Procedure Composite Process List
Get a list of ProcedureComputation objects. ProcedureComputations have a 1:1 mapping with Observations where used.
These may have a number of 2 or more components made up of combinations of Computation and Acquisition records.
The details of the underlying records have been serialised.
### Available end points:
- `/ProcedureComputations/` - Will list all ProcedureComputations in the database
- `/ProcedureComputations.json` - Will return all ProcedureComputations in json format
- `/ProcedureComputations/<object_id>/` - Returns ProcedureComputations object with that id
### Available Methods:
- `GET`
- `HEAD`
### Available filters:
None
### How to use filters:
None
GET /api/v2/composites/?format=api&offset=400
{ "count": 662, "next": "https://api.catalogue.ceda.ac.uk/api/v2/composites/?format=api&limit=100&offset=500", "previous": "https://api.catalogue.ceda.ac.uk/api/v2/composites/?format=api&limit=100&offset=300", "results": [ { "ob_id": 32014, "computationComponent": [ { "ob_id": 32010, "uuid": "5f1b78c424904c359c1c907b2bb176c2", "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.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 32013, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 12418, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/27128/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/27124/?format=api", "relatedTo": { "ob_id": 32013, "uuid": "99251b53c2e84901b4bf6ec6fd78ad51", "short_code": "acq" } }, { "ob_id": 12419, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/27130/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/27125/?format=api", "relatedTo": { "ob_id": 32013, "uuid": "99251b53c2e84901b4bf6ec6fd78ad51", "short_code": "acq" } }, { "ob_id": 12420, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/25273/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/25272/?format=api", "relatedTo": { "ob_id": 32013, "uuid": "99251b53c2e84901b4bf6ec6fd78ad51", "short_code": "acq" } }, { "ob_id": 12421, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/10906/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/14485/?format=api", "relatedTo": { "ob_id": 32013, "uuid": "99251b53c2e84901b4bf6ec6fd78ad51", "short_code": "acq" } }, { "ob_id": 12422, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/27135/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/27126/?format=api", "relatedTo": { "ob_id": 32013, "uuid": "99251b53c2e84901b4bf6ec6fd78ad51", "short_code": "acq" } }, { "ob_id": 12423, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/2629/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/2630/?format=api", "relatedTo": { "ob_id": 32013, "uuid": "99251b53c2e84901b4bf6ec6fd78ad51", "short_code": "acq" } }, { "ob_id": 12424, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/458/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/2636/?format=api", "relatedTo": { "ob_id": 32013, "uuid": "99251b53c2e84901b4bf6ec6fd78ad51", "short_code": "acq" } }, { "ob_id": 12425, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/8207/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/27121/?format=api", "relatedTo": { "ob_id": 32013, "uuid": "99251b53c2e84901b4bf6ec6fd78ad51", "short_code": "acq" } }, { "ob_id": 12426, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/8299/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/27121/?format=api", "relatedTo": { "ob_id": 32013, "uuid": "99251b53c2e84901b4bf6ec6fd78ad51", "short_code": "acq" } }, { "ob_id": 12427, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/7805/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/27122/?format=api", "relatedTo": { "ob_id": 32013, "uuid": "99251b53c2e84901b4bf6ec6fd78ad51", "short_code": "acq" } }, { "ob_id": 12428, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/7813/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/27122/?format=api", "relatedTo": { "ob_id": 32013, "uuid": "99251b53c2e84901b4bf6ec6fd78ad51", "short_code": "acq" } }, { "ob_id": 12430, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/29941/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/29938/?format=api", "relatedTo": { "ob_id": 32013, "uuid": "99251b53c2e84901b4bf6ec6fd78ad51", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/141788/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/141787/?format=api" ] }, { "ob_id": 32015, "computationComponent": [ { "ob_id": 32010, "uuid": "5f1b78c424904c359c1c907b2bb176c2", "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.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 32012, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 12411, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/458/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/2636/?format=api", "relatedTo": { "ob_id": 32012, "uuid": "8e5d8bc49927483286faa5929e012d72", "short_code": "acq" } }, { "ob_id": 12412, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/2629/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/2630/?format=api", "relatedTo": { "ob_id": 32012, "uuid": "8e5d8bc49927483286faa5929e012d72", "short_code": "acq" } }, { "ob_id": 12413, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/27135/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/27126/?format=api", "relatedTo": { "ob_id": 32012, "uuid": "8e5d8bc49927483286faa5929e012d72", "short_code": "acq" } }, { "ob_id": 12414, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/10906/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/14485/?format=api", "relatedTo": { "ob_id": 32012, "uuid": "8e5d8bc49927483286faa5929e012d72", "short_code": "acq" } }, { "ob_id": 12415, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/25273/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/25272/?format=api", "relatedTo": { "ob_id": 32012, "uuid": "8e5d8bc49927483286faa5929e012d72", "short_code": "acq" } }, { "ob_id": 12416, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/27130/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/27125/?format=api", "relatedTo": { "ob_id": 32012, "uuid": "8e5d8bc49927483286faa5929e012d72", "short_code": "acq" } }, { "ob_id": 12417, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/27128/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/27124/?format=api", "relatedTo": { "ob_id": 32012, "uuid": "8e5d8bc49927483286faa5929e012d72", "short_code": "acq" } }, { "ob_id": 12429, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/29941/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/29938/?format=api", "relatedTo": { "ob_id": 32012, "uuid": "8e5d8bc49927483286faa5929e012d72", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/141790/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/141789/?format=api" ] }, { "ob_id": 32126, "computationComponent": [ { "ob_id": 32127, "uuid": "e19d06e0fbcd4e2fa396ac94f12fe5f7", "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", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 32128, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 12442, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/32134/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/8300/?format=api", "relatedTo": { "ob_id": 32128, "uuid": "22d0f387ba5748c794006d18d94c2f57", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/142487/?format=api" ] }, { "ob_id": 32161, "computationComponent": [ { "ob_id": 32162, "uuid": "e560269d82274ed4a096af819206fe37", "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.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 32160, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 12443, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/26503/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/32158/?format=api", "relatedTo": { "ob_id": 32160, "uuid": "dd5ad889071847649a9fd58ad09fb359", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/142994/?format=api" ] }, { "ob_id": 32164, "computationComponent": [ { "ob_id": 32165, "uuid": "54e1d465d6154a2a82bd76f4d82f89c8", "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. 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(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.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 32518, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 12553, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/1664/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1802/?format=api", "relatedTo": { "ob_id": 32518, "uuid": "fe25ba369f6e4247aba9650253ef9f6a", "short_code": "acq" } }, { "ob_id": 12555, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/1679/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1802/?format=api", "relatedTo": { "ob_id": 32518, "uuid": "fe25ba369f6e4247aba9650253ef9f6a", "short_code": "acq" } }, { "ob_id": 12556, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/1693/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1802/?format=api", "relatedTo": { "ob_id": 32518, "uuid": "fe25ba369f6e4247aba9650253ef9f6a", "short_code": "acq" } }, { "ob_id": 12557, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/1809/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1802/?format=api", "relatedTo": { "ob_id": 32518, "uuid": "fe25ba369f6e4247aba9650253ef9f6a", "short_code": "acq" } }, { "ob_id": 12558, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/1824/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/10888/?format=api", "relatedTo": { "ob_id": 32518, "uuid": "fe25ba369f6e4247aba9650253ef9f6a", "short_code": "acq" } }, { "ob_id": 12559, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/27174/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/10888/?format=api", "relatedTo": { "ob_id": 32518, "uuid": "fe25ba369f6e4247aba9650253ef9f6a", "short_code": "acq" } }, { "ob_id": 12560, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/27175/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/10888/?format=api", "relatedTo": { "ob_id": 32518, "uuid": "fe25ba369f6e4247aba9650253ef9f6a", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/144770/?format=api" ] }, { "ob_id": 32525, "computationComponent": [ { "ob_id": 32524, "uuid": "d60e0d8070734e39951a356adbb8b497", "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.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 32518, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 12553, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/1664/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1802/?format=api", "relatedTo": { "ob_id": 32518, "uuid": "fe25ba369f6e4247aba9650253ef9f6a", "short_code": "acq" } }, { "ob_id": 12555, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/1679/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1802/?format=api", "relatedTo": { "ob_id": 32518, "uuid": "fe25ba369f6e4247aba9650253ef9f6a", "short_code": "acq" } }, { "ob_id": 12556, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/1693/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1802/?format=api", "relatedTo": { "ob_id": 32518, "uuid": "fe25ba369f6e4247aba9650253ef9f6a", "short_code": "acq" } }, { "ob_id": 12557, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/1809/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1802/?format=api", "relatedTo": { "ob_id": 32518, "uuid": "fe25ba369f6e4247aba9650253ef9f6a", "short_code": "acq" } }, { "ob_id": 12558, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/1824/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/10888/?format=api", "relatedTo": { "ob_id": 32518, "uuid": "fe25ba369f6e4247aba9650253ef9f6a", "short_code": "acq" } }, { "ob_id": 12559, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/27174/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/10888/?format=api", "relatedTo": { "ob_id": 32518, "uuid": "fe25ba369f6e4247aba9650253ef9f6a", "short_code": "acq" } }, { "ob_id": 12560, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/27175/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/10888/?format=api", "relatedTo": { "ob_id": 32518, "uuid": "fe25ba369f6e4247aba9650253ef9f6a", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/144801/?format=api" ] }, { "ob_id": 32596, "computationComponent": [ { "ob_id": 32595, "uuid": "6802c93ca9d64942bd1cdb88e23b3546", "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)", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 32594, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 12573, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/29959/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/29958/?format=api", "relatedTo": { "ob_id": 32594, "uuid": "2b5e6abb44844bbfa34220b674d23461", "short_code": "acq" } }, { "ob_id": 12574, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/12358/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/12365/?format=api", "relatedTo": { "ob_id": 32594, "uuid": "2b5e6abb44844bbfa34220b674d23461", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/145131/?format=api" ] }, { "ob_id": 32678, "computationComponent": [ { "ob_id": 32677, "uuid": "c763ca9f2a874b8ca75d9576efe6673b", "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/", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 32676, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 12576, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/12319/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/12313/?format=api", "relatedTo": { "ob_id": 32676, "uuid": "1555372bc28140aca7c86ae502bc8d5e", "short_code": "acq" } }, { "ob_id": 12577, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/20017/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/12313/?format=api", "relatedTo": { "ob_id": 32676, "uuid": "1555372bc28140aca7c86ae502bc8d5e", "short_code": "acq" } }, { "ob_id": 12580, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/32746/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/32675/?format=api", "relatedTo": { "ob_id": 32676, "uuid": "1555372bc28140aca7c86ae502bc8d5e", "short_code": "acq" } }, { "ob_id": 12578, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/7805/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/32747/?format=api", "relatedTo": { "ob_id": 32676, "uuid": "1555372bc28140aca7c86ae502bc8d5e", "short_code": "acq" } }, { "ob_id": 12579, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/7813/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/32747/?format=api", "relatedTo": { "ob_id": 32676, "uuid": "1555372bc28140aca7c86ae502bc8d5e", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/145711/?format=api" ] }, { "ob_id": 32698, "computationComponent": [ { "ob_id": 32697, "uuid": "29ac5e1fed1243ee8ad453ba74c5d6a4", "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", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 32696, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 12581, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/32693/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/32695/?format=api", "relatedTo": { "ob_id": 32696, "uuid": "8a71f0acff9e45199e34e879671f2b29", "short_code": "acq" } }, { "ob_id": 12582, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/32694/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/32695/?format=api", "relatedTo": { "ob_id": 32696, "uuid": "8a71f0acff9e45199e34e879671f2b29", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/145783/?format=api" ] }, { "ob_id": 32701, "computationComponent": [ { "ob_id": 32700, "uuid": "2b2b435fec204442a44ff2bf173d78b1", "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.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 32699, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 12583, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/12319/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/12313/?format=api", "relatedTo": { "ob_id": 32699, "uuid": "8ee58d233916481db9f0b25c30ae9ad1", "short_code": "acq" } }, { "ob_id": 12584, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/20017/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/12313/?format=api", "relatedTo": { "ob_id": 32699, "uuid": "8ee58d233916481db9f0b25c30ae9ad1", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/145786/?format=api" ] }, { "ob_id": 32709, "computationComponent": [ { "ob_id": 32710, "uuid": "dcb605e6db9b41c3959153f5686d6302", "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.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 27068, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 11996, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/8246/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/7872/?format=api", "relatedTo": { "ob_id": 27068, "uuid": "812b78639c2740f98090d73e5a8f938e", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/145830/?format=api" ] }, { "ob_id": 32714, "computationComponent": [ { "ob_id": 32713, "uuid": "c8de973897f042e29caf9edd71d5d705", "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.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 26443, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 11891, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/26439/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/26444/?format=api", "relatedTo": { "ob_id": 26443, "uuid": "929d929b043242e69de7b5373acfb611", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/145849/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/145850/?format=api" ] }, { "ob_id": 32755, "computationComponent": [ { "ob_id": 32756, "uuid": "1d05ff37e7124eaa8dbc7a3aedbba431", "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.", "keywords": "Synthetic Aperture Radar, Sentinel 1, Level 2, algorithm, SAR", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 12318, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 4316, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/12319/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/12313/?format=api", "relatedTo": { "ob_id": 12318, "uuid": "f95b77f14a554727a1975802b25ad8a7", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/146169/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/146170/?format=api" ] }, { "ob_id": 32764, "computationComponent": [ { "ob_id": 26519, "uuid": "179a8c9a1cd2402cb3d753071b0606a7", "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.", "keywords": "Synthetic Aperture Radar, Sentinel 1, Level 1, algorithm, SAR", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 20018, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 10863, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/20017/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/12313/?format=api", "relatedTo": { "ob_id": 20018, "uuid": "c28a3a6627354dd19363ac971116b0d8", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/146209/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/146211/?format=api" ] }, { "ob_id": 32773, "computationComponent": [ { "ob_id": 12320, "uuid": "a83b5cfca79a48599c002dfbb3de858e", "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.", "keywords": "Synthetic Aperture Radar, Sentinel 1, Level 1, algorithm, SAR", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 32774, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 12586, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/12319/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/12313/?format=api", "relatedTo": { "ob_id": 32774, "uuid": "4c4f080b0afb40b9a5522d0b3af9cf2f", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/146294/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/146295/?format=api" ] }, { "ob_id": 32777, "computationComponent": [ { "ob_id": 32778, "uuid": "e71c879b049f4ccca07542044df25f09", "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.", "keywords": "Synthetic Aperture Radar, Sentinel 1, Level 1, algorithm, SAR", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 32774, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 12586, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/12319/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/12313/?format=api", "relatedTo": { "ob_id": 32774, "uuid": "4c4f080b0afb40b9a5522d0b3af9cf2f", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/146308/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/146309/?format=api" ] }, { "ob_id": 32782, "computationComponent": [ { "ob_id": 12320, "uuid": "a83b5cfca79a48599c002dfbb3de858e", "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.", "keywords": "Synthetic Aperture Radar, Sentinel 1, Level 1, algorithm, SAR", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 12318, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 4316, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/12319/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/12313/?format=api", "relatedTo": { "ob_id": 12318, "uuid": "f95b77f14a554727a1975802b25ad8a7", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/146329/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/146330/?format=api" ] }, { "ob_id": 32791, "computationComponent": [ { "ob_id": 32778, "uuid": "e71c879b049f4ccca07542044df25f09", "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.", "keywords": "Synthetic Aperture Radar, Sentinel 1, Level 1, algorithm, SAR", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 32792, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 12587, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/12319/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/12313/?format=api", "relatedTo": { "ob_id": 32792, "uuid": "7f2db1418a374a9eb1ad4a5ead4a0936", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/146408/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/146409/?format=api" ] }, { "ob_id": 32793, "computationComponent": [ { "ob_id": 12320, "uuid": "a83b5cfca79a48599c002dfbb3de858e", "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.", "keywords": "Synthetic Aperture Radar, Sentinel 1, Level 1, algorithm, SAR", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 32792, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 12587, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/12319/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/12313/?format=api", "relatedTo": { "ob_id": 32792, "uuid": "7f2db1418a374a9eb1ad4a5ead4a0936", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/146412/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/146413/?format=api" ] }, { "ob_id": 32796, "computationComponent": [ { "ob_id": 32778, "uuid": "e71c879b049f4ccca07542044df25f09", "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.", "keywords": "Synthetic Aperture Radar, Sentinel 1, Level 1, algorithm, SAR", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 32797, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 12588, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/20017/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/12313/?format=api", "relatedTo": { "ob_id": 32797, "uuid": "4c44f3e675d0464eb47f3a7d5163c36c", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/146425/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/146426/?format=api" ] }, { "ob_id": 32800, "computationComponent": [ { "ob_id": 12320, "uuid": "a83b5cfca79a48599c002dfbb3de858e", "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.", "keywords": "Synthetic Aperture Radar, Sentinel 1, Level 1, algorithm, SAR", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 32797, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 12588, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/20017/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/12313/?format=api", "relatedTo": { "ob_id": 32797, "uuid": "4c44f3e675d0464eb47f3a7d5163c36c", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/146444/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/146445/?format=api" ] }, { "ob_id": 32811, "computationComponent": [ { "ob_id": 12320, "uuid": "a83b5cfca79a48599c002dfbb3de858e", "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.", "keywords": "Synthetic Aperture Radar, Sentinel 1, Level 1, algorithm, SAR", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 20018, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 10863, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/20017/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/12313/?format=api", "relatedTo": { "ob_id": 20018, "uuid": "c28a3a6627354dd19363ac971116b0d8", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/146506/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/146507/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/146508/?format=api" ] }, { "ob_id": 32812, "computationComponent": [ { "ob_id": 32778, "uuid": "e71c879b049f4ccca07542044df25f09", "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.", "keywords": "Synthetic Aperture Radar, Sentinel 1, Level 1, algorithm, SAR", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 32813, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 12589, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/20017/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/12313/?format=api", "relatedTo": { "ob_id": 32813, "uuid": "f2370020914949c489de445f630b3fef", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/146509/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/146510/?format=api" ] }, { "ob_id": 32816, "computationComponent": [ { "ob_id": 12320, "uuid": "a83b5cfca79a48599c002dfbb3de858e", "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.", "keywords": "Synthetic Aperture Radar, Sentinel 1, Level 1, algorithm, SAR", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 32813, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 12589, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/20017/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/12313/?format=api", "relatedTo": { "ob_id": 32813, "uuid": "f2370020914949c489de445f630b3fef", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/146524/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/146525/?format=api" ] }, { "ob_id": 32824, "computationComponent": [ { "ob_id": 32825, "uuid": "b58a89dcbf5b43bb96145e5f3833bede", "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. 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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).", "keywords": "", "inputDescription": 19, "outputDescription": 20, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 33041, "independentInstrument": [], "instrumentplatformpair_set": [] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/147577/?format=api" ] }, { "ob_id": 33047, "computationComponent": [ { "ob_id": 31952, "uuid": "73978b5dd534435abd858c94603661f5", "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. 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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). 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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.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 34694, "independentInstrument": [ 34695 ], "instrumentplatformpair_set": [] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/168717/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/168718/?format=api" ] }, { "ob_id": 34703, "computationComponent": [ { "ob_id": 34702, "uuid": "80df33b9f15c479b8786ab10c95202a1", "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.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 34700, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 12671, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/7575/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/34701/?format=api", "relatedTo": { "ob_id": 34700, "uuid": "11fa1282c74a4ce798239356afb448d1", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/168736/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/168737/?format=api" ] }, { "ob_id": 34706, 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"uuid": "68a8483aac80498ca83c5ca9b8be24cd", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/168983/?format=api" ] }, { "ob_id": 34711, "computationComponent": [ { "ob_id": 34709, "uuid": "915609dc14234791b9dd64792e32c2c1", "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", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 34710, "independentInstrument": [], "instrumentplatformpair_set": [ { 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"inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 34727, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 12672, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/846/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/847/?format=api", "relatedTo": { "ob_id": 34727, "uuid": "b5d1b194bec54f98a188433c0636f16f", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/169079/?format=api" ] }, { "ob_id": 34732, "computationComponent": [ { "ob_id": 34734, "uuid": "e77e5d3dafb44414abdf34b273f73b33", "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 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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.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 34900, "independentInstrument": [ 34901 ], "instrumentplatformpair_set": [] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/169718/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/169719/?format=api" ] }, { "ob_id": 35036, "computationComponent": [ { "ob_id": 35032, "uuid": "b1d7a82ac2844548a41b98df77007b52", "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.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 35035, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 12727, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/2629/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/2630/?format=api", "relatedTo": { "ob_id": 35035, "uuid": "7837c871e5c445ad998c0c58dfb40e19", "short_code": "acq" } }, { "ob_id": 12728, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/458/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/2636/?format=api", "relatedTo": { "ob_id": 35035, "uuid": "7837c871e5c445ad998c0c58dfb40e19", "short_code": "acq" } }, { "ob_id": 12729, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/2629/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/14771/?format=api", "relatedTo": { "ob_id": 35035, "uuid": "7837c871e5c445ad998c0c58dfb40e19", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/170187/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/170188/?format=api" ] }, { "ob_id": 37143, "computationComponent": [ { "ob_id": 37142, "uuid": "7564e6b5035341a29c17e4ab73996509", "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", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 37140, "independentInstrument": [ 37141 ], "instrumentplatformpair_set": [] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/177536/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/177537/?format=api" ] }, { "ob_id": 37148, "computationComponent": [ { "ob_id": 37147, "uuid": "35a31aaeea234553921cfe181fa84701", "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", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 37146, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 13264, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/26990/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/19032/?format=api", "relatedTo": { "ob_id": 37146, "uuid": "d74c98880ed249f5b04ebe20a38bab91", "short_code": "acq" } }, { "ob_id": 13265, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/19017/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/19032/?format=api", "relatedTo": { "ob_id": 37146, "uuid": "d74c98880ed249f5b04ebe20a38bab91", "short_code": "acq" } }, { "ob_id": 13269, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/26990/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/19027/?format=api", "relatedTo": { "ob_id": 37146, "uuid": "d74c98880ed249f5b04ebe20a38bab91", "short_code": "acq" } }, { "ob_id": 13270, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/19017/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/19027/?format=api", "relatedTo": { "ob_id": 37146, "uuid": "d74c98880ed249f5b04ebe20a38bab91", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/177542/?format=api" ] }, { "ob_id": 37274, "computationComponent": [ { "ob_id": 37275, "uuid": "bb2ca27b31884089a13c10730d9448cc", "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.", "keywords": "LiCSAR, NSBAS, GMT, Python", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 12318, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 4316, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/12319/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/12313/?format=api", "relatedTo": { "ob_id": 12318, "uuid": "f95b77f14a554727a1975802b25ad8a7", "short_code": "acq" } } ] }, { "ob_id": 20018, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 10863, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/20017/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/12313/?format=api", "relatedTo": { "ob_id": 20018, "uuid": 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"ob_id": 13288, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/34747/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/1787/?format=api", "relatedTo": { "ob_id": 37318, "uuid": "69e9443d7824428c8853865725d3feb8", "short_code": "acq" } }, { "ob_id": 13289, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/34748/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/34749/?format=api", "relatedTo": { "ob_id": 37318, "uuid": "69e9443d7824428c8853865725d3feb8", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/178280/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/178281/?format=api" ] }, { "ob_id": 37347, "computationComponent": [ { "ob_id": 37346, "uuid": "d903a8f24ba940c899704e3219e54808", "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.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 37344, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 13290, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/10906/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/10898/?format=api", "relatedTo": { "ob_id": 37344, "uuid": "604c4b0c715e4307acd61da3ffa22e6d", "short_code": "acq" } }, { "ob_id": 13291, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/10897/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/10898/?format=api", "relatedTo": { "ob_id": 37344, "uuid": "604c4b0c715e4307acd61da3ffa22e6d", "short_code": 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"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.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 37573, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 13363, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/10906/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/10898/?format=api", "relatedTo": { "ob_id": 37573, "uuid": "9a87166f55794f34a46f51e52f1c3b3d", "short_code": "acq" } }, { "ob_id": 13364, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/10897/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/10898/?format=api", "relatedTo": { "ob_id": 37573, "uuid": "9a87166f55794f34a46f51e52f1c3b3d", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/179345/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/179346/?format=api" ] }, { "ob_id": 37680, "computationComponent": [ { "ob_id": 37679, "uuid": "d129f860b47b40fdafd57a5b69457926", "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", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 26034, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 11766, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/10906/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/10898/?format=api", "relatedTo": { "ob_id": 26034, "uuid": "85bb8321bc8b42f9a39cb6d83fabe79e", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/179709/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/179710/?format=api" ] }, { "ob_id": 37710, "computationComponent": [ { "ob_id": 37711, "uuid": "d3d568f134634dda9bb06b9556756b5b", "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.", "keywords": "Sentinel, ESA, VIIRS", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 37712, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 13388, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/33004/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/37715/?format=api", "relatedTo": { "ob_id": 37712, "uuid": "30a2d92d965f48ec973f88a6eb7568db", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/179820/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/179823/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/180861/?format=api" ] }, { "ob_id": 37757, "computationComponent": [ { "ob_id": 37755, "uuid": "d015125d92c14211a83804cbf48738d9", "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", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 37756, "uuid": "f29a2dce4ec74723a75c85220f98c6cc", "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°.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 37754, "independentInstrument": [ 13716 ], "instrumentplatformpair_set": [] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/180164/?format=api" ] }, { "ob_id": 38108, "computationComponent": [ { "ob_id": 38102, "uuid": "99820bff0b994aa29f1a5557f5f14d86", "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.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], 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"https://api.catalogue.ceda.ac.uk/api/v2/instruments/13692/?format=api", "relatedTo": { "ob_id": 38288, "uuid": "09bf329dd0914affa88259c0b74585be", "short_code": "acq" } }, { "ob_id": 13416, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/26733/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/26732/?format=api", "relatedTo": { "ob_id": 38288, "uuid": "09bf329dd0914affa88259c0b74585be", "short_code": "acq" } }, { "ob_id": 13417, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/30018/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/30019/?format=api", "relatedTo": { "ob_id": 38288, "uuid": "09bf329dd0914affa88259c0b74585be", "short_code": "acq" } }, { "ob_id": 13418, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/30020/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/30021/?format=api", "relatedTo": { "ob_id": 38288, "uuid": 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"https://api.catalogue.ceda.ac.uk/api/v2/rpis/182329/?format=api" ] }, { "ob_id": 38296, "computationComponent": [ { "ob_id": 38295, "uuid": "243f6b28ccda4c3cbcf99d5537ae4195", "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.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 38291, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 13422, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/12319/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/12313/?format=api", "relatedTo": { "ob_id": 38291, "uuid": "827c76ac3cf54a49a80f6d1fc024efb1", "short_code": "acq" } }, { "ob_id": 13429, "platform": 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guide.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 38294, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 13441, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/846/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/7820/?format=api", "relatedTo": { "ob_id": 38294, "uuid": "000f3d19747c4672a6466a5432812cdf", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/182342/?format=api" ] }, { "ob_id": 38320, "computationComponent": [ { "ob_id": 19029, "uuid": "68e7fd451fd848ecb0b6e53dfd68ae4a", "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.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ], "acquisitionComponent": [ { "ob_id": 38321, "independentInstrument": [], "instrumentplatformpair_set": [ { "ob_id": 13445, "platform": "https://api.catalogue.ceda.ac.uk/api/v2/platforms/26990/?format=api", "instrument": "https://api.catalogue.ceda.ac.uk/api/v2/instruments/19027/?format=api", "relatedTo": { "ob_id": 38321, "uuid": "abf32dcb73fa4d0792107198a463c246", "short_code": "acq" } } ] } ], "identifier_set": [], "responsiblepartyinfo_set": [ "https://api.catalogue.ceda.ac.uk/api/v2/rpis/182464/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/182465/?format=api", "https://api.catalogue.ceda.ac.uk/api/v2/rpis/182466/?format=api" ] } ] }