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Abstract: Examining fast radiative processes using machine-learning-based emulators of weather (AGU25)

Examining fast radiative processes using machine-learning-based emulators of weather

Ankur Mahesh1,2, William Drew Collins3, Paul Goddard4, Shashank Subramanian5, Boris Bonev6, Travis Allen O'Brien7, Sinclaire Zebaze8, Thorsten Kurth9, Karthik Kashinath10, Christopher Stephen Bretherton11, Oliver Watt-Meyer11, Spencer Clark11, James Duncan11, Elynn Wu11 and Mike S Pritchard9, (1)Lawrence Berkeley National Laboratory, Earth and Environmental Science, Berkeley, United States, (2)University of California Berkeley, Berkeley, United States, (3)Berkeley Lab and UC Berkeley, Berkeley, United States, (4)Indiana University Bloomington, Bloomington, United States, (5)Lawrence Berkeley National Laboratory, NERSC, Berkeley, United States, (6)NVIDIA Corporation, HPC*AI, Santa Clara, United States, (7)Lawrence Berkeley National Lab, Climate & Ecosystem Sciences Division, Berkeley, United States, (8)Indiana University Bloomington, Bloomington, IN, United States, (9)NVIDIA Corporation, Santa Clara, United States, (10)NVIDIA Corporation, Santa Clara, CA, United States, (11)Allen Institute for AI, Seattle, United States
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