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Risk Perception and the Credibility of Hazard Warnings: Evidence from Tornado Warnings
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Risk Perception and the Credibility of Hazard Warnings: Evidence from Tornado Warnings

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Risk Perception and the Credibility of Hazard Warnings: Evidence from Tornado Warnings

Risk Perception and the Credibility of Hazard Warnings: Evidence from Tornado Warnings

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  • 1. Credibility of Hazard Warnings: Evidence from Tornadoes Kevin M. Simmons Austin College Fulbright Research Scholar International Centre for GeoHazards Oslo, Norway
  • 2. Economic and Societal Impacts of Tornadoes By Kevin M. Simmons and Daniel Sutter © 2010, 296 pages in paperback ISBN: 978-1-878220-99-8 AVAILABLE NOVEMBER 2010 from the American Meteorological Society and the University of Chicago Press
  • 3.  
  • 4.  
  • 5.  
  • 6.  
  • 7.  
  • 8.  
  • 9. Warnings Save Lives
    • Fatality Rate in the 1920’s was 1.8 per million
    • Today it is estimated at .11 per million
  • 10. Warning Errors
    • The value of a weather forecast or warning in an expected value framework depends on the reliability of the information signal.
    • Two error probabilities are relevant, the probability a tornado occurs unwarned, and the probability a warning is issued when no tornado occurs.
    • The second probability corresponds to the False Alarm Ratio (FAR) that the NWS reports for tornadoes and other types of warnings.
  • 11. NWS Metrics for Performance
    • The National Weather Service has 3 metrics to evaluate the performance of tornado warnings.
      • Lead Time
      • Probability of Detection
      • False Alarm Rate
  • 12. NWS Metrics for Performance
    • Leadtime
      • Time from warning to first touchdown
    • Probability of Detection
      • Warned Tornadoes / Verified Tornadoes
    • False Alarm Rate
      • 1 – (Verified Tornadoes / Warnings)
  • 13. The False Alarm Effect
    • A higher FAR should reduce the value of a tornado warning and might induce people to ignore the warning.
    • Yet evidence of a false alarm effect in the field has been elusive.
    • A recent study notes “Evidence for the cry-wolf effect in natural hazards research, however, has not been forthcoming.” (Barnes et al. 2007)
  • 14. Is there a False Alarm Problem?
    • A false alarm problem exists if a resident in the path of a tornado chooses not to respond to the warning.
  • 15. In Econo Lingo:
    • Utility of taking cover given an approaching tornado is less than the Utility of not taking cover, given an approaching tornado.
  • 16. How Can This Happen?
  • 17. Empirical Test
    • Can we find a way to test whether or not increases in false alarms have an impact on casualties from tornadoes?
  • 18. False Alarms and Casualties
    • We calculate a recent, local FAR, use this as a control variable in our casualty regressions. If false alarms reduce warning response, this should translate into more casualties, everything else equal.
    • We have tried six different definitions of the FAR in our research.
  • 19. Econometric Models
    • The number of persons killed or injured in a tornado takes on nonnegative integer values with a large portion of zeros, and thus is count data.
    • We estimate Poisson models of casualties and test for overdispersion. Injuries are generally overdispersed, but not fatalities, so we estimate Negative Binomial models for injuries.
  • 20. Data Set for Tornadoes
    • Storm Prediction Center national tornado archive
    • NWS Tornado Warning Verification records
    • County warning areas of NWS forecast offices, Doppler radar installation dates
    • Census data at county level
  • 21. Study Parameters
    • Database includes all tornadoes in the U.S. between 1986 and 2004.
    • Approximately 21,000 tornadoes.
    • County level census data from the 1980, 1990 and 2000 census.
  • 22. Control Variables in Tornado Casualty Analysis
    • Tornado characteristics: Rating on the Fujita(F) scale, path length, time of day, month of year, day of week
    • Path characteristics: Economic and demographic variables for the counties struck by each tornado
    • Warning variables: Warning in effect, lead time, false alarm ratio
  • 23. Results Injury Model
    • Injuries
      • A 1 standard deviation increase in the one year FAR increases injuries from 12.4 to 14.7%
  • 24. Results Fatality Model
    • Fatalities
      • A 1 standard deviation increase in the one year FAR increases fatalities from 12.7 to 18.4%
  • 25. THANK YOU FOR YOU ATTENTION