Can AI weather and climate emulators predict out-of-distribution gray swan extreme events?

Add to your list(s) Download to your calendar using vCal

• Prof Pedram Hassanzedeh, University of Chicago
• Friday 16 May 2025, 16:00-17:00
• MR2.

If you have a question about this talk, please contact Professor Grae Worster.

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. However, these AI models have challenges with learning the rarest yet most impactful weather extremes, particularly the gray swans (i.e., physically possible events so rare they have never been seen in the training set). They also poorly learn multi-scale chaotic dynamics. I will discuss some of these challenges, as well as some of the surprising capabilities of these models, e.g., transferring what they learn from one region to another for dynamically similar event. I will present ideas around integrating tools from applied math, climate physics, and AI to address some of these challenges and make progress. In particular, I will discuss the use if rare event sampling algorithms and the Fourier transform and adjoint of the deep neural networks.

This talk is part of the Fluid Mechanics (DAMTP) series.

This talk is included in these lists:

• All CMS events
• All Talks (aka the CURE list)
• CMS Events
• CamBridgeSens
• Cambridge talks
• Combined External Astrophysics Talks DAMTP
• Cosmology, Astrophysics and General Relativity
• DAMTP Fluids Talks
• DAMTP info aggregator
• Fluid Mechanics (DAMTP)
• Interested Talks
• Life Science Interface Seminars
• MR2
• SJC Regular Seminars
• School of Physical Sciences
• Talks related to atmosphere and ocean dynamics and climate science
• Trust & Technology Initiative - interesting events
• bld31

Note that ex-directory lists are not shown.