Observation List
Get a list of Observation objects.
GET /api/v3/observations/?format=api&offset=5100
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It has been in operation since March 2004 and is at the scientists' disposal through a scheme of project selection. \r\n\r\nData collected by this aircraft is stored in the FAAM data archive and includes \"core\" data, provided by the FAAM as a support to all flight campaigns, and \"non-core\" data, the nature of which depends on the scientific goal of the campaign.\r\n\r\nFAAM instruments provide four types of data: \r\n\r\n- parameters required for aircraft navigation; \r\n- meteorology; \r\n- cloud physics; \r\n- chemical composition. \r\n\r\nThe data are accompanied by extensive metadata, including flight logs. The FAAM apparatus includes a number of core instruments permanently onboard and operated by FAAM staff members, and a variety of other instruments, grouped into chemistry kit and cloud physics kit, that can be fitted onto the aircraft on demand. \r\nFAAM is also a member of the EUropean Facility for Airborne Research (EUFAR) fleet of research aircraft.\r\n\r\nAs per NERC data policy (see documents), FAAM data are openly available upon registration with the CEDA archive (anyone can register) under the Open Government Licence. Raw data are retained for longterm preservation but are not intended for general use." } ], "responsiblepartyinfo_set": [ 101506, 101507, 101505, 101510, 101511, 101512, 101513, 101514, 101508, 101509 ], "onlineresource_set": [ 23390, 23389 ] }, { "ob_id": 24802, "uuid": "b1c81845ad294f8399f9b564da59c68c", "title": "FAAM C013 Instrument Test flight: Airborne atmospheric measurements from core and non-core instrument suites on board the BAE-146 aircraft", "abstract": "Airborne atmospheric measurements from core and non-core instrument suites data on board the FAAM BAE-146 aircraft collected for FAAM Test, Calibration, Training and Non-science Flights and other non-specified flight projects (Instrument) project.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2024-09-11T13:15:07", "updateFrequency": "asNeeded", "dataLineage": "Data were collected by instrument scientists during the flight before preparation and delivery for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "Instrument, FAAM, airborne, atmospheric measurments", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2017-06-12T14:35:52.125274", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 4225, "bboxName": "", "eastBoundLongitude": -0.27715638, "westBoundLongitude": -5.6665139, "southBoundLatitude": 50.308342, "northBoundLatitude": 52.462685 }, "verticalExtent": null, "result_field": { "ob_id": 24801, "dataPath": "/badc/faam/data/2017/c013-may-17", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 2242456457, "numberOfFiles": 45, "fileFormat": "Data are netCDF and NASA-Ames formatted. 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It has been in operation since March 2004 and is at the scientists' disposal through a scheme of project selection. \r\n\r\nData collected by this aircraft is stored in the FAAM data archive and includes \"core\" data, provided by the FAAM as a support to all flight campaigns, and \"non-core\" data, the nature of which depends on the scientific goal of the campaign.\r\n\r\nFAAM instruments provide four types of data: \r\n\r\n- parameters required for aircraft navigation; \r\n- meteorology; \r\n- cloud physics; \r\n- chemical composition. \r\n\r\nThe data are accompanied by extensive metadata, including flight logs. The FAAM apparatus includes a number of core instruments permanently onboard and operated by FAAM staff members, and a variety of other instruments, grouped into chemistry kit and cloud physics kit, that can be fitted onto the aircraft on demand. \r\nFAAM is also a member of the EUropean Facility for Airborne Research (EUFAR) fleet of research aircraft.\r\n\r\nAs per NERC data policy (see documents), FAAM data are openly available upon registration with the CEDA archive (anyone can register) under the Open Government Licence. Raw data are retained for longterm preservation but are not intended for general use." } ], "responsiblepartyinfo_set": [ 101519, 101520, 101521, 101524, 101525, 101526, 101527, 101528, 101522, 101523 ], "onlineresource_set": [ 23393, 23392 ] }, { "ob_id": 24807, "uuid": "a3614fb00ff74999a5187d3a3767d96d", "title": "Soil-atmosphere flux measurements calculated from concentration of methane and nitrous oxide taken from the Pastaza-Marañón foreland basin, Peru", "abstract": "The research team collected data on soil-atmosphere exchange of trace gases and environmental variables during four field campaigns (two wet seasons, two dry seasons) the lowland tropical peatland forests of the Pastaza-Marañón foreland basin in Peru. The campaigns took place over a 27 month period, extending from February 2012 to May 2014. \r\n\r\nThis dataset contains measurements from field sampling of soil-atmosphere fluxes concentrated on 4 dominant vegetation types in the lowland tropical peatland forests of the Pastaza-Marañón foreland basin. Vegetation types included; forested vegetation, forested [short pole] vegetation, Mauritia flexuosa-dominated palm swamp, and mixed palm swamp. They were measured at 5 different sites in Peru including; Buena Vista, Miraflores, San Jorge, Quistococha, and Charo. \r\n\r\nGreenhouse gas (GHG) fluxes were captured from both floodplain systems and nutrient-poor bogs in order to account for underlying differences in biogeochemistry that may arise from variations in hydrology.\r\n\r\nParameters include methane and nitrous oxide fluxes, air/soil temperatures, soil pH, soil electrical conductivity, soil dissolved oxygen content, and water table depth. \r\n\r\nSee documentation and data lineage for data quality. \r\n\r\nThese data were collected in support of the NERC project: Amazonian peatlands - A potentially important but poorly characterised source of atmospheric methane and nitrous oxide (NE/I015469/2)", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2017-07-24T17:08:31.563224", "updateFrequency": "notPlanned", "dataLineage": "Gas samples were collected using a static chamber approach, samples stored in vials (Exetainers), and later analysed using a gas chromatograph with flame ionisation detector for methane detection and an electron capture detector for nitrous oxide quantification. Environmental variables were collected concomitantly, including air temperature, soil temperature, soil pH, soil electrical conductivity, soil dissolved oxygen content, and water table depth. Temperature was determined using a thermocouple; pH, electrical conductivity, and dissolved oxygen using a HACH® rugged outdoor HQ30D multi meter and pH, LDO or EC probe. Water table depth was measured using a depth measure. \r\n\r\nDiffusive gas fluxes were determined by using the JMP IN version 11 (SAS Institute, Inc., Cary, North Carolina, USA) statistical package to plot best-fit lines to the data for headspace concentration against time for individual flux chambers, with fluxes calculated from linear or non-linear regressions depending on the individual concentration trend against time. Gas mixing ratios (ppm) were converted to areal fluxes by using the Ideal Gas Law to solve for the quantity of gas in the headspace (on a mole or mass basis) and normalized by the surface area of each static flux chamber. Ebullition-derived methane fluxes were also quantified in our chambers where evidence of ebullition was found. This evidence consisted of either: (i) rapid, non-linear increases in methane concentration over time; (ii) abrupt, stochastic increases in methane concentration over time; or (iii) an abrupt stochastic increase in methane concentration, followed by a linear decline in concentration. For observations following pattern (i), flux was calculated by fitting a quadratic regression equation to the data (P < 0.05), and methane flux determined from the initial steep rise in CH4 concentration. For data following pattern (ii), the ebullition rate was determined by calculating the total methane production over the course of the bubble event, in-line with prior work conducted by the investigators. Last, for data following pattern (iii), a best-fit line was plotted to the methane concentration data after the bubble event, and a net rate of methane uptake calculated from the gradient of the line. Observations following patterns (i) and (ii) were categorized as “ebullition” (i.e. net efflux) whereas observations following pattern (iii) were categorized as “ebullition-driven methane uptake” (i.e. net influx). \r\n\r\nFlux data were removed (i.e. filtered) from the dataset if: (i) the change in gas concentration over time was not found to be statistically significant (alpha level of 0.05); (ii) evidence were found that an individual static flux chamber was not gas-tight (evidenced by a negative carbon dioxide flux); (iii) evidence were found that two or more Exetainers had been compromised by incomplete evacuation or leakage; or (iv) other evidence that an individual flux chamber had been compromised (e.g. evidence from field observations that the static flux chamber had been disturbed during the measurement process).", "removedDataReason": "", "keywords": "methane, nitrous oxide, peat, tropical peatland, Pastaza-Marañón foreland basin, Amazonia, Peru", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2017-07-24T16:13:44", "doiPublishedTime": "2017-08-25T11:00:23", "removedDataTime": null, "geographicExtent": { "ob_id": 1851, "bboxName": "Peru peatlands group", "eastBoundLongitude": -73.25, "westBoundLongitude": -74.08, "southBoundLatitude": -4.47, "northBoundLatitude": -3.83 }, "verticalExtent": null, "result_field": { "ob_id": 24913, "dataPath": "/badc/deposited2017/peru-peatlands/data/abn-fluxes", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 161215, "numberOfFiles": 7, "fileFormat": "CSV " }, "timePeriod": { "ob_id": 6723, "startTime": "2012-02-01T00:00:00", "endTime": "2014-05-29T23:00:00" }, "resultQuality": { "ob_id": 3088, "explanation": "See documentation for data quality information", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2017-06-14" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 24912, "uuid": "3aaf817078724bc284b63d8505fe5a98", "short_code": "acq", "title": "Acquisition for Soil-atmosphere flux measurements calculated from concentration of methane and nitrous oxide taken from the Pastaza-Marañón foreland basin, Peru", "abstract": "6 campaign locations for the NERC project: Amazonian peatlands - A potentially important but poorly characterised source of atmospheric methane and nitrous oxide (NE/I015469/2).\r\n\r\nAll samples were analysed back in the UK on the University of St Andrews Thermo TRACE Gas Chromatograph Ultra instrument. " }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 24805, "uuid": "a416be296ccc40ad983a98c30fb291ee", "short_code": "proj", "title": "NERC project: Amazonian peatlands - A potentially important but poorly characterised source of atmospheric methane and nitrous oxide", "abstract": "This project was funded by the Natural Environment Research Council (NERC) with the following grant reference, NE/I015469/2, led by Dr Yit Arn Teh.\r\n\r\nTropical peatlands are one of the largest global soil carbon repositories and play a critical role in regulating greenhouse gas exchange between the tropical biosphere and atmosphere. Yet despite their acknowledged importance for global atmospheric chemistry, we have relatively few ground-based measurements of greenhouse gas (GHG) fluxes from tropical peatlands. Whilst systematic gas flux measurements have been conducted in Indonesian peatlands, there is little or no data from Amazonian peatlands. Amazonian peatlands are spatially extensive, accounting for at least 150,000 square kilometres in the lowland Amazon; this is a land area that is approximately three-quarters the size of Indonesian peatlands, which are the most extensive tropical peatlands in the world. These ecosystems and their carbon stocks are currently under threat from anthropogenic intervention and future environmental change (e.g. land-use change, acid deposition, climate change), and yet we know little to nothing about their GHG dynamics or relative contribution to the carbon budget of the wider Amazon Basin as a whole.\r\n\r\nThe principal goal of this study was to collect preliminary measurements of methane and nitrous oxide fluxes from Amazonian peatlands, in order to take the critical first steps towards understanding the role of these ecosystems in regional and global atmospheric budgets of methane and nitrous oxide. Activities were focused on forested peatlands in the Pastaza-Marañón foreland basin (PMFB), one of the single largest contiguous peatland complexes in the Amazon Basin. The main objectives of this research were to: \r\n\r\n1. Quantify the magnitude and range of soil methane and nitrous oxide fluxes from a sub-set of peatlands in the PMFB that represent dominant vegetation types for the wider region\r\n2. Determine seasonal patterns of trace gas exchange\r\n3. Establish the relationship between trace gas fluxes and environmental variables\r\n\r\nSampling was concentrated on the four most dominant vegetation types in the area (forested vegetation, forested [short pole] vegetation, Mauritia flexuosa-dominated palm swamp, and mixed palm swamp). GHG fluxes were captured from both floodplain systems and nutrient-poor bogs in order to account for underlying differences in biogeochemistry that may arise from variations in hydrology. Sampling was conducted during four field campaigns (two wet season, two dry season) over a 27-month period, extending from February 2012 to May 2014." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 56169, 56170, 56171, 56172, 56173, 56174, 56175, 56176, 56177, 56178, 56179, 56180, 56181, 56182, 56183, 56184 ], "vocabularyKeywords": [], "identifier_set": [ 9328 ], "observationcollection_set": [ { "ob_id": 24806, "uuid": "d7bb874c638c40e797c3aaa6baffc65f", "short_code": "coll", "title": "Amazonian peatlands: in-situ ground based soil-atmosphere flux measurements", "abstract": "'Amazonian peatlands - A potentially important but poorly characterised source of atmospheric methane and nitrous oxide' was a NERC (Natural Environment Research Council) funded project from 2013-2014 with the following grant reference: NE/I015469/2. \r\n\r\nThis dataset collection contains measurements from field sampling of soil-atmosphere fluxes concentrated on 4 dominant vegetation types in the lowland tropical peatland forests of the Pastaza-Marañón foreland basin. 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The grounding lines have been derived from satellite observations from the ERS-1/2, TerraSAR-X and Copernicus Sentinel-1 platforms, acquired between 1995 and 2016.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2017-06-19T13:30:00", "updateFrequency": "notPlanned", "dataLineage": "Data were produced by DLR in the context of the ESA CCI Antarctic Ice Sheet project team and are archived on the ENVEO Cryoportal (http://cryoportal.enveo.at/data/) They have been supplied to CEDA in the context of the ESA CCI Open Data Portal Project.", "removedDataReason": "Request of data provider", "keywords": "", "publicationState": "removed", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "deprecated", "dataPublishedTime": null, "doiPublishedTime": null, "removedDataTime": "2018-05-15T16:29:52", "geographicExtent": { "ob_id": 1841, "bboxName": "", "eastBoundLongitude": -29.66, "westBoundLongitude": -51.51, "southBoundLatitude": -82.5, "northBoundLatitude": -77.73 }, "verticalExtent": null, "result_field": { "ob_id": 24833, "dataPath": "/neodc/esacci/ice_sheets_antarctica/data/grounding_line_locations/Slessor/v1.0/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1079, "numberOfFiles": 1, "fileFormat": "ESRI Shapefile" }, "timePeriod": { "ob_id": 6736, "startTime": "1995-10-19T23:00:00", "endTime": "2016-10-01T23:00:00" }, "resultQuality": null, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2534, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 14, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_icesheets_antarctic_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [ { "ob_id": 10187, "vocabService": "clipc_skos_vocab", "uri": "http://vocab-test.ceda.ac.uk/collection/cci/ecv/cciecv_iceSheet", "resolvedTerm": "ice sheets" }, { "ob_id": 10159, "vocabService": "clipc_skos_vocab", "uri": "http://vocab-test.ceda.ac.uk/collection/cci/dataType/dtype_gll", "resolvedTerm": "glacier grounding line location" } ], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 101652, 101656, 101651, 101653, 101655, 101650, 101654, 193977, 193978, 193979 ], "onlineresource_set": [ 23485, 23486, 23484, 23488, 23490, 23483, 23487, 23489 ] }, { "ob_id": 24837, "uuid": "d2cfd2af036b4d788d8eddf8ddf86707", "title": "Extreme weather in the UK: past, present and future - event details from the TEMPEST database", "abstract": "TEMPEST (Tracking Extremes of Meteorological Phenomena Experienced in Space and Time) is the major output of the Arts and Humanities Research Council (AHRC) funded project “Spaces of Experience and Horizons of Expectation: Extreme Weather in the UK, Past, Present and Future (which ran from 2013-2017)\". \r\n\r\nTEMPEST was designed as a freely accessible and user-friendly database resource on the UK’s weather and climate history. TEMPEST is comprised of narrative accounts of extreme weather events of all types, extracted from documentary materials located in a range of archival repositories in the UK (consequentially, please see the quality statement note below concerning data issues). The information has been extracted from a wide range of documents, including letters, diaries, church records, school log-books and many others. The entries span more than 400 years - some as early as 1346 - of weather history and relate to places across the UK, though the data search was focused in five case-study regions: Central England, Southwest England, East Anglia, Wales, and Northwest Scotland. \r\n\r\nEach event entry or narrative has been assigned to at least one weather type, is dated (at least to a year), and is geographically referenced (using digital coordinates). Many also contain material relating to the impacts of the weather event and responses to it. In addition to information on extreme weather events, TEMPEST contains details of the original documents, their authors, and the collections and repositories in which they are held. TEMPEST is searchable by all of these fields.\r\n\r\nUsers are advised to read the quality statement carefully with regards to possible issues in date and location accuracy and the way \"extreme\" events were documented. Additionally, users should be aware that the period covered by the dataset includes the change from the Julian to Gregorian calendar. In order to manage that change 11 days were omitted from the year 1752, i.e. the day after the 2 September 1752 was 14 September, in accordance with the Calendar Act of 1751. Until September 1752 the New Year began on 25 March (Lady Day) but dual dating was commonplace for many years before, adding a further layer of complication to events that took place from 1 January to 24 March, and making 1751 a short year running from 25 March to 31 December! Scotland had changed the start of the year to 1 January in 1600. Where clear, the Gregorian calendar date has been used, providing further details in the notes section.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2017-08-21T15:12:15.070000", "updateFrequency": "notPlanned", "dataLineage": "Data originally collected by the \"Spaces of Experience and Horizons of Expectation: Extreme weather in the UK, past, present and future\" project researchers from original records held in various archives. The entries were entered into the TEMPEST (Tracking Extremes of Meteorological Phenomena Experienced in Space and Time) database. Content from the TEMPEST database has then been outputted by the project participants and formatted into BADC-CSV before delivery to the Centre for Environmental Data Analysis (CEDA) for long-term archiving. Separate ancillary files contain location details and author biographies.", "removedDataReason": "", "keywords": "Extreme Weather, TEMPEST, historical events", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2017-08-21T14:13:33", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 867, "bboxName": "British Isles", "eastBoundLongitude": 3.0, "westBoundLongitude": -12.0, "southBoundLatitude": 49.0, "northBoundLatitude": 60.0 }, "verticalExtent": null, "result_field": { "ob_id": 24921, "dataPath": "/badc/deposited2017/tempest/data", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 10014654, "numberOfFiles": 4, "fileFormat": "These data are BADC-CSV formatted" }, "timePeriod": { "ob_id": 6739, "startTime": "1346-01-01T00:01:15", "endTime": "2007-12-31T23:59:59" }, "resultQuality": { "ob_id": 3092, "explanation": "Despite reviews by the project members, automated and manual checks data errors are to be expected due to : misinterpretation and transcription errors; indeterminate event start/end dates (e.g. long thermal periods); changes in calendar usage; changing location names, spelling and boundaries; variation in recording methodology during the project and between team members – including interpretation of source material; and, change in language use over time – e.g. a reference to “hurricane of wind” in a source may not map to established classification of hurricane force winds. Users are advised to interpret narratives with care, and to consult the original documents where the phrasing or dating is crucial to their research. Users are advised to read the full data quality statement linked provided by the project team linked from this record for further quality details.", "passesTest": true, "resultTitle": "Extreme Weather - past, present and future - TEMPEST database Data Quality Statement", "date": "2017-07-31" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 194 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 24836, "uuid": "292e227ad10c456296ca4a74ac05a78c", "short_code": "proj", "title": "Spaces of Experience and Horizons of Expectation: Extreme weather in the UK, past, present and future", "abstract": "The aims of the \"Spaces of Experience and Horizons of Expectation: Extreme weather in the UK, past, present and future\" project were to use documentary archives to develop local and regional climate histories in order to:\r\n\r\n - identify periods of unusual weather and extreme events; \r\n - investigate the scale of impact and the nature of human responses; \r\n - explore the nature of the recording and recollection or memory of events. \r\n\r\nAll of these elements are believed crucial for understanding the nature of the events that might take place in the future. \r\n\r\nTEMPEST (Tracking Extremes of Meteorological Phenomena Experienced in Space and Time) is the major output of the Arts and Humanities Research Council (AHRC) funded project “Spaces of Experience and Horizons of Expectation: Extreme Weather in the UK, Past, Present and Future (2013-2017)\" (AHRC grant award: AH/K005782/1). TEMPEST was designed as a freely accessible and user-friendly database resource on the UK’s weather and climate history. TEMPEST was comprised of narrative accounts of extreme weather events of all types, extracted from documentary materials located in a range of archival repositories in the UK. The information has been extracted from a wide range of documents, including letters, diaries, church records, school log-books and many others. The entries span more than 400 years of weather history and relate to places across the UK, though our data search was focused in five case-study regions: Central England, Southwest England, East Anglia, Wales, and Northwest Scotland. Each event entry or narrative has been assigned to at least one weather type, is dated (at least to a year), and is geographically referenced (using digital coordinates). Many also contain material relating to the impacts of the weather event and responses to it. In addition to information on extreme weather events, TEMPEST contains details of the original documents, their authors, and the collections and repositories in which they are held. TEMPEST is searchable by all of these fields." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 52300, 52301, 56185, 56186, 56187, 56188, 56189, 56190, 56191, 56192, 56193, 56194, 56195, 56196, 56197, 56198, 56199, 56200, 56201, 56202, 56203, 56204, 56205, 56206, 56207, 56208, 56209, 56210, 56211, 56212, 56213, 56214, 56215, 56216, 56217 ], "vocabularyKeywords": [], "identifier_set": [ 9326 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 101672, 101677, 101676, 101675, 101673, 101679, 101678, 101674, 101680, 101714, 101717, 101718, 101719, 101720, 101721, 101722, 101681 ], "onlineresource_set": [ 23606, 91836, 91837, 91838, 91839, 91840, 91841, 91842, 91843, 91844, 91845, 91846, 91847, 91848, 91849, 91850, 91851 ] }, { "ob_id": 24841, "uuid": "c4f117ba38544e8a80338b6cf1000a91", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): Ancillary data used for the \"Active\", \"Passive\" and \"Combined\" products, Version 03.2", "abstract": "These ancillary datasets were used in the production of the \"Active\", \"Passive\" and \"Combined\" soil moisture data products, created as part of the European Space Agency's (ESA) Soil Moisture Climate Change Initiative (CCI) project. The set of ancillary datasets include datasets of Average Vegetation Optical Depth data from AMSR-E, Soil Porosity, Topographic Complexity and Wetland fraction, as well as a Land Mask. This version of the ancillary datasets were used in the production of the v03.2 Soil Moisture CCI data.\r\n\r\nThe \"Active\" \"Passive\" and \"Combined\" soil moisture products which they were used in the development of are fusions of scatterometer and radiometer soil moisture products, derived from the AMI-WS, ASCAT, SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2 and SMOS satellite instruments. To access these products or for further details on them please see their dataset records. Additional reference documents and information relating to them can also be found on the CCI Soil Moisture project website.\r\n\r\nSoil moisture CCI data should be cited using all three of the following references:\r\n\r\n1. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001\r\n\r\n2. Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. 10.1109/TGRS.2017.2734070\r\n\r\n3. Liu, Y.Y., Dorigo, W.A., Parinussa, R.M., de Jeu, R.A.M. , Wagner, W., McCabe, M.F., Evans, J.P., van Dijk, A.I.J.M. (2012). Trend-preserving blending of passive and active microwave soil moisture retrievals, Remote Sensing of Environment, 123, 280-297, doi: 10.1016/j.rse.2012.03.014", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2017-06-10T20:23:54.132685", "updateFrequency": "notPlanned", "dataLineage": "Data were processed by the ESA CCI Soil Moisture project team and transferred to CEDA for the ESA CCI Open Data Portal Project. \r\nThis dataset forms part of the CCI Soil Moisture v03.2 dataset collection (doi:10.5285/d2eea061026240eb8a2f9cc64a691338. http://dx.doi.org/10.5285/d2eea061026240eb8a2f9cc64a691338 )", "removedDataReason": "", "keywords": "ESA, Soil Moisture, CCI, Ancillary", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "superseded", "dataPublishedTime": "2017-06-28T14:24:42", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 24842, "dataPath": "/neodc/esacci/soil_moisture/data/ancillary/v03.2/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1630198, "numberOfFiles": 6, "fileFormat": "Data are in NetCDF format" }, "timePeriod": { "ob_id": 7092, "startTime": "2002-06-30T23:00:00", "endTime": "2011-10-04T22:59:59" }, "resultQuality": { "ob_id": 3142, "explanation": "as provided by the CCI Soil Moisture team", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2018-06-25" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2535, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 15, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_soilmoisture_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 13332, "uuid": "c256fcfeef24460ca6eb14bf0fe09572", "short_code": "proj", "title": "ESA Soil Moisture Climate Change Initiative Project", "abstract": "The European Space Agency Soil Moisture Climate Change Initiative (Soil_Moisture_cci) project is part of the ESA Climate Change Initiative (CCI) programme, which aims to produce datasets of Essential Climate Variables (ECV's) from satellite datasets.\r\n\r\nThe Soil Moisture CCI project was set up to :\r\n - Analyse the needs of the climate research community in terms of soil moisture data.\r\n - Adapt soil moisture satellite measurements for their use by the climate research community.\r\n - Create a long-term consistent soil moisture time series, based on active and passive data, suitable for climate change studies." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 12066, 52664, 52665, 54683, 54684, 54685, 54686, 60753, 60754, 63533, 63534, 63535, 63536 ], "vocabularyKeywords": [ { "ob_id": 10104, "vocabService": "clipc_skos_vocab", "uri": "http://vocab-test.ceda.ac.uk/collection/cci/org/org70", "resolvedTerm": "Vienna University of Technology" }, { "ob_id": 10246, "vocabService": "clipc_skos_vocab", "uri": "http://vocab-test.ceda.ac.uk/collection/cci/ecv/cciecv_soilMst", "resolvedTerm": "soil moisture" } ], "identifier_set": [], "observationcollection_set": [ { "ob_id": 12877, "uuid": "6e4abd45503d44f09c74ee3a0e8fefb7", "short_code": "coll", "title": "ESA Soil Moisture (SM) Climate Change Initiative (CCI) Dataset Collection", "abstract": "As part of the European Space Agency's (ESA) Soil Moisture Climate Change Initiative (CCI) project, three surface soil moisture datasets have been created. The 'Active' and 'Passive' products have been created by fusing scatterometer and radiometer soil moisture products respectively. In the case of the 'Active' product, these have been derived from AMI-WS and ASCAT instruments and for the 'Passive' product from the instruments SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMOS and SMAP (dependent on version). The 'Combined Product' is then a blended product based on the former two data sets. The Ancillary datasets involved in their production are also available. \r\n\r\nThe homogenized and merged products present a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. The products are provided as global daily images, in NetCDF-4 classic file format, the Passive and Combined products covering the period (yyyy-mm-dd) 1978-11-01 to 2017-12-31 and the Active product covering 1991-08-05 to 2017-12-31 (dependent on version). The soil moisture data for the Passive and the Combined product are provided in volumetric units [m3 m-3], while the active soil moisture data are expressed in percent of saturation [%]. For information regarding the theoretical and algorithmic base of the datasets, please see the Algorithm Theoretical Baseline Document (ATBD) or the papers cited below. Other additional documentation and information documentation relating to the datasets can also be found on the CCI Soil Moisture project web site or in the Product Specification Document.\r\n\r\nThe data set should be cited using the complete references as follows:\r\nVersion 2.1 and v2.2\r\n1. Liu, Y. Y., W. A. Dorigo, et al. (2012). \"Trend-preserving blending of passive and active microwave soil moisture retrievals.\" Remote Sensing of Environment 123: 280-297.\r\n2. Liu, Y. Y., Parinussa, R. M., Dorigo, W. A., De Jeu, R. A. M., Wagner, W., van Dijk, A. I. J. M., McCabe, M. F., Evans, J. P. (2011). Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals. Hydrology and Earth System Sciences, 15, 425-436\r\n3. Wagner, W., W. Dorigo, R. de Jeu, D. Fernandez, J. Benveniste, E. Haas, M. Ertl (2012). Fusion of active and passive microwave observations to create an Essential Climate Variable data record on soil moisture. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences (ISPRS Annals), Volume I-7, XXII ISPRS Congress, Melbourne, Australia, 25 August-1 September 2012, 315-321\r\n\r\nVersion 3.2 onwards:\r\n1. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001\r\n\r\n2. Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. 10.1109/TGRS.2017.2734070\r\n\r\n3. Liu, Y.Y., Dorigo, W.A., Parinussa, R.M., de Jeu, R.A.M. , Wagner, W., McCabe, M.F., Evans, J.P., van Dijk, A.I.J.M. (2012). Trend-preserving blending of passive and active microwave soil moisture retrievals, Remote Sensing of Environment, 123, 280-297, doi: 10.1016/j.rse.2012.03.014" }, { "ob_id": 26164, "uuid": "d2eea061026240eb8a2f9cc64a691338", "short_code": "coll", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): Version 03.2 data collection", "abstract": "Soil Moisture data (version 03.2) from the European Space Agency's (ESA) Soil Moisture Climate Change Initiative (CCI) project. This dataset collection contains three surface soil moisture datasets, alongside ancilliary data products. The 'Active' and 'Passive' products have been created by fusing scatterometer and radiometer soil moisture products respectively. In the case of the 'Active' product, these have been derived from AMI-WS and ASCAT satellite instruments and for the 'Passive' product from the instruments SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2 and SMOS. The 'Combined Product' is then a blended product based on the former two data sets. \r\n\r\nThe homogenized and merged products present a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. The products are provided as global daily images, in NetCDF-4 classic file format, the Passive and Combined products covering the period (yyyy-mm-dd) 1978-11-01 to 2014-12-31 and the Active product covering 1991-08-05 to 2014-12-31. The soil moisture data for the Passive and the Combined product are provided in volumetric units [m3 m-3], while the active soil moisture data are expressed in percent of saturation [%]. For information regarding the theoretical and algorithmic base of the datasets, please see the Algorithm Theoretical Baseline Document (ATBD) or the paper by Wagner 2012, both available in linked documentation. Other additional documentation and information documentation relating to the datasets can also be found on the CCI Soil Moisture project web site or in the Product Specification Document.\r\n\r\nThe data set should be cited using all three of the following references:\r\n\r\n1. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001\r\n\r\n2. Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. 10.1109/TGRS.2017.2734070\r\n\r\n3. Liu, Y.Y., Dorigo, W.A., Parinussa, R.M., de Jeu, R.A.M. , Wagner, W., McCabe, M.F., Evans, J.P., van Dijk, A.I.J.M. (2012). Trend-preserving blending of passive and active microwave soil moisture retrievals, Remote Sensing of Environment, 123, 280-297, doi: 10.1016/j.rse.2012.03.014" } ], "responsiblepartyinfo_set": [ 101691, 101690, 101692, 104973, 105197, 105373, 110028, 101693, 109710, 110029, 110030, 110031, 110032, 110033, 110034, 110035, 110036, 110037 ], "onlineresource_set": [ 23506, 23507, 23511, 23509, 24912, 24913, 24914, 25076, 23513, 42140 ] }, { "ob_id": 24914, "uuid": "acf1f55cd92a41fa9a97949b2066ad07", "title": "Poles Apart: Why has Antarctic sea ice increased and why don't coupled climate models reproduce observations, climate model data", "abstract": "Data produced in support of the NERC project Poles Apart (2014 - 2017) that investigated the atmospheric drivers of changes in surface wind forcing using the UK Chemistry and Aerosols (UKCA) model. \r\n\r\nTen HadGEM3 model simulations were undertaken. Monthly model data was taken from the final 30 years of each simulation and a number of variables of interest, e.g. temperature and precipitation were extracted and analysed. \r\n\r\nThe temporal range for the perturbed simulations is 1980-2013, and the control simulations are valid between 1841-1900.\r\n\r\nSee the detailed experiment documentation for the Unified Model experiment ids and their corresponding description (these have been used to organise the data).", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2017-07-26T14:18:46.112946", "updateFrequency": "notPlanned", "dataLineage": "UK Chemistry and Aerosol (UKCA) climate model data extracted using Convsh and converted to NetCDF. The data were provided by the Poles Apart project from the British Antarctic Survey and were passed to CEDA for archival.", "removedDataReason": "", "keywords": "Climate, Ozone, GHG, Aerosol, HadGEM3, UKCA", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "The horizontal grid is N96 (equivalent to 1.875 deg latitude x 1.25 deg longitude)", "status": "completed", "dataPublishedTime": "2017-08-07T09:12:43", "doiPublishedTime": "2017-08-25T10:54:19", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 24915, "dataPath": "/badc/deposited2017/poles-apart/data", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 273217365050, "numberOfFiles": 359, "fileFormat": "The data are NetCDF formatted." }, "timePeriod": { "ob_id": 6743, "startTime": "1841-01-01T00:01:15", "endTime": "2013-12-31T23:59:59" }, "resultQuality": { "ob_id": 3091, "explanation": "The data have not been quality controlled by CEDA.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2017-07-25" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 24916, "uuid": "4b244947c9d547e9bc6761e01b78117f", "short_code": "comp", "title": "Poles Apart computation", "abstract": "The UK Met Office third generation Hadley Centre Global Environment Model (HadGEM3) run in atmosphere only mode (prescribed sea ice and sea surface temperatures) coupled to the United Kingdom Chemistry & Aerosols (UKCA) model. HadGEM3 was run using the GA4.0 and GL4.0 physics configurations (See Walters et al., 2014, Geo. Mod. Dev)" }, "procedureCompositeProcess": null, "imageDetails": [ 2 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 24917, "uuid": "b2e02ed44e184e7e8f6e6be977592f23", "short_code": "proj", "title": "Poles Apart: Why has Antarctic sea ice increased and why don't coupled climate models reproduce observations.", "abstract": "Poles Apart (Why has Antarctic sea ice increased and why don't coupled climate models reproduce observations? NE/K012150/1, NE/K011561/1) was a NERC project led by the British Antarctic Survey (BAS) and supported by the National Oceanography Centre (NOC). \r\n\r\nThe aim of this project was to model atmospheric drivers of changes in surface wind forcing. The project began in June 2014 and completed at the end of June 2017." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50559, 50561, 51581, 51582, 51584, 53790, 53860, 55676, 56955, 56961, 56967, 56969, 56972, 61259, 61264, 62186, 62187, 62188, 62189, 62190, 62191, 62192, 62193, 62194, 62195, 62196, 62197, 62198, 62199 ], "vocabularyKeywords": [], "identifier_set": [ 9327 ], "observationcollection_set": [ { "ob_id": 24918, "uuid": "2d0118fccc86444f9bdb87d2dfce4a52", "short_code": "coll", "title": "Poles Apart: Why has Antarctic sea ice increased and why don't coupled climate models reproduce observations", "abstract": "Datasets collected as part of the NERC project Poles Apart Why has Antarctic sea ice increased and why don't coupled climate models reproduce observations? (NE/K012150/1, NE/K011561/1). \r\n\r\nThe aim of this project was to model atmospheric drivers of changes in surface wind forcing. The project began in June 2014 and completed at the end of June 2017." } ], "responsiblepartyinfo_set": [ 101963, 101961, 101959, 101958, 101957, 101956, 101955, 101960, 102176, 101962, 169543 ], "onlineresource_set": [ 23601, 23603, 23608 ] }, { "ob_id": 24920, "uuid": "717a3fd92989493aae6f4982fd5fca6c", "title": "Cloud Index retrievals from MIPAS ENVISAT L1B using MIPclouds algorithm", "abstract": "This data set contains retrievals from Michelson Interferometer for Passive Atmospheric Sounding on Envisat (MIPAS-ENVISAT) cloud and aerosol and contains information on derived cloud-top height (km), cloud-top temperature (K), cloud extinction (cm-1), with uncertainties. It also includes the measured radiance in the three cloud microwindow bands (832.0-834.4 cm-1, 1232.3-1234.4 cm-1, 1973.0-1983.0 cm-1), with noise equivalent spectral radiance values. Cloud index values are also included which can be used to distinguish different clouds types based on the index value.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2017-08-07T13:44:15", "updateFrequency": "notPlanned", "dataLineage": "This work was funded by the National Centre for Earth Observation (NCEO) under the UK Natural Environment Research Council (NERC) and the ESA MIPclouds project. Data were produced by the University of Leicester Earth Observation Science (Harjinder Sembhi, David Moore, John Remedios, Rosie Leigh) and the University of Oxford AOPP group (Anu Dudhia, Jane Hurley, Andrew Smith). Data deposted by University of leicester with CEDA for archival.", "removedDataReason": "", "keywords": "MIPAS, Cloud Index, ENVISAT, cloud-top, cloud extinction", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2017-08-09T11:58:54", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 24919, "dataPath": "/neodc/mipas_clouds/data/v1.0", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 8219481926, "numberOfFiles": 2268, "fileFormat": "NetCDF" }, "timePeriod": { "ob_id": 6744, "startTime": "2002-03-31T23:00:00", "endTime": "2012-07-31T23:00:00" }, "resultQuality": null, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 24922, "uuid": "9625b2ef020f44738559033d5218d1ac", "short_code": "cmppr", "title": "Cloud Index retrievals from MIPAS ENVISAT L1B using MIPclouds algorithm", "abstract": "The purpose of the Algorithm Theoretical Basis Document (ATBD) is to present the algorithm and technical\r\ndetails necessary to realise a processor for the retrieval of cloud properties form the Level 1b (L1B) data of\r\nESA instrument MIPAS on-board the ENVISAT satellite. The infrared spectra measured by MIPAS (main information\r\nof L1B) include sufficient information to retrieve various cloud parameter. Recent publications have\r\nalready shown the large potential of the dataset ([11], [12], [5], [6]). But so far no validated and consolidated\r\nMIPAS cloud product is available for the scientific community. Consequently the development of a cloud parameter\r\nprocessor for MIPAS and the application to the time series is highly desired.\r\nThe proposed MIPAS cloud processor follows the requirements of the statement of work of the ESA-ITT\r\nAO/1-5255/06/I-OL. Therefore the development and application of the processor is so far restricted to the first\r\n\r\nDetailed information on the data available at:measurement period of MIPAS from July 2002 to March 2004 (spectral high resolution (HR) mode). After\r\n2004 MIPAS is operating with slightly reduced resolution (RR-mode). A transfer of the prototype processor\r\nfor application of the RR mode has been taken into account during the algorithm development. It is desirable,\r\nthat this will be also considered in the realisation phase of the prototype processor.\r\nPrimary task of the software is to retrieve cloud properties from the MIPAS L1B spectra. This contains various\r\nitems of interest:\r\n• the detection of cloudy spectra in the L1B data\r\n• to classify various cloud types in the measurements (e.g. polar stratospheric clouds, liquids and ice\r\nclouds)\r\n• to retrieve cloud top information on height, temperature, pressure\r\n• to retrieve profile information on cloud parameters (e.g.. extinction, ice water path or integrated limb\r\npath quantities like volume/area density path)\r\n• information on microphysical parameter like effective radius of the particle size distribution (PSD) or\r\nVolume and Area densities.\r\nThe retrievability of the cloud parameters like the ones above have been investigated in a feasibility study of\r\nthe MIPclouds project\r\n\r\n1) Spang, R., Remedios, J. J., and Barkley, M., Colour Indices for the Detection and Differentiation of Cloud Types in Infra-red Limb Emission Spectra, Adv. Space Res., 33, pp.1041-1047, 2004.\r\n2) Spang, R., Arndt, K., Dudhia, A., Höpfner, M., Hoffmann, L., Hurley, J., Grainger, R. G., Griessbach, S., Poulsen, C., Remedios, J. J., Riese, M., Sembhi, H., Siddans, R., Waterfall, A., and Zehner, C.: Fast cloud parameter retrievals of MIPAS/Envisat, Atmos. Chem. Phys., 12, 7135-7164, doi:10.5194/acp-12-7135- 2012, 2012.\r\n 3) Sembhi, H., Remedios, J., Trent, T., Moore, D. P., Spang, R., Massie, S., and Vernier, J.-P.: MIPAS detection of cloud and aerosol particle occurrence in the UTLS with comparison to HIRDLS and CALIOP, Atmos. Meas. Tech., 5, 2537- 2553, doi:10.5194/amtd-5-2537-2012, 2012\r\n4) Hurley, J., Dudhia, A., and Grainger, R. G.: Retrieval of macrophysical cloud parameters from MIPAS: algorithm description, Atmos. Meas. Tech., 4, 683- 704, doi:10.5194/amt-4-20 683-2011, 2011.\"" }, "imageDetails": [ 111, 130 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 5002, "uuid": "60e718d3f2957f742c89b2b4fc159718", "short_code": "proj", "title": "National Centre for Earth Observation (NCEO)", "abstract": "The National Centre for Earth Observation is a partnership of scientists and institutions, from a range of disciplines, who are using data from Earth observation satellites to monitor global and regional changes in the environment and to improve understanding of the Earth system so that we can predict future environmental conditions.\r\n\r\nNCEO's Vision is to unlock the full potential of Earth observation to monitor, diagnose and predict climate and environmental changes, ensuring that these scientific advances are delivered to the wider community embedded in world class science." }, { "ob_id": 24926, "uuid": "e09fed7836e940a080ef210e0b487e57", "short_code": "proj", "title": "ESA MIPclouds", "abstract": "The Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) on the ENVISAT satellite measures limb IR spectra in the 4 to 15 micron range. The instrument has measured nearly continuously from September 2002 to March 2004. The ESA operational level 1 and 2 products for this period include so far no direct information on clouds and aerosols. This is not a surprising fact, because the analysis and retrieval of cloud parameter from limb IR spectra are still challenging tasks for radiative transport applications. As part of an ESA funded study the MIPclouds consortium has investigated improvements and new developments in the detection and type classification of cloudy spectra (PSC types, liquid/ice), the retrieval of micro-physical and macro-physical parameters from the spectra, such as cloud top heights, extinction and temperature, and estimates of particle sizes and ice water content of the cloud. Results of a corresponding feasibility study will be presented. Final goal of the project is to develop a fast (near-real-time) and validated prototype processor for the analysis of the first MIPAS measurement period." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 25860, 56235, 56236, 56237, 56238, 56239, 56240, 56241, 56242, 56243, 56244, 56245, 56246, 56247, 56248, 56249 ], "vocabularyKeywords": [], "identifier_set": [ 9321 ], "observationcollection_set": [ { "ob_id": 30127, "uuid": "82b29f96b8c94db28ecc51a479f8c9c6", "short_code": "coll", "title": "National Centre for Earth Observation (NCEO) Core datasets", "abstract": "This NCEO Core data set collection contains data generated by the National Centre for Earth Observation core scientific programmes. NCEO is a National Environment Research Council (NERC) research centre with more than 80 scientists distributed across leading UK universities and research organisations and led by Professor John Remedios at the University of Leicester.\r\n\r\nNCEO provides the UK with core expertise in Earth Observation science, data sets and merging techniques, and model evaluation to underpin Earth System research and the UK’s international contribution to environmental science. NCEO scientists work strategically with space agencies, play significant roles in mission planning, and generate internationally-recognised data products from 20 different satellite instruments." } ], "responsiblepartyinfo_set": [ 102005, 102012, 102013, 102015, 102016, 102017, 102018, 102014, 102009, 102019, 101999, 102010, 102008, 102007, 102011 ], "onlineresource_set": [ 23609, 23612, 23613, 23614, 23615 ] }, { "ob_id": 24943, "uuid": "22a878b3ada24590970974588642f585", "title": "HadCRUT4: gridded dataset of global historical surface temperature anomalies, version 4.5.0.0", "abstract": "This is the HadCRUT.4.5.0.0 version of the HadCRUT4 data. \r\n\r\nData are available for each month since January 1850, on a 5 degree grid. \r\nThe gridded data are a blend of the CRUTEM4 land-surface air temperature dataset and the HadSST3 sea-surface temperature (SST) dataset. The dataset is presented as an ensemble of 100 dataset realisations that sample the distribution of uncertainty in the global temperature record. \r\n\r\nThe data consist of 100 ensemble members and the ensemble median, each are available as a separate file where the fourth component of the filename denotes the ensemble member or median. In addition, the variance information is provided under the name uncorrelated. Full error covariance data are available from the Met Office (see the link to the HadCRUT4 homepage in Docs) \r\n\r\nTo keep up to date with updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS.\r\n\r\nReferences:\r\nWhen using the dataset in a paper you must cite the following paper (see Docs for link to the publication) and this dataset (using the \"citable as\" reference) :\r\n\r\nMorice, C. P., J. J. Kennedy, N. A. Rayner, and P. D. Jones (2012), Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: The HadCRUT4 dataset, J. Geophys. Res., 117, D08101, doi:10.1029/2011JD017187", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T02:22:38", "updateFrequency": "notPlanned", "dataLineage": "HadCRUT4 is a surface air temperature product that is a blend of the CRUTEM4 and HadSST3, it is produced by the Met Office Hadley Centre in collaboration with the Climatic Research Unit of the University of East Anglia. 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The dataset is presented as an ensemble of 100 dataset realisations that sample the distribution of uncertainty in the global temperature record given current understanding of non-climatic factors affecting near-surface temperature observations. \r\n\r\nThis ensemble approach allows characterisation of spatially and temporally correlated uncertainty structure in the gridded data, for example arising from uncertainties in methods used to account for changes in SST measurement practices, homogenisation of land station records and the potential impacts of urbanisation.\r\n\r\nThe HadCRUT4 data are neither interpolated nor variance adjusted." }, "procedureCompositeProcess": null, "imageDetails": [ 157 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2561, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 32, "licenceURL": "http://www.nationalarchives.gov.uk/doc/non-commercial-government-licence/version/2/", "licenceClassifications": [ { "ob_id": 6, "classification": "personal" }, { "ob_id": 4, "classification": "academic" }, { "ob_id": 5, "classification": "policy" } ] } } ], "projects": [ { "ob_id": 13164, "uuid": "ce252c81a7bd4717834055e31716b265", "short_code": "proj", "title": "Met Office Hadley Centre - Observations and Climate", "abstract": "The Met Office Hadley Centre is one of the UK's foremost climate change research centres.\r\n\r\nThe Hadley Centre produces world-class guidance on the science of climate change and provide a focus in the UK for the scientific issues associated with climate science.\r\n\r\nLargely co-funded by Department of Energy and Climate Change (DECC) and Defra (the Department for Environment, Food and Rural Affairs), the centre provides in-depth information to, and advise, the Government on climate science issues.\r\n\r\nAs one of the world's leading centres for climate science research, the Hadley Centre scientists make significant contributions to peer-reviewed literature and to a variety of climate science reports, including the Assessment Report of the IPCC. 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The dataset is presented as an ensemble of 100 dataset realisations that sample the distribution of uncertainty in the global temperature record." } ], "responsiblepartyinfo_set": [ 102100, 102101, 102102, 102103, 102104, 102105, 102106, 102107, 102108, 168767, 102109, 168768 ], "onlineresource_set": [ 23674, 23676, 23677, 23678, 23679, 23680, 23675 ] }, { "ob_id": 24945, "uuid": "4a7b8d37184b44808296511606bd26e0", "title": "__MUST_UPDATE__20170818174601__ Atmospheric Retrievals from MIPAS/Envisat at Oxford v1.2", "abstract": "This dataset contains atmospheric profiles from the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) on Envisat, processed with the Oxford L2 retrieval algorithm MORSE. 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Computation: Envisat - MIPAS (Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) Instrument deployed on Oxford Computer; \n2. 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pollution and forecast air quality" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 102111, 102112, 102115, 102116, 102117, 102110, 102114, 102113 ], "onlineresource_set": [] }, { "ob_id": 24947, "uuid": "8b965a24e74e46dcadb63611327f1e29", "title": "Measurements of N2O5, ClNO2, HCOOH, HNO3 and HCN at the Weybourne Atmospheric Observatory", "abstract": "Chemical ionisation mass spectrometer (CIMS) measurements of N2O5, ClNO2 and other halogenated species to understand the overall oxidation budget at the Weybourne Atmospheric Observatory rural marine site.\r\n\r\n", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2017-08-22T12:10:47.770502", "updateFrequency": "", "dataLineage": "Data were collected at the Weybourne Atmospheric Observatory and obtained by the Centre of Environmental Data 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102133, 105198, 102130, 102124, 102125, 102126 ], "onlineresource_set": [] }, { "ob_id": 24958, "uuid": "233cf64c54e946e0bb691a07970ec245", "title": "BITMAP: Tracks of western disturbances transiting over Pakistan and north India in ERA-Interim reanalysis data (1979-2015)", "abstract": "This dataset contains tracks generated using a bespoke tracking algorithm developed within the BITMAP (Better understanding of Interregional Teleconnections for prediction in the Monsoon And Poles) project, identifying and linking upper-tropospheric vortices (described in Hunt et al, 2018, QJRMS - see linked documentation), using data derived from the ERA-Interim reanalysis data. Similar datasets were produced using various model output from the WCRP CMIP5 programme, available within the parent dataset collection.\r\n\r\nWestern disturbances (WDs) are upper-level vortices that can significantly impact the weather over Pakistan and north India. This is a catalogue of the tracks of WDs passing through the region (specifically 20-36.5N, 60-80E) on the 450-300 hPa. This differs from those tracks from the CMIP5 data which were carried out on the 500 hPa layer. See linked documentation for details of the algorithms used.\r\n\r\nBITMAP was an Indo-UK-German project (NERC grant award NE/P006795/1) to develop better understanding of processes linking the Arctic and Asian monsoon, leading to better prospects for prediction on short, seasonal and decadal scales in both regions. Recent work had suggested that the pole-to-equator temperature difference is an essential ingredient driving variations in the monsoon. For further details on the project itself see the linked Project record.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2024-03-09T03:07:08", "updateFrequency": "notPlanned", "dataLineage": "Data produced and prepared for archiving by the authors before supplying to the Centre of Environmental Data Analysis (CEDA) for use by the research community.", "removedDataReason": "", "keywords": "BITMAP, India, Western disturbances, Vortices", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2019-02-05T12:12:24", "doiPublishedTime": "2019-02-05T12:27:34", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 26546, "dataPath": "/badc/deposited2018/bitmap/data/era-interim-derived-wd-tracks", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 8371284, "numberOfFiles": 2, "fileFormat": "Data are BADC-CSV formatted." }, "timePeriod": { "ob_id": 6751, "startTime": "1979-01-01T00:00:00", "endTime": "2015-12-31T23:59:59" }, "resultQuality": { "ob_id": 3161, "explanation": "The user is referred to Hunt et al. (2018, QJRMS) for a full description of the tracking algorithm and statistics of the dataset.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2018-09-13" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 24957, "uuid": "f66c26bcf7684ed29d14a88825884a19", "short_code": "comp", "title": "BITMAP: Western Disturbance Tracks Algorithm", "abstract": "Tracks generated using a bespoke tracking algorithm, identifying and linking upper-tropospheric vortices (described fully in Hunt et al, 2018, QJRMS - see linked documentation to this record), using data derived from ERA-Interim reanalysis data and selected CMIP5 model runs (with some modifications such as the vorticity level used).\r\n\r\nIn essence the algorithm works by:\r\n\r\n1. locating all mid-tropospheric relative vorticity maxima;\r\n\r\n2. group multiple peaks by using a neighbourhood filter, then integrate to find the parent vortex centre;\r\n\r\n3. link potential candidates together across time steps to form tracks using a nearest-neighbour approach incorporating local wind speed;\r\n\r\n4. surviving tracks are filtered by duration (> 2 days) and location (must pass through [20-36.5N, 60-80E])." }, "procedureCompositeProcess": null, "imageDetails": [ 2 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 24956, "uuid": "6375bfb8435d42a087c9d3fc76b3603d", "short_code": "proj", "title": "BITMAP: Better understanding of Interregional Teleconnections for prediction in the Monsoon and Poles (NE/P006795/1)", "abstract": "BITMAP was an Indo-UK-German project (NERC Grant award: NE/P006795/1) to develop better understanding of processes linking the Arctic and Asian monsoon, leading to better prospects for prediction on short, seasonal and decadal scales in both regions. Recent work had suggested that the pole-to-equator temperature difference is an essential ingredient driving variations in the monsoon. \r\n\r\nBITMAP's initial focus was on the impact of the temperature difference between pole and equator on the establishment and variation of regional circulations. The project used existing databases of multiple climate models to unpack the impact of different forcing agents (e.g. greenhouse gases and polluting aerosols) on the relative warming of the northern and southern hemispheres and pole-to-equator temperature gradients. \r\n\r\nThe project then related the gradient to position of the strongest rainfall and strength and position of monsoon circulation. The project also examined the impact of different pole-to-equator temperatures on hydroclimates of the vulnerable Hindu Kush-Himalaya (HKH) region in High Asia. \r\n\r\nNext the project tested the impact on Arctic circulation patterns of \"diabatic\" heating arising from the monsoon rainfall (via waves in the atmosphere) by conducting novel experiments with climate models. The project also helped evaluate and improve these models by determining the problems caused by typical monsoon errors (e.g. misplaced tropical rainfall) on simulation of polar climates; the project also explored how errors in model Arctic sea-ice distribution affect the monsoon. Finally the project analyzed effects of variations in climate. \r\n\r\nThe project measured and modelled the impact of typical strong and weak Asian monsoon summers on atmospheric waves that travel to the poles and thereby develop a better understanding of the pathways to Arctic circulation, with implications for predicting sea-ice extent. In the other direction, the project used observations and models to assess the role of the changing Arctic temperatures on the jet stream and on the regularity of heavy rainfall and flooding events that affect South Asia.\r\n\r\nThe objectives of the BITMAP project were as follows: \r\n(1) Better understand the impact of the South Asian monsoon on temperature and circulation structure in the Arctic, including the role of changes in monsoon diabatic heating; \r\n\r\n(2) Better understand the impact of the changing equator-to-pole temperature gradient on the establishment, maintenance and variation of regional circulations over the poles and monsoons; \r\n\r\n(3) Analyze the impacts of the changing equator-to-pole temperature gradient in a warming climate on subseasonal-to-seasonal monsoon variability, with the express impact of improved scientific underpinning of forecasting at NCMRWF; \r\n\r\n(4) Better understand how dynamical connections between high- and low-latitude regions influence moisture transports reaching high Asia from higher latitudes." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 52296, 52297, 52298, 52299, 52300, 52301, 52302, 52303, 52304, 52305 ], "vocabularyKeywords": [], "identifier_set": [ 10217 ], "observationcollection_set": [ { "ob_id": 24959, "uuid": "b1f266c25cf2445f8b87d874f6ac830a", "short_code": "coll", "title": "BITMAP: Tracks of western disturbances (1979-2015)", "abstract": "Western disturbances (WDs) are upper-level vortices that can significantly impact the weather over Pakistan and north India. This collection contains a catalogue of the tracks of WDs passing through the region (specifically 20-36.5N, 60-80E) produced from various model outputs. This work was undertaken as part of the NERC funded BITMAP (Better understanding of Interregional Teleconnections for prediction in the Monsoon and Poles) project. \r\n\r\nBITMAP was an Indo-UK-German project (NERC grant award: NE/P006795/1) to develop better understanding of processes linking the Arctic and Asian monsoon, leading to better prospects for prediction on short, seasonal and decadal scales in both regions. Recent work had suggested that the pole-to-equator temperature difference is an essential ingredient driving variations in the monsoon. \r\n\r\nTracks of these WDs were generated using a bespoke tracking algorithm within the project applied to data from the European Centre for Medium-Range Weather Forecasts' (ECMWF) ERA-Interim reanalysis data and model output from various experiments of the World Climate Research Programme's Coupled Model Intercomparison Project Phase 5 (WCRP CMIP5). The algorithm, described in Hunt et al, 2017, QJRMS (see linked documentation), identified and linked upper-tropospheric vortices from the data and are available within this dataset collection. Additional details of the CMIP5 tracking algorithm are available in the Hunt et al. paper 'Representation of western disturbances in CMIP5 models' paper (see linked documentation). The principal difference between the algorithm used for the ERA-Interim data and the CMIP5 data is the choice of pressure levels on which the algorithm was run: 500 hPa for the ERA-Interim data and 450-300 hPa layer for the CMIP5 data." } ], "responsiblepartyinfo_set": [ 102185, 102186, 102187, 102189, 102190, 102191, 102193, 102192, 102188, 111543, 111544, 102194, 111575 ], "onlineresource_set": [ 25422, 87800 ] }, { "ob_id": 24961, "uuid": "d73b6fb4fcfa4dfd844227c5b9475a94", "title": "Met Éireann LIDARNET Knock Airport Vaisala CL31 ceilometer cloud base and backscatter data", "abstract": "Cloud base and backscatter data from the Met Éireann's Knock Airport Cl31 ceilometer located at Knock Airport, Charlestown, County Mayo, northwest Ireland. The Met Éireann's laser cloud base recorders network (LCBRs), or ceilometers, returns a range of products for use in forecasting and hazard detection. 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The data were created during the main years of CloudSat and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite data so that they could be compared with vertical cloud profiles from this satellite data, and the results of this analysis appear in Stein et al. 2017, Journal of Climate.\r\n\r\nEach file is one year of data (although the year may not be complete).\r\n\r\nEach variable is an array: var(nlon, nlat, [nlev], ntime)\r\nlongitude, latitude, pressure, time are variables in each file\r\nunits are attributes of each variable (except non-dimensional ones)\r\nmissing_value is 3.0E20 and is an attribute of each variable\r\n\r\nTime is in days since 19790101:00Z and is every 3hours at 00z, 03z, ... \r\nThe actual temporal frequency of the data is described for each variable below.\r\n\r\nThe data is for each 10deg X 10deg lat/lon box, 30S-30N (at outer edges of box domain), with each box defined by its centre coordinates and with boxes overlapping each other by 5deg in each direction.\r\n\r\nIn general, each variable is a spatial average over each box, with the value set to missing if more than 15% of the box is missing data.\r\nExceptions to this are given below.\r\nThe most important exception is for the brightness temperature data, used in aggregation statistics, which is filled in using neighborhood averaging if no more than 5% of the pixels are missing, but otherwise is considered to be all missing data. The percentage of missing pixels is recorded in 'bt_miss_frac'.\r\n\r\n", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2018-01-17T15:47:22.990818", "updateFrequency": "", "dataLineage": "The data were collected from various online data repositories and analysed at the University of Reading.\r\n", "removedDataReason": "", "keywords": "convective aggregation, Tropics, SCAI", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2018-02-09T09:19:04", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 1864, "bboxName": "tropical project", "eastBoundLongitude": 165.0, "westBoundLongitude": -180.0, "southBoundLatitude": -10.0, "northBoundLatitude": 10.0 }, "verticalExtent": null, "result_field": { "ob_id": 25529, "dataPath": "/badc/deposited2017/tropical_rainfall/data/observations", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 11311718931, "numberOfFiles": 8, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 6766, "startTime": "2006-06-14T23:00:00", "endTime": "2011-04-17T22:59:59" }, "resultQuality": { "ob_id": 3101, "explanation": "Data is as given by the data provider", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2018-01-17" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 25825, "uuid": "d3bf8a04e3ec4f6eb0310181a927c941", "short_code": "comp", "title": "The Organization of Tropical Rainfall: Observed convective aggregation data across the Tropics", "abstract": "Individual cloud layers identified over nearly 5 years of data (July 2006–April 2011) from two A-Train satellites, CloudSat and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) were collected and Simple Convective Aggregation Index was applied." }, "procedureCompositeProcess": null, "imageDetails": [ 2 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2543, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 2, "licenceURL": "https://artefacts.ceda.ac.uk/licences/missing_licence.pdf", "licenceClassifications": [ { "ob_id": 2, "classification": "unstated" } ] } } ], "projects": [ { "ob_id": 12144, "uuid": "6c93ff856bda41d99342702593ec6053", "short_code": "proj", "title": "The organization of tropical rainfall", "abstract": "This project was funded by the Natural Environment Research Council (NERC) with the grant reference - NE/I021012/1 - and was led by Dr Christopher Holloway (University of Reading). \r\n\r\nTropical cloud systems and rainfall help drive the global circulation of the atmosphere, transferring heat from near the Earth's surface upward for many kilometres. These convective systems can be found in groups of many different sizes, from isolated showers and thunderstorms to equatorial waves to tropical cyclones to the Madden-Julian Oscillation (MJO), an eastward-propagating weather system composed of superclusters of convection several thousand kilometres across which dominates tropical weather variability on weekly to monthly time scales. Global numerical weather forecast and climate models still do not adequately simulate these organized storm clusters and, as a result, have too little (or incorrect) variability of tropical rainfall. Improvement of the representation of organized tropical convection, and therefore the accuracy of weather forecasts, would greatly improve the lives of billions of people who rely on rainfall for agriculture in the tropics and subtropics; better forecasts of strong storms and flooding would also save countless lives and reduce property damage. Furthermore, these processes may change in the future as the climate changes due to human activities, so an improvement of the ability of global models to simulate organized convection will lead to better predictions of possible climate change scenarios over the whole globe. \r\n\r\nGlobal weather and climate models divide the Earth into grid boxes about 100 km across. These boxes are too large to directly simulate the motions responsible for small-scale rainstorms, instead estimating total rainfall based on average conditions in the box. This simplified rain estimation, necessary because of limited computer resources, ignores the interaction of isolated rain showers with each other and regional weather conditions. \r\n\r\nAn exciting new research area is the study of organized convection in high-resolution idealized models. These models, with constant sea surface temperatures and constant sunlight, can now be run on domains several thousand kilometres across and with grid boxes of only a few kilometres long, allowing convection to be represented explicitly. These models are beginning to provide insight into processes that lead to spontaneous growth of convective clusters which can ultimately grow to a single large cluster accounting for all of the rainfall in the domain. These processes act over a wide range of spatial scales which are not fully resolved in global models. \r\n\r\nHowever, the processes which lead to organized convection in idealized models are still not well understood, and it is not known whether they are also important for organizing tropical convection in nature. This project exploited a large archive of high-resolution model runs, forecast analyses, and observations from satellites to make more direct comparisons between idealized cases and observed phenomena. Ultimately, this endeavour had the potential to lead to improvements in the way that global models, especially the UK Met Office Unified Model, simulate tropical rainfall and with it global weather and climate. \r\n\r\nThis project benefited from collaborations with University of Reading, the Met Office and with other scientists approaching similar problems using different models.\r\n\r\nThe improvement of global model simulations of tropical rainfall, along with associated cloud fields and atmospheric heating, is key to reducing uncertainty in predictions of high-impact weather and climate change. Global models still fail to produce realistic large-scale clusters of tropical clouds and rainfall, which are fundamental to predicting variability on weekly and monthly timescales and have major effects on phenomena such as tropical cyclones, El Nino, and the Asian monsoon, as well as the entire global circulation. Recent high-resolution simulations for idealized conditions over tropical oceans have shown spontaneous self-aggregation of convection, with likely causes including feedbacks between convection, moisture and clouds, and radiation, as well as between convection and surface fluxes. \r\n\r\nThis project aimed to clearly identify processes important for self-aggregation of convection in idealized models and then to test whether these processes, or different processes, are active in convective organization in nature. The second part of this goal was an open question in the field, and this fellowship has the potential to connect a rapidly expanding theoretical research area with ongoing efforts to improve the understanding and prediction of tropical variability. The focus on the Unified Model benefited weather and climate prediction in the UK by exchanging ideas with Met Office scientists who were directly involved in testing and improving the model. \r\n\r\nThe specific objectives of this project, with increasing priority downwards, were to address the following three questions: \r\n1. What processes are responsible for convective self-aggregation in idealized models? \r\n2. To what extent are these idealized model processes important for convective organization in nature as represented by observations, forecast analyses, and high-resolution model case studies, particularly in conditions resembling those of idealized models? What processes lead to the simplest cases of convective organization observed? \r\n3. How do more complex features of nature, such as equatorially-trapped waves, land-sea contrast, and extratropical features, interact with more idealized mechanisms for convective organization? \r\n\r\nAddressing these specific objectives allowed for the improvement of numerical weather and climate prediction by contributing new ideas for the development of both high-resolution models with explicit convection and lower resolution models with parameterized convection." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 3894, 7565, 16668, 20957, 20958, 20959, 20960, 20961, 20962, 20963, 20964, 20965, 20966, 20968, 20969, 20970, 20971, 20972, 20973, 20974, 20975, 20976, 20977, 20978, 20979, 20980, 20981, 20982, 20983, 20984, 20985, 20986, 20987, 20988, 20989, 20990, 20991, 20992, 20993, 20994, 20995, 20996, 20997, 20998, 20999, 21000, 21001, 21002, 21003, 21004, 21005, 21006, 21007, 21008, 21009 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 25021, "uuid": "18c737a986094b1abd19cfebc9d0c374", "short_code": "coll", "title": "The Organization of Tropical Rainfall: MetUM model data and observed convective aggregation data for the Tropics", "abstract": "This project was funded by the Natural Environment Research Council (NERC) with the grant reference - NE/I021012/1 - and was led by Dr Christopher Holloway (University of Reading). \r\n\r\nThis dataset collection contains MetUM model data and observed convective aggregation data for the Tropics.\r\n\r\nThis project aimed to clearly identify processes important for self-aggregation of convection in idealized models and then to test whether these processes, or different processes, are active in convective organization in nature. The second part of this goal was an open question in the field, and this fellowship has the potential to connect a rapidly expanding theoretical research area with ongoing efforts to improve the understanding and prediction of tropical variability. The focus on the Unified Model benefited weather and climate prediction in the UK by exchanging ideas with Met Office scientists who were directly involved in testing and improving the model. \r\n" } ], "responsiblepartyinfo_set": [ 102478, 102479, 102480, 102481, 102482, 102484, 102485, 102486, 102483 ], "onlineresource_set": [] }, { "ob_id": 25024, "uuid": "fc867884cef243c5adbd081f3b14c709", "title": "Ice nucleation by fertile soil dusts: relative importance of mineral and biogenic components: Ice active site density verses temperature for a number soil samples", "abstract": "These data are associated with Ice nucleation by fertile soil dusts: relative importance of mineral and biogenic components, by D. O’Sullivan, B. J. Murray, T. L. Malkin, T. F. Whale, N. S. Umo, J. D. Atkinson, H. C. Price, K. J. Baustian, J. Browse, and M. E. Webb. Atmos. Chem. Phys., 14, 1853–1867, 2014 www.atmos-chem-phys.net/14/1853/2014/ doi:10.5194/acp-14-1853-2014. The lab measurement data comprise Ice active site density verses temperature for a number of samples. The data are in CSV format.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2017-09-05T12:34:47.703036", "updateFrequency": "notPlanned", "dataLineage": "Data supplied by project scientist. Reference to paper within the files added by CEDA staff.", "removedDataReason": "", "keywords": "", "publicationState": "published", "nonGeographicFlag": true, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2017-09-06T09:41:07", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": null, "verticalExtent": null, "result_field": { "ob_id": 25023, "dataPath": "/badc/deposited2017/osullivan_ice_nucleation2014/data", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 6350559, "numberOfFiles": 24, "fileFormat": "CSV" }, "timePeriod": null, "resultQuality": null, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 11986, "uuid": "7197d0e7d2d2298491f735e75b5dc21c", "short_code": "proj", "title": "Aerosol-Cloud Interactions - A Directed Programme to Reduce Uncertainty in Forcing through a Targeted Laboratory and Modelling Programme (ACID-PRUF)", "abstract": "ACID-PRUF was a three year NERC directed programme that investigated the complex interaction of aerosols and clouds. The overall aims of ACID-PRUF were to reduce the uncertainty in the radiative forcing associated with the aerosol indirect effects though a targeted laboratory and modelling programme. \r\nA programme of research is proposed here to \r\ni) directly investigate these processes in the laboratory, \r\nii) evaluate the sensitivity of climate relevant parameters to the studied processes, \r\niii) interpret the laboratory studies with detailed model investigations and \r\niv) to incorporate and test new descriptions of the studied processes in cloud-scale and, where possible, global scale models. \r\n\r\nThis research programme was funded by the Natural Environment Research Council. The Lead Grant Reference number for this project is NE/I020121/1." }, { "ob_id": 25025, "uuid": "fc440f19a73e4246acba79daaaa8e380", "short_code": "proj", "title": "Quantifying the efficiency with which biological particles nucleate ice when immersed in supercooled water droplets", "abstract": "Clouds composed of both ice particles and supercooled liquid droplets, known as mixed phase clouds, exist at temperatures above ~-35oC and cover a large portion of the planet. These clouds impact climate by both simultaneously warming the planet by trapping outgoing infrared radiation and cooling the planet by reflecting incoming visible light from the sun back to space. It is becoming increasingly apparent that mixed phase clouds are very sensitive to the number and type of particles, known as aerosols, present in the atmosphere. A lot of work has been done in the past to understand the role of aerosols on clouds that are entirely composed of liquid droplets and the Intergovernmental Panel on Climate Change (IPCC) attempted to quantify this impact, albeit with large uncertainties. However, the role that aerosols play in ice formation, which dramatically alters the properties of a cloud, remains very uncertain and the IPCC were not in a position to assess this forcing despite evidence that the impact is very large. Aerosols that can catalyse ice particle formation are known as ice nuclei; however their identity, concentration, global distribution and the efficiency with which they nucleate ice are all poorly quantified at present. There is mounting evidence from field studies that biogenic particles, such as bacteria, pollen or fungal spores, nucleate ice in clouds. It has been known for some time that about 25% of insoluble aerosols can be of biogenic origin, but their role in cloud formation remains highly uncertain. In the past few years technological advancements in field equipment have led to the discovery that a major fraction of particles which can serve as ice nuclei in the atmosphere are of biogenic origin. In an aircraft campaign, it was found that a third of the ice crystals in a cloud over Wyoming contained biogenic material (Pratt et al., Nature Geosci, 2, 398. 2009). In a separate study biogenic material dominated the ice nuclei populations above -25oC in the Amazon (Prenni et al., Nature Geosci, 2, 402, 2009). Hoose (Nature Geosci, 2, 385, 2009) suggests that these discoveries may represent 'the tip of the iceberg'. Hence, it is clear that biogenic aerosols are strongly correlated with ice yet their proper treatment in cloud and climate models is missing and their ice nucleation properties are very poorly characterised with huge gaps in basic knowledge. Modelling studies give conflicting results, with some models suggesting a major impact on cloud formation while others suggest a marginal impact of biogenic ice nucleation. The difference in model results and the discrepancy with the field data suggests that the laboratory data on which the models are based is inadequate. In fact, in their global model Hoose et al. (J. Atm. Sci, doi: 10.1175/2010JAS3425.1, 2010) use a crude estimate of the ice nucleating ability of fungal spores since there is no suitable experimental data on which to base the parameterisation. Given fungal spores account for 23% of the primary emissions of organic aerosol globally, their assumption will lead to major uncertainties in the model. Lab data for ice nucleation by pollen and bacteria are also very poor. In short, there is a large amount of biogenic material in the atmosphere, but we do not know how it impacts clouds and climate due to the paucity of basic data. In order to address this paucity of information we propose a set of experiments in which we make use of a unique instrument which Murray developed during his NERC fellowship. This instrument has and is being used to measure the efficiency with which mineral dust particles nucleate ice in the immersion mode. This work has resulted in the first quantitative measurements of ice nucleation by clay minerals (Murray et al. Atm. Chem. Phys. Disc. 4, 115, 2010). We plan to apply the same rigorous and quantitative techniques to fungal spores, pollen, and bacteria for the first time. NERC Reference : NE/I013466/1." }, { "ob_id": 25026, "uuid": "10bd626ecfad4873a26c1c00e549a303", "short_code": "proj", "title": "Designer ice nuclei for geoengineering of clouds", "abstract": "The collective international failure to curb rises in greenhouse gas emissions means we need to consider back up plans to avoid the worst potential impacts of climate change. A number of geoengineering schemes have been put forward and one of these involves modifying cirrus ice clouds. Thin cirrus clouds have a net warming effect on the planet, but by seeding them with efficient ice nuclei their global coverage could be reduced thus resulting in a cooling of the planet. Research is urgently needed into the basic science that will underpin schemes such as this. We propose a laboratory experimental study to identify materials which could be used to efficiently, safely and cost effectively nucleate ice (ice nuclei) in any future cloud geoengineering projects. Our strategy is to build on the experience and expertise at Asymptote Ltd who are experts in ice nucleation in the field of cryopreservation of biological samples. We already have an established working relationship with Asymptote and have published with them on freezing of water in jet fuel (see Murray's supervisor section for details). Our goal is to develop a fundamental understanding of ice nucleation by porous materials and use this to design ice nuclei which would be ideal for a range of applications. The primary focus of the student will be to identify ice nuclei for geoengineering purposes with potential benefits for society, whereas Asymptote Ltd will apply the same fundamental information to the commercial area of cryopreservation. We will therefore achieve both societal and economic impact with this work. Asymptote Ltd were recently awarded funds from the Technology Strategy Board to identifying ice nuclei which might be used in cryopreservation. This same experimental set up at Asymptote's laboratories will be used by the CASE student to screen a range of porous materials for their ice nucleating ability guided by a new theory of ice nucleation. We will then bring those same samples back to Leeds where we can quantify their ice nucleation efficiency under atmospherically relevant conditions. As well as working in a university research environment the CASE student will also spent a total of 6 months working at Asymptote Ltd. This will provide an opportunity to experience a commercial research environment and will learn skills that the School of Earth and Environment cannot offer such as management of intellectual property and the importance of commercial confidentiality. This combined with training in the fundamentals of nucleation and crystallisation as well as the transferrable skills training all Leeds students receive will give this individual a very competitive portfolio. NERC Reference : NE/I019057/1." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 56232, 56233 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 102468, 102470, 102471, 102472, 104811, 104974, 104991, 102469 ], "onlineresource_set": [ 23720 ] }, { "ob_id": 25027, "uuid": "6c1ca83355884cf5914778d960b336f0", "title": "The relevance of nanoscale biological fragments for ice nucleation in clouds: Ice active site density verses temperature for a number soil, fungus and pollen samples.", "abstract": "These data are associated with The relevance of nanoscale biological fragments for ice nucleation in clouds, by D. O'Sullivan, B. J. Murray, J. F. Ross, T. F. Whale, H. C. Price, J. D. Atkinson, N. S. Umo & M. E. Webb. Scientific Reports, DOI: 10.1038/srep08082. The lab measurement data comprise Ice active site density verses temperature for a number of samples. The data are in CSV format.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2017-09-05T12:35:48.681155", "updateFrequency": "notPlanned", "dataLineage": "Data supplier by project scientist. Reference to paper within the files added by CEDA staff.", "removedDataReason": "", "keywords": "", "publicationState": "published", "nonGeographicFlag": true, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2017-09-06T09:47:52", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": null, "verticalExtent": null, "result_field": { "ob_id": 25028, "dataPath": "/badc/deposited2017/osullivan_ice_nucleation2015/data", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1028430, "numberOfFiles": 14, "fileFormat": "CSV" }, "timePeriod": null, "resultQuality": null, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 11986, "uuid": "7197d0e7d2d2298491f735e75b5dc21c", "short_code": "proj", "title": "Aerosol-Cloud Interactions - A Directed Programme to Reduce Uncertainty in Forcing through a Targeted Laboratory and Modelling Programme (ACID-PRUF)", "abstract": "ACID-PRUF was a three year NERC directed programme that investigated the complex interaction of aerosols and clouds. The overall aims of ACID-PRUF were to reduce the uncertainty in the radiative forcing associated with the aerosol indirect effects though a targeted laboratory and modelling programme. \r\nA programme of research is proposed here to \r\ni) directly investigate these processes in the laboratory, \r\nii) evaluate the sensitivity of climate relevant parameters to the studied processes, \r\niii) interpret the laboratory studies with detailed model investigations and \r\niv) to incorporate and test new descriptions of the studied processes in cloud-scale and, where possible, global scale models. \r\n\r\nThis research programme was funded by the Natural Environment Research Council. The Lead Grant Reference number for this project is NE/I020121/1." }, { "ob_id": 25025, "uuid": "fc440f19a73e4246acba79daaaa8e380", "short_code": "proj", "title": "Quantifying the efficiency with which biological particles nucleate ice when immersed in supercooled water droplets", "abstract": "Clouds composed of both ice particles and supercooled liquid droplets, known as mixed phase clouds, exist at temperatures above ~-35oC and cover a large portion of the planet. These clouds impact climate by both simultaneously warming the planet by trapping outgoing infrared radiation and cooling the planet by reflecting incoming visible light from the sun back to space. It is becoming increasingly apparent that mixed phase clouds are very sensitive to the number and type of particles, known as aerosols, present in the atmosphere. A lot of work has been done in the past to understand the role of aerosols on clouds that are entirely composed of liquid droplets and the Intergovernmental Panel on Climate Change (IPCC) attempted to quantify this impact, albeit with large uncertainties. However, the role that aerosols play in ice formation, which dramatically alters the properties of a cloud, remains very uncertain and the IPCC were not in a position to assess this forcing despite evidence that the impact is very large. Aerosols that can catalyse ice particle formation are known as ice nuclei; however their identity, concentration, global distribution and the efficiency with which they nucleate ice are all poorly quantified at present. There is mounting evidence from field studies that biogenic particles, such as bacteria, pollen or fungal spores, nucleate ice in clouds. It has been known for some time that about 25% of insoluble aerosols can be of biogenic origin, but their role in cloud formation remains highly uncertain. In the past few years technological advancements in field equipment have led to the discovery that a major fraction of particles which can serve as ice nuclei in the atmosphere are of biogenic origin. In an aircraft campaign, it was found that a third of the ice crystals in a cloud over Wyoming contained biogenic material (Pratt et al., Nature Geosci, 2, 398. 2009). In a separate study biogenic material dominated the ice nuclei populations above -25oC in the Amazon (Prenni et al., Nature Geosci, 2, 402, 2009). Hoose (Nature Geosci, 2, 385, 2009) suggests that these discoveries may represent 'the tip of the iceberg'. Hence, it is clear that biogenic aerosols are strongly correlated with ice yet their proper treatment in cloud and climate models is missing and their ice nucleation properties are very poorly characterised with huge gaps in basic knowledge. Modelling studies give conflicting results, with some models suggesting a major impact on cloud formation while others suggest a marginal impact of biogenic ice nucleation. The difference in model results and the discrepancy with the field data suggests that the laboratory data on which the models are based is inadequate. In fact, in their global model Hoose et al. (J. Atm. Sci, doi: 10.1175/2010JAS3425.1, 2010) use a crude estimate of the ice nucleating ability of fungal spores since there is no suitable experimental data on which to base the parameterisation. Given fungal spores account for 23% of the primary emissions of organic aerosol globally, their assumption will lead to major uncertainties in the model. Lab data for ice nucleation by pollen and bacteria are also very poor. In short, there is a large amount of biogenic material in the atmosphere, but we do not know how it impacts clouds and climate due to the paucity of basic data. In order to address this paucity of information we propose a set of experiments in which we make use of a unique instrument which Murray developed during his NERC fellowship. This instrument has and is being used to measure the efficiency with which mineral dust particles nucleate ice in the immersion mode. This work has resulted in the first quantitative measurements of ice nucleation by clay minerals (Murray et al. Atm. Chem. Phys. Disc. 4, 115, 2010). We plan to apply the same rigorous and quantitative techniques to fungal spores, pollen, and bacteria for the first time. NERC Reference : NE/I013466/1." }, { "ob_id": 25026, "uuid": "10bd626ecfad4873a26c1c00e549a303", "short_code": "proj", "title": "Designer ice nuclei for geoengineering of clouds", "abstract": "The collective international failure to curb rises in greenhouse gas emissions means we need to consider back up plans to avoid the worst potential impacts of climate change. A number of geoengineering schemes have been put forward and one of these involves modifying cirrus ice clouds. Thin cirrus clouds have a net warming effect on the planet, but by seeding them with efficient ice nuclei their global coverage could be reduced thus resulting in a cooling of the planet. Research is urgently needed into the basic science that will underpin schemes such as this. We propose a laboratory experimental study to identify materials which could be used to efficiently, safely and cost effectively nucleate ice (ice nuclei) in any future cloud geoengineering projects. Our strategy is to build on the experience and expertise at Asymptote Ltd who are experts in ice nucleation in the field of cryopreservation of biological samples. We already have an established working relationship with Asymptote and have published with them on freezing of water in jet fuel (see Murray's supervisor section for details). Our goal is to develop a fundamental understanding of ice nucleation by porous materials and use this to design ice nuclei which would be ideal for a range of applications. The primary focus of the student will be to identify ice nuclei for geoengineering purposes with potential benefits for society, whereas Asymptote Ltd will apply the same fundamental information to the commercial area of cryopreservation. We will therefore achieve both societal and economic impact with this work. Asymptote Ltd were recently awarded funds from the Technology Strategy Board to identifying ice nuclei which might be used in cryopreservation. This same experimental set up at Asymptote's laboratories will be used by the CASE student to screen a range of porous materials for their ice nucleating ability guided by a new theory of ice nucleation. We will then bring those same samples back to Leeds where we can quantify their ice nucleation efficiency under atmospherically relevant conditions. As well as working in a university research environment the CASE student will also spent a total of 6 months working at Asymptote Ltd. This will provide an opportunity to experience a commercial research environment and will learn skills that the School of Earth and Environment cannot offer such as management of intellectual property and the importance of commercial confidentiality. This combined with training in the fundamentals of nucleation and crystallisation as well as the transferrable skills training all Leeds students receive will give this individual a very competitive portfolio. NERC Reference : NE/I019057/1." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 56232, 56233, 56234 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 102473, 102474, 102475, 102477, 104812, 104975, 104992, 102476 ], "onlineresource_set": [ 23721 ] }, { "ob_id": 25029, "uuid": "2499ca660bc448f281ff1a1de1d20970", "title": "The Organization of Tropical Rainfall: Idealised MetUM model output for convective aggregation studies", "abstract": "This dataset comprises of model output from 11 runs of the Met Office Unified Model (MetUM) in idealised radiative-convective equilibrium mode. All runs have fixed constant sea surface temperature (SST) and doubly-periodic lateral boundary conditions. These runs were used in several papers on convective self-aggregation: principally in Holloway and Woolnough (2016, Journal of Advances in Modeling Earth Systems) but also in Holloway (2017, Journal of Advances in Modeling Earth Systems).\r\n\r\nAll runs use the \"New Dynamics\" dynamical core, MetUM version 7.5, as described in Holloway and Woolnough (2016). The simulations are run with 4-km horizontal grid spacing. They all have a horizontal domain size of 576 km X 576 km (or 144 X 144 grid points), with 70 vertical levels. They were all run for 40 days except for the two runs with lower Sea Surface Temperatures, SSTs (295 K and 290 K) which were run for only 20 days.\r\n\r\nThe model output includes hourly model-level prognostic variables (temperature, specific humidity, pressure, wind components, liquid water, ice water) as well as some model-level increments to temperature and specific humidity. There are also many fields containing surface variables and fluxes (averaged over each hour or every 15 minutes).\r\n", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2021-06-16T15:04:27", "updateFrequency": "", "dataLineage": "The data were delivered to the Centre for Environmental Data Analysis (CEDA) for archiving.", "removedDataReason": "", "keywords": "tropical convection, Tropics, MetUM", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2018-06-14T11:05:54", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 1864, "bboxName": "tropical project", "eastBoundLongitude": 165.0, "westBoundLongitude": -180.0, "southBoundLatitude": -10.0, "northBoundLatitude": 10.0 }, "verticalExtent": null, "result_field": { "ob_id": 26162, "dataPath": "/badc/deposited2017/tropical_rainfall/data/idealised", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 2160333196526, "numberOfFiles": 36853, "fileFormat": "Data are netCDF formatted" }, "timePeriod": null, "resultQuality": { "ob_id": 3133, "explanation": "Data is as given by the data provider, no quality control has been performed by the Centre for Environmental Data Analysis (CEDA)", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2018-06-04" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 25030, "uuid": "c940eb163bf743369ea9ce7a3584fbd8", "short_code": "comp", "title": "The Organization of Tropical Rainfall: 4-km grid Met-UM simulations of tropical convection", "abstract": "Model was run on ARCHER, the UK supercomputer for academic use and comprises of model output from 11 runs of the Met Office Unified Model (MetUM) in idealised radiative-convective equilibrium mode. All runs have fixed constant sea surface temperature (SST) and doubly-periodic lateral boundary conditions. These runs were used in several papers on convective self-aggregation: principally in Holloway and Woolnough (2016, Journal of Advances in Modeling Earth Systems) but also in Holloway (2017, Journal of Advances in Modeling Earth Systems).\r\n\r\nAll runs use the \"\"New Dynamics\"\" dynamical core, MetUM version 7.5, as described in Holloway and Woolnough (2016). The simulations are run with 4-km horizontal grid spacing. They all have a horizontal domain size of 576 km X 576 km (or 144 X 144 grid points), with 70 vertical levels. They were all run for 40 days except for the two runs with lower SSTs (295 K and 290 K) which were run for only 20 days." }, "procedureCompositeProcess": null, "imageDetails": [ 2 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2543, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 2, "licenceURL": "https://artefacts.ceda.ac.uk/licences/missing_licence.pdf", "licenceClassifications": [ { "ob_id": 2, "classification": "unstated" } ] } } ], "projects": [ { "ob_id": 12144, "uuid": "6c93ff856bda41d99342702593ec6053", "short_code": "proj", "title": "The organization of tropical rainfall", "abstract": "This project was funded by the Natural Environment Research Council (NERC) with the grant reference - NE/I021012/1 - and was led by Dr Christopher Holloway (University of Reading). \r\n\r\nTropical cloud systems and rainfall help drive the global circulation of the atmosphere, transferring heat from near the Earth's surface upward for many kilometres. These convective systems can be found in groups of many different sizes, from isolated showers and thunderstorms to equatorial waves to tropical cyclones to the Madden-Julian Oscillation (MJO), an eastward-propagating weather system composed of superclusters of convection several thousand kilometres across which dominates tropical weather variability on weekly to monthly time scales. Global numerical weather forecast and climate models still do not adequately simulate these organized storm clusters and, as a result, have too little (or incorrect) variability of tropical rainfall. Improvement of the representation of organized tropical convection, and therefore the accuracy of weather forecasts, would greatly improve the lives of billions of people who rely on rainfall for agriculture in the tropics and subtropics; better forecasts of strong storms and flooding would also save countless lives and reduce property damage. Furthermore, these processes may change in the future as the climate changes due to human activities, so an improvement of the ability of global models to simulate organized convection will lead to better predictions of possible climate change scenarios over the whole globe. \r\n\r\nGlobal weather and climate models divide the Earth into grid boxes about 100 km across. These boxes are too large to directly simulate the motions responsible for small-scale rainstorms, instead estimating total rainfall based on average conditions in the box. This simplified rain estimation, necessary because of limited computer resources, ignores the interaction of isolated rain showers with each other and regional weather conditions. \r\n\r\nAn exciting new research area is the study of organized convection in high-resolution idealized models. These models, with constant sea surface temperatures and constant sunlight, can now be run on domains several thousand kilometres across and with grid boxes of only a few kilometres long, allowing convection to be represented explicitly. These models are beginning to provide insight into processes that lead to spontaneous growth of convective clusters which can ultimately grow to a single large cluster accounting for all of the rainfall in the domain. These processes act over a wide range of spatial scales which are not fully resolved in global models. \r\n\r\nHowever, the processes which lead to organized convection in idealized models are still not well understood, and it is not known whether they are also important for organizing tropical convection in nature. This project exploited a large archive of high-resolution model runs, forecast analyses, and observations from satellites to make more direct comparisons between idealized cases and observed phenomena. Ultimately, this endeavour had the potential to lead to improvements in the way that global models, especially the UK Met Office Unified Model, simulate tropical rainfall and with it global weather and climate. \r\n\r\nThis project benefited from collaborations with University of Reading, the Met Office and with other scientists approaching similar problems using different models.\r\n\r\nThe improvement of global model simulations of tropical rainfall, along with associated cloud fields and atmospheric heating, is key to reducing uncertainty in predictions of high-impact weather and climate change. Global models still fail to produce realistic large-scale clusters of tropical clouds and rainfall, which are fundamental to predicting variability on weekly and monthly timescales and have major effects on phenomena such as tropical cyclones, El Nino, and the Asian monsoon, as well as the entire global circulation. Recent high-resolution simulations for idealized conditions over tropical oceans have shown spontaneous self-aggregation of convection, with likely causes including feedbacks between convection, moisture and clouds, and radiation, as well as between convection and surface fluxes. \r\n\r\nThis project aimed to clearly identify processes important for self-aggregation of convection in idealized models and then to test whether these processes, or different processes, are active in convective organization in nature. The second part of this goal was an open question in the field, and this fellowship has the potential to connect a rapidly expanding theoretical research area with ongoing efforts to improve the understanding and prediction of tropical variability. The focus on the Unified Model benefited weather and climate prediction in the UK by exchanging ideas with Met Office scientists who were directly involved in testing and improving the model. \r\n\r\nThe specific objectives of this project, with increasing priority downwards, were to address the following three questions: \r\n1. What processes are responsible for convective self-aggregation in idealized models? \r\n2. To what extent are these idealized model processes important for convective organization in nature as represented by observations, forecast analyses, and high-resolution model case studies, particularly in conditions resembling those of idealized models? What processes lead to the simplest cases of convective organization observed? \r\n3. How do more complex features of nature, such as equatorially-trapped waves, land-sea contrast, and extratropical features, interact with more idealized mechanisms for convective organization? \r\n\r\nAddressing these specific objectives allowed for the improvement of numerical weather and climate prediction by contributing new ideas for the development of both high-resolution models with explicit convection and lower resolution models with parameterized convection." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 13296, 50542, 50543, 51580, 51581, 51582, 51583, 51584, 51585, 51586, 51587, 51588, 51589, 51590, 51591, 51592, 51593, 51594, 51595, 51596, 51597, 51598, 51599, 51600, 51601, 51602, 51603, 51604, 51605, 51606, 51607, 51608, 51609, 51610, 51611, 51612, 51613, 51614, 51615, 51616, 51617, 51618, 51619, 51620, 51621, 51622, 51623, 51624, 51625, 51626, 51627, 51628 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 25021, "uuid": "18c737a986094b1abd19cfebc9d0c374", "short_code": "coll", "title": "The Organization of Tropical Rainfall: MetUM model data and observed convective aggregation data for the Tropics", "abstract": "This project was funded by the Natural Environment Research Council (NERC) with the grant reference - NE/I021012/1 - and was led by Dr Christopher Holloway (University of Reading). \r\n\r\nThis dataset collection contains MetUM model data and observed convective aggregation data for the Tropics.\r\n\r\nThis project aimed to clearly identify processes important for self-aggregation of convection in idealized models and then to test whether these processes, or different processes, are active in convective organization in nature. The second part of this goal was an open question in the field, and this fellowship has the potential to connect a rapidly expanding theoretical research area with ongoing efforts to improve the understanding and prediction of tropical variability. The focus on the Unified Model benefited weather and climate prediction in the UK by exchanging ideas with Met Office scientists who were directly involved in testing and improving the model. \r\n" } ], "responsiblepartyinfo_set": [ 102487, 102488, 102489, 102491, 102492, 102493, 102494, 102495, 102490 ], "onlineresource_set": [ 24877, 24878 ] }, { "ob_id": 25031, "uuid": "7838d170275d460887b3043de0c71679", "title": "The Organization of Tropical Rainfall: Realistic MetUM model output for convective aggregation studies", "abstract": "This dataset comprises of model output from 25 runs (5 case studies, with 5 runs in each case study) of the Met Office Unified Model (MetUM) in realistic limited-area one-way nesting mode. The output data include values for model fields (e.g. temperature, humidity, winds, pressure) at model grid points over regularly spaced time intervals. These runs were used in a paper on convective aggregation: Holloway (2017, Journal of Advances in Modeling Earth Systems). \r\n\r\nAll runs use the \"\"New Dynamics\"\" dynamical core, MetUM version 7.5, as described in Holloway (2017). The simulations are run with 4-km horizontal grid spacing. They all have a horizontal domain size of 20 degrees latitude X 20 degrees longitude (or 574 X 574 grid points, although the grid points in the outer 8 points on all sides, the \"\"rim\"\", should be discarded before analysis), with 70 vertical levels. All runs are initialised from operational analyses from the European Centre for Medium-Range Weather Forecasting (ECMWF) taken from actual cases. Lateral boundary conditions are comprised of 6-hourly ECMWF analyses, and the model is relaxed to these conditions in and near the outer rim as described in Holloway (2017). Sea surface temperatures (SST) are taken from the initial ECMWF analysis and are held constant in time for the 15 days (but are not constant in space). There are small land regions in four of the case studies which include an interactive land surface model.\r\n\r\nEach simulation was run for 15 days. The model output includes hourly model-level prognostic variables (temperature, specific humidity, pressure, wind components, liquid water, ice water) as well as some model-level increments to temperature and specific humidity. There are also many fields containing surface variables and fluxes (averaged over each hour or every 15 minutes). Note that the \"\"control\"\" simulations have slightly more available data than the other four runs in each of the five case studies.\r\n\r\nThe five case studies are centred on the equator and occur between 2008 and 2010. See Holloway (2017) for further details:\r\nhttp://onlinelibrary.wiley.com/doi/10.1002/2017MS000980/full \r\n\r\n\r\nFor each case, there are five runs:\r\n1) control (interactive radiation, interactive surface fluxes)\r\n2) constant radiative cooling run (radiative cooling over sea points is prescribed from domain-time mean of control run)\r\n3) constant surface flux run (surface latent and sensible heat fluxes over sea points are prescribed from domain-time mean of control run)\r\n4) constant radiative cooling and constant surface flux run (combination of 2 and 3 above)\r\n5) no rain evaporation run (rain is prevented from evaporating in the atmosphere)\"\r\n", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2024-10-08T01:57:28", "updateFrequency": "", "dataLineage": "Data were delivered to the Centre for Environmental Data Analysis (CEDA) for archiving.\r\n", "removedDataReason": "", "keywords": "tropical convection, Tropics, MetUM", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2018-06-14T13:30:25", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 1864, "bboxName": "tropical project", "eastBoundLongitude": 165.0, "westBoundLongitude": -180.0, "southBoundLatitude": -10.0, "northBoundLatitude": 10.0 }, "verticalExtent": null, "result_field": { "ob_id": 26161, "dataPath": "/badc/deposited2017/tropical_rainfall/data/realistic", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 11325828430287, "numberOfFiles": 22740, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 6767, "startTime": "2008-11-19T00:00:00", "endTime": "2010-04-18T22:59:59" }, "resultQuality": { "ob_id": 3132, "explanation": "Data is as given by the data provider, no quality control has been performed by the Centre for Environmental Data Analysis (CEDA)", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2018-06-04" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 25032, "uuid": "f59ddf691aff484eadc2f0b98ec49635", "short_code": "comp", "title": "The Organization of Tropical Rainfall: Realistic MetUM model output for convective aggregation studies", "abstract": "MetUM runs were performed on either HECToR or ARCHER, the UK supercomputers for academic use. All runs use the \"\"New Dynamics\"\" dynamical core, MetUM version 7.5, as described in Holloway (2017). The simulations are run with 4-km horizontal grid spacing. They all have a horizontal domain size of 20 degrees latitude X 20 degrees longitude (or 574 X 574 grid points, although the grid points in the outer 8 points on all sides, the \"\"rim\"\", should be discarded before analysis), with 70 vertical levels. All runs are initialised from operational analyses from the European Centre for Medium-Range Weather Forecasting (ECMWF) taken from actual cases. Lateral boundary conditions are comprised of 6-hourly ECMWF analyses, and the model is relaxed to these conditions in and near the outer rim as described in Holloway (2017). Sea surface temperatures (SST) are taken from the initial ECMWF analysis and are held constant in time for the 15 days (but are not constant in space). There are small land regions in four of the case studies which include an interactive land surface model.\r\n\r\nEach simulation was run for 15 days. The model output includes hourly model-level prognostic variables (temperature, specific humidity, pressure, wind components, liquid water, ice water) as well as some model-level increments to temperature and specific humidity. There are also many fields containing surface variables and fluxes (averaged over each hour or every 15 minutes). Note that the \"\"control\"\" simulations have slightly more available data than the other four runs in each of the five case studies.\r\n\r\n\r\nThe five cases each span 10°S–10°N latitude and have the following latitude ranges and date ranges:\r\n\r\n65°E–85°E 25 Jan 2009 to 8 Feb 2009 \r\n70°E–90°E 31 Jan 2009 to 14 Feb 2009 \r\n75°E–95°E 4 Apr 2010 to 18 Apr 2010 \r\n150°E–170°E 19 Nov 2008 to 3 Dec 2008 \r\n165°E–185°E 2 May 2009 to 16 May 2009\"" }, "procedureCompositeProcess": null, "imageDetails": [ 2 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2543, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 2, "licenceURL": "https://artefacts.ceda.ac.uk/licences/missing_licence.pdf", "licenceClassifications": [ { "ob_id": 2, "classification": "unstated" } ] } } ], "projects": [ { "ob_id": 12144, "uuid": "6c93ff856bda41d99342702593ec6053", "short_code": "proj", "title": "The organization of tropical rainfall", "abstract": "This project was funded by the Natural Environment Research Council (NERC) with the grant reference - NE/I021012/1 - and was led by Dr Christopher Holloway (University of Reading). \r\n\r\nTropical cloud systems and rainfall help drive the global circulation of the atmosphere, transferring heat from near the Earth's surface upward for many kilometres. These convective systems can be found in groups of many different sizes, from isolated showers and thunderstorms to equatorial waves to tropical cyclones to the Madden-Julian Oscillation (MJO), an eastward-propagating weather system composed of superclusters of convection several thousand kilometres across which dominates tropical weather variability on weekly to monthly time scales. Global numerical weather forecast and climate models still do not adequately simulate these organized storm clusters and, as a result, have too little (or incorrect) variability of tropical rainfall. Improvement of the representation of organized tropical convection, and therefore the accuracy of weather forecasts, would greatly improve the lives of billions of people who rely on rainfall for agriculture in the tropics and subtropics; better forecasts of strong storms and flooding would also save countless lives and reduce property damage. Furthermore, these processes may change in the future as the climate changes due to human activities, so an improvement of the ability of global models to simulate organized convection will lead to better predictions of possible climate change scenarios over the whole globe. \r\n\r\nGlobal weather and climate models divide the Earth into grid boxes about 100 km across. These boxes are too large to directly simulate the motions responsible for small-scale rainstorms, instead estimating total rainfall based on average conditions in the box. This simplified rain estimation, necessary because of limited computer resources, ignores the interaction of isolated rain showers with each other and regional weather conditions. \r\n\r\nAn exciting new research area is the study of organized convection in high-resolution idealized models. These models, with constant sea surface temperatures and constant sunlight, can now be run on domains several thousand kilometres across and with grid boxes of only a few kilometres long, allowing convection to be represented explicitly. These models are beginning to provide insight into processes that lead to spontaneous growth of convective clusters which can ultimately grow to a single large cluster accounting for all of the rainfall in the domain. These processes act over a wide range of spatial scales which are not fully resolved in global models. \r\n\r\nHowever, the processes which lead to organized convection in idealized models are still not well understood, and it is not known whether they are also important for organizing tropical convection in nature. This project exploited a large archive of high-resolution model runs, forecast analyses, and observations from satellites to make more direct comparisons between idealized cases and observed phenomena. Ultimately, this endeavour had the potential to lead to improvements in the way that global models, especially the UK Met Office Unified Model, simulate tropical rainfall and with it global weather and climate. \r\n\r\nThis project benefited from collaborations with University of Reading, the Met Office and with other scientists approaching similar problems using different models.\r\n\r\nThe improvement of global model simulations of tropical rainfall, along with associated cloud fields and atmospheric heating, is key to reducing uncertainty in predictions of high-impact weather and climate change. Global models still fail to produce realistic large-scale clusters of tropical clouds and rainfall, which are fundamental to predicting variability on weekly and monthly timescales and have major effects on phenomena such as tropical cyclones, El Nino, and the Asian monsoon, as well as the entire global circulation. Recent high-resolution simulations for idealized conditions over tropical oceans have shown spontaneous self-aggregation of convection, with likely causes including feedbacks between convection, moisture and clouds, and radiation, as well as between convection and surface fluxes. \r\n\r\nThis project aimed to clearly identify processes important for self-aggregation of convection in idealized models and then to test whether these processes, or different processes, are active in convective organization in nature. The second part of this goal was an open question in the field, and this fellowship has the potential to connect a rapidly expanding theoretical research area with ongoing efforts to improve the understanding and prediction of tropical variability. The focus on the Unified Model benefited weather and climate prediction in the UK by exchanging ideas with Met Office scientists who were directly involved in testing and improving the model. \r\n\r\nThe specific objectives of this project, with increasing priority downwards, were to address the following three questions: \r\n1. What processes are responsible for convective self-aggregation in idealized models? \r\n2. To what extent are these idealized model processes important for convective organization in nature as represented by observations, forecast analyses, and high-resolution model case studies, particularly in conditions resembling those of idealized models? What processes lead to the simplest cases of convective organization observed? \r\n3. How do more complex features of nature, such as equatorially-trapped waves, land-sea contrast, and extratropical features, interact with more idealized mechanisms for convective organization? \r\n\r\nAddressing these specific objectives allowed for the improvement of numerical weather and climate prediction by contributing new ideas for the development of both high-resolution models with explicit convection and lower resolution models with parameterized convection." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 13296, 50542, 50543, 51580, 51581, 51582, 51583, 51584, 51585, 51586, 51587, 51588, 51589, 51590, 51591, 51592, 51593, 51594, 51595, 51596, 51597, 51598, 51599, 51600, 51602, 51603, 51604, 51605, 51606, 51607, 51609, 51613, 51615, 51616, 51618, 51622, 51624, 51626, 51627, 51628, 57188, 57190, 57192, 57193, 57195, 57198, 57199, 57208, 57209, 57794, 57821, 57847, 57848, 57850, 60769, 60770 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 25021, "uuid": "18c737a986094b1abd19cfebc9d0c374", "short_code": "coll", "title": "The Organization of Tropical Rainfall: MetUM model data and observed convective aggregation data for the Tropics", "abstract": "This project was funded by the Natural Environment Research Council (NERC) with the grant reference - NE/I021012/1 - and was led by Dr Christopher Holloway (University of Reading). \r\n\r\nThis dataset collection contains MetUM model data and observed convective aggregation data for the Tropics.\r\n\r\nThis project aimed to clearly identify processes important for self-aggregation of convection in idealized models and then to test whether these processes, or different processes, are active in convective organization in nature. The second part of this goal was an open question in the field, and this fellowship has the potential to connect a rapidly expanding theoretical research area with ongoing efforts to improve the understanding and prediction of tropical variability. The focus on the Unified Model benefited weather and climate prediction in the UK by exchanging ideas with Met Office scientists who were directly involved in testing and improving the model. \r\n" } ], "responsiblepartyinfo_set": [ 102498, 102499, 102500, 102501, 102502, 102504, 102505, 102506, 102503 ], "onlineresource_set": [ 24876 ] }, { "ob_id": 25036, "uuid": "131174cdad804dd1a51e79d7dbd1d2b0", "title": "ACID-PRUF: Measurements of freezing fraction of water solution droplets solute and suspended matter during the immersion freezing of pollen extracts", "abstract": "ACID-PRUF was a three year NERC directed programme that investigated the complex interaction of aerosols and clouds. The overall aims of ACID-PRUF were to reduce the uncertainty in the radiative forcing associated with the aerosol indirect effects though a targeted laboratory and modelling programme. \r\n\r\nThis dataset collection contains measurements of freezing fraction of water solution droplets-solute and suspended matter during the immersion freezing of pollen extracts (birch pollen, Betula fontinalis occidentalis, Sigma-Aldrich, P6895-1G), with a new cold electrodynamic balance (CEDB).", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2017-09-06T12:03:29.673205", "updateFrequency": "notPlanned", "dataLineage": "These data were delivered to Centre for Environmental Data Analysis (CEDA) for archiving.", "removedDataReason": "", "keywords": "ACID-PRUF, pollen, aerosols, clouds", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2017-09-06T12:45:33", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 1865, "bboxName": "University of Cambridge", "eastBoundLongitude": 0.1149, "westBoundLongitude": 0.1149, "southBoundLatitude": 52.2043, "northBoundLatitude": 52.2043 }, "verticalExtent": null, "result_field": { "ob_id": 25037, "dataPath": "/badc/deposited2017/acid-pruf/data", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 4815, "numberOfFiles": 2, "fileFormat": "Data are BADC-CSV formatted" }, "timePeriod": { "ob_id": 6768, "startTime": "2013-06-30T23:00:00", "endTime": "2014-07-31T22:59:59" }, "resultQuality": null, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 25035, "uuid": "5d73a0be5d6f4287abbb4a64983567e0", "short_code": "acq", "title": "ACID-PRUF: Measurements of freezing fraction of water solution droplets solute and suspended matter during the immersion freezing of pollen extracts", "abstract": "ACID-PRUF: Measurements of freezing fraction of water solution droplets solute and suspended matter during the immersion freezing of pollen extracts" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 18 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 11986, "uuid": "7197d0e7d2d2298491f735e75b5dc21c", "short_code": "proj", "title": "Aerosol-Cloud Interactions - A Directed Programme to Reduce Uncertainty in Forcing through a Targeted Laboratory and Modelling Programme (ACID-PRUF)", "abstract": "ACID-PRUF was a three year NERC directed programme that investigated the complex interaction of aerosols and clouds. The overall aims of ACID-PRUF were to reduce the uncertainty in the radiative forcing associated with the aerosol indirect effects though a targeted laboratory and modelling programme. \r\nA programme of research is proposed here to \r\ni) directly investigate these processes in the laboratory, \r\nii) evaluate the sensitivity of climate relevant parameters to the studied processes, \r\niii) interpret the laboratory studies with detailed model investigations and \r\niv) to incorporate and test new descriptions of the studied processes in cloud-scale and, where possible, global scale models. \r\n\r\nThis research programme was funded by the Natural Environment Research Council. The Lead Grant Reference number for this project is NE/I020121/1." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 56314, 56315 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 25033, "uuid": "53e6cf2cbb52457987466f049c734f43", "short_code": "coll", "title": "ACID-PRUF: Measurements of freezing fraction of water solution droplets solute and suspended matter during the immersion freezing of pollen extracts", "abstract": "ACID-PRUF was a three year NERC directed programme that investigated the complex interaction of aerosols and clouds. The overall aims of ACID-PRUF were to reduce the uncertainty in the radiative forcing associated with the aerosol indirect effects though a targeted laboratory and modelling programme. \r\n\r\nThis dataset collection contains measurements of freezing fraction of water solution droplets-solute and suspended matter during the immersion freezing of pollen extracts (birch pollen, Betula fontinalis occidentalis, Sigma-Aldrich, P6895-1G), with a new cold electrodynamic balance (CEDB). " } ], "responsiblepartyinfo_set": [ 102522, 102526, 102527, 102528, 102529, 102531, 102532, 102533, 102530, 102523, 102524, 102525 ], "onlineresource_set": [ 23723 ] }, { "ob_id": 25039, "uuid": "d17164eb534846dcb00c3b76e8156246", "title": "GloCAEM: Atmospheric electricity measurements at Syowa Station (10m pole), East Ongul Island, Antarctica", "abstract": "Global Coordination of Atmospheric Electricity Measurements (GloCAEM) project brought these experts together to make the first steps towards an effective global network for FW atmospheric electricity monitoring by holding workshops to discuss measurement practises and instrumentation, as well as establish recording and archiving procedures to archive electric field data in a standardised, easily accessible format, then by creating a central data repository. This project was funded in the UK under NERC grant NE/N013689/1.\r\n\r\nThis dataset contains measurements of atmospheric electricity and electric potential gradient made using a Boltek EFM 100 Instrument mounted on a 10m pole and operated by the National Institute of Polar Research and Japan Meteorological Agency at Syowa Station, East Ongul Island, Antarctica.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T03:04:06", "updateFrequency": "unknown", "dataLineage": "Data were collected by the project team and sent to CEDA", "removedDataReason": "", "keywords": "GloCAEM, GEC, electric potential, electric field", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2022-12-05T10:05:45", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3688, "bboxName": "Syowa Station, Antarctica", "eastBoundLongitude": 39.59, "westBoundLongitude": 39.59, "southBoundLatitude": -69.00611, "northBoundLatitude": -69.00611 }, "verticalExtent": null, "result_field": { "ob_id": 39230, "dataPath": "/badc/glocaem/data/syowa-high/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 7237883150, "numberOfFiles": 4957, "fileFormat": "Data are BADC-CSV format" }, "timePeriod": { "ob_id": 10882, "startTime": "2015-02-12T00:00:00", "endTime": null }, "resultQuality": { "ob_id": 3068, "explanation": "Data provided by project group", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2016-10-18" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 39231, "uuid": "5201abb5dd85431cb701e4740da4e127", "short_code": "acq", "title": "GLOCaeM potential gradient Kakioka Magnetic Observatory, Japan", "abstract": "Measurements of atmospheric electric potential gradient made at Kakioka Magnetic Observatory, Japan" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 2 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2546, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 20068, "uuid": "6ee6e0a3f57c4ca79e8cbc0daaafe76f", "short_code": "proj", "title": "Global Coordination of Atmospheric Electricity Measurements (GloCAEM)", "abstract": "It is well established that Earth has a \"Global atmospheric Electric Circuit\" (GEC), through which charge separation in thunderstorms sustains large scale current flow around the planet. The GEC generates an atmospheric electric field which is present globally, and is typically 100V/m near the surface in fair weather conditions. Measurements of electric field have been shown to include information about global thunderstorm activity, local aerosol concentrations and cloud cover, as well as changes in the space weather environment. Recent work has also suggested that atmospheric electrical changes may be effective as earthquake precursors, as well as being sensitive to release of radioactivity, as evidenced by the Fukushima disaster in 2011. \r\n\r\nThe global nature of the GEC means that in order that truly global signals are considered in understanding the processes within the circuit, many validating measurements must be made at different locations around the world. To date, no genuinely global network of FW atmospheric electricity measurements has ever existed, therefore, given the growing number of groups now involved in atmospheric electricity monitoring, such a proposal is timely. \r\n\r\nThis project brought these experts together to make the first steps towards an effective global network for FW atmospheric electricity monitoring by holding workshops to discuss measurement practises and instrumentation, as well as establish recording and archiving procedures to archive electric field data in a standardised, easily accessible format, then by creating a central data repository. This project was funded in the UK under NERC grant NE/N013689/1." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 64183, 64184, 64185, 64186, 64187, 64188, 64189, 64190, 64191, 64193, 64194, 64195, 64197, 64198, 64199, 64200, 64201, 64202, 66964, 66965, 66966, 66967, 66969, 66970, 86021 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 24981, "uuid": "bffd0262439a4ecb8fadf0134c4a4a41", "short_code": "coll", "title": "GloCAEM: Atmospheric electric potential gradient measurements", "abstract": "Global Coordination of Atmospheric Electricity Measurements (GloCAEM) project brought these experts together to make the first steps towards an effective global network for FW atmospheric electricity monitoring by holding workshops to discuss measurement practises and instrumentation, as well as establish recording and archiving procedures to archive electric field data in a standardised, easily accessible format, then by creating a central data repository. This project was funded in the UK under NERC grant NE/N013689/1.\r\n\r\nThis dataset collection contains measurements of atmospheric electricity and electric potential gradient made using a Cambell Scientific CS110 electric-field mill." } ], "responsiblepartyinfo_set": [ 102538, 102539, 102540, 102542, 102543, 102544, 102545, 102541, 190984, 190986, 190985 ], "onlineresource_set": [ 24411 ] }, { "ob_id": 25043, "uuid": "0cea8d7aca57427fae92241348ae9b03", "title": "Managing the Risks, Impacts and Uncertainties of drought and water Scarcity (MaRIUS) project: Large set of potential past and future climate time series for the UK from the weather@home2 model", "abstract": "Large data sets created within the MaRIUS project (Managing the Risks, Impacts and Uncertainties of drought and water Scarcity). \r\nThree time periods are covered: historical baseline (BS: 1900-2006), near-future (NF: 2020-2049) and far-future (FF: 2070-2099), whereby the RCP8.5 is assumed for future time slices. The 30-year baseline to which future time slices should be compared to are years 2075-2004 of the historical baseline, since oceanic variability in future time slices is based on these years.\r\nOne set of 100 time series is available for the historical baseline, while 5 sets of about 85-100 time series are available for each future time slice. The five sets sample climate model uncertainty by sampling a range of sea surface temperature warming patterns as follows: nf and ff use the median warming pattern of CMIP5 climate models. The four others sets are called nf-pYYz (near future) and ff-pYYz (far future) whereby 'YY' stands for the percentile of global mean SST warming (YY=10 or 90) and 'z' stands for the North Atlantic SST gradient (z=n for minimum, z=x for maximum). All natural forcings (e.g., volcanoes) in the future time slices are replications of years 1975-2004, while anthropogenic forcings (e.g., CO2 concentrations) follow the high-emission scenario RCP8.5.\r\nThe time series were created from single-year simulations that have been stitched together while ensuring continuity in the slowly-evolving variability (see Guillod et al., Hydrol. Earth Syst. Sci., 2017 for details). Therefore, for each set (e.g., near_future_p10n), a 'stitching table' is provided alongside with data from individual yearly simulations, which contains the simulation ID ('umid') for each year and each individual time series (i.e., each column within the table list the umid for each year of the time series).\r\nData are gridded NetCDF V3 files, provided on a rotated longitude-latitude grid at a 0.22 degree resolution (European CORDEX grid) over a domain encompassing the United Kingdom. A subset of variables are available at daily, 5-days, and monthly temporal resolutions, based on a calendar with 360 days a year (30 days per month). Included are only data from HadRM3P, the regional climate model within weather@home2. After unpacking yearly *tgz files for a given time slice and scenario (e.g., near_future_p10n) within a directory with the relevant name, the data are structured as follows:\r\ntimeslice / year / umid / temporalResolution / WAH_umid_variable_temporalResolution_g2_year.nc\r\nwhere:\r\n- The temporal resolution ('temporalResolution') available depends on the variable, but it can be 'daily', 'monthly', or '5days'\r\n- 'timeslice' can be one of: 'baseline', 'near_future', 'near_future_p10n', 'near_future_p10x', 'near_future_p90n', 'near_future_p90x', 'far_future', 'far_future_p10n', 'far_future_p10x', 'far_future_p90n', 'far_future_p90x'.\r\n- 'year' is the year of the simulation and data.\r\n- 'umid' is the ID of the simulation, to be read from the stitching table.\r\n- 'variable' is the variable, following CMOR conventions (see also Table 3 in Guillod et al., Hydrol. Earth Syst. Sci., 2017).\r\nFurther details about the data, including validation of the climate time series and analyses of some changes in the future projections, can be found in the PDF documentation as well as in Guillod et al., 2017b. A detailed validation of the model (weather@home2) can be found in Guillod et al., 2017a (see list of references below).\r\n", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2018-03-26T16:52:56.272467", "updateFrequency": "notPlanned", "dataLineage": "The Weather@Home2 climate simulation environment uses the HadAM3P Atmosphere-only General Circulation Model (AGCM) with an embedded Regional Climate Model (RCM) variant, HadRM3P, both from the UK Met Office Hadley Centre. Unlike the original Weather@Home, Weather@Home2 uses a more sophisticated land-surface model, MOSES2. These models are based upon the atmospheric component of HadCM3, a well documented and widely used coupled ocean – atmosphere model. HadRM3P is the regional model used in the Providing Regional Climates for Impacts Studies project (PRECIS)10, also originating from the UK Met Office.", "removedDataReason": "", "keywords": "", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2017-09-08T11:48:14", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 1866, "bboxName": "", "eastBoundLongitude": 6.68, "westBoundLongitude": -15.83, "southBoundLatitude": 47.59, "northBoundLatitude": 60.81 }, "verticalExtent": null, "result_field": { "ob_id": 25042, "dataPath": "/badc/weather_at_home/data/marius_time_series", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1104985878692, "numberOfFiles": 422, "fileFormat": "NetCDF" }, "timePeriod": null, "resultQuality": { "ob_id": 3095, "explanation": "Research data.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2017-09-08" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 25045, "uuid": "4eb66be638e04d759939a7af571f18ad", "short_code": "comp", "title": "Weather@Home2 climate simulation environment uses the HadAM3P Atmosphere-only General Circulation Model (AGCM) with an embedded Regional Climate Model (RCM)", "abstract": "The Weather@Home2 climate simulation environment uses the HadAM3P Atmosphere-only General Circulation Model (AGCM) \nwith an embedded Regional Climate Model (RCM) variant, HadRM3P, both from the UK Met Office Hadley Centre. Unlike the original \nWeather@Home, Weather@Home2 uses a more sophisticated land-surface model, MOSES2. \n" }, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 25044, "uuid": "a888ec077b9b4ef59c4403302614b411", "short_code": "proj", "title": "Managing the Risks, Impacts and Uncertainties of drought and water Scarcity (MaRIUS)", "abstract": "MarIUS, the “Managing the Risks, Impacts and Uncertainties of drought and water Scarcity” project, is an NERC-funded project studying the \ndrought and water scarcity in the United Kingdom. Grant number NE/L010364/1.\n" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 9461 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 102552, 102553, 102554, 102555, 102556, 102557, 102559, 102558, 102560, 102561, 102562, 102563, 102564, 102565 ], "onlineresource_set": [ 23795, 23725, 23726, 87659, 87660 ] }, { "ob_id": 25046, "uuid": "886f3142adac4acb8ed4a64328d2031d", "title": "Met Office LIDARNET Brize Norton Vaisala CL31 ceilometer cloud base and backscatter data", "abstract": "Cloud base and backscatter data from the Met Office's Vaisala CL31 ceilometer located at Brize Norton, Oxfordshire. The Met Office's laser cloud base recorders network (LCBRs), or ceilometers, returns a range of products for use in forecasting and hazard detection. The backscatter profiles can allow detection of aerosol species such as volcanic ash where suitable instrumentation is deployed. This instrument replaced a Vaisala CT25k ceilometer at the site in January 2017.\r\n", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2025-01-17T01:00:09", "updateFrequency": "daily", "dataLineage": "Data were collected by the Met Office before processed through the Met Office's Hermes system. 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These include cloud base height and also backscatter profiles. The backscatter profiles can allow detection of aerosol species such as volcanic ash where suitable instrumentation is deployed. The Met Offices operational LIDARNET network consists of Vaisala CT25k and CL31 ceilometers and the Jenoptik CHM15k Nimbus. In addition, this dataset also contains data from the development ceilometers operated by the Met Office." } ], "responsiblepartyinfo_set": [ 102569, 102570, 102571, 102573, 102574, 102575, 102576, 102572, 102581 ], "onlineresource_set": [ 23727, 23728 ] }, { "ob_id": 25049, "uuid": "df2b4915b1b84d31b7032f8eeecf6b10", "title": "Met Office LIDARNET Northolt Vaisala CL31 ceilometer cloud base and backscatter data", "abstract": "Cloud base and backscatter data from the Met Office's Vaisala CL31 ceilometer located at RAF Northolt, near Heathrow, southern England. The Met Office's laser cloud base recorders network (LCBRs), or ceilometers, returns a range of products for use in forecasting and hazard detection. The backscatter profiles can allow detection of aerosol species such as volcanic ash where suitable instrumentation is deployed. The Vaisala CL31 instrument replaced the Vaisala Ct25k instrument previously deployed at the site in October 2016.\r\n", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2025-01-17T01:00:20", "updateFrequency": "daily", "dataLineage": "Data were collected by the Met Office before processed through the Met Office's Hermes system. 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Data has been archived at the Centre for Environmental Data Anaylsis (CEDA).", "removedDataReason": "", "keywords": "S-RIP, Zonal mean, Pressure levels, Momentum equation, Transformed Eulerian mean, TEM, E-P flux", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2017-11-28T16:32:41", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 25383, "dataPath": "/badc/srip/data/zonal", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 2053423819733, "numberOfFiles": 119473, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 6774, "startTime": "1958-01-01T00:00:00", "endTime": "2016-12-31T23:59:59" }, "resultQuality": { "ob_id": 3121, "explanation": "Data is as given by the data provider, no quality control has been performed by the Centre for Environmental Data Analysis (CEDA)", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2018-03-29" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 25060, "uuid": "23c28b22413d427b923d11142e854fda", "short_code": "comp", "title": "S-RIP reanalysis", "abstract": "Three dimensional atmospheric fields were first downloaded from reanalysis data centers. Then, zonal-mean diagnostics were computed onto two distinct grids. The first is the grid originally provided by each data center. The second is a common 2.5 by 2.5 degrees grid onto which each data set is interpolated using bilinear interpolation. All diagnostics are performed using the same numerical methods for each reanalysis data set." }, "procedureCompositeProcess": null, "imageDetails": [ 208 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 25061, "uuid": "7f104d8ea82c42b6a3fb1cfcdd601abd", "short_code": "proj", "title": "SPARC Reanalysis Intercomparison Project (S-RIP)", "abstract": "SPARC (Stratosphere–troposphere Processes And their Role in Climate), is one of the core projects of the World Climate Research Programme (WCRP). S-RIP was a coordinated activity, which started in 2013, to compare global atmospheric reanalysis data sets using a variety of key diagnostics. The objectives of this project were to identify differences among reanalyses and understand their underlying causes, to provide guidance on appropriate usage of various reanalysis products in scientific studies, particularly those of relevance to SPARC, and to contribute to future improvements in the reanalysis products by establishing collaborative links between reanalysis centres and data users. The studies in S-RIP are to be published in several journal papers and the WCRP/SPARC reports.\r\n" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 21284, 21285, 21286, 21287, 21288, 21289, 21290, 21291, 50512, 51157, 56465, 56467, 57120, 57121, 57122, 57123, 57124, 57125, 57126, 57127, 57128, 57129, 57130, 57131, 57132, 57133, 57134, 57135, 57136, 57137, 57138, 57139, 57140, 57141, 57142 ], "vocabularyKeywords": [], "identifier_set": [ 9573 ], "observationcollection_set": [ { "ob_id": 25062, "uuid": "dafbd838e4cc4c68a5ccdd90690ea57f", "short_code": "coll", "title": "SPARC Reanalysis Intercomparison Project (S-RIP): Zonal-mean global atmospheric reanalyses on pressure levels", "abstract": "SPARC (Stratosphere–troposphere Processes And their Role in Climate) is an international activity started in 2013 (under the World Climate Research Programme, WCRP) to compare and evaluate all available global atmospheric reanalyses in the middle atmosphere; publish several journal papers and the WCRP/SPARC reports.\r\n\r\nThis dataset collection provides zonal-mean diagnostics computed from reanalysis data sets on pressure levels. It is divided into two components. The first provides dynamical variables like temperature, geopotential height, and wind field and derived diagnostics such as eddy fluxes and a complete budget of zonal momentum. The second provides heating rates. In both components, data is provided on two grids. The first provides the diagnostics on the same grid on which reanalysis data was obtained. The second provides, using horizontal interpolation, the diagnostics on a common grid for all data sets. All diagnostics are provided as a function of latitude and pressure from 1958 to present, depending on each reanalysis' availability.\r\n\r\nThis data set was produced to facilitate the comparison of reanalysis data sets for the collaborators of the SPARC-Reanalysis Intercomparison Project (S-RIP). The data set is substantially smaller in size compared to the full three-dimensional reanalysis fields and uses standardized numerical methods. The data set includes all global reanalyses available at the time of its development and will be extended to include new reanalysis products in the future." } ], "responsiblepartyinfo_set": [ 102653, 102654, 102655, 102656, 102657, 102659, 102660, 102661, 102658 ], "onlineresource_set": [ 23774, 24424, 89426, 89427, 89428, 89429, 87887, 87418, 87419, 87420, 87421, 87422, 87423, 87424, 87425, 87426, 87427, 87428 ] }, { "ob_id": 25063, "uuid": "c311c7948e8a47b299f8f9c7ae6cb9af", "title": "CRU TS3.25: Climatic Research Unit (CRU) Time-Series (TS) Version 3.25 of High-Resolution Gridded Data of Month-by-month Variation in Climate (Jan. 1901- Dec. 2016)", "abstract": "This version of CRU TS is superseded by version 4.01. It is being made available to assist with users moving to the new process. No further releases of version 3 are planned.\r\n\r\nThe gridded CRU TS (time-series) 3.25 data are month-by-month variations in climate over the period 1901-2016, on high-resolution (0.5x0.5 degree) grids, produced by the Climatic Research Unit (CRU) at the University of East Anglia.\r\n\r\nCRU TS 3.25 variables are cloud cover, diurnal temperature range, frost day frequency, PET, precipitation, daily mean temperature, monthly average daily maximum and minimum temperature, and vapour pressure for the period Jan. 1901 - Dec. 2016.\r\n\r\nCRU TS 3.25 data were produced using the same methodology as for the 3.21, 3.22, 3.23 and 3.24.01 datasets. This version contains updates the dataset with 2016 data, some new stations have been added for TMP and PRE only. This release is the latest release of the CRU TS data. Known issues predating this release remain.\r\n\r\nThe CRU TS 3.25 data are monthly gridded fields based on monthly observational data, which are calculated from daily or sub-daily data by National Meteorological Services and other external agents. The ASCII and NetCDF data files both contain monthly mean values for the various parameters.\r\n\r\nAll CRU TS output files are actual values - NOT anomalies.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2017-09-22T10:01:58", "updateFrequency": "notPlanned", "dataLineage": "CRU TS 3.00 data files acquired directly from CRU in 2007. CRU provided the BADC with software to generate the CRU datasets in 2010, and this was used to produce CRU TS 3.10 at the BADC in early 2011.\r\n\r\nIn July 2012, systematic errors were discovered in the CRUTS v3.10 process. The effect was, in some cases, to reduce the gridded values for PRE and therefore WET. Values of FRS were found to be unrealistic in some areas due to the algorithms used for synthetic generation. The files (pre, frs and wet) were immediately removed from BADC. The corrected run for precipitation, based on the v3.10 precipitation station data, was generated as a direct replacement and given the version number 3.10.01. There were no corrected runs produced for wet and frs.\r\n\r\nCRU TS 3.20 was produced in December 2012.\r\nIn March 2013, CRU TS observation databases for TMP and PRE variables were provided by CRU. Others are in preparation. In july 2013, two errors were found in the PRE and WET variables of CRU TS v3.20. These have been repaired in CRU TS v3.21. Details of the errors found are available in the Release Notes in the archive.\r\n\r\nCRU TS 3.21 was provided to CEDA for archival in July 2013 by CRU.\r\n\r\nCRU TS 3.22 was provided to CEDA for archival in July 2014 by CRU.\r\n\r\nCRU TS 3.23 was provided to CEDA in October 2015 by CRU. \r\n\r\nCRU TS 3.24 was provided to CEDA for archival in July 2016. This version CRU TSv3.24 has been withdrawn due to quality issues.\r\n\r\nCRU TS 3.24.01 was provided to CEDA for archival in January 2017. \r\n\r\nCRU TS 3.25 was provided to CEDA for archival in September 2017. This is the latest version available and is a replacement of the withdrawn dataset 3.24.01, it supersedes all previous data versions (which are available to allow user comparisons)", "removedDataReason": "", "keywords": "CRU, CRU TS, atmosphere, earth science, climate", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "0.5x0.5 degree grid", "status": "completed", "dataPublishedTime": "2017-09-22T09:27:54", "doiPublishedTime": "2017-12-05T14:04:00", "removedDataTime": null, "geographicExtent": { "ob_id": 513, "bboxName": "CRU High Resolution Grid", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -60.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 25068, "dataPath": "/badc/cru/data/cru_ts/cru_ts_3.25", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 8304901978, "numberOfFiles": 607, "fileFormat": "Data are provided in ASCII and NetCDF formats." }, "timePeriod": { "ob_id": 6775, "startTime": "1901-01-01T00:00:00", "endTime": "2016-12-31T23:59:59" }, "resultQuality": { "ob_id": 3075, "explanation": "The data are quality controlled by the Climatic Research Unit (CRU) at the University of East Anglia. Details are given in the paper Harries et al. 2014 and the release notes, links to both can be found in the documentation.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2017-02-14" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 6676, "uuid": "f300abf12b80415594ab776280307a31", "short_code": "comp", "title": "UEA Climatic Research Unit (CRU) High Resolution gridding software deployed on UEA Climatic Research Unit (CRU) computer system", "abstract": "This computation involved: UEA Climatic Research Unit (CRU) High Resolution gridding software deployed on UEA Climatic Research Unit (CRU) computer system. For details about the production of CRU TS and CRU CY datasets, please refer to Harris et al. (2014) - see link below." }, "procedureCompositeProcess": null, "imageDetails": [ 103 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 6672, "uuid": "b6c783922d1ce68c4293d90caede5bb9", "short_code": "proj", "title": "UEA Climatic Research Unit (CRU) Gridded Datasets production project", "abstract": "The Climatic Research Unit at the University of East Anglia is producing various resolution gridded datasets.\r\nSome of those datasets are stored at CEDA-BADC." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 21919, 21935, 21948, 21962, 21974, 21978, 25388, 25397, 25845, 25879, 25882, 25911, 25957, 25978, 25979 ], "vocabularyKeywords": [], "identifier_set": [ 9360 ], "observationcollection_set": [ { "ob_id": 6669, "uuid": "3f8944800cc48e1cbc29a5ee12d8542d", "short_code": "coll", "title": "Climatic Research Unit (CRU): Time-series (TS) datasets of variations in climate with variations in other phenomena v3", "abstract": "Time-series (TS) datasets are month-by-month variation in climate over the last century or so as produced by the Climatic Research Unit (CRU) at the University of East Anglia. These are calculated on high-resolution (0.5x0.5 degree) grids, which are based on an archive of monthly mean temperatures provided by more than 4000 weather stations distributed around the world. They allow variations in climate to be studied, and include variables such as cloud cover, diurnal temperature range, frost day frequency, precipitation, daily mean temperature, monthly average daily maximum temperature, vapour pressure, Potential Evapo-transpiration and wet day frequency.\r\n\r\nAt present, the BADC holds the latest Time Series data generated by CRU for the period 1901-2017. Those are available as CRU TS 3.26 data. The BADC also holds the preliminary CRU TS3.00 datasets for the period 1901-2006 as well as the subsequent CRU TS 3.10, 3.20, 3.21, 3.22, 3.23, 3.24 and CRU TS 3.25 datasets for the period 1901-2016.\r\n\r\nThe CRU TS data are monthly gridded fields based on daily values -hence the ASCII and netcdf files both contain monthly mean values for the various parameters." } ], "responsiblepartyinfo_set": [ 102677, 102678, 102679, 102680, 102681, 102687, 102688, 102685, 102682, 102683, 102684, 102686, 168508, 168509 ], "onlineresource_set": [ 23751, 23755, 23753, 23754, 87497, 87498, 87499, 87500, 87501, 88096, 89341, 89342, 89343 ] }, { "ob_id": 25064, "uuid": "237c618d8b2e49cf8e61d01780f11650", "title": "CRU CY3.25: Climatic Research Unit (CRU) Year-by-Year Variation of Selected Climate Variables by CountrY (CY) version 3.25 (Jan. 1901 - Dec. 2016)", "abstract": "This version of CRU CY is superseded by version 4.01. It is being made available to assist with users moving to the new process. No further releases of version 3 are planned. \r\n\r\nAn updated set of country definitions have been introduced with this version. Please see the appropriate Release Notes. The data are available as text files with the extension '.per' and can be opened by most text editors.\r\n\r\nThe CRU CY version 3.25 dataset consists of ten climate variables for country averages at a monthly, seasonal and annual frequency; including cloud cover, diurnal temperature range, frost day frequency, precipitation, daily mean temperature, monthly average daily maximum and minimum temperature, vapour pressure and potential evapotranspiration. \r\n\r\nThis dataset was produced in 2017 by the Climatic Research Unit (CRU) at the University of East Anglia and extends CRU CY 3.24.01.\r\n\r\nSpatial averages are calculated using area-weighted means. CRU CY3.25 is derived directly from the CRU TS3.25 dataset. CRU CY version 3.25 spans the period 1901-2016 for 289 countries.\r\n\r\nTo understand the CRU-CY3.25 dataset, it is important to understand the construction and limitations of the underlying dataset, CRU TS3.25. It is therefore recommended that all users read the Harris et al, 2014 paper listed in the online documentation on this record.\r\n\r\nCRU CY data are available for download to all CEDA users.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2017-12-07T14:52:41", "updateFrequency": "notPlanned", "dataLineage": "CRU CY is derived directly from the CRU TS dataset, and version numbering is matched between the two datasets. Thus, the first official version of CRU CY was v3.21 as it is based on CRU TS v3.21.\r\n\r\nCRU CY 3.24 data files supplied to CEDA for long-term archival by CRU in October 2016.\r\n\r\nThe CRU CY 3.24 data were withdrawn in January 2017 due to known issues with the data.\r\n\r\nCRU CY 3.24.01 data files supplied to CEDA for long-term archival by CRU in January 2017.\r\n\r\nCRU CY 3.25 data files supplied to CEDA for long-term archival by CRU in September 2017.", "removedDataReason": "", "keywords": "CRU, CRU CY, climate", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "0.5x0.5 degree grid", "status": "completed", "dataPublishedTime": "2017-09-22T09:25:17", "doiPublishedTime": "2017-12-04T08:59:40", "removedDataTime": null, "geographicExtent": { "ob_id": 513, "bboxName": "CRU High Resolution Grid", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -60.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 25069, "dataPath": "/badc/cru/data/cru_cy/cru_cy_3.25", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 49331502, "numberOfFiles": 2923, "fileFormat": "The CRU CY data are provided as text files with the extension \".per\", most text editors will open these files. See the linked file formats guide for more information." }, "timePeriod": { "ob_id": 6777, "startTime": "1901-01-01T12:00:00", "endTime": "2016-12-31T23:59:59" }, "resultQuality": { "ob_id": 3076, "explanation": "CRU CY is derived directly from the CRU TS dataset, and version numbering is matched between the two datasets. Thus, the first official version of CRU CY is v3.21, as it is based on CRU TS v3.21. An advisory note was issued by CRU in July 2013 regarding an issue in the spatial area of Northeast Africa, for the PRE and WET variables only. Details are given in the documentation as part of the archive.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2017-02-14" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 6676, "uuid": "f300abf12b80415594ab776280307a31", "short_code": "comp", "title": "UEA Climatic Research Unit (CRU) High Resolution gridding software deployed on UEA Climatic Research Unit (CRU) computer system", "abstract": "This computation involved: UEA Climatic Research Unit (CRU) High Resolution gridding software deployed on UEA Climatic Research Unit (CRU) computer system. For details about the production of CRU TS and CRU CY datasets, please refer to Harris et al. (2014) - see link below." }, "procedureCompositeProcess": null, "imageDetails": [ 103 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 6672, "uuid": "b6c783922d1ce68c4293d90caede5bb9", "short_code": "proj", "title": "UEA Climatic Research Unit (CRU) Gridded Datasets production project", "abstract": "The Climatic Research Unit at the University of East Anglia is producing various resolution gridded datasets.\r\nSome of those datasets are stored at CEDA-BADC." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 9358 ], "observationcollection_set": [ { "ob_id": 6889, "uuid": "116aed45b5f0d15ddc3b0e753837e8c9", "short_code": "coll", "title": "Climatic Research Unit (CRU): Year-by-Year Variation of Selected Climate Variables by CountrY (CY) v3", "abstract": "The CRU CY datasets consists of country averages at a monthly, seasonal and annual frequency, for ten climate variables in 289 countries. Spatial averages are calculated using area-weighted means. Variables include cloud cover (cld), diurnal temperature range (dtr), frost day frequency (frs), precipitation (pre), daily mean temperature (tmp), monthly average daily maximum (tmx) and minimum (tmn) temperature, vapour pressure (vap), Potential Evapo-transpiration (pet) and wet day frequency (wet). The CRU CY datasets produced by the Climatic Research Unit (CRU) at the University of East Anglia.\r\n\r\nSpatial averages are calculated using area-weighted means. CRU CY is derived directly from the CRU TS dataset and version numbering is matched between the two datasets. Thus, the first official version of CRU CY is v3.21, as it is based on CRU TS v3.21 (1901-2012) and the latest version of CRU-CY is v3.26 based on CRU TS v3.26 (1901-2017) for 289 countries. The data are available as text files with the extension '.per' and can be opened by most text editors.\r\n\r\nTo understand the CRU-CY dataset, it is important to understand the construction and limitations of the underlying dataset, CRU TS. It is therefore recommended that all users read the paper referenced below (Harris et al, 2014)." } ], "responsiblepartyinfo_set": [ 102689, 102690, 102692, 102694, 102695, 102696, 102697, 102691, 102698, 102699, 102693, 102700, 168510, 168511 ], "onlineresource_set": [ 23759, 23757, 23758, 23760, 24013, 24011 ] }, { "ob_id": 25065, "uuid": "d4e823f0172947c5ae6e6b265656c273", "title": "CRU CY4.01: Climatic Research Unit (CRU) year-by-year variation of selected climate variables by country (CY) version 4.01 (Jan. 1901 - Dec. 2016)", "abstract": "The Climatic Research Unit (CRU) Country (CY) data version 4.01 dataset consists of ten climate variables for country averages at a monthly, seasonal and annual frequency; including cloud cover, diurnal temperature range, frost day frequency, precipitation, daily mean temperature, monthly average daily maximum and minimum temperature, vapour pressure and potential evapotranspiration. This version uses the updated set of country definitions, please see the appropriate Release Notes.\r\n\r\nThis dataset was produced in 2017 by CRU at the University of East Anglia and extends the CRU CY4.00 data to include 2016. CRU CY4.01 is a full release, differing only in methodology from the existing current version 3 release, v3.25. Both are released concurrently to support comparative evaluations between these two versions, however, this will be the last release of version 3. The data are available as text files with the extension '.per' and can be opened by most text editors.\r\n\r\nSpatial averages are calculated using area-weighted means. CRU CY4.01 is derived directly from the CRU TS4.01 dataset. CRU CY version 4.01 spans the period 1901-2016 for 289 countries.\r\n\r\nTo understand the CRU CY4.01 dataset, it is important to understand the construction and limitations of the underlying dataset, CRU TS4.01. It is therefore recommended that all users read the Harris et al, 2020 paper and the CRU TS4.01 release notes listed in the online documentation on this record.\r\n\r\nCRU CY data are available for download to all CEDA users.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2017-09-22T10:00:02", "updateFrequency": "notPlanned", "dataLineage": "The Climatic Research Unit (CRU) CY data are derived directly from the CRU TS data, and version numbering is matched between the two datasets. The CRU CY data are produced by the CRU unit at the University of East Anglia and passed to the Centre for Environmental Data Analysis (CEDA) for long term archival and distribution. Previous releases of CRU CY include:\r\n\r\nCRU CY 4.01 data were passed to CEDA for archival and distribution by CRU in September 2017\r\n\r\nCRU CY 4.00 data were passed to CEDA for archival and distribution by CRU in March 2017\r\n\r\nCRU CY 3.24.01 data files supplied to CEDA for long term archival by CRU in January 2017.\r\n\r\nThe CRU CY 3.24 data were withdrawn by CRU and CEDA in January 2017 due to known issues with the data.\r\n\r\nCRU CY 3.24 data files supplied to CEDA for long term archival by CRU in October 2016.", "removedDataReason": "", "keywords": "CRU, CRU CY, climate", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "0.5x0.5 degree grid", "status": "completed", "dataPublishedTime": "2017-09-22T09:26:57", "doiPublishedTime": "2017-12-04T08:59:56", "removedDataTime": null, "geographicExtent": { "ob_id": 513, "bboxName": "CRU High Resolution Grid", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -60.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 25070, "dataPath": "/badc/cru/data/cru_cy/cru_cy_4.01", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 49331502, "numberOfFiles": 2923, "fileFormat": "The CRU CY data are provided as text files with the extension \".per\", most text editors will open these files. See the linked file formats guide for more information." }, "timePeriod": { "ob_id": 6778, "startTime": "1901-01-01T00:00:00", "endTime": "2016-12-31T23:59:59" }, "resultQuality": { "ob_id": 3080, "explanation": "CRU CY data are quality controlled by the Climatic Research Unit (CRU) at the University of East Anglia. Details are given in the paper Harris et al. 2014 and the release notes, links to both can be found in the documentation.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2017-04-07" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 20388, "uuid": "81842aa686174647ae132a4c841d73b6", "short_code": "comp", "title": "UEA Climatic Research Unit (CRU) high resolution gridding software deployed on UEA CRU computer system for v4.00", "abstract": "This computation involved: UEA Climate Research Unit (CRU) High Resolution gridding software deployed on UEA Climate Research Unit (CRU) computer system. For details about the production of CRU TS and CRU CY datasets, please refer to Harris et al. 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Spatial averages are calculated using area-weighted means. Variables include cloud cover (cld), diurnal temperature range (dtr), frost day frequency (frs), precipitation (pre), daily mean temperature (tmp), monthly average daily maximum (tmx) and minimum (tmn) temperature, vapour pressure (vap), Potential Evapo-transpiration (pet) and wet day frequency (wet). The CRU CY datasets produced by the Climatic Research Unit (CRU) at the University of East Anglia.\r\n\r\nSpatial averages are calculated using area-weighted means. CRU CY is derived directly from the CRU TS dataset and version numbering is matched between the two datasets. Thus, the first official version of CRU CY is v3.21, as it is based on CRU TS v3.21 (1901-2012) and the latest version of CRU-CY is v4.03, as it is based on CRU TS v4.03. The data are available as text files with the extension '.per' and can be opened by most text editors.\r\n\r\nTo understand the CRU-CY dataset, it is important to understand the construction and limitations of the underlying dataset, CRU TS. It is therefore recommended that all users read the paper referenced below (Harris et al, 2014)." } ], "responsiblepartyinfo_set": [ 102701, 102702, 102703, 102704, 102705, 102707, 102709, 102708, 102710, 102706, 168476, 102711, 168477 ], "onlineresource_set": [ 23764, 23762, 23785, 23761, 23763, 23765, 37075, 87731, 87732, 94684 ] }, { "ob_id": 25066, "uuid": "58a8802721c94c66ae45c3baa4d814d0", "title": "CRU TS4.01: Climatic Research Unit (CRU) Time-Series (TS) version 4.01 of high-resolution gridded data of month-by-month variation in climate (Jan. 1901- Dec. 2016)", "abstract": "The gridded Climatic Research Unit (CRU) Time-series (TS) data version 4.01 data are month-by-month variations in climate over the period 1901-2016, provided on high-resolution (0.5x0.5 degree) grids, produced by CRU at the University of East Anglia.\r\n\r\nThe CRU TS4.01 variables are cloud cover, diurnal temperature range, frost day frequency, potential evapotranspiration (PET), precipitation, daily mean temperature, monthly average daily maximum and minimum temperature, and vapour pressure for the period January 1901 - December 2016.\r\n\r\nThe CRU TS4.01 data were produced using angular-distance weighting (ADW) interpolation. All version 3 releases used triangulation routines in IDL. Please see the release notes for full details of this version update. CRU TS4.01 is a full release, differing only in methodology from the parallel release, v3.25. Both are released concurrently to support comparative evaluations between these two versions, however, this will be the last release of version 3.\r\n\r\nThe CRU TS4.01 data are monthly gridded fields based on monthly observational data calculated from daily or sub-daily data by National Meteorological Services and other external agents. The ASCII and NetCDF data files both contain monthly mean values for the various parameters. The NetCDF versions contain an additional integer variable, ’stn’, which provides, for each datum in the main variable, a count (between 0 and 8) of the number of stations used in that interpolation. The missing value code for 'stn' is -999.\r\n\r\nAll CRU TS output files are actual values - NOT anomalies.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2017-09-22T11:24:29", "updateFrequency": "notPlanned", "dataLineage": "The CRU TS data are produced by the Climatic Research Unit (CRU) at the University of East Anglia and are passed to the Centre for Environmental Data Analysis (CEDA) for long-term archival and distribution. Previous releases of the CRU TS data include:\r\n\r\nCRU TS 4.01 was provided to CEDA for archival in September 2017. \r\n\r\nCRU TS 4.00 was provided to CEDA for archival in March 2017. \r\n\r\nCRU TS 3.24.01 was provided to CEDA for archival in January 2017. This is the latest version available and is a replacement of the withdrawn dataset 3.24, it supersedes all previous data versions (which are available to allow user comparisons)\r\n\r\nCRU TS 3.24 was provided to CEDA for archival in July 2016. This is the latest version available, superseding all previous data versions (which are available to allow user comparisons), v3.24 has been withdrawn.\r\n\r\nCRU TS 3.23 was provided to CEDA in October 2015 by CRU. This is the latest version available, superseding all previous data versions (which are available to allow user comparisons).\r\n\r\nCRU TS 3.22 was provided to CEDA for archival in July 2014 by CRU.\r\n\r\nCRU TS 3.21 was provided to CEDA for archival in July 2013 by CRU.\r\n\r\nCRU TS 3.20 was produced in December 2012.\r\nIn March 2013, CRU TS observation databases for TMP and PRE variables were provided by CRU. Others are in preparation. In july 2013, two errors were found in the PRE and WET variables of CRU TS v3.20. These have been repaired in CRU TS v3.21. Details of the errors found are available in the Release Notes in the archive.\r\n\r\nCRU TS 3.10.01 In July 2012, systematic errors were discovered in the CRUTS v3.10 process. The effect was, in some cases, to reduce the gridded values for PRE and therefore WET. Values of FRS were found to be unrealistic in some areas due to the algorithms used for synthetic generation. The files (pre, frs and wet) were immediately removed from BADC. The corrected run for precipitation, based on the v3.10 precipitation station data, was generated as a direct replacement and given the version number 3.10.01. There were no corrected runs produced for wet and frs.\r\n\r\nCRU TS 3.00 data files acquired directly from CRU in 2007. 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These are calculated on high-resolution (0.5x0.5 degree) grids, which are based on an archive of monthly mean temperatures provided by more than 4000 weather stations distributed around the world. They allow variations in climate to be studied, and include variables such as cloud cover, diurnal temperature range, frost day frequency, precipitation, daily mean temperature, monthly average daily maximum temperature, vapour pressure, potential evapo-transpiration and wet day frequency.\r\n\r\nThe CRU TS data are monthly gridded fields based on daily values -hence the ASCII and netcdf files both contain monthly mean values for the various parameters." } ], "responsiblepartyinfo_set": [ 102712, 102713, 102714, 102715, 102716, 102717, 102719, 102718, 102720, 102721, 168478, 102722, 168479 ], "onlineresource_set": [ 23767, 23766, 23773, 23770, 23771, 23769, 37079, 89169, 89170, 89171, 89172, 89173, 89174, 89175, 89176, 89177, 89178, 87279, 87280, 87281, 87282, 87283, 87284, 87285, 87286, 87287, 87288, 87289, 87290, 87291, 87292, 87293, 87294, 87295, 87296, 87297, 87298, 87299, 87300, 87301, 87302, 87303, 87304, 87305, 87306, 87307, 87308, 87309, 87310, 87311, 87312, 87313, 87314, 87315, 87316, 87317, 87318, 87319, 87320, 87321, 87322, 87323, 87324, 87325, 87326, 89179, 89180, 89181, 89182, 89183, 89184, 89185, 89186, 89187, 89188, 89189, 89190, 89191, 89192, 89193, 89194, 89195, 89196, 89197, 89198, 89199, 89200, 89201, 89202, 89203, 89204, 89205, 89206, 89207, 89208, 89209, 89210, 89211, 89212, 89213, 89214, 89215, 89216, 89217, 89218, 89219, 89220, 89221, 89222, 92689 ] }, { "ob_id": 25071, "uuid": "9b8f99f449c649778cb3febb14c0408c", "title": "UGAMP: 3-D ozone column monthly average 1986", "abstract": "The UK Universities Global Atmospheric Modelling Programme (UGAMP) ozone climatology project. This dataset contains a 3-dimensional climatology of ozone monthly means, combining various satellite observations and ozone sonde data. The data are global and covers 1986.\r\n\r\nEach file contains a ligne of text followed by the variable itself, in free format.\r\n\r\nEvery single three-dimensional field var is stored as\r\n\r\n(((var(i, j, k), i=1, 144), j=1, 73), k=1, 47)\r\nwhere\r\ni is the longitude index (from 0°E to 357.5°E by 2.5°);\r\nj is the latitude index (from South Pole to North Pole by 2.5°);\r\nk is the level index (from top to bottom).\r\n\r\nEvery two-dimensional field (zonal means) is stored as\r\n\r\n((var(j, k), j=1, 73), k=1, 47)\r\nwith the same conventions as above.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "1999-05-12T11:19:59", "updateFrequency": "notPlanned", "dataLineage": "Data provided as is by the University of Reading from satellite observations (SBUV, SAGE II, SME, TOMS) as well as ozone sonde data provided by the Atmospheric Environment Service of Canada. 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Acquisition: Acquisition Process for: UGAMP Ozone Climatology; \r\n2. Computation: DETAILS NEEDED - COMPUTATION CREATED FOR SATELLITE COMPOSITE. deployed on NIMBUS 7 Satellite; \r\n3. 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UGAMP (UK Universities' Global Atmospheric Modelling Programme) funded by NERC, helped to strengthen the role of the UK universities in the vital area of numerical modelling of the large scale atmosphere.\r\n\r\nUGAMP has allowed UK university groups to lead developments in many areas of atmospheric science such as storm-tracks, monsoons and ozone loss, and to play an important part in international scientific programmes. By establishing close working relationships with two of the World's leading weather centres, ECMWF and the UK Meteorological Office, UGAMP has been able to take full advantage of their modelling developments and has enjoyed the additional benefit of access to the best available atmospheric data sets. UGAMP continues to deliver excellence in research, excellence in technical support and excellence in training for young scientists.\r\n\r\nUGAMP has close links to many UK and International organisations, including:\r\n\r\nUnited Kingdom Meteorological Office\r\nEuropean Centre for Medium-Range Weather Forecasts\r\nBritish Antarctic Survey\r\nBritish Atmospheric Data Centre\r\nCentre for Ecology and Hydrology\r\nEnvironmental Science Systems Centre (formerly NUTIS)\r\nUpper Troposphere Lower Stratosphere Ozone Scientific Research Programme (UTLS)\r\nClimatic Research Unit\r\nLeeds University , School of Chemistry\r\nLeicester University , Earth Observation Science group\r\nSouthampton Oceanography Centre\r\nLawrence Livermore National Laboratory\r\nMétéo France" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 21771, 22896, 25394 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 1272, "uuid": "bff84b935ce5aa9f04624777b0eea507", "short_code": "coll", "title": "UK Universities Global Atmospheric Modelling Programme (UGAMP) Global Ozone Climatology Project Dataset", "abstract": "The UGAMP ozone climatology consists in a 4-dimensional distribution of ozone that has been built up from the combination of several observational data sets. These data sets include satellite observations (SBUV, SAGE II, SME, TOMS) as well as ozone sonde data provided by the Atmospheric Environment Service of Canada. This global climatology, covering five years (1985 to 1989), was originally established to replace the simpler ozone climatologies used as input in the UGAMP models (ECMWF parameterization or 2-D zonal means deduced from satellite data). It provides monthly means of the ozone column above the grid levels as well as 5-year averages and zonal averages of these monthly means, on a 2.5 x 2.5 deg horizontal grid and over 47 levels, from the ground up to 0.001 mb. 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The data are global and covers 1987.\r\n\r\nEach file contains a ligne of text followed by the variable itself, in free format.\r\n\r\nEvery single three-dimensional field var is stored as\r\n\r\n(((var(i, j, k), i=1, 144), j=1, 73), k=1, 47)\r\nwhere\r\ni is the longitude index (from 0°E to 357.5°E by 2.5°);\r\nj is the latitude index (from South Pole to North Pole by 2.5°);\r\nk is the level index (from top to bottom).\r\n\r\nEvery two-dimensional field (zonal means) is stored as\r\n\r\n((var(j, k), j=1, 73), k=1, 47)\r\nwith the same conventions as above.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "1999-05-12T11:21:54", "updateFrequency": "notPlanned", "dataLineage": "Data provided as is by the University of Reading from satellite observations (SBUV, SAGE II, SME, TOMS) as well as ozone sonde data provided by the Atmospheric Environment Service of Canada. 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UGAMP (UK Universities' Global Atmospheric Modelling Programme) funded by NERC, helped to strengthen the role of the UK universities in the vital area of numerical modelling of the large scale atmosphere.\r\n\r\nUGAMP has allowed UK university groups to lead developments in many areas of atmospheric science such as storm-tracks, monsoons and ozone loss, and to play an important part in international scientific programmes. By establishing close working relationships with two of the World's leading weather centres, ECMWF and the UK Meteorological Office, UGAMP has been able to take full advantage of their modelling developments and has enjoyed the additional benefit of access to the best available atmospheric data sets. UGAMP continues to deliver excellence in research, excellence in technical support and excellence in training for young scientists.\r\n\r\nUGAMP has close links to many UK and International organisations, including:\r\n\r\nUnited Kingdom Meteorological Office\r\nEuropean Centre for Medium-Range Weather Forecasts\r\nBritish Antarctic Survey\r\nBritish Atmospheric Data Centre\r\nCentre for Ecology and Hydrology\r\nEnvironmental Science Systems Centre (formerly NUTIS)\r\nUpper Troposphere Lower Stratosphere Ozone Scientific Research Programme (UTLS)\r\nClimatic Research Unit\r\nLeeds University , School of Chemistry\r\nLeicester University , Earth Observation Science group\r\nSouthampton Oceanography Centre\r\nLawrence Livermore National Laboratory\r\nMétéo France" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 21771, 22896, 25394 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 1272, "uuid": "bff84b935ce5aa9f04624777b0eea507", "short_code": "coll", "title": "UK Universities Global Atmospheric Modelling Programme (UGAMP) Global Ozone Climatology Project Dataset", "abstract": "The UGAMP ozone climatology consists in a 4-dimensional distribution of ozone that has been built up from the combination of several observational data sets. 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UGAMP continues to deliver excellence in research, excellence in technical support and excellence in training for young scientists.\r\n\r\nUGAMP has close links to many UK and International organisations, including:\r\n\r\nUnited Kingdom Meteorological Office\r\nEuropean Centre for Medium-Range Weather Forecasts\r\nBritish Antarctic Survey\r\nBritish Atmospheric Data Centre\r\nCentre for Ecology and Hydrology\r\nEnvironmental Science Systems Centre (formerly NUTIS)\r\nUpper Troposphere Lower Stratosphere Ozone Scientific Research Programme (UTLS)\r\nClimatic Research Unit\r\nLeeds University , School of Chemistry\r\nLeicester University , Earth Observation Science group\r\nSouthampton Oceanography Centre\r\nLawrence Livermore National Laboratory\r\nMétéo France" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 21771, 22896, 25394 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 1272, "uuid": "bff84b935ce5aa9f04624777b0eea507", "short_code": "coll", "title": "UK Universities Global Atmospheric Modelling Programme (UGAMP) Global Ozone Climatology Project Dataset", "abstract": "The UGAMP ozone climatology consists in a 4-dimensional distribution of ozone that has been built up from the combination of several observational data sets. These data sets include satellite observations (SBUV, SAGE II, SME, TOMS) as well as ozone sonde data provided by the Atmospheric Environment Service of Canada. This global climatology, covering five years (1985 to 1989), was originally established to replace the simpler ozone climatologies used as input in the UGAMP models (ECMWF parameterization or 2-D zonal means deduced from satellite data). It provides monthly means of the ozone column above the grid levels as well as 5-year averages and zonal averages of these monthly means, on a 2.5 x 2.5 deg horizontal grid and over 47 levels, from the ground up to 0.001 mb. Software to convert ozone columns into mixing ratio and to interpolate the data on any required grid is also available." } ], "responsiblepartyinfo_set": [ 102766, 102767, 102768, 102769, 102771, 102772, 102773, 102770 ], "onlineresource_set": [ 23782 ] }, { "ob_id": 25086, "uuid": "8e6cbb111cfd41a19c92aadcb2d040fd", "title": "Auchencorth Moss Atmospheric Observatory (AU): Annual half-hourly meteorology since 1995, Near Edinburgh, UK - Version 1", "abstract": "The site was setup in 1995 to measure meteorology, trace gases, aerosols and their fluxes. It is (55ᵒ47’36” N, 3°14’41” W) an ombrotrophic peatland with an extensive fetch at an elevation of 270 m, lying 18 km SSW of Edinburgh, and can be categorised as a transitional lowland raised bog. The site is grazed with < 1 sheep ha^-1.\r\n\r\nDuring 2000s the site activity has increased and was established in 2006 as EMEP (European Monitoring and Evaluation Program, Level 2/3) supersite for the UK. Long term monitoring is led by NERC CEH with contributions from other organisations/research institutes including Ricardo AEA, BureauVeritas, NPL, the University of Birmingham and University of Edinburgh. In April 2014 the site was awarded WMO GAW regional station (World Meteorological Orgamisation Global Atmospheric Watch). In 2017 the site joined the ICOS network (Integrated Carbon Observation System).\r\n\r\nThe meteorological measurements were initially made to assist with interpretation of the fluxes and as such weren't installed with the intention of providing WMO standard measurements but since 2014 we have been moving towards these standards as well as enhancing instrumentation.\r\n\r\nThe dataset includes the following parameters at half hourly intervals, although not every variable is available from 1995 to 2016:\r\n-T_upper_Avg - initially used to estimate senisble heat fluxes, fine wire type-E thermocouple.\r\n-T_lower_Avg - initially used to estimate senisble heat fluxes, fine wire type-E thermocouple.\r\n-T_RHT_Avg - Temperature measured by a Vailsala relative humidity and temperature probe.\r\n-RH_RHT_Avg - Relative humidity measured by a Vailsala relative humidity and temperature probe.\r\n-P_Avg - atmospheric pressure at the sites elevation.\r\n-Tot_Solar_Avg - Total solar radiation measured by a Skye SKS1110.\r\n-PAR_Avg - Photosynthetically Averaged Radiation measured by a Skye SKP215.\r\n-NetRad_Avg - Net radiation, Kipp & Zonen NrLite.\r\n-Rainfall - tipping bucket rainfall.\r\n-SoilTavg - Average soil temperature from four type-E thermocouple probes.\r\n-Soil Heat Flux - calculated from two measurements of soil heat flux with Hukseflux HFP01 plates, corrected to surface flux using the standard formula.\r\n-Cs = Bd(Cd+fSWC.Cw)\r\n-SC = DTs.Cs.d/Dtime\r\n-SHF = Plate Average + SC\r\n-DTs = change in average soil temperature from start to end of measurement period (first and last two minutes); d = plate depth 0.2 m; Bd = soil bulk density, 100 kg m-3; cd = Specific Heat Dry Soil, 840J kg-1 K-1; fSWC = fractional soil water content, measured or 0.9; cw = Specific Capacilty Heat of Water, 4190 J kg-1 K-1; Dtime = measurement period, 1800 s\r\n-Soil Moisture - soil water content measured with TDR probes, campbell CS616\r\n-WindSpd (measured) - measured by a Gill R3 then Windmaster sonic anemometer at 3.6 m\r\n-WindSpd 10 m - for most of the time period this is estimated from the turbulence measurements and 3.6 m windspeed but from 22/06/2016 a Gill Windsonic 2D anemeometer measures at 10 m\r\n-Wind Dir - measured by the sonic anemometer at 3.6 m\r\n-snow_depth_Avg - Campbell Scientific SR50A-L Sonic Ranging Sensor\r\n-Present Weather - Vaisala FD12P Present Weather Sensor\r\n-1 hr Past Weather - Vaisala FD12P Present Weather Sensor\r\n-Visibility - Vaisala FD12P Present Weather Sensor\r\n-Evaporation - to be estimated from the water-vapout flux measurements\r\n\r\nFor modelling purposes gapfilled (variables with _gf suffixes) times series will be included, they are created by linearly initially interpolating across upto an hours missing data, filling with colocated measurements (adjusted by linear interpolation with the core data), filling with measurements from nearby sites (adjusted by linear interpolation with the core data).\r\nTa_gf\r\nP_gf\r\nRH_gf\r\nTotal_Solar_gf\r\nRainfall_gf\r\nWindspd 10m_gf\r\nWind Dir_gf", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2025-02-13T09:22:19", "updateFrequency": "annually", "dataLineage": "The site was established in 1995 and all sensors where logged using Campbell Scientific data loggers, instruments were checked regularly but not calibrated or assessed against others until 2005. Since the site has become affiliated with WMO and ICOS procedures and instruments are being modernised. This data set has been reviewed against data from colocated equivalent instruments onsite or nearby (withing ~7 km). The data was first received into the CEDA archive in 2017 with an update added each year.", "removedDataReason": "", "keywords": "Auchencorth Moss, Meteorology, Temperature, Rainfall, Wind Speed, ICOS", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2017-09-28T14:46:02", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 1867, "bboxName": "Auchencorth Moss", "eastBoundLongitude": -3.245, "westBoundLongitude": -3.245, "southBoundLatitude": 55.793, "northBoundLatitude": 55.793 }, "verticalExtent": null, "result_field": { "ob_id": 25083, "dataPath": "/badc/auchencorth/data/auchencorth_moss_met/v1", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 85039757, "numberOfFiles": 31, "fileFormat": "Data are BADC CSV formatted" }, "timePeriod": { "ob_id": 6787, "startTime": "1995-02-18T00:00:00", "endTime": null }, "resultQuality": null, "validTimePeriod": { "ob_id": 6786, "startTime": "1995-02-18T00:00:00", "endTime": null }, "procedureAcquisition": { "ob_id": 25085, "uuid": "320e3f48d917488ebbda633a0885a1b8", "short_code": "acq", "title": "Auchencorth Moss Atmospheric Observatory (AU)", "abstract": "The site was setup in 1995 to measure meteorology, trace gases, aerosols and their fluxes. It is (55ᵒ47’36” N, 3°14’41” W) an ombrotrophic peatland with an extensive fetch at an elevation of 270 m, lying 18 km SSW of Edinburgh, and can be categorised as a transitional lowland raised bog. The site is grazed with < 1 sheep ha^-1.\r\n During 2000s the site activity has increased and was established in 2006 as EMEP (European Monitoring and Evaluation Program, Level 2/3) supersite for the UK. Long term monitoring is led by NERC CEH with contributions from other organisations/research institutes including Ricardo AEA, BureauVeritas, NPL, the University of Birmingham and University of Edinburgh. In April 2014 the site was awarded WMO GAW regional station (World Meteorological Orgamisation Global Atmospheric Watch). In 2017 the site joined the ICOS network (Integrated Carbon Observation System)." }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 132 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 25088, "uuid": "3b93f94481a841f68bc1ce1e975e3ead", "short_code": "proj", "title": "Auchencorth Moss Atmospheric Observatory (AU) and associated Field sites", "abstract": "During 2000s the site activity has increased and was established in 2006 as EMEP (European Monitoring and Evaluation Program, Level 2/3) supersite for the UK. Long term monitoring is led by NERC CEH with contributions from other organisations/research institutes including Ricardo AEA, BureauVeritas, NPL, the University of Birmingham and University of Edinburgh. In April 2014 the site was awarded WMO GAW regional station (World Meteorological Orgamisation Global Atmospheric Watch). In 2017 the site joined the ICOS network (Integrated Carbon Observation System).\r\n\r\nThe meteorological measurements were initially made to assist with interpretation of the fluxes and as such weren't installed with the intention of providing WMO standard measurements but since 2014 we have been moving towards these standards as well as enhancing instrumentation. Similar measurements are also made at nearby Easter Bush Field site by the same team." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 88580, 88581, 88582, 88583, 88584, 88585, 88586, 88587, 88588, 88589, 88590, 88591, 88592, 88593, 88594, 88595, 88596, 88597, 88598, 88599, 88600, 88601, 88602, 88603, 88604, 88605, 88606, 88607, 88608, 88609, 88610, 88611, 88612, 88613, 88614, 88615, 88616, 88617, 88618, 88619, 88620, 88621, 88622, 88623, 88624, 88625, 88626, 88627, 88628, 88629, 88630, 88631, 88632, 88633, 88634, 88635, 88636, 88637, 88638, 88639, 88640, 88641, 88642, 88643, 88644, 88645, 88646, 88647, 88648, 88649, 88650, 88651, 88652, 88653, 88654, 88655, 88656, 88657, 88658, 88659 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 25087, "uuid": "55c74c02ec8e4afea309043d110a93e7", "short_code": "coll", "title": "Auchencorth Moss Atmospheric Observatory (AU) and associated field sites: Meteorological observations", "abstract": "The Auchencorth Moss Atmospheric Observatory was setup in 1995 to measure meteorology, trace gases, aerosols and their fluxes. It is (55ᵒ47’36” N, 3°14’41” W) an ombrotrophic peatland with an extensive fetch at an elevation of 270 m, lying 18 km SSW of Edinburgh, UK, and can be categorised as a transitional lowland raised bog. The site is grazed with < 1 sheep ha^-1.\r\n\r\nDuring 2000s the site activity has increased and was established in 2006 as EMEP (European Monitoring and Evaluation Program, Level 2/3) supersite for the UK. Long term monitoring is led by NERC CEH with contributions from other organisations/research institutes including Ricardo AEA, BureauVeritas, NPL, the University of Birmingham and University of Edinburgh. In April 2014 the site was awarded WMO GAW regional station (World Meteorological Orgamisation Global Atmospheric Watch). In 2017 the site joined the ICOS network (Integrated Carbon Observation System).\r\nSimilar measurements are also made at nearby Easter Bush Field site by the same team.\r\n\r\nThe meteorological measurements were initially made to assist with interpretation of the fluxes and as such weren't installed with the intention of providing WMO standard measurements." } ], "responsiblepartyinfo_set": [ 102780, 102781, 102782, 102783, 102785, 102786, 102787, 102775, 102784, 102776, 102777, 102778, 102779, 102887, 102888, 102889, 102890, 102891, 102892, 102893, 102894, 102895, 102896, 102897 ], "onlineresource_set": [ 23783 ] }, { "ob_id": 25093, "uuid": "c9cab04a8b00486e8c20d9ec68931593", "title": "MICROSCOPE: NCAS mobile X-band radar scan data from Davidstow Airfield - Version 2", "abstract": "This dataset contains scan data from the National Centre for Atmospheric Science's (NCAS) mobile X-band radar collected at the Davidstow Airfield, Cornwall, between June and August 2013 as part of the MICROphysicS of COnvective PrEcipitation (MICROSCOPE) project.\r\n\r\nThis version 2 dataset contains improved metadata and uses a revised processing method to improve data accuracy. Users should also note the change in filenaming convention for this instrument whereby the volume number given after the date-time part is no longer provided.\r\n\r\nThe X-band radar is operated as part of the NCAS Atmospheric Measurement Facility's (AMF).", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2024-03-09T03:15:38", "updateFrequency": "notPlanned", "dataLineage": "Data were collected by the NCAS instrument scientist before preparation for archiving and delivery to the BADC.", "removedDataReason": "", "keywords": "COPE, radar, precipitation, rainfall, dual-polarisation, Doppler, clouds, microphysics, clear-air echoes, convection, storms, dynamics, rainfall", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2017-09-28T13:25:04", "doiPublishedTime": "2018-11-14T13:45:38", "removedDataTime": null, "geographicExtent": { "ob_id": 523, "bboxName": "mobile X-band radar range from Davidstow", "eastBoundLongitude": -2.817, "westBoundLongitude": -6.417, "southBoundLatitude": 49.636, "northBoundLatitude": 51.636 }, "verticalExtent": null, "result_field": { "ob_id": 25094, "dataPath": "/badc/microscope/data/ncas-mobile-xband-radar/version-2", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 20308195509, "numberOfFiles": 925, "fileFormat": "Data are netCDF formatted " }, "timePeriod": { "ob_id": 3330, "startTime": "2013-07-17T23:00:00", "endTime": "2013-08-17T22:59:59" }, "resultQuality": null, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 12252, "uuid": "5c90d35e6da94e74aaf58ecdeb2ab5f7", "short_code": "acq", "title": "NCAS mobile X-band data collection for the microsCOPE project", "abstract": "The instrument was deployed at the Davidstow airfield during the microsCOPE campaign" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 13 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 6289, "uuid": "8a223610f56e7352ebdc19a31f836ea3", "short_code": "proj", "title": "The NERC MICROphysicS of COnvective PrEcipitation (MICROSCOPE) project as part of the COnvective Precipitation Experiment (COPE)", "abstract": "The COnvective Precipitation Experiment (COPE) is a project designed to understand the key processes that control precipitation intensity and to improve their representation in convective-scale forecast models. COPE is led by the National Centre for Atmospheric Science and the UK Met Office, and involves scientists at the Universities of Leeds, Manchester and Reading, as well as international partners from the Universities of Purdue and Wyoming. The MICROphysicS of COnvective PrEcipitation (MICROSCOPE) is the NERC funded part of the wider COPE (COnvective PrEcipitation) project." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 10621, 59110, 59111, 59112, 59113, 59114, 59115, 59116, 59117, 59118, 59119, 59120, 59121, 59122, 59123, 59124, 59125, 59126, 59127, 59128, 59129, 59130, 59131, 59132, 59133, 59134, 59135, 59136, 59137, 59138, 59139, 59140, 59141, 59142, 59143, 59144, 59145, 59146, 59147, 59148, 59149, 59150, 59151, 59152, 59153, 59154, 59155, 59156, 59157, 59158, 59159, 59160, 59161, 59162, 59163, 59164, 59165, 59166, 59167, 59168, 59169, 59170, 59171, 59172, 59173, 59174, 59175, 59176, 59177, 59178, 59179, 59180, 59181, 59182, 59183, 59184, 59185, 59186, 59187, 59188, 59189, 59191, 59192, 59193, 59194, 59195, 59196, 59197, 59198, 59199, 59200, 59201, 59202, 59203, 59223, 59224, 60930, 60931, 60932, 60933, 60934, 60935, 60936, 60937, 60938 ], "vocabularyKeywords": [], "identifier_set": [ 9780 ], "observationcollection_set": [ { "ob_id": 6286, "uuid": "8440933238f72f27762005c33d2aa278", "short_code": "coll", "title": "MICROphysicS of COnvective PrEcipitation (MICROSCOPE) project: In-situ airborne atmospheric and ground-based radar measurements", "abstract": "This dataset collection brings together the datasets produced from the MICROphysicS of COnvective PrEcipitation (MICROSCOPE) project, the NERC funded part of the wider COPE (COnvective Precipitation Experiment) project. COPE was led by the National Centre for Atmospheric Science (NCAS) and the UK Met Office, and involved scientists at the Universities of Leeds, Manchester and Reading, as well as international partners from the Universities of Purdue and Wyoming. As part of COPE, MICROSCOPE sought to improve predictions of severe convective rainfall by addressing the problem of the microphysics of precipitation in convective clouds. Data were collected during the project over Cornwall and Devon, UK, during July and August 2013 to study the clouds. Three research aircraft (Facility for Airborne Atmospheric Measurements (FAAM) BAe146, Met Office Civil Contingency Aircraft (MOCCA) and University of Wyoming King Air), a ground-based radar and several other ground-based instruments took measurements of exactly how the rain forms and develops. The aircraft were equipped with instruments that can distinguish between liquid and solid particles at 200 mph, for example. A major objective was to find these needles in the haystack – the first few ice crystals that form in amongst the hundreds of cloud droplets per every cubic centimetre of cloud." } ], "responsiblepartyinfo_set": [ 102817, 102816, 102822, 102823, 102821, 102820, 102818, 102819, 102910 ], "onlineresource_set": [ 23787 ] }, { "ob_id": 25099, "uuid": "d269d748cafe40ec8601125caf5cb2fa", "title": "CMIP5 20-Year AR5 Climatology Data for the IPCC DDC", "abstract": "CMIP5 monthly mean climatology fields matching those given in IPCC WG1 AR5 Annex I: Atlas of Global and Regional Climate Projections in Climate Change 2013, the Fifth Assessment Report (AR5) of the United Nations Intergovernmental Panel on Climate Change (IPCC). \r\nClimatologies have been calculated for global fields of Specific Humidity, Precipitation, Sea Level Pressure, Temperature, Wind and Downwelling Shortwave Radiation (Stoker et. al., 2013). The CMIP5 climatologies, calculated by the Centre for Environmental Data Analysis (CEDA), match those described in table AI.1 in Stoker et al (2013). Twenty-year AR5 climatologies and climatological anomalies are calculated for the averaging periods 2016-2035, 2046-2065, 2081-2100 for the CMIP5 scenario experiments rcp26, rcp45, rcp60 and rcp85. The climatologies are of global scope and are provided on latitude-longitude grids.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2025-05-25T01:52:50", "updateFrequency": "", "dataLineage": "Climatologies and climatological anomalies have been calculated at CEDA from CMIP5 model output.\r\n\r\nTime Periods:\r\n- AR5 averaging periods: 2016-2035, 2046-2065, 2081-2100\r\n\r\nAnomaly Specification: \r\nAll values are taken as anomalies from the 1986->2005 mean (base line values).\r\n\r\nOutput:\r\n- Period mean of the monthly mean of the variables\r\n- 12 monthly values per climatology\r\n- Climatology is a 2D field\r\n- One climatology per model per scenario per variable per time period\r\n- CF compliant netCDF files\r\n\r\nDirectory Structure:\r\nThe output directory structure follows the DRS specification with the root directory being /derived.\r\nEach file has the extended attribute: clim-anom-ref5. This indicates that it is a climatological mean anomaly (clim-anom) and that the reference period matches the AR5 reference period (ref5).\r\n\r\nMethod:\r\nThe algorithm follows the process below:\r\n\r\n- For every model:\r\n - For every variable (if available):\r\n calculate the 1986->2005 climatological (period average) monthly means (baseline) for the Historical experiment\r\n\r\n- For every model:\r\n - For every variable (if available):\r\n calculate the period average monthly means (baseline) of the last 20 years of the piControl run\r\n\r\n- For every model:\r\n - For every experiment:\r\n - For every time period:\r\n - For every variable (if available):\r\n calculate the climatological monthly mean for this (model, experiment, time period, variable) and subtract the relevant baseline\r\n\r\nCode:\r\nhttps://github.com/cedadev/ipcc_ddc_cmip5_clim_anoms/releases/tag/1.0", "removedDataReason": "", "keywords": "IPCC, AR5, CMIP5, Climatologies, piControl, 1pctCO2, historical, scenarios, rcp26, rcp45, rcp60, rcp85", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2018-05-22T12:47:39", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 25098, "dataPath": "/badc/ipcc-ddc/data/CMIP5-climatologies-AR5", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 28762282734, "numberOfFiles": 4842, "fileFormat": "NetCDF" }, "timePeriod": { "ob_id": 6789, "startTime": "2016-01-01T00:00:00", "endTime": "2100-12-31T23:59:59" }, "resultQuality": null, "validTimePeriod": { "ob_id": 6788, "startTime": "2017-09-28T13:48:23", "endTime": null }, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 195 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6021, 6022, 7795, 25892, 50418, 50429, 50431, 50475, 50496, 50498, 50549, 50559, 50561, 50564, 50579, 50598, 50603, 50608, 52741, 52755, 54407, 54408, 56259, 60438 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 5525, "uuid": "a0913dba885f1b1c0e3eb6dc0c04c188", "short_code": "coll", "title": "Intergovernmental Panel on Climate Change Data Distribution Centre (IPCC DDC) holdings", "abstract": "The Intergovernmental Panel on Climate Change (IPCC) Data Distribution Centre provides four main types of data and guidance:\r\n1. Observed Climate Data Sets;\r\n2. Global Climate Model Data;\r\n3. Socio-economic data and scenarios;\r\n4. Data and scenarios for other environmental changes." }, { "ob_id": 25092, "uuid": "4b873c1918854f55b11ac0e5398aaa63", "short_code": "coll", "title": "Model data described in the 2013 IPCC Fifth Assessment Report (AR5), 20 and 30 year climatologies", "abstract": "CMIP5 monthly mean climatology fields matching those given in IPCC WG1 AR5 Annex I: Atlas of Global and Regional Climate Projections in Climate Change 2013, the Fifth Assessment Report (AR5) of the United Nations Intergovernmental Panel on Climate Change (IPCC). \r\nClimatologies have been calculated for global fields of Specific Humidity, Precipitation, Sea Level Pressure, Temperature, Wind and Downwelling Shortwave Radiation (Stoker et. al., 2013). The CMIP5 climatologies, calculated by the Centre for Environmental Data Analysis (CEDA), match those described in table AI.1 in Stoker et al (2013). Twenty- and thirty-year climatologies and climatological anomalies are calculated for experiments: piControl, 1pctCO2, historical, rcp26, rcp45, rcp60 and rcp85 produced by 39 models from 22 modelling centres. The monthly climatology data covers the period from 1850-2100. The climatologies are of global scope and are provided on latitude-longitude grids." } ], "responsiblepartyinfo_set": [ 102851, 102852, 102853, 102854, 102856, 102857, 102858, 102859, 111739, 102855, 111740 ], "onlineresource_set": [ 23791, 25473, 25472 ] }, { "ob_id": 25101, "uuid": "6cf411099aa743ef81de0f96dd80bac0", "title": "CMIP5 20-Year Climatology Data for the IPCC DDC", "abstract": "CMIP5 monthly mean climatology fields matching those given in IPCC WG1 AR5 Annex I: Atlas of Global and Regional Climate Projections in Climate Change 2013, the Fifth Assessment Report (AR5) of the United Nations Intergovernmental Panel on Climate Change (IPCC). \r\nClimatologies have been calculated for global fields of Specific Humidity, Precipitation, Sea Level Pressure, Temperature, Wind and Downwelling Shortwave Radiation (Stoker et. al., 2013). The CMIP5 climatologies, calculated by the Centre for Environmental Data Analysis (CEDA), match those described in table AI.1 in Stoker et al (2013). Twenty-year climatologies and climatological anomalies are calculated for the CMIP5 experiments: piControl, 1pctCO2, historical, rcp26, rcp45, rcp60 and rcp85. The monthly climatology data covers the period from 1850-2100. The climatologies are of global scope and are provided on latitude-longitude grids.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2019-12-06T23:13:04", "updateFrequency": "", "dataLineage": "Climatologies and climatological anomalies have been calculated at CEDA from CMIP5 model output.\r\n\r\nTime periods:\r\n- periods are defined as being:\r\n - Relative to 2000 for future scenarios (RCPy.x)\r\n - Relative to 1900 for historical run \r\n - Relative to the beginning of the experiment for the piControl and 1pctCO2 runs \r\n\r\n- Twenty year averaging periods: +20 to +39, +46 to +65, +80 to +99, +180 to +199\r\n\r\nAnomaly specification: \r\nAll values are taken as anomalies from the 1986->2005 mean (base line values).\r\nFor the Historical and RCP simulations these base line values are taken from the Historical experiment.\r\nFor the 1pctCO2 and piControl simulations the base line values are taken from the last 20 years of the piControl experiment.\r\n\r\nOutput:\r\n- Period mean of the monthly mean of the variables\r\n- 12 monthly values per climatology\r\n- Climatology is a 2D field\r\n- One climatology per model per scenario per variable per time period\r\n- CF compliant netCDF files\r\n\r\nDirectory Structure:\r\nThe output directory structure follows the DRS specification with the root directory being /derived.\r\nEach file has the extended attribute: clim-anom-ref5. This indicates that it is a climatological mean anomaly (clim-anom) and that the reference period matches the AR5 reference period (ref5).\r\n\r\nMethod:\r\nThe algorithm follows the process below:\r\n\r\n- For every model:\r\n - For every variable (if available):\r\n calculate the 1986->2005 climatological (period average) monthly means (baseline) for the Historical experiment\r\n\r\n- For every model:\r\n - For every variable (if available):\r\n calculate the period average monthly means (baseline) of the last 20 years of the piControl run\r\n\r\n- For every model:\r\n - For every experiment:\r\n - For every time period:\r\n - For every variable (if available):\r\n calculate the climatological monthly mean for this (model, experiment, time period, variable) and subtract the relevant baseline\r\n\r\nCode:\r\nhttps://github.com/cedadev/ipcc_ddc_cmip5_clim_anoms/releases/tag/1.0", "removedDataReason": "", "keywords": "IPCC, AR5, CMIP5, Climatologies, piControl, 1pctCO2, historical, scenarios, rcp26, rcp45, rcp60, rcp85", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2018-05-22T12:06:24", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 25104, "dataPath": "/badc/ipcc-ddc/data/CMIP5-climatologies-20x", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 101218578886, "numberOfFiles": 16693, "fileFormat": "NetCDF" }, "timePeriod": { "ob_id": 6789, "startTime": "2016-01-01T00:00:00", "endTime": "2100-12-31T23:59:59" }, "resultQuality": null, "validTimePeriod": { "ob_id": 6788, "startTime": "2017-09-28T13:48:23", "endTime": null }, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 195 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6021, 6022, 7795, 25892, 50416, 50418, 50429, 50431, 50475, 50496, 50498, 50549, 50559, 50561, 50564, 50579, 50598, 50603, 50608, 52741, 52755, 54407, 54408, 56259 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 5525, "uuid": "a0913dba885f1b1c0e3eb6dc0c04c188", "short_code": "coll", "title": "Intergovernmental Panel on Climate Change Data Distribution Centre (IPCC DDC) holdings", "abstract": "The Intergovernmental Panel on Climate Change (IPCC) Data Distribution Centre provides four main types of data and guidance:\r\n1. Observed Climate Data Sets;\r\n2. Global Climate Model Data;\r\n3. Socio-economic data and scenarios;\r\n4. Data and scenarios for other environmental changes." }, { "ob_id": 25092, "uuid": "4b873c1918854f55b11ac0e5398aaa63", "short_code": "coll", "title": "Model data described in the 2013 IPCC Fifth Assessment Report (AR5), 20 and 30 year climatologies", "abstract": "CMIP5 monthly mean climatology fields matching those given in IPCC WG1 AR5 Annex I: Atlas of Global and Regional Climate Projections in Climate Change 2013, the Fifth Assessment Report (AR5) of the United Nations Intergovernmental Panel on Climate Change (IPCC). \r\nClimatologies have been calculated for global fields of Specific Humidity, Precipitation, Sea Level Pressure, Temperature, Wind and Downwelling Shortwave Radiation (Stoker et. al., 2013). The CMIP5 climatologies, calculated by the Centre for Environmental Data Analysis (CEDA), match those described in table AI.1 in Stoker et al (2013). Twenty- and thirty-year climatologies and climatological anomalies are calculated for experiments: piControl, 1pctCO2, historical, rcp26, rcp45, rcp60 and rcp85 produced by 39 models from 22 modelling centres. The monthly climatology data covers the period from 1850-2100. The climatologies are of global scope and are provided on latitude-longitude grids." } ], "responsiblepartyinfo_set": [ 102869, 102870, 102871, 102872, 102874, 102875, 102876, 102877, 111737, 102873, 111738 ], "onlineresource_set": [ 23793, 25471, 25470 ] }, { "ob_id": 25102, "uuid": "ed9a2b24c8944d2f8f2d6c1e86bbe130", "title": "CMIP5 30-Year Climatology Data for the IPCC DDC", "abstract": "CMIP5 monthly mean climatology fields matching those given in IPCC WG1 AR5 Annex I: Atlas of Global and Regional Climate Projections in Climate Change 2013, the Fifth Assessment Report (AR5) of the United Nations Intergovernmental Panel on Climate Change (IPCC). \r\nClimatologies have been calculated for global fields of Specific Humidity, Precipitation, Sea Level Pressure, Temperature, Wind and Downwelling Shortwave Radiation (Stoker et. al., 2013). The CMIP5 climatologies, calculated by the Centre for Environmental Data Analysis (CEDA), match those described in table AI.1 in Stoker et al (2013). Thirty-year climatologies and climatological anomalies are calculated for the CMIP5 experiments: piControl, 1pctCO2, historical, rcp26, rcp45, rcp60 and rcp85. The monthly climatology data covers the period from 1850-2100. The climatologies are of global scope and are provided on latitude-longitude grids.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2017-10-02T12:09:14", "updateFrequency": "", "dataLineage": "Climatologies and climatological anomalies have been calculated at CEDA from CMIP5 model output.\r\n\r\nTime periods:\r\n- periods are defined as being:\r\n - Relative to 2000 for future scenarios (RCPy.x)\r\n - Relative to 1900 for historical run \r\n - Relative to the beginning of the experiment for the piControl and 1pctCO2 runs \r\n\r\n- Thirty year averaging periods: +01 to +30, +31 to +60, +61 to +90\r\n\r\nAnomaly specification: \r\nAll values are taken as anomalies from the 1986->2005 mean (base line values).\r\nFor the Historical and RCP simulations these base line values are taken from the Historical experiment.\r\nFor the 1pctCO2 and piControl simulations the base line values are taken from the last 20 years of the piControl experiment.\r\n\r\nOutput:\r\n- Period mean of the monthly mean of the variables\r\n- 12 monthly values per climatology\r\n- Climatology is a 2D field\r\n- One climatology per model per scenario per variable per time period\r\n- CF compliant netCDF files\r\n\r\nDirectory Structure:\r\nThe output directory structure follows the DRS specification with the root directory being /derived.\r\nEach file has the extended attribute: clim-anom-ref5. This indicates that it is a climatological mean anomaly (clim-anom) and that the reference period matches the AR5 reference period (ref5).\r\n\r\nMethod:\r\nThe algorithm follows the process below:\r\n\r\n- For every model:\r\n - For every variable (if available):\r\n calculate the 1986->2005 climatological (period average) monthly means (baseline) for the Historical experiment\r\n\r\n- For every model:\r\n - For every variable (if available):\r\n calculate the period average monthly means (baseline) of the last 20 years of the piControl run\r\n\r\n- For every model:\r\n - For every experiment:\r\n - For every time period:\r\n - For every variable (if available):\r\n calculate the climatological monthly mean for this (model, experiment, time period, variable) and subtract the relevant baseline\r\n\r\nCode:\r\nhttps://github.com/cedadev/ipcc_ddc_cmip5_clim_anoms/releases/tag/1.0", "removedDataReason": "", "keywords": "IPCC, AR5, CMIP5, Climatologies, piControl, 1pctCO2, historical, scenarios, rcp26, rcp45, rcp60, rcp85", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2018-05-22T12:04:03", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 25103, "dataPath": "/badc/ipcc-ddc/data/CMIP5-climatologies-30a", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 64472648806, "numberOfFiles": 10538, "fileFormat": "NetCDF" }, "timePeriod": { "ob_id": 6790, "startTime": "1850-01-01T00:00:00", "endTime": "2100-12-31T23:59:59" }, "resultQuality": null, "validTimePeriod": { "ob_id": 6788, "startTime": "2017-09-28T13:48:23", "endTime": null }, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 195 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6021, 6022, 7795, 25892, 50416, 50418, 50429, 50431, 50475, 50496, 50498, 50549, 50559, 50561, 50564, 50579, 50598, 50603, 50608, 52741, 52755, 54407, 54408, 56259 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 5525, "uuid": "a0913dba885f1b1c0e3eb6dc0c04c188", "short_code": "coll", "title": "Intergovernmental Panel on Climate Change Data Distribution Centre (IPCC DDC) holdings", "abstract": "The Intergovernmental Panel on Climate Change (IPCC) Data Distribution Centre provides four main types of data and guidance:\r\n1. Observed Climate Data Sets;\r\n2. Global Climate Model Data;\r\n3. Socio-economic data and scenarios;\r\n4. Data and scenarios for other environmental changes." }, { "ob_id": 25092, "uuid": "4b873c1918854f55b11ac0e5398aaa63", "short_code": "coll", "title": "Model data described in the 2013 IPCC Fifth Assessment Report (AR5), 20 and 30 year climatologies", "abstract": "CMIP5 monthly mean climatology fields matching those given in IPCC WG1 AR5 Annex I: Atlas of Global and Regional Climate Projections in Climate Change 2013, the Fifth Assessment Report (AR5) of the United Nations Intergovernmental Panel on Climate Change (IPCC). \r\nClimatologies have been calculated for global fields of Specific Humidity, Precipitation, Sea Level Pressure, Temperature, Wind and Downwelling Shortwave Radiation (Stoker et. al., 2013). The CMIP5 climatologies, calculated by the Centre for Environmental Data Analysis (CEDA), match those described in table AI.1 in Stoker et al (2013). Twenty- and thirty-year climatologies and climatological anomalies are calculated for experiments: piControl, 1pctCO2, historical, rcp26, rcp45, rcp60 and rcp85 produced by 39 models from 22 modelling centres. The monthly climatology data covers the period from 1850-2100. The climatologies are of global scope and are provided on latitude-longitude grids." } ], "responsiblepartyinfo_set": [ 102878, 102879, 102880, 102881, 102883, 102884, 102885, 102886, 111735, 102882, 111736 ], "onlineresource_set": [ 23794, 25469, 25468 ] }, { "ob_id": 25105, "uuid": "28a48da1141447058f8d5323908e4b3d", "title": "INCOMPASS: Radiosonde measurements from the Indian Institute of Technology (IIT) airstrip, Kanpur, India", "abstract": "This dataset contains temperature, humidity, wind and GPS location information recorded from 137 radiosondes launched during July 2016 as part of the NERC/MoES Interaction of Convective Organization and Monsoon Precipitation, Atmosphere, Surface and Sea (INCOMPASS) field campaign. All radiosondes were launched from the airstrip of Indian Institute of Technology (IIT) Kanpur, Uttar Pradesh, northern India, from the location 26.519N, 80.233E, 126m above sea level. Launches took place between 5th July and 28th July at 00, 06, 12, 18UTC. The Vaisala RS41-SG radiosondes were fitted to Totex TA350 balloons. \r\n\r\nThe data were collected as part of the INCOMPASS field campaign May-July 2016, funded by Natural Environmental Research Council (NERC) (NE/L01386X/1). The aim of the project was to improve the skill of rainfall prediction in operational weather and climate models by way of better understanding and representation of interactions between the land surface, boundary layer, convection, the large-scale environment and monsoon variability on a range of scales.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2018-10-18T13:45:06.412365", "updateFrequency": "", "dataLineage": "The Vaisala MW41 DigiCora sounding station receives radiosonde measurements via telemetry and stores them in a packaged (zipped) format consisting of XML files. Only the \"SynchronizedSoundingData.xml\" file has been used (one for each launch), and converted to Netcdf (by AG Turner during September 2018) using a program running in IDL8.5. The data were then sent to the Centre for Environmental Data Analysis (CEDA) for archiving.", "removedDataReason": "", "keywords": "NERC, INCOMPASS, monsoon, radiosonde, temperature, humidity, wind", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2018-10-18T12:29:59", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 1868, "bboxName": "Indian Institute of Technology, Kanpur", "eastBoundLongitude": 80.23, "westBoundLongitude": 80.23, "southBoundLatitude": 26.519, "northBoundLatitude": 26.519 }, "verticalExtent": null, "result_field": { "ob_id": 26563, "dataPath": "/badc/sa-monsoon/data/incompass/radiosondes", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 163663579, "numberOfFiles": 140, "fileFormat": "Data are netCDF formatted." }, "timePeriod": { "ob_id": 6794, "startTime": "2016-07-05T11:00:00", "endTime": "2016-07-28T22:59:59" }, "resultQuality": { "ob_id": 3181, "explanation": "Known errors: INCOMPASS-UoR-Vaisala-MW41-RS41_IITKanpur_2016071806a.nc contains no usable ascent data. A replacement launch was made soon after, \"…2016071806b.nc\". INCOMPASS-UoR-Vaisala-MW41-RS41_IITKanpur_2016071506.nc appears to have an underfilled or leaking balloon, since there are several periods of ascent/descent within the profile.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2018-10-18" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 25108, "uuid": "f8fac9251d02473f972a9333e2882493", "short_code": "acq", "title": "INCOMPASS: Radiosonde measurements from Indian Institute of Technology (IIT) airstrip, Kanpur, India", "abstract": "INCOMPASS: Radiosonde measurements from Indian Institute of Technology (IIT) airstrip, Kanpur, India" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 2 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 19201, "uuid": "2fb5f31126a3425f9af15e3ea85c552f", "short_code": "proj", "title": "Interaction of Convective Organization and Monsoon Precipitation, Atmosphere, Surface and Sea (INCOMPASS)", "abstract": "The monsoon supplies the majority of water for agriculture and industry in South Asia, and is therefore critical to the well-being of a billion people. Active and break periods in the monsoon have a major influence on the success of farming, while year-to-year variations in the rainfall have economic consequences on an international scale. The growing population and developing economy mean that understanding and predicting the monsoon is therefore vital. Despite this, our capability to model the monsoon, and to make forecasts on scales from days to the season ahead is limited by large errors that develop quickly. The relatively poor performance of weather prediction models over India is due to a very strong and complex relationship between the land, ocean and atmosphere, which are linked by the process of convection, in the form of the rain-bringing cumulonimbus clouds. Forecast errors occur primarily because the convective clouds are not accurately linked to the large-scale circulation or to the surface conditions, and these errors persist to long time scales. Worldwide, weather and climate forecast models are gaining resolution, and yet the errors in monsoon rainfall are not diminishing. A lack of detailed observations of the land, ocean and atmospheric parts of the monsoon system, on a range of temporal and spatial scales, is preventing a more thorough understanding of processes in monsoon convective clouds and at the land surface, and their interaction with the large-scale circulation. \r\n\r\nThe project used a programme of new measurements over India and the adjacent oceans to advance monsoon forecasting capability in the Indo-UK community. The first detachment of the FAAM research aircraft to India, in combination with an intensive ground-based observation campaign, will gather new observations of the land surface, the boundary layer structure over land and ocean, and atmospheric profiles. We will institute a new long-term series of measurements of energy and water exchanges at the land surface. Research measurements from one monsoon season will be combined with long-term observations on the Indian operational networks. Observations will be focused on two transects: in the northern plains of India, covering a range of surface types from irrigated to rain-fed agriculture, and wet to dry climatic zones; and across the Western Ghats, with transitions from land to ocean and across orography. The observational analysis will represent a unique and unprecedented characterization of monsoon processes linking the land, ocean and atmospheric patterns which control the rainfall. Long-term measurements will allow the computation of statistical relationships between the various factors. \r\n\r\nThe observational analysis fed directly into improved forecasting at the Met Office and NCMRWF. The Met Office Unified Model, which is used for weather forecasting at both institutions, was set up in a range of different ways for the observational period. In particular, the project pioneered the test development of a new 100m-resolution atmospheric model, which greatly improved the representation of land-ocean-atmosphere interactions. Another priority was to improve land surface modelling in monsoon forecasts. By comparing the results of the very high resolution models on small domains with lower-resolution models representing the global weather patterns, it was possible to describe the key processes controlling monsoon rainfall, and to indicate how these need to be represented in different applications, such as weather predictions or climate predictions. Through model evaluation at a range of scales, the development of simple theoretical understanding of the rainfall processes, and working with groups responsible for operational model improvement, the project led directly to improvements in monsoon forecasts. \r\n\r\nObjectives: The grand objective of this project was to improve the skill of rainfall prediction in operational weather and climate models by way of better understanding and representation of interactions between the land surface, boundary layer, convection, the large-scale environment and monsoon variability on a range of scales.\r\n\r\nSpecific objectives:\r\n\r\n1a) To document and evaluate the characteristics of monsoon rainfall on sub-daily to intraseasonal time scales, as influenced by surface, thermodynamic and dynamic forcing, as monsoon air moves from the ocean inland and across the subcontinent.\r\n1b) To evaluate the representation of these rainfall processes in the Met Office Unified Model at a range of resolutions, and thereby to indicate the priorities for model development.\r\n\r\n2) Quantify land surface properties and fluxes, using in-situ and remote sensing measurements, as they interact with the monsoon on hourly to monthly time scales and from kilometre to continental spatial scales. \r\n\r\n3a) Quantify the role of the Indian land surface in the progression of the monsoon during the onset, and in monsoon variability, and relate it to the role of the ocean.\r\n3b) Evaluate the impact of improved land-surface representation on monsoon prediction and make recommendations for future land-atmosphere modelling strategy.\r\n\r\n4a) Evaluate the influence of local and short-term structures in convection and the boundary layer, on rainfall variability on intraseasonal and seasonal timescales, using observations, idealized models and a range of operational models. \r\n4b) Make recommendations for priorities in the parametrization of convective rainfall in the monsoon system." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 51981, 54763, 54764, 56218, 56219, 56220, 56221, 56222, 56223, 56224, 56225, 56226, 56227, 56228, 56229, 56230, 56231 ], "vocabularyKeywords": [], "identifier_set": [ 9692 ], "observationcollection_set": [ { "ob_id": 20240, "uuid": "1873b605e2a74cac8b4f5d12593e54fc", "short_code": "coll", "title": "INCOMPASS: radiosonde and in-situ airborne observations by the FAAM BAE-146 aircraft", "abstract": "Radiosonde and in-situ airborne observations by the FAAM BAE-146 aircraft for Interaction of Convective Organization and Monsoon Precipitation, Atmosphere, Surface and Sea (INCOMPASS)." } ], "responsiblepartyinfo_set": [ 102898, 102899, 102900, 102901, 102902, 102905, 102906, 102903, 143596, 111617, 111618, 111619, 111620, 111621, 111622, 111623, 111624, 111625, 111626 ], "onlineresource_set": [ 25503, 94823, 94824, 94825, 94826 ] }, { "ob_id": 25109, "uuid": "55b5d76a7edb42e39933c1edc37f7b90", "title": "ICE-D: NCAS mobile X-band radar scan data from the Praia International Airport, Santiago, Cape Verde, Version 1", "abstract": "This dataset contains scan data from the National Centre for Atmospheric Science's (NCAS) mobile X-band radar collected at Praia International Airport, Santiago, Cape Verde between July and August 2015 as part of the Ice in Clouds Experiment - Dust (ICE-D). The radar has Doppler and dual-polarisation capability and measures the location and intensity of precipitation, radial winds and polarisation parameters.\r\n\r\nThe X-band radar is operated as part of the NCAS Atmospheric Measurement Facility (AMF).", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2017-10-03T16:08:49.978704", "updateFrequency": "notPlanned", "dataLineage": "Data were collected by the NCAS instrument scientist before preparation for archiving and delivery to Centre for Environmental Data Analysis.", "removedDataReason": "", "keywords": "ICE-D, radar, clouds, microphysics, ice, rainfall, dust, convection, storms", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2018-01-08T13:13:23", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 1875, "bboxName": "Praia International Airport Coordinates", "eastBoundLongitude": -23.4939, "westBoundLongitude": -23.4939, "southBoundLatitude": 14.9242, "northBoundLatitude": 14.9242 }, "verticalExtent": null, "result_field": { "ob_id": 25110, "dataPath": "/badc/ice-d/data/ncas-mobile-xband-radar/version-1/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 312053485190, "numberOfFiles": 13342, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 6795, "startTime": "2015-07-27T13:25:07", "endTime": "2015-08-24T21:53:00" }, "resultQuality": { "ob_id": 3099, "explanation": "ICE-D AMF xband radar data, quality determined by data provider", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2018-01-08" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 25433, "uuid": "737d39b648f3425d993fbb2cd059ce78", "short_code": "acq", "title": "NCAS mobile X-band data collection for the ICE-D project", "abstract": "The instrument was deployed at the Praia airport, Cape Verde during the ICE-D campaign" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 13 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 12454, "uuid": "b0348ba21b784ae783201200213d02f4", "short_code": "proj", "title": "The UK Ice in Clouds Experiment -- Dust (UK ICE-D)", "abstract": "The UK ICE-D project was funded by the Natural Environement Research Council (NERC) with the grant references: NE/M00340X/1 and NE/M001954/1. These were led by Professor Alan Blyth (University of Leeds) and Professor Thomas William Choularton (The University of Manchester)\r\n\r\nThe goal of this research was to determine how desert dust affects the nucleation of ice particles in convective and layer clouds and the subsequent development of precipitation and glaciation of the clouds. Dust is believed to be a critical aerosol particle in the Earth system mainly because the dust particles themselves, and particles that are chemically and possibly biologically modified as they are transported from their source, are believed to be the most important ice nuclei in a global sense and because dust particles are transported to many parts of the globe. Predicting the initiation and subsequent evolution of the size distribution of ice particles in clouds from a distribution of aerosol particles is one of the most important problems in atmospheric science. It is fundamental to the NERC high-level strategy objective ``Understand and predict how our planet works'', because the lack of understanding of the processes causes uncertainty in the way global models treat the interaction of radiation with ice and mixed-phase clouds and the development of precipitation. They also cause uncertainty in Numerical Weather Prediction (NWP) models, which is concerned with the NERC strategy objective ``Resilience to environmental hazards''. The proposed research aims was to tackle this problem by making measurements of aerosols and cloud particles close to one of the largest sources of desert dust in the world. The measurements are difficult to make, which is why such detailed measurements have never been made in this region before. It is possible to do this now because the instruments are capable of determining the chemical and physical properties of aerosol particles, the aircraft cloud physics instruments can detect small ice particles, and there is a mobile dual-polarisation radar. \r\n\r\nThe UK Ice in Clouds Experiment -- Dust (UK ICE-D) was part of the US-UK aircraft and ground-based project based in Cape Verde off the coast of Senegal, Africa to be held in 2015 (mainly UK) and 2016 (mainly US). Measurements will be made in the environment around the clouds to characterise the aerosol particles and their ability to act as ice nuclei and cloud condensation nuclei, and within the clouds to determine the influence of the particles on the cloud properties. Convective clouds will be measured as a priority, but layer clouds will also be targeted. Observations will be made when dust is present in high concentrations at appropriate altitudes and when almost no dust is present. The availability of the US and UK aircraft has inadvertently provided a unique opportunity to maximise the sampling statistics of the clouds. The location and time was selected from climatology studies because dust concentrations are often large and convective and layer clouds also occur frequently. In addition, the convective clouds in the region are known to be important since they can form clusters that lead to storms and hurricanes in the Tropical Atlantic.\r\n\r\nSpecifically, UK ICE-D made measurements on days with and without the presence of dust of the following:\r\n* Aerosol particles on the ground with the instruments in the aerosol container at Cape Verde and with the BAe 146 aircraft;\r\n* Cloud droplets, supercooled raindrops, the first ice particles and development of ice and precipitation particles with the aircraft;\r\n* The altitudes of the supercooled raindrops, the location and time of the first precipitation echoes, and the radial air motions using the radar;\r\n* The thermodynamics and dynamics of the clouds and their environment with the aircraft and to some extent the radar.\r\n\r\nModel results were compared with the observations of the initiation temperatures and rates of growth and development. A spectrum of models ranging from climate through regional forecast models to explicit cloud physics process-based models, will be used as forecasting tools and as tools to interpret the data and to develop or\r\n\r\nObjectives: \r\n1. Characterise the chemical and physical properties of aerosol particles and determine the activation properties of CCN and IN.\r\n\r\n2. Given an initial well-characterised aerosol distribution, can we predict the number concentration of ice particles that will be produced in the clouds through primary nucleation? Specifically, determine if primary ice particles first form as a result of freezing of supercooled raindrops. \r\n\r\n3. Determine the physical processes responsible for the production of warm rain and the rates of growth. E.g. is the process dependent on entrainment and mixing or determined by straightforward autoconversion or by giant CCN.\r\n\r\n4. Determine whether the HM secondary ice formation process is critical to the glaciation of the convective clouds and if so (likely) the effect of dust on the process. \r\n\r\n5. Determine the influence of dust on the physical properties of the convective and layer clouds (e.g.\\ dynamics, entrainment, liquid water content distribution, development of precipitation) and if the effects can be represented with models of all scales.\r\n\r\n6. Use the new observations to test and improve the ability of regional NWP, global NWP and climate models to accurately simulate the properties of clouds and their environment. In particular, determine if the new prognostic treatment of aerosol through its effect on ice nucleation and cloud droplet number outperforms simpler non-prognostic treatments." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 10621, 59110, 59111, 59112, 59113, 59114, 59115, 59116, 59117, 59118, 59119, 59120, 59121, 59122, 59123, 59124, 59125, 59126, 59127, 59128, 59129, 59130, 59131, 59132, 59133, 59134, 59135, 59136, 59137, 59138, 59139, 59140, 59141, 59142, 59143, 59144, 59145, 59146, 59147, 59148, 59149, 59150, 59151, 59152, 59153, 59154, 59155, 59156, 59157, 59158, 59159, 59160, 59161, 59162, 59163, 59164, 59165, 59166, 59167, 59168, 59169, 59170, 59171, 59172, 59173, 59174, 59175, 59176, 59177, 59178, 59179, 59180, 59181, 59182, 59183, 59184, 59185, 59186, 59187, 59188, 59189, 59190, 59191, 59192, 59193, 59194, 59195, 59196, 59197, 59198, 59199, 59200, 59201, 59202, 59203, 59204, 59212, 59223, 59224, 59228, 60930, 60931, 60932, 60933, 60934, 60935, 60936, 60937, 60938, 69325, 69326, 69327, 69328, 69329, 69330, 69331, 69332, 69333, 69334, 69335, 69336, 69337 ], "vocabularyKeywords": [], "identifier_set": [ 9781 ], "observationcollection_set": [ { "ob_id": 13034, "uuid": "d7e02c75191a4515a28a208c8a069e70", "short_code": "coll", "title": "UK ICE-D: atmospheric measurements dataset collection", "abstract": "This dataset collection holds datasets from the FAAM BAe-146 aircraft and ground-based measurements taken in Cape Verde off the coast of Senegal, Africa during 2015 and 2016 in support of the UK Ice in Clouds Experiment - Dust (UK ICE-D) project." } ], "responsiblepartyinfo_set": [ 102911, 102912, 102913, 102915, 102916, 102917, 102918, 102914, 102919 ], "onlineresource_set": [ 23797 ] }, { "ob_id": 25111, "uuid": "f1c9c7aa210d4564bd61ed1a81d51130", "title": "ESA Fire Climate Change Initiative (Fire_cci): MODIS Fire_cci Burned Area Grid product, version 5.0", "abstract": "The ESA Fire Disturbance Climate Change Initiative (CCI) project has produced maps of global burned area developed from satellite observations. The MODIS Fire_cci v5.0 grid products described here are derived from the MODIS instrument onboard the TERRA satellite at 250m resolution for the period 2001 to 2016. This is the first time that MODIS 250m resolution images are used for global burned area (BA) mapping.\r\n\r\nThis dataset is a gridded product, derived from the MODIS Fire_cci v5.0 pixel product by summarising its burned area information into a regular grid covering the Earth for 15-day periods with 0.25 degree resolution. Information on burned area is included in 23 individual quantities: sum of burned area, standard error, fraction of burnable area, fraction of observed area, number of patches and the burned area for 18 land cover classes, as defined by the Land_Cover_cci v1.6.1 product. For further information on the product and its format see the Fire_cci product user guide in the linked documentation.\r\n\r\nPlease note, a new version of this dataset (v5.1) is now available.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2024-09-11T13:15:34", "updateFrequency": "notPlanned", "dataLineage": "Data was produced by the ESA Fire_CCI team as part of ESA's Climate Change Initiative (CCI) and is being held on the CEDA (Centre for Environmental Data Analysis) archive as part of the ESA CCI Open Data Portal Project.", "removedDataReason": "", "keywords": "ESA, CCI, GRID, Burned Area, Fire Disturbance, Climate Change, GCOS", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "0.25 degree", "status": "superseded", "dataPublishedTime": "2018-02-23T17:04:46", "doiPublishedTime": "2018-02-23T17:06:59", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 25836, "dataPath": "/neodc/esacci/fire/data/burned_area/MODIS/grid/v5.0/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 37117279164, "numberOfFiles": 403, "fileFormat": "Data are in NetCDF format." }, "timePeriod": { "ob_id": 6983, "startTime": "2001-01-01T00:00:00", "endTime": "2016-12-31T23:59:59" }, "resultQuality": { "ob_id": 1, "explanation": "See dataset associated documentation", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2012-08-15" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 25857, "uuid": "4a6d41bfb75148b4aaa20ab513ddf72c", "short_code": "cmppr", "title": "ESA Fire_cci MODIS Burned Area product", "abstract": "The MODIS Fire_CCI Burned Area product has been produced from the MODIS (Moderate Resolution Imaging Spectroradiometer) instrument on NASA's TERRA satellite, using an algorithm derived by the Fire_CCI team, as part of the European Space Agency's (ESA's) Climate Change Initiative (CCI)" }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2539, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 19, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_fire_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 13255, "uuid": "6c3584d985bd484e8beb23ff0df91292", "short_code": "proj", "title": "ESA Fire Climate Change Initiative Project (Fire CCI)", "abstract": "The European Space Agency (ESA) Fire Climate Change Initiative (Fire CCI) project, led by University of Alcala (Spain), is part of ESA's Climate Change Initiative (CCI) to produce long term datasets of Essential Climate Variables derived from global satellite data.\r\n\r\nThe Fire CCI focuses on the following issues relating to Fire Disturbance: Analysis and specification of scientific requirements relating to climate; Development and improvement of pre-processing and burned area algorithms; Inter-comparison and selection of burned area algorithms; System prototyping and production of burned area datasets; Product validation and product assessment\r\n" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6021, 6022, 6023, 57314, 57317, 59231, 59233, 59234, 59235, 59236, 59237, 60438, 68093, 68094 ], "vocabularyKeywords": [ { "ob_id": 10675, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/freq/freq_15day", "resolvedTerm": "15 days" }, { "ob_id": 10939, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/platformProg/plat_eos", "resolvedTerm": "EOS" }, { "ob_id": 10923, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/platform/plat_terra", "resolvedTerm": "Terra" }, { "ob_id": 10213, "vocabService": "clipc_skos_vocab", "uri": "http://vocab-test.ceda.ac.uk/collection/cci/ecv/cciecv_fire", "resolvedTerm": "fire" }, { "ob_id": 9505, "vocabService": "nerc_skos_vocab", "uri": "http://vocab.nerc.ac.uk/collection/P07/current/OH2X3OCY/", "resolvedTerm": "fire_area" }, { "ob_id": 10602, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/dataType/dtype_ba", "resolvedTerm": "burned area" }, { "ob_id": 11026, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/product/prod_modTe", "resolvedTerm": "MODIS_TERRA" }, { "ob_id": 10988, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/procLev/proc_level4", "resolvedTerm": "Level 4" }, { "ob_id": 10582, "vocabService": "nerc_skos_vocab", "uri": "http://vocab.nerc.ac.uk/collection/L22/current/TOOL1035/", "resolvedTerm": "Moderate Resolution Imaging Spectroradiometer" }, { "ob_id": 10746, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/org/org59", "resolvedTerm": "University of Alcala" }, { "ob_id": 11105, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/sensor/sens_modis", "resolvedTerm": "MODIS" }, { "ob_id": 10663, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/ecv/cciecv_fire", "resolvedTerm": "fire" } ], "identifier_set": [ 9778, 9516 ], "observationcollection_set": [ { "ob_id": 12683, "uuid": "bcef9e87740e4cbabc743d295afbe849", "short_code": "coll", "title": "ESA Fire Climate Change Initiative (Fire CCI) Dataset Collection", "abstract": "The ESA Fire Climate Change Initiative (Fire_cci) project is producing long-term datasets of burned area information from satellites, as part of the ESA Climate Change Initiative. The data is of use for those interested in historical burned patterns, fire management and emissions analysis and climate change research, by providing a consistent burned area time series. \r\n\r\nCurrent datasets consist of maps of global burned area for the years 1982 to 2019. Products are available at different spatial resolutions: the Pixel product (at the original resolution of the sensor data) and the Grid product (0.25 degrees resolution), the latter of which is produced from the Pixel product. They are based upon spectral information from different sensors, and in many cases also thermal information from active fires.\r\n\r\nGlobal products: \r\n\r\nFireCCI41: Medium Resolution Imaging Spectrometer (MERIS) reflectance, on board the ENVISAT ESA satellite, 300m spatial resolution, and MODIS active fires. Temporal resolution: 2005 – 2011.\r\nFireCCI50: Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance and active fires, on board the TERRA satellite, 250m spatial resolution, temporal resolution: 2001 – 2016.\r\nFireCCI51: Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance and active fires, on board the TERRA satellite, 250m spatial resolution, temporal resolution: 2001 – 2019.\r\n\r\nFireCCILT10 (beta product): Advanced Very High Resolution Radiometer (AVHRR) Land Long Term Data Record (LTDR) reflectance. Provided only as grid product. Temporal resolution: 1982-2017.\r\n\r\nContinental products:\r\n\r\nFireCCISFD11: Multispectral Instrument (MSI) reflectance, on board the Sentinel-2A satellite, 20 spatial resolution, and MODIS active fires. Temporal resolution: 2016, spatial coverage: Sub-Saharan Africa." } ], "responsiblepartyinfo_set": [ 102929, 102926, 102927, 102928, 108192, 108188, 142216, 102925, 102930, 108189, 107928, 102932, 102933, 102934, 102935, 107929, 102936, 102937, 107930, 102938, 102939, 102940, 102941, 102943, 102944, 102945, 107931, 102948, 107932, 102949, 107933 ], "onlineresource_set": [ 23803, 23802, 23798, 25991, 41945, 87709, 88102 ] }, { "ob_id": 25112, "uuid": "9c666602b89e468493e1c907a4de62ff", "title": "ESA Fire Climate Change Initiative (Fire_cci): MODIS Fire_cci Burned Area Pixel product, version 5.0", "abstract": "The ESA Fire Disturbance Climate Change Initiative (CCI) project has produced maps of global burned area derived from satellite observations. These MODIS Fire_cci v5.0 pixel products are distributed as 6 continental tiles and are based upon data from the MODIS instrument onboard the TERRA satellite at 250m resolution for the period 2001-2016. This is the first time that MODIS 250m resolution images are used for global burned area (BA) mapping.\r\n\r\nThe Fire_cci v5.0 Pixel product described here includes maps at 0.00224573-degrees (approx. 250m) resolution. Burned area(BA) information includes 3 individual files, packed in a compressed tar.gz file: date of BA detection (labelled JD), the confidence level (CL, a probability value estimating the confidence that a pixel is actually burned), and the land cover (LC) information as defined in the Land_Cover_cci v1.6.1 product.\r\n\r\nFiles are in GeoTIFF format using a geographic coordinate system based on the World Geodetic System (WGS84) reference ellipsoid and using Plate Carrée projection with geographical coordinates of equal pixel size. For further information on the product and its format see the Fire_cci Product User Guide in the linked documentation. \r\n\r\nPlease note, a new version of this product (v5.1) is now available.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2020-04-30T21:44:11", "updateFrequency": "notPlanned", "dataLineage": "Data was produced by the ESA Fire CCI team as part of the ESA Climate Change Initiative (CCI) and is being held on the CEDA (Centre for Environmental Data Analysis) archive as part of the ESA CCI Open Data Portal project.", "removedDataReason": "", "keywords": "ESA, CCI, Pixel, Burned Area, Fire Disturbance, Climate Change, GCOS", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "0.00224573 degrees (~250m)", "status": "superseded", "dataPublishedTime": "2018-02-23T16:51:58", "doiPublishedTime": "2018-02-23T17:00:00", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 25835, "dataPath": "/neodc/esacci/fire/data/burned_area/MODIS/pixel/v5.0/compressed/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 213336433225, "numberOfFiles": 1154, "fileFormat": "The Fire_cci v5.0 Pixel product described here includes maps at 0.00224573-degrees (approx. 250m) resolution. Burned area(BA) information includes 3 individual files, packed in a compressed tar.gz file: date of BA detection (labelled JD), the confidence level (CL, a probability value estimating the confidence that a pixel is actually burned), and the land cover (LC) information as defined in the Land Cover CCI v1.6.1 product.\r\n\r\nFiles are in GeoTIFF format using a geographic coordinate system based on the World Geodetic System (WGS84) reference ellipsoid and using Plate Carrée projection with geographical coordinates of equal pixel size. For further information on the product and its format see the Fire_cci Product User Guide in the linked documentation. " }, "timePeriod": { "ob_id": 6979, "startTime": "2001-01-01T00:00:00", "endTime": "2016-12-31T23:59:59" }, "resultQuality": { "ob_id": 1, "explanation": "See dataset associated documentation", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2012-08-15" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 25857, "uuid": "4a6d41bfb75148b4aaa20ab513ddf72c", "short_code": "cmppr", "title": "ESA Fire_cci MODIS Burned Area product", "abstract": "The MODIS Fire_CCI Burned Area product has been produced from the MODIS (Moderate Resolution Imaging Spectroradiometer) instrument on NASA's TERRA satellite, using an algorithm derived by the Fire_CCI team, as part of the European Space Agency's (ESA's) Climate Change Initiative (CCI)" }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2539, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 19, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_fire_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 13255, "uuid": "6c3584d985bd484e8beb23ff0df91292", "short_code": "proj", "title": "ESA Fire Climate Change Initiative Project (Fire CCI)", "abstract": "The European Space Agency (ESA) Fire Climate Change Initiative (Fire CCI) project, led by University of Alcala (Spain), is part of ESA's Climate Change Initiative (CCI) to produce long term datasets of Essential Climate Variables derived from global satellite data.\r\n\r\nThe Fire CCI focuses on the following issues relating to Fire Disturbance: Analysis and specification of scientific requirements relating to climate; Development and improvement of pre-processing and burned area algorithms; Inter-comparison and selection of burned area algorithms; System prototyping and production of burned area datasets; Product validation and product assessment\r\n" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [ { "ob_id": 10213, "vocabService": "clipc_skos_vocab", "uri": "http://vocab-test.ceda.ac.uk/collection/cci/ecv/cciecv_fire", "resolvedTerm": "fire" }, { "ob_id": 10663, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/ecv/cciecv_fire", "resolvedTerm": "fire" }, { "ob_id": 9505, "vocabService": "nerc_skos_vocab", "uri": "http://vocab.nerc.ac.uk/collection/P07/current/OH2X3OCY/", "resolvedTerm": "fire_area" } ], "identifier_set": [ 10215, 9515 ], "observationcollection_set": [ { "ob_id": 12683, "uuid": "bcef9e87740e4cbabc743d295afbe849", "short_code": "coll", "title": "ESA Fire Climate Change Initiative (Fire CCI) Dataset Collection", "abstract": "The ESA Fire Climate Change Initiative (Fire_cci) project is producing long-term datasets of burned area information from satellites, as part of the ESA Climate Change Initiative. The data is of use for those interested in historical burned patterns, fire management and emissions analysis and climate change research, by providing a consistent burned area time series. \r\n\r\nCurrent datasets consist of maps of global burned area for the years 1982 to 2019. Products are available at different spatial resolutions: the Pixel product (at the original resolution of the sensor data) and the Grid product (0.25 degrees resolution), the latter of which is produced from the Pixel product. They are based upon spectral information from different sensors, and in many cases also thermal information from active fires.\r\n\r\nGlobal products: \r\n\r\nFireCCI41: Medium Resolution Imaging Spectrometer (MERIS) reflectance, on board the ENVISAT ESA satellite, 300m spatial resolution, and MODIS active fires. Temporal resolution: 2005 – 2011.\r\nFireCCI50: Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance and active fires, on board the TERRA satellite, 250m spatial resolution, temporal resolution: 2001 – 2016.\r\nFireCCI51: Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance and active fires, on board the TERRA satellite, 250m spatial resolution, temporal resolution: 2001 – 2019.\r\n\r\nFireCCILT10 (beta product): Advanced Very High Resolution Radiometer (AVHRR) Land Long Term Data Record (LTDR) reflectance. Provided only as grid product. Temporal resolution: 1982-2017.\r\n\r\nContinental products:\r\n\r\nFireCCISFD11: Multispectral Instrument (MSI) reflectance, on board the Sentinel-2A satellite, 20 spatial resolution, and MODIS active fires. Temporal resolution: 2016, spatial coverage: Sub-Saharan Africa." } ], "responsiblepartyinfo_set": [ 102952, 102953, 102954, 108190, 102951, 142214, 142215, 102950, 102955, 108191, 107922, 102957, 102958, 102959, 102960, 107923, 102961, 102962, 107924, 102963, 102964, 102965, 102966, 102968, 102969, 102970, 107925, 102973, 107926, 102974, 107927 ], "onlineresource_set": [ 23804, 23809, 23807, 23808, 87710, 88101 ] }, { "ob_id": 25118, "uuid": "82461824b52f4c42a8e2cf09cdd9902e", "title": "FAAM C026 CLARIFY Test flight: Airborne atmospheric measurements from core instrument suite on board the BAE-146 aircraft", "abstract": "Airborne atmospheric measurements from core instrument suite data on board the FAAM BAE-146 aircraft collected for CLouds and Aerosol Radiative Impacts and Forcing: Year 2016 (CLARIFY-2016) project.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2020-07-09T20:21:25", "updateFrequency": "asNeeded", "dataLineage": "Data were collected by instrument scientists during the flight before preparation and delivery for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "CLARIFY, FAAM, airborne, atmospheric measurments", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2017-08-03T12:15:43", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 2574, "bboxName": "FAAM C026", "eastBoundLongitude": -0.3, "westBoundLongitude": -6.4, "southBoundLatitude": 50.1, "northBoundLatitude": 52.33 }, "verticalExtent": null, "result_field": { "ob_id": 25117, "dataPath": "/badc/faam/data/2017/c026-jul-31", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1128525454, "numberOfFiles": 27, "fileFormat": "Data are netCDF and NASA-Ames formatted. Ancillary files may be plain ASCII or PDF formatted. Image files may be PNG formatted." }, "timePeriod": { "ob_id": 6796, "startTime": "2017-07-31T08:28:33", "endTime": "2017-07-31T15:57:10" }, "resultQuality": { "ob_id": 3074, "explanation": "Data collected by flight participants before preparation for archival with the Centre for Environmental Data Analysis (CEDA).", "passesTest": true, "resultTitle": "FAAM to CEDA Data Quality Statement", "date": "2015-09-03" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 25120, "uuid": "f217698c58544ce99c10ea9b090f7e60", "short_code": "acq", "title": "FAAM Flight C026 Acquisition", "abstract": "FAAM Flight C026 Acquisition" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 8 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 12132, "uuid": "a3f231fadf7641c6993c7a67d52bcd2f", "short_code": "proj", "title": "CLouds and Aerosol Radiative Impacts and Forcing: Year 2016 (CLARIFY-2016)", "abstract": "The CLARIFY-2016 project was consortium of 5 university partners and the UK Met Office who aimed to measure and understand the physical, chemical, optical and radiative properties of BBAs (biomass burning aerosol) in the key South East Atlantic region. This project was funded by the Natural Environment Research Council (NERC) with the grant reference - NE/L013797/1 - and was led by Professor James Haywood (University of Exeter).\r\n\r\nBiomass burning aerosol (BBA) exerts a considerable impact on climate by impacting regional radiation budgets as it significantly reflects and absorbs sunlight, and its cloud nucleating properties perturb cloud microphysics and hence affect cloud radiative properties, precipitation and cloud lifetime. However, BBA is a complex and poorly understood aerosol species as it consists of a complex cocktail of organic carbon and inorganic compounds mixed with black carbon and hence large uncertainties exist in both the aerosol-radiation-interactions and aerosol-cloud-interactions, uncertainties that limit the ability of our current climate models to accurately reconstruct past climate and predict future climate change.\r\n \r\nThe African continent is the largest global source of BBA (around 50% of global emissions) which is transported offshore over the underlying semi-permanent cloud decks making the SE Atlantic a regional hotspot for BBA concentrations. While global climate models agree that this is a regional hotspot, their results diverge dramatically when attempting to assess aerosol-radiation-interactions and aerosol-cloud-interactions. Hence the area presents a very stringent test for climate models which need to capture not only the aerosol geographic, vertical, absorption and scattering properties, but also the cloud geographic distribution, vertical extent and cloud reflectance properties. Similarly, in order to capture the aerosol-cloud-interactions adequately, the susceptibility of the clouds in background conditions; aerosol activation processes; uncertainty about where and when BBA aerosol is entrained into the marine boundary layer and the impact of such entrainment on the microphysical and radiative properties of the cloud result in a large uncertainty. BBA overlying cloud also causes biases in satellite retrievals of cloud properties which can cause erroneous representation of stratocumulus cloud brightness; this has been shown to cause biases in other areas of the word such as biases in precipitation in Brazil via poorly understood global teleconnection processes. \r\n\r\n\r\nThese challenges can now be addressed as both measurement methods and high resolution model capabilities have developed rapidly and are now sufficiently advanced that the processes and properties of BBA can be sufficiently constrained. This measurement/high resolution model combination can be used to challenge the representation of aerosol-radiation-interaction and aerosol-cloud-interaction in coarser resolution numerical weather prediction (NWP) and climate models. Previous measurements in the region are limited to the basic measurements made during SAFARI-2000 when the advanced measurements needed for constraining the complex cloud-aerosol-radiation had not been developed and high resolution modelling was in its infancy.\r\n\r\nThe main aims of CLARIFY-2016 were to deliver a suite of ground and aircraft measurements which measure, understand, evaluate and improve:\r\na)\tthe physical, chemical, optical and radiative properties of BBAs\r\nb)\tthe physical properties of stratocumulus clouds\r\nc)\tthe representation of aerosol-radiation interactions in weather and climate models \r\nd)\tthe representation of aerosol-cloud interactions across a range of model scales. \r\n\r\nThe main field experiment took place during September 2016, based in Walvis Bay, Namibia. The UK large research aircraft (FAAM) was used to measure in-situ and remotely sensed aerosol and cloud properties while advanced radiometers on board the aircraft measured aerosol and cloud radiative impacts. This project was written on a stand-alone basis, but there was close collaboration and coordinating with both the NASA ORACLES programme (5 NASA centres, 8 USA universities) and NSF-funded ONFIRE programme (22 USA institutes).\r\n\r\n\r\nKey objectives of CLARIFY-2016 were:\r\nKO1: Measure and understand the physical, chemical, optical and radiative properties of BBAs in the key SE Atlantic region.\r\nKO2: Understand, evaluate and improve the physical properties of the SE Atlantic stratocumulus clouds and their environment in a range of models.\r\nKO3: Evaluate and improve the representation of BBA-radiation interactions over the SE Atlantic when clouds are absent/present at a range of model scales and resolutions. \r\nKO4: Evaluate and improve the representation of BBA-cloud interactions over the SE Atlantic at a range of model scales and resolutions.\r\n\r\nThese objectives were be achieved by conducting an intensive airborne field campaign with supporting surface and satellite measurements. The measurements were used to challenge, and develop improved models at different spatial scales from the cloud scale to the global scale that couple aerosols, clouds and radiation. \r\n\r\nThe enabling objectives were:- \r\nEO1: To use forecast and observations to optimise scheduling of the flight plans, balancing our operations to ensure data provision for all our enabling objectives.\r\nEO2: To characterise chemical, microphysical, optical and radiative properties of BBA over the SE Atlantic region, focussing on black carbon, absorption and single scattering albedo.\r\nEO3: To investigate the geographic and vertical profile of BBA over the region.\r\nEO4: To characterise the vertical thermodynamic structure of the MBL, residual continental polluted layer, and free troposphere and diurnal and synoptic scale variations. \r\nEO5: To characterise broad-band and spectral reflectance of the ocean surface and stratocumulus clouds when overlying BBA is present/absent from the atmospheric column.\r\nEO6: To characterise key cloud processes and parameters such as entrainment, cloud dynamics, cloud-base updraft velocities, cloud condensation nuclei (CCN), cloud droplet number concentrations (CDNC), cloud droplet effective radius, cloud liquid water path and optical depth. \r\nEO7: Use CLARIFY campaign data with representative statistical sampling as well as high-resolution models to establish robust relationships between sub-grid scale variables and large-scale model parameters suitable for constraining aerosol-cloud interactions. \r\nEO8: To use synergistic observations/model simulations to investigate impacts on NWP and climate model performance, feedback mechanisms, regional and global climate impacts and teleconnections.\r\n" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 1347, 3292, 3307, 3308, 3309, 3310, 3311, 3313, 3314, 3315, 3316, 3317, 3318, 3319, 3320, 3321, 3322, 3323, 3324, 3325, 3326, 3327, 3328, 3329, 3330, 3331, 3332, 3333, 3334, 3335, 3336, 3337, 3338, 3339, 3340, 3341, 3342, 3343, 3344, 3345, 14474, 14836, 14837, 14838, 14840, 14841, 14842, 14843, 14845, 14846, 14847, 14848, 14849, 14850, 14851, 14852, 14853, 14854, 14855, 14857, 14859, 14860, 14861, 14863, 14864, 14865, 14866, 14868, 14869, 14870, 14871, 14872, 14873, 14874, 14875, 14876, 14877, 14878, 14880, 14882, 14883, 14885, 14887, 14890, 14893, 14894, 14895, 14900, 14901, 14905, 14906, 14908, 14912, 14916, 14917, 14919, 14921, 14924, 14927, 14928, 14935, 14946, 15022, 15023, 15024, 15029, 15030, 15040, 15041, 15047, 15048, 15282, 15283, 15324, 15325, 15336, 15337, 15338, 15339, 15342, 15343, 15344, 15369, 15372, 15374, 15812, 15813, 15816, 15818, 15819, 15820, 15821, 15822, 15823, 15824, 15825, 15836, 15840, 15845, 15846, 15847, 15848, 16569, 16658, 17108, 17109, 18977, 20656, 20657, 20658, 20659, 20660, 20661, 20662, 20663, 20664, 20665, 20666, 20667, 20668, 20669, 20670, 20671, 20672, 20673, 20674, 20675, 20676, 20677, 20678, 20679, 20680, 20681, 20683, 20684, 20685, 20686, 20687, 20688, 20689, 20690, 20691, 20692, 20693, 20694, 20695, 20696, 20697, 20698, 20699, 21041, 21042, 21043, 21044, 21045, 21046, 21047, 21048, 21049, 22364, 22365, 22366, 22373, 22375, 22379, 22380, 22381, 32419, 32420, 32421, 32422, 32423, 32424, 32425, 32426, 32427, 32428, 32429, 32430, 32431, 32432, 32433 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 5782, "uuid": "affe775e8d8890a4556aec5bc4e0b45c", "short_code": "coll", "title": "Facility for Airborne Atmospheric Measurements (FAAM) flights", "abstract": "The FAAM is a large atmospheric research BAE-146 aircraft, run by the NERC (jointly with the UK Met Office until 2019). It has been in operation since March 2004 and is at the scientists' disposal through a scheme of project selection. \r\n\r\nData collected by this aircraft is stored in the FAAM data archive and includes \"core\" data, provided by the FAAM as a support to all flight campaigns, and \"non-core\" data, the nature of which depends on the scientific goal of the campaign.\r\n\r\nFAAM instruments provide four types of data: \r\n\r\n- parameters required for aircraft navigation; \r\n- meteorology; \r\n- cloud physics; \r\n- chemical composition. \r\n\r\nThe data are accompanied by extensive metadata, including flight logs. The FAAM apparatus includes a number of core instruments permanently onboard and operated by FAAM staff members, and a variety of other instruments, grouped into chemistry kit and cloud physics kit, that can be fitted onto the aircraft on demand. \r\nFAAM is also a member of the EUropean Facility for Airborne Research (EUFAR) fleet of research aircraft.\r\n\r\nAs per NERC data policy (see documents), FAAM data are openly available upon registration with the CEDA archive (anyone can register) under the Open Government Licence. Raw data are retained for longterm preservation but are not intended for general use." }, { "ob_id": 25119, "uuid": "38ab7089781a4560b067dd6c20af3769", "short_code": "coll", "title": "CLARIFY: in-situ airborne observations by the FAAM BAE-146 aircraft", "abstract": "In-situ airborne observations by the FAAM BAE-146 aircraft for CLouds and Aerosol Radiative Impacts and Forcing: CLARIFY" } ], "responsiblepartyinfo_set": [ 102996, 102997, 102998, 103001, 103002, 103003, 103004, 103005, 102999, 103000 ], "onlineresource_set": [ 23819, 23818 ] }, { "ob_id": 25123, "uuid": "1f171698c7f24ffc98431ae54fec6461", "title": "FAAM C017 STANCO flight: Airborne atmospheric measurements from core instrument suite on board the BAE-146 aircraft", "abstract": "Airborne atmospheric measurements from core instrument suite data on board the FAAM BAE-146 aircraft collected for STANCO School and Training on Aircraft New and well-established techniques for Atmospheric Composition Observation project.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2020-01-09T13:43:02", "updateFrequency": "asNeeded", "dataLineage": "Data were collected by instrument scientists during the flight before preparation and delivery for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "STANCO, FAAM, airborne, atmospheric measurments", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2017-08-03T12:15:31", "doiPublishedTime": "2018-01-18T21:15:49", "removedDataTime": null, "geographicExtent": { "ob_id": 1979, "bboxName": "", "eastBoundLongitude": 2.0722010135650635, "westBoundLongitude": -1.3252055644989014, "southBoundLatitude": 50.96731185913086, "northBoundLatitude": 52.65355682373047 }, "verticalExtent": null, "result_field": { "ob_id": 25122, "dataPath": "/badc/faam/data/2017/c017-jul-04", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 365730263, "numberOfFiles": 18, "fileFormat": "Data are netCDF and NASA-Ames formatted. Ancillary files may be plain ASCII or PDF formatted. Image files may be PNG formatted." }, "timePeriod": { "ob_id": 6797, "startTime": "2017-07-03T23:00:00", "endTime": "2017-07-04T22:59:59" }, "resultQuality": { "ob_id": 3074, "explanation": "Data collected by flight participants before preparation for archival with the Centre for Environmental Data Analysis (CEDA).", "passesTest": true, "resultTitle": "FAAM to CEDA Data Quality Statement", "date": "2015-09-03" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 25124, "uuid": "926d4ede0dfe4244b1ba5a821572bd01", "short_code": "acq", "title": "FAAM Flight C017 Acquisition", "abstract": "FAAM Flight C017 Acquisition" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 8 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 25114, "uuid": "09d7fd5586bd446aab8947274b15168f", "short_code": "proj", "title": "STANCO School and Training on Aircraft New and well-established techniques for Atmospheric Composition Observation", "abstract": "STANCO - School and Training on Aircraft New and well-established techniques for Atmospheric Composition Observation was a summer school funded by EUFAR in July 2017 at the University of Cambridge with flying on the FAAM BAe-146 aircraft based at Cranfield Airport. Flights used a scientific payload identical to most chemical campaign that includes O3, NOx, NOy (core chem instruments), CO2 and CH4 (Fast Greenhouse Gas Analyser), NO2, RO2NO2, RONO2 and HNO3 (LIF instrument), HNO3, HCN (CIMS instrument). The course was aimed at early career researchers and provided an introduction to airborne measurements, and to demonstrate how to use calibration data to retrieve atmospheric chemistry measurements. As the calibration of instruments is critical for atmospheric chemical species measurements, so detailed explanation of the main procedures for most of the instruments was given during the lectures followed by calibrations exercises on the ground and inflight exercises with instruments that need real-time calibrations. \r\n" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 14474, 14836, 14837, 14838, 14840, 14841, 14842, 14843, 14845, 14846, 14847, 14848, 14849, 14850, 14851, 14852, 14853, 14854, 14855, 14857, 14859, 14860, 14861, 14863, 14864, 14865, 14866, 14868, 14869, 14870, 14871, 14872, 14873, 14874, 14875, 14876, 14877, 14878, 14880, 14882, 14883, 14885, 14887, 14890, 14893, 14894, 14895, 14900, 14901, 14905, 14906, 14912, 14916, 14917, 14919, 14921, 14924, 14927, 14928, 14935, 14946, 15022, 15023, 15024, 15040, 15041, 15279, 15288, 15289, 15291, 15324, 15325, 15336, 15337, 15338, 15339, 15342, 15343, 15344, 15812, 15813, 15816, 15818, 15819, 15820, 15821, 15822, 15823, 15824, 15825, 15836, 15840, 15845, 15846, 15847, 15848, 16569, 16658, 17108, 17109, 18977, 20633, 20637, 20656, 20657, 20658, 20659, 20660, 20661, 20662, 20663, 20664, 20665, 20666, 20667, 20668, 20669, 20670, 20671, 20672, 20673, 20674, 20675, 20676, 20677, 20678, 20679, 20680, 20681, 20683, 20684, 20685, 20686, 20687, 20688, 20689, 20690, 20691, 20692, 20693, 20694, 20695, 20696, 20697, 20698, 20699, 21041, 21042, 21043, 21044, 21046, 22970, 22971, 22972, 22973, 22974, 22975, 22976, 22977, 22978, 22979, 22980, 22981 ], "vocabularyKeywords": [], "identifier_set": [ 9476 ], "observationcollection_set": [ { "ob_id": 5782, "uuid": "affe775e8d8890a4556aec5bc4e0b45c", "short_code": "coll", "title": "Facility for Airborne Atmospheric Measurements (FAAM) flights", "abstract": "The FAAM is a large atmospheric research BAE-146 aircraft, run by the NERC (jointly with the UK Met Office until 2019). It has been in operation since March 2004 and is at the scientists' disposal through a scheme of project selection. \r\n\r\nData collected by this aircraft is stored in the FAAM data archive and includes \"core\" data, provided by the FAAM as a support to all flight campaigns, and \"non-core\" data, the nature of which depends on the scientific goal of the campaign.\r\n\r\nFAAM instruments provide four types of data: \r\n\r\n- parameters required for aircraft navigation; \r\n- meteorology; \r\n- cloud physics; \r\n- chemical composition. \r\n\r\nThe data are accompanied by extensive metadata, including flight logs. 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Intercomparison flights took place with the FAAM BAe-146 aircraft during July 2017." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 3220, 3221, 3222, 3223, 3224, 3225, 3226, 3227, 3228, 3229, 3230, 3231, 3232, 3233, 3234, 3235, 3236, 3237, 3238, 3239, 3240, 3241, 3242, 3243, 3244, 3245, 3246, 3247, 3248, 3249, 3250, 3251, 3252, 3253, 3254, 3255, 3256, 3257, 3258, 3259, 3260, 3261, 3262, 3263, 3264, 3265, 3266, 3267, 3268, 3269, 3270, 3271, 3272, 3273, 3274, 3275, 3276, 3277, 3278, 3279, 3280, 3281, 3282, 3283, 3284, 3285, 3286, 3287, 3288, 3289, 3290, 3291, 3292, 3293, 3294, 3295, 3307, 3308, 3309, 3310, 3311, 3313, 3314, 3315, 3316, 3317, 3318, 3319, 3320, 3321, 3322, 3323, 3324, 3325, 3326, 3327, 3328, 3329, 3330, 3331, 3332, 3333, 3334, 3335, 3336, 3337, 3338, 3339, 3340, 3341, 3342, 3343, 3344, 3345, 14474, 14836, 14837, 14838, 14840, 14841, 14842, 14843, 14845, 14846, 14847, 14848, 14849, 14850, 14851, 14852, 14854, 14857, 14859, 14860, 14861, 14863, 14864, 14865, 14866, 14868, 14869, 14870, 14871, 14872, 14873, 14874, 14875, 14877, 14880, 14882, 14883, 14885, 14887, 14890, 14893, 14894, 14895, 14900, 14901, 14905, 14906, 14912, 14916, 14917, 14919, 14921, 14924, 14927, 14928, 14935, 14946, 15022, 15023, 15024, 15040, 15041, 15282, 15283, 15324, 15325, 15336, 15337, 15338, 15339, 15342, 15343, 15344, 15812, 15813, 15816, 15818, 15819, 15820, 15821, 15822, 15823, 15824, 15825, 15836, 15840, 15845, 15846, 15847, 15848, 16569, 16658, 17108, 17109, 18977, 20656, 20657, 20658, 20659, 20660, 20661, 20662, 20663, 20664, 20665, 20666, 20667, 20668, 20669, 20670, 20671, 20672, 20673, 20674, 20675, 20676, 20677, 20678, 20679, 20680, 20681, 20683, 20684, 20685, 20686, 20687, 20688, 20689, 20690, 20691, 20692, 20693, 20694, 20695, 20696, 20697, 20698, 20699, 21041, 21042, 21043, 21044, 21046 ], "vocabularyKeywords": [], "identifier_set": [ 9482 ], "observationcollection_set": [ { "ob_id": 5782, "uuid": "affe775e8d8890a4556aec5bc4e0b45c", "short_code": "coll", "title": "Facility for Airborne Atmospheric Measurements (FAAM) flights", "abstract": "The FAAM is a large atmospheric research BAE-146 aircraft, run by the NERC (jointly with the UK Met Office until 2019). It has been in operation since March 2004 and is at the scientists' disposal through a scheme of project selection. \r\n\r\nData collected by this aircraft is stored in the FAAM data archive and includes \"core\" data, provided by the FAAM as a support to all flight campaigns, and \"non-core\" data, the nature of which depends on the scientific goal of the campaign.\r\n\r\nFAAM instruments provide four types of data: \r\n\r\n- parameters required for aircraft navigation; \r\n- meteorology; \r\n- cloud physics; \r\n- chemical composition. \r\n\r\nThe data are accompanied by extensive metadata, including flight logs. The FAAM apparatus includes a number of core instruments permanently onboard and operated by FAAM staff members, and a variety of other instruments, grouped into chemistry kit and cloud physics kit, that can be fitted onto the aircraft on demand. \r\nFAAM is also a member of the EUropean Facility for Airborne Research (EUFAR) fleet of research aircraft.\r\n\r\nAs per NERC data policy (see documents), FAAM data are openly available upon registration with the CEDA archive (anyone can register) under the Open Government Licence. Raw data are retained for longterm preservation but are not intended for general use." }, { "ob_id": 6382, "uuid": "d40e4067ae0121b31bb1ba57e04707de", "short_code": "coll", "title": "EUFAR: Airborne in-situ atmospheric and hyperspectral data collection from funded flight campaigns", "abstract": "European Facility for Airborne Research in Environmental and Geo-sciences 2 (EUFAR 2) is an Integrating Activity of the 7th Framework Program of the European Commission following three previous contracts under FP5, FP6 and FP7, bringing together 24 European institutions and organisations involved in airborne research, operating 18 instrumented aircraft and providing access to 3 hyperspectral instruments.\r\n\r\nTo facilitate wide access and to maximise the potential of the valuable scientific data collected during the EUFAR FP7 projects (2008-2018), data are available through a single archive at BADC. The data itself is stored either in an accessible online archive operated by the aircraft operator (eg NERC-ARSF, FAAM-BAE-146) or lodged in the BADC EUFAR archive.\r\n\r\nData include measurements by airborne in situ atmospheric instuments and hyperspectral instruments operated on board the aircraft of the EUFAR fleet during projects funded under the transnational access part of EUFAR and during training events. The data available will vary from aircraft to aircraft depending on the instruments on board and the aims and flight patterns for each project.\r\n\r\nDuring the first FP7 project (EUFAR 2008-2013) thirteen aircraft and one additional instrument (APEX) were involved in 43 projects including a collaboration of data from volcanic ash flights following the eruption of the Icelandic volcano, Eyjafjallajokull, in April 2010. These projects are A-NEW, ACEMED, ADDRESSS, AEGEAN-GAME, AIMWETLAB, AIRES-CZM, ALISA, ARMSRACE, BIOHYPE, BLLATE1, BLLATE2, DeInVader, DeMinTIR, DRAMAC, EDOCROS, Eyjafjallajokull, GEOMAD, HABlakes, HYMEDECOS-Erosion, HyMedEcos-Gradients, HyMountEcos, HYMOWEB, HyperForest, HYPERSTRESS, i-WAKE2, ICARE-QAD, ICELAND_DEBRISFLOWS, IMROM, LADUNEX, MORE, RAIN4DUST, REFLEX, SEDMEDHY, SONATA, SRMGlaciers, SVALBD_PGLACIAL2, T-MAPP-FP7, TETRAD, UR-TIR, ValCalHyp, VESSAER, WaLiTemp. All expected data from these flight have now been archived.\r\n\r\nEUFAR2 (2014-2018) is collecting data for these projects: AHSPECT, DEHESHYRE, HIDHAZ_N_ICELAND, HILBILLY, HOLUHRAUN_HAZ, HYMOSENS2, HYPPOS, ISOTHERM, MEDHY2CON, SAVEX, SWAMP, URBSENSE. More will be added as they become available." } ], "responsiblepartyinfo_set": [ 103124, 103125, 103126, 103129, 103130, 103131, 103132, 103133, 103127, 106063, 103128 ], "onlineresource_set": [ 23841, 23840 ] }, { "ob_id": 25157, "uuid": "748c2522980545e98173f8698bc1638a", "title": "FAAM C027 CLARIFY Test flight: Airborne atmospheric measurements from core instrument suite on board the BAE-146 aircraft", "abstract": "Airborne atmospheric measurements from core instrument suite data on board the FAAM BAE-146 aircraft collected for CLouds and Aerosol Radiative Impacts and Forcing: Year 2016 (CLARIFY-2016) project.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2023-06-14T15:28:24", "updateFrequency": "asNeeded", "dataLineage": "Data were collected by instrument scientists during the flight before preparation and delivery for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "CLARIFY, FAAM, airborne, atmospheric measurments", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2017-08-06T23:15:08", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 2573, "bboxName": "", "eastBoundLongitude": 2.49, "westBoundLongitude": -0.61, "southBoundLatitude": 51.95, "northBoundLatitude": 53.0 }, "verticalExtent": null, "result_field": { "ob_id": 25156, "dataPath": "/badc/faam/data/2017/c027-aug-03", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 369762549, "numberOfFiles": 33, "fileFormat": "Data are netCDF and NASA-Ames formatted. 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Image files may be PNG formatted." }, "timePeriod": { "ob_id": 6806, "startTime": "2017-08-03T08:36:53", "endTime": "2017-08-03T12:48:49" }, "resultQuality": { "ob_id": 3074, "explanation": "Data collected by flight participants before preparation for archival with the Centre for Environmental Data Analysis (CEDA).", "passesTest": true, "resultTitle": "FAAM to CEDA Data Quality Statement", "date": "2015-09-03" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 25158, "uuid": "5d07cf3bb98a48fe9eaa0be56ef77636", "short_code": "acq", "title": "FAAM Flight C027 Acquisition", "abstract": "FAAM Flight C027 Acquisition" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 8 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 12132, "uuid": "a3f231fadf7641c6993c7a67d52bcd2f", "short_code": "proj", "title": "CLouds and Aerosol Radiative Impacts and Forcing: Year 2016 (CLARIFY-2016)", "abstract": "The CLARIFY-2016 project was consortium of 5 university partners and the UK Met Office who aimed to measure and understand the physical, chemical, optical and radiative properties of BBAs (biomass burning aerosol) in the key South East Atlantic region. This project was funded by the Natural Environment Research Council (NERC) with the grant reference - NE/L013797/1 - and was led by Professor James Haywood (University of Exeter).\r\n\r\nBiomass burning aerosol (BBA) exerts a considerable impact on climate by impacting regional radiation budgets as it significantly reflects and absorbs sunlight, and its cloud nucleating properties perturb cloud microphysics and hence affect cloud radiative properties, precipitation and cloud lifetime. However, BBA is a complex and poorly understood aerosol species as it consists of a complex cocktail of organic carbon and inorganic compounds mixed with black carbon and hence large uncertainties exist in both the aerosol-radiation-interactions and aerosol-cloud-interactions, uncertainties that limit the ability of our current climate models to accurately reconstruct past climate and predict future climate change.\r\n \r\nThe African continent is the largest global source of BBA (around 50% of global emissions) which is transported offshore over the underlying semi-permanent cloud decks making the SE Atlantic a regional hotspot for BBA concentrations. While global climate models agree that this is a regional hotspot, their results diverge dramatically when attempting to assess aerosol-radiation-interactions and aerosol-cloud-interactions. Hence the area presents a very stringent test for climate models which need to capture not only the aerosol geographic, vertical, absorption and scattering properties, but also the cloud geographic distribution, vertical extent and cloud reflectance properties. Similarly, in order to capture the aerosol-cloud-interactions adequately, the susceptibility of the clouds in background conditions; aerosol activation processes; uncertainty about where and when BBA aerosol is entrained into the marine boundary layer and the impact of such entrainment on the microphysical and radiative properties of the cloud result in a large uncertainty. BBA overlying cloud also causes biases in satellite retrievals of cloud properties which can cause erroneous representation of stratocumulus cloud brightness; this has been shown to cause biases in other areas of the word such as biases in precipitation in Brazil via poorly understood global teleconnection processes. \r\n\r\n\r\nThese challenges can now be addressed as both measurement methods and high resolution model capabilities have developed rapidly and are now sufficiently advanced that the processes and properties of BBA can be sufficiently constrained. This measurement/high resolution model combination can be used to challenge the representation of aerosol-radiation-interaction and aerosol-cloud-interaction in coarser resolution numerical weather prediction (NWP) and climate models. Previous measurements in the region are limited to the basic measurements made during SAFARI-2000 when the advanced measurements needed for constraining the complex cloud-aerosol-radiation had not been developed and high resolution modelling was in its infancy.\r\n\r\nThe main aims of CLARIFY-2016 were to deliver a suite of ground and aircraft measurements which measure, understand, evaluate and improve:\r\na)\tthe physical, chemical, optical and radiative properties of BBAs\r\nb)\tthe physical properties of stratocumulus clouds\r\nc)\tthe representation of aerosol-radiation interactions in weather and climate models \r\nd)\tthe representation of aerosol-cloud interactions across a range of model scales. \r\n\r\nThe main field experiment took place during September 2016, based in Walvis Bay, Namibia. The UK large research aircraft (FAAM) was used to measure in-situ and remotely sensed aerosol and cloud properties while advanced radiometers on board the aircraft measured aerosol and cloud radiative impacts. This project was written on a stand-alone basis, but there was close collaboration and coordinating with both the NASA ORACLES programme (5 NASA centres, 8 USA universities) and NSF-funded ONFIRE programme (22 USA institutes).\r\n\r\n\r\nKey objectives of CLARIFY-2016 were:\r\nKO1: Measure and understand the physical, chemical, optical and radiative properties of BBAs in the key SE Atlantic region.\r\nKO2: Understand, evaluate and improve the physical properties of the SE Atlantic stratocumulus clouds and their environment in a range of models.\r\nKO3: Evaluate and improve the representation of BBA-radiation interactions over the SE Atlantic when clouds are absent/present at a range of model scales and resolutions. \r\nKO4: Evaluate and improve the representation of BBA-cloud interactions over the SE Atlantic at a range of model scales and resolutions.\r\n\r\nThese objectives were be achieved by conducting an intensive airborne field campaign with supporting surface and satellite measurements. The measurements were used to challenge, and develop improved models at different spatial scales from the cloud scale to the global scale that couple aerosols, clouds and radiation. \r\n\r\nThe enabling objectives were:- \r\nEO1: To use forecast and observations to optimise scheduling of the flight plans, balancing our operations to ensure data provision for all our enabling objectives.\r\nEO2: To characterise chemical, microphysical, optical and radiative properties of BBA over the SE Atlantic region, focussing on black carbon, absorption and single scattering albedo.\r\nEO3: To investigate the geographic and vertical profile of BBA over the region.\r\nEO4: To characterise the vertical thermodynamic structure of the MBL, residual continental polluted layer, and free troposphere and diurnal and synoptic scale variations. \r\nEO5: To characterise broad-band and spectral reflectance of the ocean surface and stratocumulus clouds when overlying BBA is present/absent from the atmospheric column.\r\nEO6: To characterise key cloud processes and parameters such as entrainment, cloud dynamics, cloud-base updraft velocities, cloud condensation nuclei (CCN), cloud droplet number concentrations (CDNC), cloud droplet effective radius, cloud liquid water path and optical depth. \r\nEO7: Use CLARIFY campaign data with representative statistical sampling as well as high-resolution models to establish robust relationships between sub-grid scale variables and large-scale model parameters suitable for constraining aerosol-cloud interactions. \r\nEO8: To use synergistic observations/model simulations to investigate impacts on NWP and climate model performance, feedback mechanisms, regional and global climate impacts and teleconnections.\r\n" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 1633, 3220, 3221, 3222, 3223, 3224, 3225, 3226, 3227, 3228, 3229, 3230, 3231, 3232, 3233, 3234, 3235, 3236, 3237, 3238, 3239, 3240, 3241, 3242, 3243, 3244, 3245, 3246, 3247, 3248, 3249, 3250, 3251, 3252, 3253, 3254, 3255, 3256, 3257, 3258, 3259, 3260, 3261, 3262, 3263, 3264, 3265, 3266, 3267, 3268, 3269, 3270, 3271, 3272, 3273, 3274, 3275, 3276, 3277, 3278, 3279, 3280, 3281, 3282, 3283, 3284, 3285, 3286, 3287, 3288, 3289, 3290, 3291, 3292, 3293, 3294, 3295, 3307, 3315, 3316, 3317, 3318, 3319, 3320, 3321, 3322, 3323, 3324, 3325, 3326, 3327, 3328, 3329, 3330, 3331, 3332, 3333, 3334, 3335, 3336, 3337, 3338, 3339, 3340, 3341, 3342, 3343, 3344, 3345, 7762, 7763, 8560, 14474, 14811, 14812, 14813, 14814, 14815, 14816, 14817, 14818, 14819, 14820, 14821, 14822, 14823, 14824, 14825, 14826, 14827, 14828, 14829, 14830, 14831, 14832, 14833, 14834, 14835, 14836, 14837, 14838, 14840, 14841, 14842, 14843, 14845, 14846, 14847, 14848, 14849, 14850, 14851, 14852, 14853, 14854, 14855, 14857, 14859, 14860, 14861, 14863, 14864, 14865, 14866, 14868, 14869, 14870, 14871, 14872, 14873, 14874, 14875, 14876, 14877, 14878, 14880, 14882, 14883, 14885, 14887, 14890, 14893, 14894, 14895, 14900, 14901, 14905, 14906, 14908, 14912, 14916, 14917, 14919, 14921, 14924, 14927, 14928, 14935, 14946, 15022, 15023, 15024, 15040, 15041, 15282, 15283, 15324, 15325, 15336, 15337, 15338, 15339, 15342, 15343, 15344, 15750, 15812, 15813, 15816, 15818, 15819, 15820, 15821, 15822, 15823, 15824, 15825, 15836, 15840, 15845, 15846, 15847, 15848, 15855, 15857, 15859, 15864, 16569, 16658, 17108, 17109, 18977, 20656, 20657, 20658, 20659, 20660, 20661, 20662, 20663, 20664, 20665, 20666, 20667, 20668, 20669, 20670, 20671, 20672, 20673, 20674, 20675, 20676, 20677, 20678, 20679, 20680, 20681, 20683, 20684, 20685, 20686, 20687, 20688, 20689, 20690, 20691, 20692, 20693, 20694, 20695, 20696, 20697, 20698, 20699, 21041, 21042, 21043, 21044, 21045, 21046, 21048, 21049, 22364, 22373, 22379, 22380, 26137, 26138, 26139, 26140 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 5782, "uuid": "affe775e8d8890a4556aec5bc4e0b45c", "short_code": "coll", "title": "Facility for Airborne Atmospheric Measurements (FAAM) flights", "abstract": "The FAAM is a large atmospheric research BAE-146 aircraft, run by the NERC (jointly with the UK Met Office until 2019). It has been in operation since March 2004 and is at the scientists' disposal through a scheme of project selection. \r\n\r\nData collected by this aircraft is stored in the FAAM data archive and includes \"core\" data, provided by the FAAM as a support to all flight campaigns, and \"non-core\" data, the nature of which depends on the scientific goal of the campaign.\r\n\r\nFAAM instruments provide four types of data: \r\n\r\n- parameters required for aircraft navigation; \r\n- meteorology; \r\n- cloud physics; \r\n- chemical composition. \r\n\r\nThe data are accompanied by extensive metadata, including flight logs. The FAAM apparatus includes a number of core instruments permanently onboard and operated by FAAM staff members, and a variety of other instruments, grouped into chemistry kit and cloud physics kit, that can be fitted onto the aircraft on demand. \r\nFAAM is also a member of the EUropean Facility for Airborne Research (EUFAR) fleet of research aircraft.\r\n\r\nAs per NERC data policy (see documents), FAAM data are openly available upon registration with the CEDA archive (anyone can register) under the Open Government Licence. Raw data are retained for longterm preservation but are not intended for general use." }, { "ob_id": 25119, "uuid": "38ab7089781a4560b067dd6c20af3769", "short_code": "coll", "title": "CLARIFY: in-situ airborne observations by the FAAM BAE-146 aircraft", "abstract": "In-situ airborne observations by the FAAM BAE-146 aircraft for CLouds and Aerosol Radiative Impacts and Forcing: CLARIFY" } ], "responsiblepartyinfo_set": [ 103138, 103139, 103140, 103143, 103144, 103145, 103146, 103147, 103141, 103142 ], "onlineresource_set": [ 23844, 23843 ] }, { "ob_id": 25161, "uuid": "e40623b79b814cbdbebf2b3c57e4360e", "title": "FAAM C053 CLARIFY flight: Airborne atmospheric measurements from core instrument suite on board the BAE-146 aircraft", "abstract": "Airborne atmospheric measurements from core instrument suite data on board the FAAM BAE-146 aircraft collected for CLouds and Aerosol Radiative Impacts and Forcing: Year 2016 (CLARIFY-2016) project.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2023-06-14T15:22:53", "updateFrequency": "asNeeded", "dataLineage": "Data were collected by instrument scientists during the flight before preparation and delivery for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "CLARIFY, FAAM, airborne, atmospheric measurments", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2017-09-19T13:47:19.253331", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 4277, "bboxName": "", "eastBoundLongitude": -14.403863, "westBoundLongitude": -15.299023, "southBoundLatitude": -8.8638687, "northBoundLatitude": -6.7254543 }, "verticalExtent": null, "result_field": { "ob_id": 25160, "dataPath": "/badc/faam/data/2017/c053-sep-06", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 2091248721, "numberOfFiles": 58, "fileFormat": "Data are netCDF and NASA-Ames formatted. Ancillary files may be plain ASCII or PDF formatted. Image files may be PNG formatted." }, "timePeriod": { "ob_id": 6807, "startTime": "2017-09-05T23:00:00", "endTime": "2017-09-06T22:59:59" }, "resultQuality": { "ob_id": 3074, "explanation": "Data collected by flight participants before preparation for archival with the Centre for Environmental Data Analysis (CEDA).", "passesTest": true, "resultTitle": "FAAM to CEDA Data Quality Statement", "date": "2015-09-03" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 25162, "uuid": "de17e8a6baeb4356afabeeb28f4db58c", "short_code": "acq", "title": "FAAM Flight C053 Acquisition", "abstract": "FAAM Flight C053 Acquisition" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 8 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 12132, "uuid": "a3f231fadf7641c6993c7a67d52bcd2f", "short_code": "proj", "title": "CLouds and Aerosol Radiative Impacts and Forcing: Year 2016 (CLARIFY-2016)", "abstract": "The CLARIFY-2016 project was consortium of 5 university partners and the UK Met Office who aimed to measure and understand the physical, chemical, optical and radiative properties of BBAs (biomass burning aerosol) in the key South East Atlantic region. This project was funded by the Natural Environment Research Council (NERC) with the grant reference - NE/L013797/1 - and was led by Professor James Haywood (University of Exeter).\r\n\r\nBiomass burning aerosol (BBA) exerts a considerable impact on climate by impacting regional radiation budgets as it significantly reflects and absorbs sunlight, and its cloud nucleating properties perturb cloud microphysics and hence affect cloud radiative properties, precipitation and cloud lifetime. However, BBA is a complex and poorly understood aerosol species as it consists of a complex cocktail of organic carbon and inorganic compounds mixed with black carbon and hence large uncertainties exist in both the aerosol-radiation-interactions and aerosol-cloud-interactions, uncertainties that limit the ability of our current climate models to accurately reconstruct past climate and predict future climate change.\r\n \r\nThe African continent is the largest global source of BBA (around 50% of global emissions) which is transported offshore over the underlying semi-permanent cloud decks making the SE Atlantic a regional hotspot for BBA concentrations. While global climate models agree that this is a regional hotspot, their results diverge dramatically when attempting to assess aerosol-radiation-interactions and aerosol-cloud-interactions. Hence the area presents a very stringent test for climate models which need to capture not only the aerosol geographic, vertical, absorption and scattering properties, but also the cloud geographic distribution, vertical extent and cloud reflectance properties. Similarly, in order to capture the aerosol-cloud-interactions adequately, the susceptibility of the clouds in background conditions; aerosol activation processes; uncertainty about where and when BBA aerosol is entrained into the marine boundary layer and the impact of such entrainment on the microphysical and radiative properties of the cloud result in a large uncertainty. BBA overlying cloud also causes biases in satellite retrievals of cloud properties which can cause erroneous representation of stratocumulus cloud brightness; this has been shown to cause biases in other areas of the word such as biases in precipitation in Brazil via poorly understood global teleconnection processes. \r\n\r\n\r\nThese challenges can now be addressed as both measurement methods and high resolution model capabilities have developed rapidly and are now sufficiently advanced that the processes and properties of BBA can be sufficiently constrained. This measurement/high resolution model combination can be used to challenge the representation of aerosol-radiation-interaction and aerosol-cloud-interaction in coarser resolution numerical weather prediction (NWP) and climate models. Previous measurements in the region are limited to the basic measurements made during SAFARI-2000 when the advanced measurements needed for constraining the complex cloud-aerosol-radiation had not been developed and high resolution modelling was in its infancy.\r\n\r\nThe main aims of CLARIFY-2016 were to deliver a suite of ground and aircraft measurements which measure, understand, evaluate and improve:\r\na)\tthe physical, chemical, optical and radiative properties of BBAs\r\nb)\tthe physical properties of stratocumulus clouds\r\nc)\tthe representation of aerosol-radiation interactions in weather and climate models \r\nd)\tthe representation of aerosol-cloud interactions across a range of model scales. \r\n\r\nThe main field experiment took place during September 2016, based in Walvis Bay, Namibia. The UK large research aircraft (FAAM) was used to measure in-situ and remotely sensed aerosol and cloud properties while advanced radiometers on board the aircraft measured aerosol and cloud radiative impacts. This project was written on a stand-alone basis, but there was close collaboration and coordinating with both the NASA ORACLES programme (5 NASA centres, 8 USA universities) and NSF-funded ONFIRE programme (22 USA institutes).\r\n\r\n\r\nKey objectives of CLARIFY-2016 were:\r\nKO1: Measure and understand the physical, chemical, optical and radiative properties of BBAs in the key SE Atlantic region.\r\nKO2: Understand, evaluate and improve the physical properties of the SE Atlantic stratocumulus clouds and their environment in a range of models.\r\nKO3: Evaluate and improve the representation of BBA-radiation interactions over the SE Atlantic when clouds are absent/present at a range of model scales and resolutions. \r\nKO4: Evaluate and improve the representation of BBA-cloud interactions over the SE Atlantic at a range of model scales and resolutions.\r\n\r\nThese objectives were be achieved by conducting an intensive airborne field campaign with supporting surface and satellite measurements. 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This project was funded by the Natural Environment Research Council (NERC) with the grant reference - NE/L013797/1 - and was led by Professor James Haywood (University of Exeter).\r\n\r\nBiomass burning aerosol (BBA) exerts a considerable impact on climate by impacting regional radiation budgets as it significantly reflects and absorbs sunlight, and its cloud nucleating properties perturb cloud microphysics and hence affect cloud radiative properties, precipitation and cloud lifetime. However, BBA is a complex and poorly understood aerosol species as it consists of a complex cocktail of organic carbon and inorganic compounds mixed with black carbon and hence large uncertainties exist in both the aerosol-radiation-interactions and aerosol-cloud-interactions, uncertainties that limit the ability of our current climate models to accurately reconstruct past climate and predict future climate change.\r\n \r\nThe African continent is the largest global source of BBA (around 50% of global emissions) which is transported offshore over the underlying semi-permanent cloud decks making the SE Atlantic a regional hotspot for BBA concentrations. While global climate models agree that this is a regional hotspot, their results diverge dramatically when attempting to assess aerosol-radiation-interactions and aerosol-cloud-interactions. Hence the area presents a very stringent test for climate models which need to capture not only the aerosol geographic, vertical, absorption and scattering properties, but also the cloud geographic distribution, vertical extent and cloud reflectance properties. Similarly, in order to capture the aerosol-cloud-interactions adequately, the susceptibility of the clouds in background conditions; aerosol activation processes; uncertainty about where and when BBA aerosol is entrained into the marine boundary layer and the impact of such entrainment on the microphysical and radiative properties of the cloud result in a large uncertainty. BBA overlying cloud also causes biases in satellite retrievals of cloud properties which can cause erroneous representation of stratocumulus cloud brightness; this has been shown to cause biases in other areas of the word such as biases in precipitation in Brazil via poorly understood global teleconnection processes. \r\n\r\n\r\nThese challenges can now be addressed as both measurement methods and high resolution model capabilities have developed rapidly and are now sufficiently advanced that the processes and properties of BBA can be sufficiently constrained. This measurement/high resolution model combination can be used to challenge the representation of aerosol-radiation-interaction and aerosol-cloud-interaction in coarser resolution numerical weather prediction (NWP) and climate models. Previous measurements in the region are limited to the basic measurements made during SAFARI-2000 when the advanced measurements needed for constraining the complex cloud-aerosol-radiation had not been developed and high resolution modelling was in its infancy.\r\n\r\nThe main aims of CLARIFY-2016 were to deliver a suite of ground and aircraft measurements which measure, understand, evaluate and improve:\r\na)\tthe physical, chemical, optical and radiative properties of BBAs\r\nb)\tthe physical properties of stratocumulus clouds\r\nc)\tthe representation of aerosol-radiation interactions in weather and climate models \r\nd)\tthe representation of aerosol-cloud interactions across a range of model scales. \r\n\r\nThe main field experiment took place during September 2016, based in Walvis Bay, Namibia. The UK large research aircraft (FAAM) was used to measure in-situ and remotely sensed aerosol and cloud properties while advanced radiometers on board the aircraft measured aerosol and cloud radiative impacts. This project was written on a stand-alone basis, but there was close collaboration and coordinating with both the NASA ORACLES programme (5 NASA centres, 8 USA universities) and NSF-funded ONFIRE programme (22 USA institutes).\r\n\r\n\r\nKey objectives of CLARIFY-2016 were:\r\nKO1: Measure and understand the physical, chemical, optical and radiative properties of BBAs in the key SE Atlantic region.\r\nKO2: Understand, evaluate and improve the physical properties of the SE Atlantic stratocumulus clouds and their environment in a range of models.\r\nKO3: Evaluate and improve the representation of BBA-radiation interactions over the SE Atlantic when clouds are absent/present at a range of model scales and resolutions. \r\nKO4: Evaluate and improve the representation of BBA-cloud interactions over the SE Atlantic at a range of model scales and resolutions.\r\n\r\nThese objectives were be achieved by conducting an intensive airborne field campaign with supporting surface and satellite measurements. 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However, BBA is a complex and poorly understood aerosol species as it consists of a complex cocktail of organic carbon and inorganic compounds mixed with black carbon and hence large uncertainties exist in both the aerosol-radiation-interactions and aerosol-cloud-interactions, uncertainties that limit the ability of our current climate models to accurately reconstruct past climate and predict future climate change.\r\n \r\nThe African continent is the largest global source of BBA (around 50% of global emissions) which is transported offshore over the underlying semi-permanent cloud decks making the SE Atlantic a regional hotspot for BBA concentrations. While global climate models agree that this is a regional hotspot, their results diverge dramatically when attempting to assess aerosol-radiation-interactions and aerosol-cloud-interactions. 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BBA overlying cloud also causes biases in satellite retrievals of cloud properties which can cause erroneous representation of stratocumulus cloud brightness; this has been shown to cause biases in other areas of the word such as biases in precipitation in Brazil via poorly understood global teleconnection processes. \r\n\r\n\r\nThese challenges can now be addressed as both measurement methods and high resolution model capabilities have developed rapidly and are now sufficiently advanced that the processes and properties of BBA can be sufficiently constrained. This measurement/high resolution model combination can be used to challenge the representation of aerosol-radiation-interaction and aerosol-cloud-interaction in coarser resolution numerical weather prediction (NWP) and climate models. 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The UK large research aircraft (FAAM) was used to measure in-situ and remotely sensed aerosol and cloud properties while advanced radiometers on board the aircraft measured aerosol and cloud radiative impacts. 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It has been in operation since March 2004 and is at the scientists' disposal through a scheme of project selection. \r\n\r\nData collected by this aircraft is stored in the FAAM data archive and includes \"core\" data, provided by the FAAM as a support to all flight campaigns, and \"non-core\" data, the nature of which depends on the scientific goal of the campaign.\r\n\r\nFAAM instruments provide four types of data: \r\n\r\n- parameters required for aircraft navigation; \r\n- meteorology; \r\n- cloud physics; \r\n- chemical composition. \r\n\r\nThe data are accompanied by extensive metadata, including flight logs. The FAAM apparatus includes a number of core instruments permanently onboard and operated by FAAM staff members, and a variety of other instruments, grouped into chemistry kit and cloud physics kit, that can be fitted onto the aircraft on demand. \r\nFAAM is also a member of the EUropean Facility for Airborne Research (EUFAR) fleet of research aircraft.\r\n\r\nAs per NERC data policy (see documents), FAAM data are openly available upon registration with the CEDA archive (anyone can register) under the Open Government Licence. 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This project was funded by the Natural Environment Research Council (NERC) with the grant reference - NE/L013797/1 - and was led by Professor James Haywood (University of Exeter).\r\n\r\nBiomass burning aerosol (BBA) exerts a considerable impact on climate by impacting regional radiation budgets as it significantly reflects and absorbs sunlight, and its cloud nucleating properties perturb cloud microphysics and hence affect cloud radiative properties, precipitation and cloud lifetime. However, BBA is a complex and poorly understood aerosol species as it consists of a complex cocktail of organic carbon and inorganic compounds mixed with black carbon and hence large uncertainties exist in both the aerosol-radiation-interactions and aerosol-cloud-interactions, uncertainties that limit the ability of our current climate models to accurately reconstruct past climate and predict future climate change.\r\n \r\nThe African continent is the largest global source of BBA (around 50% of global emissions) which is transported offshore over the underlying semi-permanent cloud decks making the SE Atlantic a regional hotspot for BBA concentrations. While global climate models agree that this is a regional hotspot, their results diverge dramatically when attempting to assess aerosol-radiation-interactions and aerosol-cloud-interactions. 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BBA overlying cloud also causes biases in satellite retrievals of cloud properties which can cause erroneous representation of stratocumulus cloud brightness; this has been shown to cause biases in other areas of the word such as biases in precipitation in Brazil via poorly understood global teleconnection processes. \r\n\r\n\r\nThese challenges can now be addressed as both measurement methods and high resolution model capabilities have developed rapidly and are now sufficiently advanced that the processes and properties of BBA can be sufficiently constrained. This measurement/high resolution model combination can be used to challenge the representation of aerosol-radiation-interaction and aerosol-cloud-interaction in coarser resolution numerical weather prediction (NWP) and climate models. Previous measurements in the region are limited to the basic measurements made during SAFARI-2000 when the advanced measurements needed for constraining the complex cloud-aerosol-radiation had not been developed and high resolution modelling was in its infancy.\r\n\r\nThe main aims of CLARIFY-2016 were to deliver a suite of ground and aircraft measurements which measure, understand, evaluate and improve:\r\na)\tthe physical, chemical, optical and radiative properties of BBAs\r\nb)\tthe physical properties of stratocumulus clouds\r\nc)\tthe representation of aerosol-radiation interactions in weather and climate models \r\nd)\tthe representation of aerosol-cloud interactions across a range of model scales. \r\n\r\nThe main field experiment took place during September 2016, based in Walvis Bay, Namibia. The UK large research aircraft (FAAM) was used to measure in-situ and remotely sensed aerosol and cloud properties while advanced radiometers on board the aircraft measured aerosol and cloud radiative impacts. This project was written on a stand-alone basis, but there was close collaboration and coordinating with both the NASA ORACLES programme (5 NASA centres, 8 USA universities) and NSF-funded ONFIRE programme (22 USA institutes).\r\n\r\n\r\nKey objectives of CLARIFY-2016 were:\r\nKO1: Measure and understand the physical, chemical, optical and radiative properties of BBAs in the key SE Atlantic region.\r\nKO2: Understand, evaluate and improve the physical properties of the SE Atlantic stratocumulus clouds and their environment in a range of models.\r\nKO3: Evaluate and improve the representation of BBA-radiation interactions over the SE Atlantic when clouds are absent/present at a range of model scales and resolutions. \r\nKO4: Evaluate and improve the representation of BBA-cloud interactions over the SE Atlantic at a range of model scales and resolutions.\r\n\r\nThese objectives were be achieved by conducting an intensive airborne field campaign with supporting surface and satellite measurements. 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However, BBA is a complex and poorly understood aerosol species as it consists of a complex cocktail of organic carbon and inorganic compounds mixed with black carbon and hence large uncertainties exist in both the aerosol-radiation-interactions and aerosol-cloud-interactions, uncertainties that limit the ability of our current climate models to accurately reconstruct past climate and predict future climate change.\r\n \r\nThe African continent is the largest global source of BBA (around 50% of global emissions) which is transported offshore over the underlying semi-permanent cloud decks making the SE Atlantic a regional hotspot for BBA concentrations. While global climate models agree that this is a regional hotspot, their results diverge dramatically when attempting to assess aerosol-radiation-interactions and aerosol-cloud-interactions. 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Previous measurements in the region are limited to the basic measurements made during SAFARI-2000 when the advanced measurements needed for constraining the complex cloud-aerosol-radiation had not been developed and high resolution modelling was in its infancy.\r\n\r\nThe main aims of CLARIFY-2016 were to deliver a suite of ground and aircraft measurements which measure, understand, evaluate and improve:\r\na)\tthe physical, chemical, optical and radiative properties of BBAs\r\nb)\tthe physical properties of stratocumulus clouds\r\nc)\tthe representation of aerosol-radiation interactions in weather and climate models \r\nd)\tthe representation of aerosol-cloud interactions across a range of model scales. \r\n\r\nThe main field experiment took place during September 2016, based in Walvis Bay, Namibia. The UK large research aircraft (FAAM) was used to measure in-situ and remotely sensed aerosol and cloud properties while advanced radiometers on board the aircraft measured aerosol and cloud radiative impacts. This project was written on a stand-alone basis, but there was close collaboration and coordinating with both the NASA ORACLES programme (5 NASA centres, 8 USA universities) and NSF-funded ONFIRE programme (22 USA institutes).\r\n\r\n\r\nKey objectives of CLARIFY-2016 were:\r\nKO1: Measure and understand the physical, chemical, optical and radiative properties of BBAs in the key SE Atlantic region.\r\nKO2: Understand, evaluate and improve the physical properties of the SE Atlantic stratocumulus clouds and their environment in a range of models.\r\nKO3: Evaluate and improve the representation of BBA-radiation interactions over the SE Atlantic when clouds are absent/present at a range of model scales and resolutions. \r\nKO4: Evaluate and improve the representation of BBA-cloud interactions over the SE Atlantic at a range of model scales and resolutions.\r\n\r\nThese objectives were be achieved by conducting an intensive airborne field campaign with supporting surface and satellite measurements. The measurements were used to challenge, and develop improved models at different spatial scales from the cloud scale to the global scale that couple aerosols, clouds and radiation. \r\n\r\nThe enabling objectives were:- \r\nEO1: To use forecast and observations to optimise scheduling of the flight plans, balancing our operations to ensure data provision for all our enabling objectives.\r\nEO2: To characterise chemical, microphysical, optical and radiative properties of BBA over the SE Atlantic region, focussing on black carbon, absorption and single scattering albedo.\r\nEO3: To investigate the geographic and vertical profile of BBA over the region.\r\nEO4: To characterise the vertical thermodynamic structure of the MBL, residual continental polluted layer, and free troposphere and diurnal and synoptic scale variations. \r\nEO5: To characterise broad-band and spectral reflectance of the ocean surface and stratocumulus clouds when overlying BBA is present/absent from the atmospheric 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This project was funded by the Natural Environment Research Council (NERC) with the grant reference - NE/L013797/1 - and was led by Professor James Haywood (University of Exeter).\r\n\r\nBiomass burning aerosol (BBA) exerts a considerable impact on climate by impacting regional radiation budgets as it significantly reflects and absorbs sunlight, and its cloud nucleating properties perturb cloud microphysics and hence affect cloud radiative properties, precipitation and cloud lifetime. However, BBA is a complex and poorly understood aerosol species as it consists of a complex cocktail of organic carbon and inorganic compounds mixed with black carbon and hence large uncertainties exist in both the aerosol-radiation-interactions and aerosol-cloud-interactions, uncertainties that limit the ability of our current climate models to accurately reconstruct past climate and predict future climate change.\r\n \r\nThe African continent is the largest global source of BBA (around 50% of global emissions) which is transported offshore over the underlying semi-permanent cloud decks making the SE Atlantic a regional hotspot for BBA concentrations. While global climate models agree that this is a regional hotspot, their results diverge dramatically when attempting to assess aerosol-radiation-interactions and aerosol-cloud-interactions. Hence the area presents a very stringent test for climate models which need to capture not only the aerosol geographic, vertical, absorption and scattering properties, but also the cloud geographic distribution, vertical extent and cloud reflectance properties. Similarly, in order to capture the aerosol-cloud-interactions adequately, the susceptibility of the clouds in background conditions; aerosol activation processes; uncertainty about where and when BBA aerosol is entrained into the marine boundary layer and the impact of such entrainment on the microphysical and radiative properties of the cloud result in a large uncertainty. BBA overlying cloud also causes biases in satellite retrievals of cloud properties which can cause erroneous representation of stratocumulus cloud brightness; this has been shown to cause biases in other areas of the word such as biases in precipitation in Brazil via poorly understood global teleconnection processes. \r\n\r\n\r\nThese challenges can now be addressed as both measurement methods and high resolution model capabilities have developed rapidly and are now sufficiently advanced that the processes and properties of BBA can be sufficiently constrained. This measurement/high resolution model combination can be used to challenge the representation of aerosol-radiation-interaction and aerosol-cloud-interaction in coarser resolution numerical weather prediction (NWP) and climate models. Previous measurements in the region are limited to the basic measurements made during SAFARI-2000 when the advanced measurements needed for constraining the complex cloud-aerosol-radiation had not been developed and high resolution modelling was in its infancy.\r\n\r\nThe main aims of CLARIFY-2016 were to deliver a suite of ground and aircraft measurements which measure, understand, evaluate and improve:\r\na)\tthe physical, chemical, optical and radiative properties of BBAs\r\nb)\tthe physical properties of stratocumulus clouds\r\nc)\tthe representation of aerosol-radiation interactions in weather and climate models \r\nd)\tthe representation of aerosol-cloud interactions across a range of model scales. \r\n\r\nThe main field experiment took place during September 2016, based in Walvis Bay, Namibia. The UK large research aircraft (FAAM) was used to measure in-situ and remotely sensed aerosol and cloud properties while advanced radiometers on board the aircraft measured aerosol and cloud radiative impacts. This project was written on a stand-alone basis, but there was close collaboration and coordinating with both the NASA ORACLES programme (5 NASA centres, 8 USA universities) and NSF-funded ONFIRE programme (22 USA institutes).\r\n\r\n\r\nKey objectives of CLARIFY-2016 were:\r\nKO1: Measure and understand the physical, chemical, optical and radiative properties of BBAs in the key SE Atlantic region.\r\nKO2: Understand, evaluate and improve the physical properties of the SE Atlantic stratocumulus clouds and their environment in a range of models.\r\nKO3: Evaluate and improve the representation of BBA-radiation interactions over the SE Atlantic when clouds are absent/present at a range of model scales and resolutions. \r\nKO4: Evaluate and improve the representation of BBA-cloud interactions over the SE Atlantic at a range of model scales and resolutions.\r\n\r\nThese objectives were be achieved by conducting an intensive airborne field campaign with supporting surface and satellite measurements. 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It has been in operation since March 2004 and is at the scientists' disposal through a scheme of project selection. \r\n\r\nData collected by this aircraft is stored in the FAAM data archive and includes \"core\" data, provided by the FAAM as a support to all flight campaigns, and \"non-core\" data, the nature of which depends on the scientific goal of the campaign.\r\n\r\nFAAM instruments provide four types of data: \r\n\r\n- parameters required for aircraft navigation; \r\n- meteorology; \r\n- cloud physics; \r\n- chemical composition. \r\n\r\nThe data are accompanied by extensive metadata, including flight logs. 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However, BBA is a complex and poorly understood aerosol species as it consists of a complex cocktail of organic carbon and inorganic compounds mixed with black carbon and hence large uncertainties exist in both the aerosol-radiation-interactions and aerosol-cloud-interactions, uncertainties that limit the ability of our current climate models to accurately reconstruct past climate and predict future climate change.\r\n \r\nThe African continent is the largest global source of BBA (around 50% of global emissions) which is transported offshore over the underlying semi-permanent cloud decks making the SE Atlantic a regional hotspot for BBA concentrations. While global climate models agree that this is a regional hotspot, their results diverge dramatically when attempting to assess aerosol-radiation-interactions and aerosol-cloud-interactions. 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BBA overlying cloud also causes biases in satellite retrievals of cloud properties which can cause erroneous representation of stratocumulus cloud brightness; this has been shown to cause biases in other areas of the word such as biases in precipitation in Brazil via poorly understood global teleconnection processes. \r\n\r\n\r\nThese challenges can now be addressed as both measurement methods and high resolution model capabilities have developed rapidly and are now sufficiently advanced that the processes and properties of BBA can be sufficiently constrained. This measurement/high resolution model combination can be used to challenge the representation of aerosol-radiation-interaction and aerosol-cloud-interaction in coarser resolution numerical weather prediction (NWP) and climate models. Previous measurements in the region are limited to the basic measurements made during SAFARI-2000 when the advanced measurements needed for constraining the complex cloud-aerosol-radiation had not been developed and high resolution modelling was in its infancy.\r\n\r\nThe main aims of CLARIFY-2016 were to deliver a suite of ground and aircraft measurements which measure, understand, evaluate and improve:\r\na)\tthe physical, chemical, optical and radiative properties of BBAs\r\nb)\tthe physical properties of stratocumulus clouds\r\nc)\tthe representation of aerosol-radiation interactions in weather and climate models \r\nd)\tthe representation of aerosol-cloud interactions across a range of model scales. \r\n\r\nThe main field experiment took place during September 2016, based in Walvis Bay, Namibia. The UK large research aircraft (FAAM) was used to measure in-situ and remotely sensed aerosol and cloud properties while advanced radiometers on board the aircraft measured aerosol and cloud radiative impacts. 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However, BBA is a complex and poorly understood aerosol species as it consists of a complex cocktail of organic carbon and inorganic compounds mixed with black carbon and hence large uncertainties exist in both the aerosol-radiation-interactions and aerosol-cloud-interactions, uncertainties that limit the ability of our current climate models to accurately reconstruct past climate and predict future climate change.\r\n \r\nThe African continent is the largest global source of BBA (around 50% of global emissions) which is transported offshore over the underlying semi-permanent cloud decks making the SE Atlantic a regional hotspot for BBA concentrations. While global climate models agree that this is a regional hotspot, their results diverge dramatically when attempting to assess aerosol-radiation-interactions and aerosol-cloud-interactions. 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BBA overlying cloud also causes biases in satellite retrievals of cloud properties which can cause erroneous representation of stratocumulus cloud brightness; this has been shown to cause biases in other areas of the word such as biases in precipitation in Brazil via poorly understood global teleconnection processes. \r\n\r\n\r\nThese challenges can now be addressed as both measurement methods and high resolution model capabilities have developed rapidly and are now sufficiently advanced that the processes and properties of BBA can be sufficiently constrained. This measurement/high resolution model combination can be used to challenge the representation of aerosol-radiation-interaction and aerosol-cloud-interaction in coarser resolution numerical weather prediction (NWP) and climate models. 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This project was written on a stand-alone basis, but there was close collaboration and coordinating with both the NASA ORACLES programme (5 NASA centres, 8 USA universities) and NSF-funded ONFIRE programme (22 USA institutes).\r\n\r\n\r\nKey objectives of CLARIFY-2016 were:\r\nKO1: Measure and understand the physical, chemical, optical and radiative properties of BBAs in the key SE Atlantic region.\r\nKO2: Understand, evaluate and improve the physical properties of the SE Atlantic stratocumulus clouds and their environment in a range of models.\r\nKO3: Evaluate and improve the representation of BBA-radiation interactions over the SE Atlantic when clouds are absent/present at a range of model scales and resolutions. \r\nKO4: Evaluate and improve the representation of BBA-cloud interactions over the SE Atlantic at a range of model scales and resolutions.\r\n\r\nThese objectives were be achieved by conducting an intensive airborne field campaign with supporting surface and satellite measurements. 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Previous measurements in the region are limited to the basic measurements made during SAFARI-2000 when the advanced measurements needed for constraining the complex cloud-aerosol-radiation had not been developed and high resolution modelling was in its infancy.\r\n\r\nThe main aims of CLARIFY-2016 were to deliver a suite of ground and aircraft measurements which measure, understand, evaluate and improve:\r\na)\tthe physical, chemical, optical and radiative properties of BBAs\r\nb)\tthe physical properties of stratocumulus clouds\r\nc)\tthe representation of aerosol-radiation interactions in weather and climate models \r\nd)\tthe representation of aerosol-cloud interactions across a range of model scales. \r\n\r\nThe main field experiment took place during September 2016, based in Walvis Bay, Namibia. 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This project was written on a stand-alone basis, but there was close collaboration and coordinating with both the NASA ORACLES programme (5 NASA centres, 8 USA universities) and NSF-funded ONFIRE programme (22 USA institutes).\r\n\r\n\r\nKey objectives of CLARIFY-2016 were:\r\nKO1: Measure and understand the physical, chemical, optical and radiative properties of BBAs in the key SE Atlantic region.\r\nKO2: Understand, evaluate and improve the physical properties of the SE Atlantic stratocumulus clouds and their environment in a range of models.\r\nKO3: Evaluate and improve the representation of BBA-radiation interactions over the SE Atlantic when clouds are absent/present at a range of model scales and resolutions. \r\nKO4: Evaluate and improve the representation of BBA-cloud interactions over the SE Atlantic at a range of model scales and resolutions.\r\n\r\nThese objectives were be achieved by conducting an intensive airborne field campaign with supporting surface and satellite measurements. 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This project was funded by the Natural Environment Research Council (NERC) with the grant reference - NE/L013797/1 - and was led by Professor James Haywood (University of Exeter).\r\n\r\nBiomass burning aerosol (BBA) exerts a considerable impact on climate by impacting regional radiation budgets as it significantly reflects and absorbs sunlight, and its cloud nucleating properties perturb cloud microphysics and hence affect cloud radiative properties, precipitation and cloud lifetime. However, BBA is a complex and poorly understood aerosol species as it consists of a complex cocktail of organic carbon and inorganic compounds mixed with black carbon and hence large uncertainties exist in both the aerosol-radiation-interactions and aerosol-cloud-interactions, uncertainties that limit the ability of our current climate models to accurately reconstruct past climate and predict future climate change.\r\n \r\nThe African continent is the largest global source of BBA (around 50% of global emissions) which is transported offshore over the underlying semi-permanent cloud decks making the SE Atlantic a regional hotspot for BBA concentrations. While global climate models agree that this is a regional hotspot, their results diverge dramatically when attempting to assess aerosol-radiation-interactions and aerosol-cloud-interactions. Hence the area presents a very stringent test for climate models which need to capture not only the aerosol geographic, vertical, absorption and scattering properties, but also the cloud geographic distribution, vertical extent and cloud reflectance properties. Similarly, in order to capture the aerosol-cloud-interactions adequately, the susceptibility of the clouds in background conditions; aerosol activation processes; uncertainty about where and when BBA aerosol is entrained into the marine boundary layer and the impact of such entrainment on the microphysical and radiative properties of the cloud result in a large uncertainty. BBA overlying cloud also causes biases in satellite retrievals of cloud properties which can cause erroneous representation of stratocumulus cloud brightness; this has been shown to cause biases in other areas of the word such as biases in precipitation in Brazil via poorly understood global teleconnection processes. \r\n\r\n\r\nThese challenges can now be addressed as both measurement methods and high resolution model capabilities have developed rapidly and are now sufficiently advanced that the processes and properties of BBA can be sufficiently constrained. This measurement/high resolution model combination can be used to challenge the representation of aerosol-radiation-interaction and aerosol-cloud-interaction in coarser resolution numerical weather prediction (NWP) and climate models. Previous measurements in the region are limited to the basic measurements made during SAFARI-2000 when the advanced measurements needed for constraining the complex cloud-aerosol-radiation had not been developed and high resolution modelling was in its infancy.\r\n\r\nThe main aims of CLARIFY-2016 were to deliver a suite of ground and aircraft measurements which measure, understand, evaluate and improve:\r\na)\tthe physical, chemical, optical and radiative properties of BBAs\r\nb)\tthe physical properties of stratocumulus clouds\r\nc)\tthe representation of aerosol-radiation interactions in weather and climate models \r\nd)\tthe representation of aerosol-cloud interactions across a range of model scales. \r\n\r\nThe main field experiment took place during September 2016, based in Walvis Bay, Namibia. The UK large research aircraft (FAAM) was used to measure in-situ and remotely sensed aerosol and cloud properties while advanced radiometers on board the aircraft measured aerosol and cloud radiative impacts. This project was written on a stand-alone basis, but there was close collaboration and coordinating with both the NASA ORACLES programme (5 NASA centres, 8 USA universities) and NSF-funded ONFIRE programme (22 USA institutes).\r\n\r\n\r\nKey objectives of CLARIFY-2016 were:\r\nKO1: Measure and understand the physical, chemical, optical and radiative properties of BBAs in the key SE Atlantic region.\r\nKO2: Understand, evaluate and improve the physical properties of the SE Atlantic stratocumulus clouds and their environment in a range of models.\r\nKO3: Evaluate and improve the representation of BBA-radiation interactions over the SE Atlantic when clouds are absent/present at a range of model scales and resolutions. \r\nKO4: Evaluate and improve the representation of BBA-cloud interactions over the SE Atlantic at a range of model scales and resolutions.\r\n\r\nThese objectives were be achieved by conducting an intensive airborne field campaign with supporting surface and satellite measurements. 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However, BBA is a complex and poorly understood aerosol species as it consists of a complex cocktail of organic carbon and inorganic compounds mixed with black carbon and hence large uncertainties exist in both the aerosol-radiation-interactions and aerosol-cloud-interactions, uncertainties that limit the ability of our current climate models to accurately reconstruct past climate and predict future climate change.\r\n \r\nThe African continent is the largest global source of BBA (around 50% of global emissions) which is transported offshore over the underlying semi-permanent cloud decks making the SE Atlantic a regional hotspot for BBA concentrations. While global climate models agree that this is a regional hotspot, their results diverge dramatically when attempting to assess aerosol-radiation-interactions and aerosol-cloud-interactions. 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BBA overlying cloud also causes biases in satellite retrievals of cloud properties which can cause erroneous representation of stratocumulus cloud brightness; this has been shown to cause biases in other areas of the word such as biases in precipitation in Brazil via poorly understood global teleconnection processes. \r\n\r\n\r\nThese challenges can now be addressed as both measurement methods and high resolution model capabilities have developed rapidly and are now sufficiently advanced that the processes and properties of BBA can be sufficiently constrained. This measurement/high resolution model combination can be used to challenge the representation of aerosol-radiation-interaction and aerosol-cloud-interaction in coarser resolution numerical weather prediction (NWP) and climate models. 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It has been in operation since March 2004 and is at the scientists' disposal through a scheme of project selection. \r\n\r\nData collected by this aircraft is stored in the FAAM data archive and includes \"core\" data, provided by the FAAM as a support to all flight campaigns, and \"non-core\" data, the nature of which depends on the scientific goal of the campaign.\r\n\r\nFAAM instruments provide four types of data: \r\n\r\n- parameters required for aircraft navigation; \r\n- meteorology; \r\n- cloud physics; \r\n- chemical composition. \r\n\r\nThe data are accompanied by extensive metadata, including flight logs. 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However, BBA is a complex and poorly understood aerosol species as it consists of a complex cocktail of organic carbon and inorganic compounds mixed with black carbon and hence large uncertainties exist in both the aerosol-radiation-interactions and aerosol-cloud-interactions, uncertainties that limit the ability of our current climate models to accurately reconstruct past climate and predict future climate change.\r\n \r\nThe African continent is the largest global source of BBA (around 50% of global emissions) which is transported offshore over the underlying semi-permanent cloud decks making the SE Atlantic a regional hotspot for BBA concentrations. While global climate models agree that this is a regional hotspot, their results diverge dramatically when attempting to assess aerosol-radiation-interactions and aerosol-cloud-interactions. 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BBA overlying cloud also causes biases in satellite retrievals of cloud properties which can cause erroneous representation of stratocumulus cloud brightness; this has been shown to cause biases in other areas of the word such as biases in precipitation in Brazil via poorly understood global teleconnection processes. \r\n\r\n\r\nThese challenges can now be addressed as both measurement methods and high resolution model capabilities have developed rapidly and are now sufficiently advanced that the processes and properties of BBA can be sufficiently constrained. This measurement/high resolution model combination can be used to challenge the representation of aerosol-radiation-interaction and aerosol-cloud-interaction in coarser resolution numerical weather prediction (NWP) and climate models. Previous measurements in the region are limited to the basic measurements made during SAFARI-2000 when the advanced measurements needed for constraining the complex cloud-aerosol-radiation had not been developed and high resolution modelling was in its infancy.\r\n\r\nThe main aims of CLARIFY-2016 were to deliver a suite of ground and aircraft measurements which measure, understand, evaluate and improve:\r\na)\tthe physical, chemical, optical and radiative properties of BBAs\r\nb)\tthe physical properties of stratocumulus clouds\r\nc)\tthe representation of aerosol-radiation interactions in weather and climate models \r\nd)\tthe representation of aerosol-cloud interactions across a range of model scales. \r\n\r\nThe main field experiment took place during September 2016, based in Walvis Bay, Namibia. The UK large research aircraft (FAAM) was used to measure in-situ and remotely sensed aerosol and cloud properties while advanced radiometers on board the aircraft measured aerosol and cloud radiative impacts. This project was written on a stand-alone basis, but there was close collaboration and coordinating with both the NASA ORACLES programme (5 NASA centres, 8 USA universities) and NSF-funded ONFIRE programme (22 USA institutes).\r\n\r\n\r\nKey objectives of CLARIFY-2016 were:\r\nKO1: Measure and understand the physical, chemical, optical and radiative properties of BBAs in the key SE Atlantic region.\r\nKO2: Understand, evaluate and improve the physical properties of the SE Atlantic stratocumulus clouds and their environment in a range of models.\r\nKO3: Evaluate and improve the representation of BBA-radiation interactions over the SE Atlantic when clouds are absent/present at a range of model scales and resolutions. \r\nKO4: Evaluate and improve the representation of BBA-cloud interactions over the SE Atlantic at a range of model scales and resolutions.\r\n\r\nThese objectives were be achieved by conducting an intensive airborne field campaign with supporting surface and satellite measurements. 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However, BBA is a complex and poorly understood aerosol species as it consists of a complex cocktail of organic carbon and inorganic compounds mixed with black carbon and hence large uncertainties exist in both the aerosol-radiation-interactions and aerosol-cloud-interactions, uncertainties that limit the ability of our current climate models to accurately reconstruct past climate and predict future climate change.\r\n \r\nThe African continent is the largest global source of BBA (around 50% of global emissions) which is transported offshore over the underlying semi-permanent cloud decks making the SE Atlantic a regional hotspot for BBA concentrations. While global climate models agree that this is a regional hotspot, their results diverge dramatically when attempting to assess aerosol-radiation-interactions and aerosol-cloud-interactions. 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BBA overlying cloud also causes biases in satellite retrievals of cloud properties which can cause erroneous representation of stratocumulus cloud brightness; this has been shown to cause biases in other areas of the word such as biases in precipitation in Brazil via poorly understood global teleconnection processes. \r\n\r\n\r\nThese challenges can now be addressed as both measurement methods and high resolution model capabilities have developed rapidly and are now sufficiently advanced that the processes and properties of BBA can be sufficiently constrained. This measurement/high resolution model combination can be used to challenge the representation of aerosol-radiation-interaction and aerosol-cloud-interaction in coarser resolution numerical weather prediction (NWP) and climate models. Previous measurements in the region are limited to the basic measurements made during SAFARI-2000 when the advanced measurements needed for constraining the complex cloud-aerosol-radiation had not been developed and high resolution modelling was in its infancy.\r\n\r\nThe main aims of CLARIFY-2016 were to deliver a suite of ground and aircraft measurements which measure, understand, evaluate and improve:\r\na)\tthe physical, chemical, optical and radiative properties of BBAs\r\nb)\tthe physical properties of stratocumulus clouds\r\nc)\tthe representation of aerosol-radiation interactions in weather and climate models \r\nd)\tthe representation of aerosol-cloud interactions across a range of model scales. \r\n\r\nThe main field experiment took place during September 2016, based in Walvis Bay, Namibia. The UK large research aircraft (FAAM) was used to measure in-situ and remotely sensed aerosol and cloud properties while advanced radiometers on board the aircraft measured aerosol and cloud radiative impacts. This project was written on a stand-alone basis, but there was close collaboration and coordinating with both the NASA ORACLES programme (5 NASA centres, 8 USA universities) and NSF-funded ONFIRE programme (22 USA institutes).\r\n\r\n\r\nKey objectives of CLARIFY-2016 were:\r\nKO1: Measure and understand the physical, chemical, optical and radiative properties of BBAs in the key SE Atlantic region.\r\nKO2: Understand, evaluate and improve the physical properties of the SE Atlantic stratocumulus clouds and their environment in a range of models.\r\nKO3: Evaluate and improve the representation of BBA-radiation interactions over the SE Atlantic when clouds are absent/present at a range of model scales and resolutions. \r\nKO4: Evaluate and improve the representation of BBA-cloud interactions over the SE Atlantic at a range of model scales and resolutions.\r\n\r\nThese objectives were be achieved by conducting an intensive airborne field campaign with supporting surface and satellite measurements. 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Previous measurements in the region are limited to the basic measurements made during SAFARI-2000 when the advanced measurements needed for constraining the complex cloud-aerosol-radiation had not been developed and high resolution modelling was in its infancy.\r\n\r\nThe main aims of CLARIFY-2016 were to deliver a suite of ground and aircraft measurements which measure, understand, evaluate and improve:\r\na)\tthe physical, chemical, optical and radiative properties of BBAs\r\nb)\tthe physical properties of stratocumulus clouds\r\nc)\tthe representation of aerosol-radiation interactions in weather and climate models \r\nd)\tthe representation of aerosol-cloud interactions across a range of model scales. \r\n\r\nThe main field experiment took place during September 2016, based in Walvis Bay, Namibia. The UK large research aircraft (FAAM) was used to measure in-situ and remotely sensed aerosol and cloud properties while advanced radiometers on board the aircraft measured aerosol and cloud radiative impacts. This project was written on a stand-alone basis, but there was close collaboration and coordinating with both the NASA ORACLES programme (5 NASA centres, 8 USA universities) and NSF-funded ONFIRE programme (22 USA institutes).\r\n\r\n\r\nKey objectives of CLARIFY-2016 were:\r\nKO1: Measure and understand the physical, chemical, optical and radiative properties of BBAs in the key SE Atlantic region.\r\nKO2: Understand, evaluate and improve the physical properties of the SE Atlantic stratocumulus clouds and their environment in a range of models.\r\nKO3: Evaluate and improve the representation of BBA-radiation interactions over the SE Atlantic when clouds are absent/present at a range of model scales and resolutions. \r\nKO4: Evaluate and improve the representation of BBA-cloud interactions over the SE Atlantic at a range of model scales and resolutions.\r\n\r\nThese objectives were be achieved by conducting an intensive airborne field campaign with supporting surface and satellite measurements. The measurements were used to challenge, and develop improved models at different spatial scales from the cloud scale to the global scale that couple aerosols, clouds and radiation. \r\n\r\nThe enabling objectives were:- \r\nEO1: To use forecast and observations to optimise scheduling of the flight plans, balancing our operations to ensure data provision for all our enabling objectives.\r\nEO2: To characterise chemical, microphysical, optical and radiative properties of BBA over the SE Atlantic region, focussing on black carbon, absorption and single scattering albedo.\r\nEO3: To investigate the geographic and vertical profile of BBA over the region.\r\nEO4: To characterise the vertical thermodynamic structure of the MBL, residual continental polluted layer, and free troposphere and diurnal and synoptic scale variations. \r\nEO5: To characterise broad-band and spectral reflectance of the ocean surface and stratocumulus clouds when overlying BBA is present/absent from the atmospheric 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This project was funded by the Natural Environment Research Council (NERC) with the grant reference - NE/L013797/1 - and was led by Professor James Haywood (University of Exeter).\r\n\r\nBiomass burning aerosol (BBA) exerts a considerable impact on climate by impacting regional radiation budgets as it significantly reflects and absorbs sunlight, and its cloud nucleating properties perturb cloud microphysics and hence affect cloud radiative properties, precipitation and cloud lifetime. However, BBA is a complex and poorly understood aerosol species as it consists of a complex cocktail of organic carbon and inorganic compounds mixed with black carbon and hence large uncertainties exist in both the aerosol-radiation-interactions and aerosol-cloud-interactions, uncertainties that limit the ability of our current climate models to accurately reconstruct past climate and predict future climate change.\r\n \r\nThe African continent is the largest global source of BBA (around 50% of global emissions) which is transported offshore over the underlying semi-permanent cloud decks making the SE Atlantic a regional hotspot for BBA concentrations. While global climate models agree that this is a regional hotspot, their results diverge dramatically when attempting to assess aerosol-radiation-interactions and aerosol-cloud-interactions. Hence the area presents a very stringent test for climate models which need to capture not only the aerosol geographic, vertical, absorption and scattering properties, but also the cloud geographic distribution, vertical extent and cloud reflectance properties. Similarly, in order to capture the aerosol-cloud-interactions adequately, the susceptibility of the clouds in background conditions; aerosol activation processes; uncertainty about where and when BBA aerosol is entrained into the marine boundary layer and the impact of such entrainment on the microphysical and radiative properties of the cloud result in a large uncertainty. BBA overlying cloud also causes biases in satellite retrievals of cloud properties which can cause erroneous representation of stratocumulus cloud brightness; this has been shown to cause biases in other areas of the word such as biases in precipitation in Brazil via poorly understood global teleconnection processes. \r\n\r\n\r\nThese challenges can now be addressed as both measurement methods and high resolution model capabilities have developed rapidly and are now sufficiently advanced that the processes and properties of BBA can be sufficiently constrained. This measurement/high resolution model combination can be used to challenge the representation of aerosol-radiation-interaction and aerosol-cloud-interaction in coarser resolution numerical weather prediction (NWP) and climate models. Previous measurements in the region are limited to the basic measurements made during SAFARI-2000 when the advanced measurements needed for constraining the complex cloud-aerosol-radiation had not been developed and high resolution modelling was in its infancy.\r\n\r\nThe main aims of CLARIFY-2016 were to deliver a suite of ground and aircraft measurements which measure, understand, evaluate and improve:\r\na)\tthe physical, chemical, optical and radiative properties of BBAs\r\nb)\tthe physical properties of stratocumulus clouds\r\nc)\tthe representation of aerosol-radiation interactions in weather and climate models \r\nd)\tthe representation of aerosol-cloud interactions across a range of model scales. \r\n\r\nThe main field experiment took place during September 2016, based in Walvis Bay, Namibia. The UK large research aircraft (FAAM) was used to measure in-situ and remotely sensed aerosol and cloud properties while advanced radiometers on board the aircraft measured aerosol and cloud radiative impacts. This project was written on a stand-alone basis, but there was close collaboration and coordinating with both the NASA ORACLES programme (5 NASA centres, 8 USA universities) and NSF-funded ONFIRE programme (22 USA institutes).\r\n\r\n\r\nKey objectives of CLARIFY-2016 were:\r\nKO1: Measure and understand the physical, chemical, optical and radiative properties of BBAs in the key SE Atlantic region.\r\nKO2: Understand, evaluate and improve the physical properties of the SE Atlantic stratocumulus clouds and their environment in a range of models.\r\nKO3: Evaluate and improve the representation of BBA-radiation interactions over the SE Atlantic when clouds are absent/present at a range of model scales and resolutions. \r\nKO4: Evaluate and improve the representation of BBA-cloud interactions over the SE Atlantic at a range of model scales and resolutions.\r\n\r\nThese objectives were be achieved by conducting an intensive airborne field campaign with supporting surface and satellite measurements. The measurements were used to challenge, and develop improved models at different spatial scales from the cloud scale to the global scale that couple aerosols, clouds and radiation. \r\n\r\nThe enabling objectives were:- \r\nEO1: To use forecast and observations to optimise scheduling of the flight plans, balancing our operations to ensure data provision for all our enabling objectives.\r\nEO2: To characterise chemical, microphysical, optical and radiative properties of BBA over the SE Atlantic region, focussing on black carbon, absorption and single scattering albedo.\r\nEO3: To investigate the geographic and vertical profile of BBA over the region.\r\nEO4: To characterise the vertical thermodynamic structure of the MBL, residual continental polluted layer, and free troposphere and diurnal and synoptic scale variations. \r\nEO5: To characterise broad-band and spectral reflectance of the ocean surface and stratocumulus clouds when overlying BBA is present/absent from the atmospheric column.\r\nEO6: To characterise key cloud processes and parameters such as entrainment, cloud dynamics, cloud-base updraft velocities, cloud condensation nuclei (CCN), cloud droplet number concentrations (CDNC), cloud droplet effective radius, cloud liquid water path and optical depth. \r\nEO7: Use CLARIFY campaign data with representative statistical sampling as well as high-resolution models to establish robust relationships between sub-grid scale variables and large-scale model parameters suitable for constraining aerosol-cloud interactions. \r\nEO8: To use synergistic observations/model simulations to investigate impacts on NWP and climate model performance, feedback mechanisms, regional and global climate impacts and teleconnections.\r\n" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 1347, 1633, 3220, 3221, 3222, 3223, 3224, 3225, 3226, 3227, 3228, 3229, 3230, 3231, 3232, 3233, 3234, 3235, 3236, 3237, 3238, 3239, 3240, 3241, 3242, 3243, 3244, 3245, 3246, 3247, 3248, 3249, 3250, 3251, 3252, 3253, 3254, 3255, 3256, 3257, 3258, 3259, 3260, 3261, 3262, 3263, 3264, 3265, 3266, 3267, 3268, 3269, 3270, 3271, 3272, 3273, 3274, 3275, 3276, 3277, 3278, 3279, 3280, 3281, 3282, 3283, 3284, 3285, 3286, 3287, 3288, 3289, 3290, 3291, 3292, 3293, 3294, 3295, 3307, 3315, 3316, 3317, 3318, 3319, 3320, 3321, 3322, 3323, 3324, 3325, 3326, 3327, 3328, 3329, 3330, 3331, 3332, 3333, 3334, 3335, 3336, 3337, 3338, 3339, 3340, 3341, 3342, 3343, 3344, 3345, 7762, 7763, 8560, 14474, 14811, 14812, 14813, 14814, 14815, 14816, 14817, 14818, 14819, 14820, 14821, 14822, 14823, 14824, 14825, 14826, 14827, 14828, 14829, 14830, 14831, 14832, 14833, 14834, 14835, 14836, 14837, 14838, 14840, 14841, 14842, 14843, 14845, 14846, 14847, 14848, 14849, 14850, 14851, 14852, 14853, 14854, 14855, 14857, 14859, 14860, 14861, 14863, 14864, 14865, 14866, 14868, 14869, 14870, 14871, 14872, 14873, 14874, 14875, 14876, 14877, 14878, 14880, 14882, 14883, 14885, 14887, 14890, 14893, 14894, 14895, 14900, 14901, 14905, 14906, 14912, 14916, 14917, 14919, 14921, 14924, 14927, 14928, 14935, 14946, 15022, 15023, 15024, 15029, 15030, 15040, 15041, 15047, 15048, 15279, 15282, 15283, 15288, 15289, 15291, 15324, 15325, 15336, 15337, 15338, 15339, 15342, 15343, 15344, 15369, 15372, 15374, 15750, 15812, 15813, 15816, 15818, 15819, 15820, 15821, 15822, 15823, 15824, 15825, 15836, 15840, 15845, 15846, 15847, 15848, 15855, 15857, 15859, 15864, 16210, 16211, 16219, 16220, 16223, 16224, 16225, 16226, 16569, 16658, 17108, 17109, 18977, 20656, 20657, 20658, 20659, 20660, 20661, 20662, 20663, 20664, 20665, 20666, 20667, 20668, 20669, 20670, 20671, 20672, 20673, 20674, 20675, 20676, 20677, 20678, 20679, 20680, 20681, 20683, 20684, 20685, 20686, 20687, 20688, 20689, 20690, 20691, 20692, 20693, 20694, 20695, 20696, 20697, 20698, 20699, 21041, 21042, 21043, 21044, 21045, 21046, 21047, 21049, 22362, 22364, 22367, 22368, 22370, 22371, 22372, 22373, 22379, 22380, 22461, 22462, 22464, 22465, 22466, 22467, 22468, 22469, 22470, 22472, 23146, 23147, 23148, 23149, 23150, 23151, 23152, 23153, 25246, 25247, 25248, 25249, 25250, 25251, 25252, 26137, 26138, 26139, 26140, 26141, 26143, 26144, 26145, 26147, 26148, 26149, 26150, 26151, 26152, 26153, 26154, 26156, 26157, 26164, 26166, 26170, 26172, 26173, 26174, 26175, 26176, 26177, 26178, 26179, 26180, 26181, 26182, 26183, 26184, 26185, 26186, 26187, 26188, 26189, 26190, 26191, 26192, 26193, 26194, 26195, 26196, 26197, 26198, 26199, 26200, 26201, 26202, 26203, 26204, 32419, 32420, 32421, 32422, 32423, 32424, 32425, 32426, 32427, 32428, 32429, 32430, 32431, 32432, 32433 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 5782, "uuid": "affe775e8d8890a4556aec5bc4e0b45c", "short_code": "coll", "title": "Facility for Airborne Atmospheric Measurements (FAAM) flights", "abstract": "The FAAM is a large atmospheric research BAE-146 aircraft, run by the NERC (jointly with the UK Met Office until 2019). 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However, BBA is a complex and poorly understood aerosol species as it consists of a complex cocktail of organic carbon and inorganic compounds mixed with black carbon and hence large uncertainties exist in both the aerosol-radiation-interactions and aerosol-cloud-interactions, uncertainties that limit the ability of our current climate models to accurately reconstruct past climate and predict future climate change.\r\n \r\nThe African continent is the largest global source of BBA (around 50% of global emissions) which is transported offshore over the underlying semi-permanent cloud decks making the SE Atlantic a regional hotspot for BBA concentrations. While global climate models agree that this is a regional hotspot, their results diverge dramatically when attempting to assess aerosol-radiation-interactions and aerosol-cloud-interactions. 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The UK large research aircraft (FAAM) was used to measure in-situ and remotely sensed aerosol and cloud properties while advanced radiometers on board the aircraft measured aerosol and cloud radiative impacts. 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However, BBA is a complex and poorly understood aerosol species as it consists of a complex cocktail of organic carbon and inorganic compounds mixed with black carbon and hence large uncertainties exist in both the aerosol-radiation-interactions and aerosol-cloud-interactions, uncertainties that limit the ability of our current climate models to accurately reconstruct past climate and predict future climate change.\r\n \r\nThe African continent is the largest global source of BBA (around 50% of global emissions) which is transported offshore over the underlying semi-permanent cloud decks making the SE Atlantic a regional hotspot for BBA concentrations. While global climate models agree that this is a regional hotspot, their results diverge dramatically when attempting to assess aerosol-radiation-interactions and aerosol-cloud-interactions. 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BBA overlying cloud also causes biases in satellite retrievals of cloud properties which can cause erroneous representation of stratocumulus cloud brightness; this has been shown to cause biases in other areas of the word such as biases in precipitation in Brazil via poorly understood global teleconnection processes. \r\n\r\n\r\nThese challenges can now be addressed as both measurement methods and high resolution model capabilities have developed rapidly and are now sufficiently advanced that the processes and properties of BBA can be sufficiently constrained. This measurement/high resolution model combination can be used to challenge the representation of aerosol-radiation-interaction and aerosol-cloud-interaction in coarser resolution numerical weather prediction (NWP) and climate models. 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The UK large research aircraft (FAAM) was used to measure in-situ and remotely sensed aerosol and cloud properties while advanced radiometers on board the aircraft measured aerosol and cloud radiative impacts. 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This project was funded by the Natural Environment Research Council (NERC) with the grant reference - NE/L013797/1 - and was led by Professor James Haywood (University of Exeter).\r\n\r\nBiomass burning aerosol (BBA) exerts a considerable impact on climate by impacting regional radiation budgets as it significantly reflects and absorbs sunlight, and its cloud nucleating properties perturb cloud microphysics and hence affect cloud radiative properties, precipitation and cloud lifetime. However, BBA is a complex and poorly understood aerosol species as it consists of a complex cocktail of organic carbon and inorganic compounds mixed with black carbon and hence large uncertainties exist in both the aerosol-radiation-interactions and aerosol-cloud-interactions, uncertainties that limit the ability of our current climate models to accurately reconstruct past climate and predict future climate change.\r\n \r\nThe African continent is the largest global source of BBA (around 50% of global emissions) which is transported offshore over the underlying semi-permanent cloud decks making the SE Atlantic a regional hotspot for BBA concentrations. While global climate models agree that this is a regional hotspot, their results diverge dramatically when attempting to assess aerosol-radiation-interactions and aerosol-cloud-interactions. Hence the area presents a very stringent test for climate models which need to capture not only the aerosol geographic, vertical, absorption and scattering properties, but also the cloud geographic distribution, vertical extent and cloud reflectance properties. Similarly, in order to capture the aerosol-cloud-interactions adequately, the susceptibility of the clouds in background conditions; aerosol activation processes; uncertainty about where and when BBA aerosol is entrained into the marine boundary layer and the impact of such entrainment on the microphysical and radiative properties of the cloud result in a large uncertainty. BBA overlying cloud also causes biases in satellite retrievals of cloud properties which can cause erroneous representation of stratocumulus cloud brightness; this has been shown to cause biases in other areas of the word such as biases in precipitation in Brazil via poorly understood global teleconnection processes. \r\n\r\n\r\nThese challenges can now be addressed as both measurement methods and high resolution model capabilities have developed rapidly and are now sufficiently advanced that the processes and properties of BBA can be sufficiently constrained. This measurement/high resolution model combination can be used to challenge the representation of aerosol-radiation-interaction and aerosol-cloud-interaction in coarser resolution numerical weather prediction (NWP) and climate models. Previous measurements in the region are limited to the basic measurements made during SAFARI-2000 when the advanced measurements needed for constraining the complex cloud-aerosol-radiation had not been developed and high resolution modelling was in its infancy.\r\n\r\nThe main aims of CLARIFY-2016 were to deliver a suite of ground and aircraft measurements which measure, understand, evaluate and improve:\r\na)\tthe physical, chemical, optical and radiative properties of BBAs\r\nb)\tthe physical properties of stratocumulus clouds\r\nc)\tthe representation of aerosol-radiation interactions in weather and climate models \r\nd)\tthe representation of aerosol-cloud interactions across a range of model scales. \r\n\r\nThe main field experiment took place during September 2016, based in Walvis Bay, Namibia. The UK large research aircraft (FAAM) was used to measure in-situ and remotely sensed aerosol and cloud properties while advanced radiometers on board the aircraft measured aerosol and cloud radiative impacts. This project was written on a stand-alone basis, but there was close collaboration and coordinating with both the NASA ORACLES programme (5 NASA centres, 8 USA universities) and NSF-funded ONFIRE programme (22 USA institutes).\r\n\r\n\r\nKey objectives of CLARIFY-2016 were:\r\nKO1: Measure and understand the physical, chemical, optical and radiative properties of BBAs in the key SE Atlantic region.\r\nKO2: Understand, evaluate and improve the physical properties of the SE Atlantic stratocumulus clouds and their environment in a range of models.\r\nKO3: Evaluate and improve the representation of BBA-radiation interactions over the SE Atlantic when clouds are absent/present at a range of model scales and resolutions. \r\nKO4: Evaluate and improve the representation of BBA-cloud interactions over the SE Atlantic at a range of model scales and resolutions.\r\n\r\nThese objectives were be achieved by conducting an intensive airborne field campaign with supporting surface and satellite measurements. The measurements were used to challenge, and develop improved models at different spatial scales from the cloud scale to the global scale that couple aerosols, clouds and radiation. \r\n\r\nThe enabling objectives were:- \r\nEO1: To use forecast and observations to optimise scheduling of the flight plans, balancing our operations to ensure data provision for all our enabling objectives.\r\nEO2: To characterise chemical, microphysical, optical and radiative properties of BBA over the SE Atlantic region, focussing on black carbon, absorption and single scattering albedo.\r\nEO3: To investigate the geographic and vertical profile of BBA over the region.\r\nEO4: To characterise the vertical thermodynamic structure of the MBL, residual continental polluted layer, and free troposphere and diurnal and synoptic scale variations. \r\nEO5: To characterise broad-band and spectral reflectance of the ocean surface and stratocumulus clouds when overlying BBA is present/absent from the atmospheric column.\r\nEO6: To characterise key cloud processes and parameters such as entrainment, cloud dynamics, cloud-base updraft velocities, cloud condensation nuclei (CCN), cloud droplet number concentrations (CDNC), cloud droplet effective radius, cloud liquid water path and optical depth. \r\nEO7: Use CLARIFY campaign data with representative statistical sampling as well as high-resolution models to establish robust relationships between sub-grid scale variables and large-scale model parameters suitable for constraining aerosol-cloud interactions. \r\nEO8: To use synergistic observations/model simulations to investigate impacts on NWP and climate model performance, feedback mechanisms, regional and global climate impacts and teleconnections.\r\n" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 1347, 1633, 3220, 3221, 3222, 3223, 3224, 3225, 3226, 3227, 3228, 3229, 3230, 3231, 3232, 3233, 3234, 3235, 3236, 3237, 3238, 3239, 3240, 3241, 3242, 3243, 3244, 3245, 3246, 3247, 3248, 3249, 3250, 3251, 3252, 3253, 3254, 3255, 3256, 3257, 3258, 3259, 3260, 3261, 3262, 3263, 3264, 3265, 3266, 3267, 3268, 3269, 3270, 3271, 3272, 3273, 3274, 3275, 3276, 3277, 3278, 3279, 3280, 3281, 3282, 3283, 3284, 3285, 3286, 3287, 3288, 3289, 3290, 3291, 3292, 3293, 3294, 3295, 3307, 3315, 3316, 3317, 3318, 3319, 3320, 3321, 3322, 3323, 3324, 3325, 3326, 3327, 3328, 3329, 3330, 3331, 3332, 3333, 3334, 3335, 3336, 3337, 3338, 3339, 3340, 3341, 3342, 3343, 3344, 3345, 7762, 7763, 8560, 14474, 14811, 14812, 14813, 14814, 14815, 14816, 14817, 14818, 14819, 14820, 14821, 14822, 14823, 14824, 14825, 14826, 14827, 14828, 14829, 14830, 14831, 14832, 14833, 14834, 14835, 14836, 14837, 14838, 14840, 14841, 14842, 14843, 14845, 14846, 14847, 14848, 14849, 14850, 14851, 14852, 14853, 14854, 14855, 14857, 14859, 14860, 14861, 14863, 14864, 14865, 14866, 14868, 14869, 14870, 14871, 14872, 14873, 14874, 14875, 14876, 14877, 14878, 14880, 14882, 14883, 14885, 14887, 14890, 14893, 14894, 14895, 14900, 14901, 14905, 14906, 14912, 14916, 14917, 14919, 14921, 14924, 14927, 14928, 14935, 14946, 15022, 15023, 15024, 15029, 15030, 15040, 15041, 15047, 15048, 15279, 15282, 15283, 15288, 15289, 15291, 15324, 15325, 15336, 15337, 15338, 15339, 15342, 15343, 15344, 15369, 15372, 15374, 15750, 15812, 15813, 15816, 15818, 15819, 15820, 15821, 15822, 15823, 15824, 15825, 15836, 15840, 15845, 15846, 15847, 15848, 15855, 15857, 15859, 15864, 16210, 16211, 16219, 16220, 16223, 16224, 16225, 16226, 16569, 16658, 17108, 17109, 18977, 20656, 20657, 20658, 20659, 20660, 20661, 20662, 20663, 20664, 20665, 20666, 20667, 20668, 20669, 20670, 20671, 20672, 20673, 20674, 20675, 20676, 20677, 20678, 20679, 20680, 20681, 20683, 20684, 20685, 20686, 20687, 20688, 20689, 20690, 20691, 20692, 20693, 20694, 20695, 20696, 20697, 20698, 20699, 21041, 21042, 21043, 21044, 21045, 21046, 21047, 21049, 22362, 22364, 22367, 22368, 22370, 22371, 22372, 22373, 22379, 22380, 22461, 22462, 22464, 22465, 22466, 22467, 22468, 22469, 22470, 22472, 23146, 23147, 23148, 23149, 23150, 23151, 23152, 23153, 25246, 25247, 25248, 25249, 25250, 25251, 25252, 26137, 26138, 26139, 26140, 26141, 26143, 26144, 26145, 26147, 26148, 26149, 26150, 26151, 26152, 26153, 26154, 26156, 26157, 26164, 26166, 26172, 26173, 26174, 26175, 26176, 26177, 26178, 26179, 26180, 26181, 26182, 26183, 26184, 26185, 26186, 26187, 26188, 26189, 26190, 26191, 26192, 26193, 26194, 26195, 26196, 26197, 26198, 26199, 26200, 26201, 26202, 26203, 26204, 26205, 32419, 32420, 32421, 32422, 32423, 32424, 32425, 32426, 32427, 32428, 32429, 32430, 32431, 32432, 32433 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 5782, "uuid": "affe775e8d8890a4556aec5bc4e0b45c", "short_code": "coll", "title": "Facility for Airborne Atmospheric Measurements (FAAM) flights", "abstract": "The FAAM is a large atmospheric research BAE-146 aircraft, run by the NERC (jointly with the UK Met Office until 2019). 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