Stochastic parameterization of column physics using generative adversarial networks

We demonstrate the use of a probabilistic machine-learning technique to develop stochastic parameterizations of atmospheric column physics. After suitable preprocessing of NASA’s Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA2) data to minimize the effects of high-...

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Bibliographic Details
Published in:Environmental Data Science 2022, Vol.1, Article e22
Main Authors: Nadiga, Balasubramanya T., Sun, Xiaoming, Nash, Cody
Format: Article
Language:English
Subjects:
Column physics
generative adversarial network
machine learning
stochastic parameterization
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Summary:We demonstrate the use of a probabilistic machine-learning technique to develop stochastic parameterizations of atmospheric column physics. After suitable preprocessing of NASA’s Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA2) data to minimize the effects of high-frequency, high-wavenumber component of MERRA2 estimate of vertical velocity, we use generative adversarial networks to learn the probability distribution of vertical profiles of diabatic sources conditioned on vertical profiles of temperature and humidity. This may be viewed as an improvement over previous similar but deterministic approaches that seek to alleviate both, shortcomings of human-designed physics parameterizations, and the computational demand of the “physics” step in climate models.
ISSN:2634-4602
2634-4602
DOI:10.1017/eds.2022.32