Statistical Models for Tornado Climatology: Long and Short-Term Views.

• Other Authors: Elsner, James B (authoraut), Jagger, Thomas H (authoraut), Fricker, Tyler (authoraut)
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2016
• Subjects: Atlantic Ocean; El Nino-Southern Oscillation; Models; Theoretical; Risk Factors; Tornadoes; United States; North Atlantic; North Atlantic oscillation
• Online Access: https://diginole.lib.fsu.edu/islandora/object/fsu%3A639301/datastream/TN/view/Statistical%20Models%20for%20Tornado%20Climatology.jpg

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. Publication Note: This NIH-funded author manuscript originally appeared in PubMed Central at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5119788.