Automated Storm Classification For the Development of Probabilistic Hazard Information

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Tuesday, 4 February 2014: 2:15 PM
Room C202 (The Georgia World Congress Center )
Timothy W. Humphrey, CIMMS/Univ. of Oklahoma, Norman, OK; and V. Lakshmanan, T. M. Smith, K. L. Ortega, B. T. Smith, and R. L. Thompson

A method of estimating the probability that a storm will produce a specific severe weather hazard is presented. An automated classification technique is developed based on Hobson et. al 2012 to identify storms and assign them each to a specific convective mode. Storm characteristics including radar presentation and near-storm environment (NSE) data can then be associated with each convective mode. The algorithm then locates each storm and attempts to associate it with a specific severe weather hazard. To test the performance of the algorithm, storms from 2008 to 2010 are automatically identified and classified into a convective mode. Storm attributes are extracted from gridded data and compared to the statistical attributes for each convective mode in order to determine the probability of a severe weather event. Results show that probabilistic guidance can be provided for specific severe weather hazards and incorporated into future severe weather warnings.