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A technique for creating probabilistic spatio-temporal forecasts

Probabilistic forecasts can capture uncertainty better and provide significant economic benefits because the users of the information can calibrate risk. For forecasts in earth-centered domains to be useful, the forecasts have to be clearly demarcated in space and time. We discuss the characteristics of a good probability forecast ‐ reliability and sharpness ‐ and describe well-understood techniques of generating good probability forecasts in the literature. We then describe our approach to addressing the problem of creating good probabilistic forecasts when the entity to be forecast can move and morph. In this paper, we apply the technique to severe weather prediction by formulating the weather 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 the regions is estimated, conditioned based on the level of organization within the regions and combined with the probability that a candidate region becomes severe. For example, the probability that a candidate region becomes tornadic is estimated using a neural network with a sigmoid output node and trained on historical cases. 1. Motivation A principled estimate of the probability that a threat will materialize can be more useful than a binary yes/no prediction because a binary prediction hides the uncertainty inherent in the data and predictive model from users who will make decisions based on that prediction. A principled probabilistic prediction can enable users of the information to calibrate their risk and can aid decision making beyond what simple binary approaches yield. 1 Probabilistic forecasts can capture uncertainty better and provide significant economic benefits because the users of the information can calibrate risk. Techniques to create good probabilistic forecasts are well understood, but only in situations where the predictive model is a direct input-output relationship. If the threats in consideration move and change shape, as with short-term weather forecasts, the wellunderstood techniques can not be used directly. For forecasts in earth-centered domains to be useful, the forecasts have to be clearly demarcated in space and time. This paper presents a data mining approach to address the problem of creating principled probabilistic forecasts when the entity to be forecast can move and change shape.