Advertisement

Advances in Atmospheric Sciences

, Volume 36, Issue 6, pp 571–588 | Cite as

Current Status and Future Challenges of Weather Radar Polarimetry: Bridging the Gap between Radar Meteorology/Hydrology/Engineering and Numerical Weather Prediction

  • Guifu ZhangEmail author
  • Vivek N. Mahale
  • Bryan J. Putnam
  • Youcun Qi
  • Qing Cao
  • Andrew D. Byrd
  • Petar Bukovcic
  • Dusan S. Zrnic
  • Jidong Gao
  • Ming Xue
  • Youngsun Jung
  • Heather D. Reeves
  • Pamela L. Heinselman
  • Alexander Ryzhkov
  • Robert D. Palmer
  • Pengfei Zhang
  • Mark Weber
  • Greg M. Mcfarquhar
  • Berrien MooreIII
  • Yan Zhang
  • Jian Zhang
  • J. Vivekanandan
  • Yasser Al-Rashid
  • Richard L. Ice
  • Daniel S. Berkowitz
  • Chong-chi Tong
  • Caleb Fulton
  • Richard J. Doviak
Open Access
Review

Abstract

After decades of research and development, the WSR-88D (NEXRAD) network in the United States was upgraded with dual-polarization capability, providing polarimetric radar data (PRD) that have the potential to improve weather observations, quantification, forecasting, and warnings. The weather radar networks in China and other countries are also being upgraded with dual-polarization capability. Now, with radar polarimetry technology having matured, and PRD available both nationally and globally, it is important to understand the current status and future challenges and opportunities. The potential impact of PRD has been limited by their oftentimes subjective and empirical use. More importantly, the community has not begun to regularly derive from PRD the state parameters, such as water mixing ratios and number concentrations, used in numerical weather prediction (NWP) models.

In this review, we summarize the current status of weather radar polarimetry, discuss the issues and limitations of PRD usage, and explore potential approaches to more efficiently use PRD for quantitative precipitation estimation and forecasting based on statistical retrieval with physical constraints where prior information is used and observation error is included. This approach aligns the observation-based retrievals favored by the radar meteorology community with the model-based analysis of the NWP community. We also examine the challenges and opportunities of polarimetric phased array radar research and development for future weather observation.

Key words

weather radar polarimetry radar meteorology numerical weather prediction data assimilation microphysics parameterization forward operator 

摘 要

经过数十年的研究和发展, 天气雷达偏振技术日渐成熟, 美国新一代天气雷达(WSR-88D)已全面升级成双偏振雷达, 并提供具有改进天气观测, 量化, 预报, 和预警潜能的偏振雷达数据(PRD). 中国和其他国家的天气雷达网也正在被升级成具有双偏振功能. 现在, 雷达偏振技术已经成熟, 偏振雷达数据可在全美和全世界范围获取, 有必要理解其研发现状和未来的挑战及机遇. 偏振雷达数据潜在作用常受到主观和经验应用的限制. 更重要的是我们还没有常规的, 由偏振数据导出数值预报模式(NWP)中的状态参数.

在这篇综述中, 我们总结天气雷达技术的现状, 讨论偏振数据应用的问题和局限, 探讨在定量降水估计和预报中更有效地应用雷达数据的潜在方法, 也就是基于统计反演加物理限定, 并将先验信息和观测误差考虑在内的优化方法. 这种方法将雷达气象学领域中常用的观测反演和数值天气预报中的模式分析统一起来. 我们也将讨论用于未来天气观测的偏振相控阵雷达研发的挑战和机遇.

关键词

天气雷达偏振技术 雷达气象学 数值天气预报 资料同化 微物理参数化 前向算子 

Notes

Acknowledgements

The research was supported by the NOAA (Grant Nos. NA16AOR4320115 and NA11OAR4320072) and NSF (Grant No. AGS-1341878). The authors would like to thank the engineers at the NSSL and OU/ARRC for their support of the CPPAR development.

References

  1. Andrić, J., M. R. Kumjian, D. S. Zrnić, J. M. Straka, and V. M. Melnikov, 2013: Polarimetric Signatures above the Melting Layer in Winter Storms: An Observational and Modeling Study. Journal of Applied Meteorology and Climatology, 52, 682–700.CrossRefGoogle Scholar
  2. Bluestein, H. B., and Coauthors, 2014: Radar in atmospheric sciences and related research: Current systems, emerging technology, and future needs. Bull. Amer. Meteor. Soc., 95, 1850–1861,  https://doi.org/10.1175/BAMS-D-13-00079.1.CrossRefGoogle Scholar
  3. Brandes, E. A., and K. Ikeda, 2004: Freezing-level estimation with polarimetric radar. J. Appl. Meteor., 43, 1541–1553,  https://doi.org/10.1175/JAM2155.1.CrossRefGoogle Scholar
  4. Brandes, E. A., G. F. Zhang, and J. Vivekanandan, 2002: Experiments in rainfall estimation with a polarimetric radar in a subtropical environment. J. Appl. Meteor., 41, 674–685, https://doi.org/10.1175/1520-0450(2002)041<0674:EIREWA>2.0.CO;2.CrossRefGoogle Scholar
  5. Bringi, V. N., and V. Chandrasekar, 2001: Polarimetric Doppler Weather Radar: Principles and Applications. Cambridge University Press, 636 pp.CrossRefGoogle Scholar
  6. Brown, B. R., M. M. Bell, and A. J. Frambach, 2016: Validation of simulated hurricane drop size distributions using polarimetric radar. Geophys. Res. Lett, 43(2), 910–917,  https://doi.org/10.1002/2015GL067278.CrossRefGoogle Scholar
  7. Bukovčić, P., D. Zrnić, and G. F. Zhang, 2017: Winter precipitation liquid-ice phase transitions revealed with polarimetric radar and 2DVD observations in central Oklahoma. Journal of Applied Meteorology and Climatology, 56(5), 1345–1363,  https://doi.org/10.1175/JAMC-D-16-0239.1.CrossRefGoogle Scholar
  8. Byrd, A., C. Fulton, R. Palmer, S. Islam, D. Zrnic, R. Doviak, R. Zhang, and G. Zhang, 2017: First weather observations with a cylindrical polarimetric phased array radar. Internal Technical Report.Google Scholar
  9. Cao, Q., G. F. Zhang, E. A. Brandes, and T. J. Schuur, 2010: Polarimetric radar rain estimation through retrieval of drop size distribution using a Bayesian approach. Journal of Applied Meteorology and Climatology, 49, 973–990,  https://doi.org/10.1175/2009JAMC2227.1.CrossRefGoogle Scholar
  10. Cao, Q., G. F. Zhang, and M. Xue, 2013: A variational approach for retrieving raindrop size distribution from polarimetric radar measurements in the presence of attenuation. Journal of Applied Meteorology and Climatology, 52, 169–185,  https://doi.org/10.1175/JAMC-D-12-0101.1.CrossRefGoogle Scholar
  11. Carlin, J. T., A. V. Ryzhkov, J. C. Snyder, and A. Khain, 2016: Hydrometeor mixing ratio retrievals for storm-scale radar data assimilation: Utility of current relations and potential benefits of polarimetry. Mon. Wea. Rev., 144, 2981–3001,  https://doi.org/10.1175/MWR-D-15-0423.1.CrossRefGoogle Scholar
  12. Carlin, J. T., J. D. Gao, J. C. Snyder, and A. V. Ryzhkov, 2017: Assimilation of ZDR columns for improving the spinup and forecast of convective storms in storm-scale models: Proof-of-concept experiments. Mon. Wea. Rev., 145, 5033–5057,  https://doi.org/10.1175/MWR-D-17-0103.1.CrossRefGoogle Scholar
  13. Chandrasekar V., R. Keranen, S. Lim, and D. Moisseev, 2013: Recent advances in classification of observations from dual polarization weather radars. Atmospheric Research, 119, 97–111,  https://doi.org/10.1016/j.atmosres.2011.08.014.CrossRefGoogle Scholar
  14. Chang, W.-Y., T.-C. C. Wang, and P.-L. Lin, 2009: Characteristics of the raindrop size distribution and drop shape relation in typhoon systems in the western Pacific from the 2D video disdrometer and NCU C-band polarimetric radar. J. Atmos. Oceanic Technol., 26(10), 1973–1993,  https://doi.org/10.1175/2009JTECHA1236.1.CrossRefGoogle Scholar
  15. Chang, W.-Y., J. Vivekanandan, K. Ikeda, and P.-L. Lin, 2016: Quantitative precipitation estimation of the epic 2013 Colorado flood event: Polarization radar-based variational scheme. Journal of Applied Meteorology and Climatology, 55(7), 1477–1495,  https://doi.org/10.1175/JAMC-D-15-0222.1.CrossRefGoogle Scholar
  16. Chen, G., and Coauthors, 2017: Improving polarimetric C-band radar rainfall estimation with two-dimensional video disdrometer observations in Eastern China. Journal of Hydrometeorology, 18(5), 1375–1391,  https://doi.org/10.1175/JHM-D-16-0215.1.CrossRefGoogle Scholar
  17. Chen, H. N., and V. Chandrasekar, 2015: The quantitative precipitation estimation system for dallas-fort worth (DFW) urban remote sensing network. J. Hydrol., 531, 259–271,  https://doi.org/10.1016/j.jhydrol.2015.05.040.CrossRefGoogle Scholar
  18. Dawson, D. T., M. Xue, J. A. Milbrandt, and M. K. Yau, 2010: Comparison of evaporation and cold pool development between single-moment and multimoment bulk microphysics schemes in idealized simulations of tornadic thunderstorms. Mon. Wea. Rev., 138, 1152–1171,  https://doi.org/10.1175/2009MWR2956.1.CrossRefGoogle Scholar
  19. Dawson, D. T., M. Xue, J. A. Milbrandt, and A. Shapiro, 2015: Sensitivity of real-data simulations of the 3 May 1999 Oklahoma City tornadic supercell and associated tornadoes to multimoment microphysics. Part I: Storm- and tornado-scale numerical forecasts. Mon. Wea. Rev., 143, 2241–2265,  https://doi.org/10.1175/MWR-D-14-00279.1.CrossRefGoogle Scholar
  20. Dawson, D. T., 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.CrossRefGoogle Scholar
  21. Didlake, A. C., and M. R. Kumjian, 2017: Examining polarimetric radar observations of bulk microphysical structures and their relation to vortex kinematics in Hurricane Arthur (2014). Mon. Wea. Rev., 145(11), 4521–4541,  https://doi.org/10.1175/MWR-D-17-0035.1.CrossRefGoogle Scholar
  22. Dolan, B., S. A. Rutledge, S. Lim, V. Chandrasekar, and M. Thurai, 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,  https://doi.org/10.1175/JAMC-D-12-0275.1.CrossRefGoogle Scholar
  23. Doviak, R. J., and D. S. Zrnić, 1993: Doppler Radar and Weather Observations. 2nd ed. Academic Press, 562 pp.Google Scholar
  24. Doviak, R. J., V. Bringi, A. Ryzhkov, A. Zahrai, and D. S. Zrnić, 2000: Considerations for polarimetric upgrades to operational WSR-88D radars. J. Atmos. Oceanic Technol., 17, 257–278,  https://doi.org/10.1175/1520-0426(2000)017<0257:CFPUTO>2.0.CO;2.CrossRefGoogle Scholar
  25. Eccles, P. J., and D. Atlas, 1973: A dual-wavelength radar hail detector. J. Appl. Meteor., 12(5), 847–854,  https://doi.org/10.1175/1520-0450(1973)012<0847:ADWRHD>2.0.CO;2.CrossRefGoogle Scholar
  26. Figueras i Ventura, J., and P. Tabary, 2013: The new French operational polarimetric radar rainfall rate product. Journal of Applied Meteorology and Climatology, 52(8), 1817–1835,  https://doi.org/10.1175/JAMC-D-12-0179.1.CrossRefGoogle Scholar
  27. Finlon, J. A., G. M. McFarquhar, R. M. Rauber, D. M. Plummer, B. F. Jewett, D. Leon, and K. R. Knupp, 2016: A comparison of X-band polarization parameters with in situ microphysical measurements in the comma head of two winter cyclones. Journal of Applied Meteorology and Climatology, 55, 2549–2574,  https://doi.org/10.1175/JAMC-D-16-0059.1.CrossRefGoogle Scholar
  28. Fulton, C., and Coauthors, 2017: Cylindrical polarimetric phased array radar: Beamforming and calibration for weather applications. IEEE Trans. Geosci. Remote Sens., 55(5), 2827–2841,  https://doi.org/10.1109/TGRS.2017.2655023.CrossRefGoogle Scholar
  29. Gao, J. D., and D. J. Stensrud, 2012: Assimilation of reflectivity data in a convective-scale, cycled 3DVAR framework with hydrometeor classification. J. Atmos. Sci., 69(3), 1054–1065,  https://doi.org/10.1175/JAS-D-11-0162.1.CrossRefGoogle Scholar
  30. Ge, G. Q., J. D. Gao, and M. Xue, 2013: Impacts of assimilating measurements of different state variables with a simulated supercell storm and three-dimensional variational method. Mon. Wea. Rev., 141(8), 2759–2777,  https://doi.org/10.1175/MWR-D-12-00193.1.CrossRefGoogle Scholar
  31. Giangrande, S. E., and A. V. Ryzhkov, 2008. Estimation of rainfall based on the results of polarimetric echo classification. Journal of Applied Meteorology and Climatology, 47, 2445–2462,  https://doi.org/10.1175/2008JAMC1753.1.CrossRefGoogle Scholar
  32. Giangrande, S. E., J. M. Krause, and A. V. Ryzhkov, 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,  https://doi.org/10.1175/2007JAMC1634.1.CrossRefGoogle Scholar
  33. Giangrande, S. E., R. McGraw, and L. Lei, 2013: An application of linear programming to polarimetric radar differential phase processing. J. Atmos. Oceanic Technol., 30, 1716–1729,  https://doi.org/10.1175/JTECH-D-12-00147.1.CrossRefGoogle Scholar
  34. Gosset, M., and H. Sauvageot, 1992: A dual-wavelength radar method for ice-water characterization in mixed-phase clouds. J. Atmos. Oceanic Technol., 9, 538–547,  https://doi.org/10.1175/1520-0426(1992)009<0538:ADWRMF>2.0.CO;2.CrossRefGoogle Scholar
  35. Griffin, E. M., T. J. Schuur, and A. V. Ryzhkov, 2018: A polarimetric analysis of ice microphysical processes in snow, using quasi-vertical profiles. Journal of Applied Meteorology and Climatology, 57, 31–50,  https://doi.org/10.1175/JAMC-D-17-0033.1.CrossRefGoogle Scholar
  36. Heinselman, P. L., and S. M. Torres, 2011: High-temporal-resolution capabilities of the national weather radar testbed phased-array radar. Journal of Applied Meteorology and Climatology, 50, 579–593,  https://doi.org/10.1175/2010JAMC2588.1.CrossRefGoogle Scholar
  37. Hogan, R. J., 2007: A variational scheme for retrieving rainfall rate and hail reflectivity fraction from polarization radar. Journal of Applied Meteorology and Climatology, 46, 1544–1564,  https://doi.org/10.1175/JAM2550.1.CrossRefGoogle Scholar
  38. Hopf, A. P., J. L. Salazar, R. Medina, V. Venkatesh, E. J. Knapp, S. J. Frasier, and D. J. McLaughlin, 2009: CASA phased array radar system description, simulation and products. Proc. 2009 IEEE Int. Geoscience and Remote Sensing Symposium, Cape Town, South Africa, IEEE,  https://doi.org/10.1109/IGARSS.2009.5418262.CrossRefGoogle Scholar
  39. Hu, M., M. Xue, and K. Brewster, 2006: 3DVAR and cloud analysis with WSR-88D level-II data for the prediction of the Fort Worth, Texas, Tornadic thunderstorms. Part I: Cloud analysis and its impact. Mon. Wea. Rev., 134(2), 675–698,  https://doi.org/10.1175/MWR3092.1.CrossRefGoogle Scholar
  40. Huang, H., G. F. Zhang, K. Zhao, and S. E. Giangrande, 2017: A hybrid method to estimate specific differential phase and rainfall with linear programming and physics constraints. IEEE Trans. Geosci. Remote Sens., 55(1), 96–111,  https://doi.org/10.1109/TGRS.2016.2596295.CrossRefGoogle Scholar
  41. Huffman, G. J., R. F. Adler, D. T. Bolvin, G. Gu, E. J. Nelkin, K. P. Bowman, E. F. Stocker, and D. B. Wolff, 2007: The TRMM multi-satellite precipitation analysis: Quasi-global, multi-year, combined-sensor precipitation estimates at fine scale. Journal of Hydrometeorology, 8, 38–55.CrossRefGoogle Scholar
  42. Ice, R. L., A. K. Heck, J. G. Cunningham, and W. D. Zittel, 2014: Challenges of polarimetric weather radar calibration. Proc. 8th European Conference on Radar in Meteorology and Hydrology, Germany, Garmisch-Partenkirchen.Google Scholar
  43. Jameson, A. R., 1991: Polarization radar measurements in rain at 5 and 9 GHz. J. Appl. Meteor., 30, 1500–1513,  https://doi.org/10.1175/1520-0450(1991)030<1500:PRMIRA>2.0.CO;2.CrossRefGoogle Scholar
  44. Johnson, M., Y. Jung, D. T. Dawson II, and M. Xue, 2016: Comparison of simulated polarimetric signatures in idealized supercell storms using two-moment bulk microphysics schemes in WRF. Mon. Wea. Rev., 144, 971–996,  https://doi.org/10.1175/MWR-D-15-0233.1.CrossRefGoogle Scholar
  45. Jordan, R. L., B. L. Huneycutt, and M. Werner, 1995: The SIR-C/X-SAR synthetic aperture radar system. IEEE Trans. Geosci. Remote Sens., 33(4), 829–839,  https://doi.org/10.1109/36.406669.CrossRefGoogle Scholar
  46. Jung, Y., G. F. Zhang, and M. Xue, 2008a: Assimilation of simulated polarimetric radar data for a convective storm using the ensemble Kalman filter. Part I: Observation operators for reflectivity and polarimetric variables. Mon. Wea. Rev., 136(6), 2228–2245,  https://doi.org/10.1175/2007MWR2083.1.CrossRefGoogle Scholar
  47. Jung, Y., M. Xue, G. F. Zhang, and J. M. Straka, 2008b: Assimilation of simulated polarimetric radar data for a convective storm using the ensemble Kalman filter. Part II: Impact of polarimetric data on storm analysis. Mon. Wea. Rev., 136(6), 2246–2260,  https://doi.org/10.1175/2007MWR2288.1.CrossRefGoogle Scholar
  48. Jung, Y., M. Xue, and G. F. Zhang, 2010: Simulations of polarimetric radar signatures of a supercell storm using a two-moment bulk microphysics scheme. Journal of Applied Meteorology and Climatology, 49(1), 146–163,  https://doi.org/10.1175/2009JAMC2178.1.CrossRefGoogle Scholar
  49. Jung, Y., M. Xue, and M. J. Tong, 2012: Ensemble Kalman filter analyses of the 29–30 may 2004 Oklahoma Tornadic thunderstorm using one- and two-moment bulk microphysics schemes, with verification against polarimetric radar data. Mon. Wea. Rev., 140(5), 1457–1475,  https://doi.org/10.1175/MWR-D-11-00032.1.CrossRefGoogle Scholar
  50. Kalnay, E., 2003: Atmospheric Modeling, Data Assimilation and Predictability. Cambridge University Press, 368 pp.Google Scholar
  51. Karimkashi, S., and G. F. Zhang, 2015: Optimizing radiation patterns of a cylindrical polarimetric phased-array radar for multimissions. IEEE Trans. Geosci. Remote Sens., 53, 2810–2818,  https://doi.org/10.1109/TGRS.2014.2365362.CrossRefGoogle Scholar
  52. Khain, A. P., and Coauthors, 2015: Representation of microphysical processes in cloud-resolving models: Spectral (bin) microphysics versus bulk parameterization. Rev. Geophys., 53, 247–322,  https://doi.org/10.1002/2014RG000468.CrossRefGoogle Scholar
  53. Kumjian, M. R., 2013: Principles and applications of dual-polarization weather radar. Part 2: Warm and cold season applications. Journal of Operational Meteorology, 1(20), 243–264,  https://doi.org/10.15191/nwajom.2013.0120.CrossRefGoogle Scholar
  54. Kumjian, M. R., and A. V. Ryzhkov, 2008: Polarimetric signatures in supercell thunderstorms. Journal of Applied Meteorology and Climatology, 47, 1940–1961,  https://doi.org/10.1175/2007JAMC1874.1.CrossRefGoogle Scholar
  55. 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. Journal of Applied Meteorology and Climatology, 53(7), 1820–1843,  https://doi.org/10.1175/JAMC-D-13-0354.1.CrossRefGoogle Scholar
  56. Li, X. L., and J. R. Mecikalski, 2010: Assimilation of the dual-polarization Doppler radar data for a convective storm with a warm-rain radar forward operator. J. Geophys. Res., 115, D16208,  https://doi.org/10.1029/2009JD013666.CrossRefGoogle Scholar
  57. Li, X. L., J. R. Mecikalski, and D. Posselt, 2017: An ice-phase microphysics forward model and preliminary results of polarimetric radar data assimilation. Mon. Wea. Rev., 145, 683–708,  https://doi.org/10.1175/MWR-D-16-0035.1.CrossRefGoogle Scholar
  58. Liu, H. P., and V. Chandrasekar, 2000: Classification of hydrometeors based on polarimetric radar measurements: Development of fuzzy logic and neuro-fuzzy systems, and in situ verification. J. Atmos. Oceanic Technol., 17, 140–164,  https://doi.org/10.1175/1520-0426(2000)017<0140:COHBOP>2.0.CO;2.CrossRefGoogle Scholar
  59. Lorenc, A. C., 1986: Analysis methods for numerical weather prediction. Quart. J. Roy. Meteor. Soc., 112, 1177–1194,  https://doi.org/10.1002/qj.49711247414.CrossRefGoogle Scholar
  60. Mahale, V., G. Zhang, M. Xue, J. Gao, and H. D. Reeves, 2019: Variational retrieval of rain microphysics and related parameters from polarimetric radar data with a parameterized operator. J. Atmos. Oceanic Technol., in-review.Google Scholar
  61. May, P. T., J. D. Kepert, and T. D. Keenan, 2008: Polarimetric radar observations of the persistently asymmetric structure of tropical cyclone Ingrid. Mon. Wea. Rev., 136(2), 616–630,  https://doi.org/10.1175/2007MWR2077.1.CrossRefGoogle Scholar
  62. May, P. T., T. D. Keenan, D. S. Zrnić, L. D. Carey, and S. A. Rutledge, 1999: Polarimetric radar measurements of tropical rain at a 5-cm wavelength. J. Appl. Meteor., 38, 750–765,  https://doi.org/10.1175/1520-0450(1999)038<0750:PRMOTR>2.0.CO;2.CrossRefGoogle Scholar
  63. Milbrandt, J. A., and M. K. Yau, 2005a: A multimoment bulk microphysics parameterization. Part I: Analysis of the role of the spectral shape parameter. J. Atmos. Sci., 62, 3051–3064,  https://doi.org/10.1175/JAS3534.1.CrossRefGoogle Scholar
  64. Milbrandt, J. A., and M. K. Yau, 2005b. A multimoment bulk microphysics parameterization. Part II: A proposed three-moment closure and scheme description. J. Atmos. Sci., 62, 3065–3081,  https://doi.org/10.1175/JAS3535.1.CrossRefGoogle Scholar
  65. Morrison, H., and J. Milbrandt, 2011: Comparison of two-moment bulk microphysics schemes in idealized supercell thunderstorm simulations. Mon. Wea. Rev., 139, 1103–1130,  https://doi.org/10.1175/2010MWR3433.1.CrossRefGoogle Scholar
  66. Morrison, H., G. Thompson, and V. Tatarskii, 2009: Impact of cloud microphysics on the development of trailing stratiform precipitation in a simulated squall line: Comparison of one-and two-moment schemes. Mon. Wea. Rev., 137, 991–1007,  https://doi.org/10.1175/2008MWR2556.1.CrossRefGoogle Scholar
  67. Morrison, H., J. A. Curry, and V. I. Khvorostyanov, 2005: A new double-moment microphysics parameterization for application in cloud and climate models. Part I: Description. J. Atmos. Sci., 62, 1665–1677,  https://doi.org/10.1175/JAS3446.1.CrossRefGoogle Scholar
  68. 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. Journal of Applied Meteorology and Climatology, 55, 829–848,  https://doi.org/10.1175/JAMC-D-15-0203.1.CrossRefGoogle Scholar
  69. Overeem, A., H. Leijnse, and R. Uijlenhoet, 2013. Country-wide rainfall maps from cellular communication networks. Proc. Natl. Acad. Sci. USA, 110(8), 2741–2745,  https://doi.org/10.1073/pnas.1217961110.CrossRefGoogle Scholar
  70. Park, H. S., A. V. Ryzhkov, D. S. Zrnić, and K.-E. Kim, 2009: The hydrometeor classification algorithm for the polarimetric WSR-88D: Description and application to an MCS. Wea. Forecasting, 24(3), 730–748,  https://doi.org/10.1175/2008WAF2222205.1.CrossRefGoogle Scholar
  71. Pfeifer, M., G. C. Craig, M. Hagen, and C. Keil, 2008: A polarimetric radar forward operator for model evaluation. Journal of Applied Meteorology and Climatology, 47(12), 3202–3220,  https://doi.org/10.1175/2008JAMC1793.1.CrossRefGoogle Scholar
  72. Posselt, D. J., X. L. Li, S. A. Tushaus, and J. R. Mecikalski, 2015: Assimilation of dual-polarization radar observations in mixed- and ice-phase regions of convective storms: Information content and forward model errors. Mon. Wea. Rev., 143, 2611–2636,  https://doi.org/10.1175/MWR-D-14-00347.1.CrossRefGoogle Scholar
  73. Putnam, B. J., M. Xue, Y. Jung, N. Snook, and G. F. Zhang, 2014: The analysis and prediction of microphysical states and polarimetric radar variables in a mesoscale convective system using double-moment microphysics, multinetwork radar data, and the ensemble Kalman filter. Mon. Wea. Rev., 142(1), 141–162,  https://doi.org/10.1175/MWR-D-13-00042.1.CrossRefGoogle Scholar
  74. Putnam, B. J., M. Xue, Y. Jung, G. F. Zhang, and F. Y. Kong, 2017b: Simulation of polarimetric radar variables from 2013 CAPS spring experiment storm-scale ensemble forecasts and evaluation of microphysics schemes. Mon. Wea. Rev., 145, 49–73,  https://doi.org/10.1175/MWR-D-15-0415.1.CrossRefGoogle Scholar
  75. Putnam, B. J., M. Xue, Y. Jung, N. A. Snook, and G. F. Zhang, 2017a: Ensemble probabilistic prediction of a mesoscale convective system and associated polarimetric radar variables using single-moment and double-moment microphysics schemes and EnKF radar data assimilation. Mon. Wea. Rev., 145, 2257–2279,  https://doi.org/10.1175/MWR-D-16-0162.1.CrossRefGoogle Scholar
  76. Putnam, B. J., M. Xue, Y. Jung, N. A. Snook, and G. Zhang, 2019: EnKF assimilation of polarimetric radar observations for the 20 may 2013 Oklahoma Tornadic Supercell case. Mon. Wea. Rev. (in press)Google Scholar
  77. Rauber, R. M., and Coauthors, 2007: Rain in shallow cumulus over the ocean: The Rico campaign. Bull. Amer. Meteor. Soc., 88, 1912–1928,  https://doi.org/10.1175/BAMS-88-12-1912.CrossRefGoogle Scholar
  78. Reeves, H. D., 2016: The uncertainty of precipitation-type observations and its effect on the validation of forecast precipitation type. Wea. Forecasting, 31, 1961–1971,  https://doi.org/10.1175/WAF-D-16-0068.1.CrossRefGoogle Scholar
  79. Reeves, H. D., K. L. Elmore, A. Ryzhkov, T. Schuur, and J. Krause, 2014: Sources of uncertainty in precipitation-type forecasting. Wea. Forecasting, 29, 936–953,  https://doi.org/10.1175/WAF-D-14-00007.1.CrossRefGoogle Scholar
  80. Rodgers, C. D., 2000: Inverse Methods for Atmospheric Sounding: Theory and Practice. World Scientific Press, 258 pp.CrossRefGoogle Scholar
  81. Romine, G. S., D. W. Burgess, and R. B. Wilhelmson, 2008: A dual-polarization-radar-based assessment of the 8 May 2003 Oklahoma City area tornadic supercell. Mon. Wea. Rev., 136, 2849–2870,  https://doi.org/10.1175/2008MWR2330.1.CrossRefGoogle Scholar
  82. Ryzhkov, A., and D. Zrnić, 1996. Assessment of rainfall measurement that uses specific differential phase. J. Appl. Meteor., 35, 2080–2090,  https://doi.org/10.1175/1520-0450(1996)035<2080:AORMTU>2.0.CO;2.CrossRefGoogle Scholar
  83. Ryzhkov, A., M. Pinsky, A. Pokrovsky, and A. Khain, 2011: Polarimetric radar observation operator for a cloud model with spectral microphysics. Journal of Applied Meteorology and Climatology, 50(4), 873–894,  https://doi.org/10.1175/2010JAMC2363.1.CrossRefGoogle Scholar
  84. Ryzhkov, A., M. Diederich, P. F. Zhang, and C. Simmer, 2014: Potential utilization of specific attenuation for rainfall estimation, mitigation of partial beam blockage, and radar networking. J. Atmos. Oceanic Technol., 31, 599–619,  https://doi.org/10.1175/JTECH-D-13-00038.1.CrossRefGoogle Scholar
  85. Ryzhkov, A. V., and D. S. Zrnic, 1995. Comparison of dual-polarization radar estimators of rain. J. Atmos. Oceanic Technol., 12, 249–256,  https://doi.org/10.1175/1520-0426(1995)012<0249:CODPRE>2.0.CO;2.CrossRefGoogle Scholar
  86. Ryzhkov, A. V., T. J. Schuur, D. W. Burgess, and D. S. Zrnic, 2005: Polarimetric tornado detection. J. Appl. Meteor., 44, 557–570,  https://doi.org/10.1175/JAM2235.1.CrossRefGoogle Scholar
  87. Ryzhkov, A. V., M. R. Kumjian, S. M. Ganson, and A. P. Khain, 2013a: Polarimetric radar characteristics of melting hail. Part I: Theoretical simulations using spectral microphysical modeling. Journal of Applied Meteorology and Climatology, 52(12), 2849–2870,  https://doi.org/10.1175/JAMC-D-13-073.1.CrossRefGoogle Scholar
  88. Ryzhkov, A. V., M. R. Kumjian, S. M. Ganson, and P. F. Zhang, 2013b: Polarimetric radar characteristics of melting hail. Part II: Practical implications. Journal of Applied Meteorology and Climatology, 52(12), 2871–2886,  https://doi.org/10.1175/JAMC-D-13-074.1.CrossRefGoogle Scholar
  89. Sachidananda, M., and D. S. Zrnić, 1987: Rain rate estimates from differential polarization measurements. J. Atmos. Oceanic Technol., 4, 588–598,  https://doi.org/10.1175/1520-0426(1987)004<0588:RREFDP>2.0.CO;2.CrossRefGoogle Scholar
  90. Saeidi-Manesh, H., M. Mirmozafari, and G. Zhang, 2017: Low cross-polarisation high-isolation frequency scanning aperture coupled microstrip patch antenna array with matched dualpolarisation radiation patterns. Electronics Letters, 53(14), 901–902,  https://doi.org/10.1049/el.2017.1282.CrossRefGoogle Scholar
  91. Seliga, T. A., and V. N. Bringi, 1976: Potential use of radar differential reflectivity measurements at orthogonal polarizations for measuring precipitation. J. Appl. Meteor., 15, 69–76,  https://doi.org/10.1175/1520-0450(1976)015<0069:PUORDR>2.0.CO;2.CrossRefGoogle Scholar
  92. Seliga, T. A., V. N. Bringi, and H. H. Al-Khatib, 1979: Differential reflectivity measurements in rain: First experiments. IEEE Transactions on Geoscience Electronics, 17, 240–244,  https://doi.org/10.1109/TGE.1979.294652.CrossRefGoogle Scholar
  93. Snook, N., Y. Jung, J. Brotzge, B. Putnam, and M. Xue, 2016: Prediction and ensemble forecast verification of hail in the supercell storms of 20 May 2013. Wea. Forecasting, 31, 811–825,  https://doi.org/10.1175/WAF-D-15-0152.1.CrossRefGoogle Scholar
  94. 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(6), 1819–1844,  https://doi.org/10.1175/WAF-D-15-0068.1.CrossRefGoogle Scholar
  95. Stailey J. E., and K. D. Hondl, 2016: Multifunction phased array radar for aircraft and weather surveillance. Proceedings of the IEEE, 104(3), 649–659,  https://doi.org/10.1109/JPROC.2015.2491179.CrossRefGoogle Scholar
  96. Straka, J. M., 1996: Hydrometeor fields in a supercell storm as deduced from dual-polarization radar. Preprints, 18th Conf. on Severe Local Storms, San Francisco, CA, Amer. Meteor. Soc., 551–554.Google Scholar
  97. Straka, J. M., and D. S. Zrnić, 1993: An algorithm to deduce hydrometeor types and contents from multiparameter radar data. Preprints, 26th Int. Conf. on Radar Meteorology, Norman, OK, Amer. Meteor. Soc., 513–515.Google Scholar
  98. 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.CrossRefGoogle Scholar
  99. Sun, J. Z., and A. N. Crook, 1997: Dynamical and microphysical retrieval from doppler radar observations using a cloud model and its adjoint. Part I: Model development and simulated data experiments. J. Atmos. Sci., 54(12), 1642–1661,  https://doi.org/10.1175/1520-0469(1997)054<1642:DAMRFD>2.0.CO;2.CrossRefGoogle Scholar
  100. Sun, J. Z., and N. A. Crook, 1998: Dynamical and microphysical retrieval from Doppler radar observations using a cloud model and its adjoint. Part II: Retrieval experiments of an observed Florida convective storm. J. Atmos. Sci., 55(5), 835–852,  https://doi.org/10.1175/1520-0469(1998)055<0835:DAMRFD>2.0.CO;2.CrossRefGoogle Scholar
  101. Ulbrich, C. W., and D. Atlas, 1984: Assessment of the contribution of differential polarization to improved rainfall measurements. Radio Sci., 19(1), 49–57,  https://doi.org/10.1029/RS019i001p00049.CrossRefGoogle Scholar
  102. Van Den Broeke, M. S., and S. T. Jauernic, 2014: Spatial and temporal characteristics of polarimetric tornadic debris signatures Journal of Applied Meteorology and Climatology, 53, 2217–2231,  https://doi.org/10.1175/JAMC-D-14-0094.1.
  103. Vivekanandan, J., D. S. Zrnic, S. M. Ellis, R. Oye, A. V. Ryzhkov, and J. Straka, 1999: Cloud microphysics retrieval using S-band dual-polarization radar measurements. Bull. Amer. Meteor. Soc., 80(3), 381–388,  https://doi.org/10.1175/1520-0477(1999)080<0381:CMRUSB>2.0.CO;2.CrossRefGoogle Scholar
  104. Vivekanandan, J., W. M. Adams, and V. N. Bringi, 1991: Rigorous approach to polarimetric radar modeling of hydrometeor orientation distributions. J. Appl. Meteor., 30, 1053–1063,  https://doi.org/10.1175/1520-0450(1991)030<1053:RATPRM>2.0.CO;2.CrossRefGoogle Scholar
  105. Waterman, P. C., 1965: Matrix formulation of electromagnetic scattering. Proceedings of the IEEE, 53, 805–812,  https://doi.org/10.1109/PROC.1965.4058.CrossRefGoogle Scholar
  106. Weber, M. E., J. Y. N. Cho, J. S. Herd, J. M. Flavin, W. E. Benner, and G. S. Torok, 2007: The next-generation multimission U.S. surveillance radar network. Bull. Amer. Meteor. Soc., 88, 1739–1752,  https://doi.org/10.1175/BAMS-88-11-1739.CrossRefGoogle Scholar
  107. Wen, G., A. Protat, P. T. May, X. Z. Wang, and W. Moran, 2015: Cluster-based method for hydrometeor classification using polarimetric variables. Part I: Interpretation and analysis. J. Atmos. Oceanic Technol., 32, 1320–1340,  https://doi.org/10.1175/JTECH-D-13-00178.1.CrossRefGoogle Scholar
  108. Wen, G., A. Protat, P. T. May, W. Moran, and M. Dixon, 2016: Cluster-based method for hydrometeor classification using polarimetric variables. Part II: Classification. J. Atmos. Oceanic Technol., 33, 45–60,  https://doi.org/10.1175/JTECH-D-14-00084.1.CrossRefGoogle Scholar
  109. Wheatley, D. M., N. Yussouf, and D. J. Stensrud, 2014: Ensemble Kalman filter analyses and forecasts of a severe mesoscale convective system using different choices of microphysics schemes. Mon. Wea. Rev., 142, 3243–3263,  https://doi.org/10.1175/MWR-D-13-00260.1.CrossRefGoogle Scholar
  110. Wu, B., J. Verlinde, and J. Z. Sun, 2000: Dynamical and micro-physical retrievals from doppler radar observations of a deep convective cloud. J. Atmos. Sci., 57(2), 262–283,  https://doi.org/10.1175/1520-0469(2000)057<0262:DAMRFD>2.0.CO;2.CrossRefGoogle Scholar
  111. Yoshikawa, E., V. Chandrasekar, and T. Ushio, 2014. Raindrop size distribution (DSD) retrieval for X-band dual-polarization radar. J. Atmos. Oceanic Technol., 31(2), 387–403,  https://doi.org/10.1175/JTECH-D-12-00248.1.CrossRefGoogle Scholar
  112. Yussouf, N., and D. J. Stensrud, 2010: Impact of phased-array radar observations over a short assimilation period: Observing system simulation experiments using an ensemble Kalman filter. Mon. Wea. Rev., 138, 517–538,  https://doi.org/10.1175/2009MWR2925.1.CrossRefGoogle Scholar
  113. Yussouf, N., D. C. Dowell, L. J. Wicker, K. H. Knopfmeier, and D. M. Wheatley, 2015: Storm-scale data assimilation and ensemble forecasts for the 27 April 2011 severe weather outbreak in Alabama. Mon. Wea. Rev., 143, 3044–3066,  https://doi.org/10.1175/MWR-D-14-00268.1.CrossRefGoogle Scholar
  114. Yussouf, N., E. R. Mansell, L. J. Wicker, D. M. Wheatley, and D. J. Stensrud, 2013: The ensemble Kalman filter analyses and forecasts of the 8 May 2003 Oklahoma city tornadic supercell storm using single- and double-moment microphysics schemes. Mon. Wea. Rev., 141, 3388–3412,  https://doi.org/10.1175/MWR-D-12-00237.1.CrossRefGoogle Scholar
  115. Zhang, G. F., 2016: Weather Radar Polarimetry. CRC Press, 304 pp.CrossRefGoogle Scholar
  116. Zhang, G. F., J. Z. Sun, and E. A. Brandes, 2006: Improving parameterization of rain microphysics with disdrometer and radar observations. J. Atmos. Sci., 63, 1273–1290,  https://doi.org/10.1175/JAS3680.1.CrossRefGoogle Scholar
  117. Zhang, G. F., R. J. Doviak, D. S. Zrnic, J. Crain, D. Staiman, and Y. Al-Rashid, 2009: Phased array radar polarimetry for weather sensing: A theoretical formulation for bias corrections. IEEE Trans. Geosci. Remote Sens., 47, 3679–3689,  https://doi.org/10.1109/TGRS.2009.2029332.CrossRefGoogle Scholar
  118. Zhang, G. F., R. J. Doviak, D. S. Zrnić, R. Palmer, L. Lei, and Y. Al-Rashid, 2011: Polarimetric phased-array radar for weather measurement: A planar or cylindrical configuration? J. Atmos. Oceanic Technol., 28, 63–73,  https://doi.org/10.1175/2010JTECHA1470.1.
  119. Zhang, J., and Coauthors, 2016: Multi-radar multi-sensor (MRMS) quantitative precipitation estimation: Initial operating capabilities. Bull. Amer. Meteor. Soc., 97, 621–638,  https://doi.org/10.1175/BAMS-D-14-00174.1.CrossRefGoogle Scholar
  120. Zhang, J., and Coauthors, 2017: MRMS dual-polarization radar synthetic QPE. Proc. 38th Conf. Radar Meteorology, 28 August–1 September 2017, Chicago, AMS.Google Scholar
  121. Zrnic, D. S., and K. Aydin, 1992: Polarimetric signatures of precipitation. Applied Computational Electromagnetics Society (ACES) Newsletter, 7(2), 12–14.Google Scholar
  122. Zrnic, 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.CrossRefGoogle Scholar
  123. Zrnic, D. S., V. M. Melnikov, and J. K. Carter, 2006: Calibrating differential reflectivity on the WSR-88D. J. Atmos. Oceanic Technol., 23, 944–951,  https://doi.org/10.1175/JTECH1893.1.CrossRefGoogle Scholar
  124. Zrnic, D. S., and Coauthors, 2007: Agile-beam phased array radar for weather observations. Bull. Amer. Meteor. Soc., 88, 1753–1766,  https://doi.org/10.1175/BAMS-88-11-1753.CrossRefGoogle Scholar

Copyright information

© The Author(s) 2019

Open Access This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.

Authors and Affiliations

  • Guifu Zhang
    • 1
    Email author
  • Vivek N. Mahale
    • 2
  • Bryan J. Putnam
    • 1
  • Youcun Qi
    • 1
  • Qing Cao
    • 3
  • Andrew D. Byrd
    • 1
  • Petar Bukovcic
    • 1
  • Dusan S. Zrnic
    • 4
  • Jidong Gao
    • 4
  • Ming Xue
    • 1
  • Youngsun Jung
    • 1
  • Heather D. Reeves
    • 4
  • Pamela L. Heinselman
    • 4
  • Alexander Ryzhkov
    • 1
  • Robert D. Palmer
    • 1
  • Pengfei Zhang
    • 1
  • Mark Weber
    • 1
  • Greg M. Mcfarquhar
    • 1
  • Berrien MooreIII
    • 1
  • Yan Zhang
    • 1
  • Jian Zhang
    • 4
  • J. Vivekanandan
    • 5
  • Yasser Al-Rashid
    • 6
  • Richard L. Ice
    • 7
  • Daniel S. Berkowitz
    • 7
  • Chong-chi Tong
    • 1
  • Caleb Fulton
    • 1
  • Richard J. Doviak
    • 4
  1. 1.University of OklahomaNormanUSA
  2. 2.NOAA/National Weather ServiceNormanUSA
  3. 3.Enterprise Electronics CorporationEnterpriseUSA
  4. 4.NOAA/National Severe Storms LaboratoryNormanUSA
  5. 5.National Center for Atmospheric ResearchBoulderUSA
  6. 6.Raytheon CompanyWalthamUSA
  7. 7.Radar Operations CenterNormanUSA

Personalised recommendations