Tyler Fricker

Empirical estimates of kinetic energy from some recent U.S. tornadoes

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.

Population and energy elasticity of tornado casualties

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 harms 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.

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.

The Relationship between Elevation Roughness and Tornado Activity: A Spatial Statistical Model Fit to Data from the Central Great Plains

AbstractThe statistical relationship between elevation roughness and tornado activity is quantified using a spatial model that controls for the effect of population on the availability of reports. Across a large portion of the central Great Plains the model shows that areas with uniform elevation tend to have more tornadoes on average than areas with variable elevation. The effect amounts to a 2.3% [(1.6%, 3.0%) = 95% credible interval] increase in the rate of a tornado occurrence per meter of decrease in elevation roughness, defined as the highest minus the lowest elevation locally. The effect remains unchanged if the model is fit to the data starting with the year 1995. The effect strengthens for the set of intense tornadoes and is stronger using an alternative definition of roughness. The elevation-roughness effect appears to be strongest over Kansas, but it is statistically significant over a broad domain that extends from Texas to South Dakota. The research is important for developing a local climato...

Statistical Models For Predicting Tornado Rates: Case Studies From Oklahoma And The Mid South Usa

The destructive impact tornadoes have on communities has sparked interest in predicting the risk of impacts on seasonal time scales. Here, the authors demonstrate how to build statistical models for predicting tornado rates. They test the models with tornado counts accumulated over a 45-year period aggregated to counties in the State of Oklahoma and to cells in a latitude/longitude grid across a large portion of south central United States. The spatial model provides a fit to the counts, which includes terms for the spatial correlation and the population effect. A space-time model not only provides a similar fit to annual counts but also includes a term for a time-varying climate factor. This work contributes to methods for forecasting severe convective storms on the seasonal time scale.

A dasymetric method to spatially apportion tornado casualty counts

ABSTRACT This paper describes a dasymetric technique to spatially apportion casualty counts from tornado events in the US Storm Prediction Centers 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.

A model for U.S. tornado casualties involving interaction between damage path estimates of population density and energy dissipation

AbstractA recent study showed the importance of tornado energy as a factor in a model for tornado deaths and injuries (casualties). The model was additive under the assumption of uniform threat. He...

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 (β = 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.