Analyzing High-Resolution Clouds and Convection using Multi-Channel VAEs
This work addresses the problem of overwhelming data analysis for atmospheric scientists by providing a data-driven tool to extract critical weather characteristics, though it is incremental as it builds on existing VAE methods.
The paper tackled the challenge of analyzing high-resolution atmospheric simulations by developing a multi-channel VAE to embed spatial arrays of wind velocities, temperatures, and water vapor, resulting in more interpretable latent structures and unsupervised identification of weather patterns.
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.