High-Resolution Numerical Simulations for Severe Hail Producing Thunderstorms on 19 May 2013 using Multi-Moment Microphysics Schemes
Individual hail storms can cause more than $2 billion in damage in the United States. Despite the potential to extend severe weather warning lead times and mitigate damage, explicit hail prediction using convection-resolving models remains relatively understudied. In this study an ensemble Kalman filter (EnKF) is used to assimilate surface and radar observations into ensembles run using either the Milbrandt and Yau double moment (MY2), triple moment (MY3), or the NSSL double moment microphysics scheme. The data assimilation configuration developed for this study is used to improve the microphysical state of simulated hail producing supercell thunderstorms on 19 May 2013 in Oklahoma City. High-resolution (500 m horizontal grid spacing) ensemble forecasts of surface hail size are verified against radar derived hail products including a hydrometeor classification algorithm.
This study aims to assess the skill of ensemble hail forecasts produced using the different microphysics schemes. Results indicate accurate surface hail size forecasts cannot be produced until microphysics schemes properly represent the growth and decay of hail particles. The ensemble run using the NSSL scheme produces forecasts with the most skill in terms of the spatial extent and size of hail at the surface. The NSSL scheme has improved representation of hail in the melting layer, and uses the variable density of rimed ice to create realistic hail growth processes. Ensemble forecasts run using the MY2 and MY3 schemes predict hail size with less skill. Both schemes over predict hail size due to poor representation of hail in the melting layer. The MY3 prognostic hail shape parameter reduces hail size biases by narrowing the hail size distribution in hail cores.