A proposed methodology for model-based feature-specific prediction designed for high impact weather

Timely and accurate delivery of weather information is an integral part of the forecasting process; however, forecasters continue to be presented with an ever-increasing volume of data. While convection-permitting numerical weather prediction models can provide valuable forecast information for high-impact weather events by producing realistic mesoscale spatial structure useful in identifying convective mode, they also contribute substantially to the volume of data that the forecaster needs to interpret. A feature-specific forecasting method that takes advantage of high-resolution numerical weather prediction models and spatial forecast verification methodology is proposed. By identifying model-predicted mesoscale features of forecast interest (e.g. mesocyclonic features – Fig. 1), the methodology proposed herein offers guidance on the most relevant model-output products to consider, thus maximizing forecaster efficiency. An application of this method to the prediction of a severe convective-storm event is given and is intended to facilitate further development and implementation of the warn-on-forecast framework.