Prof. Hassanzadeh’s research spans climate dynamics, scientific machine learning, fluid dynamics, computational physics, and dynamical systems. He leads the Climate Extremes Theory and Data (CeTD) Group at the University of Chicago.
Can AI emulators predict out-of-distribution gray swan weather extremes?
Artificial intelligence (AI) is transforming weather and climate modeling. For example, neural network–based weather models can now outperform physics-based models for up to 15-day forecasts at a fraction of the computing time. In this talk, I will present examples of both successes and limitations of current AI models and discuss ways to make progress through integrating tools and insights from AI theory, weather and climate dynamics, and applied mathematics. As one example of success, I will present recent work on AI-based forecasting of Indian monsoon onset and the dissemination of this information to millions of farmers. As examples of current limitations, I will highlight challenges in learning the rarest yet most impactful weather extremes, particularly the “gray swans”. I will also discuss difficulties in capturing broad temporal scales, for example, predicting the quasi-biennial oscillation (QBO). In addition to outlining potential solutions to these challenges (e.g., AI-boosted rare-event sampling), I will show some of the surprising capabilities of these models, such as transferring what they learn from one region to another for dynamically similar events, which can be further exploited in future model developments.
