Neural Network Parameterization of Subgrid‐Scale Physics From a Realistic Geography Global Storm‐Resolving Simulation

Neural Network Parameterization of Subgrid‐Scale Physics From a Realistic Geography Global Storm‐Resolving Simulation

Abstract Parameterization of subgrid‐scale processes is a major source of uncertainty in global atmospheric model simulations. Global storm‐resolving simulations use a finer grid (less than 5 km) to reduce this uncertainty by explicitly resolving deep convection and details of orography. This study...

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Bibliographic Details
Main Authors: Oliver Watt‐Meyer, Noah D. Brenowitz, Spencer K. Clark, Brian Henn, Anna Kwa, Jeremy McGibbon, W. Andre Perkins, Lucas Harris, Christopher S. Bretherton
Format: Article
Language:English
Published: American Geophysical Union (AGU) 2024-02-01
Series:Journal of Advances in Modeling Earth Systems
Subjects:
machine learning
parameterization
atmospheric modeling
global storm‐resolving simulations
Online Access:https://doi.org/10.1029/2023MS003668
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https://doi.org/10.1029/2023MS003668

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