In our continuing work on the implementation and applications of an operational mesoscale modeling system dubbed "Deep Thunder", we examine its forecast performance for convective events over the several geographic regions in the United States. Initially, this protoype provided nested 24-hour forecasts, which are typically updated twice daily, for the New York City metropolitan area to 1 km resolution. It has been recently extended to also provide forecasts for the Chicago and Kansas City metropolitan areas at 2 km resolution, at least once per day. Explicit, bulk cloud microphysics are included in the model predictions for all three geographies. All of the processing, modelling and visualization are completed in one to two hours on relatively modest hardware to enable sufficiently timely dissemination of forecast products for potential weather-sensitive applications.

In order to evaluate the quality of the forecasts produced by Deep Thunder at a storm-scale and its potential skill, we have identified a number of interesting cases for moderate and severe convective events in each of the three aforementioned geographies. We compare the model results with observational data and other available forecasts as well as the operational availability of specific forecast products. Such performance is examined by considering forecast timing, locality, and intensity of convective events.