Characterizing and Reducing Microphysics Parameterization Error with Machine Learning-Enabled Bayesian Inference

Warm microphysics parameterizations (simplified representations of the properties and evolution of liquid cloud and precipitation particles) are an important—but highly uncertain—component of atmospheric modeling from cloud-resolving to global scales. Here, we use machine learning (ML) to accelerate Bayesian and process-based workflows with the goal of characterizing and reducing errors associated with warm microphysics parameterizations. Specifically, we consider errors driven by parameter selection (parametric error) and errors driven by the choice of prognostic variables and process rate formulations (structural error), as these two error types require separate paths for characterization and reduction. We focus on the performance of a standard (two-moment, two-category) bulk microphysics scheme in large-eddy simulations and use as a target emergent cloud and precipitation properties simulated with a more complex bin microphysics scheme. We use ML-enabled Bayesian parameter inference to perform quantitative parameter selection, to separate parametric and structural error, and to characterize parametric error quantitatively and structural error qualitatively. We improve parameterization performance by implementing process-level structural changes driven by insights from the Bayesian analysis. This work highlights how ML (in concert with a process-level perspective) can enable more systematic parameterization development, evaluation, and uncertainty quantification.