• Bayesian;
  • hierarchical;
  • Poisson;
  • uncertainty;
  • spatiotemporal

[1] Recent evidence suggests that tornado report counts for monthly or longer periods may be correlated to climate indices in space and time. However, the climatological analysis of tornado reports is complicated by reporting errors, by the non-Gaussian nature of count data and rare events, and by the presence of spatial and temporal correlation. Typically, such factors have not been formally included in the underlying statistical model used for inferential decisions. We present an analysis in which a statistical model is constructed so that the aforementioned characteristics of tornado reports are explicitly accommodated in the model structure. In particular, a hierarchical Bayesian framework is considered, in which the various complicated structures are considered in a series of conditional models, formally linked by basic probability rules. This formalism allows one to evaluate characteristics of the spatiotemporal variability of an underlying (unobservable) tornado count process given the noisy observations (tornado reports). In addition, one can consider the effects of exogenous climate processes on this tornado count process. We find that an index of El Niño activity is significantly associated with tornado reports over the continental U.S. and that there is substantial regional variability in this relationship. In addition, we find evidence of temporal trends in tornado counts, with spatial variation in the magnitude and sign of the trend.