Revisiting Machine Learning Approaches for Short‐ and Longwave Radiation Inference in Weather and Climate Models

Abstract This paper explores Machine Learning (ML) parameterizations for radiative transfer in the ICOsahedral Nonhydrostatic weather and climate model (ICON) and investigates the achieved ML model speed‐up with ICON running on graphics processing units (GPUs). Five ML models, with varying complexit...

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Principais autores:	Guillaume Bertoli, Salman Mohebi, Firat Ozdemir, Jonas Jucker, Stefan Rüdisühli, Fernando Perez‐Cruz, Mathieu Salzmann, Sebastian Schemm
格式:	Artigo
語言:	Inglês
出版:	American Geophysical Union (AGU) 2025-09-01
叢編:	Journal of Advances in Modeling Earth Systems
主題:	global climate model GPU ICON model neural networks parameterization radiative transfer
在線閱讀:	https://doi.org/10.1029/2025MS004956
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