An ML‐Based P3‐Like Multimodal Two‐Moment Ice Microphysics in the ICON Model

Abstract Machine learning (ML) is used to build a bulk microphysical parameterization including ice processes. Simulations of the Lagrangian super‐particle model McSnow are used as training data. The ML performs a coarse‐graining of the particle‐resolved microphysics to multi‐category two‐moment bulk equations. Besides mass and number, prognostic particle properties (P3) like melt water, rime mass, and rime volume are predicted by the ML‐based bulk model. The ML‐based scheme is tested with simulations of increasing complexity. As a box model, the ML‐based bulk scheme can reproduce the simulations of McSnow quite accurately. In 3d idealized squall line simulations, the ML‐based P3‐like scheme provides a more realistic extended stratiform region when compared to the standard two‐moment bulk scheme in ICON. In a realistic case study, the ML‐based scheme runs stably, but can not significantly improve the results. This shows that ML can be used to coarse‐grain super‐particle simulations to a bulk scheme of arbitrary complexity.