doi:10.1007/s00376-019-8172-4

Andrić, J., M. R. Kumjian, D. S. Zrnić, J. M. Straka, 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- 700.10.1175/JAMC-D-12-028.1

c3c737351a138518a113b4d966134214

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2013JApMC..52..682A

http://journals.ametsoc.org/doi/abs/10.1175/JAMC-D-12-028.1Polarimetric radar observations above the melting layer in winter storms reveal enhanced differential reflectivity Z(DR) and specific differential phase shift K-DP, collocated with reduced copolar correlation coefficient rho(hv); these signatures often appear as isolated "pockets." High-resolutionRHIs and vertical profiles of polarimetric variables were analyzed for a winter storm that occurred in Oklahoma on 27 January 2009, observed with the polarimetric Weather Surveillance Radar-1988 Doppler (WSR-88D) in Norman. The Z(DR) maximum and rhv minimum are located within the temperature range between -10 degrees and -15 degrees C, whereas the K-DP maximum is located just below the Z(DR) maximum. These signatures are coincident with reflectivity factor Z(H) that increases toward the ground. A simple kinematical, one-dimensional, two-moment bulk microphysical model is developed and coupled with electromagnetic scattering calculations to explain the nature of the observed polarimetric signature. The microphysics model includes nucleation, deposition, and aggregation and considers only ice-phase hydrometeors. Vertical profiles of the polarimetric radar variables (Z(H), Z(DR), K-DP, and rho(hv)) were calculated using the output from the microphysical model. The base model run reproduces the general profile and magnitude of the observed Z(H) and rho(hv) and the correct shape (but not magnitude) of Z(DR) and K-DP. Several sensitivity experiments were conducted to determine if the modeled signatures of all variables can match the observed ones. The model was incapable of matching both the observed magnitude and shape of all polarimetric variables, however. This implies that some processes not included in the model (such as secondary ice generation) are important in producing the signature.

Bluestein , H. B.,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

Brand es, E. A.,K. Ikeda, 2004: Freezing-level estimation with polarimetric radar. J. Appl. Meteor., 43, 1541-1553, https://doi.org/10.1175/JAM2155.1

Brand es, E. A., G. F. Zhang, 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

Bringi V. N.,V. Chandrasekar, 2001: Polarimetric Doppler Weather Radar: Principles and Applications. Cambridge University Press,636 pp.10.1016/S0169-8095(02)00024-8

37634c46891517cba5c5a1246035f492

http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0169809502000248

http://www.sciencedirect.com/science/article/pii/S0169809502000248Not Available

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

Bukovčić, P., D. Zrnić, 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

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.

Cao Q.,G. F. Zhang, E. A. Brand es, 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

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

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

Carlin J. T.,J. D. Gao, J. C. Snyder, and A. V. Ryzhkov, 2017: Assimilation of Z_DR 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

Chand rasekar V., R. Keränen, S. Lim, 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

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

Chang W.-Y.,J. Vivekanand an, 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

Chen, G., 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

Chen H. N.,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

Dawson D. T.,M. Xue, J. A. Milbrand t, 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

Dawson D. T.,M. Xue, J. A. Milbrand t, 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

Dawson D. T.,E. R. Mansell, Y. Jung, L. J. Wicker, M. R. Kumjian, and M. Xue, 2014: Low-level Z_DR 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

Didlake A. C.,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

Dolan B.,S. A. Rutledge, S. Lim, V. Chand rasekar, 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

Doviak R. J.,D. S. Zrnić, 1993: Doppler Radar and Weather Observations. 2nd ed. Academic Press, 562 pp.10.1016/B978-0-12-221422-6.50008-5

59b2349a2c90b40c0e2f4118f9bf0504

http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FB9780122214226500231

http://www.sciencedirect.com/science/article/pii/B9780122214226500231Doppler radar and weather observations Richard J. Doviak and Du08an S. Zrni04 Dover Publications, 2006 2nd ed

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

Eccles P. J.,D. Atlas, 1973: A dual-wavelength radar hail detector. J. Appl. Meteor., 12(5), 847, https://doi.org/10.1175/1520-0450(1973)012<0847:ADWRHD>2.0.CO;2

Figueras i Ventura, J., 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

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

Fulton, C., 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

Gao J. D.,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

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

Giangrande S. E.,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

Giangrand e, S. E., J. M. Krause, 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

Giangrand e, S. E., R. McGraw, 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

Gosset M.,H. Sauvageot, 1992: A dual-wavelength radar method for ice-water characterization in mixed-phase clouds. J. Atmos. Oceanic Technol., 9, 538, https://doi.org/10.1175/1520-0426(1992)009<0538:ADWRMF>2.0.CO;2

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

Heinselman P. L.,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

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

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

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

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

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- 55.10.1175/JHM560.1

14b3f7c5f11319d65bcf0e8383044009

http%3A%2F%2Fwww.bioone.org%2Fservlet%2Flinkout%3Fsuffix%3Dbibr04%26amp%3Bdbid%3D16%26amp%3Bdoi%3D10.5814%252Fj.issn.1674-764x.2012.04.009%26amp%3Bkey%3D10.1175%252FJHM560.1

http://journals.ametsoc.org/doi/abs/10.1175/JHM560.1

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.

1c6994042b4e35df6eabeac64df7a859

http%3A%2F%2Froc.noaa.gov%2FWSR88D%2FPublicDocs%2FPublications%2FPolarimetric_Calibration_Challenges_ERAD_2014_Ice_final_July18.pdf

http://roc.noaa.gov/WSR88D/PublicDocs/Publications/Polarimetric_Calibration_Challenges_ERAD_2014_Ice_final_July18.pdfThe United States' Next Generation Weather Radar (NEXRAD) program has deployed a polarimetric upgrade to the WSR-88D network radars. This modification provides new base variables to the operational community and has opened exciting new possibilities for improved forecasts and warnings. One variable in particular, differential reflectivity (ZDR), is critically important to improved precipitation estimates and hydrometeor classification. However, the quality of the differential reflectivity estimate is highly dependent on the removal of biases induced by the radar system hardware. These biases must be measured to an uncertainty of 0.1 dB in order to obtain maximum benefit from the polarimetric data and meteorological algorithms.

Jameson A. R.,1991: Polarization radar measurements in rain at 5 and 9 GHz. J. Appl. Meteor., 30, 1500, https://doi.org/10.1175/1520-0450(1991)030<1500:PRMIRA>2.0.CO;2

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

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

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

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

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

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

Kalnay E.,2003: Atmospheric Modeling, Data Assimilation and Predictability.Cambridge University Press, 368 pp.10.1198/tech.2005.s326

24ad4992e47c8e246389c1c272eda9b9

http%3A%2F%2Famstat.tandfonline.com%2Fdoi%2Fpdf%2F10.1198%2Ftech.2005.s326

http://www.tandfonline.com/doi/abs/10.1198/tech.2005.s326This comprehensive text and reference work on numerical weather prediction covers for the first time, not only methods for numerical modeling, but also the important related areas of data assimilation and predictability. It incorporates all aspects of environmental computer modeling including an historical overview of the subject, equations of motion and their approximations, a modern and clear description of numerical methods, and the determination of initial conditions using weather observations (an important new science known as data assimilation).

Karimkashi S.,G. F. Zhang, 2015: Optimizing radiation patterns of a cylindrical polarimetric phased-array radar for multimissions. IEEE Trans. Geosci. Remote Sens., 53, 2810, https://doi.org/10.1109/TGRS.2014.2365362

Khain, A. P.,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

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

Kumjian M. R.,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

Kumjian M. R.,A. P. Khain, N. Benmoshe, E. Ilotoviz, A. V. Ryzhkov, and V. T. J. Phillips, 2014: The anatomy and physics of Z_DR 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

Li X. L.,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

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

Liu H. P.,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

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

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.

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

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

Milbrand t, J. A.,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

Milbrand t, J. A.,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

Morrison H.,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

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

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

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

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

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

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

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

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

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

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

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)

Rauber, R. M.,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

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

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

Rodgers C. D.,2000: Inverse Methods for Atmospheric Sounding: Theory and Practice. World Scientific Press, 258 pp.

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

Ryzhkov A.,D. Zrnić, 1996. Assessment of rainfall measurement that uses specific differential phase. J. Appl. Meteor., 35, 2080, https://doi.org/10.1175/1520-0450(1996)035<2080:AORMTU>2.0.CO;2

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

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

Ryzhkov A. V.,D. S. Zrnić, 1995. Comparison of dual-polarization radar estimators of rain. J. Atmos. Oceanic Technol., 12, 249, https://doi.org/10.1175/1520-0426(1995)012<0249:CODPRE>2.0.CO;2

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

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

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

Sachidanand a, M., D. S. Zrnić, 1987: Rain rate estimates from differential polarization measurements. J. Atmos. Oceanic Technol., 4, 588, https://doi.org/10.1175/1520-0426(1987)004<0588:RREFDP>2.0.CO;2

Saeidi-Manesh H.,M. Mirmozafari, and G. Zhang, 2017: Low cross-polarisation high-isolation frequency scanning aperture coupled microstrip patch antenna array with matched dual-polarisation radiation patterns. Electronics Letters, 53(14), 901-902, https://doi.org/10.1049/el.2017.1282

Seliga T. A.,V. N. Bringi, 1976: Potential use of radar differential reflectivity measurements at orthogonal polarizations for measuring precipitation. J. Appl. Meteor., 15, 69, https://doi.org/10.1175/1520-0450(1976)015<0069:PUORDR>2.0.CO;2

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

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

Snyder J. C.,A. V. Ryzhkov, M. R. Kumjian, A. P. Khain, and J. Picca, 2015: A Z_DR column detection algorithm to examine convective storm updrafts. Wea. Forecasting, 30(6), 1819-1844, https://doi.org/10.1175/WAF-D-15-0068.1

Stailey J. E.,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

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- 554.0953252dddba8f0fe82681a9870718c7

http://xueshurefer.baidu.com/nopagerefer?id=ee8fc6c9353dcbc813d6663925ea6ded

Straka J. M.,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.

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, https://doi.org/10.1175/1520-0450(2000)039<1341:BHCAQU>2.0.CO;2

Sun J. Z.,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, https://doi.org/10.1175/1520-0469(1997)054<1642:DAMRFD>2.0.CO;2

Sun J. Z.,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

Ulbrich C. W.,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

Van Den Broeke, M. S.,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

Vivekanand an, J., D. S. Zrnic, S. M. Ellis, R. Oye, A. V. Ryzhkov, J. Straka, 1999: Cloud microphysics retrieval using S-band dual-polarization radar measurements. Bull. Amer. Meteor. Soc., 80(3), 381, https://doi.org/10.1175/1520-0477(1999)080<0381:CMRUSB>2.0.CO;2

Vivekanand an, J., W. M. Adams, V. N. Bringi, 1991: Rigorous approach to polarimetric radar modeling of hydrometeor orientation distributions. J. Appl. Meteor., 30, 1053, https://doi.org/10.1175/1520-0450(1991)030<1053:RATPRM>2.0.CO;2

Waterman P. C.,1965: Matrix formulation of electromagnetic scattering. Proceedings of the IEEE, 53, 805-812, https://doi.org/10.1109/PROC.1965.4058

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

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

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

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

Wu B.,J. Verlinde, and J. Z. Sun, 2000: Dynamical and microphysical retrievals from doppler radar observations of a deep convective cloud. J. Atmos. Sci., 57(2), 262, https://doi.org/10.1175/1520-0469(2000)057<0262:DAMRFD>2.0.CO;2

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

Yussouf N.,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

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

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

Zhang G. F.,2016: Weather Radar Polarimetry. CRC Press, 304 pp.

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

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, https://doi.org/10.1109/TGRS.2009.2029332

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

Zhang, J., 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

Zhang, J., Coauthors, 2017: MRMS dual-polarization radar synthetic QPE. Proc. 38th Conf., Radar Meteorology, 28 August -1 September 2017, Chicago, AMS.

Zrnic D. S.,K. Aydin, 1992: Polarimetric signatures of precipitation. Applied Computational Electromagnetics Society (ACES) Newsletter, 7( 2), 12- 14.

Zrnic D. S.,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

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

Zrnic, D. S.,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