• Adams-Selin, R., and C. L. Ziegler, 2016: Forecasting hail using a one-dimensional hail growth model within WRF. Mon. Wea. Rev., 144, 49194939, https://doi.org/10.1175/MWR-D-16-0027.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Adlerman, E. J., and R. P. Davies-Jones, 1999: A numerical simulation of cyclic mesocyclogenesis. J. Atmos. Sci., 56, 20452069, https://doi.org/10.1175/1520-0469(1999)056<2045:ANSOCM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Adlerman, E. J., and K. K. Droegemeier, 2002: The sensitivity of numerically simulated cyclic mesocyclogenesis to variations in model physical and computational parameters. Mon. Wea. Rev., 130, 26712691, https://doi.org/10.1175/1520-0493(2002)130<2671:TSONSC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Adlerman, E. J., and K. K. Droegemeier, 2005: The dependence of numerically simulated cyclic mesocyclogenesis upon environmental vertical wind shear. Mon. Wea. Rev., 133, 35953623, https://doi.org/10.1175/MWR3039.1.

    • 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 (3), https://www.ejssm.org/ojs/index.php/ejssm/article/view/149.

    • Search Google Scholar
    • Export Citation
  • Allen, J. T., I. M. Giammanco, M. R. Kumjian, H. J. Punge, Q. Zhang, P. Groenemeijer, M. Kunz, and K. Ortega, 2020: Understanding hail in the Earth system. Rev. Geophys., 58, e2019RG000665, https://doi.org/10.1029/2019RG000665.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Aydin, K., T. A. Seliga, and V. Balaji, 1986: Remote sensing of hail with a dual linear polarized radar. J. Climate Appl. Meteor., 25, 14751484, https://doi.org/10.1175/1520-0450(1986)025<1475:RSOHWA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Beck, J. R., J. L. Schroeder, and J. M. Wurman, 2006: High-resolution dual-Doppler analyses of the 29 May 2001 Kress, Texas, cyclic supercell. Mon. Wea. Rev., 134, 31253148, https://doi.org/10.1175/MWR3246.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Blair, S. F., D. R. Deroche, J. M. Boustead, J. W. Leighton, B. L. Barjenbruch, and W. P. Gargan, 2011: A radar-based assessment of the detectability of giant hail. Electron. J. Severe Storms Meteor., 6 (7), https://www.ejssm.org/ojs/index.php/ejssm/article/viewArticle/87.

    • 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
  • Brook, J. P., A. Protat, J. Soderholm, J. T. Carlin, H. McGowan, and R. A. Warren, 2021: HailTrack—Improving radar-based hailfall estimates by modeling hail trajectories. J. Appl. Meteor. Climatol., 60, 237254, https://doi.org/10.1175/JAMC-D-20-0087.1.

    • 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
  • Browning, K. A., and G. B. Foote, 1976: Air-flow and hail growth in supercell storms and some implications for hail suppression. Quart. J. Roy. Meteor. Soc., 102, 499533, https://doi.org/10.1002/qj.49710243303.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bryan, G. H., and J. M. Fritsch, 2002: A benchmark simulation for moist nonhydrostatic numerical models. Mon. Wea. Rev., 130, 29172928, https://doi.org/10.1175/1520-0493(2002)130<2917:ABSFMN>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bunkers, M. J., B. A. Klimowski, J. W. Zeitler, R. L. Thompson, and M. L. Weisman, 2000: Predicting supercell motion using a new hodograph technique. Wea. Forecasting, 15, 6179, https://doi.org/10.1175/1520-0434(2000)015<0061:PSMUAN>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Changnon, S., D. Changnon, and S. D. Hilberg, 2009: Hailstorms across the nation: An atlas about hail and its damages. Illinois State Water Survey Contract Rep. 2009-12, 92 pp.

  • Coffer, B. E., and M. Parker, 2017: Simulated supercells in nontornadic and tornadic VORTEX2 environments. Mon. Wea. Rev., 145, 149180, https://doi.org/10.1175/MWR-D-16-0226.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dahl, J., 2017: Tilting of horizontal shear vorticity and the development of updraft rotation in supercell thunderstorms. J. Atmos. Sci., 74, 29973020, https://doi.org/10.1175/JAS-D-17-0091.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Davies-Jones, R., 1984: Streamwise vorticity: The origin of updraft rotation in supercell storms. J. Atmos. Sci., 41, 29913006, https://doi.org/10.1175/1520-0469(1984)041<2991:SVTOOU>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Davies-Jones, R., R. J. Trapp, and H. B. Bluestein, 2001: Tornadoes and tornadic storms. Severe Convective Storms, Meteor. Monogr., No. 28, Amer. Meteor. Soc., 167–221, https://doi.org/10.1175/0065-9401-28.50.167.

    • Crossref
    • Export Citation
  • Deardorff, J. W., 1980: Stratocumulus-capped mixed layers derived from a three-dimensional model. Bound.-Layer Meteor., 18, 495527, https://doi.org/10.1007/BF00119502.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dennis, E. J., and M. R. Kumjian, 2017: The impact of vertical wind shear on hail growth in simulated supercells. J. Atmos. Sci., 74, 641663, https://doi.org/10.1175/JAS-D-16-0066.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Depue, T. K., P. C. Kennedy, and S. A. Rutledge, 2007: Performance of the hail differential reflectivity (HDR) polarimetric radar hail indicator. J. Appl. Meteor., 46, 12901301, https://doi.org/10.1175/JAM2529.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Efron, B., and R. J. Tibshirani, 1993: An Introduction to the Bootstrap. 1st ed. Chapman and Hall, 456 pp.

  • French, M. M., H. B. Bluestein, D. C. Dowell, L. J. Wicker, M. R. Kramar, and A. L. Pazmany, 2008: High-resolution, mobile Doppler radar observations of cyclic mesocyclogenesis in a supercell. Mon. Wea. Rev., 136, 49975016, https://doi.org/10.1175/2008MWR2407.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • French, M. M., H. B. Bluestein, I. PopStefanija, C. A. Baldi, and R. T. Bluth, 2013: Reexamining the vertical development of tornadic vortex signatures in supercells. Mon. Wea. Rev., 141, 45764601, https://doi.org/10.1175/MWR-D-12-00315.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • French, M. M., D. W. Burgess, E. R. Mansell, and L. J. Wicker, 2015: Hook echo raindrop sizes retrieved using mobile, polarimetric Doppler radar observations. J. Appl. Meteor. Climatol., 54, 423450, https://doi.org/10.1175/JAMC-D-14-0171.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grant, L. D., and S. C. van den Heever, 2014: Microphysical and dynamical characteristics of low-precipitation and classic supercells. J. Atmos. Sci., 71, 26042624, https://doi.org/10.1175/JAS-D-13-0261.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grieser, J., and M. Hill, 2019: How to express hail intensity—Modeling the hailstone size distribution. J. Appl. Meteor. Climatol., 58, 23292345, https://doi.org/10.1175/JAMC-D-18-0334.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gutierrez, R. E., and M. R. Kumjian, 2021: Environmental and radar characteristics of gargantuan hail-producing storms. Mon. Wea. Rev., 149, 25232538, https://doi.org/10.1175/MWR-D-20-0298.1.

    • Search Google Scholar
    • Export Citation
  • Heymsfield, A. J., 1983: Case study of a hailstorm in Colorado. Part IV: Graupel and hail growth mechanisms deduced through particle trajectory calculations. J. Atmos. Sci., 40, 14821509, https://doi.org/10.1175/1520-0469(1983)040<1482:CSOAHI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Katona, B., and P. Markowski, 2021: Assessing the influence of complex terrain on severe convective environments in northeastern Alabama. Wea. Forecasting, 36, 10031029, https://doi.org/10.1175/WAF-D-20-0136.1.

    • Search Google Scholar
    • Export Citation
  • Katona, B., P. Markowski, C. Alexander, and S. Benjamin, 2016: The influence of topography on convective storm environments in the eastern United States as deduced from the HRRR. Wea. Forecasting, 31, 14811490, https://doi.org/10.1175/WAF-D-16-0038.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Klemp, J. B., and R. Wilhelmson, 1978: The simulation of three-dimensional convective storm dynamics. J. Atmos. Sci., 35, 10701096, https://doi.org/10.1175/1520-0469(1978)035<1070:TSOTDC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Knight, N. C., 1981: The climatology of hail embryos. J. Appl. Meteor., 20, 750755, https://doi.org/10.1175/1520-0450(1981)020<0750:TCOHE>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kumjian, M. R., and A. V. Ryzhkov, 2008: Polarimetric signatures in supercell thunderstorms. J. Appl. Meteor. Climatol., 47, 19401961, https://doi.org/10.1175/2007JAMC1874.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kumjian, M. R., and K. Lombardo, 2020: A hail growth trajectory model for exploring the environmental controls on hail size: Model physics and idealized tests. J. Atmos. Sci., 77, 27652791, https://doi.org/10.1175/JAS-D-20-0016.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kumjian, M. R., A. V. Ryzhkov, V. M. Melnikov, and T. J. Schuur, 2010: Rapid-scan super-resolution observations of a cyclic supercell with a dual-polarization WSR-88D. Mon. Wea. Rev., 138, 37623786, https://doi.org/10.1175/2010MWR3322.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kumjian, M. R., and et al. , 2020: Gargantuan hail in Argentina. Bull. Amer. Meteor. Soc., 101, E1241E1258, https://doi.org/10.1175/BAMS-D-19-0012.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lebo, Z. J., and H. C. Morrison, 2015: Effects of horizontal and vertical grid spacing on mixing in simulated squall lines and implications for convective strength and structure. Mon. Wea. Rev., 143, 43554375, https://doi.org/10.1175/MWR-D-15-0154.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Letkewicz, C. E., and M. D. Parker, 2011: Impact of environmental variations on simulated squall lines interacting with terrain. Mon. Wea. Rev., 139, 31633183, https://doi.org/10.1175/2011MWR3635.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lombardo, K. A., 2020: Squall line response to coastal mid-Atlantic thermodynamic heterogeneities. J. Atmos. Sci., 77, 41434170, https://doi.org/10.1175/JAS-D-20-0044.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lombardo, K. A., and T. Kading, 2018: The behavior of squall lines in horizontally heterogeneous coastal environments. J. Atmos. Sci., 75, 12431269, https://doi.org/10.1175/JAS-D-17-0248.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Markowski, P. M., 2020: What is the intrinsic predictability of tornadic supercell thunderstorms? Mon. Wea. Rev., 148, 31573180, https://doi.org/10.1175/MWR-D-20-0076.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Markowski, P. M., and Y. P. Richardson, 2010: Mesoscale Meteorology in Midlatitudes. 1st ed. Wiley-Blackwell, 407 pp.

  • Markowski, P. M., and N. Dotzek, 2011: A numerical study of the effects of orography on supercells. Atmos. Res., 100, 457478, https://doi.org/10.1016/j.atmosres.2010.12.027.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Markowski, P. M., and Y. P. Richardson, 2014: The influence of environmental low-level shear and cold pools on tornadogenesis: Insights from idealized simulations. J. Atmos. Sci., 71, 243275, https://doi.org/10.1175/JAS-D-13-0159.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Miller, L. J., J. D. Tuttle, and C. A. Knight, 1988: Airflow and hail growth in a severe northern plains supercell. J. Atmos. Sci., 45, 736762, https://doi.org/10.1175/1520-0469(1988)045<0736:AAHGIA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Morgan, G. M., and N. G. Towery, 1975: Small-scale variability of hail and its significance for hail prevention experiments. J. Appl. Meteor., 14, 763770, https://doi.org/10.1175/1520-0450(1975)014<0763:SSVOHA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Morrison, H., 2016: Impacts of updraft size and dimensionality on the perturbation pressure and vertical velocity in cumulus convection: Part I: Simple, generalized analytic solutions. J. Atmos. Sci., 73, 14411454, https://doi.org/10.1175/JAS-D-15-0040.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Morrison, H., G. Thompson, and V. Tatarskii, 2009: Impact of cloud microphysics on the development of trailing stratiform precipitation in a simulated squall line: Comparison of one- and two-moment schemes. Mon. Wea. Rev., 137, 9911007, https://doi.org/10.1175/2008MWR2556.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mulholland, J. P., S. W. Nesbitt, R. J. Trapp, and J. M. Peters, 2020: The influence of terrain on the convective environment and associated convective morphology from an idealized modeling prospective. J. Atmos. Sci., 77, 39293949, https://doi.org/10.1175/JAS-D-19-0190.1.

    • 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
  • National Weather Service, 2016: Weather event summary: San Antonio April 2016. NWS Rep., 5 pp., https://www.weather.gov/media/ewx/wxevents/ewx-20160412.pdf.

  • Nelson, S. P., 1983: The influence of storm flow structure on hail growth. J. Atmos. Sci., 40, 19651983, https://doi.org/10.1175/1520-0469(1983)040<1965:TIOSFS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Orf, L., R. Wilhelmoson, B. Lee, C. Finley, and A. Houston, 2017: Evolution of a long-track violent tornado within a simulated supercell. Bull. Amer. Meteor. Soc., 98, 4568, https://doi.org/10.1175/BAMS-D-15-00073.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ortega, K. L., T. M. Smith, K. L. Manross, A. G. Kolodziej, K. A. Scharfenberg, A. Witt, and J. J. Gourley, 2009: The Severe Hazards Analysis and Verification Experiment. Bull. Amer. Meteor. Soc., 90, 15191530, https://doi.org/10.1175/2009BAMS2815.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ortega, K. L., J. M. Krause, and A. V. Ryzhkov, 2016: Polarimetric radar characteristics of melting hail. Part III: Validation of the algorithm for hail size discrimination. J. Appl. Meteor. Climatol., 55, 829848, https://doi.org/10.1175/JAMC-D-15-0203.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Peters, J. M., C. J. Nowotarski, and H. C. Morrison, 2019: The role of vertical wind shear in modulating maximum supercell updraft velocities. J. Atmos. Sci., 76, 31693189, https://doi.org/10.1175/JAS-D-19-0096.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Peters, J. M., C. J. Nowotarski, J. P. Mulholland, and R. L. Thompson, 2020: The influences of effective inflow layer streamwise vorticity and storm-relative flow on supercell updraft properties. J. Atmos. Sci., 77, 30333057, https://doi.org/10.1175/JAS-D-19-0355.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Púčik, T., C. Castellano, P. Groenemeijer, T. Kühne, A. T. Rädler, B. Antonescu, and E. Faust, 2019: Large hail incidence and its economic and societal impacts across Europe. Mon. Wea. Rev., 147, 39013916, https://doi.org/10.1175/MWR-D-19-0204.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Punge, H. J., and M. Kunz, 2016: Hail observations and hailstorm characteristics in Europe: A review. Atmos. Res., 176–177, 159184, https://doi.org/10.1016/j.atmosres.2016.02.012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rasmussen, R. M., and A. J. Heymsfield, 1987: Melting and shedding of graupel and hail. Part III: Investigation of the role of shed drops as hail embryos in the 1 August CCOPE severe storm. J. Atmos. Sci., 44, 27832803, https://doi.org/10.1175/1520-0469(1987)044<2783:MASOGA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Richardson, Y. P., K. K. Droegemeier, and R. P. Davies-Jones, 2007: The influence of horizontal environmental variability on numerically simulated convective storms. Part I: Variations in vertical shear. Mon. Wea. Rev., 135, 34293455, https://doi.org/10.1175/MWR3463.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ryzhkov, A. V., M. R. Kumjian, S. M. Ganson, and P. Zhang, 2013: Polarimetric radar characteristics of melting hail. Part II: Practical implications. J. Appl. Meteor. Climatol., 52, 28712886, https://doi.org/10.1175/JAMC-D-13-074.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Soderholm, J., H. McGowan, H. Richter, K. Walsh, T. Weckwerth, and M. Coleman, 2017: An 18-year climatology of hailstorm trends and related drivers across southeast Queensland, Australia. Quart. J. Roy. Meteor. Soc., 143, 11231135, https://doi.org/10.1002/qj.2995.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Soderholm, J., M. R. Kumjian, N. McCarthy, P. Maldonado, and M. Wang, 2020: Quantifying hail size distributions from the sky—Application of drone aerial photogrammetry. Atmos. Meas. Tech., 13, 747754, https://doi.org/10.5194/amt-13-747-2020.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tessendorf, S. A., L. J. Miller, K. C. Wiens, and S. A. Rutledge, 2005: The 29 June supercell observed during STEPS. Part I: Kinematics and microphysics. J. Atmos. Sci., 62, 41274150, https://doi.org/10.1175/JAS3585.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Trapp, R. J., G. R. Marion, and S. W. Nesbitt, 2017: The regulation of tornado intensity by updraft width. J. Atmos. Sci., 74, 41994211, https://doi.org/10.1175/JAS-D-16-0331.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Warren, R. A., H. Richter, H. A. Ramsay, S. T. Siems, and M. J. Manton, 2017: Impact of variations in upper-level shear on simulated supercells. Mon. Wea. Rev., 145, 26592681, https://doi.org/10.1175/MWR-D-16-0412.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weisman, M. L., and J. B. Klemp, 1984: The structure and classification of numerically simulated convective storms in directionally varying wind shears. Mon. Wea. Rev., 112, 24792498, https://doi.org/10.1175/1520-0493(1984)112<2479:TSACON>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weisman, M. L., and R. Rotunno, 2000: The use of vertical wind shear versus helicity in interpreting supercell dynamics. J. Atmos. Sci., 57, 14521472, https://doi.org/10.1175/1520-0469(2000)057<1452:TUOVWS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Witt, A., and J. C. Snyder, 2018: WSR-88D observations of an extreme hail event impacting western South Dakota on 20 June 2015. 29th Conf. on Severe Local Storms, Stowe, VT, Amer. Meteor. Soc., 9, https://ams.confex.com/ams/29SLS/webprogram/Paper348276.html.

  • Witt, A., N. 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
  • Witt, A., D. W. Burgess, A. Seimon, J. T. Allen, J. C. Snyder, and H. B. Bluestein, 2018: Rapid-scan radar observations of an Oklahoma tornadic hailstorm producing giant hail. Wea. Forecasting, 33, 12631282, https://doi.org/10.1175/WAF-D-18-0003.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ziegler, C. L., P. S. Ray, and N. C. Knight, 1983: Hail growth in an Oklahoma multicell storm. J. Atmos. Sci., 40, 17681791, https://doi.org/10.1175/1520-0469(1983)040<1768:HGIAOM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
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The Evolution of Hail Production in Simulated Supercell Storms

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  • 1 a Department of Meteorology and Atmospheric Science, The Pennsylvania State University, University Park, Pennsylvania
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Abstract

Hailstorms pose a significant socioeconomic risk, necessitating detailed assessments of how the hail threat changes throughout their lifetimes. Hail production involves the favorable juxtaposition of ingredients, but how storm evolution affects these ingredients is unknown, limiting understanding of how hail production evolves. Unfortunately, neither surface hail reports nor radar-based swath estimates have adequate resolution or details needed to assess evolving hail production. Instead, we use a novel approach of coupling a detailed hail trajectory model to idealized convective storm simulations to better understand storm evolution’s influence on hail production. Hail production varies substantially throughout storms’ mature phases: maximum sizes vary by a factor of 2 and the concentration of severe hail by more than fivefold during 45–60-min periods. This variability arises from changes in updraft properties, which come from (i) changes in low-level convergence and (ii) internal storm dynamics, including anticyclonic vortex shedding/storm splitting, and the response of the updraft’s airflow and supercooled liquid water content to these events. Hodograph shape strongly affects such behaviors. Straighter hodographs lead to more prolific hail production through wider updrafts and weaker mesocyclones and a periodicity in hail size metrics associated with anticyclonic vortex shedding and/or storm splitting. In contrast, a curved hodograph (favorable for tornadoes) led to a storm with a stronger but more compact updraft, which occasionally produced giant (10-cm) hail but that was a less-prolific severe hail producer overall. Unless storms are adequately sampled throughout their life cycles, snapshots from ground reports will insufficiently resolve the true nature of hail production.

© 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: Matthew R. Kumjian, kumjian@psu.edu

Abstract

Hailstorms pose a significant socioeconomic risk, necessitating detailed assessments of how the hail threat changes throughout their lifetimes. Hail production involves the favorable juxtaposition of ingredients, but how storm evolution affects these ingredients is unknown, limiting understanding of how hail production evolves. Unfortunately, neither surface hail reports nor radar-based swath estimates have adequate resolution or details needed to assess evolving hail production. Instead, we use a novel approach of coupling a detailed hail trajectory model to idealized convective storm simulations to better understand storm evolution’s influence on hail production. Hail production varies substantially throughout storms’ mature phases: maximum sizes vary by a factor of 2 and the concentration of severe hail by more than fivefold during 45–60-min periods. This variability arises from changes in updraft properties, which come from (i) changes in low-level convergence and (ii) internal storm dynamics, including anticyclonic vortex shedding/storm splitting, and the response of the updraft’s airflow and supercooled liquid water content to these events. Hodograph shape strongly affects such behaviors. Straighter hodographs lead to more prolific hail production through wider updrafts and weaker mesocyclones and a periodicity in hail size metrics associated with anticyclonic vortex shedding and/or storm splitting. In contrast, a curved hodograph (favorable for tornadoes) led to a storm with a stronger but more compact updraft, which occasionally produced giant (10-cm) hail but that was a less-prolific severe hail producer overall. Unless storms are adequately sampled throughout their life cycles, snapshots from ground reports will insufficiently resolve the true nature of hail production.

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Corresponding author: Matthew R. Kumjian, kumjian@psu.edu
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