Back-propagating through the fluid model during training (online learning) makes CNN subgrid parameterizations of two-layer quasi-geostrophic turbulence more skillful and stable than offline training.
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Adjoint-based online learning of two-layer quasi-geostrophic baroclinic turbulence
Back-propagating through the fluid model during training (online learning) makes CNN subgrid parameterizations of two-layer quasi-geostrophic turbulence more skillful and stable than offline training.