Polarimetric Characteristics of Deep Convective Storms

  • Alexander V. Ryzhkov
  • Dusan S. Zrnic
Part of the Springer Atmospheric Sciences book series (SPRINGERATMO)


Overview of polarimetric measurements in deep convective storms is presented in this chapter. General characteristics of the spatial distributions of polarimetric radar variables in mesoscale convective systems (MCSs), hailstorms, and supercell tornadic storms are examined. Spatial pattern of the polarimetric variables in the MCSs is consistent with the accepted conceptual model. Combinations of polarimetric variables that correspond uniquely to locations within storms where specific scatterer types reside are identified and named “polarimetric signatures.” Prominent among these is the column of differential reflectivity indicative of convective updraft and preferred location for hail formation. The bottom of the column of specific differential phase is identified as location of precipitation-laden downdraft. Other important signatures associated with tornadic storms and discussed in this chapter are tornado debris signature (TDS), ZDR arc, and midlevel “rings” of enhanced ZDR and depressed ρhv. Examples of polarimetric variables in hailstorms are illustrated and related to the kinematic and microphysical features within these storms. Observations of large hail are presented, and comparisons between measurements at C and S band are made. Examples of tornado debris signatures observed with S-, C-, and X-band radars are also included. Modeling of the polarimetric characteristics of these deep convective storms is the subject of the last section, and examples from the literature are used to illustrate the inferred polarimetric signatures and compare these with observations.


Polarimetric observations Deep convective storms Mesoscale convective systems Differential reflectivity column Column of differential phase Tornado debris signature ZDR arc Hail storm Modeling polarimetric characteristics 


  1. Anderson, M., Carey, L., Petersen, W., & Knupp, K. (2011). C-band dual-polarization radar signatures of hail. Electronic Journal of Operational Meteorology, 2011-EJ2. Retrieved from
  2. Aydin, K., Seliga, T., & Balaji, V. (1986). Remote sensing of hail with a dual linear polarization radar. Journal of Climate and Applied Meteorology, 25, 1475–1484.CrossRefGoogle Scholar
  3. Aydin, K., & Zhao, Y. (1990). A computational study of polarimetric radar observables in hail. IEEE Transactions on Geoscience and Remote Sensing, 28, 412–422.CrossRefGoogle Scholar
  4. Balakrishnan, N., & Zrnic, D. (1990). Use of polarization to characterize precipitation and discriminate large hail. Journal of the Atmospheric Sciences, 47, 1525–1540.CrossRefGoogle Scholar
  5. Blair, S., & Leighton, J. (2012). Creating high-resolution hail datasets using social media and post-storm ground surveys. Electronic Journal of Operational Meteorology, 13, 32–45.Google Scholar
  6. Bluestein, H. (2013). Severe convective storms and tornadoes. Observations and dynamics (p. 456). Berlin, Germany: Springer.CrossRefGoogle Scholar
  7. Bluestein, H., French, M., Tanamachi, R., Frasier, S., Hardwick, K., Junyent, F., & Pazmany, A. (2007). Close-range observations of tornadoes in supercells made with a dual-polarization, X-band, mobile Doppler radar. Monthly Weather Review, 135, 1522–1543.CrossRefGoogle Scholar
  8. Bluestein, H., Snyder, J., & Houser, J. (2015). A multiscale overview of the El Reno, Oklahoma, tornadic supercell of 31 May 2013. Weather and Forecasting, 30, 525–552.CrossRefGoogle Scholar
  9. Bodine, D., Kumjian, M., Palmer, R., Heinselman, P., & Ryzhkov, A. (2013). Tornado damage estimation using polarimetric radar. Weather and Forecasting, 28, 139–158.CrossRefGoogle Scholar
  10. Bodine, D., Palmer, R., & Zhang, G. (2014). Dual-wavelength polarimetric radar analyses of tornadic debris signatures. Journal of Applied Meteorology and Climatology, 53, 242–261.CrossRefGoogle Scholar
  11. Borowska, L., Ryzhkov, A., Zrnic, D., Simmer, C., & Palmer, R. (2011). Attenuation and differential attenuation of the 5 cm wavelength radiation in melting hail. Journal of Applied Meteorology and Climatology, 50, 59–76.CrossRefGoogle Scholar
  12. Bringi, V., Vivekanandan, J., & Tuttle, J. (1986). Multiparameter radar measurements in Colorado convective storms. Part II: Hail detection studies. Journal of the Atmospheric Sciences, 43, 2564–2577.CrossRefGoogle Scholar
  13. Brown, R. A., Lemon, L. R., & Burgess, D. W. (1978). Tornado detection by pulsed Doppler radar. Monthly Weather Review, 106, 29–38.CrossRefGoogle Scholar
  14. Burgess, D., et al. (2014). 20 May 2013 Moore, Oklahoma, Tornado: Damage survey and analysis. Weather and Forecasting, 29, 1229–1237.CrossRefGoogle Scholar
  15. Carlin, J., Ryzhkov, A., Snyder, J., & Khain, A. (2016). Hydrometeor mixing ratio retrievals for strom-scale radar data assimilation: Utility of current equations and potential benefits of polarimetry. Monthly Weather Review, 144, 2981–3001.CrossRefGoogle Scholar
  16. Conway, J., & Zrnic, D. (1993). A study of embryo production and hail growth using dual-Doppler and multiparameter radars. Monthly Weather Review, 121, 2511–2528.CrossRefGoogle Scholar
  17. Dawson, D., Mansell, E., Jung, Y., Wicker, L., Kumjian, M., & Xue, M. (2014). Low-level ZDR signatures in supercell forward flanks: The role of size sorting and melting of hail. Journal of the Atmospheric Sciences, 71, 276–299.CrossRefGoogle Scholar
  18. Entremont, C., & Lamb, D. (2014). Relationship between tornado debris signature (TDS) height and tornado intensity. Training Material for Forecasters.
  19. Feral, L., Sauvageot, H., & Soula, S. (2003). Hail detection using S- and C-band radar reflectivity difference. Journal of Atmospheric and Oceanic Technology, 20, 233–248.CrossRefGoogle Scholar
  20. Gao, J., Xue, M., Brewster, K., & Droegemeier, K. (2004). A three-dimensional variational data analysis method with recursive filter for Doppler radars. Journal of Atmospheric and Oceanic Technology, 21, 457–469.CrossRefGoogle Scholar
  21. Gu, J.-Y., Ryzhkov, A., Zhang, P., Neilley, P., Knight, M., Wolf, B., & Lee, D.-I. (2011). Polarimetric attenuation correction in heavy rain at C band. Journal of Applied Meteorology, 50, 39–58.CrossRefGoogle Scholar
  22. Holler, H., Bringi, V., Hubbert, J., Hagen, M., & Meischner, P. (1994). Life cycle and precipitation formation in a hybrid-type hailstorm revealed by polarimetric and Doppler radar measurements. Journal of the Atmospheric Sciences, 51, 2500–2522.CrossRefGoogle Scholar
  23. Houze, R. (1993). Cloud dynamics (p. 573). Amsterdam, Netherlands: Academic Press.Google Scholar
  24. Houze, R., Rutledge, S., Biggerstaff, M., & Smuul, B. (1989). Interpretation of Doppler weather radar displays of midlatitude mesoscale convective systems. Bulletin of the American Meteorological Society, 70, 608–619.CrossRefGoogle Scholar
  25. Hubbert, J., Bringi, V., Carey, L., & Bolen, S. (1998). CSU-CHILL polarimetric radar measurements from a severe hail storm in eastern Colorado. Journal of Applied Meteorology, 37, 749–775.CrossRefGoogle Scholar
  26. Illingworth, A., Goddard, J., & Cherry, S. (1987). Polarization radar studies of precipitation development in convective storms. Quarterly Journal of the Royal Meteorological Society, 113, 469–489.CrossRefGoogle Scholar
  27. Ilotoviz, E., Benmoshe, N., Khain, A., Phillips, V., & Ryzhkov, A. (2016). Effect of aerosols on freezing drops, hail, and precipitation in a mid-latitude storm. Journal of the Atmospheric Sciences, 73, 109–144.CrossRefGoogle Scholar
  28. Ilotoviz, E., Khain, A., Ryzhkov, A., & Snyder, J. (2018). Relationship between aerosols, hail microphysics, and ZDR columns. Journal of the Atmospheric Sciences, 75, 1755–1781.CrossRefGoogle Scholar
  29. Johnson, M., Jung, Y., Dawson, D., & Xue, M. (2016). Comparison of simulated polarimetric signatures in idealized supercell storms using two-moment bulk microphysics schemes in WRF. Monthly Weather Review, 144, 971–996.CrossRefGoogle Scholar
  30. Jung, Y., Xue, M., & Zhang, G. (2010). Simulations of polarimetric radar signatures of a supercell storm using a two-moment bulk microphysics scheme. Journal of Applied Meteorology and Climatology, 49, 146–163.CrossRefGoogle Scholar
  31. Jung, Y., Zhang, G., & Xue, M. (2008). Assimilation of simulated polarimetric radar data for a convective storm using the Ensemble Kalman Filter. Part I: Observation operators for reflectivity and polarimetric variables. Monthly Weather Review, 136, 2228–2245.CrossRefGoogle Scholar
  32. Kaltenboeck, R., & Ryzhkov, A. (2013). Comparison of polarimetric signatures of hail at S and C bands for different hail sizes. Atmospheric Research, 123, 323–336.CrossRefGoogle Scholar
  33. Kennedy, P., Rutledge, S., Dolan, B., & Thaller, E. (2014). Observations of the 14 July 2011 Fort Collins hailstorm: Implications for WSR-88D-based hail detection and warnings. Weather and Forecasting, 29, 623–638.CrossRefGoogle Scholar
  34. Kennedy, P., Rutledge, S., Petersen, W., & Bringi, V. (2001). Polarimetric radar observations of hail formation. Journal of Applied Meteorology, 40, 1347–1366.CrossRefGoogle Scholar
  35. Khain, A., & Pinsky, M. (2018). Physical processes in clouds and cloud modeling (p. 686). Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  36. Khain, A., Pokrovsky, A., Pinsky, M., Seifert, A., & Phillips, V. (2004). Simulation of effects of atmospheric aerosols on deep turbulent convective clouds using a spectral microphysics mixed-phase cumulus cloud model. Part I: Model description and possible applications. Journal of the Atmospheric Sciences, 61, 2963–2982.CrossRefGoogle Scholar
  37. Khain, A., Rosenfeld, D., Pokrovsky, A., Blahak, U., & Ryzhkov, A. (2011). The role of CCN in precipitation and hail in a midlatitude storm as seen in simulations using a spectral (bin) microphysics model in a 2D dynamic frame. Atmospheric Research, 99, 129–146.CrossRefGoogle Scholar
  38. Kumjian, M. (2011). Precipitation properties of supercell hook echoes. Electronic Journal of Severe Storms Meteorology, 6(5), 1–21 Retrieved from,php/ejssm/issue/view/30.Google Scholar
  39. Kumjian, M. (2013). Principles and applications of dual-polarization weather radar. Part II: Warm- and cold-season applications. Journal of Operational Meteorology, 1(20), 243–264.CrossRefGoogle Scholar
  40. Kumjian, M., Khain, A., Benmoshe, N., Ilotoviz, E., Ryzhkov, A., & Phillips, V. (2014). The anatomy and physics of ZDR columns: Investigating a polarimetric radar signature with a spectral bin microphysical model. Journal of Applied Meteorology and Climatology, 53, 1820–1843.CrossRefGoogle Scholar
  41. Kumjian, M., & Ryzhkov, A. (2009). Storm-relative helicity revealed from polarimetric radar measurements. Journal of the Atmospheric Sciences, 66, 667–685.CrossRefGoogle Scholar
  42. Kumjian, M., Ryzhkov, A., Melnikov, V., & Schuur, T. (2010). Rapid-scan super-resolution observations of a cyclic supercell with a dual-polarization WSR-88D. Monthly Weather Review, 138, 3762–3786.CrossRefGoogle Scholar
  43. Kumjian, M. R., & Ryzhkov, A. (2008). Polarimetric signatures in supercell storms. Journal of Applied Meteorology and Climatology, 47, 1940–1961.CrossRefGoogle Scholar
  44. Kurdzo, J., Bodine, D., Cheong, B.-L., & Palmer, R. (2015). High-temporal resolution polarimetric X-band Doppler radar observations of the 20 May 2013 Moore, Oklahoma, tornado. Monthly Weather Review, 143, 2711–2735.CrossRefGoogle Scholar
  45. Loney, M., Zrnic, D., Straka, J., & Ryzhkov, A. (2002). Enhanced polarimetric signatures above the melting level in a supercell storm. Journal of Applied Meteorology, 41, 1179–1194.CrossRefGoogle Scholar
  46. Mansel, E. (2010). On sedimentation and advection in multimoment bulk microphysics. Journal of the Atmospheric Sciences, 67, 3084–3094.CrossRefGoogle Scholar
  47. Markowski, P., & Richardson, Y. (2010). Mesoscale meteorology in midlatitudes (p. 407). Chichester, UK: Wiley.CrossRefGoogle Scholar
  48. Mildbrand, J., & Yau, M. (2005). A multimoment bulk microphysical parameterization. Part II: A proposed three-moment closure and scheme description. Journal of the Atmospheric Sciences, 62, 3065–3081.CrossRefGoogle Scholar
  49. Ortega, K., Krause, J., & Ryzhkov, A. (2016). Polarimetric radar characteristics of melting hail. Part III: Validation of the algorithm for hail size discrimination. Journal of Applied Meteorology and Climatology, 55, 829–848.CrossRefGoogle Scholar
  50. Palmer, R., Bodine, D., Kumjian, M., Cheong, B., Zhang, G., Cao, Q., Bluestein, H., Ryzhkov, A., Yu, T., & Wang, Y. (2011). Observations of the 10 May 2010 tornado outbreak using OU-PRIME: Potential for new science with high-resolution polarimetric radar. Bulletin of the American Meteorological Society, 92, 871–891.CrossRefGoogle Scholar
  51. Payne, C., Schuur, T., MacGorman, D., Biggerstaff, M., Kumjian, M., & Rust, W. (2010). Polarimetric and electrical characteristics of a lightning ring in a supercell storm. Monthly Weather Review, 138, 2405–2425.CrossRefGoogle Scholar
  52. Picca, J., & Ryzhkov, A. (2012). A dual-wavelength polarimetric analysis of the 16 May 2010 Oklahoma City extreme hailstorm. Monthly Weather Review, 140, 1385–1403.CrossRefGoogle Scholar
  53. Romine, G., Burgess, D., & Wilhelmson, R. (2008). A dual-polarization-radar -based assessment of the 8 May 2003 Oklahoma City area tornadic supercell. Monthly Weather Review, 136, 2849–2870.CrossRefGoogle Scholar
  54. Ryzhkov, A., Burgess, D., Zrnic, D., Smith, T., & Giangrande, S. (2002). Polarimetric analysis of a 3 May 1999 Tornado. Preprints, 21 Conference on Severe Local Storms, San Antonio, TX, Amer. Meteor. Soc., 14.2. Retrieved from
  55. Ryzhkov, A., Kumjian, M., Ganson, S., & Khain, A. (2013a). Polarimetric radar characteristics of melting hail. Pt I: Theoretical simulations using spectral microphysical modeling. Journal of Applied Meteorology and Climatology, 52, 2849–2870.CrossRefGoogle Scholar
  56. Ryzhkov, A., Kumjian, M., Ganson, S., & Zhang, P. (2013b). Polarimetric radar characteristics of melting hail. Pt II: Practical implications. Journal of Applied Meteorology and Climatology, 52, 2871–2886.CrossRefGoogle Scholar
  57. Ryzhkov, A., Matrosov, S., Melnikov, V., Zrnic, D., Zhang, P., Cao, Q., Knight, M., Simmer, C., & Troemel, S. (2017). Estimation of depolarization ratio using radars with simultaneous transmission/reception. Journal of Applied Meteorology and Climatology, 56, 1797–1816.CrossRefGoogle Scholar
  58. Ryzhkov, A., Pinsky, M., Pokrovsky, A., & Khain, A. (2011). Polarimetric radar observation operator for a cloud model with spectral microphysics. Journal of Applied Meteorology and Climatology, 50, 873–894.CrossRefGoogle Scholar
  59. Ryzhkov, A., Schuur, T., Burgess, D., & Zrnic, D. (2005). Polarimetric tornado detection. Journal of Applied Meteorology, 44, 557–570.CrossRefGoogle Scholar
  60. Ryzhkov, A., & Zrnic, D. (1995). Precipitation and attenuation measurements at a 10 cm wavelength. Journal of Applied Meteorology, 34, 2121–2134.CrossRefGoogle Scholar
  61. Ryzhkov, A., Zrnic, D., Zhang, P., Krause, J., Park, H., Hudak, D., et al. (2007). Comparison of polarimetric algorithms for hydrometeor classification at S and C bands. In Extended Abstracts, 33rd Conference Radar Meteorology, Cairns, Australia, Amer. Meteor. Soc., 10.3. Retrieved from
  62. Scharfenberg, K., Miller, D., Schuur, T., Schlatter, P., Giangrande, S., Melnikov, V., et al. (2005). The Joint Polarization Experiment: Polarimetric radar in forecasting and warning decision making. Weather and Forecasting, 20, 775–788.Google Scholar
  63. Schultz, C., Nelson, S., Carey, L., Belanger, C., Carcione, B., Darden, C., et al. (2012a). Dual-polarization tornadic debris signatures. Part I: Examples and utility in an operational setting. Electronic Journal of Operational Meteorology, 13(9), 120–137.Google Scholar
  64. Schultz, C., Nelson, S., Carey, L., Belanger, L., Carcione, B., Darden, C., et al. (2012b). Dual-polarization tornadic debris signatures. Part II: Comparisons and caveats. Electronic Journal of Operational Meteorology, 13(10), 138–150.Google Scholar
  65. Snyder, J., & Bluestein, H. (2014). Some considerations for the use of high-resolution mobile radar data in tornado intensity determination. Weather and Forecasting, 29, 799–827.CrossRefGoogle Scholar
  66. Snyder, J., Bluestein, H., Dawson, D., & Jung, Y. (2017a). Simulations of polarimetric, X-band radar signatures in supercells. Part I: Description of experiment and simulated ρhv rings. Journal of Applied Meteorology and Climatology, 56, 1977–1999.CrossRefGoogle Scholar
  67. Snyder, J., Bluestein, H., Dawson, D., & Jung, Y. (2017b). Simulations of polarimetric, X-band radar signatures in supercells. Part II: ZDR columns and rings and KDP columns. Journal of Applied Meteorology and Climatology, 56, 2001–2026.CrossRefGoogle Scholar
  68. Snyder, J., Bluestein, H., Venkatesh, V., & Frasier, S. (2013). Observations of polarimetric signatures in supercells by an X-band mobile Doppler radar. Monthly Weather Review, 141, 3–29.CrossRefGoogle Scholar
  69. Snyder, J., Ryzhkov, A., Kumjian, M., Picca, J., & Khain, A. (2015). Developing a ZDR column detection algorithm to examine convective storm updrafts. Weather and Forecasting, 30, 1819–1844.CrossRefGoogle Scholar
  70. Tabary, P., Fradon, B., Illingworth, A.J., & Vulpiani, G. (2009). Hail detection and quantification with a C-band polarimetric radar: Challenges and promises. In Extended Abstracts, 34th Conference on Radar Meteorology, Williamsburg, VA, Amer. Meteor. Soc., 10A.4. Retrieved from
  71. Tessendorf, S., Miller, J., Wiens, K., & Rutledge, S. (2005). The 29 June 2000 supercell observed during STEPS. Part I: Kinematics and microphysics. Journal of the Atmospheric Sciences, 62, 4127–4150.CrossRefGoogle Scholar
  72. Van Den Broeke, M., & Jauernic, S. (2014). Spatial and temporal characteristics of polarimetric tornadic debris signatures. Journal of Applied Meteorology and Climatology, 53, 2217–2231.CrossRefGoogle Scholar
  73. Van Den Broeke, M., Straka, J., & Rasmussen, E. (2008). Polarimetric radar observations at low levels during tornado life cycles in a small sample of classic southern plains supercells. Journal of Applied Meteorology and Climatology, 47, 1232–1247.CrossRefGoogle Scholar
  74. Van Lier-Walqui, M., Fridlind, A., Helmus, A., MacGorman, D., North, K., Kollias, P., & Posselt, D. (2016). On polarimetric radar signatures of deep convection for model evaluation: Columns of specific differential phase observed during MC3E. Monthly Weather Review, 144, 737–758.CrossRefGoogle Scholar
  75. Wurman, J., Kosiba, K., Robinson, P., & Marshall, T. (2014). The role of multiple-vortex tornado structure in causing storm researcher fatalities. Bulletin of the AMS, 95, 31–45.Google Scholar
  76. Xue, G., Wang, D., Gao, J., Brewster, K., & Droegemeier, K. (2003). The Advanced Regional Prediction System (ARPS), storm-scale numerical weather prediction and data assimilation. Meteorology and Atmospheric Physics, 82, 139–170.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Alexander V. Ryzhkov
    • 1
  • Dusan S. Zrnic
    • 2
  1. 1.Cooperative Institute for Mesoscale Meteorological StudiesThe University of OklahomaNormanUSA
  2. 2.National Severe Storms Laboratory, National Oceanic and Atmospheric AdministrationNormanUSA

Personalised recommendations