Summer 2025 Lecture in Climate Data Science: KATIE DAGON

JOIN THE LEARNING THE EARTH WITH ARTIFICIAL INTELLIGENCE + PHYSICS (LEAP) CENTER AT COLUMBIA FOR A SEMINAR ON CLIMATE DATA SCIENCE.

By LEAP Center

Date and time

Location

Columbia Innovation Hub - Tang Family Hall

2276 12th Avenue Room 202 New York, NY 10027

About this event

Event lasts 1 hour

TITLE: "Machine Learning-Based Detection of Precipitation Extremes and Regional Climate Impacts"

SPEAKER: Katie Dagon (NSF NCAR)

Date: July 24, 2025

Time: 12:30 p.m.

Format: Hybrid

Virtual: Zoom link provided upon registration

In-person: Columbia Innovation Hub, 2276 12th Avenue, Second Floor, Room 202, New York, NY 10027

Katie will present remotely, but attendees are welcome to gather and watch together at the Columbia Engineering Innovation Hub.

*Please note that in-person space is limited.*

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Abstract: Extreme precipitation events have wide-ranging impacts on humans and the environment. Machine learning-based detection algorithms can help with the automated classification of the synoptic-scale weather features that produce extreme precipitation events, such as fronts and atmospheric rivers (ARs). Here we use a suite of deep learning algorithms to identify weather fronts and ARs in high resolution Community Earth System Model (CESM) simulations and evaluate the results using observational and reanalysis products. To study how these features might change with climate change, we compare results between CESM simulations using present-day and future climate forcing. We further investigate regional climate impacts such as precipitation associated with fronts and ARs over the United States. We find that detected synoptic events in CESM have seasonally varying spatial patterns and responses to climate change and are found to be associated with modeled changes in large scale circulation.

Bio: Katie Dagon is a climate scientist at the NSF National Center for Atmospheric Research in Boulder, Colorado. Her research focuses on modeling the impacts of climate change on land-atmosphere interactions, climate variability, and extreme events. She is also interested in machine learning approaches to climate science and modeling. She received her Ph.D. in Earth and Planetary Sciences from Harvard University and her B.S. in Mathematics-Physics from Brown University.

Learn More: LEAP

KATIE DAGON
NSF NCAR

Organized by

LEAP Center

LEAP’s primary research strategy is to improve near-term climate projections by merging physical modeling with machine learning across a continuum from expertise in climate science and climate modeling to cutting-edge machine learning algorithms. The benefits will be significant for both the climate and data sciences communities. Climate scientists and modelers struggle to fully integrate the wealth of existing datasets into their models, while machine learning algorithms have been good at emulating and interpolating but have difficulties extrapolating or predicting extremes. By combining both approaches, LEAP will trigger a significant advancement for data science algorithms applied to physical problems. LEAP will incorporate physics and causal mechanisms into machine learning algorithms for better generalization and extrapolation, while optimally using the wealth of data available to climate science, in order to better predict the future.

Free