Evaluation of moist processes during intense precipitation in km-scale NWP models using remote sensing and in-situ data: Impact of microphysics size distribution assumptions

This study investigates the sensitivity of moist processes and surface precipitation during three extreme precipitation events over Belgium to the representation of rain, snow and hail size distributions in a bulk one-moment microphysics parameterisation scheme. Sensitivities included the use of empirically derived relations to calculate the slope parameter and diagnose the intercept parameter of the exponential snow and rain size distributions and sensitivities to the treatment of hail/graupel. A detailed evaluation of the experiments against various high temporal resolution and spatially distributed observational data was performed to understand how moist processes responded to the implemented size distribution modifications. pour consumption by microphysical processes was found to be unaffected by snow or rain size distribution modifications, while it was reduced replacing formulations for hail by those typical for graupel, mainly due to intense sublimation of graupel. Cloud optical thickness was overestimated in all experiments and all cases, likely due to overestimated snow amounts. The overestimation slightly deteriorated by modifying the rain and snow size distributions due to increased snow depositional growth, while it was reduced by including graupel. The latter was mainly due to enhanced cloud water collection by graupel and reduced snow depositional growth. Radar reflectivity and cloud optical thickness could only be realistically represented by inclusion of graupel during a stratiform case, while hail was found indispensable to simulate the vertical reflectivity profile and the surface precipitation structure. Precipitation amount was not much altered by any of the modifications made and the general overestimation was only decreased slightly during a supercell convective case.