Fidelity of Analytic Drop Size Distributions in Drizzling Stratiform Clouds Based on Large-Eddy Simulations

Abstract

Abstract Cloud microphysical parameterizations and retrievals rely heavily on knowledge of the shape of drop size distributions (DSDs). Many investigations assume that DSDs in the entire or partial drop size range may be approximated by known analytical functions. The most frequently employed approximations of function are of the type of gamma, lognormal, Khrgian–Mazin, and Marshall–Palmer. At present, little is known about the accuracy of these approximations. The authors employ a DSD dataset generated by the Cooperative Institute for Mesoscale Meteorological Studies Large-Eddy Simulation (CIMMS LES) explicit microphysics model for stratocumulus cases observed during the Atlantic Stratocumulus Transition Experiment (ASTEX) field project. The fidelity of analytic lognormal- and gamma-type DSD functions is evaluated according to how well they represent the higher-order moments of the drop spectra, such as precipitation flux and radar reflectivity. It is concluded that for boundary layer marine drizzling stratocumuli, a DSD based on the two-mode gamma distribution provides a more accurate estimate of precipitation flux and radar reflectivity than the DSD based on the lognormal distribution. The gamma distribution also provides a more accurate radar reflectivity field in two- and three-moment bulk microphysical models compared to the conventional Z–R relationship.