Clouds and the precipitation they produce are an critical component for accurate prediction of the Earth’s water cycle, high impact weather such as hurricanes, as well as for simulating the Earth’s radiative balance. However, the multiscale nature of cloud microphysics (ranging from microscopic cloud droplets to weather systems that span hundreds of kilometers) presents a challenge for simulation within numerical models of the Earth system. I’ll briefly discuss the sources of uncertainties in the modeling of cloud microphysical processes, how scientists have traditionally addressed them, and how they limit the accuracy of weather forecasts and climate projections. I’ll then present how Bayesian statistical methods and machine learning have been brought to bear, in particular how these methods have leveraged observations of the atmosphere. Finally I will present areas of active research, including how observational insights at multiple scales can be unified, and how stochastic may address apparently intractable challenges.
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