AI for Social Impact Seminar Series: Weather and climate emulation with state-of-the-art physics-informed AI algorithms

Abstract:

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.

Bio:

Romit Maulik is an Assistant Professor in the College of Information Sciences and Technology at Penn State. He is also a co-hire in the Institute for Computational and Data Sciences at Penn State and a Joint Appointment Faculty at Argonne National Laboratory. He obtained his PhD in Mechanical and Aerospace Engineering at Oklahoma State University in 2019 and was the Margaret Butler Postdoctoral Fellow from 2019-2021 before becoming an Assistant Computational Scientist at Argonne National Laboratory from 2021-2023. His group studies high-performance multifidelity scientific machine learning algorithm development with applications to various multiphysical nonlinear dynamical systems such as those that arise in fluid dynamics, weather and climate modeling, nuclear fusion, and beyond. He is an Early Career Awardee from the Army Research Office.