In this paper, we describe our approach to addressing the problem of creating good probabilistic forecasts when the entity to be forecast can move and morph. We formulate the tornado prediction problem to be one of estimating the probability of an event at a particular spatial location within a given time window. The technique involves clustering Doppler radar-derived fields such as low-level shear and reflectivity to form candidate regions. Assuming stationarity, the spatial probability distribution of this region T minutes ahead is estimated and combined with the probability that the candidate region becomes tornadic T minutes later. Using these two probabilities and the variability of the motion estimates, a spatio-temporal probability field is derived.

The neural network training required to correctly estimate the probabilities has not yet been developed. Therefore, this paper illustrates the underlying idea using fuzzy logic, storm half-life and motion variability.