The past two decades have witnessed natural disasters and extreme weather events that affect millions of lives. At the same time, the data volume from high-resolution climate models, satellite, in-situ and ground-based measurements have substantially increased to petabyte scales. These new and readily accessible datasets create the previously missing pipeline for scientific machine learning (ML), which in turn can improve our understanding and ability to predict extreme climate events. 

This project will develop deep latent variable models (LVMs) to discover hidden physical structures in high-dimensional, spatiotemporal data of extreme climate events such as droughts or heatwaves.