Generative Data‐Driven Approaches for Stochastic Subgrid Parameterizations in an Idealized Ocean Model

Generative Data‐Driven Approaches for Stochastic Subgrid Parameterizations in an Idealized Ocean Model

Abstract Subgrid parameterizations of mesoscale eddies continue to be in demand for climate simulations. These subgrid parameterizations can be powerfully designed using physics and/or data‐driven methods, with uncertainty quantification. For example, Guillaumin and Zanna (2021, https://doi.org/10.1...

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Bibliographic Details
Main Authors: Pavel Perezhogin, Laure Zanna, Carlos Fernandez‐Granda
Format: Article
Language:English
Published: American Geophysical Union (AGU) 2023-10-01
Series:Journal of Advances in Modeling Earth Systems
Subjects:
deep learning
generative model
stochastic parameterization
turbulence
ocean
Online Access:https://doi.org/10.1029/2023MS003681

Internet

https://doi.org/10.1029/2023MS003681

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