Announcement Detail

Tuesday, February 24, 2026

8:00 AM PST

Join via Zoom: https://us06web.zoom.us/j/89805920868?pwd=UaxOZeVHZAs9BeKkjqRWbNadZYCIZo.1

Energy & Earth Systems TTA Webinar

The Ensemble Kalman Inversion Race

Rebecca Gjini, University of California, San Diego

Abstract:

Ensemble Kalman methods were initially developed to solve nonlinear data assimilation problems in oceanography, but are now popular in applications far beyond their original use cases. Of particular interest is climate model calibration. As hybrid physics and machine-learning models evolve, the number of parameters and complexity of parameterizations in climate models will continue to grow. Thus, robust calibration of these parameters plays an increasingly important role. We focus on learning climate model parameters from minimizing the misfit between modeled and observed climate statistics in an idealized setting. Ensemble Kalman methods are a natural choice for this problem because they are derivative-free, scalable to high dimensions, and robust to noise caused by statistical observations. Given the many variants of ensemble methods proposed, an important question is: Which ensemble Kalman method should be used for climate model calibration? To answer this question, we perform systematic numerical experiments to explore the relative computational efficiencies of several ensemble Kalman methods. The numerical experiments involve statistical observations of Lorenz-type models of increasing complexity, frequently used to represent simplified atmospheric systems, and some feature neural network parameterizations. For each test problem, several ensemble Kalman methods and a derivative-based method "race" to reach a specified accuracy, and we measure the computational cost required to achieve the desired accuracy. We investigate how prior information and the parameter or data dimensions play a role in choosing the ensemble method variant. The derivative-based method consistently fails to complete the race because it does not adaptively handle the noisy loss landscape.

Bio:

Rebecca is a 5th-year PhD candidate in the Institute of Geophysics and Planetary Physics within the Scripps Institution of Oceanography at the University of California, San Diego.  She started her PhD in 2021 after receiving her Bachelor of Science in mathematics from Lehigh University.  Her current work focuses on derivative-free parameter estimation and uncertainty quantification for understanding the climate system under the guidance of her advisor, Matthias Morzfeld.

Upcoming Talks:

Tuesday, March 31: Dr. Hadi Hajibeygi, Delft University of Technology

Tuesday, April 21: Marianna Maiaru, Columbia University

Tuesday, May 19: Elizabeth Barnes, Boston University

Tuesday, June 2: Shamina Shahrin Hossain-McKenzie, Sandia National Laboratories