Dataset
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.
Details
| Previous Info: |
No news update for this record
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| Previously used record identifiers: |
No related previous identifiers.
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| Access rules: |
Public data: access to these data is available to both registered and non-registered users.
Use of these data is covered by the following licence(s): http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/ When using these data you must cite them correctly using the citation given on the CEDA Data Catalogue record. |
| Data lineage: |
Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA). |
| Data Quality: |
Global driving model is using configuration known as GA6 (Walters et al 2017, doi.org/10.5194/gmd-10-1487-2017). Individual LAMs are using configuration known as RA1 (Bush et al 2019, doi: 10.5194/gmd-2019-130). The nesting suite is documented by Webster et al 2008, doi: 10.1002/asl.172). The data was used for a paper entitled -Machine learning of coarse-grained convection-permitting numerical weather prediction model thermodynamic tendencies- by Cyril Morcrette, code for which is available from https://github.com/CyrilMorcrette/ML_CoarseGrained_CRM_NWP . Data are as given by the data provider, no quality control has been performed by the Centre for Environmental Data Analysis (CEDA)
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| File Format: |
Data are netCDF formatted.
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Related Documents
Process overview
| Title | Met Office Unified Model (MetUM) |
| Abstract | Met Office Unified Model (MetUM) deployed on xce, xcf and xcs in Exeter. Model data generated using Met Office Unified Model rose suite-id u-bw210. This is a nesting suite 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). |
| Input Description | None |
| Output Description | None |
| Software Reference | None |
- units: km
- var_id: DeltaXkmSubDomainWRTRegionCentre
- units: km
- var_id: DeltaYkmSubDomainWRTRegionCentre
- units: m
- var_id: Height
- units: hours
- var_id: HoursSince2016_01_01_00Z
- units: degrees
- var_id: LatRegionCentreDeg
- units: degrees
- var_id: LonRegionCentreDeg
- units: K
- var_id: air_potential_temperature
- long_name: liquid_ice_static_potential_temperature
- names: liquid_ice_static_potential_temperature
- units: K s-1
- long_name: net_advective_flux_liquid_ice_static_potential_temperature
- var_id: net_flux_air_potential_temperature
- names: net_advective_flux_liquid_ice_static_potential_temperature
- long_name: net_advective_flux_total_specific_humidity
- units: kg kg-1 s-1
- var_id: net_flux_specific_humidity
- names: net_advective_flux_total_specific_humidity
- units: kg m-2 s-1
- var_id: stratiform_rainfall_flux
- units: kg m-2 s-1
- var_id: stratiform_snowfall_flux
- units: W m-2
- var_id: surface_upward_latent_heat_flux
- units: W m-2
- var_id: surface_upward_sensible_heat_flux
- units: W m-2
- var_id: toa_incoming_shortwave_flux
- units: kg kg-1
- var_id: specific_humidity
- long_name: total_specific_humidity
- names: total_specific_humidity
Co-ordinate Variables
Temporal Range
2016-01-01T00:00:00
2017-06-30T23:59:59
Geographic Extent
81.6200° |
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-171.6200° |
171.6200° |
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-71.6200° |