Environmental and Radar Characteristics of Gargantuan Hail–Producing Storms

Rachel E. Gutierrez aDepartment of Meteorology and Atmospheric Science, The Pennsylvania State University, University Park, Pennsylvania

Search for other papers by Rachel E. Gutierrez in
Current site
Google Scholar
PubMed
Close
https://orcid.org/0000-0001-5787-1913
and
Matthew R. Kumjian aDepartment of Meteorology and Atmospheric Science, The Pennsylvania State University, University Park, Pennsylvania

Search for other papers by Matthew R. Kumjian in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

Storms that produce gargantuan hail (defined here as ≥6 in. or 15 cm in maximum dimension), although seemingly rare, can cause extensive damage to property and infrastructure, and cause injury or even death to humans and animals. Currently, we are limited in our ability to accurately predict gargantuan hail and detect gargantuan hail on radar. In this study, we analyze the environmental and radar characteristics of gargantuan hail–producing storms to define the parameter space of environments in which gargantuan hail occurs, and compare environmental parameters and radar signatures in these storms to storms producing other sizes of hail. We find that traditionally used environmental parameters used for severe storm prediction, such as most unstable convective available potential energy (MUCAPE) and 0–6-km vertical wind shear, display considerable overlap between gargantuan hail–producing storm environments and those that produce smaller hail. There is a slight tendency for larger MUCAPE values for gargantuan hail cases, however. Additionally, gargantuan hail–producing storms seem to have larger low-level storm-relative winds and larger updraft widths than those storms producing smaller hail, implying updrafts less diluted by entrainment and perhaps maximizing the liquid water content available for hail growth. Moreover, radar reflectivity or products derived from it are not different from cases of smaller hail sizes. However, inferred mesocyclonic rotational velocities within the hail growth region of storms that produce gargantuan hail are significantly stronger than the rotational velocities found for smaller hail categories.

© 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: Rachel E. Gutierrez, rachel.gutierrez@noaa.gov

Abstract

Storms that produce gargantuan hail (defined here as ≥6 in. or 15 cm in maximum dimension), although seemingly rare, can cause extensive damage to property and infrastructure, and cause injury or even death to humans and animals. Currently, we are limited in our ability to accurately predict gargantuan hail and detect gargantuan hail on radar. In this study, we analyze the environmental and radar characteristics of gargantuan hail–producing storms to define the parameter space of environments in which gargantuan hail occurs, and compare environmental parameters and radar signatures in these storms to storms producing other sizes of hail. We find that traditionally used environmental parameters used for severe storm prediction, such as most unstable convective available potential energy (MUCAPE) and 0–6-km vertical wind shear, display considerable overlap between gargantuan hail–producing storm environments and those that produce smaller hail. There is a slight tendency for larger MUCAPE values for gargantuan hail cases, however. Additionally, gargantuan hail–producing storms seem to have larger low-level storm-relative winds and larger updraft widths than those storms producing smaller hail, implying updrafts less diluted by entrainment and perhaps maximizing the liquid water content available for hail growth. Moreover, radar reflectivity or products derived from it are not different from cases of smaller hail sizes. However, inferred mesocyclonic rotational velocities within the hail growth region of storms that produce gargantuan hail are significantly stronger than the rotational velocities found for smaller hail categories.

© 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: Rachel E. Gutierrez, rachel.gutierrez@noaa.gov
Save
  • Allen, J. T., M. K. Tippett, and A. H. Sobel, 2015: 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
  • Allen, J. T., M. K. Tippett, Y. Kaheil, A. H. Sobel, C. Lepore, S. Nong, and A. Muehlbauer, 2017: An extreme value model for U.S. hail size. Mon. Wea. Rev., 145, 45014519, https://doi.org/10.1175/MWR-D-17-0119.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Allen, J. T., I. M. Giammanco, M. R. Kumjian, H. Jurgen 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
  • Anderson-Frey, A. K., Y. P. Richardson, A. R. Dean, R. L. Thompson, and B. T. Smith, 2016: Investigation of near-storm environments for tornado events and warnings. Wea. Forecasting, 31, 17711790, https://doi.org/10.1175/WAF-D-16-0046.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Aydin, K., T. A. Seliga, and V. Balaji, 1986: Remote sensing of hail with a dual linear polarization 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
  • Bang, S. D., and D. J. Cecil, 2019: Constructing a multifrequency passive microwave hail retrieval and climatology in the GPM domain. J. Appl. Meteor. Climatol., 58, 18891904, https://doi.org/10.1175/JAMC-D-19-0042.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Benjamin, S. G., and Coauthors, 2016: A North American hourly assimilation and model forecast cycle: The Rapid Refresh. Mon. Wea. Rev., 144, 16691694, https://doi.org/10.1175/MWR-D-15-0242.1.

    • 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 Coauthors, 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
  • 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: Airflow 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
  • Bruick, Z. S., K. L. Rasmussen, and D. J. Cecil, 2019: Subtropical South American hailstorm characteristics and environments. Mon. Wea. Rev., 147, 42894304, https://doi.org/10.1175/MWR-D-19-0011.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brunner, C., 2015: Eastern Butte County hit by hail storm. Accessed 10 August 2019, rapidcityjournal.com.

  • 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., 2018: Observations of right-moving supercell motion forecast errors. Wea. Forecasting, 33, 145159, https://doi.org/10.1175/WAF-D-17-0133.1.

    • 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. 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., 2008: Temporal and spatial distributions of damaging hail in the continental United States. Phys. Geogr., 29, 341350, https://doi.org/10.2747/0272-3646.29.4.341.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Childs, S., 2018: Destructive 2018 hail season a sign of things to come. Colorado State University, accessed 12 August 2019, https://theconversation.com/destructive-2018-hail-season-a-sign-of-things-to-come-102879.

  • 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
  • Coffer, B. E., and M. D. 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
  • 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
  • Donaldson, R. J. J., 1961: Radar reflectivity profiles in thunderstorms. J. Meteor., 18, 292305, https://doi.org/10.1175/1520-0469(1961)018<0292:RRPIT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Doviak, R. J., and D. S. Zrnić, 1993: Doppler Radar and Weather Observations. 2nd ed. Dover Publications, Inc., 562 pp.

  • Edwards, R., and R. L. Thompson, 1998: Nationwide comparisons of hail size with WSR-88D vertically integrated liquid water and derived thermodynamic sounding data. Wea. Forecasting, 13, 277285, https://doi.org/10.1175/1520-0434(1998)013<0277:NCOHSW>2.0.CO;2.

    • 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.

    • Crossref
    • Export Citation
  • Foote, G. B., 1984: A study of hail growth utilizing observed storm conditions. J. Climate Appl. Meteor., 23, 84–101, https://doi.org/10.1175/1520-0450(1984)023<0084:ASOHGU>2.0.CO;2.

    • Crossref
    • Export Citation
  • Forster, L., 2019: 2 Colorado hailstorms were among nation’s billion-dollar disasters in 2018. Gazette News, 11 February, accessed 12 August 2019.

  • Geotis, S. G., 1963: Some radar measurements of hailstorms. J. Appl. Meteor., 2, 270275, https://doi.org/10.1175/1520-0450(1963)002<0270:SRMOH>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Giammanco, I. M., T. M. Brown, R. G. Grant, D. L. Dewey, J. D. Hodel, and R. A. Stumpf, 2015: Evaluating the hardness characteristics of hail through compressive strength measurements. J. Atmos. Oceanic Technol., 32, 21002113, https://doi.org/10.1175/JTECH-D-15-0081.1.

    • Crossref
    • 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
  • Heymsfield, A. J., I. M. Giammanco, and R. Wright, 2014: Terminal velocities and kinetic energies of natural hailstones. Geophys. Res. Lett., 41, 86668672, https://doi.org/10.1002/2014GL062324.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jewell, R., and J. Brimelow, 2009: Evaluation of Alberta hail growth model using severe hail proximity soundings from the United States. Wea. Forecasting, 24, 15921609, https://doi.org/10.1175/2009WAF2222230.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Johnson, A. W., and K. E. Sugden, 2014: Evaluation of sounding-derived thermodynamic and wind-related parameters associated with large hail events. Electron. J. Severe Storms Meteor., 9 (5), https://www.ejssm.org/ojs/index.php/ejssm/article/viewArticle/137.

  • Knight, C. A., and N. C. Knight, 2005: Very large hailstones from Aurora, Nebraska. Bull. Amer. Meteor. Soc., 86, 17731782, https://doi.org/10.1175/BAMS-86-12-1773.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kumjian, M. R., and K. A. 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., Z. J. Lebo, and A. M. Ward, 2019: Storms producing large accumulations of small hail. J. Appl. Meteor. Climatol., 58, 341364, https://doi.org/10.1175/JAMC-D-18-0073.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kumjian, M. R., and Coauthors, 2020: Gargantuan hail in Argentina. Bull. Amer. Meteor. Soc., 101, E1241–E1258, https://doi.org/10.1175/BAMS-D-19-0012.1.

    • Crossref
    • Export Citation
  • Marion, G. R., and R. J. Trapp, 2019: The dynamical coupling of convective updrafts, downdrafts, and cold pools in simulated supercell thunderstorms. J. Geophys. Res. Atmos., 124, 664683, https://doi.org/10.1029/2018JD029055.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Markowski, P., and Y. Richardson, 2010: Mesoscale Meteorology in Midlatitudes. Wiley-Blackwell, 430 pp.

    • Crossref
    • Export Citation
  • May, R., S. Arms, P. Marsh, E. Bruning, and J. Leeman, 2021: Metpy: A Python package for meteorological data. Boulder, CO, accessed 3 June 2019, https://doi.org/10.5065/D6WW7G29, https://github.com/Unidata/MetPy.

    • Crossref
    • Export Citation
  • Mezher, R. N., M. Doyle, and V. Barros, 2012: Climatology of hail in Argentina. Atmos. Res., 114–115, 7082, https://doi.org/10.1016/j.atmosres.2012.05.020.

    • 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 high 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
  • Morrison, H., 2016a: 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., 2016b: Impacts of updraft size and dimensionality on the perturbation pressure and vertical velocity in cumulus convection. Part II: Comparison of theoretical and numerical solutions and fully dynamical simulations. J. Atmos. Sci., 73, 14551480, https://doi.org/10.1175/JAS-D-15-0041.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • MunichRe, 2013: 2013 Natural Catastrophe Year in Review. MunichRe NatCat Services, 92 pp., https://www.iii.org/sites/default/files/docs/pdf/MunichRe-010714.pdf.

  • 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
  • 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
  • Nelson, S. P., 1987: The hybrid multicellular–supercellular storm—An efficient hail producer. Part II: General characteristics and implications for hail growth. J. Atmos. Sci., 44, 20602073, https://doi.org/10.1175/1520-0469(1987)044<2060:THMSEH>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nick, A., T. Lang, J. J. Helmus, and S. Nesbitt, 2016: artview: Artview release 1.2.3. Accessed 2 April 2019, https://doi.org/10.5281/zenodo.47224.

    • Crossref
    • Export Citation
  • NOAA, 2019: NOAA’s weather and climate toolkit (viewer and data exporter). NOAA, accessed 5 March 2018, https://www.ncdc.noaa.gov/wct/.

  • NOAA/National Centers for Environmental Information, 2019: NCDC storm events database: Storm events data. NOAA, accessed 3 November 2017, https://data.nodc.noaa.gov/cgi-bin/iso?id=gov.noaa.ncdc:C00510#.

  • NOAA/NWS, 2007: Very large hail from Dante, SD—August 21, 2007. U.S. Department of Commerce, NOAA/National Weather Service, accessed 3 June 2019, https://www.weather.gov/fsd/20070821-hail-dante.

  • NOAA/NWS, 2010a: Record setting hail event in Vivian, South Dakota on July 23, 2010. U.S. Department of Commerce, NOAA/National Weather Service, accessed 3 June 2019, https://www.weather.gov/abr/vivianhailstone.

  • NOAA/NWS, 2010b: September 15th 2010 record setting hail. U.S. Department of Commerce, NOAA/National Weather Service, accessed 3 June 2019, https://www.weather.gov/ict/event.

  • Ortega, K. L., 2018: Evaluating multi-radar, multi-sensor products for surface hail-fall diagnosis. Electron. J. Severe Storms Meteor, 13 (1), https://ejssm.org/ojs/index.php/ejssm/article/viewArticle/163.

    • Search Google Scholar
    • Export Citation
  • Parker, M. D., 2010: Relationship between system slope and updraft intensity in squall lines. Mon. Wea. Rev., 138, 35723578, https://doi.org/10.1175/2010MWR3441.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Parker, M. D., 2014: Composite VORTEX2 supercell environments from near-storm soundings. Mon. Wea. Rev., 142, 508529, https://doi.org/10.1175/MWR-D-13-00167.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Peel, S., and L. J. Wilson, 2008: Modeling the distribution of precipitation forecasts from the Canadian ensemble prediction system using kernel density estimation. Wea. Forecasting, 23, 575595, https://doi.org/10.1175/2007WAF2007023.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Peters, J. M., 2020: Are supercells resistant to entrainment because of their rotation? J. Atmos. Sci., 77, 14751495, https://doi.org/10.1175/JAS-D-19-0316.1.

    • 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., H. C. Morrison, C. J. Nowotarski, J. P. Mulholland, and R. L. Thompson, 2020a: A formula for the maximum vertical velocity in supercell updrafts. J. Atmos. Sci., 77, 37473757, https://doi.org/10.1175/JAS-D-20-0103.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Peters, J. M., H. C. Morrison, C. J. Nowotarski, J. P. Mulholland, and R. L. Thompson, 2020b: 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., P. Groenemeijer, D. Rýva, and M. Kolář, 2015: Proximity soundings of severe and nonsevere thunderstorms in central Europe. Mon. Wea. Rev., 143, 48054821, https://doi.org/10.1175/MWR-D-15-0104.1.

    • 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, 2783–2803, https://doi.org/10.1175/1520-0469(1987)044<2783:MASOGA>2.0.CO;2.

    • Crossref
    • Export Citation
  • Roeder, P. E., 2012: Severe Weather in North America: Perils, Risks, and Insurance. Knowledge Series: Natural Hazards, Munich RE, 274 pp.

  • Ryzhkov, A. V., T. J. Schuur, D. W. Burgess, P. L. Heinselman, S. E. Giangrande, and D. S. Zrnić, 2005: The Joint Polarization Experiment polarimetric rainfall measurements and hydrometeor classification. Bull. Amer. Meteor. Soc., 86, 809824, https://doi.org/10.1175/BAMS-86-6-809.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Storm Prediction Center, 2019: Help—Significant hail parameter. Accessed 20 July 2019, https://www.spc.noaa.gov/exper/mesoanalysis/help/help.

  • 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
  • Tessendorf, S. A., L. J. Miller, K. C. Wiens, and S. A. Rutledge, 2005: The 29 June 2000 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
  • Torres, K., 2018: Cheyenne Mountain Zoo reopens after hail damages buildings, kills several animals. FOX 31 Denver, 10 August 2018, https://kdvr.com/on-air/on-channel-2/cheyenne-mountain-zoo-to-reopen-after-hail-damages-buildings-kills-several-animals/.

  • 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
  • Weather Forecast Office San Antonio, 2016: Austin/San Antonio Weather Forecast Office, Weather event summary: San Antonio April 2016. NOAA/NWS, 5 pp., https://www.weather.gov/media/ewx/wxevents/ewx-20160412.pdf.

  • Weisman, M. L., R. Rotunno, M. L. Weisman, 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., 1998: The relationship between WSR-88D measured midlatitude rotation and maximum hail size. Preprints, 19th Conf. on Severe Local Storms, Minneapolis, MN, Amer. Meteor. Soc., 740–743.

  • 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
  • Zhang, G. J., 2009: Effects of entrainment on convective available potential energy and closure assumptions in convection parameterization. J. Geophys. Res., 114, D07109, https://doi.org/10.1029/2008JD010976.

    • 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
All Time Past Year Past 30 Days
Abstract Views 626 0 0
Full Text Views 1250 548 59
PDF Downloads 1423 547 63