The Intersection of AI and Climate Modeling: Emulators for Physical Parameterizations and Frameworks for Testing AI Weather Models
Published in University of Michigan, Ann Arbor, 2025
This dissertation examines ML integration across climate and weather modeling from emulating physical processes to evaluating AI-driven forecasting systems; identifying key barriers and paths toward more rigorous, physically interpretable approaches.
Recommended citation: Limon, G. C. (2025), The Intersection of AI and Climate Modeling: Emulators for Physical Parameterizations and Frameworks for Testing AI Weather Models, Ph.D. dissertation, Univ. of Michigan, Ann Arbor. hdl.handle.net/2027.42/201169 https://doi.org/10.7302/28253
