What Defines a Relatively Normal Tornado Day? Exploring the Relative Risk of Tornadoes Using Practically Perfect Hindcasts for All U.S. Tornado Days from 1950 to 2021

Sean R. Ernst Institute for Public Policy Research and Analysis, The University of Oklahoma, Norman, Oklahoma
OU Cooperative Institute for Severe and High-Impact Weather Research and Operations, Norman, Oklahoma
NOAA/Storm Prediction Center, Norman, Oklahoma
School of Meteorology, The University of Oklahoma, Norman, Oklahoma

Search for other papers by Sean R. Ernst in
Current site
Google Scholar
PubMed
Close
https://orcid.org/0000-0002-7053-9621
,
Makenzie J. Krocak NOAA/National Severe Storms Laboratory, Norman, Oklahoma
Institute for Public Policy Research and Analysis, The University of Oklahoma, Norman, Oklahoma
School of Meteorology, The University of Oklahoma, Norman, Oklahoma

Search for other papers by Makenzie J. Krocak in
Current site
Google Scholar
PubMed
Close
,
Harold Brooks NOAA/National Severe Storms Laboratory, Norman, Oklahoma
School of Meteorology, The University of Oklahoma, Norman, Oklahoma

Search for other papers by Harold Brooks in
Current site
Google Scholar
PubMed
Close
, and
Joseph Ripberger Institute for Public Policy Research and Analysis, The University of Oklahoma, Norman, Oklahoma

Search for other papers by Joseph Ripberger in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

Risks associated with rare events that occur infrequently but have serious impacts, such as tornadoes, are difficult to communicate. The U.S. National Weather Service Storm Prediction Center (SPC) communicates tornado risks by forecasting the absolute likelihood of tornadoes within 25 mi of a point in their regularly issued convective outlooks. The most common forecast likelihood of tornadoes in these outlooks is subjectively low, at 2% and 5%. Studies of probabilistic risk communication for natural disasters have suggested that normalizing the absolute likelihood of rare events by their baseline rate of occurrence can help users better understand small absolute changes in their risk of negative impacts. This study seeks to develop and investigate the distribution of relative risk, defined as the absolute likelihood divided by the climatological likelihood for tornadoes within 25 mi of a point, across the contiguous United States using the 1950–2021 SPC tornado report database. The analysis reveals that relative risk values vary greatly across time and space, primarily due to the annual and regional climatology of tornado events. Overall, relative risk may be able to provide useful context for tornado risk communication in areas that have higher rates of tornado occurrence, but it can be greatly inflated in regions with low climatological risks. Future work should seek to understand how broadcast meteorologists, emergency managers, and members of the public interact with relative risk information and in doing so identify the types of tornado events where relative risk improves or complicates risk messaging.

Significance Statement

Forecasts for rare events like tornadoes are difficult to communicate to weather messaging recipients because of their very low forecast likelihoods. However, risk communication literature suggests that relative risk, which compares the forecast probability of a hazard to how likely a hazard is at a given time, can add important context to risk messages. By calculating relative risk for all reported tornadoes from 1950 to 2021, we observe that relative risk values are highest in areas that infrequently see tornadoes, and extremely large values of relative risk can occur when a single tornado occurs at a time of year and place where no others are recorded. Future work should identify how individuals react to relative risk values in tornado forecasts.

© 2025 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Sean Ernst, sean.ernst@ou.edu

Abstract

Risks associated with rare events that occur infrequently but have serious impacts, such as tornadoes, are difficult to communicate. The U.S. National Weather Service Storm Prediction Center (SPC) communicates tornado risks by forecasting the absolute likelihood of tornadoes within 25 mi of a point in their regularly issued convective outlooks. The most common forecast likelihood of tornadoes in these outlooks is subjectively low, at 2% and 5%. Studies of probabilistic risk communication for natural disasters have suggested that normalizing the absolute likelihood of rare events by their baseline rate of occurrence can help users better understand small absolute changes in their risk of negative impacts. This study seeks to develop and investigate the distribution of relative risk, defined as the absolute likelihood divided by the climatological likelihood for tornadoes within 25 mi of a point, across the contiguous United States using the 1950–2021 SPC tornado report database. The analysis reveals that relative risk values vary greatly across time and space, primarily due to the annual and regional climatology of tornado events. Overall, relative risk may be able to provide useful context for tornado risk communication in areas that have higher rates of tornado occurrence, but it can be greatly inflated in regions with low climatological risks. Future work should seek to understand how broadcast meteorologists, emergency managers, and members of the public interact with relative risk information and in doing so identify the types of tornado events where relative risk improves or complicates risk messaging.

Significance Statement

Forecasts for rare events like tornadoes are difficult to communicate to weather messaging recipients because of their very low forecast likelihoods. However, risk communication literature suggests that relative risk, which compares the forecast probability of a hazard to how likely a hazard is at a given time, can add important context to risk messages. By calculating relative risk for all reported tornadoes from 1950 to 2021, we observe that relative risk values are highest in areas that infrequently see tornadoes, and extremely large values of relative risk can occur when a single tornado occurs at a time of year and place where no others are recorded. Future work should identify how individuals react to relative risk values in tornado forecasts.

© 2025 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Sean Ernst, sean.ernst@ou.edu
Save
  • Ashley, W. S., A. M. Haberlie, and J. Strohm, 2019: A climatology of quasi-linear convective systems and their hazards in the United States. Wea, Forecasting, 34, 16051631, https://doi.org/10.1175/WAF-D-19-0014.1.

    • Search Google Scholar
    • Export Citation
  • Bitterman, A., M. J. Krocak, J. T. Ripberger, S. Ernst, J. E. Trujillo-Falcón, A. Gaviria Pabón, C. Silva, and H. Jenkins-Smith, 2023: Assessing public interpretation of original and linguist-suggested SPC risk categories in Spanish. Wea. Forecasting, 38, 10951106, https://doi.org/10.1175/WAF-D-22-0110.1.

    • Search Google Scholar
    • Export Citation
  • Brooks, H. E., M. Kay, and J. A. Hart, 1998: Objective limits on forecasting skill of rare events. Preprints, 19th Conf. Severe Local Storms, Minneapolis, MN, Amer. Meteor. Soc., 552555, https://www.nssl.noaa.gov/users/brooks/public_html/papers/prague2k1.pdf.

    • Search Google Scholar
    • Export Citation
  • Brooks, H. E., C. A. Doswell III, and M. P. McKay, 2003: Climatological estimates of local daily tornado probability for the United States. Wea. Forecasting, 18, 626640, 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, 349352, https://doi.org/10.1126/science.1257460.

    • Search Google Scholar
    • Export Citation
  • Brown, B. G., R. W. Katz, and A. H. Murphy, 1986: On the economic value of seasonal-precipitation forecasts: The fallowing/planting problem. Bull. Amer. Meteor. Soc., 67, 833841, https://doi.org/10.1175/1520-0477(1986)067%3C0833:OTEVOS%3E2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Drobny, D., 2020: March 3 tornado. Nashville Severe Weather, accessed 30 January 2025, https://nashvillesevereweather.com/march-3-tornado/.

    • Search Google Scholar
    • Export Citation
  • Edwards, R., and F. Ostby, 2022: Time line of SELS and SPC. Storm Prediction Center, accessed 13 March 2023, https://www.spc.noaa.gov/history/timeline.html.

    • Search Google Scholar
    • Export Citation
  • Edwards, R., H. E. Brooks, and H. Cohn, 2021: Changes in Tornado climatology accompanying the enhanced Fujita scale. J. Appl. Meteor. Climatol., 60, 14651482, https://doi.org/10.1175/JAMC-D-21-0058.1.

    • Search Google Scholar
    • Export Citation
  • Ernst, S., 2020: Colorful language: Investigating the interpretation of the Storm Prediction Center’s Convective Outlook by broadcast meteorologists and the US public. M.S. thesis, Dept. of Atmospheric and Geographic Sciences, University of Oklahoma, 115 pp.

    • Search Google Scholar
    • Export Citation
  • Ernst, S., B. J. Fellman, Z. Rosen, M. J. Krocack, I. L. Jirak, J. Ripberger, and H. Jenkins-Smith, 2025: Investigating how NWS meteorologists, emergency managers, and the public interpret conditional intensity forecasts for severe weather. Wea. Forecasting, 40, 507524, https://doi.org/10.1175/WAF-D-24-0109.1.

    • Search Google Scholar
    • Export Citation
  • Gensini, V. A., A. M. Haberlie, and P. T. Marsh, 2020: Practically perfect hindcasts of severe convective storms. Bull. Amer. Meteor. Soc., 101, E1259E1278, https://doi.org/10.1175/BAMS-D-19-0321.1.

    • Search Google Scholar
    • Export Citation
  • Gigerenzer, G., R. Hertwig, E. V. Den Broek, B. Fasolo, and K. V. Katsikopoulos, 2005: “A 30% chance of rain tomorrow”: How does the public understand probabilistic weather forecasts? Risk Anal., 25, 623629, https://doi.org/10.1111/j.1539-6924.2005.00608.x.

    • Search Google Scholar
    • Export Citation
  • Grams, J., B. Bunting, and S. Weiss, 2014: SPC convective outlooks. Storm Prediction Center, accessed 9 April 2019, https://www.spc.noaa.gov/misc/SPC_probotlk_info.html.

    • Search Google Scholar
    • Export Citation
  • Hitchens, N. M., and H. E. Brooks, 2012: Evaluation of the storm prediction center’s day 1 convective outlooks. Wea. Forecasting, 27, 15801585, https://doi.org/10.1175/WAF-D-12-00061.1.

    • Search Google Scholar
    • Export Citation
  • Hitchens, N. M., and H. E. Brooks, 2014: Evaluation of the storm prediction center’s convective outlooks from Day 3 through day 1. Wea. Forecasting, 29, 11341142, https://doi.org/10.1175/WAF-D-13-00132.1.

    • Search Google Scholar
    • Export Citation
  • Hitchens, N. M., H. E. Brooks, and M. P. Kay, 2013: Objective limits on forecasting skill of rare events. Wea. Forecasting, 28, 525534, https://doi.org/10.1175/WAF-D-12-00113.1.

    • Search Google Scholar
    • Export Citation
  • Joslyn, S. L., and J. E. LeClerc, 2012: Uncertainty forecasts improve weather-related decisions and attenuate the effects of forecast error. J. Exp. Psychol. Applied, 18, 126140, https://doi.org/10.1037/a0025185.

    • Search Google Scholar
    • Export Citation
  • Juanchich, M., and M. Sirota, 2019: Not as gloomy as we thought: Reassessing how the public understands probability of precipitation forecasts. J. Cognit. Psychol., 31, 116129, https://doi.org/10.1080/20445911.2018.1553884.

    • Search Google Scholar
    • Export Citation
  • Klockow-McClain, K. E., and Coauthors, 2020: Putting multiple probabilistic products before end-users: The 2019 HWT emergency manager experiments. 15th Symp. on Societal Applications: Policy, Research, and Practice, Boston, MA, Amer. Meteor. Soc., 12A.6, https://ams.confex.com/ams/2020Annual/webprogram/Paper369752.html.

    • Search Google Scholar
    • Export Citation
  • Krocak, M. J., and H. E. Brooks, 2018: Climatological estimates of hourly Tornado probability for the United States. Wea. Forecasting, 33, 5969, https://doi.org/10.1175/WAF-D-17-0123.1.

    • Search Google Scholar
    • Export Citation
  • Krocak, M. J., J. T. Ripberger, S. Ernst, C. L. Silva, and H. C. Jenkins-Smith, 2022: Exploring the differences in SPC convective outlook interpretation using categorical and numeric information. Wea. Forecasting, 37, 303311, https://doi.org/10.1175/WAF-D-21-0123.1.

    • Search Google Scholar
    • Export Citation
  • Murphy, A. H., 1991: Probabilities, odds, and forecasts of rare events. Wea. Forecasting, 6, 302307, https://doi.org/10.1175/1520-0434(1991)006<0302:POAFOR>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Murphy, A. H., 1993: What is a good forecast? An essay on the nature of goodness in weather forecasting. Wea. Forecasting, 8, 281293, https://doi.org/10.1175/1520-0434(1993)008<0281:WIAGFA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • NCEI, 2011: Monthly Tornadoes Report for June 2011. National Centers for Environmental Information, accessed 13 February 2023, https://www.ncei.noaa.gov/access/monitoring/monthly-report/tornadoes/201106.

    • Search Google Scholar
    • Export Citation
  • NCEI, 2023: U.S. Billion-dollar Weather and Climate Disasters. National Centers for Environmental Information, accessed 13 February 2023, https://doi.org/10.25921/stkw-7w73.

    • Search Google Scholar
    • Export Citation
  • Neace, W. P., S. Michaud, L. Bolling, K. Deer, and L. Zecevic, 2008: Frequency formats, probability formats, or problem structure? A test of the nested-sets hypothesis in an extensional reasoning task. Judgment Decis. Making, 3, 140152, https://doi.org/10.1017/S1930297500001480.

    • Search Google Scholar
    • Export Citation
  • NWS, 2013: The tornado outbreak of May 20, 2013. NOAA, accessed 13 February 2023, https://www.weather.gov/oun/events-20130520.

  • Potvin, C. K., C. Broyles, P. S. Skinner, H. E. Brooks, and E. Rassmussen, 2019: A Bayesian hierarchical modeling framework for correcting reporting bias in the U.S. Tornado database. Wea. Forecasting, 34, 1530, https://doi.org/10.1175/WAF-D-18-0137.1.

    • Search Google Scholar
    • Export Citation
  • Ripberger, J., A. Bell, A. Fox, A. Forney, W. Livingston, C. Gaddie, C. Silva, and J. Jenkins-Smith, 2022: Communicating probability information in weather forecasts: Findings and recommendations from a living systematic review of the research literature. Wea. Climate Soc., 14, 481498, https://doi.org/10.1175/WCAS-D-21-0034.1.

    • 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., 215220.

    • Search Google Scholar
    • Export Citation
  • Shivers-Williams, C. A., and K. E. Klockow-McClain, 2021: Geographic scale and probabilistic forecasts: A trade-off for protective decisions? Nat. Hazards, 105, 22832306, https://doi.org/10.1007/s11069-020-04400-2.

    • Search Google Scholar
    • Export Citation
  • SPC, 2022: 1950-2021_actual_tornadoes. Accessed 22 August 2022, https://www.spc.noaa.gov/wcm/#data.

  • Spiegelhalter, D., 2017: Risk and uncertainty communication. Annu. Rev. Stat. Appl., 4, 3160, https://doi.org/10.1146/annurev-statistics-010814-020148.

    • Search Google Scholar
    • Export Citation
  • Strathie, A., G. Netto, G. H. Walker, and G. Pender, 2017: How presentation format affects the interpretation of probabilistic flood risk information. J. Flood Risk Manage., 10, 8796, https://doi.org/10.1111/jfr3.12152.

    • Search Google Scholar
    • Export Citation
  • Thompson, R., 2004: SPC probabilistic outlook information. Accessed 13 February 2024, https://www.spc.noaa.gov/products/outlook/archive/2004/probinfo.html.

    • Search Google Scholar
    • Export Citation
  • Vancil, J. T., I. L. Jirak, and C. D. Karstens, 2022: Conditional intensity forecast verification using significant severe local storm reports. NOAA Tech Doc. 214, 7 pp., https://www.spc.noaa.gov/publications/vancil/condints.pdf.

    • 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, 8693, https://doi.org/10.1175/WAF910.1.

    • Search Google Scholar
    • Export Citation
  • Woo, G., and W. Marzocchi, 2014: Operational earthquake forecasting and decision-making. Early Warning for Geological Disasters, F. Wenzel and J. Zschau, Eds., Advanced Technologies in Earth Sciences, Springer, 353367.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 148 148 148
Full Text Views 137 137 59
PDF Downloads 131 131 67