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Tornado damage ratings estimated with cumulative logistic regression

Author

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  • Elsner, James B.
  • Schroder, Zoe
Abstract
Empirical studies have led to improvements in evaluating and quantifying the tornado threat. However more work is needed to put the research onto a solid statistical foundation. Here the authors begin to build this foundation by introducing and then demonstrating a statistical model to estimate damage rating probabilities. A goal is to alert researchers to available statistical technology for improving severe weather warnings. The model is cumulative logistic regression and the parameters are determined using Bayesian inference. The model is demonstrated by estimating damage rating probabilities from values of known environmental factors on days with many tornadoes in the United States. Controlling for distance-to-nearest town/city, which serves as a proxy variable for damage target density, the model quantifies the chance that a particular tornado will be assigned any damage rating given specific environmental conditions. Under otherwise average conditions the model estimates a 65% chance that a tornado occurring in a city or town will be rated EF0 when bulk shear is weak (10 m/s). This probability drops to 38% when the bulk shear is strong (40 m/s). The model quantifies the corresponding increases in the chance of the same tornado receiving higher damage ratings. Quantifying changes to the probability distribution on the ordered damage rating categories is a natural application of cumulative logistic regression.

Suggested Citation

  • Elsner, James B. & Schroder, Zoe, 2019. "Tornado damage ratings estimated with cumulative logistic regression," Earth Arxiv k9wv6, Center for Open Science.
    • Handle: RePEc:osf:eartha:k9wv6
    DOI: 10.31219/osf.io/k9wv6
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    References listed on IDEAS

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    1. Carpenter, Bob & Gelman, Andrew & Hoffman, Matthew D. & Lee, Daniel & Goodrich, Ben & Betancourt, Michael & Brubaker, Marcus & Guo, Jiqiang & Li, Peter & Riddell, Allen, 2017. "Stan: A Probabilistic Programming Language," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 76(i01).
    2. Bürkner, Paul-Christian, 2017. "brms: An R Package for Bayesian Multilevel Models Using Stan," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 80(i01).
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    Cited by:

    1. Fahim Sufi & Edris Alam & Musleh Alsulami, 2022. "Automated Analysis of Australian Tropical Cyclones with Regression, Clustering and Convolutional Neural Network," Sustainability, MDPI, vol. 14(16), pages 1-23, August.
    2. Fahim Sufi & Edris Alam & Musleh Alsulami, 2022. "A New Decision Support System for Analyzing Factors of Tornado Related Deaths in Bangladesh," Sustainability, MDPI, vol. 14(10), pages 1-18, May.

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