Unpacking AI Weather Emulators

Description

Recently, AI models, mainly those based on deep neural networks, have shown surprisingly skillful performance in short-term weather forecasting and long-term emulation. While these models primarily excel at in-distribution interpolation, emerging evidence suggests they may also be capturing aspects of the underlying physics. This raises a fundamental question at the intersection of AI theory, mathematics, and atmospheric physics: Do AI models truly learn multi-scale, chaotic physics, and if so, what aspects and how? Addressing this question could accelerate the development of more accurate and physically consistent models while also improving our understanding of atmospheric and Earth system dynamics. However, the necessary mathematical and statistical tools to explore these kinds of questions remain underdeveloped. This workshop aims to bridge this gap by bringing together researchers from applied and computational mathematics, statistics, atmospheric and Earth sciences, and computer science. Our goal is to foster interdisciplinary discussions and collaborations that drive the development of novel methodologies and insights, deepening our understanding of how AI forecast models and long-term emulators learn.

Registration

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