Improving the Skill of Predicted Surface Hail in a High-Resolution Model Simulation using a Multi-Moment Microphysics Scheme

Several studies that have analyzed the skill of explicit hail prediction in convective-scale NWP forecasts have indicated that forecasts under-predict the spatial distribution and size of hail near the surface. Forecasts produced using the double-moment microphysics scheme of Milbrandt and Yau (2005) (hereafter MY2) predicted relatively little hail at the surface, even in regions where radar-derived products indicated severe hail was present and where the model predicted abundant hail, including large hail, aloft. 

In this study, we examine the impact of varying the shape parameter of the MY2 hail particle size distribution (PSD) on the hail fields predicted by a storm-scale ensemble forecast system.  Previous research suggests that using a non-zero shape parameter may improve the representation of sedimentation of hail, as well as the skill of surface hail forecasts.  Several experiments are performed using a 40-member ensemble with a 500 m horizontal grid spacing for the case of a severe hail event over central Oklahoma on 19 May 2013. Simulations are run using the Advanced Regional Prediction System (ARPS), using the ARPS ensemble Kalman filter (EnKF) system to assimilate surface and weather radar observations and provide the ensemble initial conditions. These experiments include using a constant non-zero shape parameter in the hail PSD, and using a diagnostic function to predict the shape parameter of the hail PSD in the MY2 scheme. The Milbrandt and Yau (2005) three-moment microphysics scheme, which has a fully prognostic shape parameter is also tested.  The severe hail event of 19 May 2013 occurred over a densely populated region, providing a large number of surface hail reports with which to verify surface hail fields predicted by the ensemble. Particle size distributions, both near and below the melting layer, are analyzed to examine the melting of hail within the model.