To better understand the microphysical and dynamical effects of melting in mesoscale convective storms, a bin microphysics scheme was used in the Weather Research and Forecasting (WRF) model for two idealized cases: a supercell thunderstorm and a squall line. The simulations were initialized based on various physical attributes from two mesoscale extreme events: a forecast sounding near the May 24, 2011, EF5 tornado-producing supercell near El Reno, Oklahoma, and a squall line that occurred on May 20, 2011, during the Midlatitude Continental Convective Clouds Experiment (MC3E).
Physically based continuous melting, instantaneous melting, and bulk-like melting schemes were used to examine the role of melting hydrometeors in mesoscale convective storms as well as the impact of different melting parameterizations. The results suggest that the amount of precipitation is dependent on the representation of melting. Moreover, the dynamic and thermodynamic environments are found to differ substantially between the melting scenarios, resulting in varied storm system evolution; these differences are found to be dependent on the ambient aerosol concentration, although the differences induced by changing the representation of melting generally outweigh those of changing the aerosol loading. With respect to the supercell simulations, storm structure varies based on the employed melting scheme. Well-defined "hooks" in the reflectivity field, elongated rear flanking downdrafts, and a focused convergence zone near the base of the mesocyclone arise when continuous melting is used. Moreover, for the squall line simulations, the melting assumption has a profound impact on cold pool formation and strength, storm propagation, rear inflow jet characteristics, and the transition from convective to stratiform precipitation.
The results highlight the significant role of melting on convective storm characteristics, and the need for further model improvements in the near future to be able to accurately account for the impacts melting has when forecasting mesoscale extreme events.