# Stochastic Parameterization of Column Physics using Generative Adversarial Networks

arXiv: https://arxiv.org/abs/2211.16654 · HTML: https://mlsanity.com/paper/2211.16654/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 35 references that are in the map.
- First posted on arXiv 30 Nov 2022.

## Builds on (most cited first, 6 of 6)

- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Variational Inference with Normalizing Flows](https://mlsanity.com/paper/1505.05770/) (2015)
- [Towards Principled Methods for Training Generative Adversarial Networks](https://mlsanity.com/paper/1701.04862/) (2017)
- [Deep learning to represent sub-grid processes in climate models](https://mlsanity.com/paper/1806.04731/) (2018)
- [A Bayesian Deep Learning Approach to Near-Term Climate Prediction](https://mlsanity.com/paper/2202.11244/) (2022)
- [High-Precision Inversion of Dynamic Radiography Using Hydrodynamic Features](https://mlsanity.com/paper/2112.01627/) (2021)

## Built on by (most cited first, 3 of 3)

- [climt-paraformer: Stable Emulation of Convective Parameterization using a Temporal Memory-aware Transformer](https://mlsanity.com/paper/2604.21085/) (2026)
- [Machine Learning for Stochastic Parametrisation](https://mlsanity.com/paper/2402.09471/) (2024)
- [Paraformer: Parameterization of Sub-grid Scale Processes Using Transformers](https://mlsanity.com/paper/2412.16763/) (2024)

## Abstract (arXiv)

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.