Deep Learning for Subgrid‐Scale Turbulence Modeling in Large‐Eddy Simulations of the Convective Atmospheric Boundary Layer

Abstract In large‐eddy simulations, subgrid‐scale (SGS) processes are parameterized as a function of filtered grid‐scale variables. First‐order, algebraic SGS models are based on the eddy‐viscosity assumption, which does not always hold for turbulence. Here we apply supervised deep neural networks (...

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Detalhes bibliográficos

Principais autores:	Yu Cheng, Marco G. Giometto, Pit Kauffmann, Ling Lin, Chen Cao, Cody Zupnick, Harold Li, Qi Li, Yu Huang, Ryan Abernathey, Pierre Gentine
Formato:	Artigo
Idioma:	Inglês
Publicado em:	American Geophysical Union (AGU) 2022-05-01
coleção:	Journal of Advances in Modeling Earth Systems
Assuntos:	turbulence large‐eddy simulation machine learning
Acesso em linha:	https://doi.org/10.1029/2021MS002847
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