• Allen, J. T., 2017: Atmospheric hazards hail potential heating up. Nat. Climate Change, 7, 474475, https://doi.org/10.1038/nclimate3327.

  • Allen, J. T., and D. J. Karoly, 2014: A climatology of Australian severe thunderstorm environments 1979–2011: Inter-annual variability and ENSO influence. Int. J. Climatol., 34, 8197, https://doi.org/10.1002/joc.3667.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Allen, J. T., and M. K. Tippett, 2015: The characteristics of United States hail reports: 1955–2014. Electron. J. Severe Storms Meteor., 10, 131, https://www.ejssm.org/ojs/index.php/ejssm/article/viewArticle/149.

    • Search Google Scholar
    • Export Citation
  • Allen, J. T., M. K. Tippett, and A. H. Sobel, 2015a: 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
  • Allen, J. T., M. K. Tippett, and A. H. Sobel, 2015b: An empirical model relating U.S. monthly hail occurrence to large-scale meteorological environment. J. Adv. Model. Earth Syst., 7, 226243, https://doi.org/10.1002/2014MS000397.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Augustine, J. A., and F. Caracena, 1994: Lower-tropospheric precursors to nocturnal MCS development over the central United States. Wea. Forecasting, 9, 116135, https://doi.org/10.1175/1520-0434(1994)009<0116:LTPTNM>2.0.CO;2.

    • 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
  • Berg, L. K., L. D. Riihimaki, Y. Qian, H. P. Yan, and M. Y. Huang, 2015: The low-level jet over the Southern Great Plains determined from observations and reanalyses and its impact on moisture transport. J. Climate, 28, 66826706, https://doi.org/10.1175/JCLI-D-14-00719.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Blair, S. F., and et al. , 2017: High-resolution hail observations: Implications for NWS warning operations. Wea. Forecasting, 32, 11011119, https://doi.org/10.1175/WAF-D-16-0203.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Blunden, J., and D. S. Arndt, 2013: State of the climate in 2012. Bull. Amer. Meteor. Soc., 94, S1S258, https://doi.org/10.1175/2013BAMSStateoftheClimate.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bouwer, L. M., 2011: Have disaster losses increased due to anthropogenic climate change? Bull. Amer. Meteor. Soc., 92, 3946, https://doi.org/10.1175/2010BAMS3092.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bouwer, L. M., 2013: Projections of future extreme weather losses under changes in climate and exposure. Risk Anal., 33, 915930, https://doi.org/10.1111/j.1539-6924.2012.01880.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bowman, K. P., and C. R. Homeyer, 2017: GridRad—Three-dimensional gridded NEXRAD WSR-88D radar data. Research data archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory, Boulder, CO, accessed 31 October 2018, https://doi.org/10.5065/D6NK3CR7.

    • Crossref
    • Export Citation
  • Brimelow, J. C., W. R. Burrows, and J. M. Hanesiak, 2017: The changing hail threat over North America in response to anthropogenic climate change. Nat. Climate Change, 7, 516523, https://doi.org/10.1038/nclimate3321.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bromley, G. T., T. Gerken, A. F. Prein, and P. C. Stoy, 2020: Recent trends in the near-surface climatology of the northern North American Great Plains. J. Climate, 33, 461475, https://doi.org/10.1175/JCLI-D-19-0106.1.

    • Crossref
    • 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.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brooks, H. E., C. A. Doswell III, and M. P. Kay, 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.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brooks, H. E., A. R. Anderson, K. Riemann, I. Ebbers, and H. Flachs, 2007: Climatological aspects of convective parameters from the NCAR/NCEP reanalysis. Atmos. Res., 83, 294305, https://doi.org/10.1016/j.atmosres.2005.08.005.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brown, T. M., W. H. Pogorzelski, and I. M. Giammanco, 2015: Evaluating hail damage using property insurance claims data. Wea. Climate Soc., 7, 197210, https://doi.org/10.1175/WCAS-D-15-0011.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Changnon, S. A., 1999: Data and approaches for determining hail risk in the contiguous United States. J. Appl. Meteor., 38, 17301739, https://doi.org/10.1175/1520-0450(1999)038<1730:DAAFDH>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Changnon, S. A., 2009: Increasing major hail losses in the U.S. Climatic Change, 96, 161166, https://doi.org/10.1007/s10584-009-9597-z.

  • Changnon, S. A., and D. Changnon, 2000: Long-term fluctuations in hail incidences in the United States. J. Climate, 13, 658664, https://doi.org/10.1175/1520-0442(2000)013<0658:LTFIHI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, W. Y., 1982: Assessment of Southern Oscillation sea-level pressure indexes. Mon. Wea. Rev., 110, 800807, https://doi.org/10.1175/1520-0493(1982)110<0800:AOSOSL>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cintineo, J. L., T. M. Smith, V. Lakshmanan, H. E. Brooks, and K. L. Ortega, 2012: An objective high-resolution hail climatology of the contiguous United States. Wea. Forecasting, 27, 12351248, https://doi.org/10.1175/WAF-D-11-00151.1.

    • Crossref
    • 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.

    • Crossref
    • 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, 24552478, https://doi.org/10.1175/JAMC-D-16-0249.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cotton, W. R., M. S. Lin, R. L. McAnelly, and C. J. Tremback, 1989: A composite model of mesoscale convective complexes. Mon. Wea. Rev., 117, 765783, https://doi.org/10.1175/1520-0493(1989)117<0765:ACMOMC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Crum, T. D., R. E. Saffle, and J. W. Wilson, 1998: An update on the NEXRAD program and future WSR-88D support to operations. Wea. Forecasting, 13, 253262, https://doi.org/10.1175/1520-0434(1998)013<0253:AUOTNP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Doswell, C. A., H. E. Brooks, and M. P. Kay, 2005: Climatological estimates of daily local nontornadic severe thunderstorm probability for the United States. Wea. Forecasting, 20, 577595, https://doi.org/10.1175/WAF866.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • French, A. J., and M. D. Parker, 2010: The response of simulated nocturnal convective systems to a developing low-level jet. J. Atmos. Sci., 67, 33843408, https://doi.org/10.1175/2010JAS3329.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gagne, D. J., A. McGovern, S. E. Haupt, R. A. Sobash, J. K. Williams, and M. Xue, 2017: Storm-based probabilistic hail forecasting with machine learning applied to convection-allowing ensembles. Wea. Forecasting, 32, 18191840, https://doi.org/10.1175/WAF-D-17-0010.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gensini, V. A., and W. S. Ashley, 2011: Climatology of potentially severe convective environments from North American regional reanalysis. Electron. J. Severe Storms Meteor., 6, https://www.ejssm.org/ojs/index.php/ejssm/article/viewArticle/85.

    • 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.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gimeno, L., A. Drumond, R. Nieto, R. M. Trigo, and A. Stohl, 2010: On the origin of continental precipitation. Geophys. Res. Lett., 37, L13804, https://doi.org/10.1029/2010GL043712.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gimeno, L., et al. , 2012: Oceanic and terrestrial sources of continental precipitation. Rev. Geophys., 50, 50, https://doi.org/10.1029/2012RG000389.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gunturi, P., and M. Tippett, 2017: Managing severe thunderstorm risk: Impact of ENSO on U.S. tornado and hail frequencies. Willis Re Tech. Rep., 5 pp., http://www.columbia.edu/~mkt14/files/WillisRe_Impact_of_ENSO_on_US_Tornado_and_Hail_frequencies_Final.pdf.

  • Hodges, D., and Z. X. Pu, 2019: Characteristics and variations of low-level jets and environmental factors associated with summer precipitation extremes over the Great Plains. J. Climate, 32, 51235144, https://doi.org/10.1175/JCLI-D-18-0553.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jeong, J.-H., J. Fan, C. R. Homeyer, and Z. Hou, 2020: Understanding hailstone temporal variability and contributing factors over the United States southern Great Plains. J. Climate, 33, 39473966, https://doi.org/10.1175/JCLI-D-19-0606.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Johns, R. H., and C. A. Doswell, 1992: Severe local storms forecasting. Wea. Forecasting, 7, 588612, https://doi.org/10.1175/1520-0434(1992)007<0588:SLSF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lanicci, J. M., and T. T. Warner, 1991: A synoptic climatology of the elevated mixed-layer inversion over the southern Great Plains in spring. Part I: Structure, dynamics, and seasonal evolution. Wea. Forecasting, 6, 181197, https://doi.org/10.1175/1520-0434(1991)006<0181:ASCOTE>2.0.CO;2.

    • Crossref
    • 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.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, L., W. Li, and Y. Kushnir, 2012: Variation of the North Atlantic subtropical high western ridge and its implication to southeastern US summer precipitation. Climate Dyn., 39, 14011412, https://doi.org/10.1007/s00382-011-1214-y.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, W. H., L. F. Li, R. Fu, Y. Deng, and H. Wang, 2011: Changes to the North Atlantic subtropical high and its role in the intensification of summer rainfall variability in the southeastern United States. J. Climate, 24, 14991506, https://doi.org/10.1175/2010JCLI3829.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, Y. C., P. Di, S. H. Chen, and J. DaMassa, 2018: Relationships of rainy season precipitation and temperature to climate indices in California: Long-term variability and extreme events. J. Climate, 31, 19211942, https://doi.org/10.1175/JCLI-D-17-0376.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lukach, M., L. Foresti, O. Giot, and L. Delobbe, 2017: Estimating the occurrence and severity of hail based on 10 years of observations from weather radar in Belgium. Meteor. Appl., 24, 250259, https://doi.org/10.1002/met.1623.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Maddox, R. A., 1983: Large-scale meteorological conditions associated with mid-latitude, mesoscale convective complexes. Mon. Wea. Rev., 111, 14751493, https://doi.org/10.1175/1520-0493(1983)111<1475:LSMCAW>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Manzato, A., 2013: Hail in northeast Italy: A neural network ensemble forecast using sounding-derived indices. Wea. Forecasting, 28, 328, https://doi.org/10.1175/WAF-D-12-00034.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Marzban, C., and J. T. Schaefer, 2001: The correlation between U.S. tornadoes and Pacific sea surface temperatures. Mon. Wea. Rev., 129, 884895, https://doi.org/10.1175/1520-0493(2001)129<0884:TCBUST>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Melick, C. J., I. L. Jirak, J. Correia Jr., A. R. Dean, and S. J. Weiss, 2014: Exploration of the NSSL maximum expected size of hail (MESH) product for verifying experimental hail forecasts in the 2014 Spring Forecasting Experiment. 27th Conf. on Severe Local Storms, Madison, WI, Amer. Meteor. Soc., 76, https://ams.confex.com/ams/27SLS/webprogram/Paper254292.html.

  • Mesinger, F., et al. , 2006: North American Regional Reanalysis. Bull. Amer. Meteor. Soc., 87, 343360, https://doi.org/10.1175/BAMS-87-3-343.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Molina, M. J., R. P. Timmer, and J. T. Allen, 2016: Importance of the Gulf of Mexico as a climate driver for U.S. severe thunderstorm activity. Geophys. Res. Lett., 43, 12 29512 304, https://doi.org/10.1002/2016GL071603.

    • Crossref
    • 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.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Murillo, E. M., and C. R. Homeyer, 2019: Severe hail fall and hailstorm detection using remote sensing observations. J. Appl. Meteor. Climatol., 58, 947970, https://doi.org/10.1175/JAMC-D-18-0247.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Newell, R. E., and Y. Zhu, 1994: Tropospheric rivers: A one-year record and a possible application to ice core data. Geophys. Res. Lett., 21, 113116, https://doi.org/10.1029/93GL03113.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nisi, L., O. Martius, A. Hering, M. Kunz, and U. Germann, 2016: Spatial and temporal distribution of hailstorms in the Alpine region: A long-term, high resolution, radar-based analysis. Quart. J. Roy. Meteor. Soc., 142, 15901604, https://doi.org/10.1002/qj.2771.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ortega, K. L., T. M. Smith, G. J. Stumpf, J. Hocker, and L. Lopez, 2005: A comparison of multi-sensor hail diagnosis techniques. 21st Conf. on Interactive Information Processing Systems, San Diego, CA, Amer. Meteor. Soc., P1.11.

  • Picca, J., and A. Ryzhkov, 2012: A dual-wavelength polarimetric analysis of the 16 May 2010 Oklahoma City extreme hailstorm. Mon. Wea. Rev., 140, 13851403, https://doi.org/10.1175/MWR-D-11-00112.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Prein, A. F., and G. J. Holland, 2018: Global estimates of damaging hail hazard. Wea. Climate Extremes, 22, 1023, https://doi.org/10.1016/j.wace.2018.10.004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pu, B., and R. E. Dickinson, 2014: Diurnal spatial variability of Great Plains summer precipitation related to the dynamics of the low-level jet. J. Atmos. Sci., 71, 18071817, https://doi.org/10.1175/JAS-D-13-0243.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rasmussen, E. N., and D. O. Blanchard, 1998: A baseline climatology of sounding-derived supercell and tornado forecast parameters. Wea. Forecasting, 13, 11481164, https://doi.org/10.1175/1520-0434(1998)013<1148:ABCOSD>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Raymond, D. J., 1978: Instability of the low-level jet and severe storm formation. J. Atmos. Sci., 35, 22742280, https://doi.org/10.1175/1520-0469(1978)035<2274:IOTLLJ>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rotunno, R., J. B. Klemp, and M. L. Weisman, 1988: A theory for strong, long-lived squall lines. J. Atmos. Sci., 45, 463485, https://doi.org/10.1175/1520-0469(1988)045<0463:ATFSLL>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schaefer, J. T., J. J. Levit, S. J. Weiss, and D. W. McCarthy, 2004: The frequency of large hail over the contiguous United States. 14th Conf. on Applied Meteorology, Seattle, WA, Amer. Meteor. Soc, 3.3.

  • Schlie, E. E. J., D. Wuebbles, S. Stevens, R. Trapp, and B. Jewett, 2019: A radar-based study of severe hail outbreaks over the contiguous United States for 2000–2011. Int. J. Climatol., 39, 278291, https://doi.org/10.1002/joc.5805.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schubert, S. D., M. J. Suarez, P. J. Pegion, R. D. Koster, and J. T. Bacmeister, 2004: Causes of long-term drought in the U.S. Great Plains. J. Climate, 17, 485503, https://doi.org/10.1175/1520-0442(2004)017<0485:COLDIT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sen, P. K., 1968: Estimates of the regression coefficient based on Kendall’s tau. J. Amer. Stat. Assoc., 63, 13791389, https://doi.org/10.1080/01621459.1968.10480934.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stensrud, D. J., 1996: Importance of low-level jets to climate: A review. J. Climate, 9, 16981711, https://doi.org/10.1175/1520-0442(1996)009<1698:IOLLJT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tang, B. H., V. A. Gensini, and C. R. Homeyer, 2019: Trends in United States large hail environments and observations. npj Climate Atmos. Sci., 2, 45, https://doi.org/10.1038/s41612-019-0103-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Taszarek, M., H. E. Brooks, and B. Czernecki, 2017: Sounding-derived parameters associated with convective hazards in Europe. Mon. Wea. Rev., 145, 15111528, https://doi.org/10.1175/MWR-D-16-0384.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Taszarek, M., J. T. Allen, H. E. Brooks, N. Pilguj, and B. Czernecki, 2021: Differing trends in United States and European severe thunderstorm environments in a warming climate. Bull. Amer. Meteor. Soc., 102, 296322, https://doi.org/10.1175/BAMS-D-20-0004.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Trapp, R. J., N. S. Diffenbaugh, H. E. Brooks, M. E. Baldwin, E. D. Robinson, and J. S. Pal, 2007: Changes in severe thunderstorm environment frequency during the 21st century caused by anthropogenically enhanced global radiative forcing. Proc. Natl. Acad. Sci. USA, 104, 19 71919 723, https://doi.org/10.1073/pnas.0705494104.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E., 1984: Signal versus noise in the southern oscillation. Mon. Wea. Rev., 112, 326332, https://doi.org/10.1175/1520-0493(1984)112<0326:SVNITS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E., 1997: The definition of El Niño. Bull. Amer. Meteor. Soc., 78, 27712777, https://doi.org/10.1175/1520-0477(1997)078<2771:TDOENO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E., and D. P. Stepaniak, 2001: Indices of El Niño evolution. J. Climate, 14, 16971701, https://doi.org/10.1175/1520-0442(2001)014<1697:LIOENO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Walters, C. K., J. A. Winkler, S. Husseini, R. Keeling, J. Nikolic, and S. Y. Zhong, 2014: Low-level jets in the North American Regional Reanalysis (NARR): A comparison with rawinsonde observations. J. Appl. Meteor. Climatol., 53, 20932113, https://doi.org/10.1175/JAMC-D-13-0364.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, H., S. Schubert, M. Suarez, and R. Koster, 2010: The physical mechanisms by which the leading patterns of SST variability impact U.S. precipitation. J. Climate, 23, 1815183, https://doi.org/10.1175/2009JCLI3188.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wei, W., W. H. Li, Y. Deng, and S. Yang, 2019: Intraseasonal variation of the summer rainfall over the southeastern United States. Climate Dyn., 53, 11711183, https://doi.org/10.1007/s00382-018-4345-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weisman, M. L., and J. B. Klemp, 1982: The dependence of numerically simulated convective storms on vertical wind shear and buoyancy. Mon. Wea. Rev., 110, 504520, https://doi.org/10.1175/1520-0493(1982)110<0504:TDONSC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Whiteman, C. D., X. Bian, and S. Zhong, 1997: Low-level jet climatology from enhanced rawinsonde observations at a site in the southern Great Plains. J. Appl. Meteor., 36, 13631376, https://doi.org/10.1175/1520-0450(1997)036<1363:LLJCFE>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Witt, A., M. D. Eilts, G. J. Stumpf, J. T. Johnson, E. D. Mitchell, and K. W. Thomas, 1998: An enhanced hail detection algorithm for the WSR-88D. Wea. Forecasting, 13, 286303, https://doi.org/10.1175/1520-0434(1998)013<0286:AEHDAF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wolter, K., and M. S. Timlin, 2011: El Niño/Southern Oscillation behaviour since 1871 as diagnosed in an extended multivariate ENSO index (MEI.ext). Int. J. Climatol., 31, 10741087, https://doi.org/10.1002/joc.2336.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, T., A. Hoell, J. Perlwitz, J. Eischeid, D. Murray, M. Hoerling, and T. M. Hamill, 2019: Towards probabilistic multivariate ENSO monitoring. Geophys. Res. Lett., 46, 10 53210 540, https://doi.org/10.1029/2019GL083946.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhu, Y., and R. E. Newell, 1998: A proposed algorithm for moisture fluxes from atmospheric rivers. Mon. Wea. Rev., 126, 725735, https://doi.org/10.1175/1520-0493(1998)126<0725:APAFMF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 167 167 33
Full Text Views 62 62 6
PDF Downloads 81 81 8

Spatial and Temporal Trends and Variabilities of Hailstones in the United States Northern Great Plains and Their Possible Attributions

View More View Less
  • 1 a Atmospheric Science and Global Change Division, Pacific Northwest National Laboratory, Richland, Washington
  • | 2 b School of Meteorology, University of Oklahoma, Norman, Oklahoma
© Get Permissions Rent on DeepDyve
Restricted access

Abstract

Following on our study of hail for the southern Great Plains (SGP), we investigated the spatial and temporal hail trends and variabilities for the northern Great Plains (NGP) and the contributing factors for summers (June–August) focusing on the period of 2004–16 using two independent hail datasets. Analysis for an extended period (1994–2016) with the hail reports was also conducted to more reliably investigate the contributing factors. Both severe hail (diameter between 1 and 2 inches) and significant severe hail (SSH; diameter > 2 inches) were examined and similar results were obtained. The occurrence of hail over the NGP demonstrated a large interannual variability, with a positive slope overall. Spatially, the increase is mainly located in the western part of Nebraska, South Dakota, and North Dakota. We find the three major dynamical factors that most likely contribute to the hail interannual variability in the NGP are El Niño–Southern Oscillation (ENSO), the North Atlantic subtropical high (NASH), and the low-level jet (LLJ). With a thermodynamical variable integrated water vapor transport that is strongly controlled by LLJ, the four factors can explain 78% of the interannual variability in the number of SSH reports. Hail occurrences in the La Niña years are higher than the El Niño years since the jet stream is stronger and NASH extends farther into the southeastern United States, thereby strengthening the LLJ and in turn water vapor transport. Interestingly, the important factors impacting hail interannual variability over the NGP are quite different from those for the SGP, except for ENSO.

© 2021 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: Jiwen Fan, jiwen.fan@pnnl.gov

This article has a companion article which can be found at http://journals.ametsoc.org/doi/abs/10.1175/JCLI-D-19-0606.1.

Abstract

Following on our study of hail for the southern Great Plains (SGP), we investigated the spatial and temporal hail trends and variabilities for the northern Great Plains (NGP) and the contributing factors for summers (June–August) focusing on the period of 2004–16 using two independent hail datasets. Analysis for an extended period (1994–2016) with the hail reports was also conducted to more reliably investigate the contributing factors. Both severe hail (diameter between 1 and 2 inches) and significant severe hail (SSH; diameter > 2 inches) were examined and similar results were obtained. The occurrence of hail over the NGP demonstrated a large interannual variability, with a positive slope overall. Spatially, the increase is mainly located in the western part of Nebraska, South Dakota, and North Dakota. We find the three major dynamical factors that most likely contribute to the hail interannual variability in the NGP are El Niño–Southern Oscillation (ENSO), the North Atlantic subtropical high (NASH), and the low-level jet (LLJ). With a thermodynamical variable integrated water vapor transport that is strongly controlled by LLJ, the four factors can explain 78% of the interannual variability in the number of SSH reports. Hail occurrences in the La Niña years are higher than the El Niño years since the jet stream is stronger and NASH extends farther into the southeastern United States, thereby strengthening the LLJ and in turn water vapor transport. Interestingly, the important factors impacting hail interannual variability over the NGP are quite different from those for the SGP, except for ENSO.

© 2021 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: Jiwen Fan, jiwen.fan@pnnl.gov

This article has a companion article which can be found at http://journals.ametsoc.org/doi/abs/10.1175/JCLI-D-19-0606.1.

Save