# Analyzing High-Resolution Clouds and Convection using Multi-Channel VAEs

arXiv: https://arxiv.org/abs/2112.01221 · HTML: https://mlsanity.com/paper/2112.01221/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 28 references that are in the map.
- First posted on arXiv 1 Dec 2021.

## Builds on (most cited first, 8 of 8)

- [Generating Sentences from a Continuous Space](https://mlsanity.com/paper/1511.06349/) (2015)
- [Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks](https://mlsanity.com/paper/1701.04722/) (2017)
- [IntroVAE: Introspective Variational Autoencoders for Photographic Image Synthesis](https://mlsanity.com/paper/1807.06358/) (2018)
- [Anomaly Detection With Conditional Variational Autoencoders](https://mlsanity.com/paper/2010.05531/) (2020)
- [On the Quantitative Analysis of Decoder-Based Generative Models](https://mlsanity.com/paper/1611.04273/) (2016)
- [Shape optimization in laminar flow with a label-guided variational autoencoder](https://mlsanity.com/paper/1712.03599/) (2017)
- [Generative Modeling for Atmospheric Convection](https://mlsanity.com/paper/2007.01444/) (2020)
- [Resampling with neural networks for stochastic parameterization in multiscale systems](https://mlsanity.com/paper/2004.01457/) (2020)

## Built on by (most cited first, 1 of 1)

- [Understanding and Visualizing Droplet Distributions in Simulations of Shallow Clouds](https://mlsanity.com/paper/2310.20168/) (2023)

## Abstract (arXiv)

Understanding the details of small-scale convection and storm formation is crucial to accurately represent the larger-scale planetary dynamics. Presently, atmospheric scientists run high-resolution, storm-resolving simulations to capture these kilometer-scale weather details. However, because they contain abundant information, these simulations can be overwhelming to analyze using conventional approaches. This paper takes a data-driven approach and jointly embeds spatial arrays of vertical wind velocities, temperatures, and water vapor information as three "channels" of a VAE architecture. Our "multi-channel VAE" results in more interpretable and robust latent structures than earlier work analyzing vertical velocities in isolation. Analyzing and clustering the VAE's latent space identifies weather patterns and their geographical manifestations in a fully unsupervised fashion. Our approach shows that VAEs can play essential roles in analyzing high-dimensional simulation data and extracting critical weather and climate characteristics.