SuperdropNet: a Stable and Accurate Machine Learning Proxy for Droplet-based Cloud Microphysics

arXiv:2402.18354v1 Announce Type: cross
Abstract: Cloud microphysics has important consequences for climate and weather phenomena, and inaccurate representations can limit forecast accuracy. While atmospheric models increasingly resolve storms and clouds, the accuracy of the underlying microphysics remains limited by computationally expedient bulk moment schemes based on simplifying assumptions. Droplet-based Lagrangian schemes are more accurate but are underutilized due to their large computational overhead. Machine learning (ML) based schemes can bridge this gap by learning from vast droplet-based simulation datasets, but …

abstract accuracy arxiv assumptions bulk climate cloud consequences cs.lg forecast machine machine learning physics.ao-ph physics.comp-ph physics.flu-dyn simplifying type weather