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Published in Journal of Advances in Modeling Earth Systems, 2023
We show that random forest emulators perform well in simplified climate model configurations, but skill degrades systematically as model complexity increases.
Recommended citation: Limon, G. C. & Jablonowski, C., 2023, "Probing the Skill of Random Forests Emulators for Physical Parameterizations via a Hierarchy of Simple CAM6 Configurations." Journal of Advances in Modeling Earth Systems. 15. https://doi.org/10.1029/2022MS003395
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
Published:
G. C. Limon and T. Luchko. First Place, Outstanding Oral Presenter. Presented as part of M.S. research in computational physics.
Published:
G. C. Limon and C. Jablonowski. doi: 10.1002/essoar.10501799.1
Published:
C. Jablonowski and G. C. Limon. Presented by C. Jablonowski as an invited speaker.
Published:
G. C. Limon and C. Jablonowski.
Published:
G. C. Limon and C. Jablonowski.
Published:
G. C. Limon and C. Jablonowski.
Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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