Coarsened fine-grid model data for: A machine learning parameterization of clouds in a coarse-resolution climate model for unbiased radiation

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 radi..., 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., , # 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 stru...