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- Author or Editor: James B. Elsner x
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Abstract
Environmental variables are routinely used in estimating when and where tornadoes are likely to occur, but more work is needed to understand how tornado and casualty counts of severe weather outbreak vary with the larger-scale environmental factors. Here the authors demonstrate a method to quantify “outbreak”-level tornado and casualty counts with respect to variations in large-scale environmental factors. They do this by fitting negative binomial regression models to cluster-level environmental data to estimate the number of tornadoes and the number of casualties on days with at least 10 tornadoes. Results show that a 1000 J kg−1 increase in CAPE corresponds to a 5% increase in the number of tornadoes and a 28% increase in the number of casualties, conditional on at least 10 tornadoes and holding the other variables constant. Further, results show that a 10 m s−1 increase in deep-layer bulk shear corresponds to a 13% increase in tornadoes and a 98% increase in casualties, conditional on at least 10 tornadoes and holding the other variables constant. The casualty-count model quantifies the decline in the number of casualties per year and indicates that outbreaks have a larger impact in the Southeast than elsewhere after controlling for population and geographic area.
Abstract
Environmental variables are routinely used in estimating when and where tornadoes are likely to occur, but more work is needed to understand how tornado and casualty counts of severe weather outbreak vary with the larger-scale environmental factors. Here the authors demonstrate a method to quantify “outbreak”-level tornado and casualty counts with respect to variations in large-scale environmental factors. They do this by fitting negative binomial regression models to cluster-level environmental data to estimate the number of tornadoes and the number of casualties on days with at least 10 tornadoes. Results show that a 1000 J kg−1 increase in CAPE corresponds to a 5% increase in the number of tornadoes and a 28% increase in the number of casualties, conditional on at least 10 tornadoes and holding the other variables constant. Further, results show that a 10 m s−1 increase in deep-layer bulk shear corresponds to a 13% increase in tornadoes and a 98% increase in casualties, conditional on at least 10 tornadoes and holding the other variables constant. The casualty-count model quantifies the decline in the number of casualties per year and indicates that outbreaks have a larger impact in the Southeast than elsewhere after controlling for population and geographic area.
Abstract
Property losses from tornadoes in Florida are estimated by combining a 1-km spatial grid of structural values from the Department of Revenue’s 2014 cadastral database with historical tornado events since 1950. There are 91 180 grid cells in the state with at least some structural value. Total and residential structural values total $942 billion and $619 billion, respectively. Over the period 1950 through 2015 there were 3233 individual tornado reports in the state with a peak frequency during June. The property value exposed to tornadoes is estimated using a geometric model for the path. Annual statewide total and residential structural property exposure to tornadoes is estimated at $171 million and $103 million, respectively. Property exposure to tornadoes peaks in February. A regression model quantifies the relationship between actual losses since 2007 and exposures. A doubling of the residential exposure increases actual recorded losses by 26% since 2007, and a doubling of nonresidential exposure increases losses by 21%, controlling for changes over time. Randomization of the historical tornado paths provides alternative exposure scenarios that are used to determine the probability of extreme loss years. Results from the Monte Carlo algorithm indicate a 1% chance that the annual loss will exceed $430 million and a 0.1% chance that it will exceed $1 billion. These findings, and the procedure to obtain them, should help property insurance and reinsurance companies gauge their risk of losses and prioritize their management actions.
Abstract
Property losses from tornadoes in Florida are estimated by combining a 1-km spatial grid of structural values from the Department of Revenue’s 2014 cadastral database with historical tornado events since 1950. There are 91 180 grid cells in the state with at least some structural value. Total and residential structural values total $942 billion and $619 billion, respectively. Over the period 1950 through 2015 there were 3233 individual tornado reports in the state with a peak frequency during June. The property value exposed to tornadoes is estimated using a geometric model for the path. Annual statewide total and residential structural property exposure to tornadoes is estimated at $171 million and $103 million, respectively. Property exposure to tornadoes peaks in February. A regression model quantifies the relationship between actual losses since 2007 and exposures. A doubling of the residential exposure increases actual recorded losses by 26% since 2007, and a doubling of nonresidential exposure increases losses by 21%, controlling for changes over time. Randomization of the historical tornado paths provides alternative exposure scenarios that are used to determine the probability of extreme loss years. Results from the Monte Carlo algorithm indicate a 1% chance that the annual loss will exceed $430 million and a 0.1% chance that it will exceed $1 billion. These findings, and the procedure to obtain them, should help property insurance and reinsurance companies gauge their risk of losses and prioritize their management actions.
Abstract
Tornado–hazard assessment is hampered by a population bias in the available data. Here, the authors demonstrate a way to statistically quantify this bias using the ratio of city to country report densities. The expected report densities come from a model of the number of reports as a function of distance from the nearest city center. On average since 1950, reports near cities with populations of at least 1000 in a 5.5° latitude × 5.5° longitude region centered on Russell, Kansas, exceed those in the country by 70% [54%, 84%; 95% confidence interval (CI)]. The model is applied to 10-yr moving windows to show that the percentage is decreasing with time. Over the most recent period (2002–11), the tornado report density in the city is slightly fewer than 3 reports (100 km2)−1 (100 yr)−1, and this value is statistically indistinguishable from the report density in the country. On average, the population bias is less pronounced for Fujita (F) scale F0 tornadoes, but the bias disappears more quickly over time for the F1 and stronger tornadoes. The authors show evidence that this decline could be related in part to an increase in the number of storm chasers. The population-bias model can enhance the usefulness of the Storm Prediction Center's tornado database and help create more meaningful spatial climatologies.
Abstract
Tornado–hazard assessment is hampered by a population bias in the available data. Here, the authors demonstrate a way to statistically quantify this bias using the ratio of city to country report densities. The expected report densities come from a model of the number of reports as a function of distance from the nearest city center. On average since 1950, reports near cities with populations of at least 1000 in a 5.5° latitude × 5.5° longitude region centered on Russell, Kansas, exceed those in the country by 70% [54%, 84%; 95% confidence interval (CI)]. The model is applied to 10-yr moving windows to show that the percentage is decreasing with time. Over the most recent period (2002–11), the tornado report density in the city is slightly fewer than 3 reports (100 km2)−1 (100 yr)−1, and this value is statistically indistinguishable from the report density in the country. On average, the population bias is less pronounced for Fujita (F) scale F0 tornadoes, but the bias disappears more quickly over time for the F1 and stronger tornadoes. The authors show evidence that this decline could be related in part to an increase in the number of storm chasers. The population-bias model can enhance the usefulness of the Storm Prediction Center's tornado database and help create more meaningful spatial climatologies.
