• 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.

    • Crossref
    • 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.

    • Crossref
    • 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, 27902795, https://doi.org/10.1002/grl.50522.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barrett, B. S., and B. N. Henley, 2015: Intraseasonal variability of hail in the contiguous United States: Relationship to the Madden–Julian oscillation. Mon. Wea. Rev., 143, 10861103, https://doi.org/10.1175/MWR-D-14-00257.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bauer, P., A. Thorpe, and G. Brunet, 2015: The quiet revolution of numerical weather prediction. Nature, 525, 4755, https://doi.org/10.1038/nature14956.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brooks, H. E., 2007: Ingredients-based forecasting. Atmospheric Convection: Research and Operational Forecasting Aspects, Springer, 133140.

    • Crossref
    • 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.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Doswell, C. A., III, G. W. Carbin, and H. E. Brooks, 2012: The tornadoes of spring 2011 in the USA: An historical perspective. Weather, 67, 8894, https://doi.org/10.1002/wea.1902.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gensini, V. A., and A. Marinaro, 2016: Tornado frequency in the United States related to global relative angular momentum. Mon. Wea. Rev., 144, 801810, https://doi.org/10.1175/MWR-D-15-0289.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gensini, V. A., and J. T. Allen, 2018: US hail frequency and the global wind oscillation. Geophys. Res. Lett., 45, 16111620, https://doi.org/10.1002/2017GL076822.

    • Crossref
    • 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.

    • Crossref
    • 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.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grice, G., and Coauthors, 1999: The golden anniversary celebration of the first tornado forecast. Bull. Amer. Meteor. Soc., 80, 13411348, https://doi.org/10.1175/1520-0477(1999)080<1341:TGACOT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hamill, T. M., R. S. Schneider, H. E. Brooks, G. S. Forbes, H. B. Bluestein, M. Steinberg, D. Meléndez, and R. M. Dole, 2005: The May 2003 extended tornado outbreak. Bull. Amer. Meteor. Soc., 86, 531542, https://doi.org/10.1175/BAMS-86-4-531.

    • Crossref
    • 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.

    • Crossref
    • 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.

    • Crossref
    • 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.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hoffman, R. R., D. S. LaDue, H. M. Mogil, P. J. Roebber, and J. G. Trafton, 2017: Minding the Weather: How Expert Forecasters Think. MIT Press, 488 pp.

    • 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.

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

    • Crossref
    • 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.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Moore, T. W., 2018: Annual and seasonal tornado activity in the United States and the global wind oscillation. Climate Dyn ., 50, 43234334, https://doi.org/10.1007/s00382-017-3877-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Moore, T. W., and M. P. McGuire, 2019: Tornado-days in the United States by phase of the Madden–Julian oscillation and global wind oscillation. Climate Dyn ., 54, 1736, https://doi.org/10.1007/S00382-019-04983-Y.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Muller, C., L. Chapman, S. Johnston, C. Kidd, S. Illingworth, G. Foody, A. Overeem, and R. Leigh, 2015: Crowdsourcing for climate and atmospheric sciences: Current status and future potential. Int. J. Climatol., 35, 31853203, https://doi.org/10.1002/joc.4210.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • NCEI, 2019: U.S. billion-dollar weather and climate disasters. NOAA, www.ncdc.noaa.gov/billions/.

    • Search Google Scholar
  • Robertson, A. W., A. Kumar, M. Peña, and F. Vitart, 2015: Improving and promoting subseasonal to seasonal prediction. Bull. Amer. Meteor. Soc., 96, ES49ES53, https://doi.org/10.1175/BAMS-D-14-00139.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schubert, S., R. Dole, H. vandenDool, M. Suarez, and D. Waliser, 2002: Prospects for improved forecasts of weather and short-term climate variability on subseasonal (2-week to 2-month) times scales. NASA Tech. Memo. NASA/TM-2002-104606, Vol. 23, 171 pp.

    • Search Google Scholar
    • Export Citation
  • Simmons, K. M., and D. Sutter, 2012: The 2011 tornadoes and the future of tornado research. Bull. Amer. Meteor. Soc., 93, 959961, https://doi.org/10.1175/BAMS-D-11-00126.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Simmons, K. M., and D. Sutter, 2013: Economic and Societal Impacts of Tornadoes. Springer Science and Business Media, 296 pp.

  • Smith, A. B., and J. L. Matthews, 2015: Quantifying uncertainty and variable sensitivity within the US billion-dollar weather and climate disaster cost estimates. Nat. Hazards, 77, 18291851, https://doi.org/10.1007/s11069-015-1678-x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thompson, D. B., and P. E. Roundy, 2013: The relationship between the Madden–Julian oscillation and U.S. violent tornado outbreaks in the spring. Mon. Wea. Rev., 141, 20872095, https://doi.org/10.1175/MWR-D-12-00173.1.

    • Crossref
    • 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.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tian, D., E. Wood, and X. Yuan, 2017: CFSv2-based sub-seasonal precipitation and temperature forecast skill over the contiguous United States. Hydrol. Earth Syst. Sci., 21, 14771490, https://doi.org/10.5194/hess-21-1477-2017.

    • Crossref
    • 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.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Trapp, R. J., 2014: On the significance of multiple consecutive days of tornado activity. Mon. Wea. Rev., 142, 14521459, https://doi.org/10.1175/MWR-D-13-00347.1.

    • Crossref
    • 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.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vigaud, N., A. W. Robertson, and M. K. Tippett, 2017: Multimodel ensembling of subseasonal precipitation forecasts over North America. Mon. Wea. Rev., 145, 39133928, https://doi.org/10.1175/MWR-D-17-0092.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wanders, N., and E. F. Wood, 2016: Improved sub-seasonal meteorological forecast skill using weighted multi-model ensemble simulations. Environ. Res. Lett., 11, 094007, https://doi.org/10.1088/1748-9326/11/9/094007.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, L., and A. W. Robertson, 2019: Week 3–4 predictability over the United States assessed from two operational ensemble prediction systems. Climate Dyn ., 52, 58615875, https://doi.org/10.1007/s00382-018-4484-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weickmann, K., and E. Berry, 2009: The tropical Madden–Julian oscillation and the global wind oscillation. Mon. Wea. Rev., 137, 16011614, https://doi.org/10.1175/2008MWR2686.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilks, D. S., 2011: Statistical Methods in the Atmospheric Sciences. 3rd ed. Academic Press, 704 pp.

  • Zhang, C., 2013: Madden–Julian oscillation: Bridging weather and climate. Bull. Amer. Meteor. Soc., 94, 18491870, https://doi.org/10.1175/BAMS-D-12-00026.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
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The Extended-Range Tornado Activity Forecast (ERTAF) Project

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  • 1 Department of Geographic and Atmospheric Sciences, Northern Illinois University, DeKalb, Illinois
  • | 2 Oceanography Department, United States Naval Academy, Annapolis, Maryland
  • | 3 Department of Earth and Atmospheric Science, Central Michigan University, Mount Pleasant, Michigan
  • | 4 Weather Analytics Center of Competency, Global Business Services, IBM, Armonk, New York
  • | 5 Meteorology Program, College of DuPage, Glen Ellyn, Illinois
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Abstract

Large-scale weather patterns favorable for tornado occurrence have been understood for many decades. Yet prediction of tornadoes, especially at extended lead periods of more than a few days, remains an arduous task, partly due to the space and time scales involved. Recent research has shown that tropical convection, sea surface temperatures, and the Earth-relative atmospheric angular momentum can induce jet stream configurations that may increase or decrease the probability of tornado frequency across the United States. Applying this recent theoretical work in practice, on 1 March 2015, the authors began the Extended-Range Tornado Activity Forecast (ERTAF) project, with the following goals: 1) to have a map room–style discussion of the anticipated atmospheric state in the 2–3-week lead window; 2) to predict categorical level of tornado activity in that lead window; and 3) to learn from the forecasts through experience by identifying strengths and weaknesses in the methods, as well as identifying any potential scientific knowledge gaps. Over the last five years, the authors have shown skill in predicting U.S. tornado activity two to three weeks in advance during boreal spring. Unsurprisingly, skill is shown to be greater for forecasts spanning week 2 versus week 3. This manuscript documents these forecasting efforts, provides verification statistics, and shares the challenges and lessons learned from predicting tornado activity on the subseasonal time scale.

© 2020 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: Vittorio A. Gensini, vgensini@niu.edu

Abstract

Large-scale weather patterns favorable for tornado occurrence have been understood for many decades. Yet prediction of tornadoes, especially at extended lead periods of more than a few days, remains an arduous task, partly due to the space and time scales involved. Recent research has shown that tropical convection, sea surface temperatures, and the Earth-relative atmospheric angular momentum can induce jet stream configurations that may increase or decrease the probability of tornado frequency across the United States. Applying this recent theoretical work in practice, on 1 March 2015, the authors began the Extended-Range Tornado Activity Forecast (ERTAF) project, with the following goals: 1) to have a map room–style discussion of the anticipated atmospheric state in the 2–3-week lead window; 2) to predict categorical level of tornado activity in that lead window; and 3) to learn from the forecasts through experience by identifying strengths and weaknesses in the methods, as well as identifying any potential scientific knowledge gaps. Over the last five years, the authors have shown skill in predicting U.S. tornado activity two to three weeks in advance during boreal spring. Unsurprisingly, skill is shown to be greater for forecasts spanning week 2 versus week 3. This manuscript documents these forecasting efforts, provides verification statistics, and shares the challenges and lessons learned from predicting tornado activity on the subseasonal time scale.

© 2020 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: Vittorio A. Gensini, vgensini@niu.edu
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