Stanford University

ESS Seminar Series: Dr. Maria J. Molina “Earth System Prediction: Defying Chaos Theory with Machine Learning"

When:
-
Where:
Mitchell Earth Sciences, Hartley Conference Room
More Info:
ESS Seminar Series: Dr. Maria J. Molina “Earth System Prediction: Defying Chao…
Audience:
Students - Undergraduates, Students - Graduates, Students, Staff - Managers, Staff - Academic, Staff, Postdocs, Members, Faculty, Alumni, Affiliates
Sponsors:
Department of Earth System Science

Please join us Thursday - October 6th for the return of our Autumn Seminar Series with our guest speaker:Maria J. Molina, Ph.D A special thanks to Professor Morgan O’Neill for bringing this speaker to us for this seminar. 

 

 

 DEPARTMENT OF EARTH SYSTEM SCIENCE

SEMINAR SERIES AUTUMN 2022

 IN-PERSON EVENT  

 12:00 - 1:20pm

 Thursday, October 6, 2022

  

Maria J. Molina, Ph.D

Assistant Professor, University of Maryland at College Park

Department of Atmospheric and Oceanic Science

 

 

Earth System Prediction: Defying Chaos Theory with Machine Learning

Chaos theory, which is the concept of sensitivity to initial state for numerical weather prediction, is at the root of why the skillful deterministic prediction of weather at the subseasonal-to-seasonal (S2S) timescales using state-of-the-art forecasting models remains extremely challenging. Predictability stemming from atmospheric initial conditions is also substantially reduced beyond approximately two weeks and the ocean generally does not offer added predictability until a trajectory reaches the seasonal timescale. These challenges motivate the use of machine learning methods for S2S prediction. Two approaches for S2S prediction will be highlighted: (1) an unsupervised learning approach and (2) a supervised learning approach. The first study focuses on assessing the representation and predictability of North American weather regimes, which are persistent large-scale atmospheric patterns. The second study focuses on the use of a deep learning model (specifically a U-Net) for bias correction of S2S forecasts of global temperature and precipitation. We also highlight several forecasts that exhibit high predictability at the subseasonal time scale, seemingly "defying chaos theory," along with the potential reasons for skill.

 

Bio

Dr. Maria J. Molina is an Assistant Professor within the Department of Atmospheric and Oceanic Science at the University of Maryland, College Park and runs a research group that focuses on data science for climate and extremes. Maria is also affiliated with the National Center for Atmospheric Research in Boulder, Colorado and is an Adjunct Assistant Professor within the Department of Marine, Earth, and Atmospheric Sciences at North Carolina State University. Maria also serves as Co-Chair of the AMS Early Career Leadership Academy, is a member of the AMS Board on Representation, Accessibility, Inclusion, and Diversity (BRAID), and serves as an Academia Ambassador for the AMS Committee for Hispanic and Latinx Advancement (CHALA).

 

Websites:

 https://mariajmolina.github.io/

 

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