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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Bibliografski detalji

Glavni autori:	Guillaume Bertoli, Salman Mohebi, Firat Ozdemir, Jonas Jucker, Stefan Rüdisühli, Fernando Perez‐Cruz, Mathieu Salzmann, Sebastian Schemm
Format:	Artigo
Jezik:	Inglês
Izdano:	American Geophysical Union (AGU) 2025-09-01
Serija:	Journal of Advances in Modeling Earth Systems
Teme:	global climate model GPU ICON model neural networks parameterization radiative transfer
Online pristup:	https://doi.org/10.1029/2025MS004956
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