Editorial Type:
Article
Article Type:
Research Article

Distinguishing Characteristics of Tornadic and Nontornadic Supercell Storms from Composite Mean Analyses of Radar Observations

Cameron R. Homeyer School of Meteorology, University of Oklahoma, Norman, Oklahoma

Search for other papers by Cameron R. Homeyer in
Current site
Google Scholar
PubMed Close
,
Thea N. Sandmæl Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, Norman, Oklahoma
School of Meteorology, University of Oklahoma, Norman, Oklahoma
NOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma

Search for other papers by Thea N. Sandmæl in
Current site
Google Scholar
PubMed Close
,
Corey K. Potvin NOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma
School of Meteorology, University of Oklahoma, Norman, Oklahoma

Search for other papers by Corey K. Potvin in
Current site
Google Scholar
PubMed Close
, and
Amanda M. Murphy School of Meteorology, University of Oklahoma, Norman, Oklahoma

Search for other papers by Amanda M. Murphy in
Current site
Google Scholar
PubMed Close
Online Publication:
10 Dec 2020
Print Publication:
01 Dec 2020
DOI:
https://doi.org/10.1175/MWR-D-20-0136.1
Page(s):
5015–5040
Restricted access

Abstract

An improved understanding of common differences between tornadic and nontornadic supercells is sought using a large set of observations from the operational NEXRAD WSR-88D polarimetric radar network in the contiguous United States. In particular, data from 478 nontornadic and 294 tornadic supercells during a 7-yr period (2011–17) are used to produce probability-matched composite means of microphysical and kinematic variables. Means, which are centered on echo-top maxima and in a horizontal coordinate system rotated such that storm motion points in the positive x dimension, are created in altitude relative to ground level at times of peak echo-top altitude and peak midlevel rotation for nontornadic supercells and times at and prior to the first tornado in tornadic supercells. Robust differences between supercell types are found, with consistent characteristics at and preceding tornadogenesis in tornadic storms. In particular, the mesocyclone is found to be vertically aligned in tornadic supercells and misaligned in nontornadic supercells. Microphysical differences found include a low-level radar reflectivity hook echo aligned with and ~10 km right of storm center in tornadic supercells and displaced 5–10 km down-motion in nontornadic supercells, a low-to-midlevel differential radar reflectivity dipole that is oriented more parallel to storm motion in tornadic supercells and more perpendicular in nontornadic supercells, and a separation between enhanced differential radar reflectivity and specific differential phase (with unique displacement-relative correlation coefficient reductions) at low levels that is more perpendicular to storm motion in tornadic supercells and more parallel in nontornadic supercells.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/MWR-D-20-0136.s1.

© 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: Cameron R. Homeyer, chomeyer@ou.edu

Abstract

An improved understanding of common differences between tornadic and nontornadic supercells is sought using a large set of observations from the operational NEXRAD WSR-88D polarimetric radar network in the contiguous United States. In particular, data from 478 nontornadic and 294 tornadic supercells during a 7-yr period (2011–17) are used to produce probability-matched composite means of microphysical and kinematic variables. Means, which are centered on echo-top maxima and in a horizontal coordinate system rotated such that storm motion points in the positive x dimension, are created in altitude relative to ground level at times of peak echo-top altitude and peak midlevel rotation for nontornadic supercells and times at and prior to the first tornado in tornadic supercells. Robust differences between supercell types are found, with consistent characteristics at and preceding tornadogenesis in tornadic storms. In particular, the mesocyclone is found to be vertically aligned in tornadic supercells and misaligned in nontornadic supercells. Microphysical differences found include a low-level radar reflectivity hook echo aligned with and ~10 km right of storm center in tornadic supercells and displaced 5–10 km down-motion in nontornadic supercells, a low-to-midlevel differential radar reflectivity dipole that is oriented more parallel to storm motion in tornadic supercells and more perpendicular in nontornadic supercells, and a separation between enhanced differential radar reflectivity and specific differential phase (with unique displacement-relative correlation coefficient reductions) at low levels that is more perpendicular to storm motion in tornadic supercells and more parallel in nontornadic supercells.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/MWR-D-20-0136.s1.

© 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: Cameron R. Homeyer, chomeyer@ou.edu

Supplementary Materials

    • Supplemental Materials (ZIP 76.9 MB)
Save
Cover Monthly Weather Review
Monthly Weather Review

  • Balakrishnan, N., and D. S. Zrnić, 1990: Use of polarization to characterize precipitation and discriminate large hail. J. Atmos. Sci., 47, 1525–1540, https://doi.org/10.1175/1520-0469(1990)047<1525:UOPTCP>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, 3125–3148, https://doi.org/10.1175/MWR3246.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Bedka, K., E. M. Murillo, C. R. Homeyer, B. Scarino, and H. Mersiovsky, 2018: The above anvil cirrus plume: An important severe weather indicator in visible and infrared satellite imagery. Wea. Forecasting, 33, 1159–1181, https://doi.org/10.1175/WAF-D-18-0040.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Bringi, V. N., and V. Chandrasekar, 2001: Polarimetric Doppler Weather Radar. 1st ed. Cambridge University Press, 636 pp.

    • Crossref
    • Export Citation

  • Brooks, H. E., and J. Correia, 2018: Long-term performance metrics for National Weather Service tornado warnings. Wea. Forecasting, 33, 1501–1511, https://doi.org/10.1175/WAF-D-18-0120.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Brooks, H. E., C. A. Doswell III, and J. Cooper, 1994: On the environments of tornadic and nontornadic mesocyclones. Wea. Forecasting, 9, 606–618, https://doi.org/10.1175/1520-0434(1994)009<0606:OTEOTA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Brown, M., and C. J. Nowotarski, 2019: The influence of lifting condensation level on low-level outflow and rotation in simulated supercell thunderstorms. J. Atmos. Sci., 76, 1349–1372, https://doi.org/10.1175/JAS-D-18-0216.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Cintineo, J. L., M. J. Pavolonis, J. M. Sieglaff, and A. K. Heidinger, 2013: Evolution of severe and nonsevere convection inferred from GOES-derived cloud properties. J. Appl. Meteor. Climatol., 52, 2009–2023, https://doi.org/10.1175/JAMC-D-12-0330.1.

    • 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, 149–180, https://doi.org/10.1175/MWR-D-16-0226.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Coffer, B. E., and M. D. Parker, 2018: Is there a “tipping point” between simulated nontornadic and tornadic supercells in VORTEX2 environments? Mon. Wea. Rev., 146, 2667–2693, https://doi.org/10.1175/MWR-D-18-0050.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Coffer, B. E., M. D. Parker, J. M. L. Dahl, L. J. Wicker, and A. J. Clark, 2017: Volatility of tornadogenesis: An ensemble of simulated nontornadic and tornadic supercells in VORTEX2 environments. Mon. Wea. Rev., 145, 4605–4625, https://doi.org/10.1175/MWR-D-17-0152.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Coffer, B. E., M. D. Parker, R. L. Thompson, B. T. Smith, and R. E. Jewell, 2019: Using near-ground storm relative helicity in supercell tornado forecasting. Wea. Forecasting, 34, 1417–1435, https://doi.org/10.1175/WAF-D-19-0115.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Crowe, C. C., C. J. Schultz, M. Kumjian, L. D. Carey, and W. A. Petersen, 2012: Use of dual-polarization signatures in diagnosing tornadic potential. Electron. J. Oper. Meteor., 13, 57–78.

    • Search Google Scholar
    • Export Citation

  • Crum, T. D., and R. L. Alberty, 1993: The WSR-88D and the WSR-88D operational support facility. Bull. Amer. Meteor. Soc., 74, 1669–1687, https://doi.org/10.1175/1520-0477(1993)074<1669:TWATWO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Dahl, J. M. L., 2015: Near-ground rotation in simulated supercells: On the robustness of the baroclinic mechanism. Mon. Wea. Rev., 143, 4929–4942, https://doi.org/10.1175/MWR-D-15-0115.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Davies-Jones, R., 1984: Streamwise vorticity: The origin of updraft rotation in supercell storms. J. Atmos. Sci., 41, 2991–3006, https://doi.org/10.1175/1520-0469(1984)041<2991:SVTOOU>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Davies-Jones, R., 2015: A review of supercell and tornado dynamics. Atmos. Res., 158–159, 274–291, https://doi.org/10.1016/j.atmosres.2014.04.007.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Davies-Jones, R., and H. Brooks, 1993: Mesocyclogenesis from a theoretical perspective. The Tornado: Its Structure, Dynamics, Prediction, and Hazards, Geophys. Monogr., Vol. 79, Amer. Geophys. Union, 105–114, https://doi.org/10.1029/GM079p0105.

    • Crossref
    • Export Citation

  • Dawson, D. T., II, E. R. Mansell, Y. Jung, L. J. Wicker, M. R. Kumjian, and M. Xue, 2014: Low-level ZDR signatures in supercell forward flanks: The role of size sorting and melting of hail. J. Atmos. Sci., 71, 276–299, https://doi.org/10.1175/JAS-D-13-0118.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Doviak, R. J., and D. S. Zrnić, 1993: Doppler Radar and Weather Observations. Academic Press, 562 pp.

  • Dowell, D. C., and H. B. Bluestein, 2002: The 8 June 1995 McLean, Texas, storm. Part II: Cyclic tornado formation, maintenance, and dissipation. Mon. Wea. Rev., 130, 2649–2670, https://doi.org/10.1175/1520-0493(2002)130<2649:TJMTSP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Dworak, R., K. Bedka, J. Brunner, and W. Feltz, 2012: Comparison between/textitGOES-12 overshooting-top detections, WSR-88D radar reflectivity, and severe storm reports. Wea. Forecasting, 27, 684–699, https://doi.org/10.1175/WAF-D-11-00070.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Ebert, E. E., 2001: Ability of a poor man’s ensemble to predict the probability and distribution of precipitation. Mon. Wea. Rev., 129, 2461–2480, https://doi.org/10.1175/1520-0493(2001)129<2461:AOAPMS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Feist, M. M., C. D. Westbrook, P. A. Clark, T. H. Stein, H. W. Lean, and A. J. Stirling, 2019: Statistics of convective cloud turbulence from a comprehensive turbulence retrieval method for radar observations. Quart. J. Roy. Meteor. Soc., 145, 727–744, https://doi.org/10.1002/qj.3462.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • French, M. M., and D. M. Kingfield, 2019: Dissipation characteristics of tornadic vortex signatures associated with long-duration tornadoes. Mon. Wea. Rev., 58, 317–339, https://doi.org/10.1175/JAMC-D-18-0187.1.

    • Search Google Scholar
    • Export Citation

  • French, M. M., D. W. Burgess, E. R. Mansell, and L. J. Wicker, 2015: Bulk hook echo raindrop sizes retrieved using mobile, polarimetric Doppler radar observations. J. Appl. Meteor. Climatol., 54, 423–450, https://doi.org/10.1175/JAMC-D-14-0171.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Grzych, M. L., B. D. Lee, and C. A. Finley, 2007: Thermodynamic analysis of supercell rear-flank downdrafts from project ANSWERS. Mon. Wea. Rev., 135, 240–246, https://doi.org/10.1175/MWR3288.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Guarriello, F., C. J. Nowotarski, and C. C. Epifanio, 2018: Effects of the low-level wind profile on outflow position and near-surface vertical vorticity in simulated supercell thunderstorms. J. Atmos. Sci., 75, 731–753, https://doi.org/10.1175/JAS-D-17-0174.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Helmus, J. J., and S. M. Collis, 2016: The Python ARM Radar toolkit (Py-ART), a library for working with weather radar data in the Python programming language. J. Open Res. Software, 4, e25, https://doi.org/10.5334/jors.119.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Herzegh, P. H., and A. R. Jameson, 1992: Observing precipitation through dual-polarization radar measurements. Bull. Amer. Meteor. Soc., 73, 1365–1374, https://doi.org/10.1175/1520-0477(1992)073<1365:OPTDPR>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Hirth, B. D., J. L. Schroeder, and C. C. Weiss, 2008: Surface analysis of the rear-flank downdraft outflow in two tornadic supercells. Mon. Wea. Rev., 136, 2344–2363, https://doi.org/10.1175/2007MWR2285.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Homeyer, C. R., and M. R. Kumjian, 2015: Microphysical characteristics of overshooting convection from polarimetric radar observations. J. Atmos. Sci., 72, 870–891, https://doi.org/10.1175/JAS-D-13-0388.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Homeyer, C. R., and K. P. Bowman, 2017: Algorithm description document for version 3.1 of the three-dimensional gridded NEXRAD WSR-88D radar (GridRad) dataset. Tech. Rep., 23 pp., http://gridrad.org/pdf/GridRad-v3.1-Algorithm-Description.pdf.

  • Homeyer, C. R., J. D. McAuliffe, and K. M. Bedka, 2017: On the development of above-anvil cirrus plumes in extratropical convection. J. Atmos. Sci., 74, 1617–1633, https://doi.org/10.1175/JAS-D-16-0269.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Hubbert, J. C., V. N. Bringi, L. D. Carey, and S. Bolen, 1998: CSU-CHILL polarimetric radar measurements from a severe hail storm in eastern Colorado. J. Appl. Meteor., 37, 749–775, https://doi.org/10.1175/1520-0450(1998)037<0749:CCPRMF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Klees, A. M., Y. P. Richardson, P. M. Markowski, C. Weiss, J. M. Wurman, and K. K. Kosiba, 2016: Comparison of the tornadic and nontornadic supercells intercepted by VORTEX2 on 10 June 2010. Mon. Wea. Rev., 144, 3201–3231, https://doi.org/10.1175/MWR-D-15-0345.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Kosiba, K., J. Wurman, Y. Richardson, P. Markowski, P. Robinson, and J. Marquis, 2013: Genesis of the Goshen County, Wyoming, tornado on 5 June 2009 during VORTEX2. Mon. Wea. Rev., 141, 1157–1181, https://doi.org/10.1175/MWR-D-12-00056.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Kumjian, M. R., 2011: Precipitation properties of supercell hook echoes. Electron. J. Severe Storms Meteor., 6 (5), https://ejssm.org/ojs/index.php/ejssm/article/viewArticle/93.

    • Search Google Scholar
    • Export Citation

  • Kumjian, M. R., 2013a: Principles and applications of dual-polarization weather radar. Part I: Description of the polarimetric radar variables. J. Oper. Meteor., 1, 226–242, https://doi.org/10.15191/nwajom.2013.0119.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Kumjian, M. R., 2013b: Principles and applications of dual-polarization weather radar. Part II: Warm and cold season applications. J. Oper. Meteor., 1, 243–264, https://doi.org/10.15191/nwajom.2013.0120.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Kumjian, M. R., 2013c: Principles and applications of dual-polarization weather radar. Part III: Artifacts. J. Oper. Meteor., 1, 265–274, https://doi.org/10.15191/nwajom.2013.0121.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Kumjian, M. R., and A. V. Ryzhkov, 2008a: Microphysical differences between tornadic and nontornadic supercell rear-flank downdrafts revealed by dual-polarization radar measurements. 24th Conf. on Severe Local Storms, Savannah, GA, Amer. Meteor. Soc., 3B.4, https://ams.confex.com/ams/24SLS/techprogram/paper_141912.htm.

  • Kumjian, M. R., and A. V. Ryzhkov, 2008b: Polarimetric signatures in supercell thunderstorms. J. Appl. Meteor. Climatol., 47, 1940–1961, https://doi.org/10.1175/2007JAMC1874.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Kumjian, M. R., and A. V. Ryzhkov, 2012: The impact of size sorting on the polarimetric radar variables. J. Atmos. Sci., 69, 2042–2060, https://doi.org/10.1175/JAS-D-11-0125.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Kumjian, M. R., A. P. Khain, N. Benmoshe, E. Ilotoviz, A. V. Ryzhkov, and V. T. J. Phillips, 2014: The anatomy and physics of ZDR columns: Investigating a polarimetric radar signature with a spectral bin microphysical model. J. Appl. Meteor. Climatol., 53, 1820–1843, https://doi.org/10.1175/JAMC-D-13-0354.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Kuster, C. M., J. C. Snyder, T. J. Schuur, T. T. Lindley, P. L. Heinselman, J. C. Furtado, J. W. Brogden, and R. Toomey, 2019: Rapid-update radar observations of ZDR column depth and its use in the warning decision process. Wea. Forecasting, 34, 1173–1188, https://doi.org/10.1175/WAF-D-19-0024.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Lagerquist, R., A. McGovern, C. R. Homeyer, D. J. Gagne II, and T. Smith, 2020: Deep learning on three-dimensional multiscale data for next-hour tornado prediction. Mon. Wea. Rev., 148, 2837–2861, https://doi.org/10.1175/MWR-D-19-0372.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Loeffler, S. D., M. R. Kumjian, M. Jurewicz, and M. M. French, 2020: Differentiating between tornadic and nontornadic supercells using polarimetric radar signatures of hydrometeor size sorting. Geophys. Res. Lett., 47, e2020GL088242, https://doi.org/10.1029/2020GL088242.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Markowski, P. M., 2008: A comparison of the midlevel kinematic characteristics of a pair of supercell thunderstorms observed by airborne Doppler radar. Atmos. Res., 88, 314–322, https://doi.org/10.1016/j.atmosres.2007.11.026.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Markowski, P. M., 2016: An idealized numerical simulation investigation of the effects of surface drag on the development of near-surface vertical vorticity in supercell thunderstorms. J. Atmos. Sci., 73, 4349–4385, https://doi.org/10.1175/JAS-D-16-0150.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Markowski, P. M., and Y. P. Richardson, 2009: Tornadogenesis: Our current understanding, forecasting considerations, and questions to guide future research. Atmos. Res., 93, 3–10, https://doi.org/10.1016/j.atmosres.2008.09.015.

    • 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, 243–275, https://doi.org/10.1175/JAS-D-13-0159.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Markowski, P. M., J. M. Straka, and E. N. Rasmussen, 2002: Direct surface thermodynamic observations within the rear-flank downdrafts of nontornadic and tornadic supercells. Mon. Wea. Rev., 130, 1692–1721, https://doi.org/10.1175/1520-0493(2002)130<1692:DSTOWT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Markowski, P. M., C. Hannon, J. Frame, E. Lancaster, A. Pietrycha, R. Edwards, and R. L. Thompson, 2003: Characteristics of vertical wind profiles near supercells obtained from the Rapid Update Cycle. Wea. Forecasting, 18, 1262–1272, https://doi.org/10.1175/1520-0434(2003)018<1262:COVWPN>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Marquis, J., Y. Richardson, P. Markowski, D. Dowell, and J. Wurman, 2012: Tornado maintenance investigated with high-resolution dual-Doppler and EnKF analysis. Mon. Wea. Rev., 140, 3–27, https://doi.org/10.1175/MWR-D-11-00025.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Murdzek, S. S., P. M. Markowski, Y. P. Richardson, and R. L. Tanamachi, 2020: Processes preventing the development of a significant tornado in a Colorado supercell on 26 May 2010. Mon. Wea. Rev., 148, 1753–1778, https://doi.org/10.1175/MWR-D-19-0288.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • National Centers for Environmental Information, 2020: NOAA’s severe weather data inventory. National Centers for Environmental Information, accessed May 2017 to May 2018, https://www.ncdc.noaa.gov/severe-weather/severe-weather-data-inventory.

  • NOAA/NCEI, 1988: ETOPO5 5-minute gridded elevation data. Data announcement 88-MGG-02, digital relief of the surface of the Earth, NOAA/National Geophysical Data Center, accessed June 2018, https://www.ngdc.noaa.gov/mgg/global/etopo5.HTML.

  • NOAA/NWS/ROC, 1991: NOAA Next Generation Radar (NEXRAD) Level 2 base data. NOAA/National Centers for Environmental Information, accessed May 2017 to May 2018, https://doi.org/10.7289/V5W9574V.

    • Crossref
    • 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, 829–848, https://doi.org/10.1175/JAMC-D-15-0203.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Parker, M. D., 2014: Composite VORTEX2 supercell environments from near-storm soundings. Mon. Wea. Rev., 142, 508–529, https://doi.org/10.1175/MWR-D-13-00167.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Potvin, C. K., K. L. Elmore, and S. J. Weiss, 2010: Assessing the impacts of proximity sounding criteria on the climatology of significant tornado environments. Wea. Forecasting, 25, 921–930, https://doi.org/10.1175/2010WAF2222368.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Potvin, C. K., C. Broyles, P. S. Skinner, H. E. Brooks, and E. Rasmussen, 2019a: A Bayesian hierarchical modeling framework for correcting reporting bias in the U.S. tornado database. Wea. Forecasting, 34, 15–30, https://doi.org/10.1175/WAF-D-18-0137.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Potvin, C. K., and Coauthors, 2019b: Systematic comparison of convection-allowing models during the 2017 NOAA HWT spring forecasting experiment. Wea. Forecasting, 34, 1395–1416, https://doi.org/10.1175/WAF-D-19-0056.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Potvin, C. K., and Coauthors, 2020: Assessing systematic impacts of PBL schemes in the NOAA Warn-on-Forecast system. Mon. Wea. Rev., 148, 2567–2590, https://doi.org/10.1175/MWR-D-19-0389.1.

    • 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, 1148–1164, https://doi.org/10.1175/1520-0434(1998)013<1148:ABCOSD>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Roberts, B., M. Xue, I. Dawson, and T. Daniel, 2020: The effect of surface drag strength on mesocyclone intensification and tornadogenesis in idealized supercell simulations. J. Atmos. Sci., 77, 1699–1721, https://doi.org/10.1175/JAS-D-19-0109.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Rotunno, R., and J. Klemp, 1985: On the rotation and propagation of simulated supercell thunderstorms. J. Atmos. Sci., 42, 271–292, https://doi.org/10.1175/1520-0469(1985)042<0271:OTRAPO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Rotunno, R., P. M. Markowski, and G. H. Bryan, 2017: “Near ground” vertical vorticity in supercell thunderstorm models. J. Atmos. Sci., 74, 1757–1766, https://doi.org/10.1175/JAS-D-16-0288.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Ryzhkov, A. V., and D. S. Zrnić, 2019: Radar Polarimetry for Weather Observations. 1st ed. Springer, 497 pp., https://doi.org/10.1007/978-3-030-05093-1.

    • Crossref
    • Export Citation

  • 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, 809–824, https://doi.org/10.1175/BAMS-86-6-809.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Sandmæl, T. N., 2017: An evaluation of radar- and satellite-data based products to discriminate between tornadic and non-tornadic storms. M.S. thesis, School of Meteorology, University of Oklahoma, 98 pp., https://shareok.org/handle/11244/52775.

  • Sandmæl, T. N., C. R. Homeyer, K. M. Bedka, J. M. Apke, J. R. Mecikalski, and K. Khlopenkov, 2019: Evaluating the ability of remote sensing observations to identify significantly severe and potentially tornadic storms. J. Appl. Meteor. Climatol., 58, 2569–2590, https://doi.org/10.1175/JAMC-D-18-0241.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Schenkman, A. D., M. Xue, and M. Hu, 2014: Tornadogenesis in a high-resolution simulation of the 8 May 2003 Oklahoma city supercell. J. Atmos. Sci., 71, 130–154, https://doi.org/10.1175/JAS-D-13-073.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Schultz, C. J., L. D. Carey, E. V. Schultz, and R. J. Blakeslee, 2017: Kinematic and microphysical significance of lightning jumps versus nonjump increases in total flash rate. Wea. Forecasting, 32, 275–288, https://doi.org/10.1175/WAF-D-15-0175.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Skinner, P. S., C. C. Weiss, M. M. French, H. B. Bluestein, P. M. Markowski, and Y. P. Richardson, 2014: VORTEX2 observations of a low-level mesocyclone with multiple internal rear-flank downdraft momentum surges in the 18 May 2010 Dumas, Texas, supercell. Mon. Wea. Rev., 142, 2935–2960, https://doi.org/10.1175/MWR-D-13-00240.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Smith, T. M., and K. L. Elmore, 2004: The use of radial velocity derivative to diagnose rotation and divergence. 11th Conf. on Aviation, Range, and Aerospace, Hyannis, MA, Amer. Meteor. Soc., P5.6, https://ams.confex.com/ams/11aram22sls/techprogram/paper_81827.htm.

  • Snook, N., and M. Xue, 2008: Effects of microphysical drop size distribution on tornadogenesis in supercell thunderstorms. Geophys. Res. Lett., 35, L24803, https://doi.org/10.1029/2008GL035866.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Snyder, J. C., A. V. Ryzhkov, M. R. Kumjian, A. P. Khain, and J. Picca, 2015: A ZDR column detection algorithm to examine convective storm updrafts. Wea. Forecasting, 30, 1819–1844, https://doi.org/10.1175/WAF-D-15-0068.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Stensrud, D. J., and Coauthors, 2013: Progress and challenges with Warn-on-Forecast. Atmos. Res., 123, 2–16, https://doi.org/10.1016/j.atmosres.2012.04.004.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Straka, J. M., D. S. Zrnić, and A. V. Ryzhkov, 2000: Bulk hydrometeor classification and quantification using polarimetric radar data: Synthesis of relations. J. Appl. Meteor., 39, 1341–1372, https://doi.org/10.1175/1520-0450(2000)039<1341:BHCAQU>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Straka, J. M., E. Rasmussen, R. Davies-Jones, and P. Markowski, 2007: An observational and idealized numerical examination of low-level counter-rotating vortices in the rear flank of supercells. Electron. J. Severe Storms Meteor., 2 (8), https://www.ejssm.org/ojs/index.php/ejssm/article/viewArticle/32.

    • Search Google Scholar
    • Export Citation

  • Stumpf, G. J., A. Witt, E. D. Mitchell, P. L. Spencer, J. T. Johnson, M. D. Eilts, K. W. Thomas, and D. W. Burgess, 1998: The National Severe Storms Laboratory mesocyclone detection algorithm for the WSR-88D. Wea. Forecasting, 13, 304–326, https://doi.org/10.1175/1520-0434(1998)013<0304:TNSSLM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Tanamachi, R. L., H. B. Bluestein, J. B. Houser, S. J. Frasier, and K. M. Hardwick, 2012: Mobile, X-band, polarimetric Doppler radar observations of the 4 May 2007 Greensburg, Kansas, tornadic supercell. Mon. Wea. Rev., 140, 2103–2125, https://doi.org/10.1175/MWR-D-11-00142.1.

    • 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, 1243–1261, https://doi.org/10.1175/1520-0434(2003)018<1243:CPSWSE>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Thompson, R. L., C. M. Mead, and R. Edwards, 2007: Effective storm-relative helicity and bulk shear in supercell thunderstorm environments. Wea. Forecasting, 22, 102–115, https://doi.org/10.1175/WAF969.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Trapp, R. J., 1999: Observations of nontornadic low-level mesocyclones and attendant tornadogenesis failure during VORTEX. Mon. Wea. Rev., 127, 1693–1705, https://doi.org/10.1175/1520-0493(1999)127<1693:OONLLM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Trapp, R. J., G. J. Stumpf, and K. L. Manross, 2005a: A reassessment of the percentage of tornadic mesocyclones. Wea. Forecasting, 20, 680–687, https://doi.org/10.1175/WAF864.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Trapp, R. J., S. A. Tessendorf, E. S. Godfrey, and H. E. Brooks, 2005b: Tornadoes from squall lines and bow echoes. Part I: Climatological distribution. Wea. Forecasting, 20, 23–34, https://doi.org/10.1175/WAF-835.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Van Den Broeke, M. S., 2020: A preliminary polarimetric radar comparison of pre-tornadic and nontornadic supercell storms. Mon. Wea. Rev., 148, 1567–1584, https://doi.org/10.1175/MWR-D-19-0296.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Wakimoto, R. M., and H. Cai, 2000: Analysis of a nontornadic storm during VORTEX 95. Mon. Wea. Rev., 128, 565–592, https://doi.org/10.1175/1520-0493(2000)128<0565:AOANSD>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Wicker, L. J., 1996: The role of near-surface wind shear on low-level mesocyclone generation and tornadoes. Preprints, 18th Conf. on Severe Local Storms, San Francisco, CA, Amer. Meteor. Soc, 115–119.

  • Wicker, L. J., and R. B. Wilhelmson, 1995: Simulation and analysis of tornado development and decay within a three-dimensional supercell thunderstorm. J. Atmos. Sci., 52, 2675–2703, https://doi.org/10.1175/1520-0469(1995)052<2675:SAAOTD>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Witt, A., M. D. Eilts, G. J. Stumpf, E. D. Mitchell, J. T. Johnson, and K. W. Thomas, 1998: Evaluating the performance of WSR-88D severe storm detection algorithms. Wea. Forecasting, 13, 513–518, https://doi.org/10.1175/1520-0434(1998)013<0513:ETPOWS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Zrnić, D. S., and A. V. Ryzhkov, 1999: Polarimetry for weather surveillance radars. Bull. Amer. Meteor. Soc., 80, 389–406, https://doi.org/10.1175/1520-0477(1999)080<0389:PFWSR>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 554 0 0
Full Text Views 5752 4097 263
PDF Downloads 1655 411 53