Using Machine Learning for Model Physics: an Overview
Atmospheric and Oceanic Physics
2022-06-22 v2 Machine Learning
Abstract
In the overview, a generic mathematical object (mapping) is introduced, and its relation to model physics parameterization is explained. Machine learning (ML) tools that can be used to emulate and/or approximate mappings are introduced. Applications of ML to emulate existing parameterizations, to develop new parameterizations, to ensure physical constraints, and control the accuracy of developed applications are described. Some ML approaches that allow developers to go beyond the standard parameterization paradigm are discussed.
Keywords
Cite
@article{arxiv.2002.00416,
title = {Using Machine Learning for Model Physics: an Overview},
author = {Vladimir Krasnopolsky and Aleksei A. Belochitski},
journal= {arXiv preprint arXiv:2002.00416},
year = {2022}
}
Comments
66 pages, 6 figures, 1 table