Observation Instance

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{
    "ob_id": 32008,
    "uuid": "615144dec7bb4882a5a4bfed89862b93",
    "title": "Coarse-grained numerical-weather-prediction cloud-resolving model data for use in machine learning thermodynamic tendencies",
    "abstract": "This dataset contains a series of 99 limited-area models (LAMs) nested within the Met Office global model. Met Office Unified Model (MetUM) deployed on xce, xcf and xcs in Exeter. Model data generated using Met Office Unified Model using a nesting suite (u-bw210) that runs an N512 global forecast and 99 embedded limited-area models each using a convection-permitting grid-length of 1.5km. The LAMs are each 360x360 grid points. The outer region is deemed to be a spin-up region and is ignored. The central 240x240 is then coarse-grained onto a 45km scale using 30x30 horizontal averaging to produce a 8x8=64 grid of spatially averaged data. Each file contains data from only one of these 64 subdomains, but data from every one the 99 regions around the globe. The nesting simulations are free-running within each LAM, but the driving model is re-initialised every 00Z using operational atmospheric analyses. All 99 regions are wholly over the sea. The central lat/lon for each of the 99 regions are: (80,-150), (70,0), (60,-35), (60,-15), (50,-160), (50,-140), (50,-45), (50,-25), (50,-149), (50,170), (40,-160), (40,-140), (40,-65), (40,-45), (40,-25), (40,150), (40,170), (30,-170), (30,-150), (30,-130), (29,-65), (30,-45), (30,-25), (30,145), (30,170), (20,-170), (20,-145), (21,-115), (20,-55), (20,-30), (20,65), (20,135), (20,170), (10,-170), (10,-140), (10,-120), (10,-100), (10,-50), (10,-30), (10,60), (10,88), (10,145), (10,160), (0,-160), (0,-130), (0,-100), (0,-30), (0,-15), (0,0), (0,50), (0,70), (0,88), (0,160), (-10,-170), (-10,-140), (-10,-120), (-10,-90), (-10,-30), (-10,-15), (-10,5), (-10,60), (-10,88), (-10,170), (-20,-160), (-20,-130), (-20,-100), (-20,-30), (-20,0), (-20,55), (-20,80), (-20,105), (-30,-160), (-30,-130), (-30,-100), (-30,-40), (-30,-15), (-30,10), (-30,60), (-30,88), (-40,-160), (-40,-130), (-40,-100), (-40,-50), (-40,0), (-40,50), (-40,100), (-50,-150), (-50,-90), (-50,-30), (-50,30), (-50,88), (-50,150), (-60,-140), (-60,-70), (-60,0), (-60,70), (-60,140), (-70,-160), (-70,-40). The data has near global coverage, but using this series of small domains. Training data is available for 6 months: Jan, Mar, Apr, Jul, Oct, Dec 2016. Test data is available for Jun 2017.",
    "keywords": "Coarse, cloud, LAMs, Met Office, Machine learning, unified model",
    "publicationState": "citable",
    "dataPublishedTime": "2020-11-25T14:13:47",
    "doiPublishedTime": "2020-11-25T15:12:06.385900",
    "updateFrequency": "",
    "status": "completed",
    "result_field": {
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        "dataPath": "/badc/deposited2020/coarsened-cloud-resolving-tq/data/",
        "oldDataPath": [],
        "storageLocation": "internal",
        "storageStatus": "online",
        "volume": 38620234868,
        "numberOfFiles": 449,
        "fileFormat": "Data are netCDF formatted."
    },
    "timePeriod": "https://api.catalogue.ceda.ac.uk/api/v2/times/8779/?format=api",
    "geographicExtent": "https://api.catalogue.ceda.ac.uk/api/v2/bboxes/2721/?format=api",
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    "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).",
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    "language": "English",
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