A Comparison of Data‐Driven Approaches to Build Low‐Dimensional Ocean Models

A Comparison of Data‐Driven Approaches to Build Low‐Dimensional Ocean Models

Abstract We present a comprehensive inter‐comparison of linear regression (LR), stochastic, and deep‐learning approaches for reduced‐order statistical emulation of ocean circulation. The reference data set is provided by an idealized, eddy‐resolving, double‐gyre ocean circulation model. Our goal is...

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Bibliographic Details
Main Authors: Niraj Agarwal, D. Kondrashov, P. Dueben, E. Ryzhov, P. Berloff
Format: Article
Language:English
Published: American Geophysical Union (AGU) 2021-09-01
Series:Journal of Advances in Modeling Earth Systems
Subjects:
data‐driven modeling
reduced order modeling
ocean models
machine learning
Online Access:https://doi.org/10.1029/2021MS002537

Internet

https://doi.org/10.1029/2021MS002537

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