Weather and Climate Simulation with State-of-the-Art Physics-Informed AI Algorithms

Recently, advances in machine learning, hardware (e.g. GPUs/TPUs), and availability of high-quality data have set the stage for machine learning (ML) to tackle problems for weather and climate. This has led to a paradigm shift in operational weather forecasting, most evidently seen by the vast amount of resources being invested into AI models at the leading operational centers including NOAA, ECMWF, and others.  This has been motivated by the influx of deep learning-based models in the last 3 years for weather forecasting which have been demonstrated to have forecasting skill approaching or even exceeding the best available numerical weather prediction (NWP) models. In this seminar, we explore the rise of ML-based modeling for weather and climate prediction, specifically, by looking at (1) a vision transformer-based model for medium-range weather forecasting called Stormer and, (2) one of the first systematic evaluations of machine learning-based emulators for climate research. We conclude by discussing some exciting new directions that are a consequence of our developed models.