Stochastic Parameterization of Column Physics using Generative Adversarial Networks

TL;DR

This paper introduces a probabilistic machine learning approach using generative adversarial networks to develop stochastic parameterizations of atmospheric column physics, aiming to improve climate model accuracy and efficiency.

Contribution

It presents a novel application of GANs for stochastic parameterization of vertical profiles in atmospheric physics, enhancing previous deterministic methods.

Findings

GANs successfully learn the probability distribution of vertical profiles.

The method reduces computational demand compared to traditional physics parameterizations.

Improves representation of atmospheric variability in climate models.

Abstract

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.