DOI: 10.5061/dryad.9p8cz8wpz
title: Coarsened fine-grid model data for: A machine learning parameterization of clouds in a coarse-resolution climate model for unbiased radiation

description: Coarse-grid weather and climate models rely particularly on
 parameterizations of cloud fields, and coarse-grained cloud fields from a
 fine-grid reference model are a natural target for a machine-learned
 parameterization. We machine-learn the coarsened-fine cloud properties as
 a function of coarse-grid model state in each grid cell of NOAA's
 FV3GFS global atmosphere model with 200 km grid spacing, trained using a
 3-km fine-grid reference simulation with a modified version of FV3GFS. The
 ML outputs are coarsened-fine fractional cloud cover and liquid and ice
 cloud condensate mixing ratios, and the inputs are coarse model
 temperature, pressure, relative humidity, and ice cloud condensate. The
 predicted fields are skillful and unbiased, but somewhat under-dispersed,
 resulting in too many partially-cloudy model columns. When the predicted
 fields are applied diagnostically (offline) in FV3GFS's radiation
 scheme, they lead to small biases in global-mean top-of-atmosphere (TOA)
 and surface radiative fluxes. An unbiased global-mean TOA net radiative
 flux is obtained by setting to zero any predicted cloud with grid-cell
 mean cloud fraction less than a threshold of 6.5%; this does not
 significantly degrade the ML prediction of cloud properties. The
 diagnostic, ML-derived radiative fluxes are far more accurate than those
 obtained with the existing cloud parameterization in the nudged
 coarse-grid model, as they leverage the accuracy of the fine-grid
 reference simulation's cloud properties.This dataset provides the
 coarsened fine-grid model outputs needed to run the nudged coarse climate
 model, including running with prescribed coarsened fine-grid cloud fields
 and to train the ML model that predicts coarsened-fine cloud fields as
 functions of nudged coarse model state. 
description: This dataset was generated by running a 10-day simulation of NOAA
 GFDL's X-SHiELD global storm-resolving atmospheric model at C3072
 (3km) resolution. X-SHiELD shares the same FV3 dynamical core and most of
 its physics parameterizations with NOAA's Global Forecast System
 (GFS), NOAA's operational global weather forecast model. The
 simulation was run on GFDL's GAEA supercomputing system and was
 coarse-grained online to C48 (~200km) resolution to produce the model
 state and diagnostic files included here. Please see Harris et al, 2020,
 "GFDL SHiELD: A Unified System for Weather-to-Seasonal
 Prediction" (JAMES) doi:10.1029/2020MS002223 for more information on
 X-SHiELD.
description: # Coarsened fine-grid model data for "A machine learning
 parameterization of clouds in a coarse-resolution climate model for
 unbiased radiation" 
 Contains the coarsened fine-grid model state files (restart files) for
 each 15 minute timestep over a 10-day simulation period. Allows for
 conducting a nudged run of a coarse model to the coarsened-fine model
 state, and for training machine learning with coarsend-fine cloud fields
 as targets. Also contains coarsened fine-grid model diagnostic fields such
 radiation that are used evaluation metrics in the manuscript. The
 fine-grid model used to generate the coarsened-fine files here is NOAA
 GFDL X-SHiELD. The model used to run the coarse-grid simulations,
 including those with prescribed cloud (coarsened-fine grid cloud and ML
 cloud), is NOAA GFDL FV3GFS. Both models are based on the GFDL FV3
 dynamical core, and use the RRTMG radiation and NOAA GFDL microphysics
 schemes. See and
 for more information. ##
 Description of the data and file structure Contains two major sections: *
 A set of folders containing coarsened X-SHiELD model restart files (one
 folder for each 15-minute timestep over 10 days). Each folder contains
 files with the full 3D model state needed to nudge or restart a
 coarse-grid model. These are saved as netCDF files. These file formats are
 defined by the NOAA GFDL FV3GFS/SHiELD modeling environment and are
 expected by the model executable to be in this format in order to
 initialize the model. For upload purposes, the folders are grouped into
 tarballed archives by day. For example, for the timestep folder
 `20200731.001500`: * Each file is a netCDF, and prepended by the timestamp
 name * Each file has a suffix (e.g., `tile*`) that indicates which tile on
 the 6-tile cubed-sphere global grid it corresponds to. So there are 6
 files for each file type category. * There are the following file type
 categories: * "fv_core.res" files: These contain the core 3-D
 prognostic variables of the model on its cubed-sphere, hybrid
 sigma/pressure vertical level grids, including u- and v- winds on the D-
 and A-grids, vertical winds, model layer height thicknesses, temperatures,
 model layer thicknesses, and surface geopotential height. Variables:
 'u', 'v', 'ua', 'va',
 'W', 'DZ', 'T', 'delp', and
 'phis'. * "fv_srf_wnd.res" files: These contain 2-D
 surface winds. Variables: 'u_srf' and 'v_srf'. *
 "fv_tracer.res" files: These contain 3-D fields of the tracers
 (water species and ozone, primarily) associated with the model physics
 packages. Variables: 'sphum', 'liq_wat',
 'rainwat', 'ice_wat', 'snowwat',
 'graupel', 'sgs_tke', 'pbl_clock',
 'tro_pbl_clock', 'o3mr', and 'cld_amt'. Note
 that 'liq_wat', 'ice_wat', and 'cld_amt' are
 the machine learning targets in the manuscript. * "sfc_data"
 files: These contain 2-D fields associated with the model's land
 surface simulation, such as albedo, soil moisture, vegetation, snow, ice,
 roughness, etc. Variables: 'slmsk', 'tsea',
 'sheleg', 'tg3', 'zorl', 'alvsf',
 'alvwf', 'alnsf', 'alnwf',
 'facsf', 'facwf', 'vfrac',
 'canopy', 'f10m', 't2m', 'q2m',
 'vtype', 'stype', 'uustar',
 'ffmm', 'ffhh', 'hice', 'fice',
 'tisfc', 'tprcp', 'srflag',
 'snwdph', 'shdmin', 'shdmax',
 'slope', 'snoalb', 'sncovr',
 'stc', 'smc', and 'slc'. * A dataset of 2D
 model diagnostics output at the same time intervals. Diagnostics are
 different from restart files in that they are outputs of the model, but
 are not descriptions of the model state needed to initialize a new model
 simulation. These include the radiative fluxes at earth's surface and
 top of atmosphere that are used as evaluation metrics in the manuscript.
 These diagnostics are saved as a tarballed zarr archive. Zarr is a more
 convenient form for cloud-native data storage than netCDF files, as it
 allows for scalable reading and writing of only the portion of a dataset
 that are needed, and direct read from cloud storage without copying. Note
 that the zarr format is also self-describing; it contains a file called
 ".zmetadata" that is a JSON-formatted description of all of the
 variables included in the zarr store, including their names, units,
 long-form text descriptions, dimensions, and chunking and encoding into
 binary format. See for more information on
 getting started with zarr. * The relevant coarsened-fine model radiative
 flux variables saved here that are used in the manuscript include: *
 'USWRFtoa_coarse': upward total-sky shortwave radiative flux at
 top of atmosphere * 'ULWRFtoa_coarse': upward total-sky longwave
 radiative flux at top of atmosphere * 'DSWRFsfc_coarse':
 downward total-sky shortwave radiative flux at the surface *
 'DLWRFsfc_coarse': downward total-sky longwave radiative flux at
 the surface