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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Cited articles

Alexeev, D.: PyTorch bindings for Fortran (v0.4), GitHub [code], (last access: 6 September 2023), 2023. a
Arnold, C., Sharma, S., and Weigel, T.: DKRZ-AIM/dkrz-hereon-icon-superdropnet: Integrating SuperdropNet (v0.1.0), Zenodo [code],, 2023a. a
Arnold, C., Sharma, S., and Weigel, T.: ICON Code v 2.6.5 including coupling schemes for integrating SuperdropNet, Zenodo [code],, 2023b. a
Arnold, C., Sharma, S., and Weigel, T.: Data set for: Efficient and Stable Coupling of the SuperdropNet Deep Learning-based Cloud Microphysics (v0.1.0) to the ICON Climate and Weather Model (v2.6.5), Zenodo [data set],, 2023c. a
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.