Editorial Type:
Article
Article Type:
Research Article

Improving Estimates of U.S. Tornado Frequency by Accounting for Unreported and Underrated Tornadoes

Corey K. Potvin aNOAA/OAR National Severe Storms Laboratory, Norman, Oklahoma
bSchool of Meteorology, University of Oklahoma, Norman, Oklahoma

Search for other papers by Corey K. Potvin in
Current site
Google Scholar
PubMed Close
https://orcid.org/0000-0002-2752-3085
,
Chris Broyles cNOAA/NWS/NCEP Storm Prediction Center, Norman, Oklahoma

Search for other papers by Chris Broyles in
Current site
Google Scholar
PubMed Close
,
Patrick S. Skinner dCooperative Institute for Severe and High-Impact Weather Research and Operations, University of Oklahoma, Norman, Oklahoma
aNOAA/OAR National Severe Storms Laboratory, Norman, Oklahoma
bSchool of Meteorology, University of Oklahoma, Norman, Oklahoma

Search for other papers by Patrick S. Skinner in
Current site
Google Scholar
PubMed Close
, and
Harold E. Brooks aNOAA/OAR National Severe Storms Laboratory, Norman, Oklahoma
bSchool of Meteorology, University of Oklahoma, Norman, Oklahoma

Search for other papers by Harold E. Brooks in
Current site
Google Scholar
PubMed Close
Online Publication:
26 Jul 2022
Print Publication:
01 Jul 2022
DOI:
https://doi.org/10.1175/JAMC-D-21-0225.1
Page(s):
909–930
Restricted access

Abstract

Many tornadoes are unreported because of lack of observers or are underrated in intensity, width, or track length because of lack of damage indicators. These reporting biases substantially degrade estimates of tornado frequency and thereby undermine important endeavors such as studies of climate impacts on tornadoes and cost–benefit analyses of tornado damage mitigation. Building on previous studies, we use a Bayesian hierarchical modeling framework to estimate and correct for tornado reporting biases over the central United States during 1975–2018. The reporting biases are treated as a univariate function of population density. We assess how these biases vary with tornado intensity, width, and track length and over the analysis period. We find that the frequencies of tornadoes of all kinds, but especially stronger or wider tornadoes, have been substantially underestimated. Most strikingly, the Bayesian model estimates that there have been approximately 3 times as many tornadoes capable of (E)F2+ damage as have been recorded as (E)F2+ [(E)F indicates a rating on the (enhanced) Fujita scale]. The model estimates that total tornado frequency changed little over the analysis period. Statistically significant trends in frequency are found for tornadoes within certain ranges of intensity, pathlength, and width, but it is unclear what proportion of these trends arise from changes in damage survey practices. Simple analyses of the tornado database corroborate many of the inferences from the Bayesian model.

Significance Statement

Prior studies have shown that the probabilities of a tornado being reported and of its intensity, track length, and width being accurately estimated are strongly correlated with the local population density. We have developed a sophisticated statistical model that accounts for these population-dependent tornado reporting biases to improve estimates of tornado frequency in the central United States. The bias-corrected tornado frequency estimates differ markedly from the official tornado climatology and have important implications for tornado risk assessment, damage mitigation, and studies of climate change impacts on tornado activity.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Corey K. Potvin, corey.potvin@noaa.gov

Abstract

Many tornadoes are unreported because of lack of observers or are underrated in intensity, width, or track length because of lack of damage indicators. These reporting biases substantially degrade estimates of tornado frequency and thereby undermine important endeavors such as studies of climate impacts on tornadoes and cost–benefit analyses of tornado damage mitigation. Building on previous studies, we use a Bayesian hierarchical modeling framework to estimate and correct for tornado reporting biases over the central United States during 1975–2018. The reporting biases are treated as a univariate function of population density. We assess how these biases vary with tornado intensity, width, and track length and over the analysis period. We find that the frequencies of tornadoes of all kinds, but especially stronger or wider tornadoes, have been substantially underestimated. Most strikingly, the Bayesian model estimates that there have been approximately 3 times as many tornadoes capable of (E)F2+ damage as have been recorded as (E)F2+ [(E)F indicates a rating on the (enhanced) Fujita scale]. The model estimates that total tornado frequency changed little over the analysis period. Statistically significant trends in frequency are found for tornadoes within certain ranges of intensity, pathlength, and width, but it is unclear what proportion of these trends arise from changes in damage survey practices. Simple analyses of the tornado database corroborate many of the inferences from the Bayesian model.

Significance Statement

Prior studies have shown that the probabilities of a tornado being reported and of its intensity, track length, and width being accurately estimated are strongly correlated with the local population density. We have developed a sophisticated statistical model that accounts for these population-dependent tornado reporting biases to improve estimates of tornado frequency in the central United States. The bias-corrected tornado frequency estimates differ markedly from the official tornado climatology and have important implications for tornado risk assessment, damage mitigation, and studies of climate change impacts on tornado activity.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Corey K. Potvin, corey.potvin@noaa.gov
Save
Cover Journal of Applied Meteorology and Climatology
Journal of Applied Meteorology and Climatology

  • Agee, E., and S. Childs, 2014: Adjustments in tornado counts, F-scale intensity, and path width for assessing significant tornado destruction. J. Appl. Meteor. Climatol., 53, 1494–1505, https://doi.org/10.1175/JAMC-D-13-0235.1.

    • Search Google Scholar
    • Export Citation

  • Allen, J. T., M. K. Tippett, and A. H. Sobel, 2015: Influence of El Niño–Southern oscillation on tornado and hail frequency in the United States. Nat. Geosci., 8, 278–283, https://doi.org/10.1038/ngeo2385.

    • Search Google Scholar
    • Export Citation

  • Anderson, C. J., C. K. Wikle, Q. Zhou, and J. A. Royle, 2007: Population influences on tornado reports in the United States. Wea. Forecasting, 22, 571–579, https://doi.org/10.1175/WAF997.1.

    • Search Google Scholar
    • Export Citation

  • Ashley, W. S., and S. M. Strader, 2016: Recipe for disaster: How the dynamic ingredients of risk and exposure are changing the tornado disaster landscape. Bull. Amer. Meteor. Soc., 97, 767–786, https://doi.org/10.1175/BAMS-D-15-00150.1.

    • Search Google Scholar
    • Export Citation

  • Ashley, W. S., S. Strader, T. Rosencrants, and A. J. Krmenec, 2014: Spatiotemporal changes in tornado hazard exposure: The case of the expanding bull’s-eye effect in Chicago, Illinois. Wea. Climate Soc., 6, 175–193, https://doi.org/10.1175/WCAS-D-13-00047.1.

    • Search Google Scholar
    • Export Citation

  • Barrett, B. S., and V. A. Gensini, 2013: Variability of central United States April–May tornado day likelihood by phase of the Madden–Julian oscillation. Geophys. Res. Lett., 40, 2790–2795, https://doi.org/10.1002/grl.50522.

    • Search Google Scholar
    • Export Citation

  • Brooks, H. E., 2004: On the relationship of tornado path length and width to intensity. Wea. Forecasting, 19, 310–319, https://doi.org/10.1175/1520-0434(2004)019<0310:OTROTP>v2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Brooks, H. E., C. A. Doswell, and M. P. Kay, 2003: Climatological estimates of local daily tornado probability for the United States. Wea. Forecasting, 18, 626–640, https://doi.org/10.1175/1520-0434(2003)018<0626:CEOLDT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Brooks, H. E., G. W. Carbin, and P. T. Marsh, 2014: Increased variability of tornado occurrence in the United States. Science, 346, 349–352, https://doi.org/10.1126/science.1257460.

    • Search Google Scholar
    • Export Citation

  • Broyles, J. C., and K. C. Crosbie, 2004: Evidence of smaller tornado alleys across the United States based on a long track F3–F5 tornado climatology study from 1880–2003. 22nd Conf. on Severe Local Storms, Hyannis, MA, Amer. Meteor. Soc., P5.6, https://ams.confex.com/ams/pdfpapers/81872.pdf.

  • Bubnicki, J. W., M. Churski, K. Schmidt, T. A. Diserens, and D. P. J. Kuijper, 2019: Linking spatial patterns of terrestrial herbivore community structure to trophic interactions. eLife, 8, e44937, https://doi.org/10.7554/eLife.44937.001.

    • Search Google Scholar
    • Export Citation

  • Cheng, V. Y. S., G. B. Arhonditsis, D. M. L. Sills, H. Auld, M. W. Shephard, W. A. Gough, and J. Klaassen, 2013: Probability of tornado occurrence across Canada. J. Climate, 26, 9415–9428, https://doi.org/10.1175/JCLI-D-13-00093.1.

    • Search Google Scholar
    • Export Citation

  • Cheng, V. Y. S., G. B. Arhonditsis, D. M. L. Sills, W. A. Gough, and H. Auld, 2015: A Bayesian modelling framework for tornado occurrences in North America. Nat. Commun., 6, 6599, https://doi.org/10.1038/ncomms7599.

    • Search Google Scholar
    • Export Citation

  • Cheng, V. Y. S., G. B. Arhonditsis, D. M. L. Sills, W. A. Gough, and H. Auld, 2016: Predicting the climatology of tornado occurrences in North America with a Bayesian hierarchical modeling framework. J. Climate, 29, 1899–1917, https://doi.org/10.1175/JCLI-D-15-0404.1.

    • Search Google Scholar
    • Export Citation

  • Childs, S. J., R. S. Schumacher, and J. L. Demuth, 2020: Agricultural perspectives on hailstorm severity, vulnerability, and risk messaging in eastern Colorado. Wea. Climate Soc., 12, 897–911, https://doi.org/10.1175/WCAS-D-20-0015.1.

    • Search Google Scholar
    • Export Citation

  • Clark, A. J., J. S. Kain, P. T. Marsh, J. Correia, M. Xue, and F. Kong, 2012: Forecasting tornado pathlengths using a three-dimensional object identification algorithm applied to convection-allowing forecasts. Wea. Forecasting, 27, 1090–1113, https://doi.org/10.1175/WAF-D-11-00147.1.

    • Search Google Scholar
    • Export Citation

  • Coleman, T. A., and P. G. Dixon, 2014: An objective analysis of tornado risk in the United States. Wea. Forecasting, 29, 366–376, https://doi.org/10.1175/WAF-D-13-00057.1.

    • Search Google Scholar
    • Export Citation

  • Cook, A. R., L. M. Leslie, D. B. Parsons, and J. T. Schaefer, 2017: The impact of El Niño–Southern Oscillation (ENSO) on winter and early spring U.S. tornado outbreaks. J. Appl. Meteor. Climatol., 56, 2455–2478, https://doi.org/10.1175/JAMC-D-16-0249.1.

    • Search Google Scholar
    • Export Citation

  • Dahl, N. A., D. S. Nolan, G. H. Bryan, and R. Rotunno, 2017: Using high-resolution simulations to quantify underestimates of tornado intensity from in situ observations. Mon. Wea. Rev., 145, 1963–1982, https://doi.org/10.1175/MWR-D-16-0346.1.

    • Search Google Scholar
    • Export Citation

  • Doswell, C. A., III, and D. Burgess, 1988: On some issues of United States tornado climatology. Mon. Wea. Rev., 116, 495–501, https://doi.org/10.1175/1520-0493(1988)116<0495:OSIOUS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Doswell, C. A., III, H. E. Brooks, and N. Dotzek, 2009: On the implementation of the enhanced Fujita scale in the USA. Atmos. Res., 93, 554–563, https://doi.org/10.1016/j.atmosres.2008.11.003.

    • Search Google Scholar
    • Export Citation

  • Edwards, R., R. L. Thompson, K. C. Brosbie, J. A. Hart, and C. A. Doswell III, 2004: Proposals for modernizing the definitions of tornado and severe thunderstorm outbreaks. 22nd Conf. on Severe Local Storms, Hyannis, MA, Amer. Meteor. Soc., 7.B.2, http://ams.confex.com/ams/pdfpapers/81342.pdf.

  • Edwards, R., H. E. Brooks, and H. Cohn, 2021: Changes in tornado climatology accompanying the enhanced Fujita scale. J. Appl. Meteor. Climatol., 60, 1465–1482, https://doi.org/10.1175/JAMC-D-21-0058.1.

    • Search Google Scholar
    • Export Citation

  • Elsner, J. B., and H. M. Widen, 2014: Predicting spring tornado activity in the central Great Plains by 1 March. Mon. Wea. Rev., 142, 259–267, https://doi.org/10.1175/MWR-D-13-00014.1.

    • Search Google Scholar
    • Export Citation

  • Elsner, J. B., L. E. Michaels, K. N. Scheitlin, and I. J. Elsner, 2013: The decreasing population bias in tornado reports across the central plains. Wea. Climate Soc., 5, 221–232, https://doi.org/10.1175/WCAS-D-12-00040.1.

    • Search Google Scholar
    • Export Citation

  • Elsner, J. B., T. H. Jagger, and T. Fricker, 2016: Statistical models for tornado climatology: Long and short-term views. PLOS ONE, 11, e0166895, https://doi.org/10.1371/journal.pone.0166895.

    • Search Google Scholar
    • Export Citation

  • Fuhrmann, C. M., C. E. Konrad, M. M. Kovach, J. T. McLeod, W. G. Schmitz, and P. G. Dixon, 2014: Ranking of tornado outbreaks across the United States and their climatological characteristics. Wea. Forecasting, 2, 684–701, https://doi.org/10.1175/WAF-D-13-00128.1.

    • Search Google Scholar
    • Export Citation

  • Gensini, V. A., and H. E. Brooks, 2018: Spatial trends in United States tornado frequency. npj Climate Atmos. Sci., 1, 38, https://doi.org/10.1038/s41612-018-0048-2.

    • Search Google Scholar
    • Export Citation

  • Grieser, J., and F. Terenzi, 2016: Modeling financial losses resulting from tornadoes in European countries. Wea. Climate Soc., 8, 313–326, https://doi.org/10.1175/WCAS-D-15-0036.1.

    • Search Google Scholar
    • Export Citation

  • Guo, L., K. Wang, and H. B. Bluestein, 2016: Variability of tornado occurrence over the continental United States since 1950. J. Geophys. Res. Atmos., 121, 6943–6953, https://doi.org/10.1002/2015JD024465.

    • Search Google Scholar
    • Export Citation

  • Hall, S. G., and W. S. Ashley, 2008: Effects of urban sprawl on the vulnerability to a significant tornado impact in northeastern Illinois. Nat. Hazards Rev., 9, 209–219, https://doi.org/10.1061/(ASCE)1527-6988(2008)9:4(209).

    • Search Google Scholar
    • Export Citation

  • Hamed, K. H., and A. R. Rao, 1998: A modified Mann–Kendall trend test for autocorrelated data. J. Hydrol., 204, 182–196, https://doi.org/10.1016/S0022-1694(97)00125-X.

    • Search Google Scholar
    • Export Citation

  • Hoffman, M. D., and A. Gelman, 2014: The no-U-turn sampler: Adaptively setting path lengths in Hamiltonian Monte Carlo. J. Mach. Learn. Res., 15, 1593–1623.

    • Search Google Scholar
    • Export Citation

  • Hughes, J., and M. Haran, 2013: Dimension reduction and alleviation of confounding for spatial generalized linear mixed models. J. Roy. Stat. Soc., 75A, 139–159, https://doi.org/10.1111/j.1467-9868.2012.01041.x.

    • Search Google Scholar
    • Export Citation

  • Hussain, M., and I. Mamhud, 2019: pyMannKendall: A Python package for non parametric Mann Kendall family of trend tests. J. Open Source Software, 4, 1556, https://doi.org/10.21105/joss.01556.

    • Search Google Scholar
    • Export Citation

  • Lee, B. D., and R. B. Wilhelmson, 1997: The numerical simulation of non-supercell tornadogenesis. Part I: Initiation and evolution of pretornadic misocyclone circulations along a dry outflow boundary. J. Atmos. Sci., 54, 32–60, https://doi.org/10.1175/1520-0469(1997)054<0032:TNSONS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Lee, C. C., 2012: Utilizing synoptic climatological methods to assess the impacts of climate change on future tornado-favorable environments. Nat. Hazards, 62, 325–343, https://doi.org/10.1007/s11069-011-9998-y.

    • Search Google Scholar
    • Export Citation

  • McCarthy, D. W., and J. T. Schaefer, 2004: Tornado trends over the past thirty years. 14th Conf. on Applied Climatology, Seattle, WA, Amer. Meteor. Soc., 3.4, https://ams.confex.com/ams/pdfpapers/72089.pdf.

  • McFarquhar, G. M., and Coauthors, 2020: Current and future uses of UAS for improved forecasts/warnings and scientific studies. Bull. Amer. Meteor. Soc., 101, E1322–E1328, https://doi.org/10.1175/BAMS-D-20-0015.1.

    • Search Google Scholar
    • Export Citation

  • Nouri, N., N. Devineni, V. Were, and R. Khanbilvardi, 2021: Explaining the trends and variability in the United States tornado records using climate teleconnections and shifts in observational practices. Sci. Rep., 11, 1741, https://doi.org/10.1038/s41598-021-81143-5.

  • Potvin, C. K., C. Broyles, P. S. Skinner, H. E. Brooks, and E. Rasmussen, 2019: A Bayesian hierarchical modeling framework for correcting reporting bias in the U.S. tornado database. Wea. Forecasting, 34, 15–30, https://doi.org/10.1175/WAF-D-18-0137.1.

    • Search Google Scholar
    • Export Citation

  • Reich, B. J., J. S. Hodges, and V. Zadnik, 2006: Effects of residual smoothing on the posterior of the fixed effects in disease-mapping models. Biometrics, 62, 1197–1206, https://doi.org/10.1111/j.1541-0420.2006.00617.x.

    • Search Google Scholar
    • Export Citation

  • Romanic, D., M. Refan, C.-H. Wu, and G. Michel, 2016: Oklahoma tornado risk and variability: A statistical model. Int. J. Disaster Risk Reduct., 16, 19–32, https://doi.org/10.1016/j.ijdrr.2016.01.011.

    • Search Google Scholar
    • Export Citation

  • Rue, H., and L. Held, 2005: Gaussian Markov Random Fields: Theory and Applications. Chapman and Hall/CRC Press, 280 pp.

  • Salvatier, J., T. V. Wiecki, and C. Fonnesbeck, 2016: Probabilistic programming in Python using PyMC3. PeerJ Comput. Sci., 2, e55, https://doi.org/10.7717/peerj-cs.55.

    • Search Google Scholar
    • Export Citation

  • Schaefer, J. T., and R. Edwards, 1999: The SPC tornado/severe thunderstorm database. Preprints, 11th Conf. on Applied Climatology, Dallas, TX, Amer. Meteor. Soc., 603–606, https://ams.confex.com/ams/99annual/abstracts/1360.htm.

    • Search Google Scholar
    • Export Citation

  • Schaefer, J. T., D. L. Kelly, and R. F. Abbey, 1986: A minimum assumption tornado-hazard probability model. J. Climate Appl. Meteor., 25, 1934–1945, https://doi.org/10.1175/1520-0450(1986)025<1934:AMATHP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Simmons, K. M., P. Kovacs, and G. A. Kopp, 2015: Tornado damage mitigation: Benefit–cost analysis of enhanced building codes in Oklahoma. Wea. Climate Soc., 7, 169–178, https://doi.org/10.1175/WCAS-D-14-00032.1.

    • Search Google Scholar
    • Export Citation

  • Strader, S. M., and W. S. Ashley, 2015: The expanding bull’s-eye effect. Weatherwise, 68, 23–29, https://doi.org/10.1080/00431672.2015.1067108.

    • Search Google Scholar
    • Export Citation

  • Strader, S. M., T. J. Pingel, and W. S. Ashley, 2016: A Monte Carlo model for estimating tornado impacts. Meteor. Appl., 23, 269–281, https://doi.org/10.1002/met.1552.

    • Search Google Scholar
    • Export Citation

  • Strader, S. M., W. S. Ashley, T. J. Pingel, and A. J. Krmenec, 2017: Observed and projected changes in United States tornado exposure. Wea. Climate Soc., 9, 109–123, https://doi.org/10.1175/WCAS-D-16-0041.1.

    • Search Google Scholar
    • Export Citation

  • Tippett, M. K., J. T. Allen, V. A. Gensini, and H. E. Brooks, 2015: Climate and hazardous convective weather. Curr. Climate Change Rep., 1, 60–73, https://doi.org/10.1007/s40641-015-0006-6.

    • Search Google Scholar
    • Export Citation

  • Trapp, R. J., and K. A. Hoogewind, 2018: Exploring a possible connection between U.S. tornado activity and Arctic sea ice. npj Climate Atmos. Sci., 1, 14, https://doi.org/10.1038/s41612-018-0025-9.

    • Search Google Scholar
    • Export Citation

  • Verbout, S. M., H. E. Brooks, L. M. Leslie, and D. M. Schultz, 2006: Evolution of the U.S. tornado database: 1954–2003. Wea. Forecasting, 21, 86–93, https://doi.org/10.1175/WAF910.1.

    • Search Google Scholar
    • Export Citation

  • Wagner, M., R. K. Doe, A. Johnson, Z. Chen, J. Das, and R. S. Cerveny, 2019: Unpiloted aerial systems (UASs) application for tornado damage surveys: Benefits and procedures. Bull. Amer. Meteor. Soc., 100, 2405–2409, https://doi.org/10.1175/BAMS-D-19-0124.1.

    • Search Google Scholar
    • Export Citation

  • Wagner, M., R. K. Doe, C. Wang, E. Rasmussen, M. C. Coniglio, K. L. Elmore, R. C. Balling, and R. S. Cerveny, 2021: High-resolution observations of microscale influences on a tornado track using unpiloted aerial systems (UAS). Mon. Wea. Rev., 149, 2819–2834, https://doi.org/10.1175/MWR-D-20-0213.1.

    • Search Google Scholar
    • Export Citation

  • Widen, H. M. , and Coauthors, 2013: Adjusted tornado probabilities. Electron. J. Severe Storms Meteor., 8 (7), https://ejssm.org/archives/wp-content/uploads/2021/10/vol8-7.pdf.

    • Search Google Scholar
    • Export Citation

  • Wikle, C. K., and C. J. Anderson, 2003: Climatological analysis of tornado report counts using a hierarchical Bayesian spatiotemporal model. J. Geophys. Res., 108, 9005, https://doi.org/10.1029/2002JD002806.

    • Search Google Scholar
    • Export Citation

  • Wind Science and Engineering Center, 2006: A recommendation for an enhanced Fujita scale (EF-scale), revision 2. Texas Tech University Doc., 95 pp., http://www.depts.ttu.edu/nwi/pubs/fscale/efscale.pdf.

  • Wurman, J., J. Straka, E. Rasmussen, M. Randall, and A. Zahrai, 1997: Design and deployment of a portable, pencil-beam, pulsed, 3-cm Doppler radar. J. Atmos. Oceanic Technol., 14, 1502–1512, https://doi.org/10.1175/1520-0426(1997)014<1502:DADOAP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Wurman, J., K. Kosiba, T. White, and P. Robinson, 2021: Supercell tornadoes are much stronger and wider than damage-based ratings indicate. Proc. Natl. Acad. Sci., 118, e2021535118, https://doi.org/10.1073/pnas.2021535118.

All Time Past Year Past 30 Days
Abstract Views 903 266 0
Full Text Views 490 202 27
PDF Downloads 435 159 14