Use of Neural Networks for Stable, Accurate and Physically Consistent Parameterization of Subgrid Atmospheric Processes With Good Performance at Reduced Precision

Abstract A promising approach to improve climate‐model simulations is to replace traditional subgrid parameterizations based on simplified physical models by machine learning algorithms that are data‐driven. However, neural networks (NNs) often lead to instabilities and climate drift when coupled to...

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Bibliographic Details

Published in:	Geophysical Research Letters
Main Authors:	Janni Yuval, Paul A. O'Gorman, Chris N. Hill
Format:	Article
Language:	English
Published:	Wiley 2021-03-01
Subjects:	Atmospheric modeling Convection machine learning parameterization subgrid physics
Online Access:	https://doi.org/10.1029/2020GL091363