Statistical Models for Predicting Tornado Rates: Case Studies from Oklahoma and the Mid South USA

Statistical Models for Predicting Tornado Rates: Case Studies from Oklahoma and the Mid South USA

James B. Elsner Tyler Fricker Thomas H. Jagger Victor Mesev 

Department of Geography, Florida State University

Page: 
1-9
|
DOI: 
https://doi.org/10.2495/SAFE-V6-N1-1-9
Received: 
N/A
| |
Accepted: 
N/A
| | Citation

OPEN ACCESS

Abstract: 

The destructive impact tornadoes have on communities has sparked interest in predicting the risk of impacts on seasonal time scales. Here, the authors demonstrate how to build statistical models for  predicting tornado rates. They test the models with tornado counts accumulated over a 45-year period aggregated to counties in the State of Oklahoma and to cells in a latitude/longitude grid across a large portion of south central United States. The spatial model provides a fit to the counts, which includes terms for the spatial correlation and the population effect. A space-time model not only provides a  similar fit to annual counts but also includes a term for a time-varying climate factor. This work  contributes to methods for forecasting severe convective storms on the seasonal time scale

Keywords: 

climate, risk prediction, space-time model, statistical model, tornadoes

  References

[1] Elsner, J.B. & Widen, H.M., Predicting spring tornado activity in the central great plains by March 1st. Monthly Weather Review, 142, pp. 259–267, 2014. http://dx.doi.org/10.1175/MWR-D-13-00014.1

[2] Allen, J.T., Tippett, M.K. & Sobel, A.H., Influence of the El Nin˜o/Southern  Oscillation on tornado and hail frequency in the United States. Nature Geosciences, 8, pp. 278–283, 2015. http://dx.doi.org/10.1038/ngeo2385

[3] King, P., On the absence of population bias in the tornado climatology of southwestern Ontario. Weather and Forecasting, 12, pp. 939–946, 1997.

http://dx.doi.org/10.1175/1520-0434(1997)012<0939:OTAOPB>2.0.CO;2

[4] Ray, P.S., Bieringer, P., Niu, X. & Whissel, B., An improved estimate of tornado  occurrence in the central plains of the United States. Monthly Weather Review, 131, pp. 1026–1031, 2003. http://dx.doi.org/10.1175/1520-0493(2003)131<1026:AIEOTO>2.0.CO;2

[5] Anderson, C.J., Wikle, C.K. & Zhou, Q., Population influences on tornado reports in the United States. Weather and Forecasting, 22, pp. 571–579, 2007.

http://dx.doi.org/10.1175/WAF997.1

[6] Brooks, H.E., Doswell, C.A. & Kay, M.P., Climatological estimates of local daily t ornado probability for the United States. Weather and Forecasting, 18, pp. 626–640, 2003.

http://dx.doi.org/10.1175/1520-0434(2003)018<0626:CEOLDT>2.0.CO;2

[7] Dixon, P.G., Mercer, A.E., Choi, J. & Allen, J.S., Tornado risk analysis: is dixie alley an extension of tornado alley? Bulletin of the American Meteorological Society, 92, pp. 433–441, 2011. http://dx.doi.org/10.1175/2010BAMS3102.1

[8] Shafer, C.M. & Doswell, C.A., Using kernel density estimation to identify, rank, and classify severe weather outbreak events. Electronic Journal of Severe Storms  Meteorology, 6, pp. 1–28, 2011.

[9] Doswell, C.A. & Burgess, D.W., On some issues of United States Tornado climatology. 

Monthly Weather Review, 116, pp. 495–501, 1988. http://dx.doi.org/10.1175/1520-0493(1988)116<0495:OSIOUS>2.0.CO;2 [10] Elsner, J.B., Michaels, L.E., Scheitlin, K.N. & Elsner, I.J., The decreasing population bias in tornado reports. Weather, Climate, and Society, 5, pp. 221–232, 2013.

http://dx.doi.org/10.1175/WCAS-D-12-00040.1

[11] Doswell, C.A., Brooks, H.E. & Kay, M.P., Climatological estimates of daily local  nontornadic severe thunderstorm probability for the United States. Weather and  Forecasting, 20, pp. 577–595, 2005.

http://dx.doi.org/10.1175/WAF866.1

[12] Verbout, S.M., Brooks, H.E., Leslie, L.M. & Schultz, D.M., Evolution of the U.S. 

 tornado database: 1954-2003. Weather and Forecasting, 21, pp. 86–93, 2006. http://dx.doi.org/10.1175/WAF910.1

[13] Jagger, T.H., Elsner, J.B. & Widen, H.M., A statistical model for regional tornado  climate studies. PLoS ONE, 10(8), p. e0131876, 2015.

[14] Elsner, J.B., Jagger, T.H. & Elsner, I.J., Tornado intensity estimated from damage path dimensions. PLoS ONE, 9(9), p. e107571, 2014.

[15] Besag, J., Statistical analysis of non-lattice data. Journal of the Royal Statistical  Society: 

Series D (The Statistician), 24, pp. 179–195, 1975. http://dx.doi.org/10.2307/2987782

[16] Rue, H., Martino, S. & Chopin, N., Approximate bayesian inference for latent G aussian models by using integrated nested Laplace approximations. Journal of the Royal  Statistical Society: Series B (Statistical Methodology), 71, pp. 319–392, 2009. http://dx.doi.org/10.1111/j.1467-9868.2008.00700.x

[17] Rue, H., Martino, S., Lindgren, F., Simpson, D., Riebler, A. & Krainski, E.T., INLA: Functions which allow to perform full Bayesian analysis of latent Gaussian models using Integrated Nested Laplace Approximaxion, 2014 + 0100). R package version 0.01417182342.

[18] Marzban, C. & Schaefer, J.T., The correlation between US tornadoes and Pacific sea surface temperatures. Monthly Weather Review, 129(4), pp. 884–895, 2001.

http://dx.doi.org/10.1175/1520-0493(2001)129<0884:TCBUST>2.0.CO;2

[19] Cook, A.R. & Schaefer, J.T., The relation of El Nin˜o-Southern Oscillation (ENSO) to winter tornado outbreaks. Monthly Weather Review, 136, pp. 3121–3137, 2008. http://dx.doi.org/10.1175/2007MWR2171.1

[20] Mun˜oz, E. & Enfield, D., The boreal spring variability of the Intra-Americas low-level jet and its relation with precipitation and tornadoes in the eastern United States. Climate Dynamics, 36(1–2), pp. 247–259, 2011.

[21] Lee, S.K., Atlas, R., Enfield, D., Wang, C. & Liu, H., Is there an optimal ENSO pattern that enhances large-scale atmospheric processes conducive to tornado outbreaks in the United States? Journal of Climate, 26, pp. 1626–1642, 2013. http://dx.doi.org/10.1175/JCLI-D-12-00128.1

[22] Hijmans, R.J., raster: Geographic Data Analysis and Modeling, 2015. R package  version 2, pp. 4–18.

[23] Krishnamurthy, L., Vecchi, G.A., Msadek, R., Wittenberg, A., Delworth, T.L. & Zeng, F., The seasonality of the great plains low-level jet and ENSO relationship. Journal of Climate, 28, pp. 4525–4544, 2015. http://dx.doi.org/10.1175/JCLI-D-14-00590.1