Correcting Coarse‐Grid Weather and Climate Models by Machine Learning From Global Storm‐Resolving Simulations

Correcting Coarse‐Grid Weather and Climate Models by Machine Learning From Global Storm‐Resolving Simulations

Abstract Global atmospheric “storm‐resolving” models with horizontal grid spacing of less than 5 km resolve deep cumulus convection and flow in complex terrain. They promise to be reference models that could be used to improve computationally affordable coarse‐grid global climate models across a ran...

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Bibliographic Details
Main Authors: Christopher S. Bretherton, Brian Henn, Anna Kwa, Noah D. Brenowitz, Oliver Watt‐Meyer, Jeremy McGibbon, W. Andre Perkins, Spencer K. Clark, Lucas Harris
Format: Article
Language:English
Published: American Geophysical Union (AGU) 2022-02-01
Series:Journal of Advances in Modeling Earth Systems
Subjects:
climate modeling
machine learning
land‐atmosphere coupling
global storm resolving model
coarse‐graining
Online Access:https://doi.org/10.1029/2021MS002794

Internet

https://doi.org/10.1029/2021MS002794

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