Rethinking Moment-Based Microphysics to Eliminate Conversion Rates (Invited Presentation)

The dominant method of representing warm-phase hydrometeors in today’s climate and weather models involves separating liquid droplets into a cloud and rain category and tracking one, two, or three moments of each. This distinction introduces a requirement to model conversion of cloud droplets into rain droplets, replacing a physically interpretable process (coalescence based on droplet sizes) with artificial rates of autoconversion and accretion. Resolving the size distribution explicitly avoids this issue, but existing bin or quadrature methods are too computationally expensive for operational use. We present an alternative approach to resolving size distributions through a flexible moment-based closure using a mixture subdistributions with associated moments. This approach bridges traditional bulk microphysics and the particle-based Superdroplet Method to span a much broader range of structural complexity, while reducing parametric uncertainty. Using idealized kinematic flow and large-eddy simulations, we demonstrate this novel approach in both traditional two-category configurations as well as nontraditional multi-subdistribution configurations to illustrate the benefits of eliminating conversion rates and increasing the complexity of microphysics model structure. This work elucidates best-practices for future microphysics model configuration and development while providing a functional method and framework for representing liquid processes.