SuperdropNet: a Stable and Accurate Machine Learning Proxy for
  Droplet-based Cloud Microphysics
Shivani Sharma David Greenberg
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 have so far
struggled to match the accuracy and stability of bulk moment schemes. To
address this challenge, we developed SuperdropNet, an ML-based emulator of the
Lagrangian superdroplet simulations. To improve accuracy and stability, we
employ multi-step autoregressive prediction during training, impose physical
constraints, and carefully control stochasticity in the training data.
Superdropnet predicted hydrometeor states and cloud-to-rain transition times
more accurately than previous ML emulators, and matched or outperformed bulk
moment schemes in many cases. We further carried out detailed analyses to
reveal how multistep autoregressive training improves performance, and how the
performance of SuperdropNet and other microphysical schemes hydrometeors' mass,
number and size distribution. Together our results suggest that ML models can
effectively emulate cloud microphysics, in a manner consistent with
droplet-based simulations.