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

Data sets

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) C. Arnold et al.

Model code and software

DKRZ-AIM/dkrz-hereon-icon-superdropnet: Integrating SuperdropNet (v0.1.0) C. Arnold et al.

ICON Code v 2.6.5 including coupling schemes for integrating SuperdropNet C. Arnold et al.

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.