A proposed methodology for model-based feature-specific prediction designed for high impact weather
Jacob R. Carley, Purdue University, West Lafayette, IN; and B. R. J. Schwedler, M. E. Baldwin, R. J. Trapp, J. Kwiatkowski, J. Logsdon, and S. J. Weiss
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.
Poster Session 7, Numerical Weather Prediction Posters
Wednesday, 13 October 2010, 3:15 PM-5:00 PM, Grand Mesa Ballroom ABC
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