A Stochastic Statistical Model for U.S. Outbreak-Level Tornado Occurrence Based on the Large-Scale Environment

Kelsey Malloy aDepartment of Applied Physics and Applied Mathematics, Columbia University, New York, New York

Search for other papers by Kelsey Malloy in
Current site
Google Scholar
PubMed
Close
https://orcid.org/0000-0002-1989-7490
and
Michael K. Tippett aDepartment of Applied Physics and Applied Mathematics, Columbia University, New York, New York

Search for other papers by Michael K. Tippett in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

Tornado outbreaks—when multiple tornadoes occur within a short period of time—are rare yet impactful events. Here we developed a two-part stochastic tornado outbreak index for the contiguous United States (CONUS). The first component produces a probability map for outbreak tornado occurrence based on spatially resolved values of convective precipitation, storm relative helicity (SRH), and convective available potential energy. The second part of the index provides a probability distribution for the total number of tornadoes given the outbreak tornado probability map. Together these two components allow stochastic simulation of location and number of tornadoes that is consistent with environmental conditions. Storm report data from the Storm Prediction Center for the 1979–2021 period are used to train the model and evaluate its performance. In the first component, the probability of an outbreak-level tornado is most sensitive to SRH changes. In the second component, the total number of CONUS tornadoes depends on the sum and gridpoint maximum of the probability map. Overall, the tornado outbreak index represents the climatology, seasonal cycle, and interannual variability of tornado outbreak activity well, particularly over regions and seasons when tornado outbreaks occur most often. We found that El Niño–Southern Oscillation (ENSO) modulates the tornado outbreak index such that La Niña is associated with enhanced U.S. tornado outbreak activity over the Ohio River Valley and Tennessee River Valley regions during January–March, similar to the behavior seen in storm report data. We also found an upward trend in U.S. tornado outbreak activity during winter and spring for the 1979–2021 period using both observations and the index.

Significance Statement

Tornado outbreaks are when multiple tornadoes happen in a short time span. Because of the rare, sporadic nature of tornadoes, it can be challenging to use observational tornado reports directly to assess how climate affects tornado and tornado outbreak activity. Here, we developed a statistical model that produces a U.S. map of the likelihood that an outbreak-level tornado would occur based on environmental conditions. In addition, using that likelihood map, the model predicts a range of how many tornadoes could occur in these events. We found that “storm relative helicity” (a proxy for potential rotation in a storm’s updraft) is especially important for predicting outbreak tornado likelihood, and the sum and maximum value of the likelihood map is important for predicting total numbers for an event. Overall, this model can represent the typical behavior and fluctuations in tornado outbreak activity well. Both the tornado outbreak model and observations agree that the state of sea surface temperature in the tropical Pacific (El Niño–Southern Oscillation) is linked to tornado outbreak activity over the Ohio River Valley and Tennessee River Valley in winter through early spring and that there are upward trends in tornado outbreak activity.

© 2024 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: Kelsey Malloy, kmm2374@columbia.edu

Abstract

Tornado outbreaks—when multiple tornadoes occur within a short period of time—are rare yet impactful events. Here we developed a two-part stochastic tornado outbreak index for the contiguous United States (CONUS). The first component produces a probability map for outbreak tornado occurrence based on spatially resolved values of convective precipitation, storm relative helicity (SRH), and convective available potential energy. The second part of the index provides a probability distribution for the total number of tornadoes given the outbreak tornado probability map. Together these two components allow stochastic simulation of location and number of tornadoes that is consistent with environmental conditions. Storm report data from the Storm Prediction Center for the 1979–2021 period are used to train the model and evaluate its performance. In the first component, the probability of an outbreak-level tornado is most sensitive to SRH changes. In the second component, the total number of CONUS tornadoes depends on the sum and gridpoint maximum of the probability map. Overall, the tornado outbreak index represents the climatology, seasonal cycle, and interannual variability of tornado outbreak activity well, particularly over regions and seasons when tornado outbreaks occur most often. We found that El Niño–Southern Oscillation (ENSO) modulates the tornado outbreak index such that La Niña is associated with enhanced U.S. tornado outbreak activity over the Ohio River Valley and Tennessee River Valley regions during January–March, similar to the behavior seen in storm report data. We also found an upward trend in U.S. tornado outbreak activity during winter and spring for the 1979–2021 period using both observations and the index.

Significance Statement

Tornado outbreaks are when multiple tornadoes happen in a short time span. Because of the rare, sporadic nature of tornadoes, it can be challenging to use observational tornado reports directly to assess how climate affects tornado and tornado outbreak activity. Here, we developed a statistical model that produces a U.S. map of the likelihood that an outbreak-level tornado would occur based on environmental conditions. In addition, using that likelihood map, the model predicts a range of how many tornadoes could occur in these events. We found that “storm relative helicity” (a proxy for potential rotation in a storm’s updraft) is especially important for predicting outbreak tornado likelihood, and the sum and maximum value of the likelihood map is important for predicting total numbers for an event. Overall, this model can represent the typical behavior and fluctuations in tornado outbreak activity well. Both the tornado outbreak model and observations agree that the state of sea surface temperature in the tropical Pacific (El Niño–Southern Oscillation) is linked to tornado outbreak activity over the Ohio River Valley and Tennessee River Valley in winter through early spring and that there are upward trends in tornado outbreak activity.

© 2024 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: Kelsey Malloy, kmm2374@columbia.edu
Save
  • Allen, J. T., M. K. Tippett, and A. H. Sobel, 2015: Influence of the El Niño/Southern Oscillation on tornado and hail frequency in the United States. Nat. Geosci., 8, 278283, https://doi.org/10.1038/ngeo2385.

    • Search Google Scholar
    • Export Citation
  • Baggett, C. F., K. M. Nardi, S. J. Childs, S. N. Zito, E. A. Barnes, and E. D. Maloney, 2018: Skillful subseasonal forecasts of weekly tornado and hail activity using the Madden-Julian Oscillation. J. Geophys. Res. Atmos., 123, 12 66112 675, https://doi.org/10.1029/2018JD029059.

    • Search Google Scholar
    • Export Citation
  • Benjamini, Y., and Y. Hochberg, 1995: Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. Roy. Stat. Soc., 57B, 289300, https://doi.org/10.1111/j.2517-6161.1995.tb02031.x.

    • Search Google Scholar
    • Export Citation
  • Bloemendaal, N., I. D. Haigh, H. de Moel, S. Muis, R. J. Haarsma, and J. C. J. H. Aerts, 2020: Generation of a global synthetic tropical cyclone hazard dataset using storm. Sci. Data, 7, 40, https://doi.org/10.1038/s41597-020-0381-2.

    • Search Google Scholar
    • Export Citation
  • Brooks, H. E., 2013: Severe thunderstorms and climate change. Atmos. Res., 123, 129138, https://doi.org/10.1016/j.atmosres.2012.04.002.

    • Search Google Scholar
    • Export Citation
  • Brooks, H. E., C. A. Doswell III, and J. Cooper, 1994: On the environments of tornadic and nontornadic mesocyclones. Wea. Forecasting, 9, 606618, https://doi.org/10.1175/1520-0434(1994)009<0606:OTEOTA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Brooks, H. E., J. W. Lee, and J. P. Craven, 2003: The spatial distribution of severe thunderstorm and tornado environments from global reanalysis data. Atmos. Res., 6768, 7394, https://doi.org/10.1016/S0169-8095(03)00045-0.

    • Search Google Scholar
    • Export Citation
  • Bukovsky, M. S., and D. J. Karoly, 2007: A brief evaluation of precipitation from the North American Regional Reanalysis. J. Hydrometeor., 8, 837846, https://doi.org/10.1175/JHM595.1.

    • Search Google Scholar
    • Export Citation
  • Carbin, G. W., M. K. Tippett, S. P. Lillo, and H. E. Brooks, 2016: Visualizing long-range severe thunderstorm environment guidance from CFSv2. Bull. Amer. Meteor. Soc., 97, 10211031, https://doi.org/10.1175/BAMS-D-14-00136.1.

    • 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, 18991917, https://doi.org/10.1175/JCLI-D-15-0404.1.

    • Search Google Scholar
    • Export Citation
  • Chu, J.-E., A. Timmermann, and J.-Y. Lee, 2019: North American April tornado occurrences linked to global sea surface temperature anomalies. Sci. Adv., 5, eaaw9950, https://doi.org/10.1126/sciadv.aaw9950.

    • Search Google Scholar
    • Export Citation
  • Coffer, B. E., M. D. Parker, R. L. Thompson, B. T. Smith, and R. E. Jewell, 2019: Using near-ground storm relative helicity in supercell tornado forecasting. Wea. Forecasting, 34, 14171435, https://doi.org/10.1175/WAF-D-19-0115.1.

    • Search Google Scholar
    • Export Citation
  • Colquhoun, J. R., and P. A. Riley, 1996: Relationships between tornado intensity and various wind and thermodynamic variables. Wea. Forecasting, 11, 360371, https://doi.org/10.1175/1520-0434(1996)011<0360:RBTIAV>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Cook, A. R., and J. T. Schaefer, 2008: The relation of El Niño–Southern Oscillation (ENSO) to winter tornado outbreaks. Mon. Wea. Rev., 136, 31213137, https://doi.org/10.1175/2007MWR2171.1.

    • Search Google Scholar
    • Export Citation
  • Czado, C., T. Gneiting, and L. Held, 2009: Predictive model assessment for count data. Biometrics, 65, 12541261, https://doi.org/10.1111/j.1541-0420.2009.01191.x.

    • Search Google Scholar
    • Export Citation
  • Daneshvaran, S., and R. E. Morden, 2007: Tornado risk analysis in the United States. J. Risk Finance, 8, 97111, https://doi.org/10.1108/15265940710732314.

    • Search Google Scholar
    • Export Citation
  • Davies, G., 2019: Tsunami variability from uncalibrated stochastic earthquake models: Tests against deep ocean observations 2006–2016. Geophys. J. Int., 218, 19391960, https://doi.org/10.1093/gji/ggz260.

    • Search Google Scholar
    • Export Citation
  • Davies, J. M., 1993: Hourly helicity, instability, and EHI in forecasting supercell tornadoes. Preprints, 17th Conf. on Severe Local Storms, St. Louis, MO, Amer. Meteor. Soc., 107–111.

  • Davies, J. M., 2006: Hurricane and tropical cyclone tornado environments from RUC proximity soundings. Preprints, 23rd Conf. on Severe Local Storms, St. Louis, MO, Amer. Meteor. Soc., 12.6A, https://ams.confex.com/ams/23SLS/techprogram/paper_115483.htm.

  • Diffenbaugh, N. S., M. Scherer, and R. J. Trapp, 2013: Robust increases in severe thunderstorm environments in response to greenhouse forcing. Proc. Natl. Acad. Sci. USA, 110, 16 36116 366, https://doi.org/10.1073/pnas.1307758110.

    • Search Google Scholar
    • Export Citation
  • Doswell, C., R. Edwards, R. Thompson, J. Hart, and K. Crosbie, 2006: A simple and flexible method for ranking severe weather events. Wea. Forecasting, 21, 939951, https://doi.org/10.1175/WAF959.1.

    • Search Google Scholar
    • Export Citation
  • Droegemeier, K. K., S. M. Lazarus, and R. Davies-Jones, 1993: The influence of helicity on numerically simulated convective storms. Mon. Wea. Rev., 121, 20052029, https://doi.org/10.1175/1520-0493(1993)121<2005:TIOHON>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Edwards, R., 2012: Tropical cyclone tornadoes: A review of knowledge in research and prediction. Electron. J. Severe Storms Meteor., 7 (6), https://doi.org/10.55599/ejssm.v7i6.42.

    • Search Google Scholar
    • Export Citation
  • Edwards, R., and R. Mosier, 2022: Over a quarter century of TCTOR: Tropical cyclone tornadoes in the WSR-88D era. 30th Conf. on Severe Local Storms, Santa Fe, NM, Amer. Meteor. Soc., P171, https://ams.confex.com/ams/30SLS/meetingapp.cgi/Paper/407018.

  • Edwards, R., A. R. Dean, R. L. Thompson, and B. T. Smith, 2012: Convective modes for significant severe thunderstorms in the contiguous United States. Part III: Tropical cyclone tornadoes. Wea. Forecasting, 27, 15071519, https://doi.org/10.1175/WAF-D-11-00117.1.

    • 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
  • Emanuel, K., S. Ravela, E. Vivant, and C. Risi, 2006: A statistical deterministic approach to hurricane risk assessment. Bull. Amer. Meteor. Soc., 87, 299314, https://doi.org/10.1175/BAMS-87-3-299.

    • Search Google Scholar
    • Export Citation
  • Fan, F., and W. Pang, 2019: Stochastic track model for tornado risk assessment in the US. Front. Built Environ., 5, 37, https://doi.org/10.3389/fbuil.2019.00037.

    • Search Google Scholar
    • Export Citation
  • Fuhrmann, C. M., C. E. Konrad III, 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, 29, 684701, https://doi.org/10.1175/WAF-D-13-00128.1.

    • Search Google Scholar
    • Export Citation
  • Gensini, V. A., and W. S. Ashley, 2011: Climatology of potentially severe convective environments from the North American Regional Reanalysis. Electron. J. Severe Storms Meteor., 6 (8), https://doi.org/10.55599/ejssm.v6i8.35.

    • 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
  • Gensini, V. A., and M. K. Tippett, 2019: Global Ensemble Forecast System (GEFS) predictions of days 1–15 US tornado and hail frequencies. Geophys. Res. Lett., 46, 29222930, https://doi.org/10.1029/2018GL081724.

    • Search Google Scholar
    • Export Citation
  • Gensini, V. A., T. L. Mote, and H. E. Brooks, 2014: Severe-thunderstorm reanalysis environments and collocated radiosonde observations. J. Appl. Meteor. Climatol., 53, 742751, https://doi.org/10.1175/JAMC-D-13-0263.1.

    • Search Google Scholar
    • Export Citation
  • Gensini, V. A., D. Gold, J. T. Allen, and B. S. Barrett, 2019: Extended U.S. tornado outbreak during late May 2019: A forecast of opportunity. Geophys. Res. Lett., 46, 10 15010 158, https://doi.org/10.1029/2019GL084470.

    • Search Google Scholar
    • Export Citation
  • Gensini, V. A., B. S. Barrett, J. T. Allen, D. Gold, and P. Sirvatka, 2020a: The Extended-Range Tornado Activity Forecast (ERTAF) project. Bull. Amer. Meteor. Soc., 101, E700E709, https://doi.org/10.1175/BAMS-D-19-0188.1.

    • Search Google Scholar
    • Export Citation
  • Gensini, V. A., A. M. Haberlie, and P. T. Marsh, 2020b: 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
  • Gneiting, T., F. Balabdaoui, and A. E. Raftery, 2007: Probabilistic forecasts, calibration and sharpness. J. Roy. Stat. Soc., 69B, 243268, https://doi.org/10.1111/j.1467-9868.2007.00587.x.

    • Search Google Scholar
    • Export Citation
  • Greene, W., 2008: Functional forms for the negative binomial model for count data. Econ. Lett., 99, 585590, https://doi.org/10.1016/j.econlet.2007.10.015.

    • Search Google Scholar
    • Export Citation
  • Guillaume, B., B. Porterie, A. Batista, P. Cottle, and A. Albergel, 2019: Improving the uncertainty assessment of economic losses from large destructive wildfires. Int. J. Wildland Fire, 28, 420430, https://doi.org/10.1071/WF18104.

    • Search Google Scholar
    • Export Citation
  • Hamill, T. M., 2001: Interpretation of rank histograms for verifying ensemble forecasts. Mon. Wea. Rev., 129, 550560, https://doi.org/10.1175/1520-0493(2001)129<0550:IORHFV>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Hart, J. A., and A. E. Cohen, 2016a: The challenge of forecasting significant tornadoes from June to October using convective parameters. Wea. Forecasting, 31, 20752084, https://doi.org/10.1175/WAF-D-16-0005.1.

    • Search Google Scholar
    • Export Citation
  • Hart, J. A., and A. E. Cohen, 2016b: The statistical severe convective risk assessment model. Wea. Forecasting, 31, 16971714, https://doi.org/10.1175/WAF-D-16-0004.1.

    • Search Google Scholar
    • Export Citation
  • Hatzis, J. J., J. Koch, and H. E. Brooks, 2020: A tornado daily impacts simulator for the central and southern United States. Meteor. Appl., 27, e1882, https://doi.org/10.1002/met.1882.

    • Search Google Scholar
    • Export Citation
  • Herman, G. R., E. R. Nielsen, and R. S. Schumacher, 2018: Probabilistic verification of storm prediction center convective outlooks. Wea. Forecasting, 33, 161184, https://doi.org/10.1175/WAF-D-17-0104.1.

    • Search Google Scholar
    • Export Citation
  • Hill, A. J., G. R. Herman, and R. S. Schumacher, 2020: Forecasting severe weather with random forests. Mon. Wea. Rev., 148, 21352161, https://doi.org/10.1175/MWR-D-19-0344.1.

    • 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
  • Kim, D., S.-K. Lee, and H. Lopez, 2020: Madden–Julian oscillation–induced suppression of Northeast Pacific convection increases U.S. tornadogenesis. J. Climate, 33, 49274939, https://doi.org/10.1175/JCLI-D-19-0992.1.

    • Search Google Scholar
    • Export Citation
  • King, A. T., and A. D. Kennedy, 2019: North American supercell environments in atmospheric reanalyses and RUC-2. J. Appl. Meteor. Climatol., 58, 7192, https://doi.org/10.1175/JAMC-D-18-0015.1.

    • Search Google Scholar
    • Export Citation
  • Knupp, K. R., and Coauthors, 2014: Meteorological overview of the devastating 27 April 2011 tornado outbreak. Bull. Amer. Meteor. Soc., 95, 10411062, https://doi.org/10.1175/BAMS-D-11-00229.1.

    • Search Google Scholar
    • Export Citation
  • Koch, E., J. Koh, A. C. Davison, C. Lepore, and M. K. Tippett, 2021: Trends in the extremes of environments associated with severe U.S. thunderstorms. J. Climate, 34, 12591272, https://doi.org/10.1175/JCLI-D-19-0826.1.

    • Search Google Scholar
    • Export Citation
  • Lee, C.-Y., M. K. Tippett, A. H. Sobel, and S. J. Camargo, 2018: An environmentally forced tropical cyclone hazard model. J. Adv. Model. Earth Syst., 10, 223241, https://doi.org/10.1002/2017MS001186.

    • Search Google Scholar
    • Export Citation
  • Lee, S.-K., R. Atlas, D. Enfield, C. Wang, and H. Liu, 2013: Is there an optimal ENSO pattern that enhances large-scale atmospheric processes conducive to tornado outbreaks in the United States? J. Climate, 26, 16261642, https://doi.org/10.1175/JCLI-D-12-00128.1.

    • Search Google Scholar
    • Export Citation
  • Lee, S.-K., A. T. Wittenberg, D. B. Enfield, S. J. Weaver, C. Wang, and R. Atlas, 2016: U.S. regional tornado outbreaks and their links to spring ENSO phases and North Atlantic SST variability. Environ. Res. Lett., 11, 044008, https://doi.org/10.1088/1748-9326/11/4/044008.

    • Search Google Scholar
    • Export Citation
  • Lepore, C., M. K. Tippett, and J. T. Allen, 2017: ENSO-based probabilistic forecasts of March–May U.S. tornado and hail activity. Geophys. Res. Lett., 44, 90939101, https://doi.org/10.1002/2017GL074781.

    • Search Google Scholar
    • Export Citation
  • Lepore, C., M. K. Tippett, and J. T. Allen, 2018: CFSv2 monthly forecasts of tornado and hail activity. Wea. Forecasting, 33, 12831297, https://doi.org/10.1175/WAF-D-18-0054.1.

    • Search Google Scholar
    • Export Citation
  • Lepore, C., R. Abernathey, N. Henderson, J. T. Allen, and M. K. Tippett, 2021: Future global convective environments in CMIP6 models. Earth’s Future, 9, e2021EF002277, https://doi.org/10.1029/2021EF002277.

    • Search Google Scholar
    • Export Citation
  • Miller, D. E., Z. Wang, R. J. Trapp, and D. S. Harnos, 2020: Hybrid prediction of weekly tornado activity out to week 3: Utilizing weather regimes. Geophys. Res. Lett., 47, e2020GL087253, https://doi.org/10.1029/2020GL087253.

    • Search Google Scholar
    • Export Citation
  • Miller, D. E., V. A. Gensini, and B. S. Barrett, 2022: Madden-Julian oscillation influences United States springtime tornado and hail frequency. npj Climate Atmos. Sci., 5, 37, https://doi.org/10.1038/s41612-022-00263-5.

    • Search Google Scholar
    • Export Citation
  • Molina, M. J., J. T. Allen, and V. A. Gensini, 2018: The Gulf of Mexico and ENSO influence on subseasonal and seasonal CONUS winter tornado variability. J. Appl. Meteor. Climatol., 57, 24392463, https://doi.org/10.1175/JAMC-D-18-0046.1.

    • Search Google Scholar
    • Export Citation
  • Murugavel, P., S. Pawar, and V. Gopalakrishnan, 2012: Trends of convective available potential energy over the Indian region and its effect on rainfall. Int. J. Climatol., 32, 13621372, https://doi.org/10.1002/joc.2359.

    • Search Google Scholar
    • Export Citation
  • Quinn, N., P. D. Bates, J. Neal, A. Smith, O. Wing, C. Sampson, J. Smith, and J. Heffernan, 2019: The spatial dependence of flood hazard and risk in the United States. Water Resour. Res., 55, 18901911, https://doi.org/10.1029/2018WR024205.

    • Search Google Scholar
    • Export Citation
  • Rädler, A. T., P. Groenemeijer, E. Faust, and R. Sausen, 2018: Detecting severe weather trends using an additive regressive convective hazard model (AR-CHaMo). J. Appl. Meteor. Climatol., 57, 569587, https://doi.org/10.1175/JAMC-D-17-0132.1.

    • Search Google Scholar
    • Export Citation
  • Rädler, A. T., P. H. Groenemeijer, E. Faust, R. Sausen, and T. Púčik, 2019: Frequency of severe thunderstorms across Europe expected to increase in the 21st century due to rising instability. npj Climate Atmos. Sci., 2, 30, https://doi.org/10.1038/s41612-019-0083-7.

    • Search Google Scholar
    • Export Citation
  • Schroder, Z., and J. B. Elsner, 2021: Estimating “outbreak”-level tornado counts and casualties from environmental variables. Wea. Climate Soc., 13, 473485, https://doi.org/10.1175/WCAS-D-20-0130.1.

    • Search Google Scholar
    • Export Citation
  • Smith, B. T., R. L. Thompson, J. S. Grams, C. Broyles, and H. E. Brooks, 2012: Convective modes for significant severe thunderstorms in the contiguous United States. Part I: Storm classification and climatology. Wea. Forecasting, 27, 11141135, https://doi.org/10.1175/WAF-D-11-00115.1.

    • Search Google Scholar
    • Export Citation
  • Sobash, R. A., C. S. Schwartz, G. S. Romine, K. R. Fossell, and M. L. Weisman, 2016: Severe weather prediction using storm surrogates from an ensemble forecasting system. Wea. Forecasting, 31, 255271, https://doi.org/10.1175/WAF-D-15-0138.1.

    • Search Google Scholar
    • Export Citation
  • Sobash, R. A., G. S. Romine, and C. S. Schwartz, 2020: A comparison of neural-network and surrogate-severe probabilistic convective hazard guidance derived from a convection-allowing model. Wea. Forecasting, 35, 19812000, https://doi.org/10.1175/WAF-D-20-0036.1.

    • 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, 269281, https://doi.org/10.1002/met.1552.

    • Search Google Scholar
    • Export Citation
  • Thompson, R. L., 2023: A comparison of right-moving supercell and quasi-linear convective system tornadoes in the contiguous United States 2003–2021. Wea. Forecasting, 38, 14971513, https://doi.org/10.1175/WAF-D-23-0006.1.

    • Search Google Scholar
    • Export Citation
  • Thompson, R. L., R. Edwards, J. A. Hart, K. L. Elmore, and P. Markowski, 2003: Close proximity soundings within supercell environments obtained from the rapid update cycle. Wea. Forecasting, 18, 12431261, https://doi.org/10.1175/1520-0434(2003)018<1243:CPSWSE>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Tippett, M. K., 2018: Robustness of relations between the MJO and U.S. tornado occurrence. Mon. Wea. Rev., 146, 38733884, https://doi.org/10.1175/MWR-D-18-0207.1.

    • Search Google Scholar
    • Export Citation
  • Tippett, M. K., and C. Lepore, 2021: ENSO-based predictability of a regional severe thunderstorm index. Geophys. Res. Lett., 48, e2021GL094907, https://doi.org/10.1029/2021GL094907.

    • Search Google Scholar
    • Export Citation
  • Tippett, M. K., A. H. Sobel, and S. J. Camargo, 2012: Association of U.S. tornado occurrence with monthly environmental parameters. Geophys. Res. Lett., 39, L02801, https://doi.org/10.1029/2011GL050368.

    • Search Google Scholar
    • Export Citation
  • Tippett, M. K., A. H. Sobel, S. J. Camargo, and J. T. Allen, 2014: An empirical relation between U.S. tornado activity and monthly environmental parameters. J. Climate, 27, 29832999, https://doi.org/10.1175/JCLI-D-13-00345.1.

    • 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, 6073, https://doi.org/10.1007/s40641-015-0006-6.

    • Search Google Scholar
    • Export Citation
  • Tippett, M. K., C. Lepore, and J. E. Cohen, 2016: More tornadoes in the most extreme U.S. tornado outbreaks. Science, 354, 14191423, https://doi.org/10.1126/science.aah7393.

    • Search Google Scholar
    • Export Citation
  • Tippett, M. K., C. Lepore, and M. L. L’Heureux, 2022: Predictability of a tornado environment index from El Niño–Southern Oscillation (ENSO) and the Arctic Oscillation. Wea. Climate Dyn., 3, 10631075, https://doi.org/10.5194/wcd-3-1063-2022.

    • Search Google Scholar
    • Export Citation
  • Trapp, R. J., N. S. Diffenbaugh, and A. Gluhovsky, 2009: Transient response of severe thunderstorm forcing to elevated greenhouse gas concentrations. Geophys. Res. Lett., 36, L01703, https://doi.org/10.1029/2008GL036203.

    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E., A. Dai, R. M. Rasmussen, and D. B. Parsons, 2003: The changing character of precipitation. Bull. Amer. Meteor. Soc., 84, 12051218, https://doi.org/10.1175/BAMS-84-9-1205.

    • 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
  • Vitart, F., and Coauthors, 2019: Sub-seasonal to seasonal prediction of weather extremes. Sub-Seasonal to Seasonal Prediction: The Gap between Weather and Climate Forecasting, A. W. Robertson and F. Vitart, Eds., Elsevier, 365–386.

  • Welker, C., T. Röösli, and D. N. Bresch, 2021: Comparing an insurer’s perspective on building damages with modelled damages from pan-European winter windstorm event sets: A case study from Zurich, Switzerland. Nat. Hazards Earth Syst. Sci., 21, 279299, https://doi.org/10.5194/nhess-21-279-2021.

    • Search Google Scholar
    • Export Citation
  • Wing, O. E. J., and Coauthors, 2020: Toward global stochastic river flood modeling. Water Resour. Res., 56, e2020WR027692, https://doi.org/10.1029/2020WR027692.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 1047 1047 189
Full Text Views 208 208 56
PDF Downloads 279 279 70