Spatially Extended Tests of a Neural Network Parametrization Trained by Coarse‐Graining
Spatially Extended Tests of a Neural Network Parametrization Trained by Coarse‐Graining
Abstract General circulation models (GCMs) typically have a grid size of 25–200 km. Parametrizations are used to represent diabatic processes such as radiative transfer and cloud microphysics and account for subgrid‐scale motions and variability. Unlike traditional approaches, neural networks (NNs) can readily exploit recent observational data sets and global cloud‐system …