11A.2
Trends in Severe Thunderstorms from High-Resolution Dynamical Downscaling and an Artificial Neural Network

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Wednesday, 7 November 2012: 1:45 PM
Symphony I (Loews Vanderbilt Hotel)
Eric D. Robinson, Purdue University, West Lafayette, IN; and R. J. Trapp and M. E. Baldwin

Trends in severe thunderstorms and the associated phenomena of tornadoes, hail, and damaging winds have been difficult to determine due to the many uncertainties in the report-based historical record. The authors produce a synthetic record based on high-resolution numerical modeling to address this issue. Specifically, global reanalysis data are dynamically downscaled over the period of 1990-2009 for the months of April, May, and June using the Weather Research and Forecasting model. An artificial neural network is trained and then utilized to identify occurrences of severe thunderstorms in the model. Results indicate that over this 20-y period, there is no statistically significant trend in simulated warm-season severe-thunderstorm occurrence over much of the United States.