Lost in latent land: examples from weather and climate with Dr. Pierre Gentine
When and Where
Speakers
Description
The School of the Environment invites you to join us for 'Lost in latent land: examples from weather and climate' with Dr. Pierre Gentine, a Maurice Ewing and J. Lamar Worzel Professor of Geophysics in the departments of Earth & Environmental Engineering and Earth & Environmental Sciences at Columbia University.
The talk will take place on Thursday, October 23rd, 2025, from 10:30 AM to 12:00 PM in SS 2098 (100 St. George Street, Toronto, ON M5S 3G3) and virtually on Zoom (meeting details sent to participants the week of event).
Registration is required.
Registration will close at 10:30 AM on Friday, October 17th, 2025.
About the Speaker
Pierre Gentine is the Maurice Ewing and J. Lamar Worzel Professor of Geophysics in the Departments of Earth and Environmental Engineering and Earth and Environmental Sciences at Columbia University. He studies terrestrial water and carbon cycles and their changes with climate change. Pierre Gentine is the recipient of the National Science Foundation (NSF), NASA, and Department of energy (DOE) early career awards, as well as the American Geophysical Union Global Environmental Changes Early Career, the James B. Macelwane Medal, and the American Meteorological Society Meisinger Award. He is the Director of the NSF Science and Technology Center (STC) for Learning the Earth with Artificial intelligence and Physics (LEAP), the largest funding mechanism of the NSF.
Seminar Abstract
Machine learning and artificial intelligence have been revolutionizing weather and climate modeling and data analysis over the past few years. However, it remains unclear how much understanding has been gained from those models, even though they are reaching unprecedented accuracy. Through the study and analysis of carefully chosen latent spaces, I will demonstrate how we can get new understanding on the terrestrial water and carbon cycle as well as on atmospheric processes, specifically on convection. Those latent spaces can also be used to better characterize complex stochastic processes such as turbulence and combined with data assimilation in order to achieve improved performance in those models.