A Stochastic Closure for Two-Moment Bulk Microphysics of Warm Clouds: Part II, Validation

TL;DR

This paper validates a stochastic two-moment bulk microphysics model for warm clouds, demonstrating its accuracy and efficiency in reproducing detailed collision-coalescence processes with potential for use in weather prediction models.

Contribution

It provides a theoretical and empirical validation of a simplified stochastic bulk model, enhancing cloud microphysics representation in climate models.

Findings

Model accurately reproduces detailed collision-coalescence results

Can be used with 30-second time steps without loss of accuracy

Validated against an established droplet size distribution model

Abstract

The representation of clouds and associated processes of rain and snow formation remains one of the major uncertainties in climate and weather prediction models. In a companion paper (Part I), we systematically derived a two moment bulk cloud microphysics model for collision and coalescence in warm rain based on the kinetic coalescence equation (KCE) and used stochastic approximations to close the higher order moment terms, and do so independently of the collision kernel. Conservation of mass and consistency of droplet number concentration of the evolving cloud properties were combined with numerical simulations to reduce the parametrization problem to three key parameters. Here, we constrain these three parameters based on the physics of collision and coalescence resulting in a "region of validity." Furthermore, we theoretically validate the new bulk model by deriving a subset of the "region of validity" that contains stochastic parameters that skillfully reproduces an existing model based on an a priori droplet size distribution by Seifert and Beheng (2001). The stochastic bulk model is empirically validated against this model, and parameter values that faithfully reproduce detailed KCE results are identified. Furthermore, sensitivity tests indicate that the stochastically derived model can be used with a time step as large as 30 seconds without significantly compromising accuracy, which makes it very attractive to use in medium to long range weather prediction models. These results can be explored in the future to select parameters in the "region of validity" that are conditional on environmental conditions and the age of the cloud.