Interpreting and Stabilizing Machine-learning Parametrizations of Convection

14 Mar 2020 Brenowitz Noah D. Beucler Tom Pritchard Michael Bretherton Christopher S.

Neural networks are a promising technique for parameterizing sub-grid-scale physics (e.g. moist atmospheric convection) in coarse-resolution climate models, but their lack of interpretability and reliability prevents widespread adoption. For instance, it is not fully understood why neural network parameterizations often cause dramatic instability when coupled to atmospheric fluid dynamics... (read more)

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  • ATMOSPHERIC AND OCEANIC PHYSICS