Public perception of climatological tornado risk in Tennessee, USA.

The southeastern United States experiences some of the greatest tornado fatality rates in the world, with a peak in the western portion of the state of Tennessee. Understanding the physical and social characteristics of the area that may lead to increased fatalities is a critical research need. Residents of 12 Tennessee counties from three regions of the state (N = 1804) were asked questions about their perception of climatological tornado risk in their county. Approximately half of participants underestimated their local tornado risk calculated from 50 years of historical tornado data. The percentage of participants underestimating their climatological risk increased to 81% when using model estimates of tornado frequencies that account for likely missed tornadoes. A mixed effects, ordinal logistic regression model suggested that participants with prior experience with tornadoes are more likely to correctly estimate or overestimate (rather than underestimate) their risk compared to those lacking experience (ß = 0.52, p < 0.01). Demographic characteristics did not have a large influence on the accuracy of climatological tornado risk perception. Areas where more tornadoes go unreported may be at a disadvantage for understanding risk because residents' prior experience is based on limited observations. This work adds to the literature highlighting the importance of personal experiences in determining hazard risk perception and emphasizes the uniqueness of tornadoes, as they may occur in rural areas without knowledge, potentially prohibiting an accumulation of experiences.

Statistical Models for Tornado Climatology: Long and Short-Term Views.

This paper estimates regional tornado risk from records of past events using statistical models. First, a spatial model is fit to the tornado counts aggregated in counties with terms that control for changes in observational practices over time. Results provide a long-term view of risk that delineates the main tornado corridors in the United States where the expected annual rate exceeds two tornadoes per 10,000 square km. A few counties in the Texas Panhandle and central Kansas have annual rates that exceed four tornadoes per 10,000 square km. Refitting the model after removing the least damaging tornadoes from the data (EF0) produces a similar map but with the greatest tornado risk shifted south and eastward. Second, a space-time model is fit to the counts aggregated in raster cells with terms that control for changes in climate factors. Results provide a short-term view of risk. The short-term view identifies a shift of tornado activity away from the Ohio Valley under El Niño conditions and away from the Southeast under positive North Atlantic oscillation conditions. The combined predictor effects on the local rates is quantified by fitting the model after leaving out the year to be predicted from the data. The models provide state-of-the-art views of tornado risk that can be used by government agencies, the insurance industry, and the general public.

Kinetic Energy of Tornadoes in the United States.

Tornadoes can cause catastrophic destruction. Here total kinetic energy (TKE) as a metric of destruction is computed from the fraction of the tornado path experiencing various damage levels and a characteristic wind speed for each level. The fraction of the path is obtained from a model developed for the Nuclear Regulatory Commission that combines theory with empirical data. TKE is validated as a useful metric by comparing it to other indexes and loss indicators. Half of all tornadoes have TKE exceeding 62.1 GJ and a quarter have TKE exceeding 383.2 GJ. One percent of the tornadoes have TKE exceeding 31.9 TJ. April has more energy than May with fewer tornadoes; March has more energy than June with half as many tornadoes. September has the least energy but November and December have the fewest tornadoes. Alabama ranks number one in terms of tornado energy with 2.48 PJ over the period 2007-2013. TKE can be used to help better understand the changing nature of tornado activity.