Probabilistic forecasts of severe convection with a WRF-DART analysis and convection-permitting forecast system

While convection-permitting models are capable of providing forecasters with useful guidance about expected convective mode, they often struggle with accurately forecasting timing and location, especially for small-scale and extreme events. Additionally, with horizontal grid spacing on the order of 1-4 km, small-scale convective features and severe weather phenomena cannot be fully resolved. Because of these limitations, ensemble forecasts of severe storm proxies and a probabilistic verification approach are examined. A five-member ensemble is produced with the Weather Research and Forecasting (WRF) model and used to retrospectively assess the performance of convection-permitting model guidance in forecasting severe storms that occurred on 19-20 May 2012 across the central Great Plains. Forecasts are initialized with an analysis from the real-time WRF-DART ensemble data assimilation system. Model forecasts are evaluated by comparing radar observations and storm reports to model fields such as simulated reflectivity and maximum updraft helicity, which can be used as a proxy for supercells in convection-permitting models. Also, precipitation forecasts are verified using a “neighborhood-based” approach. This approach has been shown to produce more representative forecast skill scores when verifying high-resolution model output. Preliminary results suggest the ensemble forecasts were tightly clustered and provided useful guidance on storm mode, location, and intensity. Additional results of the subjective evaluation and objective verification of the model forecasts will be presented.