Quantification of errors in polarimetric radar variables simulated from bulk microphysics parameterizations

Currently, there are two approaches used to account for cloud and precipitation physics in storm-resolving numerical weather models: bulk parameterizations, where a functional form of the particle size distribution (PSD) is assumed, and bin or spectral methods, whereupon the number of particles in each “bin” is allowed to vary independently via different kinematic and microphysical processes. Polarimetric radar measurements are very sensitive to changes in shape of the PSD, especially those occurring for the largest sizes. Therefore, errors resulting from assuming PSD shapes a priori (as is done in bulk parameterizations) can significantly affect simulated fields of polarimetric radar variables and cause problems for assimilation of polarimetric observations into numerical models.

In this study, we quantify the errors in simulated polarimetric radar variables resulting from assumptions about the PSD used in bulk parameterization schemes. The benchmark for such comparisons are explicit bin microphysical models of varying complexity, including simple one- and two-dimensional models describing sedimentation, evaporation of raindrops, stochastic freezing of drops within updrafts, and size sorting by wind shear, and the state-of-the-art Hebrew University Cloud Model. Results illustrate that substantial errors are possible in single-moment bulk parameterizations, owing to incorrect treatment of microphysical processes and inappropriate assumptions regarding PSD shape. Double-moment bulk parameterizations, while an improvement over single-moment schemes, can still produce errors in certain situations (i.e., when specific microphysical processes such as size sorting are dominant). More sophisticated methods designed to alleviate these problems are discussed.