A Posteriori Learning for Quasi‐Geostrophic Turbulence Parametrization
A Posteriori Learning for Quasi‐Geostrophic Turbulence Parametrization
Abstract The use of machine learning to build subgrid parametrizations for climate models is receiving growing attention. State‐of‐the‐art strategies address the problem as a supervised learning task and optimize algorithms that predict subgrid fluxes based on information from coarse resolution models. In practice, training data are generated from higher resolution …