Bailey M. P., J. Hallett, 2009: A comprehensive habit diagram for atmospheric ice crystals: confirmation from the laboratory,AIRS II, and other field studies. J. Atmos. Sci., 66, 2888-2899, .https://doi.org/10.1175/2009JAS2883.1
Brand es, E. A., J. Vivekanand an, J. D. Tuttle, C. J. Kessinger, 1995: A study of thunderstorm microphysics with multiparameter radar and aircraft observations. Mon. Wea. Rev.,123, 3129-3143, .https://doi.org/10.1175/1520-0493(1995)123<3129:ASOTMW>2.0.CO;2
Chen X. C., F. G. Zhang, and K. Zhao, 2016: Diurnal variations of the Land-sea breeze and its related precipitation over South China.J. Atmos. Sci.,73, 4793-4815, .https://doi.org/10.1175/JAS-D-16-0106.1
Chen X. C., K. Zhao, and M. Xue, 2014: Spatial and temporal characteristics of warm season convection over Pearl River Delta region,China, based on 3 years of operational radar data. J. Geophys. Res., 119, 12 447-12 465, https://doi.org/10.1002/2014JD021965.
Chen X. C., K. Zhao, M. Xue, B. W. Zhou, X. X. Huang, and W. X. Xu, 2015: Radar observed diurnal cycle and propagation of convection over the Pearl River Delta during Mei-Yu season.J. Geophys. Res. Atmos.,120, 12 557-12 575, https://doi.org/10.1002/2015JD023872.
Elmore K. L., 2011: The NSSL hydrometeor classification algorithm in winter surface precipitation: Evaluation and future development.Wea. Forecasting,26, 756-765, .https://doi.org/10.1175/WAF-D-10-05011.1
Giangrand e, S. E., A. V. Ryzhkov, 2005: Calibration of dualpolarization radar in the presence of partial beam blockage.J. Atmos. Oceanic Technol.,22, 1156-1166, .https://doi.org/10.1175/JTECH1766.1
Giangrand e, S. E., A. V. Ryzhkov, 2008: Estimation of rainfall based on the results of polarimetric echo classification.J. Appl. Meteor. Climatol.,47, 2445-2462, .https://doi.org/10.1175/2008JAMC1753.1
Heinselman P. L., A. V. Ryzhkov, 2006: Validation of polarimetric hail detection.Wea. Forecasting,21, 839-850, .https://doi.org/10.1175/WAF956.1
Hong Y. C., H. Xiao, H. Y. Li, and Z. X. Hu, 2002: Studies on microphysical processes in hail cloud.Chinese Journal of Atmospheric Sciences,26, 421-432, . (in Chinese)https://doi.org/10.3878/j.issn.1006-9895.2002.03.13
Hu Z. Q., L. P. Liu, and L. L. Wu, 2014: Comparison among several system biases calibration methods on c-band polarimetric radar. Plateau Meteorology, 33, 104- 107. (in Chinese)1d418214146632ec93daddaa1e7ed544http%3A%2F%2Fen.cnki.com.cn%2FArticle_en%2FCJFDTotal-GYQX201401022.htmhttp://en.cnki.com.cn/Article_en/CJFDTotal-GYQX201401022.htmThe principles of system biases calibration of horizontal reflectivity( ZH) and differential reflectivity( ZDR) on C-band dual linear polarimetric radar are introduced. Further analysis are performed by the actual data w hich w ere detected in Yunnan and Anhui Provinces w ith tw o same type mobile C-band dual polarization radars, w hich w ere both produced by Anhui Sun-Create Electronics Co,Ltd. The results suggest that,for ZDRcalibration methods,sun method is difficult to actual application as the consistency betw een horizontal and vertical receiver are not w ell in the w eak signal; vertical detection one need the radar antenna can be lifted to 90° elevation angle, w hich has some mechanical limitation; the elevation one need a very uniform rain region w hich is difficult to satisfy; the change of ZDRcaused by ground clutter have not regularity in the statistical analysis,therefore,the clutter calibration method is also excluded from practical application; for dry snow method,the value of ZDRof dry snow do not equal 0 dB,and the 0℃ level height need to know in advance,furthermore,the data w hich satisfy the requirement of SNR( signal to noise ratio) are less above the height of 0℃ level,and the phase of hydrometeor is indefinable as dry snow,so there are certain limitation in dry snow method; micro-raindrop method can be explained clearly in theory,and the conclusion is convinced,w hich do not need special span mode,and can obtain a large number of data that satisfy the thresholds of SNR、ZHfrom a scan volume,therefore,micro-raindrop method is a better one to calibrate ZDRusing meteorological target. After ZDRbiases corrected,the feasibility of reflectivity ZHcalibration by self-consistency technique is verified. The results suggest that self-consistency method can approximately test the correctness of ZHcalibration,how ever,w hile the method is used to calibrate ZH, high quality of polarization parameters are needed,and the coefficients in the self-consistency relationship need to be validated further more.
Hu Z. Q., L. P. Liu, L. L. Wu, and Q. Wei, 2015: A comparison of de-noising methods for differential phase shift and associated rainfall estimation.J. Meteor. Res.,29, 315-327, https://doi.org/10.1007/s13351-015-4062-6.
Illingworth A. J., J. W. F. Goddard, and S. M. Cherry, 1987: Polarization radar studies of precipitation development in convective storms.Quart. J. Roy. Meteor. Soc.,113, 469-489, .https://doi.org/10.1002/qj.49711347604
Jameson A. R., M. L. Larsen, and A. B. Kostinski, 2015: On the variability of drop size distributions over areas.J. Atmos. Sci.,72, 1386-1397, .https://doi.org/10.1175/JAS-D-14-0258.1
Jameson A. R., 2016: Quantifying drop size distribution variability over areas: Some implications for ground validation experiments.Journal of Hydrometeorology,17, 2689-2698, .https://doi.org/10.1175/JHM-D-16-0094.1
Johnson M., Y. Jung, D. T. Dawson, 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
Kumjian M. R., A. V. Ryzhkov, 2008: Polarimetric signatures in supercell thunderstorms.J. Appl. Meteor. Climatol.,47, 1940-1961, .https://doi.org/10.1175/2007JAMC1874.1
Lim S., V. Chand rasekar, and V. N. Bringi, 2005: Hydrometeor classification system using dual-polarization radar measurements: Model improvements and in situ verification. IEEE Trans. Geosci. Remote Sens.,43, 792-801, .https://doi.org/10.1109/TGRS.2004.843077
Liu H., 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
Liu L. P., B. X. Xu, Z. J. Wang, and J. Wang, 1992: Study of Hail with C-B and dual linear polarization radar. Chinese Journal of Atmospheric Sciences, 16, 370- 376. (in Chinese)bef4e685dd0cdabda2765dcd099f5f2dhttp%3A%2F%2Fen.cnki.com.cn%2FArticle_en%2FCJFDTOTAL-DQXK199203013.htmhttp://en.cnki.com.cn/Article_en/CJFDTOTAL-DQXK199203013.htmThis paper presents the calculations of the scattering properties of hydrometeors with different shapes and phases. Based on these properties and ZDR features of rain and clond echos, the dual linear polarization radar RHI data observed during hailstorm processes on August 9 and August 30, 1990 are analysed . We deduced that the area with negative ZDR values corresponded to hail shafts where may appear large oblate hailstones and small conical hailstones, and the area with great positive Z D R values corresponded to raining area . The Z D R greater than 5dB was caused by huge raindrops (D0.5cm). It is possible to detect the presence of hail using ZDR as a criterion. Compared with a S-band radar, the C-band dual linear polarization radar is unique in several aspects : the Z DR values are larger in raining area and smaller and negative in hail shooting area , which are useful for the study of microphysics and spatial structure of storms. The dual linear polarization radar will be impartant to cloud physics and weather modification .
Liu L. P., Y. F. Qian, Z. J. Wang, and R. Z. Chu, 1996: Comparative study on dual linear polarization radar measuring rainfall rate.Chinese Journal of Atmospheric Sciences,20, 615-619, . (in Chinese)https://doi.org/10.3878/j.issn.1006-9895.1996.05.13
Liu L. P., Z. Q. Hu, and C. Wu, 2016: Development and application of dual linear polarization radar and phased-array radar. Advances in Meteorological Science and Technology, 6, 28- 33. (in Chinese)883461d4d051d0ae35725365e706a300http%3A%2F%2Fwww.en.cnki.com.cn%2FArticle_en%2FCJFDTOTAL-QXKZ201603009.htmhttp://www.en.cnki.com.cn/Article_en/CJFDTOTAL-QXKZ201603009.htmThe Polarimetric upgrades to NEXRAD radar(WSR-88D) and development of phased-array radar in USA are reviewed,the application of the two radars on watching and warning of severe weather are analyzed,especially in quantitative precipitation estimate(QPE) and hydrometer classification of dual polarization radar,tornado watching and warning with phased-array radar.The current status and development of dual polarization radar and phased-array radar in China are presented in this paper.This paper is value for application of these two kinds of radars.
Liu Y. N., X. Xiao, Z. D. Yao, and L. Feng, 2012: Analyses of hydrometeor identification based on X-band polarimetric radar. Climatic and Environmental Research, 17, 925- 936. (in Chinese)10.1007/s11783-011-0280-z1a0555d852751f57c8fd9b52f78390e6http%3A%2F%2Fen.cnki.com.cn%2FArticle_en%2FCJFDTotal-QHYH201206031.htmhttp://en.cnki.com.cn/Article_en/CJFDTotal-QHYH201206031.htmThe study of weather modification must exactly identify the phases of cloud hydrometeor particles to improve the weather modification performance.The vehicle-borne X-band dual-polarization radar system set up by the Laboratory of Cloud-Precipitation and Severe Storms (LACS),Institute of Atmospheric Physics (IAP),Chinese Academy of Sciences,can provide several dual-polarization radar observables,including radar reflectivity,differential reflectivity,specific differential propagation phase,and correlation coefficient,which are related to the sizes,shapes,and phases of hydrometeor particles.In this paper,the four polarimetric observables combined with environmental temperature are considered as five input parameters,and a fuzzy logic algorithm for hydrometeor particle identification is developed and implemented to discriminate ten different hydrometeor types including drizzle,rain,wet graupel,dry graupel,small hail,large hail,rain and hail mixture,wet snow,dry snow,and ice crystals.The identification algorithm is tested and estimated by using the radar data observed in southern and northern China,and comparing the results with the surface field observation and airborne instrument observations.The classification results indicate that the fuzzy logic algorithm is reasonable and practicable.
Loney M. L., D. S. Zrnic, J. M. Straka, and A. V. Ryzhkov, 2002: Enhanced polarimetric radar signatures above the melting level in a supercell storm. J. Appl. Meteor.,41, 1179-1194, .https://doi.org/10.1175/1520-0450(2002)041<1179:EPRSAT>2.0.CO;2
Luo, Y. L., Coauthors, 2016: The southern China monsoon rainfall experiment (SCMREX).Bull. Amer. Meteor. Soc.,98, 999-1013, .https://doi.org/10.1175/BAMS-D-15-00235.1
Mohr C. G., L. J. Miller, R. L. Vaughan, and H. W. Frank, 1986: The merger of mesoscale datasets into a common Cartesian format for efficient and systematic analyses. J. Atmos. Oceanic Technol.,3, 143-161, .https://doi.org/10.1175/1520-0426(1986)003<0143:TMOMDI>2.0.CO;2
Pan Y. J., K. Zhao, and Y. N. Pan, 2010: Single-doppler radar observations of a high precipitation supercell accompanying the 12 April 2003 Severe Squall Line in Fujian Province. Acta Meteorologica Sinica, 24, 50- 65.2886eb910758a94e08ea5eb8c0070072http%3A%2F%2Fkns.cnki.net%2FKCMS%2Fdetail%2Fdetail.aspx%3Ffilename%3Dqxxw201001006%26dbname%3DCJFD%26dbcode%3DCJFQ
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, 730-748, .https://doi.org/10.1175/2008WAF2222205.1
Ryzhkov A. V., S. E. Giangrand e, V. M. Melnikov, and T. J. Schuur, 2005: Calibration issues of dual-polarization radar measurements.J. Atmos. Oceanic Technol.,22, 1138-1155, .https://doi.org/10.1175/JTECH1772.1
Richardson L. M., W. David Zittel, R. R. Lee, V. M. Melnikov, I. L. Ice, and J. G. Cunningham, 2017: Bragg scatter detection by the WSR-88D.Part I: Algorithm development. J. Atmos. Oceanic Technol.,34, 479-493, .https://doi.org/10.1175/JTECH-D-16-0030.1
Ryzhkov A. V., 2007: The impact of beam broadening on the quality of radar polarimetric data.J. Atmos. Oceanic Technol.,24, 729-744, .https://doi.org/10.1175/JTECH2003.1
Schuur T. J., A. V. Ryzhkov, and P. L. Heinselman, D. Zrnic, and D. Burgess, 2003: Observations and classification of echoes with the polarimetric WSR-88D radar. NOAA/National Severe Storms Laboratory Rep,46 pp.e42251e7b3bc3aa4e6dacd937607d847http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F241142902_Observations_and_Classification_of_Echoes_with_the_Polarimetric_WSR-88D_radarhttp://www.researchgate.net/publication/241142902_Observations_and_Classification_of_Echoes_with_the_Polarimetric_WSR-88D_radarIn the Spring of 2002, several years of effort on the NOAA research WSR-88D radar culminated in generation and display of dual polarization radar data and products. As part of the Joint Polarization Experiment (JPOLE), high quality polarimetric data sets were
Schuur T. J., H. S. Park, A. V. Ryzhkov, and H. D. Reeves, 2012: Classification of precipitation types during transitional winter weather using the RUC model and polarimetric radar retrievals.J. Appl. Meteor. Climatol.,51, 763-779, .https://doi.org/10.1175/JAMC-D-11-091.1
Straka J. M., D. S. Zrnić, 1993: An algorithm to deduce hydrometeor types and contents from multi-parameter radar data. Preprints, 26th Conf. on Radar Meteorology. Norman, OK., Amer. Meteor. Soc., 513- 515.9716cd7c92965e9a7593c01201c59654http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F282384606_Algorithm_to_deduce_hydrometeor_types_and_contents_from_multi-parameter_radar_datahttp://www.researchgate.net/publication/282384606_Algorithm_to_deduce_hydrometeor_types_and_contents_from_multi-parameter_radar_dataAbstract A new algorithm has been developed to deduce bulk hydrometeor types and contents from polarimetric, reflectivity, dual-wavelength and Doppler radar data. The algorithm was developed to impove moisture and thermodynamic initializations in cloud/mesoscale models. Analyses made with the algorithm may provide information to help understand microphysical processes in both stable and convective precipitation systems, as well as interactions between dynamical and microhphysical processes associated with convective storms.
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- 554.0953252dddba8f0fe82681a9870718c7http://xueshurefer.baidu.com/nopagerefer?id=ee8fc6c9353dcbc813d6663925ea6dedhttp://xueshurefer.baidu.com/nopagerefer?id=ee8fc6c9353dcbc813d6663925ea6ded
Su D. B., J. L. Ma, Q. Zhang, and D. R. Lu, 2011: Preliminary research on method of hail detection with X band dual linear polarization radar. Meteorological Monthly,, 37, 1228- 1232. (in Chinese)10.1007/s00376-010-1000-5e80b96ef47e0a023769151ef969e5a12http%3A%2F%2Fen.cnki.com.cn%2FArticle_en%2FCJFDTotal-QXXX201110007.htmhttp://en.cnki.com.cn/Article_en/CJFDTotal-QXXX201110007.htmBased on the Beijing Weather Modification Office X-band dual polarization radar's observations of different types of rainfall in April to October 2009,the characteristics of raindrop size distribution of Z_H-Z_(DR) are given out,and can be expressed as a piecewise function,from which the hail parameters H_(DR) are derived.If H_(DR)0,then there is hail,and if H_(DR)0,it means no hail.It is also pointed that the attenuation of electromagnetic waves can affect the results of H_(DR) hail identification.By comparing the relations between H_(DR) and the ground-observation hail data,the preliminary results have shown that the H_(DR) above zero region is in good agreement with hail on the ground.
Vivekanand an, J., D. S. Zrnić, S. Ellis, D. Oye, A. V. Ryzhkov, J. M. Straka, 1999: Cloud microphysics retrieval using S-band dual-polarization radar measurements. Bull. Amer. Meteor. Soc.,80, 381-388, .https://doi.org/10.1175/1520-0477(1999)080<0381:CMRUSB>2.0.CO;2
Wakimoto R. M., V. N. Bringi, 1988: Dual-polarization observations of microbursts associated with intense convection: The 20 July storm during the MIST project. Mon. Wea. Rev.,116, 1521-1539, .https://doi.org/10.1175/1520-0493(1988)116<1521:DPOOMA>2.0.CO;2
Wang H., Y. L. Luo, and B. J. Jou, 2014: Initiation,maintenance, and properties of convection in an extreme rainfall event during SCMREX: Observational analysis. J. Geophys. Res., 119, 13 206-13 232, https://doi.org/10.1002/2014JD022339.
Wei Q., Z. Q. Hu, L. P. Liu, and L. L. Wu, 2016: C-band polarization radar data preprocessing and its application to rainfall estimation.Plateau Meteorology,35, 231-243, . (in Chinese)https://doi.org/10.7522/j.issn.1000-0534.2014.00131
Xiao Y. J., B. Wang, X. H. Chen, J. W. Cao, and X. M. Yang, 2012: Differential phase data quality control of mobile X-band dual-polarimetric Doppler weather radar. Plateau Meteorology, 31, 223- 230. (in Chinese)10.1007/s11783-011-0280-z2b2622dbb252f0b4e36056d236262b3dhttp%3A%2F%2Fen.cnki.com.cn%2FArticle_en%2FCJFDTotal-GYQX201201024.htmhttp://en.cnki.com.cn/Article_en/CJFDTotal-GYQX201201024.htmThe specific differential propagation phase is an important parameter for meteorological applications,because it is not affected by propagation attenuation and closely related to rain intensity.To estimate the specific differential propagation phase requires to compute the derivative of range profiles of the differential phase.The existence of possible phase wrapping,ground clutter,noise,backscattering phase and associated fluctuation in the differential phase makes the derivative evaluation having an unstable numerical process.In this paper,for mobile X-band dual-polarimetric doppler weather radar in the alternative transmission mode developed by Anhui Sun-Create Electronics Co.,Ltd.,a set of algorithm is presented to control the data quality of the differential phase,including ground clutter removing,phase unfolding,initial phase adjusting and phase filtering.Two cases are studied to evaluate the algorithm.The results show the algorithm can remove effectively the ground clutter,unfold wrapped phases and filter fluctuations associated with noise and backscattering phase.
Yu X. D., Y. Y. Zheng, Y. F. Liao, Y. Q. Yao, and C. Fang, 2008: Observational investigation of a tornadic heavy precipitation supercell storm. Chinese Journal of Atmospheric Sciences, 32, 508- 522. (in Chinese)10.3724/SP.J.1148.2008.00288d1ea83554de7f549e4f4398187792369http%3A%2F%2Fen.cnki.com.cn%2FArticle_en%2FCJFDTOTAL-DQXK200803007.htmhttp://en.cnki.com.cn/Article_en/CJFDTOTAL-DQXK200803007.htmA detailed analysis of a tornadic heavy precipitation(HP) supercell's environmental conditions,structure and evolution is made based on the Doppler weather radar data,routine upper-air and surface observation,and intense automatic weather station observation.The main results are as follows: (1) This HP supercell occurs in the moderate convective available potential energy(CAPE) and significant vertical wind shear environment,with high value of low level vertical wind shear and low lifting condensation level(LCL) at the same time.The moderate CAPE and significant vertical wind shear favor the generation of supercell,while the high value of low level vertical wind shear and low LCL favor the occurrence of strong tornadoes.(2) This HP supercell begins to develop when the pre-existing long convective rainbelt widens and shortens,the mesocyclone first appears in the middle cell of the rainbelt,beginning at 4-km height and then developing upward and downward.Soon,the mesocyclone also develops in the southern cell of the rainbelt,and gradually the southern cell merges with the middle cell,forming a strong HP supercell,with a 12-km diameter and 9-km height mesocyclone embedded.The vertical vorticity associated with the mesocyclone is 1.5-10~(-2) s~(-1).The radar echo of this supercell successively displays kidney bean,spiral,"S" shapes,and finally evolves into bow echo,lasting more than 2 hours.(3) The F3 tornado occurs during the "S" shape period.Before tornado touches down,a tornado vortex signature(TVS) appears in the central part of the large mesocyclone,corresponding to a vertical vorticity value of 6.0 10~(-2) s~(-1).When the tornado is underway,strong divergence occurs at the storm top above the tornado,with a divergent value of 0.8 10~(-2) s~(-1).The mesocyclone that leads to the tornado lasts 2 hours and 13 minutes.The mechanisms for mesocyclone generation and HP echo revolution are discussed in details.
Zhang, J., Coauthors, 2011: National Mosaic and Multi-Sensor QPE (NMQ) system-Description,results and future plans. Bull. Amer. Meteor. Soc., 92, 1321-1338, .https://doi.org/10.1175/2011BAMS-D-11-00047.1
Zhang J., Y. Qi, D. Kingsmill, and K. Howard, 2012: Radar-based quantitative precipitation estimation for the cool season in complex terrain: Case studies from the NOAA Hydrometeorology Testbed.J. Hydrometeor.,13, 1836-1854, .https://doi.org/10.1175/JHM-D-11-0145.1
Zrnić, 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