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

TL;DR

This paper introduces a multi-channel VAE approach to analyze high-resolution atmospheric simulation data, enabling better interpretation and clustering of weather patterns from complex, high-dimensional datasets.

Contribution

It presents a novel multi-channel VAE architecture that jointly embeds multiple atmospheric variables, improving interpretability and robustness over previous methods analyzing variables separately.

Findings

Latent space effectively clusters weather patterns

Method enhances interpretability of complex simulation data

Unsupervised identification of geographical weather features

Abstract

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.