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A neural network is developed to diagnose which circulations detected by the National Severe Storms Laboratory's Mesocyclone Detection Algorithm yield damaging wind. In particular, 23 variables characterizing the circulations are selected to be used as the input nodes of a feed-forward, supervised neural network. The outputs of the network represent the existence/nonexistence of damaging wind, based on ground observations. A set of 14 scalar, nonprobabilistic measures and a set of two multidimensional, probabilistic measures are employed to assess the performance of the network. The former set includes measures of accuracy, association, discrimination, skill, and the latter consists of reliability and refinement diagrams. Two classification schemes are also examined.
Marzban et al. (Sun,) studied this question.
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