Advertisement

Polarimetric Classification of Radar Echo

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

Abstract

Automatic classification of radar returns using the polarimetric variables and environmental conditions is presented in this chapter. General principles of classification are reviewed with emphasis on the fuzzy logic method. Then, the hydrometeor classification algorithm operational on WSR-88D network is described, and other classification algorithms are discussed. The method for melting layer detection as an important part of the most classification schemes is described in detail. A section of the chapter is devoted to detection of hail and estimation of its size together with some verification. Also presented is automated detection of tornado debris signatures in the context of tornado detection, and tracks of detections are plotted along the damage paths of several tornadoes. Automatic detection of convective updrafts is based on the columns of differential reflectivity, and examples are included. A separate section is devoted to classification specifically tailored for winter precipitation. This implies combined use of the polarimetric data and numerical weather prediction model output. Finally, classification of radar returns other than from hydrometeors is described. Specifically, polarimetric methods to identify land and sea clutter, biological scatterers, chaff, smoke plumes, dust storms, and volcanic ash are presented.

Keywords

Classification Fuzzy logic Hydrometeor classification algorithm (HCA) Melting layer detection Hail detection Hail size Detection of tornado debris Winter precipitation Classification of nonmeteorological returns Land and sea clutter Biological scatterers Chaff Smoke plumes Dust storms Volcanic ash 

References

  1. Achtemeier, G. (1991). The use of insects as tracers for “clear air” boundary-layer studies by Doppler radar. Journal of Atmospheric and Oceanic Technology, 8, 746–765.CrossRefGoogle Scholar
  2. Al-Sakka, H., Boumahmoud, A., Fradon, B., Frasier, S., & Tabary, P. (2013). A new fuzzy logic hydrometeor classification scheme applied to the French X-, C-, and S-band polarimetric radars. Journal of Applied Meteorology and Climatology, 52, 2328–2344.CrossRefGoogle Scholar
  3. Atlas, D., & Ludlam, F. (1961). Multi-wavelength radar reflectivity of hailstorms. Quarterly Journal of the Royal Meteorological Society, 87, 523–534.CrossRefGoogle Scholar
  4. Aydin, K., Seliga, T., & Balaji, V. (1986). Remote sensing of hail with a dual linear polarization radar. Journal of Applied Meteorology, 25, 1475–1484.CrossRefGoogle Scholar
  5. 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
  6. Bechini, R., & Chandrasekar, V. (2015). A semisupervised robust hydrometeor classification method for dual-polarization radar applications. Journal of Atmospheric and Oceanic Technology, 32, 22–47.CrossRefGoogle Scholar
  7. Besic, N., Figueras i Ventura, J., Grazioli, J., Gabella, M., Germann, U., & Berne, A. (2016). Hydrometeor classification through statistical clustering of polarimetric radar measurements: A semi-supervised approach. Atmospheric Measurement Techniques, 9, 4425–4445.CrossRefGoogle Scholar
  8. Boodoo, S., Hudak, D., Donaldson, N., & Leduc, M. (2010). Application of dual-polarization radar melting-layer detection algorithm. Journal of Applied Meteorology and Climatology, 49, 1779–1793.CrossRefGoogle Scholar
  9. Brandes, E., & Ikeda, K. (2004). Freezing-level estimation with polarimetric radar. Journal of Applied Meteorology, 43, 1541–1553.CrossRefGoogle Scholar
  10. 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
  11. Brown, J. M., Smirnova, T. G., Olson, J. B., Grell, G. A., Dowell, D. C., Benjamin, S., et al. (2011). Improvement and testing of WRF physics options for application to rapid refresh and high resolution rapid refresh. In Preprints, 14th Conf. on Mesoscale Processes/15th Conf. on Aviation, Range, and Aerospace Meteorology, Los Angeles, CA, Amer. Meteor. Soc. 5.5. Retrieved from https://ams.confex.com/ams/14Meso15ARAM/webprogram/Paper191234.html
  12. Cao, Q., Zhang, G., Palmer, R., Knight, M., May, R., & Stafford, R. (2012). Spectrum – Time estimation and processing (STEP) for improving weather radar data quality. IEEE Transactions on Geoscience and Remote Sensing, 50, 4670–4683.CrossRefGoogle Scholar
  13. Chandrasekar, V., Keranen, R., Lim, S., & Moisseev, D. (2013). Recent advances in classification of observations from dual-polarization weather radars. Atmospheric Research, 119, 97–111.CrossRefGoogle Scholar
  14. Chilson, P., Frick, W., Kelly, J., Howard, K., Larkin, R., Diehl, R., Westbrook, J., Kelly, T., & Kunz, T. (2012). Partly cloudy with a chance of migration: Weather, radars, and aeroecology. Bulletin of the American Meteorological Society, 93, 669–686.CrossRefGoogle Scholar
  15. Depue, T., Kennedy, P., & Rutledge, S. (2007). Performance of the hail differential reflectivity (HDR) polarimetric radar hail indicator. Journal of Applied Meteorology and Climatology, 46, 1290–1301.CrossRefGoogle Scholar
  16. Dolan, B., & Rutledge, S. (2009). A theory-based hydrometeor identification algorithm for X-band polarimetric radars. Journal of Atmospheric and Oceanic Technology, 26, 20171–22088.CrossRefGoogle Scholar
  17. Dolan, B., Rutledge, S., Lim, S., Chandrasekar, V., & Thurai, M. (2013). A robust C-band hydrometeor identification algorithm and application to a long-term polarimetric radar dataset. Journal of Applied Meteorology and Climatology, 52, 2162–2186.CrossRefGoogle Scholar
  18. Eccles, P., & Atlas, D. (1973). A dual-wavelength radar hail detector. Journal of Applied Meteorology, 12, 847–854.CrossRefGoogle Scholar
  19. Elmore, K., Flamig, Z., Lakshmanan, V., Kaney, B., Reeves, H., Farmer, V., & Rothfusz, L. (2014). mPING: Crowd-sourcing weather reports for research. Bulletin of the American Meteorological Society, 95, 1335–1342.CrossRefGoogle Scholar
  20. Fabry, F. (2015). Radar meteorology. Principles and practice (p. 256). Cambridge, UK: Cambridge University Press.CrossRefGoogle Scholar
  21. 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
  22. Giangrande, S. E., Krause, J. M., & Ryzhkov, A. V. (2008). Automatic designation of the melting layer with a polarimetric prototype of the WSR-88D radar. Journal of Applied Meteorology and Climatology, 47, 1354–1364.CrossRefGoogle Scholar
  23. Grazioli, J., Turia, D., & Berne, A. (2015). Hydrometeor classification from polarimetric radar measurements: A clustering approach. Atmospheric Measurement Techniques, 8, 149–170.CrossRefGoogle Scholar
  24. Greene, D., & Clark, R. (1972). Vertically integrated liquid water – A new analysis tool. Monthly Weather Review, 100, 548–552.CrossRefGoogle Scholar
  25. Hannessen, R., & Weipert, A. (2011a). A probability-based sea clutter suppression method for polarimetric weather radar systems. In Weather radar and hydrology, proceedings of a symposium held in Exeter, UK, April 2011 (pp. 52–57).Google Scholar
  26. Hannessen, R., & Weipert, A. (2011b). An algorithm to detect and quantify volcanic eruptions using polarimetric X-band radar data. In International workshop on X-band Weather Radar, Delft, The Netherlands.Google Scholar
  27. Heinselman, P., & Ryzhkov, A. (2006). Validation of polarimetric hail detection. Weather and Forecasting, 21, 839–850.CrossRefGoogle Scholar
  28. Holler, H., Brini, V., Hubbert, J., Hagen, M., & Meischner, P. (1994). Life cycle and precipitation formation in hybrid-type hailstorm revealed by polarimetric and Doppler radar measurements. Journal of the Atmospheric Sciences, 51, 2500–2522.CrossRefGoogle Scholar
  29. Hubbert, J., Dixon, M., & Ellis, S. (2009b). Weather radar ground clutter. Part II: Real-time identification and filtering. Journal of Atmospheric and Oceanic Technology, 26, 1181–1197.CrossRefGoogle Scholar
  30. Hubbert, J., Dixon, M., Ellis, S., & Meymaris, G. (2009a). Weather radar ground clutter. Part I: Identification, modeling, and simulation. Journal of Atmospheric and Oceanic Technology, 26, 1165–1180.CrossRefGoogle Scholar
  31. 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.Google Scholar
  32. Keenan, T. (2003). Hydrometeor classification with a C-band polarimetric radar. Australian Meteorological Magazine, 52, 23–31.Google Scholar
  33. Kennedy, P., Rutledge, S., Petersen, W., & Bringi, V. (2001). Polarimetric radar observations of hail formation. Journal of Applied Meteorology, 40, 1347–1366.CrossRefGoogle Scholar
  34. Keranen, R., Saltikoff, E., Chandrasekar, V., Lim, S., Holmes, J., & Selzler, J. (2007). Real-time hydrometeor classification for the operational forecasting environment. In Preprints, 33rd Conf. on Radar Meteorology, Cairns, Australia, Amer. Meteor. Soc. P11B.11. Retrieved from https://ams.confex.com/ams/pdfpapers/123476.pdf
  35. 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
  36. Kurdzo, J., Williams, E., Smalley, D., Bennett, B., Patterson, D., Veillette, M., & Donovan, M. (2018). Polarimetric observations of chaff using the WSR-88D network. Journal of Applied Meteorology and Climatology, 57, 1063–1081.CrossRefGoogle Scholar
  37. Leitao, M., & Watson, P. (1984). Application of dual linearly polarized radar data to prediction of microwave path attenuation at 10 – 30 GHz. Radio Science, 19, 209–221.CrossRefGoogle Scholar
  38. Li, Y., Zhang, G., Doviak, R., Lei, L., & Cao, Q. (2013). A new approach to detect ground clutter mixed with weather signals. IEEE Transactions on Geoscience and Remote Sensing, 51, 2373–2387.CrossRefGoogle Scholar
  39. Lim, S., Chandrasekar, V., & Bringi, V. (2005). Hydrometeor classification system using dual-polarization radar measurements: Model improvements and in-situ verification. IEEE Transactions on Geoscience and Remote Sensing, 42, 792–801.CrossRefGoogle Scholar
  40. Lim, S., Moisseev, D., Chandrasekar, V., & Lee, D.-R. (2013). Classification and quantification of rimed snow based on spatial variability of radar reflectivity. Journal of the Meteorological Society of Japan, 91, 763–774.CrossRefGoogle Scholar
  41. Liu, H., & Chandrasekar, V. (2000). Classification of hydrometeors based on polarimetric radar measurements: Development of fuzzy logic and neuro-fuzzy systems, and in situ verification. Journal of Atmospheric and Oceanic Technology, 17, 140–167.CrossRefGoogle Scholar
  42. Marzano, F., Scaranari, D., & Vulpiani, G. (2007). Supervised fuzzy-logic classification of hydrometeors using C-band weather radars. IEEE Transactions on Geoscience and Remote Sensing, 45, 3784–3799.CrossRefGoogle Scholar
  43. Matrosov, S., Clark, K., & Kingsmill, D. (2007). A polarimetric radar approach to identify rain, melting layer, and snow regions for applying corrections to vertical profiles of reflectivity. Journal of Applied Meteorology and Climatology, 46, 154–166.CrossRefGoogle Scholar
  44. Melnikov, V., Istok, M., & Westbrook, J. (2015). Asymmetric radar echo patterns from insects. Journal of Atmospheric and Oceanic Technology, 32, 659–674.CrossRefGoogle Scholar
  45. Melnikov, V., Zrnic, D., Rabin, R., & Zhang, P. (2008). Radar polarimetric signatures of fire plums in Oklahoma. Geophysical Research Letters, 35, L14815.CrossRefGoogle Scholar
  46. Meyers, M., DeMott, P., & Cotton, W. (1992). New primary ice-nucleation parameterization in an explicit cloud model. Journal of Applied Meteorology, 31, 708–721.CrossRefGoogle Scholar
  47. Montopoli, M., Vulpiani, G., Cimini, D., Piccotti, E., & Marzano, F. (2013). Interpretation of observed microwave signatures from ground dual-polarization radar and space multi-frequency radiometer for the 2011 Grimsvotn volcanic eruption. Atmospheric Measurement Techniques Discussions, 6, 6215–6248.CrossRefGoogle Scholar
  48. Mueller, E., & Larkin, R. (1985). Insects observed using dual-polarization radar. Journal of Atmospheric and Oceanic Technology, 2, 49–54.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. Ortega, K. L., Smith, T. M., Manross, K. L., Kolodziej, A. G., Scharfenberg, K. A., Witt, A., & Gourley, J. (2009). The severe hazards analysis and verification experiment. Bulletin of the American Meteorological Society, 90, 1519–1530.CrossRefGoogle Scholar
  51. Park, H.-S., Ryzhkov, A., Zrnic, D., & Kim K.-E. (2007). Optimization of the matrix of weights in the polarimetric algorithm for classification of radar echoes. In Preprints, 33rd conf. on radar meteorology, Cairns, Australia, Amer. Meteor. Soc. P11B.4. Retrieved from http://ams.confex.com/ams/pdfpapers/123123.pdf
  52. Park, H.-S., Ryzhkov, A., Zrnic, D., & Kim, K.-E. (2009). The hydrometeor classification algorithm for the polarimetric WSR-88D. Description and application to an MCS. Weather and Forecasting, 24, 730–748.CrossRefGoogle Scholar
  53. Picca, J., Kumjian, M., & Ryzhkov, A. (2010). ZDR columns as a predictive tool for hail growth and storm evolution. In Preprints, 25th conf. on severe local storms, Denver, CO, Amer. Meteor. Soc. 113. Retrieved from https://ams.confex.com/ams/pdfpapers/175750.pdf
  54. 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
  55. Reeves, H., Elmore, K., Ryzhkov, A., Schuur, T., & Krause, J. (2014). Source of uncertainty in precipitation-type forecasting. Weather and Forecasting, 29, 936–953.CrossRefGoogle Scholar
  56. Reeves, H., Ryzhkov, A., & Krause, J. (2016). Discrimination between winter precipitation types based on spectral-bin microphysical modeling. Journal of Applied Meteorology and Climatology, 55, 1747–1761.CrossRefGoogle Scholar
  57. Rico-Ramirez, A., & Cluckie, I. (2008). Classification of ground clutter and anomalous propagation using dual-polarization weather radar. IEEE Transactions on Geoscience and Remote Sensing, 46, 1892–1904.CrossRefGoogle Scholar
  58. 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
  59. 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
  60. Ryzhkov, A., Reeves, H., Krause, J., & Burcham, H. (2014). Discrimination between winter precipitation types based on explicit microphysical modeling of melting and refreezing in the polarimetric hydrometeor classification algorithm. In Eighth European conf. on radar meteorology, Garmisch-Partenkirchen, Germany. Europeran Meteorological Society. MIC.P07. Retrieved from http://www.pa.op.dlr.de/erad2014/programme/ExtendedAbstracts/198_Ryzhkov.pdf
  61. Ryzhkov, A., Zhang, P., Doviak, R., & Kessinger, C. (2002). Discrimination between weather and sea clutter using Doppler and dual-polarization weather radars. In Proc. 27th general assembly of the international union of radio science, Maastricht, Netherlands. F1.P.3. Retrieved from https://www.ursi.org/Proceedings/ProcGA02/papers/p1383.pdf
  62. Ryzhkov, A. V. (2007). The impact of beam broadening on the quality of radar polarimetric data. Journal of Atmospheric and Oceanic Technology, 24, 729–744.CrossRefGoogle Scholar
  63. Ryzhkov, A. V., Schuur, T. J., Burgess, D. W., Giangrande, S., & Zrnic, D. S. (2005). The joint polarization experiment: Polarimetric rainfall measurements and hydrometeor classification. Bulletin of American Meteorological Society, 86, 809–824.CrossRefGoogle Scholar
  64. Schuur, T., Krause, J., & Ryzhkov A. (2015). A new melting layer detection algorithm that combines polarimetric radar-based detection with thermodynamic output from numerical models. In 37th Conference on Radar Meteorology. 7B.1.Google Scholar
  65. Schuur, T., Park, H.-S., Ryzhkov, A., & Reeves, H. (2012). Classification of precipitation types during transitional winter weather using the RUC model and polarimetric radar retrievals. Journal of Applied Meteorology and Climatology, 51, 763–779.CrossRefGoogle Scholar
  66. Schuur, T., Ryzhkov, A., & Heinselman, P. (2003). Observations and classification of echoes with the polarimetric WSR-88D radar. NOAA/National Severe Storms Laboratory report (46pp).Google Scholar
  67. Siggia, A., & Passarelli, R., Jr. (2004). Gaussian model adaptive processing (GMAP) for improved ground clutter cancellation and moment calculation. In Proceedings of ERAD (2004), Visby, Island of Gotland, Sweden (pp. 63–73).Google Scholar
  68. Snyder, J., Bluestein, H., Zhang, G., & Frasier, S. (2010). Attenuation correction and hydrometeor classification of high resolution, X-band, dual-polarized mobile radar measurements in severe convective storms. Journal of Atmospheric and Oceanic Technology, 27, 1979–2001.CrossRefGoogle Scholar
  69. Snyder, J., & Ryzhkov, A. (2015). Automated detection of polarimetric tornado debris signatures. Journal of Applied Meteorology and Climatology, 54, 1861–1870.CrossRefGoogle Scholar
  70. 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
  71. Stepanian, P., Horton, K., Melnikov, V., Zrnic, D., & Gauthreaux, S., Jr. (2016). Dual-polarization radar products for biological applications. Ecosphere, 7(11), e01539. https://doi.org/10.1002/ecs2.1539.CrossRefGoogle Scholar
  72. Straka, J. (1996). Hydrometeor fields in a supercell storm as deduced from dual-polarization radar. In Preprints, 18th Conf. on Severe Local Storms, San Francisco, CA, Amer. Meteor. Soc. (pp. 551–554).Google Scholar
  73. Straka, J., & Zrnic, D. (1993). An algorithm to deduce hydrometeor types and contents from multiparameter radar data. In Preprints, 26th Int. conf. on radar meteorology, Norman, OK, Amer. Meteor. Soc. (pp. 513–516).Google Scholar
  74. Straka, J. M., Zrnic, D. S., & Ryzhkov, A. V. (2000). Bulk hydrometeor classification and quantification using multiparameter radar data. Synthesis of relations. Journal of Applied Meteorology, 39, 1341–1372.CrossRefGoogle Scholar
  75. Tabary, P., Le Henaff, A., Vulpiani, G., Parent-du-Chatelet, J., & Gourley, J. J. (2006). Melting layer characterization and identification with a C-band dual-polarization radar: A long-term analysis. In Preprints, Fourth European conf. on radar in meteorology and hydrology (ERAD 2006), Barcelona, Spain (pp. 17–20).Google Scholar
  76. Thompson, E., Rutledge, S., Dolan, B., Chandrasekar, V., & Cheong, B.-L. (2014). A dual-polarization radar hydrometeor classification algorithm for winter precipitation. Journal of Atmospheric and Oceanic Technology, 31, 1457–1481.CrossRefGoogle Scholar
  77. Torres, S., & Warde, D. (2014). Ground clutter mitigation for weather radars using the autocorrelation spectral density. Journal of Atmospheric and Oceanic Technology, 31, 2049–2066.CrossRefGoogle Scholar
  78. Van den Broeke, M. (2013). Polarimetric radar observations of biological scatterers in hurricanes Irene (2011) and Sandy (2012). Journal of Atmospheric and Oceanic Technology, 30, 2754–2767.CrossRefGoogle Scholar
  79. Van den Broeke, M., & Alsarraf, H. (2016). Polarimetric radar observations of dust storms at C and S bands. Journal Of Operational Meteorology, 4(9), 123–131.CrossRefGoogle Scholar
  80. 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
  81. Vivekanandan, J., Zrnic, D. S., Ellis, S. M., Oye, D., Ryzhkov, A. V., & Straka, J. (1999). Cloud microphysics retrieval using S-band dual-polarization radar measurements. Bulletin of the American Meteorological Society, 80, 381–388.CrossRefGoogle Scholar
  82. Waldvogel, A., Federer, B., & Grimm, P. (1979). Criteria for the detection of hail cells. Journal of Applied Meteorology, 18, 1521–1525.CrossRefGoogle Scholar
  83. Wen, G., Protat, A., May, P., Moran, W., & Dixon, M. (2016). Cluster-based method for hydrometeor classification using polarimetric variables. Part II: Classification. Journal of Atmospheric and Oceanic Technology, 33, 45–59.CrossRefGoogle Scholar
  84. Wen, G., Protat, A., May, P., Wang, X., & Moran, W. (2015). Cluster-based method for hydrometeor classification using polarimetric variables. Part I: Interpretation and analysis. Journal of Atmospheric and Oceanic Technology, 32, 1320–1330.CrossRefGoogle Scholar
  85. Williams, E., Nathou, E., Hicks, E., Pontikis, C., Russell, B., Miller, M., & Bartholomew, M. (2009). The electrification of dust-lofting gust fronts (‘haboobs’) in the Sahel. Atmospheric Research, 91, 292–298.CrossRefGoogle Scholar
  86. Witt, A., Eilts, M., Stumpf, G., Johnson, J., Mitchell, E., & Thomas, K. (1998). An enhanced hail detection algorithm for the WSR-88D. Weather and Forecasting, 13, 286–303.CrossRefGoogle Scholar
  87. Wolfensberger, D., Scipion, D., & Berne, A. (2016). Detection and characterization of the melting layer based on polarimetric radar scans. Quarterly Journal of the Royal Meteorological Society, 142, 108–124.CrossRefGoogle Scholar
  88. Zeng, Z., Yuter, S., House, R., & Kingsmill, D. (2001). Microphysics of the rapid development of heavy convective precipitation. Monthly Weather Review, 129, 1882–1904.CrossRefGoogle Scholar
  89. Zhang, G. (2016). Weather radar Polarimetry (p. 304). Boca Raton, FL: CRC Press.CrossRefGoogle Scholar
  90. Zhang, P., Zrnic, D., & Ryzhkov, A. (2015) Observations of negative ZDR in sandstorms and storm outflows. In Preprints, 37th conference on Radar Meteorology, Norman, OK, Amer. Meteor. Soc. (p. 259). Retrieved from https://ams.confex.com/ams/37RADAR/webprogram/Handout/Paper275678/37thPoster_PZhang.pdf
  91. Zrnic, D., & Ryzhkov, A. (1998). Observations of insects and birds with a polarimetric radar. IEEE Transactions on Geoscience and Remote Sensing, 36, 661–668.CrossRefGoogle Scholar
  92. Zrnic, D., & Ryzhkov, A. (1999). Polarimetry for weather surveillance radars. Bulletin of the American Meteorological Society, 80, 389–406.CrossRefGoogle Scholar
  93. Zrnic, D. S., Bringi, V. N., Balakrishnan, N., Aydin, K., Chandrasekar, V., & Hubbert, J. (1993). Polarimetric measurements in a severe hailstorm. Monthly Weather Review, 121, 2221–2238.CrossRefGoogle Scholar
  94. Zrnic, D. S., & Ryzhkov, A. V. (2004). Polarimetric properties of chaff. Journal of Atmospheric and Oceanic Technology, 21, 1017–1024.CrossRefGoogle Scholar
  95. Zrnic, D. S., Ryzhkov, A. V., Straka, J., Liu, Y., & Vivekanandan, J. (2001). Testing a procedure for the automatic classification of hydrometeor types. Journal of Atmospheric and Oceanic Technology, 18, 892–913.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