Dr. Tyler A Fricker

Storm reports show an upward trend in the power of tornadoes. Quantifying the magnitude of the increase is difficult given diurnal and seasonal influences on tornadoes embedded within natural variations and made worse by changes in practices for rating damage. Here the authors solve this problem by fitting a statistical model to a metric of tornado power during the period 1994-2016. They find an increase of 5.5% [(4.6, 6.5%), 95% CI] per year in power controlling for the diurnal cycle, seasonality, natural climate variability, and the switch to a new damage scale. A portion of the trend is attributed to long-term changes in convective storm environments involving dynamic and thermodynamic variables and their interactions. Increasing power is occurring in environments where the effect of convective available potential energy is enhanced by increasing vertical wind shear. However, a majority of the trend is not attributable to changes in storm environments.

This paper describes a dasymetric technique to spatially apportion casualty counts from tornado events in the US Storm Prediction Center's database. Apportionment is a calculation of the number of casualties within the area of the tornado damage path and with respect to the underlying population density. The method is illustrated with raster grids on tornadoes occurring between 1955 and 2016 within the most tornado-prone region of the United States. Results suggest a relatively uniform spatial distribution of tornado-induced casualties with slightly higher rates in the mid-south, particularly in northern Mississippi and Alabama, and also in many metropolitan areas. In addition, there is some degree of spatial variation over time, particularly clusters of high injury rates across the northern half of Alabama. Validation of the results at the county- and grid-level indicate that casualty numbers correlate strongly with the dasymetric estimates. Future work that includes socioeconomic variables (demographics, ethnicity, poverty and housing stock/value) might allow populations to be profiled with regards to vulnerability.

Tornadoes are capable of catastrophic destruction and mass casualties, but there are yet no estimates of how sensitive the number of casualties are to changes in the number of people in harm's way or to changes in tornado energy. Here the relationship between tornado casualties (deaths and injuries), population, and energy dissipation is quantified using the economic concept of elasticity. Records of casualties from individual tornadoes over the period 2007-2015 are fit to a regression model. The coefficient on the population term (population elasticity) indicates that a doubling in population increases the casualty rate by 21% [(17, 24)%, 95% credible interval]. The coefficient on the energy term (energy elasticity) indicates that a doubling in energy dissipation leads to a 33% [(30, 35)%, 95% credible interval] increase in the casualty rate. The difference in elasticity values show that on average, changes in energy dissipation have been relatively more important in explaining tornado casualties than changes in population. Assuming no changes in warning effectiveness or mitigation efforts, these elasticity estimates can be used to project changes in casualties given the known population trends and possible trends in tornado activity.

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.

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.

Data from some recent tornado damage assessments are used to compute the percentage of damage path area by enhanced Fujita (EF) rating and to estimate kinetic energy. Only a small fraction of the damage area gets the highest damage rating, and this fraction is lower than a model used by the U. S. Nuclear Regulatory Commission. However, estimates of kinetic energy derived from a characteristic wind speed for each EF rating and the fraction of area with that rating match kinetic energy estimates using the model percentages. On average, the higher the EF rating, the larger the kinetic energy, but there is large variability in the relationship. The average total kinetic energy over the EF1 tornadoes examined in the study is 0.61 TJ, which compares with an average of 2.37 TJ, 40.1 TJ, 36.5 TJ, and 50.4 TJ for the EF2, EF3, EF4, and EF5 tornadoes, respectively. The most energetic tornado examined had a maximum damage rating of EF3.