Assessing the Offline and Online Performance of Physical Parameterization Emulators via Simplified Climate Model Configurations
Oral Presentation, AMS Annual Meeting, New Orleans, LA
G. C. Limon and C. Jablonowski.
Talks and presentations
Practical Limitations of Machine Learning Approaches for Online Emulation of Simplified Physical Processes in CAM6
Oral Presentation, AMS Annual Meeting, Baltimore, MD
G. C. Limon and C. Jablonowski.
A First Look at Coupling Machine Learning Emulators for Simple Physics Parameterizations in the Community Atmosphere Model
Oral Presentation, AMS Annual Meeting, Virtual
G. C. Limon and C. Jablonowski.
Assessing Machine Learning Approaches for Physical Parameterizations in Atmospheric General Circulation Models
Oral Presentation, ECMWF-ESA Workshop on Machine Learning for Earth System Observation and Prediction, Reading, U.K. (Virtual)
C. Jablonowski and G. C. Limon. Presented by C. Jablonowski as an invited speaker.
An Assessment of Machine Learning Techniques for Replicating Physical Forcing Mechanisms in Climate Models
Poster Presentation, American Geophysical Union Annual Meeting, San Francisco, CA
G. C. Limon and C. Jablonowski. doi: 10.1002/essoar.10501799.1
Development and Implementation of Multi-Grid Solver into 3D-RISM
Oral Presentation, 22nd Annual Research & Creative Works Symposium, California State University Northridge, Northridge, CA
G. C. Limon and T. Luchko. First Place, Outstanding Oral Presenter. Presented as part of M.S. research in computational physics.