| Abstract: | To alert the public to the possibility of tornado (T), hail (H), or convective wind (C), the National Weather Service (NWS) issues watches (V) and warnings (W). There are severe thunderstorm watches (SV), tornado watches (TV), and particularly dangerous situation watches (PV); and there are severe thunderstorm warnings (SW), and tornado warnings (TW). Two stochastic models are formulated that quantify uncertainty in severe weather alarms for the purpose of making decisions: a one-stage model for deciders who respond to warnings, and a two-stage model for deciders who respond to watches and warnings. The models identify all possible sequences of watches, warnings, and events, and characterize the associated uncertainties in terms of transition probabilities. The modeling approach is demonstrated on data from the NWS Norman, Oklahoma, warning area, years 2000–2007. The major findings are these. (i) Irrespective of its official designation, every warning type {SW, TW} predicts with a significant probability every event type {T, H, C}. (ii) An ordered intersection of SW and TW, defined as reinforced warning (RW), provides additional predictive information and outperforms SW and TW. (iii) A watch rarely leads directly to an event, and most frequently is false. But a watch that precedes a warning does matter. The watch type ({SV), TV, (PV}) is a predictor of the warning type ({SW), RW, (TW}) and of the warning performance: It sharpens the false alarm rate of the warning and the predictive probability of an event, and it increases the average lead time of the warning. |
