A spatiotemporal approach to tornado prediction

Automated tornado detection or prediction techniques in the literature have all been based on analyzing signatures of tornadoes that appear in Doppler radar velocity data. Attributes of these signatures are derived from radar data, as well as the near-storm environment, and associated with observed tornadoes. This associated database has been used to train neural networks and support vector machines to automatically classify radar signatures. In this paper, we formulate the tornado prediction problem differently. Instead of devising a machine intelligence approach to classify detections, we formulate the problem as a spatiotemporal one: of estimating the probability of a tornado event at a particular spatial location within a given time window. In this paper, we also describe our initial approach to addressing this differently formulated problem. We use a least-squares methodology to estimate shear, morphological image processing to estimate gradients, fuzzy logic to generate compact measures of tornado possibility and a classification neural network to generate the final spatio-temporal probability field.