wave and currents applications may contain the following geophysical components derived from the SAR data:\r\n- Ocean Wind field (OWI)\r\n- Ocean Swell spectra (OSW)\r\n- Surface Radial Velocity (RVL)\r\nThe availability of components depends on the acquisition mode. The OSW component cannot be generated from IW and EW mode, since individual looks with sufficient time separation are required. The obtained inter look time separation within one burst is too short due to the progressive scanning (i.e. short dwell time).\r\n\r\nThe metadata referring to OWI are derived from an internally processed GRD product. The metadata referring to RVL (and OSW, for SM and WV mode) are derived from an internally processed SLC product.\r\n\r\nFor more information on the changes for this processing version please see the Sentinel 1 document libary under the docs tab."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 20018,
                    "uuid": "c28a3a6627354dd19363ac971116b0d8",
                    "short_code": "acq",
                    "title": "Acquisition Process for: Sentinel 1B C-band Synthetic Aperture Radar (SAR) Interferometric Wide (IW) mode.",
                    "abstract": "The acquisition process for the collection of raw radar data from the European Space Agency (ESA) Sentinel 1B C-band Synthetic Aperture Radar (SAR) in Interferometric Wide (IW) mode."
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                146209,
                146211
            ]
        },
        {
            "ob_id": 32773,
            "uuid": "07df50df040f4559a7d4da60620bb9f0",
            "title": "Composite Process for: Level 1 data from the Sentinel 1A C-band Synthetic Aperture Radar (SAR), Stripmap (SM) mode, Instrument Processing Facility (IPF) v3",
            "abstract": "Composite process for Level 1 data from the C-band Synthetic Aperture Radar (SAR) deployed on Sentinel 1. This consists of the Acquisition process for raw radar data from the Sentinel 1 SAR and the computation component to produce processed Level 1 radar data.",
            "computationComponent": [
                {
                    "ob_id": 12320,
                    "uuid": "a83b5cfca79a48599c002dfbb3de858e",
                    "short_code": "comp",
                    "title": "Level 1 processing algorithm applied to Sentinel 1 raw data, Instrument Processing Facility (IPF) v3",
                    "abstract": "This computation involves the Level 1 processing algorithm applied to raw Synthetic Aperture Radar (SAR) data. This consists of Level 1 preprocessing, special handling for TOPSAR mode, Doppler centroid estimation, Level 1 Single Look Complex (SLC) processing algorithms and Level 1 post-processing to generate the output Single Look Complex (SLC) and Ground Range Detected (GRD) products as well as quicklook images. \r\n\r\nLevel-1 Single Look Complex (SLC) products consist of focused SAR data, geo-referenced using orbit and attitude data from the satellite, and provided in slant-range geometry. Slant range is the natural radar range observation coordinate, defined as the line-of-sight from the radar to each reflecting object. The products are in zero-Doppler orientation where each row of pixels represents points along a line perpendicular to the sub-satellite track.\r\n\r\nThe products include a single look in each dimension using the full available signal bandwidth and complex samples (real and imaginary) preserving the phase information. The products have been geo-referenced using the orbit and attitude data from the satellite and have been corrected for azimuth bi-static delay, elevation antenna pattern and range spreading loss.\r\n\r\nFor more information on the changes for this processing version please see the Sentinel 1 document libary under the docs tab."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 32774,
                    "uuid": "4c4f080b0afb40b9a5522d0b3af9cf2f",
                    "short_code": "acq",
                    "title": "Acquisition Process for: Sentinel 1A C-band Synthetic Aperture Radar (SAR), Stripmap (SM) mode.",
                    "abstract": "The acquisition process for the collection of raw radar data from the European Space Agency (ESA) Sentinel 1A C-band Synthetic Aperture Radar (SAR) in Stripmap (SM) mode."
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                146294,
                146295
            ]
        },
        {
            "ob_id": 32777,
            "uuid": "2a273bb40a9c44a7bfc46ac89f623d28",
            "title": "Composite Process for: Level 1 data from the Sentinel 1A C-band Synthetic Aperture Radar (SAR), Stripmap (SM) mode, Instrument Processing Facility (IPF) v2",
            "abstract": "Composite process for Level 1 data from the C-band Synthetic Aperture Radar (SAR) deployed on Sentinel 1. This consists of the Acquisition process for raw radar data from the Sentinel 1 SAR and the computation component to produce processed Level 1 radar data.",
            "computationComponent": [
                {
                    "ob_id": 32778,
                    "uuid": "e71c879b049f4ccca07542044df25f09",
                    "short_code": "comp",
                    "title": "Level 1 processing algorithm applied to Sentinel 1 raw data, Instrument Processing Facility (IPF) v2",
                    "abstract": "This computation involves the Level 1 processing algorithm applied to raw Synthetic Aperture Radar (SAR) data. This consists of Level 1 preprocessing, special handling for TOPSAR mode, Doppler centroid estimation, Level 1 Single Look Complex (SLC) processing algorithms and Level 1 post-processing to generate the output Single Look Complex (SLC) and Ground Range Detected (GRD) products as well as quicklook images. \r\n\r\nLevel-1 Single Look Complex (SLC) products consist of focused SAR data, geo-referenced using orbit and attitude data from the satellite, and provided in slant-range geometry. Slant range is the natural radar range observation coordinate, defined as the line-of-sight from the radar to each reflecting object. The products are in zero-Doppler orientation where each row of pixels represents points along a line perpendicular to the sub-satellite track.\r\n\r\nThe products include a single look in each dimension using the full available signal bandwidth and complex samples (real and imaginary) preserving the phase information. The products have been geo-referenced using the orbit and attitude data from the satellite and have been corrected for azimuth bi-static delay, elevation antenna pattern and range spreading loss.\r\n\r\nFor more information on the changes for this processing version please see the Sentinel 1 document libary under the docs tab."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 32774,
                    "uuid": "4c4f080b0afb40b9a5522d0b3af9cf2f",
                    "short_code": "acq",
                    "title": "Acquisition Process for: Sentinel 1A C-band Synthetic Aperture Radar (SAR), Stripmap (SM) mode.",
                    "abstract": "The acquisition process for the collection of raw radar data from the European Space Agency (ESA) Sentinel 1A C-band Synthetic Aperture Radar (SAR) in Stripmap (SM) mode."
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                146308,
                146309
            ]
        },
        {
            "ob_id": 32782,
            "uuid": "972c15a7c83a419caf2ddce1d5f0bb20",
            "title": "Composite Process for: Level 1 data from the Sentinel 1A C-band Synthetic Aperture Radar (SAR), Interferometric Wide (IW), Instrument Processing Facility (IPF) v3",
            "abstract": "Composite process for Level 1 data from the C-band Synthetic Aperture Radar (SAR) deployed on Sentinel 1. This consists of the Acquisition process for raw radar data from the Sentinel 1 SAR and the computation component to produce processed Level 1 radar data.",
            "computationComponent": [
                {
                    "ob_id": 12320,
                    "uuid": "a83b5cfca79a48599c002dfbb3de858e",
                    "short_code": "comp",
                    "title": "Level 1 processing algorithm applied to Sentinel 1 raw data, Instrument Processing Facility (IPF) v3",
                    "abstract": "This computation involves the Level 1 processing algorithm applied to raw Synthetic Aperture Radar (SAR) data. This consists of Level 1 preprocessing, special handling for TOPSAR mode, Doppler centroid estimation, Level 1 Single Look Complex (SLC) processing algorithms and Level 1 post-processing to generate the output Single Look Complex (SLC) and Ground Range Detected (GRD) products as well as quicklook images. \r\n\r\nLevel-1 Single Look Complex (SLC) products consist of focused SAR data, geo-referenced using orbit and attitude data from the satellite, and provided in slant-range geometry. Slant range is the natural radar range observation coordinate, defined as the line-of-sight from the radar to each reflecting object. The products are in zero-Doppler orientation where each row of pixels represents points along a line perpendicular to the sub-satellite track.\r\n\r\nThe products include a single look in each dimension using the full available signal bandwidth and complex samples (real and imaginary) preserving the phase information. The products have been geo-referenced using the orbit and attitude data from the satellite and have been corrected for azimuth bi-static delay, elevation antenna pattern and range spreading loss.\r\n\r\nFor more information on the changes for this processing version please see the Sentinel 1 document libary under the docs tab."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 12318,
                    "uuid": "f95b77f14a554727a1975802b25ad8a7",
                    "short_code": "acq",
                    "title": "Acquisition Process for: Sentinel 1A C-band Synthetic Aperture Radar (SAR), Interferometric Wide (IW) mode.",
                    "abstract": "The acquisition process for the collection of raw radar data from the European Space Agency (ESA) Sentinel 1A C-band Synthetic Aperture Radar (SAR) in Interferometric Wide (IW) mode."
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                146329,
                146330
            ]
        },
        {
            "ob_id": 32791,
            "uuid": "5a90ab33b85c4cfb80ccb3e8f99f8345",
            "title": "Composite Process for: Level 1 data from the Sentinel 1A C-band Synthetic Aperture Radar (SAR), Wave (WV) mode, Instrument Processing Facility (IPF) v2",
            "abstract": "Composite process for Level 1 data from the C-band Synthetic Aperture Radar (SAR) deployed on Sentinel 1. This consists of the Acquisition process for raw radar data from the Sentinel 1 SAR and the computation component to produce processed Level 1 radar data.",
            "computationComponent": [
                {
                    "ob_id": 32778,
                    "uuid": "e71c879b049f4ccca07542044df25f09",
                    "short_code": "comp",
                    "title": "Level 1 processing algorithm applied to Sentinel 1 raw data, Instrument Processing Facility (IPF) v2",
                    "abstract": "This computation involves the Level 1 processing algorithm applied to raw Synthetic Aperture Radar (SAR) data. This consists of Level 1 preprocessing, special handling for TOPSAR mode, Doppler centroid estimation, Level 1 Single Look Complex (SLC) processing algorithms and Level 1 post-processing to generate the output Single Look Complex (SLC) and Ground Range Detected (GRD) products as well as quicklook images. \r\n\r\nLevel-1 Single Look Complex (SLC) products consist of focused SAR data, geo-referenced using orbit and attitude data from the satellite, and provided in slant-range geometry. Slant range is the natural radar range observation coordinate, defined as the line-of-sight from the radar to each reflecting object. The products are in zero-Doppler orientation where each row of pixels represents points along a line perpendicular to the sub-satellite track.\r\n\r\nThe products include a single look in each dimension using the full available signal bandwidth and complex samples (real and imaginary) preserving the phase information. The products have been geo-referenced using the orbit and attitude data from the satellite and have been corrected for azimuth bi-static delay, elevation antenna pattern and range spreading loss.\r\n\r\nFor more information on the changes for this processing version please see the Sentinel 1 document libary under the docs tab."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 32792,
                    "uuid": "7f2db1418a374a9eb1ad4a5ead4a0936",
                    "short_code": "acq",
                    "title": "Acquisition Process for: Sentinel 1A C-band Synthetic Aperture Radar (SAR), Wave (WV) mode.",
                    "abstract": "The acquisition process for the collection of raw radar data from the European Space Agency (ESA) Sentinel 1A C-band Synthetic Aperture Radar (SAR) in Wave (WV) mode."
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                146408,
                146409
            ]
        },
        {
            "ob_id": 32793,
            "uuid": "82df069ac1ad48ae8e548aaae496acf3",
            "title": "Composite Process for: Level 1 data from the Sentinel 1A C-band Synthetic Aperture Radar (SAR), Wave (WV) mode, Instrument Processing Facility (IPF) v3",
            "abstract": "Composite process for Level 1 data from the C-band Synthetic Aperture Radar (SAR) deployed on Sentinel 1. This consists of the Acquisition process for raw radar data from the Sentinel 1 SAR and the computation component to produce processed Level 1 radar data.",
            "computationComponent": [
                {
                    "ob_id": 12320,
                    "uuid": "a83b5cfca79a48599c002dfbb3de858e",
                    "short_code": "comp",
                    "title": "Level 1 processing algorithm applied to Sentinel 1 raw data, Instrument Processing Facility (IPF) v3",
                    "abstract": "This computation involves the Level 1 processing algorithm applied to raw Synthetic Aperture Radar (SAR) data. This consists of Level 1 preprocessing, special handling for TOPSAR mode, Doppler centroid estimation, Level 1 Single Look Complex (SLC) processing algorithms and Level 1 post-processing to generate the output Single Look Complex (SLC) and Ground Range Detected (GRD) products as well as quicklook images. \r\n\r\nLevel-1 Single Look Complex (SLC) products consist of focused SAR data, geo-referenced using orbit and attitude data from the satellite, and provided in slant-range geometry. Slant range is the natural radar range observation coordinate, defined as the line-of-sight from the radar to each reflecting object. The products are in zero-Doppler orientation where each row of pixels represents points along a line perpendicular to the sub-satellite track.\r\n\r\nThe products include a single look in each dimension using the full available signal bandwidth and complex samples (real and imaginary) preserving the phase information. The products have been geo-referenced using the orbit and attitude data from the satellite and have been corrected for azimuth bi-static delay, elevation antenna pattern and range spreading loss.\r\n\r\nFor more information on the changes for this processing version please see the Sentinel 1 document libary under the docs tab."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 32792,
                    "uuid": "7f2db1418a374a9eb1ad4a5ead4a0936",
                    "short_code": "acq",
                    "title": "Acquisition Process for: Sentinel 1A C-band Synthetic Aperture Radar (SAR), Wave (WV) mode.",
                    "abstract": "The acquisition process for the collection of raw radar data from the European Space Agency (ESA) Sentinel 1A C-band Synthetic Aperture Radar (SAR) in Wave (WV) mode."
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                146412,
                146413
            ]
        },
        {
            "ob_id": 32796,
            "uuid": "540c178877e94c4082492e210954716d",
            "title": "Composite Process for: Level 1 data from the Sentinel 1B C-band Synthetic Aperture Radar (SAR), Wave (WV) mode, Instrument Processing Facility (IPF) v2",
            "abstract": "Composite process for Level 1 data from the C-band Synthetic Aperture Radar (SAR) deployed on Sentinel 1. This consists of the Acquisition process for raw radar data from the Sentinel 1 SAR and the computation component to produce processed Level 1 radar data.",
            "computationComponent": [
                {
                    "ob_id": 32778,
                    "uuid": "e71c879b049f4ccca07542044df25f09",
                    "short_code": "comp",
                    "title": "Level 1 processing algorithm applied to Sentinel 1 raw data, Instrument Processing Facility (IPF) v2",
                    "abstract": "This computation involves the Level 1 processing algorithm applied to raw Synthetic Aperture Radar (SAR) data. This consists of Level 1 preprocessing, special handling for TOPSAR mode, Doppler centroid estimation, Level 1 Single Look Complex (SLC) processing algorithms and Level 1 post-processing to generate the output Single Look Complex (SLC) and Ground Range Detected (GRD) products as well as quicklook images. \r\n\r\nLevel-1 Single Look Complex (SLC) products consist of focused SAR data, geo-referenced using orbit and attitude data from the satellite, and provided in slant-range geometry. Slant range is the natural radar range observation coordinate, defined as the line-of-sight from the radar to each reflecting object. The products are in zero-Doppler orientation where each row of pixels represents points along a line perpendicular to the sub-satellite track.\r\n\r\nThe products include a single look in each dimension using the full available signal bandwidth and complex samples (real and imaginary) preserving the phase information. The products have been geo-referenced using the orbit and attitude data from the satellite and have been corrected for azimuth bi-static delay, elevation antenna pattern and range spreading loss.\r\n\r\nFor more information on the changes for this processing version please see the Sentinel 1 document libary under the docs tab."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 32797,
                    "uuid": "4c44f3e675d0464eb47f3a7d5163c36c",
                    "short_code": "acq",
                    "title": "Acquisition Process for: Sentinel 1B C-band Synthetic Aperture Radar (SAR), Wave (WV) mode.",
                    "abstract": "The acquisition process for the collection of raw radar data from the European Space Agency (ESA) Sentinel 1B C-band Synthetic Aperture Radar (SAR) in Wave (WV) mode."
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                146425,
                146426
            ]
        },
        {
            "ob_id": 32800,
            "uuid": "d6eea898cc2048198e842f081f17070e",
            "title": "Composite Process for: Level 1 data from the Sentinel 1B C-band Synthetic Aperture Radar (SAR), Wave (WV) mode, Instrument Processing Facility (IPF) v3",
            "abstract": "Composite process for Level 1 data from the C-band Synthetic Aperture Radar (SAR) deployed on Sentinel 1. This consists of the Acquisition process for raw radar data from the Sentinel 1 SAR and the computation component to produce processed Level 1 radar data.",
            "computationComponent": [
                {
                    "ob_id": 12320,
                    "uuid": "a83b5cfca79a48599c002dfbb3de858e",
                    "short_code": "comp",
                    "title": "Level 1 processing algorithm applied to Sentinel 1 raw data, Instrument Processing Facility (IPF) v3",
                    "abstract": "This computation involves the Level 1 processing algorithm applied to raw Synthetic Aperture Radar (SAR) data. This consists of Level 1 preprocessing, special handling for TOPSAR mode, Doppler centroid estimation, Level 1 Single Look Complex (SLC) processing algorithms and Level 1 post-processing to generate the output Single Look Complex (SLC) and Ground Range Detected (GRD) products as well as quicklook images. \r\n\r\nLevel-1 Single Look Complex (SLC) products consist of focused SAR data, geo-referenced using orbit and attitude data from the satellite, and provided in slant-range geometry. Slant range is the natural radar range observation coordinate, defined as the line-of-sight from the radar to each reflecting object. The products are in zero-Doppler orientation where each row of pixels represents points along a line perpendicular to the sub-satellite track.\r\n\r\nThe products include a single look in each dimension using the full available signal bandwidth and complex samples (real and imaginary) preserving the phase information. The products have been geo-referenced using the orbit and attitude data from the satellite and have been corrected for azimuth bi-static delay, elevation antenna pattern and range spreading loss.\r\n\r\nFor more information on the changes for this processing version please see the Sentinel 1 document libary under the docs tab."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 32797,
                    "uuid": "4c44f3e675d0464eb47f3a7d5163c36c",
                    "short_code": "acq",
                    "title": "Acquisition Process for: Sentinel 1B C-band Synthetic Aperture Radar (SAR), Wave (WV) mode.",
                    "abstract": "The acquisition process for the collection of raw radar data from the European Space Agency (ESA) Sentinel 1B C-band Synthetic Aperture Radar (SAR) in Wave (WV) mode."
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                146444,
                146445
            ]
        },
        {
            "ob_id": 32811,
            "uuid": "dda769b3edc247b19fac1828052001cd",
            "title": "Composite Process for: Level 1 data from the Sentinel 1B C-band Synthetic Aperture Radar (SAR) Interferometric Wide (IW), Instrument Processing Facility (IPF) v3",
            "abstract": "Composite process for Level 1 data from the C-band Synthetic Aperture Radar (SAR) deployed on Sentinel 1B. This consists of the Acquisition process for raw radar data from the Sentinel 1 SAR and the computation component to produce processed Level 1 radar data.",
            "computationComponent": [
                {
                    "ob_id": 12320,
                    "uuid": "a83b5cfca79a48599c002dfbb3de858e",
                    "short_code": "comp",
                    "title": "Level 1 processing algorithm applied to Sentinel 1 raw data, Instrument Processing Facility (IPF) v3",
                    "abstract": "This computation involves the Level 1 processing algorithm applied to raw Synthetic Aperture Radar (SAR) data. This consists of Level 1 preprocessing, special handling for TOPSAR mode, Doppler centroid estimation, Level 1 Single Look Complex (SLC) processing algorithms and Level 1 post-processing to generate the output Single Look Complex (SLC) and Ground Range Detected (GRD) products as well as quicklook images. \r\n\r\nLevel-1 Single Look Complex (SLC) products consist of focused SAR data, geo-referenced using orbit and attitude data from the satellite, and provided in slant-range geometry. Slant range is the natural radar range observation coordinate, defined as the line-of-sight from the radar to each reflecting object. The products are in zero-Doppler orientation where each row of pixels represents points along a line perpendicular to the sub-satellite track.\r\n\r\nThe products include a single look in each dimension using the full available signal bandwidth and complex samples (real and imaginary) preserving the phase information. The products have been geo-referenced using the orbit and attitude data from the satellite and have been corrected for azimuth bi-static delay, elevation antenna pattern and range spreading loss.\r\n\r\nFor more information on the changes for this processing version please see the Sentinel 1 document libary under the docs tab."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 20018,
                    "uuid": "c28a3a6627354dd19363ac971116b0d8",
                    "short_code": "acq",
                    "title": "Acquisition Process for: Sentinel 1B C-band Synthetic Aperture Radar (SAR) Interferometric Wide (IW) mode.",
                    "abstract": "The acquisition process for the collection of raw radar data from the European Space Agency (ESA) Sentinel 1B C-band Synthetic Aperture Radar (SAR) in Interferometric Wide (IW) mode."
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                146506,
                146507,
                146508
            ]
        },
        {
            "ob_id": 32812,
            "uuid": "05fbcb5c4c2b440e9949b05f03de17b7",
            "title": "Composite Process for: Level 1 data from the Sentinel 1B C-band Synthetic Aperture Radar (SAR), Stripmap (SM) mode, Instrument Processing Facility (IPF) v2",
            "abstract": "Composite process for Level 1 data from the C-band Synthetic Aperture Radar (SAR) deployed on Sentinel 1. This consists of the Acquisition process for raw radar data from the Sentinel 1 SAR and the computation component to produce processed Level 1 radar data.",
            "computationComponent": [
                {
                    "ob_id": 32778,
                    "uuid": "e71c879b049f4ccca07542044df25f09",
                    "short_code": "comp",
                    "title": "Level 1 processing algorithm applied to Sentinel 1 raw data, Instrument Processing Facility (IPF) v2",
                    "abstract": "This computation involves the Level 1 processing algorithm applied to raw Synthetic Aperture Radar (SAR) data. This consists of Level 1 preprocessing, special handling for TOPSAR mode, Doppler centroid estimation, Level 1 Single Look Complex (SLC) processing algorithms and Level 1 post-processing to generate the output Single Look Complex (SLC) and Ground Range Detected (GRD) products as well as quicklook images. \r\n\r\nLevel-1 Single Look Complex (SLC) products consist of focused SAR data, geo-referenced using orbit and attitude data from the satellite, and provided in slant-range geometry. Slant range is the natural radar range observation coordinate, defined as the line-of-sight from the radar to each reflecting object. The products are in zero-Doppler orientation where each row of pixels represents points along a line perpendicular to the sub-satellite track.\r\n\r\nThe products include a single look in each dimension using the full available signal bandwidth and complex samples (real and imaginary) preserving the phase information. The products have been geo-referenced using the orbit and attitude data from the satellite and have been corrected for azimuth bi-static delay, elevation antenna pattern and range spreading loss.\r\n\r\nFor more information on the changes for this processing version please see the Sentinel 1 document libary under the docs tab."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 32813,
                    "uuid": "f2370020914949c489de445f630b3fef",
                    "short_code": "acq",
                    "title": "Acquisition Process for: Sentinel 1B C-band Synthetic Aperture Radar (SAR), Stripmap (SM) mode.",
                    "abstract": "The acquisition process for the collection of raw radar data from the European Space Agency (ESA) Sentinel 1B C-band Synthetic Aperture Radar (SAR) in Stripmap (SM) mode."
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                146509,
                146510
            ]
        },
        {
            "ob_id": 32816,
            "uuid": "772b1c2ca7254513bd19d05ea8c25a53",
            "title": "Composite Process for: Level 1 data from the Sentinel 1B C-band Synthetic Aperture Radar (SAR), Stripmap (SM) mode, Instrument Processing Facility (IPF) v3",
            "abstract": "Composite process for Level 1 data from the C-band Synthetic Aperture Radar (SAR) deployed on Sentinel 1. This consists of the Acquisition process for raw radar data from the Sentinel 1 SAR and the computation component to produce processed Level 1 radar data.",
            "computationComponent": [
                {
                    "ob_id": 12320,
                    "uuid": "a83b5cfca79a48599c002dfbb3de858e",
                    "short_code": "comp",
                    "title": "Level 1 processing algorithm applied to Sentinel 1 raw data, Instrument Processing Facility (IPF) v3",
                    "abstract": "This computation involves the Level 1 processing algorithm applied to raw Synthetic Aperture Radar (SAR) data. This consists of Level 1 preprocessing, special handling for TOPSAR mode, Doppler centroid estimation, Level 1 Single Look Complex (SLC) processing algorithms and Level 1 post-processing to generate the output Single Look Complex (SLC) and Ground Range Detected (GRD) products as well as quicklook images. \r\n\r\nLevel-1 Single Look Complex (SLC) products consist of focused SAR data, geo-referenced using orbit and attitude data from the satellite, and provided in slant-range geometry. Slant range is the natural radar range observation coordinate, defined as the line-of-sight from the radar to each reflecting object. The products are in zero-Doppler orientation where each row of pixels represents points along a line perpendicular to the sub-satellite track.\r\n\r\nThe products include a single look in each dimension using the full available signal bandwidth and complex samples (real and imaginary) preserving the phase information. The products have been geo-referenced using the orbit and attitude data from the satellite and have been corrected for azimuth bi-static delay, elevation antenna pattern and range spreading loss.\r\n\r\nFor more information on the changes for this processing version please see the Sentinel 1 document libary under the docs tab."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 32813,
                    "uuid": "f2370020914949c489de445f630b3fef",
                    "short_code": "acq",
                    "title": "Acquisition Process for: Sentinel 1B C-band Synthetic Aperture Radar (SAR), Stripmap (SM) mode.",
                    "abstract": "The acquisition process for the collection of raw radar data from the European Space Agency (ESA) Sentinel 1B C-band Synthetic Aperture Radar (SAR) in Stripmap (SM) mode."
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                146524,
                146525
            ]
        },
        {
            "ob_id": 32824,
            "uuid": "33712e8a0e0741a7b16f891a6a26b984",
            "title": "Composite Process for: Level 1A data from the Sentinel 3 Synthetic Aperture Radar Altimeter (SRAL)",
            "abstract": "Composite process for Level 1 data from the Synthetic Aperture Radar Altimeter (SRAL) deployed on Sentinel 3. This consists of the Acquisition process for raw data from the Sentinel 3 SRAL and the computation component to produce processed Level 1A data.",
            "computationComponent": [
                {
                    "ob_id": 32825,
                    "uuid": "b58a89dcbf5b43bb96145e5f3833bede",
                    "short_code": "comp",
                    "title": "Computation Component: Level 1A processing algorithm applied to Sentinel 3 SRAL raw data.",
                    "abstract": "This computation involves the Level 1A processing algorithm applied to raw Synthetic Aperture Radar Altimeter (SRAL) data. \r\n\r\nThe main algorithms of the Level-1 SAR_Ku chain are:\r\nDetermine surface type: This algorithm computes the surface type (\"open ocean or semi-enclosed seas\", \"enclosed seas or lakes\", \"continental ice\" or \"land\") determining the position of a \"land-sea mask\" Auxiliary Data File nearest to the geolocated measurement. The latitude and longitude resolution of this land-sea mask is 2 minutes.\r\nCompute tracker ranges corrected for USO frequency drift: This algorithm computes the USO correction from an Auxiliary Data File called \"USO file\" and this correction is applied to the tracker range. The \"USO file\" provides the real USO frequency drift measured on-board wrt the USO frequency nominal value. This algorithm also computes the tracker range rate converted into distance versus time.\r\nCompute tracker ranges corrected for internal path correction: This algorithm computes the internal path correction from an Auxiliary Data File called \"CAL1 LTM file\" and this correction is applied to the tracker range. The \"CAL1 LTM file\" provides the internal path delay measured on-board thanks to the CAL1 calibration mode, which measures the difference of travel between the transmission and the reference lines within the altimeter. This algorithm also computes and applies the instrumental delay correction measured on-ground, due to the distance between the duplexer and the antenna reference point.\r\nCorrect the AGC for instrumental errors: This algorithm computes the Automatic Gain Control (AGC) instrumental correction and applies this correction to the AGC. The AGC instrumental correction is computed taking into account the real gain value applied on-board and stored as a matrix table on an Auxiliary Data File called \"characterisation file\".\r\nCorrect and apply power & phase corrections: This algorithm computes and applies to each burst the phase and power variations within all the echoes of every burst. These phase and power corrections are measured on-board through a sequence of calibration echoes in CAL1 calibration mode.\r\nCorrect the waveforms: On-board, there is a calibration mode called CAL2 that is able to compute the Gain Profile Range Window (GPRW) that provides the information of the attenuation of the samples of the Level. The GPRW accounts for several instrumental effects (e.g. intermediate frequency filters gain response) that have an impact on the Level 0 waveforms power. This algorithm corrects these Level-0 waveforms by the GPRW instrumental effects.\r\nCompute surface locations: In the SAR_Ku processing chain, the output measurements are referenced to surface locations along the satellite track. These surface locations correspond with the intersection of the Doppler beams with an estimation of the surface elevations. These surface locations are used along all L1 SAR_Ku processing.\r\nDetermine Doppler beams direction: This algorithm determines the angular spacing between the instantaneous zero Doppler plane and the lines defined by the burst centre and the reference surface locations \"observed\" within the burst sequence.\r\nDoppler beams generation: This algorithm generates the Doppler beams in the frequency domain. Each burst of pulse-limited time domain echoes are transformed into the frequency domain using an FFT (Fast Fourier Transform) in the along track direction.\r\nCompute and apply Doppler correction: This algorithm computes and applied the Doppler correction to the tracker ranges. This correction is needed to remove the echoes frequency shifts due to sensor-target velocity. The Doppler correction is computed and applied in the frequency domain to each Doppler beam. This correction is a function of the emitted frequency, the pulse emitted duration, the satellite velocity of the beams, the emitted bandwidth and the sign of the slope of the transmitted chirp.\r\nCompute and apply slant range corrections: This algorithm computes the slant corrections (both fine and coarse) that correct the range-migration due to the motion of the sensor along the orbit.\r\nRange compression: This algorithm performs a range compression of the waveform that is the conversion of each Doppler processed burst of pulse-width time domain echoes to the frequency domain.\r\nTracker alignment correction: This algorithm corrects the azimuth processed echo stack for on-board tracker variation. It means that for each surface location, the waveforms are aligned before multi-looking.\r\nDoppler beams stack & multi-looking: This algorithm computes the stacked Doppler beams (I2+Q2 power waveforms) through the non-coherent summation of all the beams corresponding to each surface location.\r\nCompute sigma0 scaling factor: This algorithm computes the sigma0 scaling factor that is used at Level2 to determine the backscatter coefficients from the retracked amplitudes. The sigma0 scaling factor accounts for all power attenuations and gains which have an impact on the signal received on-board."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 19015,
                    "uuid": "1c1605795ae247c28a64d003c686bdb2",
                    "short_code": "acq",
                    "title": "Acquisition Process for: Sentinel 3A Synthetic Aperture Radar Altimeter (SRAL)",
                    "abstract": "The acquisition process for the collection of raw data from the European Space Agency (ESA) Sentinel 3A Synthetic Aperture Radar Altimeter (SRAL)."
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                146568,
                146564,
                146565
            ]
        },
        {
            "ob_id": 32859,
            "uuid": "2fbdd374d2e245c38808ec69d41cad11",
            "title": "Composite process for the ESA Greenhouse Gases Climate Change Initiative CH4_GO2_SRPR product",
            "abstract": "The CH4_GO2_SRPR product was derived from data from the TANSO-FTS/2 instrument on the GOSAT satellite, using the SRPR (Remotec) retrieval algorithm.",
            "computationComponent": [
                {
                    "ob_id": 32854,
                    "uuid": "680031416869407292a724be855271f3",
                    "short_code": "comp",
                    "title": "The SRPR (Remotec) Proxy retrieval algorithm",
                    "abstract": "The RemoTeC retrieval algorithm  has been jointly developed at SRON and KIT to retrieve column-averaged methane, using a Proxy retrieval technique.   \r\n\r\nDetails of the technical aspects of the retrievals can be found in the ATBD (see documentation links)"
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 32858,
                    "uuid": "74166c97d5e74d51ac946aa36431ae95",
                    "short_code": "acq",
                    "title": "Aquisition for the ESA Greenhouse Gases Climate Change Initiative CH4_GO2_SRPR dataset",
                    "abstract": "The CH4_GO2_SRPR dataset is derived from data from the TANSO-FTS/2 instrument on the GOSAT-2 satellite."
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                146757
            ]
        },
        {
            "ob_id": 32861,
            "uuid": "25daadec315b4127a5c80d4e1354c690",
            "title": "Composite process for the ESA Greenhouse Gases Climate Change Initiative CH4_GO2_SRFP and CO2_GOS_SRFP products",
            "abstract": "The CH4_GO2_SRFP and CO2_GO2_SRFP products were derived from data from the TANSO-FTS/2 instrument on the GOSAT satellite, using the SRFP (Remotec) retrieval algorithm.",
            "computationComponent": [
                {
                    "ob_id": 32860,
                    "uuid": "0396726bba52483588fd9db95aebed52",
                    "short_code": "comp",
                    "title": "The SRFP (Remotec) Full Physics retrieval algorithm",
                    "abstract": "The RemoTeC retrieval algorithm  has been jointly developed at SRON and KIT to retrieve column-averaged methane and carbon dioxide, using a 'Full Physics' retrieval technique.   \r\n\r\nDetails of the technical aspects of the retrievals can be found in the ATBD (see documentation links)"
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 32858,
                    "uuid": "74166c97d5e74d51ac946aa36431ae95",
                    "short_code": "acq",
                    "title": "Aquisition for the ESA Greenhouse Gases Climate Change Initiative CH4_GO2_SRPR dataset",
                    "abstract": "The CH4_GO2_SRPR dataset is derived from data from the TANSO-FTS/2 instrument on the GOSAT-2 satellite."
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                146760
            ]
        },
        {
            "ob_id": 32867,
            "uuid": "78c4897e8b5c4818b81a0213ddc198dd",
            "title": "Composite process for the ESA Greenhouse Gases Climate Change Initiative CH4_S5P_WFMD product",
            "abstract": "The ESA Greenhouse Gases Climate Change Initiative CH4_S5P_WFMD product has been derived from the Tropomi instrument on the Sentinel-5P satellite, using the WFM-DOAS retrieval algorithm.",
            "computationComponent": [
                {
                    "ob_id": 32866,
                    "uuid": "bec27be51fdd44a69ce1223ae068e518",
                    "short_code": "comp",
                    "title": "Derivation of the CH4_S5P_WFMD product from the  WFM-DOAS Retrieval algorithm",
                    "abstract": "The Weighting Function Modified Differential Optical Absorption Spectroscopy (WFM-DOAS) algorithm is a least-squares retrieval method based on scaling (or shifting) pre-selected atmospheric vertical profiles.   The column-averaged dry air mole fractions of  methane (denoted XCH4) are derived from the vertical column amounts of methane by normalising with the dry air column, which is obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF) analysis. The corresponding vertical columns of CH4 are retrieved from the measured sun-normalised radiance using spectral fitting windows in the SWIR spectral region (2311-2315.5 nm and 2320-2338 nm).\r\nFor further details see the documentation section."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 26443,
                    "uuid": "929d929b043242e69de7b5373acfb611",
                    "short_code": "acq",
                    "title": "Acquisition Process for: Sentinel 5P TROPOspheric Monitoring Instrument (TROPOMI)",
                    "abstract": "The acquisition process for the collection of data from the European Space Agency (ESA) Sentinel 5P TROPOspheric Monitoring Instrument (TROPOMI)."
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                146780
            ]
        },
        {
            "ob_id": 32941,
            "uuid": "a0b041d6de0341b1a6218014d3964417",
            "title": "Composite process for the ESA Ozone Climate Change Initiative: ACE-FTS Level 3 monthly mean zonal mean ozone profiles on an altitude grid, v0001",
            "abstract": "The ACE-FTS L3 Monthly Zonal Mean (MZM) data was derived from the ACE-FTS instrument on SCISAT.   The data was derived as detailed in the computation section.",
            "computationComponent": [
                {
                    "ob_id": 32939,
                    "uuid": "ec4421f8744d414d95e23ba2b788bed0",
                    "short_code": "comp",
                    "title": "Derivation of the ESA Ozone CCI ACE-FTS monthly zonal mean ozone profile data",
                    "abstract": "The monthly zonal mean ozone profile data are based on  ACE-FTS Level 2 profiles (Bernath et al., 2005; Bernath, 2017), retrieved with the University of Toronto processor UoT v3.5/3.6 and included into the new version of the HARMonized datasets of OZone profiles (HARMOZ_ALT, Sofieva et al., 2013). A more detailed description of the MZM data processing and dataset parameters can be found in the README file with the data and in (Sofieva et al., 2017)."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 32940,
                    "uuid": "aef67d6db6584dae9787a3942e0fd03c",
                    "short_code": "acq",
                    "title": "The ACE-FTS instrument on SCISAT",
                    "abstract": "Acquisition of data from the ACE-FTS (Atmospheric Chemistry Experiment Fourier Transform Spectrometer) instrument onboard SCISAT"
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                147110,
                147111
            ]
        },
        {
            "ob_id": 32947,
            "uuid": "48aa2b83890b49c8bde874553141555a",
            "title": "Composite process for the ESA Ozone Climate Change Initiative: GOMOS Level 3 monthly mean zonal mean ozone profiles on an altitude grid, v0001",
            "abstract": "The GOMOS L3 Monthly Zonal Mean (MZM) data was derived from the GOMOS instrument on the ENVISAT.   The data was derived as detailed in the computation section.",
            "computationComponent": [
                {
                    "ob_id": 32946,
                    "uuid": "358982e67544438e832a6f1d8c867a6e",
                    "short_code": "comp",
                    "title": "Derivation of the ESA Ozone CCI GOMOS monthly zonal mean ozone profile data",
                    "abstract": "The monthly zonal mean ozone profile data are based on  GOMOS Level 2 profiles, retrieved with the \r\nscientific ALGOM2s v1 processor (Sofieva et al., 2017a)  and included into the new version of the HARMonized datasets of OZone profiles (HARMOZ_ALT, Sofieva et al., 2013). \r\n\r\nA more detailed description of the GOMOS Monthly zonal mean data can be found in the README and in (Sofieva et al., 2017b)."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 11047,
                    "uuid": "36a1661ea0484e58957173cbdc85d531",
                    "short_code": "acq",
                    "title": "Acquisition Process for: Data from ENVISAT GOMOS at Envisat for the ENVISAT Campaign",
                    "abstract": "This acquisition is comprised of the following: INSTRUMENTS: ENVISAT GOMOS; PLATFORMS: Envisat; "
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                147125,
                147126
            ]
        },
        {
            "ob_id": 32951,
            "uuid": "101d3d9c256d4b259725cf05d11afc87",
            "title": "Composite process for the ESA Ozone Climate Change Initiative: MIPAS Level 3 monthly mean zonal mean ozone profiles on an altitude grid, v0001",
            "abstract": "The MIPAS L3 Monthly Zonal Mean (MZM) data was derived from the MIPAS instrument on the ENVISAT satellite.   The data was derived as detailed in the computation section.",
            "computationComponent": [
                {
                    "ob_id": 32949,
                    "uuid": "21dc87fc7da349239858a7e804dccb4b",
                    "short_code": "comp",
                    "title": "Derivation of the ESA  Ozone CCI MIPAS monthly zonal mean ozone profile data",
                    "abstract": "The monthly zonal mean ozone profile data are based on  MIPAS Level 2 profiles retrieved with the Karlsruhe Institute of Technology processor KIT/IAA V7R_O3_240 (von Clarmann et al., 2003; 2009) and included into the new version of the HARMonized datasets of Ozone profiles (HARMOZ_ALT, Sofieva et al., 2013). \r\n\r\nA more detailed description of the MIPAS Monthly zonal mean data can be found in the README and in (Sofieva et al., 2017)."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 11449,
                    "uuid": "f9c56f83e09845029eac0ac84f256d60",
                    "short_code": "acq",
                    "title": "Acquisition Process for: Data from Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) on ENVISAT at Envisat for the European Space Agency  (ESA)",
                    "abstract": "This acquisition is comprised of the following: INSTRUMENTS: Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) on ENVISAT; PLATFORMS: Envisat; "
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                147139,
                147140
            ]
        },
        {
            "ob_id": 32952,
            "uuid": "0f121956ea97454f824f27bd4fe48596",
            "title": "__MUST_UPDATE__20210729172258__ Composite process for the ESA Ozone Climate Change Initiative: GOMOS Level 3 monthly mean zonal mean ozone profiles on an altitude grid, v0001",
            "abstract": "The GOMOS L3 Monthly Zonal Mean (MZM) data was derived from the GOMOS instrument on the ENVISAT.   The data was derived as detailed in the computation section.",
            "computationComponent": [
                {
                    "ob_id": 32946,
                    "uuid": "358982e67544438e832a6f1d8c867a6e",
                    "short_code": "comp",
                    "title": "Derivation of the ESA Ozone CCI GOMOS monthly zonal mean ozone profile data",
                    "abstract": "The monthly zonal mean ozone profile data are based on  GOMOS Level 2 profiles, retrieved with the \r\nscientific ALGOM2s v1 processor (Sofieva et al., 2017a)  and included into the new version of the HARMonized datasets of OZone profiles (HARMOZ_ALT, Sofieva et al., 2013). \r\n\r\nA more detailed description of the GOMOS Monthly zonal mean data can be found in the README and in (Sofieva et al., 2017b)."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 11047,
                    "uuid": "36a1661ea0484e58957173cbdc85d531",
                    "short_code": "acq",
                    "title": "Acquisition Process for: Data from ENVISAT GOMOS at Envisat for the ENVISAT Campaign",
                    "abstract": "This acquisition is comprised of the following: INSTRUMENTS: ENVISAT GOMOS; PLATFORMS: Envisat; "
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                147141,
                147142
            ]
        },
        {
            "ob_id": 32953,
            "uuid": "dcc20903b5214fc0973cb00450a8cabc",
            "title": "Composite process for the ESA Ozone Climate Change Initiative: SCIAMACHY Level 3 monthly mean zonal mean ozone profiles on an altitude grid, v0001",
            "abstract": "The SCIAMACHY L3 Monthly Zonal Mean (MZM) data was derived from the SCIAMACHY instrument on the ENVISAT.   The data was derived as detailed in the computation section.",
            "computationComponent": [
                {
                    "ob_id": 32954,
                    "uuid": "eb01c2e26aee45969ce71926e5070cb1",
                    "short_code": "comp",
                    "title": "Derivation of the ESA Ozone CCI SCIAMACHY monthly zonal mean ozone profile data",
                    "abstract": "The monthly zonal mean ozone profile data are based on  SCIAMACHY Level 2 profiles, retrieved with the \r\nUniversity of Bremen processor UBr v3.5 (Jia et al., 2015) and included into the new version of the HARMonized datasets of Ozone profiles (HARMOZ_ALT, Sofieva et al., 2013).\r\n\r\nA more detailed description of the GOMOS Monthly zonal mean data can be found in the README and in (Sofieva et al., 2017b)."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 8036,
                    "uuid": "fa6a8b1a91cf4a4cb78ac3aa64fd2659",
                    "short_code": "acq",
                    "title": "Acquisition Process for: SCIAMACHY Level 2 vertical columns of trace gases products",
                    "abstract": "This acquisition is comprised of the following: INSTRUMENTS: Envisat - SCIAMACHY; PLATFORMS: Envisat; "
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                147143,
                147144
            ]
        },
        {
            "ob_id": 32960,
            "uuid": "6afb33b6bc8145d49ade2e2cc7784993",
            "title": "Composite process for the ESA Ozone Climate Change Initiative: OSIRIS Level 3 monthly mean zonal mean ozone profiles on an altitude grid, v0002",
            "abstract": "The OSIRIS L3 Monthly Zonal Mean (MZM) data was derived from the OSIRIS instrument on the Odin satellite.   The data was derived as detailed in the computation section.",
            "computationComponent": [
                {
                    "ob_id": 32956,
                    "uuid": "4a614e5b61824f508ec7d362e1f187be",
                    "short_code": "comp",
                    "title": "Derivation of the ESA Ozone CCI OSIRIS monthly zonal mean ozone profile data",
                    "abstract": "The monthly zonal mean ozone profile data are based on OSIRIS Level 2 profiles, retrieved with the University of Saskatchewan processor USask v.5.10 (Bourassa et al., 2017; Degenstein et al., 2009) and included into the new version of the HARMonized datasets of Ozone profiles (HARMOZ_ALT, Sofieva et al., 2013). \r\n\r\nA more detailed description of the GOMOS Monthly zonal mean data can be found in the README and in (Sofieva et al., 2017)."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 20004,
                    "uuid": "97f19ea394fd429bbec2d4251612c02c",
                    "short_code": "acq",
                    "title": "OSIRIS",
                    "abstract": "Canada's Optical Spectrograph and InfraRed Imaging System (OSIRIS) is the optical payload on Sweden's Odin satellite. It works in synergy with Sweden's advanced radiometer and measures atmospheric composition."
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                147151,
                147152
            ]
        },
        {
            "ob_id": 33002,
            "uuid": "696802f3a2c34d7f9e78492720a9ca16",
            "title": "Composite process for the ESA Ozone Climate Change Initiative: MErged GRIdded Dataset of Ozone Profiles (MEGRIDOP), v0001",
            "abstract": "The MErged GRIdded Dataset of Ozone Profiles (MEGRIDOP) in the stratosphere with a resolved longitudinal structure, which is derived from data by six limb and occultation satellite instruments: GOMOS, SCIAMACHY and MIPAS on Envisat, OSIRIS on Odin, OMPS on Suomi-NPP, and MLS on Aura.\r\nThe merging is performed using deseasonalized anomalies.\r\n\r\nFor further information see the associated readme file and Sofieva, V. F., Szelag, M., Tamminen, J., Kyrölä, E., Degenstein, D., Roth, C., Zawada, D., Rozanov, A., Arosio, C., Burrows, J. P., Weber, M., Laeng, A., Stiller, G., von Clarmann, T., Froidevaux, L., Livesey, N., van Roozendael, M., and Retscher,\r\nC.: Measurement report: Regional trends of stratospheric ozone evaluated using the MErged GRIdded Dataset of Ozone Profiles (MEGRIDOP), Atmos. Chem. Phys. Discuss. [preprint], https://doi.org/10.5194/acp-2020-1117, in review, 2020.",
            "computationComponent": [
                {
                    "ob_id": 33008,
                    "uuid": "ccabd72bca1c4444a7a538fc8b0a989d",
                    "short_code": "comp",
                    "title": "Computation of the MErged GRIdded Dataset of Ozone Profiles (MEGRIDOP)",
                    "abstract": "For information on the computation of the MErged Gridded Dataset of Ozone Profiles(MEGRIDOP), see the  associated readme file and the attached documentation."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 33006,
                    "uuid": "e97292028fac4449b72a05d173087430",
                    "short_code": "acq",
                    "title": "Acquisition process for the ESA Ozone Climate Change Initiative: MErged GRIdded Dataset of Ozone Profiles (MEGRIDOP)",
                    "abstract": "The MErged GRIdded Dataset of Ozone Profiles (MEGRIDOP) in the stratosphere with a resolved longitudinal structure, which is derived from data by six limb and occultation satellite instruments: GOMOS, SCIAMACHY and MIPAS on Envisat, OSIRIS on Odin, OMPS on Suomi-NPP, and MLS on Aura."
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                147377,
                147378
            ]
        },
        {
            "ob_id": 33011,
            "uuid": "fb3a31dac5d04a10bc3333d6f13832c3",
            "title": "Composite process for the ESA Ozone Climate Change Initiative: Merged SAGE II, Ozone_cci and OMPS-LP dataset of ozone profiles",
            "abstract": "The merged SAGE-CCI-OMPS+ dataset of ozone profiles is created using the data from several satellite instruments: SAGE II on ERBS; GOMOS, SCIAMACHY and MIPAS on Envisat; OSIRIS on Odin; ACE-FTS on SCISAT; OMPS on Suomi-NPP; POAM III on SPOT 4 and SAGE III on ISS. The SAGE-CCI-OMPS+ dataset is created by computation and merging of deseasonalized anomalies from individual instruments. The detailed description of the dataset can be found in (Sofieva et al., 2017) and (Sofieva et al., 2023).",
            "computationComponent": [
                {
                    "ob_id": 33009,
                    "uuid": "0f7e38b29ac843a7a8485c9b2fcb36bd",
                    "short_code": "comp",
                    "title": "Computation of the Merged SAGE II, Ozone_cci and OMPS-LP dataset of ozone profiles",
                    "abstract": "The long-term SAGE-CCI-OMPS+ dataset has been created by computation and merging of deseasonalized anomalies from the individual instruments.\r\n\r\nThe detailed description of the dataset can be found in Sofieva et al. (2017) and Sofieva et al. (2023)."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 33010,
                    "uuid": "1beeae47665547c2b65ab98af80325b8",
                    "short_code": "acq",
                    "title": "Acquisition process for the ESA Ozone Climate Change Initiative: Merged SAGE II, Ozone_cci and OMPS-LP dataset of ozone profiles",
                    "abstract": "The merged SAGE-CCI-OMPS+ dataset of ozone profiles is created using the data from several satellite instruments: SAGE II on ERBS; GOMOS, SCIAMACHY and MIPAS on Envisat; OSIRIS on Odin; ACE-FTS on SCISAT; OMPS on Suomi-NPP; POAM III on SPOT 4 and SAGE III on ISS."
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                147389,
                147390
            ]
        },
        {
            "ob_id": 33015,
            "uuid": "4304ad00744341cc9c8fa2ce53f6fe6a",
            "title": "Composite process for the ESA Ozone Climate Change Initiative: Level 3 Total Ozone Merged Data Product",
            "abstract": "The L3 Total Ozone Merged Data Product from the ESA Ozone Climate Change intitative project is a merged data record combining harmonized data from the Global Ozone Monitoring Experiment (GOME) onboard the second European Remote Sensing satellite (ERS-2), the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) onboard the Environmental Satellite (ENVISAT), the Ozone Monitoring Instrument (OMI) onboard Aura, the GOME-2 instruments onboard the Meteorological Operational (MetOp) satellites A and B, and the Tropospheric Monitoring Instrument (TROPOMI) onboard the Seninel-5 Precursor (S5-P) satellite.",
            "computationComponent": [
                {
                    "ob_id": 33013,
                    "uuid": "75a592a7130347cb8212599149a3a077",
                    "short_code": "comp",
                    "title": "Computation of the ESA Ozone CCI Level 3 Total Ozone Merged Data Product",
                    "abstract": "For information on the computation of this dataset see the associated Algorithm Theoretical Basis Document (ATBD)."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 33014,
                    "uuid": "fa1b258d150441779629cc077ff077b4",
                    "short_code": "acq",
                    "title": "Acquisition process for the ESA Ozone Climate Change Initiative: Level 3 Total Ozone Merged Data Product",
                    "abstract": "The dataset is a merged data record combining harmonized data from the Global Ozone Monitoring Experiment (GOME) onboard the second European Remote Sensing satellite (ERS-2), the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) onboard the Environmental Satellite (ENVISAT), the Ozone Monitoring Instrument (OMI) onboard Aura, the GOME-2 instruments onboard the Meteorological Operational (MetOp) satellites A and B, and the Tropospheric Monitoring Instrument (TROPOMI) onboard the Seninel-5 Precursor (S5-P) satellite."
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                147403,
                147404
            ]
        },
        {
            "ob_id": 33043,
            "uuid": "1edad8d1d7414d798ac441790d5f92ee",
            "title": "Kruger DEM and Orthomosaics",
            "abstract": "The Kruger DEM and Orthomsaics",
            "computationComponent": [
                {
                    "ob_id": 33042,
                    "uuid": "be654a396581427da1a8f977502f098d",
                    "short_code": "comp",
                    "title": "Kruger DEM and Orthomosaics; semi-global matching",
                    "abstract": "In recent years, semi-global matching (SGM) approaches have proven to be among the most popular and successful algorithms in the fields of stereo vision and photogrammetry (Klette et al. 2011, Michael et al. 2013). To extract the height information from the aerial imagery, we used this matching approach, which utilizes intensity differences, mutual information (as the cost function) and an approximation of the global energy function that is being optimized path-wise (16 paths in this study) from all directions over the image. The cost function is significantly influenced by the use of penalty values, which were chosen based on performance tests and represent varying magnitudes of disparity changes. These variables have a strong impact on the matching performance and the robustness that is related to this processing step. The term 'semi-global arises from the combination of both global and local methods in a way that the complexity of the process is lowered and the quality of the matching is drastically improved. While the computation time for these global methods is often considerably higher, the overall performance increases compared to local matching algorithms. Further, pixel-wise calculated matching cost, contrary to the calculation along image paths, poses negative effects of insufficient correspondences related to low texture and ambiguity (Hirschmüller 2007)."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 33041,
                    "uuid": "b44b3f35d2174ed7ab0b326562489ff8",
                    "short_code": "acq",
                    "title": "Kruger DEM and Orthomosaics",
                    "abstract": "Data acquisition: Flight campaign using multispectral camera\\n2.) Processing of raw aerial imagery using Catalyst Enterprise: metadata preparation, tie-point collection, height derivation, post-processing\\n3.) Data upload preparation: resampling, subsetting and projection of data"
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                147577
            ]
        },
        {
            "ob_id": 33047,
            "uuid": "d2d16e94cd4e4f5b95cebf99dc7fe2dd",
            "title": "Composite process for the ESA Sea Level CCI Altimeter coastal sea level anomalies datasets based on XTRACK/ALES processing and ENVISAT and SARAL data.",
            "abstract": "The coastal sea level data products are based on a complete reprocessing of raw radar altimetry waveforms from the ENVISAT and SARAL missions to derive satellite-sea surface ranges as close as possible to the coast (a process called ‘retracking’) and optimization of the geophysical corrections applied to the range measurements to produce sea level time series at monthly interval, from 20 km offshore to the coast",
            "computationComponent": [
                {
                    "ob_id": 31952,
                    "uuid": "73978b5dd534435abd858c94603661f5",
                    "short_code": "comp",
                    "title": "Computation of altimeter coastal sea level anomalies based on the XTRACK/ALES processing",
                    "abstract": "The altimeter coastal sea level anomalies products are based on the XTRACK/ALES processing.  The products benefit from the spatial resolution provided by high-rate data, the Adaptive Leading Edge Subwaveform Retracker (ALES) and the post-processing strategy of the along-track (X-TRACK) algorithm, both developed for the processing of coastal altimetry data, as well as the best possible set of geophysical corrections.  \r\n\r\nFor details of the processing see the Algorithm Theoretical Baseline Document in the linked documentation."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 33046,
                    "uuid": "8d6bd3620bf24eefb63e86861c8d5eeb",
                    "short_code": "acq",
                    "title": "Acquisition for the ESA Climate Change Initiative Sea Level altimeter (ENVISAT and SARAL) coastal sea level anomalies datasets",
                    "abstract": "Acquisition for the ESA Climate Change Initiative Sea Level altimeter coastal sea level anomalies datasets from the ENVISAT and SARAL satellites."
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                147593,
                147592
            ]
        },
        {
            "ob_id": 33058,
            "uuid": "4d90fa654eda41b982c9b59de3b0fc4e",
            "title": "Composite process for the ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column averaged carbon dioxide from OCO-2 generated with the FOCAL algorithm",
            "abstract": "The data has been generated from the OCO-2 satellite using the FOCAL-OCO2 algorithm",
            "computationComponent": [
                {
                    "ob_id": 33057,
                    "uuid": "1c59ff1ebc6849d9b31c0dbeffe51a40",
                    "short_code": "comp",
                    "title": "Derivation of the ESA GHG CCI column averaged carbon dioxide from OCO-2 generated with the FOCAL algorithm",
                    "abstract": "Column-averaged carbon dioxide (XCO2) has been retrieved using the FOCAL-OCO2 algorithm, by analysing hyper spectral solar backscattered radiance measurements from NASA's Orbiting Carbon Observatory 2 (OCO-2) satellite. FOCAL includes a radiative transfer model which has been developed to approximate light scattering effects by multiple scattering at an optically thin scattering layer. This reduces the computational costs by several orders of magnitude. FOCAL's radiative transfer model is utilised to simulate the radiance in all three OCO-2 spectral bands allowing the simultaneous retrieval of CO2, H2O, and solar induced chlorophyll fluorescence. The product is limited to cloud-free scenes on the Earth's day side."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 33056,
                    "uuid": "2bfe33f3daa24ebca447f98367edc5dd",
                    "short_code": "acq",
                    "title": "Acquisition process for the ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column averaged carbon dioxide from OCO-2 generated with the FOCAL algorithm",
                    "abstract": "Data was acquired from the OCO-2 satellite instrument."
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                147635
            ]
        },
        {
            "ob_id": 33168,
            "uuid": "9df00141e4ac4f83a209f0e9f737c232",
            "title": "Surface velocity map of the Afar Rift Zone from 2014-19",
            "abstract": "Surface velocity map of the Afar Rift Zone from 2014-19",
            "computationComponent": [
                {
                    "ob_id": 33167,
                    "uuid": "71a0d12647a04cc9a05197cffdd5f24a",
                    "short_code": "comp",
                    "title": "Surface velocity map of the Afar Rift Zone from 2014-19",
                    "abstract": "We used frequent Sentinel-1 satellite Interferometric Synthetic Aperture Radar (InSAR) observations to measure surface displacements through time across the whole region. We related these to ground based Global Navigation Satellite Systems (GNSS) observations and combine data from different satellite tracks to produce maps of the average surface velocity in three directions (perpendicular to the rift zone, parallel to the rift zone, and vertical)."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 33112,
                    "uuid": "586b8018048f428093160db7af90c7b7",
                    "short_code": "acq",
                    "title": "Surface Velocity Map of the Afar Rift Zone from 2014-19",
                    "abstract": "Surface Velocity Map of the Afar Rift Zone from 2014-19"
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                148083
            ]
        },
        {
            "ob_id": 33268,
            "uuid": "c57edbf3a959498f9a7d7bcbbb951d59",
            "title": "CCI Biomass v3.0",
            "abstract": "CCI Biomass",
            "computationComponent": [
                {
                    "ob_id": 33267,
                    "uuid": "c8478b0eb248451c8a938a66a72005d0",
                    "short_code": "comp",
                    "title": "The ESA Biomass Climate Change Initiative above ground biomass retrieval algorithm, v3.0",
                    "abstract": "For information on the derivation of the Biomass CCI data, please see the ATBD (Algorithm Theoretical Baseline Document)."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 33266,
                    "uuid": "c7698febb7fd4fff9a53d526cb54d866",
                    "short_code": "acq",
                    "title": "CCI Biomass, v3.0",
                    "abstract": "CCI Biomass, v3.0"
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                148506,
                148507
            ]
        },
        {
            "ob_id": 33286,
            "uuid": "1d6b6c09b11040359ada94fe2a03595c",
            "title": "ESA Water Vapour Climate Change Initiative: Total Column Water Vapour over land, v3.1",
            "abstract": "The ESA Water Vapour Climate Change Initiative Total Column Water Vapour dataset has been derived from the following satellite instruments:  MERIS on ENVISAT, MODIS on TERRA and OLCI on Sentinel-3.   For more details, see the documenation at https://climate.esa.int/projects/water-vapour.",
            "computationComponent": [
                {
                    "ob_id": 33285,
                    "uuid": "f9ae51ce1ad24618b22afc7f7bf6fbc6",
                    "short_code": "comp",
                    "title": "Derivation of the ESA Water Vapour Climate Change Initiative Total Column Water Vapour over land (TCWV-land) product, v3.1",
                    "abstract": "For information on the derivation of the ESA Water Vapour CCI Total Column Water Vapour over land (TCWV-land) data, please see the ATBD (Algorithm Theoretical Baseline Document)."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 33284,
                    "uuid": "8551b133e021456ebafd1e325bed0d90",
                    "short_code": "acq",
                    "title": "ESA CCI Water Vapour:   Total Column Water Vapour over land (TCWV-land), v3.1",
                    "abstract": "The ESA Climate Change Initiatve Water Vapour (Water_Vapour_cci) Total Column Water Vapour over land (TCWV-land) data set has been produced from observations from the following satellite instruments: MERIS on ENVISAT, MODIS on TERRA and OLCI on Sentinel-3."
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                148651,
                148652
            ]
        },
        {
            "ob_id": 33348,
            "uuid": "f71b64ad4ab14213b6110b5e7f775799",
            "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Normalised Difference Vegetation Index (NDVI) v1",
            "abstract": "All index equations from Sentinel-hub custom scripts (links in Related Documents).\r\nSentinel-2 surface reflectance products are obtained from Defra and JNCC Sentinel-2 ARD CEDA dataset. ARD granules are masked using the corresponding cloud and topographic shadow masks. NDVI index files are then generated in R 3.6.1 using the raster::calc function.",
            "computationComponent": [
                {
                    "ob_id": 33349,
                    "uuid": "cd8b228f5be942189c2cb2e45077d92d",
                    "short_code": "comp",
                    "title": "Computation for JNCC Sentinel-2 indices Analysis Ready Data (ARD) Normalised Difference Vegetation Index (NDVI) v1",
                    "abstract": "All index equations from Sentinel-hub custom scripts (links in Related Documents).\r\nSentinel-2 surface reflectance products are obtained from Defra and JNCC Sentinel-2 ARD CEDA dataset. ARD granules are masked using the corresponding cloud and topographic shadow masks. NDVI index files are then generated in R 3.6.1 using the raster::calc function."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 25439,
                    "uuid": "18f84df32d934058862f2c3990885a4c",
                    "short_code": "acq",
                    "title": "Acquisition Process for: Sentinel 2B Multispectral Instrument (MSI)",
                    "abstract": "The acquisition process for the collection of raw imaging data from the European Space Agency (ESA) Sentinel 2B Multispectral Instrument (MSI)."
                },
                {
                    "ob_id": 13191,
                    "uuid": "e05a470bb02a4bf5bba845b1fcc3aca6",
                    "short_code": "acq",
                    "title": "Acquisition Process for: Sentinel 2A Multispectral Instrument (MSI)",
                    "abstract": "The acquisition process for the collection of raw imaging data from the European Space Agency (ESA) Sentinel 2A Multispectral Instrument (MSI)."
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                148919
            ]
        },
        {
            "ob_id": 33373,
            "uuid": "96cc894239c341d182941c9205e62ca6",
            "title": "Composite process for ESA Land Surface Temperature Climate Change Initiative (LST_cci): All-weather daily MicroWave Land Surface Temperature (MW-LST) global data record (1996-2020)",
            "abstract": "The land surface temperature (LST) data has been derived from the microwave instruments Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager / Sounder (SSMIS). Observations available at frequencies close to 18, 22, 26, and 85 GHz are used as an input to a retrieval algorithm that produces LST over all continental surfaces, twice per day (6 am/pm), at a spatial resolution of ~25 km, and over 25 years (1996-2020).",
            "computationComponent": [
                {
                    "ob_id": 33371,
                    "uuid": "5ac2c6ccc9904ee087b1409dbc78c1ec",
                    "short_code": "comp",
                    "title": "Derivation of ESA Land Surface Temperature Climate Change Initiative (LST_cci): All-weather daily MicroWave Land Surface Temperature (MW-LST) global data record (1996-2020)",
                    "abstract": "For more information on the retrieval algorithm used see the documentation on the LST CCI webpage"
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 33372,
                    "uuid": "62b6d9e61692493e938a4ef8b98e5989",
                    "short_code": "acq",
                    "title": "Aquistion for ESA Land Surface Temperature Climate Change Initiative (LST_cci): All-weather daily MicroWave Land Surface Temperature (MW-LST) global data record (1996-2020)",
                    "abstract": "The Land Surface Temperature dataset has been derived from the microwave instruments Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager / Sounder (SSMIS)."
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                149024,
                149025
            ]
        },
        {
            "ob_id": 33378,
            "uuid": "cb31c83f45104421b6866bc2f1a81259",
            "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Normalised Difference Water Index (NDWI) v1",
            "abstract": "All index equations from Sentinel-hub custom scripts (links in Related Documents).\r\nSentinel-2 surface reflectance products are obtained from Defra and JNCC Sentinel-2 ARD CEDA dataset. ARD granules are masked using the corresponding cloud and topographic shadow masks. NDWI index files are then generated in R 3.6.1 using the raster::calc function.",
            "computationComponent": [
                {
                    "ob_id": 33379,
                    "uuid": "fc47446cc51f49c9b304b08c75ac66da",
                    "short_code": "comp",
                    "title": "Computation for JNCC Sentinel-2 indices Analysis Ready Data (ARD) Normalised Difference Water Index (NDWI) v1",
                    "abstract": "All index equations from Sentinel-hub custom scripts (links in Related Documents).\r\nSentinel-2 surface reflectance products are obtained from Defra and JNCC Sentinel-2 ARD CEDA dataset. ARD granules are masked using the corresponding cloud and topographic shadow masks. NDWI index files are then generated in R 3.6.1 using the raster::calc function."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 13191,
                    "uuid": "e05a470bb02a4bf5bba845b1fcc3aca6",
                    "short_code": "acq",
                    "title": "Acquisition Process for: Sentinel 2A Multispectral Instrument (MSI)",
                    "abstract": "The acquisition process for the collection of raw imaging data from the European Space Agency (ESA) Sentinel 2A Multispectral Instrument (MSI)."
                },
                {
                    "ob_id": 25439,
                    "uuid": "18f84df32d934058862f2c3990885a4c",
                    "short_code": "acq",
                    "title": "Acquisition Process for: Sentinel 2B Multispectral Instrument (MSI)",
                    "abstract": "The acquisition process for the collection of raw imaging data from the European Space Agency (ESA) Sentinel 2B Multispectral Instrument (MSI)."
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                149042
            ]
        },
        {
            "ob_id": 33381,
            "uuid": "0b93bf0463a54e3ba6e590552af19cfe",
            "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Normalised Difference Moisture Index (NDMI) v1",
            "abstract": "All index equations from Sentinel-hub custom scripts (links in Related Documents).\r\nSentinel-2 surface reflectance products are obtained from Defra and JNCC Sentinel-2 ARD CEDA dataset. ARD granules are masked using the corresponding cloud and topographic shadow masks. NDMI index files are then generated in R 3.6.1 using the raster::calc function.",
            "computationComponent": [
                {
                    "ob_id": 33380,
                    "uuid": "3e92184223a84da2857f1496034ae04b",
                    "short_code": "comp",
                    "title": "Computation for JNCC Sentinel-2 indices Analysis Ready Data (ARD) Normalised Difference Moisture Index (NDMI) v1",
                    "abstract": "All index equations from Sentinel-hub custom scripts (links in Related Documents).\r\nSentinel-2 surface reflectance products are obtained from Defra and JNCC Sentinel-2 ARD CEDA dataset. ARD granules are masked using the corresponding cloud and topographic shadow masks. NDMI index files are then generated in R 3.6.1 using the raster::calc function."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 13191,
                    "uuid": "e05a470bb02a4bf5bba845b1fcc3aca6",
                    "short_code": "acq",
                    "title": "Acquisition Process for: Sentinel 2A Multispectral Instrument (MSI)",
                    "abstract": "The acquisition process for the collection of raw imaging data from the European Space Agency (ESA) Sentinel 2A Multispectral Instrument (MSI)."
                },
                {
                    "ob_id": 25439,
                    "uuid": "18f84df32d934058862f2c3990885a4c",
                    "short_code": "acq",
                    "title": "Acquisition Process for: Sentinel 2B Multispectral Instrument (MSI)",
                    "abstract": "The acquisition process for the collection of raw imaging data from the European Space Agency (ESA) Sentinel 2B Multispectral Instrument (MSI)."
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                149045
            ]
        },
        {
            "ob_id": 33406,
            "uuid": "b842c42d1ad74be9a5d0ebb8afa3ee66",
            "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Enhanced Vegetation Index (EVI)",
            "abstract": "All index equations from Sentinel-hub custom scripts (links in Related Documents).\r\nSentinel-2 surface reflectance products are obtained from Defra and JNCC Sentinel-2 ARD CEDA dataset. ARD granules are masked using the corresponding cloud and topographic shadow masks. EVI index files are then generated in R 3.6.1 using the raster::calc function.",
            "computationComponent": [
                {
                    "ob_id": 33407,
                    "uuid": "c4d9a3c4351241ea9a62843ce4e1c109",
                    "short_code": "comp",
                    "title": "Computation for JNCC Sentinel-2 indices Analysis Ready Data (ARD) Enhanced Vegetation Index (EVI)",
                    "abstract": "All index equations from Sentinel-hub custom scripts (links in Related Documents).\r\nSentinel-2 surface reflectance products are obtained from Defra and JNCC Sentinel-2 ARD CEDA dataset. ARD granules are masked using the corresponding cloud and topographic shadow masks. EVI index files are then generated in R 3.6.1 using the raster::calc function."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 13191,
                    "uuid": "e05a470bb02a4bf5bba845b1fcc3aca6",
                    "short_code": "acq",
                    "title": "Acquisition Process for: Sentinel 2A Multispectral Instrument (MSI)",
                    "abstract": "The acquisition process for the collection of raw imaging data from the European Space Agency (ESA) Sentinel 2A Multispectral Instrument (MSI)."
                },
                {
                    "ob_id": 25439,
                    "uuid": "18f84df32d934058862f2c3990885a4c",
                    "short_code": "acq",
                    "title": "Acquisition Process for: Sentinel 2B Multispectral Instrument (MSI)",
                    "abstract": "The acquisition process for the collection of raw imaging data from the European Space Agency (ESA) Sentinel 2B Multispectral Instrument (MSI)."
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                149166
            ]
        },
        {
            "ob_id": 33409,
            "uuid": "4e83f489b880411ab9eed49276a76476",
            "title": "JNCC Sentinel-2 indices Analysis Ready Data (ARD) Normalised Burn Ratio (NBR)",
            "abstract": "All index equations from Sentinel-hub custom scripts (links in Related Documents).\r\nSentinel-2 surface reflectance products are obtained from Defra and JNCC Sentinel-2 ARD CEDA dataset. ARD granules are masked using the corresponding cloud and topographic shadow masks. NBR index files are then generated in R 3.6.1 using the raster::calc function.",
            "computationComponent": [
                {
                    "ob_id": 33408,
                    "uuid": "0286bcc4ac924662a29cee886689817b",
                    "short_code": "comp",
                    "title": "Computation for JNCC Sentinel-2 indices Analysis Ready Data (ARD) Normalised Burn Ratio (NBR)",
                    "abstract": "All index equations from Sentinel-hub custom scripts (links in Related Documents).\r\nSentinel-2 surface reflectance products are obtained from Defra and JNCC Sentinel-2 ARD CEDA dataset. ARD granules are masked using the corresponding cloud and topographic shadow masks. NBR index files are then generated in R 3.6.1 using the raster::calc function."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 13191,
                    "uuid": "e05a470bb02a4bf5bba845b1fcc3aca6",
                    "short_code": "acq",
                    "title": "Acquisition Process for: Sentinel 2A Multispectral Instrument (MSI)",
                    "abstract": "The acquisition process for the collection of raw imaging data from the European Space Agency (ESA) Sentinel 2A Multispectral Instrument (MSI)."
                },
                {
                    "ob_id": 25439,
                    "uuid": "18f84df32d934058862f2c3990885a4c",
                    "short_code": "acq",
                    "title": "Acquisition Process for: Sentinel 2B Multispectral Instrument (MSI)",
                    "abstract": "The acquisition process for the collection of raw imaging data from the European Space Agency (ESA) Sentinel 2B Multispectral Instrument (MSI)."
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                149169
            ]
        },
        {
            "ob_id": 33427,
            "uuid": "afe4dc7900e1420484f55e80345db858",
            "title": "The RAL extended IMS retrieval scheme applied to the CrIS instrument on Suomi-NPP",
            "abstract": "The Rutherford Appleton Laboratory (RAL) extended Infrared Microwave Sounder (IMS) data set contains vertical profiles of temperature, water vapour (H2O), ozone (O3), carbon monoxide (CO), together with estimated total columns of other minor gases, cloud optical depth and effective radius, dust and sulfuric acid aerosol optical depth. The scheme also provides surface temperature and surface spectral emissivity spanning infrared and microwave. Data are retrieved from the infra-red and microwave sounders on platforms Metop (IASI, AMSU and MHS) and Suomi-NPP (CrIS and ATMS).\r\n\r\nColumn amounts of the following minor gases are retrieved: Nitric acid (HNO3), ammonia (NH3), sulfur dioxide (SO2), methanol (CH3OH), formic acid (HCOOH) and (for Suomi-NPP only) isoprene (C5H8).\r\n\r\nIn this dataset, the scheme has been applied to the CrIS instrument on the Suomi-NPP satellite.",
            "computationComponent": [
                {
                    "ob_id": 33426,
                    "uuid": "f0adab0324864be387e64fc53fba2641",
                    "short_code": "comp",
                    "title": "RAL extended Infrared Microware Sounder IMS retrieval scheme",
                    "abstract": "For information on the RAL extended Infrared Microwave Sounder (IMS) retrieval scheme see the ATBD in the linked docmentation"
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 33425,
                    "uuid": "b111709fd6454b358a4af7117eabeebe",
                    "short_code": "acq",
                    "title": "Aquisition for the RAL extended IMS retrieval scheme applied to Suomi-NPP data",
                    "abstract": "The IMS retrieval scheme has been applied to data from the CHRIS instrument on Suomi-NPP"
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                149248,
                149249
            ]
        },
        {
            "ob_id": 33436,
            "uuid": "f529533367f445e4b024ef8d8018e44a",
            "title": "Composite process for ESA Sea Level Climate Change Initiative (Sea_level_cci): Arctic Sea Level Anomalies from ENVISAT and SARAL/Altika satellite altimetry missions (by CLS/PML)",
            "abstract": "Estimations of Arctic sea level anomalies have beenproduced by the ESA Sea Level Climate Change Initiative project (Sea_level_cci), based on satellite altimetry from the ENVISAT and SARAL/Altika satellites. It has been produced by Collecte Localisation Satellites (CLS) and the Plymouth Marine Laboratory (PML).\r\n\r\nThe retrieval of sea level in the Arctic sea ice covered region requires specific processing steps of the satellite altimetry measurements. For this dataset, a specific radar waveform classification method has been applied based on a neural network approach. And the waveform retracking is based on a new adaptive retracking that is able to process both open ocean and peaky echoes measured in leads without introducing any bias between the two types of surfaces. Editing and mapping processing steps have been optimized for this dataset",
            "computationComponent": [
                {
                    "ob_id": 33435,
                    "uuid": "fdc12089c12b4c6984b3c2fff32bc324",
                    "short_code": "comp",
                    "title": "Retrieval algorithm for the ESA Sea Level Climate Change Initiative (Sea_level_cci): Arctic Sea Level Anomalies from ENVISAT and SARAL/Altika satellite altimetry missions (by CLS/PML)",
                    "abstract": "The retrieval of sea level in the Arctic sea ice covered region requires specific processing steps of the satellite altimetry measurements. For this dataset, a specific radar waveform classification method has been applied based on a neural network approach, and the waveform retracking is based on a new adaptive retracking that is able to process both open ocean and peaky echoes measured in leads without introducing any bias between the two types of surfaces. Editing and mapping processing steps have been optimized for this dataset"
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 33434,
                    "uuid": "c721934aea5447ab819c14cfb25ecd8b",
                    "short_code": "acq",
                    "title": "Aquisition for the ESA Sea Level Climate Change Initiative (Sea_level_cci): Arctic Sea Level Anomalies from ENVISAT and SARAL/Altika satellite altimetry missions (by CLS/PML)",
                    "abstract": "Data from the Radar Altimeter -2 (RA-2) on ENVISAT  (Ku band only) and the Altika Instrument on the SARAL satellite were used to derive the Arctic Sea Level Anomalies data"
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                149275,
                149276
            ]
        },
        {
            "ob_id": 33445,
            "uuid": "0297f0c7495941b3bfdde3f74f9e9925",
            "title": "Composite process for ESA Sea Level Climate Change Initiative (Sea_Level_cci): High Latitude Sea Level Anomalies from satellite altimetry (by DTU/TUM)",
            "abstract": "The data comprises weekly means from August 1991 to April 2017 and has been obtained using satellite altimetry data from four satellite missions: ERS1 (weeks 0 - 217); ERS2 (weeks 218 - 573); Envisat (weeks 574 - 1020); CryoSat-2 (weeks 1021 - 1336).\r\n\r\nTwo datasets are available: dataset #1 is based on the ALES+ retracking without correction of the inverse barometer whereas dataset #2 has been corrected for this effect.\r\n\r\nDataset #1 is provided both 'masked' and 'unmasked', where the masked data have been masked using sea ice concentrations downloaded from osisaf.met.no/p/ice. Dataset #2 is provided both 'masked' and 'unmasked', where the masked data have had data points retrieved over land removed from the files",
            "computationComponent": [
                {
                    "ob_id": 33444,
                    "uuid": "2e362e12d8e444338e7e14301e377184",
                    "short_code": "comp",
                    "title": "Retrieval algorithm for ESA Sea Level Climate Change Initiative (Sea_Level_cci): High Latitude Sea Level Anomalies from satellite altimetry (by DTU/TUM)",
                    "abstract": "For more details see the linked technical notes in the documentation section"
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 33443,
                    "uuid": "f8550972e507447fab3d9ca3c8e55cb3",
                    "short_code": "acq",
                    "title": "Acquisition for the ESA Sea Level Climate Change Initiative (Sea_Level_cci): High Latitude Sea Level Anomalies from satellite altimetry (by DTU/TUM)",
                    "abstract": "Satellite altimetry data from four satellite missions were used: ERS1 (weeks 0 - 217); ERS2 (weeks 218 - 573); Envisat (weeks 574 - 1020); CryoSat-2 (weeks 1021 - 1336)."
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                149307,
                149308
            ]
        },
        {
            "ob_id": 33464,
            "uuid": "c473bb55244b472285bb9d01545757bd",
            "title": "STFC RAL methane retrieval from IASI on METOP B",
            "abstract": "Retrieval of methane from MetOp-B IASI",
            "computationComponent": [
                {
                    "ob_id": 14468,
                    "uuid": "a12e65652edb42b9bc85f27aa55eb143",
                    "short_code": "comp",
                    "title": "STFC RAL IASI methane processor",
                    "abstract": "Retrieval of methane using the STFC RAL IASI methane processor. Full retrieval diagnostic information including averaging kernels, estimated error and retrieved cloud parameters is produced."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 8302,
                    "uuid": "747d50622dc5490fbe04b65343158a33",
                    "short_code": "acq",
                    "title": "Acquisition Process for: Level 1C data from the IASI (Infrared Atmospheric Sounding Interferometer) Instrument on board the MetOp-B satellite.",
                    "abstract": "This acquisition is comprised of the following: INSTRUMENTS: IASI; PLATFORMS: Metop-B; "
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                149522,
                149523
            ]
        },
        {
            "ob_id": 33465,
            "uuid": "3591ac5232f94aaa93dc142f6fe03e80",
            "title": "Composite process for the ESA Snow Climate Change Initiative SCFG MODIS v2.0 product",
            "abstract": "The snow_cci SCFG products from MODIS are based on the MODIS/Terra Calibrated Radiances 5-Min L1B Swath 1km (MOD021KM) and the MODIS/Terra Geolocation Fields 5-Min L1A Swath 1km (MOD03) Collection 6.1 data sets, provided by NASA.\r\n\r\nThe retrieval method of the snow_cci SCFG product from MODIS data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module developed by ENVEO. For the SCFG product generation from MODIS, multiple reflective and emissive spectral bands are used. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Metsämäki et al., 2015). All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 0.55 µm and 1.6 µm, and an emissive band centred at about 11 µm. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFG retrieval method is applied. Improvements of the snow_cci SCFG version 2.0 compared to the snow_cci version 1.0 include (i) the utilisation of an updated background reflectance map derived from statistical analyses of an extended MODIS time series, (ii) an update of the forest canopy transmissivity map, and (iii) an update of the constant reflectance value for wet snow based on the analysis of time series of the MODIS reflectance at 0.55 µm. \r\n\r\nThe main differences compared to the GlobSnow approach, and the method 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 a spatially variable background reflectance map instead of a global constant value for snow free land, (iv) the update of the constant value for wet snow based on analyses of spatially distributed reflectance time series of MODIS data, and (v) the update of the forest canopy transmissivity to assure in forested areas consistency of the SCFG and the SCFV CRDP v2.0 from MODIS data (https://catalogue.ceda.ac.uk/uuid/ebe625b6f77945a68bda0ab7c78dd76b) using the same retrieval approach. \r\n\r\nPermanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the pixel spacing of the SCFG product. Water areas are masked if more than 30 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map. The product uncertainty for observed land pixels is provided as unbiased root mean square error (RMSE) per pixel in the ancillary variable.",
            "computationComponent": [
                {
                    "ob_id": 33469,
                    "uuid": "b89c9a5ea15e4fb7941ddc5330540a7a",
                    "short_code": "comp",
                    "title": "ESA Snow Climate Change Initiative: Derivation of SCFG MODIS v2.0 product.",
                    "abstract": "The retrieval method of the Snow_cci SCFG product from MODIS data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module developed by ENVEO. For the SCFG product generation from MODIS, multiple reflective and emissive spectral bands are used. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Metsämäki et al., 2015). All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 0.55 µm and 1.6 µm, and an emissive band centred at about 11 µm. The Snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFG retrieval method is applied. \r\n\r\nThe main differences of the Snow_cci snow cover mapping algorithm compared to the GlobSnow algorithm described in Metsämäki et al. (2015) are (i) improvements of the cloud screening approach applicable on a global scale, (ii) the pre-classification of snow free areas on global land areas, (iii) the usage of spatially variable background reflectance and forest reflectance maps instead of global constant values for snow free land and forest, (iv) the update of the constant value for wet snow based on analyses of spatially distributed reflectance time series of MODIS data, and (v) the update of the global forest canopy transmissivity based on forest density from Hansen et al. (2013) and forest type layers from Land Cover CCI (Defourny, 2019) to assure in forested areas consistency of the SCFG and the SCFV CRDP v2.0 from MODIS data (https://catalogue.ceda.ac.uk/uuid/ebe625b6f77945a68bda0ab7c78dd76b) using the same retrieval approach.\r\n\r\nImprovements of the Snow_cci SCFG version 2.0 compared to the Snow_cci version 1.0 include (i) the utilisation of an updated background reflectance map derived from statistical analyses of an extended MODIS time series, (ii) an update of the forest canopy transmissivity map, and (iii) an update of the constant reflectance value for wet snow based on the analysis of time series of the MODIS reflectance at 0.55 µm. \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. The product uncertainty for observed land pixels is provided as unbiased root mean square error (RMSE) per pixel in the ancillary variable."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 32512,
                    "uuid": "b7f993e0c3e745dc9975da8aa580a654",
                    "short_code": "acq",
                    "title": "Aquisition for: ESA Snow Climate Change Initiative (snow_cci) Snow Cover Fraction from MODIS, v1.0",
                    "abstract": "The snow_cci SCFG and SCFV products from MODIS are based on the MODIS/Terra Calibrated Radiances 5-Min L1B Swath 1km (MOD021KM) and the MODIS/Terra Geolocation Fields 5-Min L1A Swath 1km (MOD03) Collection 6.1 data sets, provided by NASA.\r\n\r\nThe snow_cci SCF processing chain for MODIS includes the masking of clouds, the identification of certainly snow free areas, and the classification of snow cover fraction per pixel for all remaining observed pixels. Finally, permanent snow and ice areas as well as water bodies are masked in the SCFG products using the corresponding classes from the Land Cover CCI map of the year 2000 as auxiliary layers. All SCFG products are prepared according to the CCI data standards.\r\n\r\nAn automated and a manual quality check was performed on the full time series."
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                149525
            ]
        },
        {
            "ob_id": 33470,
            "uuid": "64b26970ed084e8bbdbd38d4e948c7cc",
            "title": "Composite process for the ESA Snow Climate Change Initiative SCFV AVHRR v2.0 product",
            "abstract": "The ESA Snow Climate Change Initiative SCFG AVHRR v2.0 product is based on an AVHRR baseline FCDR pre-processed using pyGAC and pySTAT in the frame of the ESA CCI Cloud project.\r\n\r\nThe retrieval method of the snow_cci SCFV product from AVHRR data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module. All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 630 nm 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 two-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFV retrieval method is applied. \r\n\r\nThe 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.",
            "computationComponent": [
                {
                    "ob_id": 33466,
                    "uuid": "c89218fb8dc045b2875c06e8042f25a5",
                    "short_code": "comp",
                    "title": "ESA Snow Climate Change Initiative: Derivation of SCFV AVHRR v2.0 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.\r\n\r\nThe following auxiliary data set is used for product generation: ESA CCI Land Cover from 2000; water bodies and permanent snow and ice areas are masked based on this dataset. Both classes were separately aggregated to the pixel spacing of the SCF product. Water areas are masked if more than 50 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 32518,
                    "uuid": "fe25ba369f6e4247aba9650253ef9f6a",
                    "short_code": "acq",
                    "title": "Aquisition for: ESA Snow Climate Change Initiative (snow_cci) Snow Cover Fraction from AVHRR, v1.0",
                    "abstract": "The snow_cci SCFG and SCFV products from AVHRR are based on the AVHRR baseline FCDR that was pre-processed using pyGAC and pySTAT in the frame of the ESA CCI Cloud project."
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                149534
            ]
        },
        {
            "ob_id": 33471,
            "uuid": "552e5a4245d24972902cb667ac881dca",
            "title": "Composite process for the ESA Snow Climate Change Initiative SCFG AVHRR v2.0 product",
            "abstract": "The ESA Snow Climate Change Initiative SCFG AVHRR v2.0 product is based on an AVHRR baseline FCDR pre-processed using pyGAC and pySTAT in the frame of the ESA CCI Cloud project.\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 630 nm 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 two-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFG retrieval method is applied. \r\n\r\nThe 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.",
            "computationComponent": [
                {
                    "ob_id": 33467,
                    "uuid": "0b0e05e096b24d4a92edac27d43fb6cc",
                    "short_code": "comp",
                    "title": "ESA Snow Climate Change Initiative: Derivation of SCFG AVHRR v2.0 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 630 nm 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 two-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFG retrieval method is applied. \r\n\r\nThe 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."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 32518,
                    "uuid": "fe25ba369f6e4247aba9650253ef9f6a",
                    "short_code": "acq",
                    "title": "Aquisition for: ESA Snow Climate Change Initiative (snow_cci) Snow Cover Fraction from AVHRR, v1.0",
                    "abstract": "The snow_cci SCFG and SCFV products from AVHRR are based on the AVHRR baseline FCDR that was pre-processed using pyGAC and pySTAT in the frame of the ESA CCI Cloud project."
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                149535
            ]
        },
        {
            "ob_id": 33472,
            "uuid": "5996caf104734a74bdd627da97db10e5",
            "title": "Composite process for the ESA Snow Climate Change Initiative SCFV MODIS v2.0 product",
            "abstract": "The snow_cci SCFV products from MODIS are based on the MODIS/Terra Calibrated Radiances 5-Min L1B Swath 1km (MOD021KM) and the MODIS/Terra Geolocation Fields 5-Min L1A Swath 1km (MOD03) Collection 6.1 data sets, provided by NASA.\r\n\r\nThe retrieval method of the snow_cci SCFV product from MODIS data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module developed by ENVEO. For the SCFV product generation from MODIS, multiple reflective and emissive spectral bands are used. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Metsämäki et al., 2015). All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 550 nm and 1.6 µm, and an emissive band centred at about 11 µm. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFV retrieval method is applied. Improvements to the GlobSnow algorithm implemented for snow_cci version 1 include (i) the utilisation of a background reflectance map derived from statistical analyses of MODIS time series replacing the constant values for snow free ground used in the GlobSnow approach, and (ii) the adaptation of the retrieval method for mapping in forested areas the SCFV. \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. The product uncertainty for observed land pixels is provided as unbiased root mean square error (RMSE) per pixel in the ancillary variable.",
            "computationComponent": [
                {
                    "ob_id": 33468,
                    "uuid": "255c8d91fdc64e21b9e3f10e34284748",
                    "short_code": "comp",
                    "title": "ESA Snow Climate Change Initiative: Derivation of SCFV MODIS v2.0 product.",
                    "abstract": "The retrieval method of the Snow_cci SCFV product from MODIS data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module developed by ENVEO. For the SCFV product generation from MODIS, multiple reflective and emissive spectral bands are used. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Metsämäki et al., 2015). All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 550 nm and 1.6 µm, and an emissive band centred at about 11 µm. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the Snow_cci SCFV retrieval method is applied. \r\n\r\nThe main differences of the Snow_cci snow cover mapping algorithm compared to the GlobSnow algorithm described in Metsämäki et al. (2015) are (i) improvements of the cloud screening approach applicable on a global scale, (ii) the pre-classification of snow free areas on global land areas, (iii) the adaptation of the retrieval method using of a spatially variable ground reflectance instead of global constant values for snow free land, (iv) the update of the constant value for wet snow based on analyses of spatially distributed reflectance time series of MODIS data to assure in forested areas consistency of the SCFV and the SCFG CRDP v2.0 from MODIS data (https://catalogue.ceda.ac.uk/uuid/ebe625b6f77945a68bda0ab7c78dd76b) using the same retrieval approach.\r\n\r\nImprovements of the Snow_cci SCFV version 2.0 compared to the Snow_cci version 1.0 include (i) the utilisation of an updated ground reflectance map derived from statistical analyses of an extended MODIS time series, (ii) an update of the forest mask used for the transmissivity estimation, and (iii) an update of the constant reflectance value for wet snow based on the analysis of time series of the MODIS reflectance at 0.55 µm.\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. The product uncertainty for observed land pixels is provided as unbiased root mean square error (RMSE) per pixel in the ancillary variable."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 32512,
                    "uuid": "b7f993e0c3e745dc9975da8aa580a654",
                    "short_code": "acq",
                    "title": "Aquisition for: ESA Snow Climate Change Initiative (snow_cci) Snow Cover Fraction from MODIS, v1.0",
                    "abstract": "The snow_cci SCFG and SCFV products from MODIS are based on the MODIS/Terra Calibrated Radiances 5-Min L1B Swath 1km (MOD021KM) and the MODIS/Terra Geolocation Fields 5-Min L1A Swath 1km (MOD03) Collection 6.1 data sets, provided by NASA.\r\n\r\nThe snow_cci SCF processing chain for MODIS includes the masking of clouds, the identification of certainly snow free areas, and the classification of snow cover fraction per pixel for all remaining observed pixels. Finally, permanent snow and ice areas as well as water bodies are masked in the SCFG products using the corresponding classes from the Land Cover CCI map of the year 2000 as auxiliary layers. All SCFG products are prepared according to the CCI data standards.\r\n\r\nAn automated and a manual quality check was performed on the full time series."
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                149536
            ]
        },
        {
            "ob_id": 34667,
            "uuid": "6c409ce178ec4d5eab40c1d6feaa070b",
            "title": "Composite process for the  ESA Land Surface Temperature Climate Change Initiative (LST_cci): Along-Track Scanning Radiometer 2 (ATSR-2) level 3 collated (L3C) global product (1995-2003), version 3.00",
            "abstract": "Data has been derived from the Along-Track Scanning Radiometer 2 (ATSR-2) on the European Remote-sensing Satellite (ERS-2)\r\n\r\nFor more information on the retrieval algorithm used see the documentation on the LST CCI webpage: https://climate.esa.int/sites/default/files/LST-CCI-D2.2-ATBD%20-%20i1r1%20-%20Algorithm%20Theoretical%20Basis%20Document.pdf.",
            "computationComponent": [
                {
                    "ob_id": 34666,
                    "uuid": "45354bf51d2a475d9b7abfdd04bcbcab",
                    "short_code": "comp",
                    "title": "Derivation of ESA Land Surface Temperature Climate Change Initiative (LST_cci): Along-Track Scanning Radiometer 2 (ATSR-2) level 3 collated (L3C) global product (1995-2003), version 3.00",
                    "abstract": "For more information on the retrieval algorithm used see the documentation on the LST CCI webpage: https://climate.esa.int/sites/default/files/LST-CCI-D2.2-ATBD%20-%20i1r1%20-%20Algorithm%20Theoretical%20Basis%20Document.pdf"
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 34668,
                    "uuid": "54d6d8a51efd49e28e9fa8e9ce2e294c",
                    "short_code": "acq",
                    "title": "Acquisition Process for the ESA Land Surface Temperature Climate Change Initiative (LST_cci): Along-Track Scanning Radiometer 2 (ATSR-2) level 3 collated (L3C) global product (1995-2003), version 3.00",
                    "abstract": "This acquisition is comprised of the following: INSTRUMENTS: ERS2 ATSR2; PLATFORMS: ERS-2;"
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                168070
            ]
        },
        {
            "ob_id": 34691,
            "uuid": "a46e2ab2b2c5468893e3273ab94809df",
            "title": "Composite Process for Iceland Greenland Seas Project (IGP): water isotope measurements from the University of Iceland vapour isotope analyzer at Húsavik and samples of precipitation and surface snow in Iceland and southern Norway within SNOWPACE",
            "abstract": "Composite process covering Acquisition for: Iceland Greenland Seas Project (IGP): water isotope measurements from the University of Iceland vapour isotope analyzer at Húsavik and samples of precipitation and surface snow in Iceland and southern Norway within SNOWPACE and Post processing of station data at the University of Iceland and the University of Bergen.",
            "computationComponent": [
                {
                    "ob_id": 34690,
                    "uuid": "27a2d430de2e4f36bbb341eee55e9502",
                    "short_code": "comp",
                    "title": "Post processing of station data at the University of Iceland and the University of Bergen",
                    "abstract": "The vapour measurements averaged to 15 min time intervals. In total, the instrument operated on 60 days, with a total of 51.7 days of ambient air measurements. Uncertainty after calibration is 0.4, 2.0 and 1.8 for δO18, δD and the d-excess, respectively. The vapour isotope data are joined with the meteorological data from nearby automatic weather stations with a 15 min averaging time using the processing tool isofuse (naming: IGP2018_SNOWPACE_Husavik_station_data.nc; format: netcdf). Weather station data for the 7 closest automatic weather stations near the sampling transects are joined in a common data file at the original 10 min time resolution (naming: IGP2018_vedur_met_stations.nc; format: netcdf). Results from precipitation and surface snow sample analysis for water isotopes from the Iceland and southern Norway are available in one datafile (naming:IGP2018_SNOWPACE_water_isotope_samples_stations.csv, format: csv).\\n\\n\\nPost processing of the vapour isotope data was done by Jean-Lionel Lacour (UoI). Post processing of the discrete sample data was done by Harald Sodemann (UiB), who also acts as data contact."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 34688,
                    "uuid": "2b79ae0d652f439b9e46e426e6ba3bc7",
                    "short_code": "acq",
                    "title": "Acquisition for: Iceland Greenland Seas Project (IGP): water isotope measurements from the University of Iceland vapour isotope analyzer at Húsavik and samples of precipitation and surface snow in Iceland and southern Norway within SNOWPACE",
                    "abstract": ""
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                168698,
                168699
            ]
        },
        {
            "ob_id": 34697,
            "uuid": "9852714eb53f4851b4b7a5e22c971fc8",
            "title": "Composite Process for Iceland Greenland Seas Project (IGP): water isotope measurements from the University of Bergen vapour isotope analyzer on board the NATO Research Vessel Alliance within SNOWPACE",
            "abstract": "Composite process covering Acquisition for: Iceland Greenland Seas Project (IGP): water isotope measurements from the University of Bergen vapour isotope analyzer on board the NATO Research Vessel Alliance within SNOWPACE and Post processing of ship data at University of Bergen.",
            "computationComponent": [
                {
                    "ob_id": 34696,
                    "uuid": "99163507794a465e9261bfe7ac3c5af6",
                    "short_code": "comp",
                    "title": "Post processing of ship data at University of Bergen",
                    "abstract": "Data files recorded by the analyzer in *.dat format are converted to netCDF format using a python routine. The raw data are then processed using the calibration routines FaVaCal, in use at FARLAB, University of Bergen, Norway. Calibration periods are identified and removed for separate processing with plots and quality evaluation. Water vapour isotope measurements are corrected for the humidity-isotope ratio dependency, as documented by Weng et al., 2020. The complete data processing is described in more detail in the data report for stable water isotope measurements from MASIN aircraft during IGP. The vapour isotope data are joined with the meteorological data from the R/V Alliance obtained during IGP at a 60 s averaging time using the processing tool isofuse. Output from this conversion is stored as one single datafiles (naming: IGP2018_SNOWPACE_Alliance_cruise_data_V3.1_60s.nc; format: netcdf). Results from liquid sample analysis for water isotopes from the R/V Alliance are available in one datafile (naming:IGP2018_SNOWPACE_water_isotope_samples_Alliance.csv, format: csv). Post processing was done by Harald Sodemann (UiB), who also acts as data contact."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 34694,
                    "uuid": "14619c3b7cbd4a72bbd36a26145ecdaa",
                    "short_code": "acq",
                    "title": "Acquisition for: Iceland Greenland Seas Project (IGP): water isotope measurements from the University of Bergen vapour isotope analyzer on board the NATO Research Vessel Alliance within SNOWPACE",
                    "abstract": ""
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                168717,
                168718
            ]
        },
        {
            "ob_id": 34703,
            "uuid": "349ebcee33884eb88dee5e109fc0bcd2",
            "title": "Composite Process for Iceland Greenland Seas Project (IGP): water isotope measurements from the University of Bergen vapour isotope analyzer on board the BAS research aircraft MASIN within SNOWPACE",
            "abstract": "Composite process covering Acquisition for: Iceland Greenland Seas Project (IGP): water isotope measurements from the University of Bergen vapour isotope analyzer on board the BAS research aircraft MASIN within SNOWPACE and Post processing of aircraft data at University of Bergen.",
            "computationComponent": [
                {
                    "ob_id": 34702,
                    "uuid": "80df33b9f15c479b8786ab10c95202a1",
                    "short_code": "comp",
                    "title": "Post processing of aircraft data at University of Bergen",
                    "abstract": "Data files recorded by the analyzer in *.dat format are converted to netCDF format using apython routine. The raw data are then processed using the calibration routines FaVaCal, in use at FARLAB, University of Bergen, Norway. Calibration periods are identified and removed for separate processing with plots and quality evaluation. Water vapour isotope measurements are corrected for the humidity-isotope ratio dependency, as documented by Weng et al., 2020. The complete data processing is described in more detail in the data report for stable water isotope measurements from aircraft during IGP. The vapour isotope data are joined with the meteorological data from the MASIN aircraft obtained during IGP at a 2s, 10s, 30s and 60 s averaging time using the processing tool isofuse. Output from this conversion is stored as in separate datafiles for each averaging time and flight (naming: MASIN_isotopes_IGP2018_V3.3_20180304_f295_02s_final.nc; folder: IGP2018_SNOWPACE_MASIN_flight_data_V3.3/02s; format: netcdf). Results from liquid sample analysis for water isotopes from MASIN are available in one datafile (naming:IGP2018_SNOWPACE_water_isotope_samples_MASIN.csv, format: csv).Post processing was done by Harald Sodemann (UiB), who also acts as data contact."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 34700,
                    "uuid": "11fa1282c74a4ce798239356afb448d1",
                    "short_code": "acq",
                    "title": "Acquisition for: Iceland Greenland Seas Project (IGP): water isotope measurements from the University of Bergen vapour isotope analyzer on board the BAS research aircraft MASIN within SNOWPACE",
                    "abstract": "The Picarro instrument installed on board of MASIN was a flight-enabled Picarro L2130-i (Ser. No. HIDS2254), provided by FARLAB, University of Bergen. This particular L2130-i is a custom-made, flight-enabled modification that has an additional laser, allowing faster switching between wavelengths and thus faster measurement frequency, regulation of the flow rate through the cavity at changing ambient pressure, and a measurement cavity certified down to 200 ppmv H2O, lower than regular instruments of the same type. In addition to mixing ratio, isotope delta values and a range of internal control parameters, the instrument records ambient pressure, which allows for an alignment of the vapour isotope measurements with meteorological measurements onboard the aircraft. A backward-facing inlet was installed for vapour measurements. Inlet tubing was heated to about 60 deg C by self-regulating heat trace (Thermon Inc., USA). Less than 1 m stainless steel tubing (1/4 inch diameter) was used from the point where a T-valve allowed to draw air from either the forward or backward facing inlet line. From there, another 2.5 m of 3/8 inch stainless steel tubing with Sulfinert coating (Silcotek Inc., USA) for lower moisture retention led to a connection with a T-valve, that allowed to bypass the KNF manifold pump (N022AN.18) with a flow rate of about 5–10 lpm. During two of the flights, this valve was by mistake turned such that the pump did not flush the inlet, such that only the CRDS analyzer with a flow rate of about 40 sccm provided flow through the inlet. Less than 1 m of 1/4 inch stainless steel tubing then branched off to the analyzer, connected to an critical orifice required for low-flow mode, and protected by a check valve against reverse flow. An additional T-valve allowed for switching between flow from the inlet or from the calibration unit. These last parts of tubing had a flow rate of about 30–40 sccm, as the analyzer was operated in low-flow mode.\"\r\n\r\nPost processing of aircraft data at University of Bergen\r\nData files recorded by the analyzer in *.dat format are converted to netCDF format using apython routine. The raw data are then processed using the calibration routines FaVaCal, in use at FARLAB, University of Bergen, Norway. Calibration periods are identified and removed for separate processing with plots and quality evaluation. Water vapour isotope measurements are corrected for the humidity-isotope ratio dependency, as documented by Weng et al., 2020. The complete data processing is described in more detail in the data report for stable water isotope measurements from aircraft during IGP. The vapour isotope data are joined with the meteorological data from the MASIN aircraft obtained during IGP at a 2s, 10s, 30s and 60 s averaging time using the processing tool isofuse. Output from this conversion is stored as in separate datafiles for each averaging time and flight (naming: MASIN_isotopes_IGP2018_V3.3_20180304_f295_02s_final.nc; folder: IGP2018_SNOWPACE_MASIN_flight_data_V3.3/02s; format: netcdf). Results from liquid sample analysis for water isotopes from MASIN are available in one datafile (naming:IGP2018_SNOWPACE_water_isotope_samples_MASIN.csv, format: csv).Post processing was done by Harald Sodemann (UiB), who also acts as data contact."
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                168736,
                168737
            ]
        },
        {
            "ob_id": 34706,
            "uuid": "d117a265c9d24309b895d62c168d39ef",
            "title": "Composite process for the  ESA Land Surface Temperature Climate Change Initiative (LST_cci): Moderate resolution Infra-red Spectroradiometer (MODIS) on Aqua level 3 collated (L3C) global product (2002-2018), version 3.00",
            "abstract": "Data has been derived from the Moderate resolution Infra-red Spectroradiometer (MODIS) on the Aqua\r\n satellite.\r\n\r\nFor more information on the retrieval algorithm used see the documentation on the LST CCI webpage: https://climate.esa.int/sites/default/files/LST-CCI-D2.2-ATBD%20-%20i1r1%20-%20Algorithm%20Theoretical%20Basis%20Document.pdf.",
            "computationComponent": [
                {
                    "ob_id": 34705,
                    "uuid": "a3299e95950e4934802b93455cb25150",
                    "short_code": "comp",
                    "title": "Derivation of ESA Land Surface Temperature Climate Change Initiative (LST_cci): Moderate resolution Infra-red Spectroradiometer (MODIS) on Aqua level 3 collated (L3C) global product (2002-2018), version 3.00",
                    "abstract": "For more information on the retrieval algorithm used see the documentation on the LST CCI webpage: https://climate.esa.int/sites/default/files/LST-CCI-D2.2-ATBD%20-%20i1r1%20-%20Algorithm%20Theoretical%20Basis%20Document.pdf"
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 34707,
                    "uuid": "68a8483aac80498ca83c5ca9b8be24cd",
                    "short_code": "acq",
                    "title": "Acquisition Process for the ESA Land Surface Temperature Climate Change Initiative (LST_cci): Moderate resolution Infra-red Spectroradiometer (MODIS) on Aqua level 3 collated (L3C) global product (2002-2018), version 3.00",
                    "abstract": "This acquisition is comprised of the following: INSTRUMENTS: MODIS; PLATFORMS: Aqua"
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                168983
            ]
        },
        {
            "ob_id": 34711,
            "uuid": "415abc3d33ef49a39b5383945f3fe220",
            "title": "Composite process for the  ESA Land Surface Temperature Climate Change Initiative (LST_cci): Moderate resolution Infra-red Spectroradiometer (MODIS) on Terra level 3 collated (L3C) global product (2000-2018), version 3.00",
            "abstract": "Data has been derived from the Moderate resolution Infra-red Spectroradiometer (MODIS) on the Aqua\r\n satellite.\r\n\r\nFor more information on the retrieval algorithm used see the documentation on the LST CCI webpage: https://climate.esa.int/sites/default/files/LST-CCI-D2.2-ATBD%20-%20i1r1%20-%20Algorithm%20Theoretical%20Basis%20Document.pdf.",
            "computationComponent": [
                {
                    "ob_id": 34709,
                    "uuid": "915609dc14234791b9dd64792e32c2c1",
                    "short_code": "comp",
                    "title": "Derivation of ESA Land Surface Temperature Climate Change Initiative (LST_cci): Moderate resolution Infra-red Spectroradiometer (MODIS) on Terra level 3 collated (L3C) global product (2000-2018), version 3.00",
                    "abstract": "For more information on the retrieval algorithm used see the documentation on the LST CCI webpage: https://climate.esa.int/sites/default/files/LST-CCI-D2.2-ATBD%20-%20i1r1%20-%20Algorithm%20Theoretical%20Basis%20Document.pdf"
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 34710,
                    "uuid": "811ac73c575844d1a8d4a5e777ed0f63",
                    "short_code": "acq",
                    "title": "Acquisition Process for the ESA Land Surface Temperature Climate Change Initiative (LST_cci): Moderate resolution Infra-red Spectroradiometer (MODIS) on Terra level 3 collated (L3C) global product (2002-2018), version 3.00",
                    "abstract": "This acquisition is comprised of the following: INSTRUMENTS: MODIS; PLATFORMS: Terra"
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                168997
            ]
        },
        {
            "ob_id": 34728,
            "uuid": "d0c234bef9ce4607bb252fe87053a3a7",
            "title": "Composite process for the  ESA Land Surface Temperature Climate Change Initiative (LST_cci): Advanced Along-Track Scanning Radiometer (AATSR) level 3 collated (L3C) global product (2002-2012), version 3.00\\",
            "abstract": "Data has been derived from the Advanced Along-Track Scanning Radiometer 2 (AATSR) on the Envisat satellite.\r\n\r\nFor more information on the retrieval algorithm used see the documentation on the LST CCI webpage: https://climate.esa.int/sites/default/files/LST-CCI-D2.2-ATBD%20-%20i1r1%20-%20Algorithm%20Theoretical%20Basis%20Document.pdf.",
            "computationComponent": [
                {
                    "ob_id": 34726,
                    "uuid": "1c8ebc22681d48f8a67008e900ece8ef",
                    "short_code": "comp",
                    "title": "Derivation of ESA Land Surface Temperature Climate Change Initiative (LST_cci): Advanced Along-Track Scanning Radiometer (AATSR) level 3 collated (L3C) global product (2002-2012), version 3.00",
                    "abstract": "For more information on the retrieval algorithm used see the documentation on the LST CCI webpage: https://climate.esa.int/sites/default/files/LST-CCI-D2.2-ATBD%20-%20i1r1%20-%20Algorithm%20Theoretical%20Basis%20Document.pdf"
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 34727,
                    "uuid": "b5d1b194bec54f98a188433c0636f16f",
                    "short_code": "acq",
                    "title": "Acquisition Process for the ESA Land Surface Temperature Climate Change Initiative (LST_cci): Advanced Along-Track Scanning Radiometer (AATSR) level 3 collated (L3C) global product (2002-2012), version 3.00\\",
                    "abstract": "This acquisition is comprised of the following: INSTRUMENTS: AATSR; PLATFORMS: ENVISAT;"
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                169079
            ]
        },
        {
            "ob_id": 34732,
            "uuid": "4b56444be9cd4f4e99d8244ccf5a66f2",
            "title": "Composite process for the  ESA Land Surface Temperature Climate Change Initiative (LST_cci): Sea and Land Surface Temperature Radiometer (SLSTR) on Sentinel 3A level 3 collated (L3C) global product (2016-2020), version 3.00",
            "abstract": "Data has been derived from the Sea and Land Surface Temperature Radiometer (SLSTR) on the Sentinel 3A satellite.\r\n\r\nFor more information on the retrieval algorithm used see the documentation on the LST CCI webpage: https://climate.esa.int/sites/default/files/LST-CCI-D2.2-ATBD%20-%20i1r1%20-%20Algorithm%20Theoretical%20Basis%20Document.pdf.",
            "computationComponent": [
                {
                    "ob_id": 34734,
                    "uuid": "e77e5d3dafb44414abdf34b273f73b33",
                    "short_code": "comp",
                    "title": "Derivation of ESA Land Surface Temperature Climate Change Initiative (LST_cci): Sea and Land Surface Temperature Radiometer (SLSTR) on Sentinel 3A level 3 collated (L3C) global product (2016-2020), version 3.00",
                    "abstract": "For more information on the retrieval algorithm used see the documentation on the LST CCI webpage: https://climate.esa.int/sites/default/files/LST-CCI-D2.2-ATBD%20-%20i1r1%20-%20Algorithm%20Theoretical%20Basis%20Document.pdf"
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 34733,
                    "uuid": "e02575af28404c7e9c4453fa4f3e1fad",
                    "short_code": "acq",
                    "title": "Acquisition Process for the ESA Land Surface Temperature Climate Change Initiative (LST_cci): Sea and Land Surface Temperature Radiometer (SLSTR) on Sentinel 3A level 3 collated (L3C) global product (2016-2020), version 3.00",
                    "abstract": "This acquisition is comprised of the following: INSTRUMENTS: SLSTR; PLATFORMS: Sentinel3A;"
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                169098
            ]
        },
        {
            "ob_id": 34736,
            "uuid": "0969e83cf7134b0c89011f5c4ac8e8ca",
            "title": "Composite process for the  ESA Land Surface Temperature Climate Change Initiative (LST_cci): Sea and Land Surface Temperature Radiometer (SLSTR) on Sentinel 3B level 3 collated (L3C) global product (2018-2020), version 3.00",
            "abstract": "Data has been derived from the Sea and Land Surface Temperature Radiometer (SLSTR) on the Sentinel 3B satellite.\r\n\r\nFor more information on the retrieval algorithm used see the documentation on the LST CCI webpage: https://climate.esa.int/sites/default/files/LST-CCI-D2.2-ATBD%20-%20i1r1%20-%20Algorithm%20Theoretical%20Basis%20Document.pdf.",
            "computationComponent": [
                {
                    "ob_id": 34738,
                    "uuid": "fb22d4204c5943ccbc0a22d293a5aa01",
                    "short_code": "comp",
                    "title": "Derivation of ESA Land Surface Temperature Climate Change Initiative (LST_cci): Sea and Land Surface Temperature Radiometer (SLSTR) on Sentinel 3B level 3 collated (L3C) global product (2018-2020), version 3.00",
                    "abstract": "For more information on the retrieval algorithm used see the documentation on the LST CCI webpage: https://climate.esa.int/sites/default/files/LST-CCI-D2.2-ATBD%20-%20i1r1%20-%20Algorithm%20Theoretical%20Basis%20Document.pdf"
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 34737,
                    "uuid": "9cd8a82f4ed94ef1a692e3c166344302",
                    "short_code": "acq",
                    "title": "Acquisition Process for the ESA Land Surface Temperature Climate Change Initiative (LST_cci): Sea and Land Surface Temperature Radiometer (SLSTR) on Sentinel 3B level 3 collated (L3C) global product (2018-2020), version 3.00",
                    "abstract": "This acquisition is comprised of the following: INSTRUMENTS: SLSTR; PLATFORMS: Sentinel3B;"
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                169106
            ]
        },
        {
            "ob_id": 34742,
            "uuid": "860f3c1333c44302bd01911d53cdaeea",
            "title": "Composite process for the  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 2.00",
            "abstract": "For more information on the retrieval algorithm used see the documentation on the LST CCI webpage: https://climate.esa.int/sites/default/files/LST-CCI-D2.2-ATBD%20-%20i1r1%20-%20Algorithm%20Theoretical%20Basis%20Document.pdf.",
            "computationComponent": [
                {
                    "ob_id": 34741,
                    "uuid": "950e57621baf4a2dbeecc0ac69f63c48",
                    "short_code": "comp",
                    "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 2.00",
                    "abstract": "For more information on the retrieval algorithm used see the documentation on the LST CCI webpage: https://climate.esa.int/sites/default/files/LST-CCI-D2.2-ATBD%20-%20i1r1%20-%20Algorithm%20Theoretical%20Basis%20Document.pdf"
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 34740,
                    "uuid": "6825231fd8d1437788ddb667d64c0720",
                    "short_code": "acq",
                    "title": "Acquisition Process for the 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 2.00",
                    "abstract": "The dataset is comprised of LSTs from a series of instruments with a common heritage: the Along-Track Scanning Radiometer 2 (ATSR-2), the Advanced Along-Track Scanning Radiometer (AATSR) and the Sea and Land Surface Temperature Radiometer on Sentinel 3A (SLSTRA); and data from; the Moderate Imaging Spectroradiometer on Earth Observation System - Terra (MODIS Terra) to fill the gap between AATSR and SLSTR. So, the instruments contributing to the time series are: ATSR-2 from August 1995 to July 2002; AATSR from August 2002 to March 2012; MODIS Terra from April 2012 to July 2016; and SLSTRA from August 2016 to December 2020. Inter-instrument biases are accounted for by cross-calibration with the Infrared Atmospheric Sounding Interferometer (IASI) instruments on Meteorological Operational (METOP) satellites"
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                169117
            ]
        },
        {
            "ob_id": 34746,
            "uuid": "b6501a3e35d24202ad89d3d5544ee6f4",
            "title": "Composite process for the ESA Land Surface Temperature Climate Change Initiative (LST_cci): Multisensor Infra-Red (IR) Low Earth Orbit (LEO) and Geostationary Earth Orbit (GEO) Land surface temperature (LST) level 3 supercollated (L3S) global product (2009-2020), version 1.00",
            "abstract": "For more information on the retrieval algorithm used see the documentation on the LST CCI webpage: https://climate.esa.int/sites/default/files/LST-CCI-D2.2-ATBD%20-%20i1r1%20-%20Algorithm%20Theoretical%20Basis%20Document.pdf.",
            "computationComponent": [
                {
                    "ob_id": 34743,
                    "uuid": "37c5516b18df4545b3a271c639e3db63",
                    "short_code": "comp",
                    "title": "Derivation of the ESA Land Surface Temperature Climate Change Initiative (LST_cci): Multisensor Infra-Red (IR) Low Earth Orbit (LEO) and Geostationary Earth Orbit (GEO) Land surface temperature (LST) level 3 supercollated (L3S) global product (2009-2020), version 1.00",
                    "abstract": "For more information on the retrieval algorithm used see the documentation on the LST CCI webpage: https://climate.esa.int/sites/default/files/LST-CCI-D2.2-ATBD%20-%20i1r1%20-%20Algorithm%20Theoretical%20Basis%20Document.pdf"
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 34744,
                    "uuid": "8dd100ae2cd64c1bbe86336a3dd96c9c",
                    "short_code": "acq",
                    "title": "Acquisition Process for the ESA Land Surface Temperature Climate Change Initiative (LST_cci): Multisensor Infra-Red (IR) Low Earth Orbit (LEO) and Geostationary Earth Orbit (GEO) Land surface temperature (LST) level 3 supercollated (L3S) global product (2009-2020), version 1.00",
                    "abstract": "Data from the following instruments is included in the dataset: \r\ngeostationary: Imagers on Geostationary Operational Environmental Satellite (GOES) 12 and GOES 13, Advanced Baseline Imager (ABI) on GOES 16, Spinning Enhanced Visible Infra-Red Imager (SEVIRI) on Meteosat Second Generation (MSG) 1, MSG 2, MSG 3, and MSG 4, Japanese Advanced Meteorological Imager (JAMI) on Multifunctional Transport Satellite MTSAT) 1, and MTSAT 2; \r\nand polar:\r\nAdvanced Along-Track Scanning Radiometer (AATSR) on Environmental Satellite (Envisat), Moderate-resolution Imaging Spectroradiometer (MODIS) on Earth Observation System (EOS) - Aqua and EOS - Terra, Sea and Land Surface Temperature Radiometer SLSTR on Sentinel-3A and Sentinel-3B. However, it should be noted that which instruments contribute to a particular product file depends on depends on mission start and end dates and instrument downtimes"
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                169122
            ]
        },
        {
            "ob_id": 34903,
            "uuid": "60ae08f9d4ae47d1a471729c88b9cfef",
            "title": "Composite Process for Water budget and Lagrangian analysis of tropical tropopause in simulations with Hadley Centre Global Environmental Model version 3 (HadGEM3)",
            "abstract": "Composite process covering Acquisition for: Water budget and Lagrangian analysis of tropical tropopause in simulations with Hadley Centre Global Environmental Model version 3 (HadGEM3) and HadGEM3, Global Atmosphere (GA) 7.0.",
            "computationComponent": [
                {
                    "ob_id": 34902,
                    "uuid": "7688b3764f4e47dca47b3246f19b8924",
                    "short_code": "comp",
                    "title": "HadGEM3, Global Atmosphere (GA) 7.0",
                    "abstract": "The atmosphere component of HadGEM3, Global Atmosphere (GA) 7.0, was run for three different scenarios. Based on QBOi experiments 2,3,4, these force the atmosphere  model with year 2002 conditions (e.g. of solar radiation and sea surface temperatures) every year for 21 years. The first scenario has no modifications (as a control), the second has doubled CO2 concentrations and sea surface temperatures (SSTs) are increased by 2K, and the same again where CO2 concentrations are quadrupled and SSTs are increased by 4K. Simulations were allowed 10 years to stabilise to their modified forcing conditions and the final 11 years were analysed further."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 34900,
                    "uuid": "adeac87c5fca4bd199d3c2bc005f6e1e",
                    "short_code": "acq",
                    "title": "Acquisition for: Water budget and Lagrangian analysis of tropical tropopause in simulations with Hadley Centre Global Environmental Model version 3 (HadGEM3)",
                    "abstract": ""
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                169718,
                169719
            ]
        },
        {
            "ob_id": 35036,
            "uuid": "518a0b2bf5604cd8b0110e047f45d877",
            "title": "Composite process for: ESA Snow Climate Change Initiative (Snow_cci): Snow Water Equivalent (SWE) level 3C daily global climate research data package (CRDP)  (1979 – 2020), version 2.0",
            "abstract": "The product is based on data from the Scanning Multichannel Microwave Radiometer (SMMR) operated on National Aeronautics and Space Administration’s (NASA) Nimbus-7 satellite, the  Special Sensor Microwave / Imager (SSM/I) and the Special Sensor Microwave Imager / Sounder (SSMI/S) carried onboard the Defense Meteorological Satellite Program (DMSP) 5D- and F-series satellites. The satellite bands provide spatial resolutions between 15 and 69 km.  The retrieval methodology combines 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.",
            "computationComponent": [
                {
                    "ob_id": 35032,
                    "uuid": "b1d7a82ac2844548a41b98df77007b52",
                    "short_code": "comp",
                    "title": "ESA Snow Climate Change Initiative (snow_cci): SWE, v2",
                    "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. Improvements to the GlobSnow algorithm implemented for snow_cci version 1 include 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.\r\n\r\nThe version 2 dataset has some notable differences compared to the v1 data. In v2, 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-sets/) the grid spacing is reduced from 25 km to 12.5 km, and spatially and temporally varying snow density fields are used to adjust SWE retrievals in post processing. The output grid spacing is reduced from 0.25-degree to 0.10-degree WGS84 latitude / longitude to be compatible with other Snow_cci products. The time series has been extended by two years with data from 2018 to 2020 added.\r\n\r\nSWE products are based on SMMR, SSM/I and SSMIS passive microwave radiometer data for non-alpine regions of the Northern Hemisphere."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 35035,
                    "uuid": "7837c871e5c445ad998c0c58dfb40e19",
                    "short_code": "acq",
                    "title": "Acquisition for: ESA Snow Climate Change Initiative (Snow_cci): Snow Water Equivalent (SWE) level 3C daily global climate research data package (CRDP) (1979 – 2020), version 2.0",
                    "abstract": "The ESA Snow_cci Snow Water Equivalent (SWE) data product is based on data from the Scanning Multichannel Microwave Radiometer (SMMR) operated on National Aeronautics and Space Administration’s (NASA) Nimbus-7 satellite, the  Special Sensor Microwave / Imager (SSM/I) and the Special Sensor Microwave Imager / Sounder (SSMI/S) carried onboard the Defense Meteorological Satellite Program (DMSP) 5D- and F-series satellites. The satellite bands provide spatial resolutions between 15 and 69 km."
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                170187,
                170188
            ]
        },
        {
            "ob_id": 37143,
            "uuid": "7722d59232124e3b9134cffae3141753",
            "title": "Composite Process for Transition Air: Street side measurements of dispersed NO from an NO bottle in Oxford. ",
            "abstract": "Composite process covering Acquisition for: Transition Air: Street side measurements of dispersed NO from an NO bottle in Oxford.  and Simcenter STAR-CCM+.",
            "computationComponent": [
                {
                    "ob_id": 37142,
                    "uuid": "7564e6b5035341a29c17e4ab73996509",
                    "short_code": "comp",
                    "title": "Simcenter STAR-CCM+",
                    "abstract": "The dispersion model hs been developed using a commercial CFD software STAR-CCM+. \n\n\nSimcenter STAR-CCM+ is a multiphysics computational fluid dynamics (CFD) software for the simulation of products operating under real-world conditions. Simcenter STAR-CCM+ uniquely brings automated design exploration and optimization to the CFD simulation toolkit of every engineer.\n\nThe single integrated environment includes everything from CAD, automated meshing, multiphysics CFD, sophisticated postprocessing, and design exploration. This allows engineers to efficiently explore the entire design space to make better design decisions faster.\n\n https://www.plm.automation.siemens.com/global/en/products/simcenter/STAR-CCM.html\n"
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 37140,
                    "uuid": "a96d74062b6e42a78ab41430cb0072fc",
                    "short_code": "acq",
                    "title": "Acquisition for: Transition Air: Street side measurements of dispersed NO from an NO bottle in Oxford. ",
                    "abstract": ""
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                177536,
                177537
            ]
        },
        {
            "ob_id": 37148,
            "uuid": "f90d5bdc7c3e415b9ae4bd9e8dcda9eb",
            "title": "Composite process for the ESA Fire Climate Change Initiative (Fire_cci): Sentinel-3 SYN Burned Area products, version 1.0",
            "abstract": "For more information see the documentation at https://climate.esa.int/projects/fire/key_documents/",
            "computationComponent": [
                {
                    "ob_id": 37147,
                    "uuid": "35a31aaeea234553921cfe181fa84701",
                    "short_code": "comp",
                    "title": "Derivation of the ESA Fire Climate Change Initiative (Fire_cci): Sentinel-3 SYN Burned Area products, v1.0",
                    "abstract": "For more information see the documentation at https://climate.esa.int/projects/fire/key_documents"
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 37146,
                    "uuid": "d74c98880ed249f5b04ebe20a38bab91",
                    "short_code": "acq",
                    "title": "Acquisition Process for the ESA Fire Climate Change Initiative (Fire_cci): Sentinel-3 SYN Burned Area products, version 1.0",
                    "abstract": "Products were derived from the Sentinel-3 SYN product, which is based on data from the OLCI and SLSTR instruments on Sentinel-3A and Sentinel-3B."
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                177542
            ]
        },
        {
            "ob_id": 37274,
            "uuid": "bd12bcc31ce44614bb69eb31454ba711",
            "title": "Composite process for the velocity and strain rate fields of the Northeast Tibetan Plateau.",
            "abstract": "Insert some info here!",
            "computationComponent": [
                {
                    "ob_id": 37275,
                    "uuid": "bb2ca27b31884089a13c10730d9448cc",
                    "short_code": "comp",
                    "title": "Computation for the velocity and strain rate fields of the Northeast Tibetan Plateau",
                    "abstract": "The interferograms are processed from Sentinel-1 Level 1 (L1) Synthetic Aperture Radar (SAR) imagery using the Looking Into Continents from Space with Synthetic Aperture Radar (LiCSAR) routine. The average line-of-sight (LOS) velocities and associated uncertainties are derived from frame-based five-year time series, which are inverted from networks of short temporal baseline interferograms using the New Small Baseline Subset (NSBAS) method. The scaled uncertainties are the LOS uncertainties with referencing effects corrected by fitting a spherical model to the scatter points between uncertainty and distance from the reference. The stitched LOS velocities in the reference frame of the Global Navigational Satellite System (GNSS) velocities are the results of mosaicking frame-sized LOS velocities into tracks by adding a planar ramp per frame to close the differences between overlapping pixels in consecutive LOS frames and between InSAR and GNSS LOS velocities. The stitched LOS velocities in two line-of-sights were then decomposed into Cartesian velocities in two steps, first into an east component and a combination of the north and vertical components, and then resolving the vertical component from the combination component using an interpolated north component from the GNSS velocities. The strain rate fields are calculated from the horizontal gradients of the filtered InSAR east velocities and interpolated GNSS north velocities."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 20018,
                    "uuid": "c28a3a6627354dd19363ac971116b0d8",
                    "short_code": "acq",
                    "title": "Acquisition Process for: Sentinel 1B C-band Synthetic Aperture Radar (SAR) Interferometric Wide (IW) mode.",
                    "abstract": "The acquisition process for the collection of raw radar data from the European Space Agency (ESA) Sentinel 1B C-band Synthetic Aperture Radar (SAR) in Interferometric Wide (IW) mode."
                },
                {
                    "ob_id": 12318,
                    "uuid": "f95b77f14a554727a1975802b25ad8a7",
                    "short_code": "acq",
                    "title": "Acquisition Process for: Sentinel 1A C-band Synthetic Aperture Radar (SAR), Interferometric Wide (IW) mode.",
                    "abstract": "The acquisition process for the collection of raw radar data from the European Space Agency (ESA) Sentinel 1A C-band Synthetic Aperture Radar (SAR) in Interferometric Wide (IW) mode."
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                178105,
                178108
            ]
        },
        {
            "ob_id": 37279,
            "uuid": "df5034f23d4742818b6e422928c98267",
            "title": "Composite process for ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Gravimetric Mass Balance from GRACE data (CSR RL06), derived by DTU Space, v2.2",
            "abstract": "The DTU Space v2.2  Greenland Gravimetric Mass Balance (GMB) product has been derived from monthly gravity field solutions (L2) of release 06 generated at the Center for Space Research (University of Texas at Austin) primarily using K-Band ranging, accelerometer and GPS observations acquired by the GRACE and GRACE-FO twin-satellite missions.\r\n\r\nThe mass change estimation is based on inversion method developed at DTU Space.",
            "computationComponent": [
                {
                    "ob_id": 37277,
                    "uuid": "b28242c2531f4186a2189b9cecba8e50",
                    "short_code": "comp",
                    "title": "Derivation of the ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Gravimetric Mass Balance from GRACE data (CSR RL06), derived by DTU Space, v2.2",
                    "abstract": "Estimates of mass change have been derived based on inversion methods developped at DTU Space.\r\n\r\nThe underlying L2 monthly gravity field solutions used in the derivation were generated by the Center for Space Research (University of Texas at Austin) primarily using K-Band ranging, accelerometer and GPS observations acquired by the GRACE and GRACE-FO twin-satellite missions.\r\n\r\n For more information see the linked documentation."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 37278,
                    "uuid": "7ccd2fd6e55946beb2a49b8d5b6456c5",
                    "short_code": "acq",
                    "title": "Acquisition for the ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Gravimetric Mass Balance from GRACE data (CSR RL06), derived by DTU Space, v2.2",
                    "abstract": "The underlying L2 monthly gravity field solutions were generated by the Center for Space Research (University of Texas at Austin) primarily using K-Band ranging, accelerometer and GPS observations acquired by the GRACE and GRACE-FO twin-satellite missions"
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                178124,
                178125
            ]
        },
        {
            "ob_id": 37319,
            "uuid": "117901813f274f678a643c35e6a0c03b",
            "title": "Composite process for ESA Land Surface Temperature Climate Change Initiative (LST_cci): Geostationary Operational Environmental Satellite (GOES) level 3 (L3U) product (2009-2020), version 1.00",
            "abstract": "Data has been retrieved from the IMAGER onboard the Geostationary Operational Environmental Satellite (GOES-12 and GOES-13) and from the Advanced Baseline Imager (ABI) onboard GOES-16.\r\n\r\nFor information on the retrieval algorithm used see the documentation on the LST CCI webpage.",
            "computationComponent": [
                {
                    "ob_id": 37317,
                    "uuid": "01d2d0615b5346c6a3a7ec198d8e7a98",
                    "short_code": "comp",
                    "title": "Derivation of ESA Land Surface Temperature Climate Change Initiative (LST_cci): Geostationary Operational Environmental Satellite (GOES) level 3 (L3U) product (2009-2020), version 1.00",
                    "abstract": "For information on the retrieval algorithm used see the documentation on the LST CCI webpage"
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 37318,
                    "uuid": "69e9443d7824428c8853865725d3feb8",
                    "short_code": "acq",
                    "title": "Acquisition for ESA Land Surface Temperature Climate Change Initiative (LST_cci): Geostationary Operational Environmental Satellite (GOES) level 3 (L3U) product (2009-2020), version 1.00",
                    "abstract": "Data has been retrieved from the IMAGER onboard the Geostationary Operational Environmental Satellite (GOES-12 and GOES-13) and from the Advanced Baseline Imager (ABI) onboard GOES-16."
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                178280,
                178281
            ]
        },
        {
            "ob_id": 37347,
            "uuid": "c8c31aecb8ee48318cadc3ec81c1a207",
            "title": "Composite process for Invisible Tracks: Collocation of wind-advected ship locations and shipping emissions with data from the MODIS cloud product",
            "abstract": "The dataset contains data from the MODIS cloud product, collocated to wind-advected ship locations and shipping emissions. It is the product of three data sources: AIS data giving ship locations, ERA5 winds used to advect the emissions up to the time of the Aqua and Terra overpasses, as well as the level-2 cloud product MOD06.",
            "computationComponent": [
                {
                    "ob_id": 37346,
                    "uuid": "d903a8f24ba940c899704e3219e54808",
                    "short_code": "comp",
                    "title": "Derivation of Invisible Tracks: Collocation of wind-advected ship locations and shipping emissions with data from the MODIS cloud product",
                    "abstract": "The dataset contains data from the MODIS cloud product, collocated to wind-advected ship locations and shipping emissions.   It is the product of three data sources: AIS data giving ship locations, ERA5 winds used to advect the emissions up to the time of the Aqua and Terra overpasses, as well as the level-2 cloud product MOD06."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 37344,
                    "uuid": "604c4b0c715e4307acd61da3ffa22e6d",
                    "short_code": "acq",
                    "title": "Aquisition for Invisible Tracks: Collocation of wind-advected ship locations and shipping emissions with data from the MODIS cloud product",
                    "abstract": "The dataset 'Invisible Tracks: Collocation of wind-advected ship locations and shipping emissions with data from the MODIS cloud product' is the product of three data sources: AIS data giving ship locations, ERA5 winds used to advect the emissions up to the time of the Aqua and Terra satellite overpasses, as well as the MODIS level-2 cloud product MOD06."
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                178404,
                178405
            ]
        },
        {
            "ob_id": 37376,
            "uuid": "4667b80536d240ec973127f856cd12fc",
            "title": "Composite process for ESA Land Surface Temperature Climate Change Initiative (LST_cci): Spinning Enhanced Visible and Infrared Imager (SEVIRI) on MSG level 3 (L3U) product (2004-2020), version 3.00",
            "abstract": "Data has been retrieved from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation series (Meteosat 8 - 11, also known as MSG1-4). \r\n\r\nFor information on the retrieval algorithm used see the documentation on the LST CCI webpage.",
            "computationComponent": [
                {
                    "ob_id": 37375,
                    "uuid": "b32ca49d33644065b52401a6119774b1",
                    "short_code": "comp",
                    "title": "Derivation of ESA Land Surface Temperature Climate Change Initiative (LST_cci): Spinning Enhanced Visible and Infrared Imager (SEVIRI) on MSG level 3 (L3U) product (2004-2020), version 3.00",
                    "abstract": "For information on the retrieval algorithm used see the documentation on the LST CCI webpage"
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 37369,
                    "uuid": "b53836ac3d2d48a29e0a6055dc8d74c8",
                    "short_code": "acq",
                    "title": "Acquisition for ESA Land Surface Temperature Climate Change Initiative (LST_cci): Spinning Enhanced Visible and Infrared Imager (SEVIRI) on MSG level 3 (L3U) product (2004-2020), version 3.00",
                    "abstract": "This dataset was retrieved from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) instruments onboard the Meteosat Second Generation series (MSG1-4)."
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                178554,
                178555
            ]
        },
        {
            "ob_id": 37379,
            "uuid": "b3aa750910c349198aaf4950d535c2b8",
            "title": "Composite process for ESA Land Surface Temperature Climate Change Initiative (LST_cci): Multi-Functional Transport Satellite (MTSAT) level 3 (L3U) product (2009-2015), version 1.00",
            "abstract": "Data has been derived from the JApanese Advanced Meteorological Imager (JAMI) onboard the Multi-Functional Transport Satellite (MTSAT1 and 2, also known as Himiwari-6 and 7).\r\n\r\nFor information on the retrieval algorithm used see the documentation on the LST CCI webpage.",
            "computationComponent": [
                {
                    "ob_id": 37380,
                    "uuid": "94eae3a57e984e2ba1920f5f775511d3",
                    "short_code": "comp",
                    "title": "Derivation of ESA Land Surface Temperature Climate Change Initiative (LST_cci): Multi-Functional Transport Satellite (MTSAT) level 3 (L3U) product (2009-2015), version 1.00",
                    "abstract": "For information on the retrieval algorithm used see the documentation on the LST CCI webpage"
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 37378,
                    "uuid": "cbefad86db1d4b56b9dbd71752c8444e",
                    "short_code": "acq",
                    "title": "Acquisition for ESA Land Surface Temperature Climate Change Initiative (LST_cci): Multi-Functional Transport Satellite (MTSAT) level 3 (L3U) product (2009-2015), version 1.00",
                    "abstract": "This dataset was retrieved from the Japanese Advanced Meteorological Imager (JAMI) onboard the Multi-Functional Transport Satelitte series (MTSAT1-2, also known as Himawari-6 and 7)"
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                178567,
                178568
            ]
        },
        {
            "ob_id": 37575,
            "uuid": "e1c6eda586694c7facb951c569c604f6",
            "title": "Composite process for 'Cloud droplet number concentration, calculated from the MODIS (Moderate resolution imaging spectroradiometer) cloud optical properties retrieval and gridded using different sampling strategies'",
            "abstract": "Cloud droplet number concentrations were gridded to 1 by 1 degree resolution using a variety of sampling methods to select valid retrievals. Data from the MODIS (Moderate resolution imaging spectroradiometer) instruments on both the Terra (morning overpass) and Aqua (Afternoon overpass) satellites are available (indicated by a T or A in the filename). These sampling methods have been compared against multiple flight campaigns, see Gryspeerdt et al., The impact of sampling strategy on the cloud droplet number concentration estimated from satellite data. Atmos. Meas. Tech. 2022.\"",
            "computationComponent": [
                {
                    "ob_id": 37574,
                    "uuid": "8f7400954c7f4946afdda0ac202211ce",
                    "short_code": "comp",
                    "title": "Derivation of the dataset: Cloud droplet number concentration, calculated from the MODIS (Moderate resolution imaging spectroradiometer) cloud optical properties retrieval and gridded using different sampling strategies",
                    "abstract": "For more information see Gryspeerdt et al., The impact of sampling strategy on the cloud droplet number concentration estimated from satellite data. Atmos. Meas. Tech. 2022."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 37573,
                    "uuid": "9a87166f55794f34a46f51e52f1c3b3d",
                    "short_code": "acq",
                    "title": "Aquisition for Cloud droplet number concentration, calculated from the MODIS (Moderate resolution imaging spectroradiometer) cloud optical properties retrieval and gridded using different sampling strategies",
                    "abstract": "Data from the MODIS (Moderate resolution imaging spectroradiometer) instruments on both the Terra (morning overpass) and Aqua (Afternoon overpass) satellites have been used."
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                179345,
                179346
            ]
        },
        {
            "ob_id": 37680,
            "uuid": "35164039bf2a417fa41578d29df5929a",
            "title": "Composite Process for Shiptrack clouds inferred from MODIS (MODerate Imaging Spectroradiometer) by deep learning global dataset for 2002-2021",
            "abstract": "Composite process covering Acquisition for: Shiptrack clouds inferred from MODIS (MODerate Imaging Spectroradiometer) by deep learning global dataset for 2002-2021 and shiptrack_semantic_segmentation_v1.",
            "computationComponent": [
                {
                    "ob_id": 37679,
                    "uuid": "d129f860b47b40fdafd57a5b69457926",
                    "short_code": "comp",
                    "title": "shiptrack_semantic_segmentation_v1",
                    "abstract": "A convolutional neural network with a Unet architecture, with a RESNET-152 backbone, trained to segment shiptrack clouds from enhanced day_microphysics imagery from AQUA MODIS Input Description AQUA MODIS level 1B day microphysics composite granules, enhanced with histogram stretch. Output Description Netcdf files with a single variable 'shiptracks' that contains shiptrack inference values and shares the coordinates of the original AQUA MODIS granule from which they are derived. Post-processing is required to extract contours and filter them by brightness temperature to obtain final results used in publication. Software Reference https://github.com/duncanwp/shiptrack-detection"
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 26034,
                    "uuid": "85bb8321bc8b42f9a39cb6d83fabe79e",
                    "short_code": "acq",
                    "title": "MODIS data from the Aqua Satellite",
                    "abstract": "The Moderate Resolution Imaging Spectroradiometer (MODIS) provides high radiometric sensitivity in 36 spectral bands ranging from 0.4 to 14.4 micrometres. Two bands are imaged at a nominal resolution of 250 m at nadir, with five bands at 500 m, and the remaining 29 bands at 1 km.\r\n\r\nIt is flown on NASA's Aqua Satellite  which was launched on May 4, 2002, and has six Earth-observing instruments on board, collecting a variety of global data sets\r\n\r\nA MODIS instrument is also flown on NASA's Terra satellite"
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                179709,
                179710
            ]
        },
        {
            "ob_id": 37710,
            "uuid": "2e9c4e2cab344e60a2cf7f02759daa8a",
            "title": "Composite process for the Visible Infra-red Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership (SNPP), Top of Atmosphere (TOA) level 2 (L2) S5P-NP-BDx cloud products.",
            "abstract": "This process generates auxiliary data for observations collected by the Copernicus Sentinel 5 Precursor Tropospheric Monitoring Instrument (S5P/TROPOMI, see catalogue record link in documentation). The auxiliary data are cloud information relevant to each TROPOMI point of view and are produced by the VIIRS instrument on the SNPP platform (see catalogue link in documentation). \r\n\r\nComputed NPP-VIIRS data (consisting of a VCM/ECM cloud mask product) is used for cloud screening in order to enhance the accuracy of the methane (CH4) column retrieval algorithm on Sentinel 5P data, due to its superior spatial resolution and spectral band coverage compared to S5P.\r\n\r\nFor more information on the cloud retrieval algorithms used see the documentation on the Sentinel webpage: https://sentinel.esa.int/documents/247904/0/Sentinel-5P-TROPOMI-ATBD-Clouds/29d45625-f34c-45bd-ac85-985eb9250a47. These include ICFA (Initial Cloud Fitting Algorithm), FRESCO (Fast REtrieval Scheme for Clouds from the Oxygen band), SACURA (Semi-Analytical CloUd Retrieval Algorithm), OCRA (Optical Cloud Recognition Algorithm) and ROCINN (Retrieval of Cloud Information using Neural Networks).",
            "computationComponent": [
                {
                    "ob_id": 37711,
                    "uuid": "d3d568f134634dda9bb06b9556756b5b",
                    "short_code": "comp",
                    "title": "Level 2 (L2) cloud retrieval algorithm applied to Suomi National Polar-orbiting Partnership (SNPP) Visible Infra-red Imaging Radiometer Suite (VIIRS) instrument: level 2 Suomi-NPP VIIRS raw cloud data.",
                    "abstract": "The computation process for the transformation of cloud data from the VIIRS instrument on board the JPSS (Joint Polar Satellite System) operational meterological satellite prototype SNPP, to \r\nTo view the retrieval algorithms for these products, visit the following Sentinel/Copernicus page: \r\nhttps://sentinels.copernicus.eu/documents/247904/2476257/Sentinel-5P-NPP-ATBD-NPP-Clouds\r\n\r\nThe S5P-NPP Cloud processor is a stand-alone code separate from other TROPOMI-related computations. The retrieval algorithm requires the following input fields:\r\n• Semi-major and semi-minor axis, and eccentricity, of the WGS'84 ellipsoid\r\n• Time window of tolerance to identify relevant NPP files for a given S5P scan line\r\n• Number of VIIRS cloud classifcations, defined S5P FOVs, and VIIRS moderate resolution channels used in the S5P-NPP product\r\n• Maximum and minimum values of transformed coordinates (y & z) which define a specific S5P FOV.\r\n• Instantaneous spatial response function\r\n• Latitude and longitude of S5P pixel corner, centre or sensor, and altitude of sensor\r\n• View zenith angle of S5P pixel and line-of-sight zenith angle of a VIIRS pixel\r\n• Sensing time of S5P scan line and a VIIRS scan line\r\n• Sensing start and stop time of S5P L1 file\r\n• Latitude and longitude of VIIRS pixel\r\n• Sensing start and stop time of NPP L1 file\r\n• Sensing start and stop time of NPP L1 granule\r\n• Sensing start and stop centre-scan latitude and longitude of NPP L1 granule\r\n• VIIRS pixel level geolocation, radiance and cloud mask data quality flags\r\n• VIIRS band averaged spectral radiance\r\n• VIIRS pixel cloud mask\r\n• Ratio of nominal FOV extent to that of L1GPC in across- and along-track directions\r\n• Spatial response function (SRF), including effects of satellite motion\r\n\r\nThe retrieval is performed in 3 steps:\r\n1) S5P L1 file is ingested, containing the geolocation of each scene for which the S5P-NPP-Cloud information is required.\r\n2) The time difference, ∆tsat, between S5P centre-swath observations and those of spatially co-located VIIRS measurements is calculated to an accuracy of ≈1 minute using metadata in the VIIRS geololcation file.\r\n3) S5P scan lines are looped over: a) VIIRS files which contain information relevant to these scan lines are identified b) The relevant information from these VIIRS files is read and stored in memory c) S5P across-track pixels within the current scan line are looped over.\r\n \r\nThe NPP-Cloud product is then the re-gridding of the VIIRS L1 and cloud mask products from the SNPP to the resolution acquired by the TROPOMI instrument on the Sentinel 5P satellite.\r\n\r\nFor more information on the processing algorithm please look at the ATBD (Algorithm Theoretical Baseline Document) on the Sentinel Copernicus webpage, URL linked in the documentation."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 37712,
                    "uuid": "30a2d92d965f48ec973f88a6eb7568db",
                    "short_code": "acq",
                    "title": "Acquisition Process for: Sentinel 5P level 2 Suomi-NPP VIIRS cloud data.",
                    "abstract": "The acquisition process for the collection of cloud data from the SNPP operational meteorological satellite (prototype of the Joint Polar Satellite System, JPSS). This satellite flies in loose formation with the Sentinel 5-Precursor satellite, with an observational gap of approximately 3.5 minutes, so that data from the two platforms may be co-located. The instruments of interest regarding collection of these data are the Sentinel 5P TROPOspheric Monitoring Instrument (TROPOMI) and the SNPP Visible Infra-red Imaging Radiometer Suite (VIIRS).\r\n\r\nThe data are from operational products from the Visible Infrared Imaging Radiometer Suite (VIIRS) \r\non board the Suomi NPP platform, and are used to derive information on cloud and scene homogeneity for TROPOMI scenes on board the Sentinel 5-Precursor satellite which flies in loose formation with SNPP."
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                179820,
                179823,
                180861
            ]
        },
        {
            "ob_id": 37757,
            "uuid": "64f9a34007ef425880561a49913aa85f",
            "title": "Composite process for Geochem model and NAME model for  MOYA Bolivia BAS twin Otter Flights",
            "abstract": "Aircraft measurements Geochem model and NAME model for  MOYA Bolivia BAS twin Otter Flights",
            "computationComponent": [
                {
                    "ob_id": 37755,
                    "uuid": "d015125d92c14211a83804cbf48738d9",
                    "short_code": "comp",
                    "title": "NAME Model computation for MOYA Bolivia BAS Twin Otter flights with modelling",
                    "abstract": "NAME model simulation which ran using  the Met Office NAME model at of 0.14° × 0.09° and temporal resolution of 3 hourly. A footprint was simulated for each minute of aircraft sampling to capture the Llanos de Moxos wetlands in Bolivia 2019-03-08 to 2019-03-09\r\nThe NAME model inversion was carried out using footprints simulated from  the NAME Lagrangian particle dispersion model and a hierarchical Bayesian Markov chain Monte Carlo (MCMC) framework"
                },
                {
                    "ob_id": 37756,
                    "uuid": "f29a2dce4ec74723a75c85220f98c6cc",
                    "short_code": "comp",
                    "title": "GEOSChem model simulation for  MOYA BAS flights in Bolivia",
                    "abstract": "A nested GEOS-Chem simulation for the MOYA Bolivia flights 2019-03-08 to 2019-03-09.\r\n\r\nThe GEOS-Chem inverse modeling methodology followed a Bayesian synthesis inversion framework (4). The state vector included 100 elements, 99 corresponding to emissions and one describing the baseline mole fraction. Measurements were averaged into 1-minute  means. Model-measurement uncertainties included the standard deviation of measurements with in each one-minute period and a fixed 8 ppb model uncertainty. A flat prior emissions distribution was used within the Llanos de Moxos basin with emissions of 48 mg CH4/m2/day. A nested GEOS-Chem simulation at 0.25° x 0.3125° was used to map the relationship between emissions  and aircraft measurements in a regional domain bounded by 24 - 0 °S and 75 – 55 °W. Initial  boundary conditions for the nested domain were created by a global GEOS-Chem simulation at 2° x 2.5°."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 37754,
                    "uuid": "1c244a85e1a944f6a42ae43bb95e451e",
                    "short_code": "acq",
                    "title": "Acquisition for: In CH4 flight campaign samples, Llanos de Moxos, Bolivia 2019",
                    "abstract": ""
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                180164
            ]
        },
        {
            "ob_id": 38108,
            "uuid": "dbd00d9ac8fa4c7f80a953f3c46d95c5",
            "title": "Composite process for SASSO Australian Wildfires",
            "abstract": "Data were collected by the CALIOP and OMPS-LS instruments and zonally averaged and placed into subsets and then converted into the products required as input by the UKESM1 model by the project team",
            "computationComponent": [
                {
                    "ob_id": 38102,
                    "uuid": "99820bff0b994aa29f1a5557f5f14d86",
                    "short_code": "comp",
                    "title": "Computation for SASSO Australian Wildfires",
                    "abstract": "A combination based on CALIOP and OMPS-LP satellite retrievals that have been zonally averaged and placed into subsets and then converted into the products required as input by the UKESM1 model by the project team."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 38107,
                    "uuid": "3cc6897f1405409c8f4027e29f3bc780",
                    "short_code": "acq",
                    "title": "Acquisition for SASSO Australian Wildfires",
                    "abstract": "Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP)  and Ozone Mapping and Profiler Suite -Limb Profiler (OMPS-LP) instruments satellite retrievals of the aerosol extinction coefficient and ozone anomalies"
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                181577
            ]
        },
        {
            "ob_id": 38290,
            "uuid": "df96995ca0ce43158f8b2f00a6d9de51",
            "title": "Composite process for the ESA Sea State Climate Change Initiative (Sea_State_cci): Global remote sensing multi-mission along-track significant wave height from altimetery, version 3 datasets",
            "abstract": "The altimeter data used in the ESA Sea State Climate Change Initiative (Sea_State_cci): Global remote sensing multi-mission along-track significant wave height from altimetery, version 3 datasets come from multiple satellite missions (Envisat, CryoSat-2, Jason-1, Jason-2, Jason-3, SARAL, Sentinel-3A).",
            "computationComponent": [
                {
                    "ob_id": 38289,
                    "uuid": "0bca71f1e90c41b1ba78ba0e87b67583",
                    "short_code": "comp",
                    "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 3 datasets",
                    "abstract": "For information on the derivation of the Sea_State_cci Global remote sensingmulti-mission along-track significant wave height from altimetery version 3 products please see the product user guide."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 38288,
                    "uuid": "09bf329dd0914affa88259c0b74585be",
                    "short_code": "acq",
                    "title": "Acquisition for the ESA Sea State Climate Change Initiative (Sea_State_cci) : Global remote sensing merged multi-mission gridded significant wave height from altimetery, v3 datasets",
                    "abstract": "The altimeter data used in the ESA Sea State Climate Change Initiative (Sea_State_cci): Global remote sensing multi-mission gridded significant wave height from altimeter, v3 datasets come from multiple satellite missions spanning from 2002 to 2020 ( Envisat, CryoSat-2, Jason-1, Jason-2, Jason-3, SARAL, Sentinel-3A)."
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                182329
            ]
        },
        {
            "ob_id": 38296,
            "uuid": "61335301ac8649ec9f24912e7c85cb8d",
            "title": "Composite process for the ESA Sea State Climate Change Initiative (Sea_State_cci): Global remote sensing multi-mission along-track significant wave height (SWH) from SAR WV onboard Sentinel-1A & 1B, L2P product, version 3",
            "abstract": "The SAR Wave Mode data used in this dataset comes from Sentinel-1 satellite missions spanning from 2015 to 2021 (Sentinel-1 A, Sentinel-1 B).\r\n\r\nFor more information on the dataset see the product user guide.",
            "computationComponent": [
                {
                    "ob_id": 38295,
                    "uuid": "243f6b28ccda4c3cbcf99d5537ae4195",
                    "short_code": "comp",
                    "title": "Computation for the ESA Sea State Climate Change Initiative (Sea_State_cci) v3 products",
                    "abstract": "For information on the derivation of the ESA Sea_State_cci v3 products please see the product user guide."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 38291,
                    "uuid": "827c76ac3cf54a49a80f6d1fc024efb1",
                    "short_code": "acq",
                    "title": "Acquisition for the ESA Sea State Climate Change Initiative (Sea_State_cci) : Global remote sensing multi-mission along-track significant wave height (SWH) from SAR WV onboard Sentinel-1A & 1B, L2P product, version 3",
                    "abstract": "The SAR Wave Mode data used in the Sea State CCI dataset v3 come from Sentinel-1 satellite missions spanning from 2015 to 2021 (Sentinel-1 A, Sentinel-1 B)"
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                182340
            ]
        },
        {
            "ob_id": 38297,
            "uuid": "d4cce7ac48b944bd9877ba67955c361e",
            "title": "Composite process for the ESA Sea State Climate Change Initiative (Sea_State_cci): Global remote sensing multi-mission along-track Integrated Sea State Parameters (ISSP) from SAR WV onboard Sentinel-1A & 1B, L2P product, version 3",
            "abstract": "The SAR Wave Mode data used in this dataset comes from Sentinel-1 satellite missions spanning from 2014 to 2021 (Sentinel-1 A, Sentinel-1 B).\r\n\r\nFor more information on the dataset see the product user guide.",
            "computationComponent": [
                {
                    "ob_id": 38295,
                    "uuid": "243f6b28ccda4c3cbcf99d5537ae4195",
                    "short_code": "comp",
                    "title": "Computation for the ESA Sea State Climate Change Initiative (Sea_State_cci) v3 products",
                    "abstract": "For information on the derivation of the ESA Sea_State_cci v3 products please see the product user guide."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 38293,
                    "uuid": "d1735b9c9282475aae88a322e22b405f",
                    "short_code": "acq",
                    "title": "Acquisition for the ESA Sea State Climate Change Initiative (Sea_State_cci) : Global remote sensing multi-mission along-track Integrated Sea State Parameters (ISSP) from SAR WV onboard Sentinel-1A & 1B, L2P product, version 3",
                    "abstract": "The SAR Wave Mode data used in the Sea State CCI dataset v3 come from Sentinel-1 satellite missions spanning from 2014 to 2021 (Sentinel-1 A, Sentinel-1 B)"
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                182341
            ]
        },
        {
            "ob_id": 38298,
            "uuid": "a8c3649ece4f4f5098544c5acd54d6ed",
            "title": "Composite process for the ESA Sea State Climate Change Initiative (Sea_State_cci): Global remote sensing multi-mission along-track Integrated Sea State Parameters (ISSP) from SAR Wave Mode onboard ENVISAT, L2P product, version 3",
            "abstract": "The SAR Wave Mode data used in this dataset come from the ENVISAT satellite mission spanning from 2002 to 2012.\r\n\r\nFor more information on the dataset see the product user guide.",
            "computationComponent": [
                {
                    "ob_id": 38295,
                    "uuid": "243f6b28ccda4c3cbcf99d5537ae4195",
                    "short_code": "comp",
                    "title": "Computation for the ESA Sea State Climate Change Initiative (Sea_State_cci) v3 products",
                    "abstract": "For information on the derivation of the ESA Sea_State_cci v3 products please see the product user guide."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 38294,
                    "uuid": "000f3d19747c4672a6466a5432812cdf",
                    "short_code": "acq",
                    "title": "Acquisition for the ESA Sea State Climate Change Initiative (Sea_State_cci): Global remote sensing multi-mission along-track Integrated Sea State Parameters (ISSP) from SAR Wave Mode onboard ENVISAT, L2P product, version 3",
                    "abstract": "The SAR Wave Mode data used in the Sea State CCI SAR WV onboard ENVISAT Level 2P (L2P) ISSP v3 dataset come from the ENVISAT satellite mission spanning from 2002 to 2012."
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                182342
            ]
        },
        {
            "ob_id": 38320,
            "uuid": "e714abd02bbd40bf96abfbfb851ceadc",
            "title": "Composite Process for: Level 1 data from the Sentinel 3B Ocean and Land Colour Instrument (OLCI)",
            "abstract": "Composite process for Level 1 data from the Ocean and Land Colour Instrument (OLCI) deployed on Sentinel 3B. This consists of the Acquisition process for raw data from the Sentinel 3 OLCI and the computation component to produce processed data.",
            "computationComponent": [
                {
                    "ob_id": 19029,
                    "uuid": "68e7fd451fd848ecb0b6e53dfd68ae4a",
                    "short_code": "comp",
                    "title": "Level 1 processing algorithm applied to Sentinel 3 Ocean and Land Colour Instrument (OLCI) raw data",
                    "abstract": "This computation involves the Level 1 processing algorithm applied to raw Ocean and Land Colour Instrument (OLCI) data. This product consists of the physical measurements from the instrument. These are Top Of Atmosphere (TOA) radiances, calibrated to geophysical units (W.m-2. sr-1 µm-1), georeferenced onto the Earth's surface, spatially resampled onto an evenly spaced grid, and annotated with illumination and observation geometry, environment data (meteorological data) and quality and classification flags.\r\nThe product is available at two spatial resolutions: full resolution and reduced resolution.\r\n\r\nThe processing involves the following steps:\r\n\r\n1. Data extraction and quality checks.\r\n2. Radiometric scaling.\r\n3. Stray light correction.\r\n4. Georeferencing.\r\n5. Pixel classification functions.\r\n6. Spatial re-sampling.\r\n7. Product formatting (into the two spatial resolutions)\r\n\r\nFor more information please see the OLCI user guide in the docs tab."
                }
            ],
            "acquisitionComponent": [
                {
                    "ob_id": 38321,
                    "uuid": "abf32dcb73fa4d0792107198a463c246",
                    "short_code": "acq",
                    "title": "Acquisition Process for: Sentinel 3B Ocean and Land Colour Instrument (OLCI)",
                    "abstract": "The acquisition process for the collection of raw data from the European Space Agency (ESA) Sentinel 3B Ocean and Land Colour Instrument (OLCI)."
                }
            ],
            "identifier_set": [],
            "responsiblepartyinfo_set": [
                182464,
                182465,
                182466
            ]
        }
    ]
}