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  • Author or Editor: Kevin M. Simmons x
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Kevin M. Simmons
and
Daniel Sutter

Abstract

This paper extends prior research on the societal value of tornado warnings to the impact of false alarms. Intuition and theory suggest that false alarms will reduce the response to warnings, yet little evidence of a “false alarm effect” has been unearthed. This paper exploits differences in the false-alarm ratio across the United States to test for a false-alarm effect in a regression model of tornado casualties from 1986 to 2004. A statistically significant and large false-alarm effect is found: tornadoes that occur in an area with a higher false-alarm ratio kill and injure more people, everything else being constant. The effect is consistent across false-alarm ratios defined over different geographies and time intervals. A one-standard-deviation increase in the false-alarm ratio increases expected fatalities by between 12% and 29% and increases expected injuries by between 14% and 32%. The reduction in the national tornado false-alarm ratio over the period reduced fatalities by 4%–11% and injuries by 4%–13%. The casualty effects of false alarms and warning lead times are approximately equal in magnitude, suggesting that the National Weather Service could not reduce casualties by trading off a higher probability of detection for a higher false-alarm ratio, or vice versa.

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Kevin M. Simmons
,
Paul Kovacs
, and
Gregory A. Kopp

Abstract

In April 2014, the city of Moore, Oklahoma, adopted enhanced building codes designed for wind-resistant construction. This action came after Moore suffered three violent tornadoes in 14 yr. Insured loss data and a rigorous approach to estimating how much future damage can be mitigated is used to conduct a benefit–cost analysis of the Moore standards applied to the entire state of Oklahoma. The results show that the new codes easily pass the benefit–cost test for the state of Oklahoma by a factor of 3 to 1. Additionally, a sensitivity analysis is conducted on each of the five input variables to identify the threshold where each variable causes the benefit–cost test to fail. Variables include the estimate of future losses, percent of damage that can be reduced, added cost, residential share of overall losses, and the discount rate.

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Seth P. Howard
,
Alison P. Boehmer
,
Kevin M. Simmons
, and
Kim E. Klockow-Mcclain

Abstract

Tornadoes are nature’s most violent storm and annually cause billions of dollars in damage along with the threat of fatalities and injuries. To improve tornado warnings, the National Weather Service is considering a change from a deterministic to a probabilistic paradigm. While studies have been conducted on how individual behavior may change with the new warnings, no study of which we are aware has considered the effect this change may have on businesses. This project is a response to the Weather Research and Forecasting Innovation Act of 2017, House of Representatives (H.R.) bill 353, which calls for the use of social and behavioral science to study and improve storm warning systems. The goal is to discuss business response to probabilistic tornado warnings through descriptive and regression-based statistics using a survey administered to businesses in north Texas. Prior to release, the survey was vetted by a focus group composed of businesses in Grayson County, Texas, who assisted in the creation of a behavior ranking scale. The scale ranked behaviors from low to high effort. Responses allowed for determining whether the business reacted to the warning in a passive or active manner. Returned surveys came from large and small businesses in north Texas and represent a wide variety of industries. Regression analysis explores which variables have the greatest influence on the behavior of businesses and show that, beyond increases in probability from the probabilistic warnings, trust in the warning provides the most significant change to behavior.

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