Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 17, issue 9
Articles | Volume 17, issue 9
https://doi.org/10.5194/gmd-17-4017-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/gmd-17-4017-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 17, issue 9
Development and technical paper
 | 
16 May 2024
Development and technical paper |  | 16 May 2024

Efficient and stable coupling of the SuperdropNet deep-learning-based cloud microphysics (v0.1.0) with the ICON climate and weather model (v2.6.5)

Caroline Arnold, Shivani Sharma, Tobias Weigel, and David S. Greenberg

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Short summary
In atmospheric models, rain formation is simplified to be computationally efficient. We trained a machine learning model, SuperdropNet, to emulate warm-rain formation based on super-droplet simulations. Here, we couple SuperdropNet with an atmospheric model in a warm-bubble experiment and find that the coupled simulation runs stable and produces reasonable results, making SuperdropNet a viable ML proxy for droplet simulations. We also present a comprehensive benchmark for coupling architectures.
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