Advanced Search
Article Contents

Comparison of the Observation Capability of an X-band Phased-array Radar with an X-band Doppler Radar and S-band Operational Radar

Fund Project:

doi: 10.1007/s00376-013-3072-5

  • An X-band phased-array meteorological radar (XPAR) was developed in China and will be installed in an airplane to observe precipitation systems for research purposes. In order to examine the observational capability of the XPAR and to test the operating mode and calibration before installation in the airplane, a mobile X-band Doppler radar (XDR) and XPAR were installed at the same site to observe convective precipitation events. Nearby S-band operational radar (SA) data were also collected to examine the reflectivity bias of XPAR. An algorithm for quantitative analysis of reflectivity and velocity differences and radar sensitivity of XPAR is presented. The reflectivity and velocity biases of XPAR are examined with SA and XDR. Reflectivity sensitivities, the horizontal and vertical structures of reflectivity by the three radars are compared and analyzed. The results indicated that while the XPRA with different operating modes can capture the main characteristic of 3D structures of precipitation, and the averaged reflectivity differences between XPAR and XDR, and XDR and SA, were 0.4 dB and 6.6 dB on 13 July and -4.5 dB and 5.1 dB on 2 August 2012, respectively. The minimum observed reflectivities at a range of 50 km for XPAR, XDR and SA were about 15.4 dBZ, 13.5 dBZ and -3.5 dBZ, respectively. The bias of velocity between XPAR and XDR was negligible. This study provides a possible method for the quantitative comparison of the XPAR data, as well as the sensitivity of reflectivity, calibration, gain and bias introduced by pulse compression.
    摘要: An X-band phased-array meteorological radar (XPAR) was developed in China and will be installed in an airplane to observe precipitation systems for research purposes. In order to examine the observational capability of the XPAR and to test the operating mode and calibration before installation in the airplane, a mobile X-band Doppler radar (XDR) and XPAR were installed at the same site to observe convective precipitation events. Nearby S-band operational radar (SA) data were also collected to examine the reflectivity bias of XPAR. An algorithm for quantitative analysis of reflectivity and velocity differences and radar sensitivity of XPAR is presented. The reflectivity and velocity biases of XPAR are examined with SA and XDR. Reflectivity sensitivities, the horizontal and vertical structures of reflectivity by the three radars are compared and analyzed. The results indicated that while the XPRA with different operating modes can capture the main characteristic of 3D structures of precipitation, and the averaged reflectivity differences between XPAR and XDR, and XDR and SA, were 0.4 dB and 6.6 dB on 13 July and -4.5 dB and 5.1 dB on 2 August 2012, respectively. The minimum observed reflectivities at a range of 50 km for XPAR, XDR and SA were about 15.4 dBZ, 13.5 dBZ and -3.5 dBZ, respectively. The bias of velocity between XPAR and XDR was negligible. This study provides a possible method for the quantitative comparison of the XPAR data, as well as the sensitivity of reflectivity, calibration, gain and bias introduced by pulse compression.
  • Bluestein, H. B.,M. M. French,I. Popstefanija,R. T. Bluth, and J. B. Knorr, 2010: A mobile, phased-array Doppler radar for the study of severe convective storms. Bull. Amer. Meteor. Soc., 91, 579-600.
    Heinselman, P. L.,D. L. Priegnitz,K. L. Manross,T. M. Smith, and R. W. Adams, 2008: Rapid sampling of severe storms by the national weather radar testbed phased array radar. Wea. Forecasting, 23, 808-824.
    Knorr, J. B., 2007: Weather radar equation correction for frequency agile and phased array radars. IEEE Transactions on Aerospace and Electronic Systems, 43, 1220-1227.
    Probert-Jones, J. R., 1962: The radar equation in meteorology. Quart. J. Roy. Meteor. Soc., 88, 485-495.
    Wurman, J.,D. Dowell,Y. Richardson,P. Markowski,E. Rasmussen,D. Burgess,L. Wicker, and H. B. Bluestein, 2012: The second verification of the origins of rotation in tornadoes experiment: VORTEX2. Bull. Amer. Meteor. Soc., 93, 1147-1170.
    Zrnić, D. S., and Coauthors, 2007: Agile-beam phased array radar for weather observations. Bull. Amer. Meteor. Soc., 88, 1753-1766.
    Zhang, Z. Q., and L. P. Liu, 2011: A simulation and analysis of the observation errors of cloud intensity and structure with the S-band phased array radar and the CINRAD/SA. Acta Meteorologica Sinica, 69, 729-735. (in Chinese)
  • [1] LIU Liping, ZHANG Zhiqiang, YU Danru, YANG Hu, ZHAO Chonghui, ZHONG Lingzhi, 2012: Comparison of Precipitation Observations from a Prototype Space-based Cloud Radar and Ground-based Radars, ADVANCES IN ATMOSPHERIC SCIENCES, 29, 1318-1329.  doi: 10.1007/s00376-012-1233-6
    [2] LIN Yinjing, WANG Hongqing, HAN Lei, ZHENG Yongguang, WANG Yu, 2010: Quantitative Analysis of Meso-β-scale Convective Cells and Anvil Clouds over North China, ADVANCES IN ATMOSPHERIC SCIENCES, 27, 1089-1098.  doi: 10.1007/s00376-010-9154-8
    [3] DING Yihui, LI Chongyin, LIU Yanju, 2004: Overview of the South China Sea Monsoon Experiment, ADVANCES IN ATMOSPHERIC SCIENCES, 21, 343-360.  doi: 10.1007/BF02915563
    [4] Xiaoqiong ZHEN, Shuqing MA, Hongbin CHEN, Guorong WANG, Xiaoping XU, Siteng LI, 2022: A New X-band Weather Radar System with Distributed Phased-Array Front-ends: Development and Preliminary Observation Results, ADVANCES IN ATMOSPHERIC SCIENCES, 39, 386-402.  doi: 10.1007/s00376-021-1114-y
    [5] Jianli MA, Zhiqun HU, Meilin YANG, Siteng LI, 2020: Improvement of X-Band Polarization Radar Melting Layer Recognition by the Bayesian Method and ITS Impact on Hydrometeor Classification, ADVANCES IN ATMOSPHERIC SCIENCES, 37, 105-116.  doi: 10.1007/s00376-019-9007-z
    [6] Kong Fanyou, Mao jietai, 1994: A Model Study of Three Dimensional Wind Field Analysis from Dual-Doppler Radar Data, ADVANCES IN ATMOSPHERIC SCIENCES, 11, 162-174.  doi: 10.1007/BF02666543
    [7] SUN Lan, XUE Yongkang, 2004: Validation of SSiB Model over Grassland with CHeRES Field Experiment Data in 2001, ADVANCES IN ATMOSPHERIC SCIENCES, 21, 547-556.  doi: 10.1007/BF02915722
    [8] YANG Junli, SHEN Xueshun, 2011: The Construction of SCM in GRAPES and Its Applications in Two Field Experiment Simulations, ADVANCES IN ATMOSPHERIC SCIENCES, 28, 534-550.  doi: 10.1007/s00376-010-0062-8
    [9] Kanghui ZHOU, Jisong SUN, Yongguang ZHENG, Yutao ZHANG, 2022: Quantitative Precipitation Forecast Experiment Based on Basic NWP Variables Using Deep Learning, ADVANCES IN ATMOSPHERIC SCIENCES, 39, 1472-1486.  doi: 10.1007/s00376-021-1207-7
    [10] Yang Xiaosong, Lin Zhaohui, Dai Yongjiu, Guo Yufu, 2001: Validation of IAP94 Land Surface Model over the Huaihe River Basin with HUBEX Field Experiment Data, ADVANCES IN ATMOSPHERIC SCIENCES, 18, 139-154.  doi: 10.1007/s00376-001-0009-1
    [11] Jiang HUANGFU, Zhiqun HU, Jiafeng ZHENG, Lirong WANG, Yongjie ZHU, 2024: Study on Quantitative Precipitation Estimation by Polarimetric Radar Using Deep Learning, ADVANCES IN ATMOSPHERIC SCIENCES.  doi: 10.1007/s00376-023-3039-0
    [12] Ma Zhenhua, Liu Guosheng, Liu Wei, 1985: PRINCIPAL STUDY OF THE FM RADAR FOR IMPROVING THE ACCURACY IN QUANTITATIVE RAINFALL RATE MEASUREMENT, ADVANCES IN ATMOSPHERIC SCIENCES, 2, 341-346.  doi: 10.1007/BF02677250
    [13] ZHI Hai, ZHANG Rong-Hua, LIN Pengfei, WANG Lanning, 2015: Quantitative Analysis of the Feedback Induced by the Freshwater Flux in the Tropical Pacific Using CMIP5, ADVANCES IN ATMOSPHERIC SCIENCES, 32, 1341-1353.  doi: 10.1007/s00376-015-5064-0
    [14] T.N.Krishnamurti, Sheng Jian, 1985: THE HEATING FIELD IN AN ASYMMETRIC HURRICANE -PART I:SCALE ANALYSIS, ADVANCES IN ATMOSPHERIC SCIENCES, 2, 402-413.  doi: 10.1007/BF02677256
    [15] Yang CAO, Debin SU, Xingang FAN, Hongbin CHEN, 2019: Evaluating the Algorithm for Correction of the Bright Band Effects in QPEs with S-, C- and X-Band Dual-Polarized Radars, ADVANCES IN ATMOSPHERIC SCIENCES, 36, 41-54.  doi: 10.1007/s00376-018-8032-7
    [16] Peng LIU, Chung-Hsiung SUI, 2014: An Observational Analysis of the Oceanic and Atmospheric Structure of Global-Scale Multi-decadal Variability, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 316-330.  doi: 10.1007/s00376-013-2305-y
    [17] Yawei QU, Tijian WANG, Yanfeng CAI, Shekou WANG, Pulong CHEN, Shu LI, Mengmeng LI, Cheng YUAN, Jing WANG, Shaocai XU, 2018: Influence of Atmospheric Particulate Matter on Ozone in Nanjing, China: Observational Study and Mechanistic Analysis, ADVANCES IN ATMOSPHERIC SCIENCES, 35, 1381-1395.  doi: 10.1007/s00376-018-8027-4
    [18] WANG Gaili, WONG Waikin, LIU Liping, WANG Hongyan, 2013: Application of Multi-Scale Tracking Radar Echoes Scheme in Quantitative Precipitation Nowcasting, ADVANCES IN ATMOSPHERIC SCIENCES, 30, 448-460.  doi: 10.1007/s00376-012-2026-7
    [19] Haibo ZOU, Shanshan WU, Miaoxia TIAN, 2023: Radar Quantitative Precipitation Estimation Based on the Gated Recurrent Unit Neural Network and Echo-Top Data, ADVANCES IN ATMOSPHERIC SCIENCES, 40, 1043-1057.  doi: 10.1007/s00376-022-2127-x
    [20] Ma Zhenhua, 1985: THE EFFECTS OF EARTH PARTIAL SPECULAR REFLECTION ON THE QUANTITATIVE RAINFALL-RATE MEASUREMENTS BY RADAR, ADVANCES IN ATMOSPHERIC SCIENCES, 2, 104-111.  doi: 10.1007/BF03179742

Get Citation+

Export:  

Share Article

Manuscript History

Manuscript received: 10 April 2013
Manuscript revised: 02 December 2013
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Comparison of the Observation Capability of an X-band Phased-array Radar with an X-band Doppler Radar and S-band Operational Radar

  • 1. State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Science, Beijing 100081
  • 2. Chengdu University of Information Technology, Chengdu 610225
Fund Project:  The authors wish to thank the Jiangsu Meteorological Bureau for providing the SA radar data. This study was funded by National High-Tech Research and Development Projects (863; Grant No. 2007AA061901), the National Key Program for Developing Basic Sciences (Grant No. 2012CB417202), the National Natural Science Foundation of China (Grant No. 41175038), and the Public Welfare Meteorological Special Project (Grant No. GYHY201106046).

Abstract: An X-band phased-array meteorological radar (XPAR) was developed in China and will be installed in an airplane to observe precipitation systems for research purposes. In order to examine the observational capability of the XPAR and to test the operating mode and calibration before installation in the airplane, a mobile X-band Doppler radar (XDR) and XPAR were installed at the same site to observe convective precipitation events. Nearby S-band operational radar (SA) data were also collected to examine the reflectivity bias of XPAR. An algorithm for quantitative analysis of reflectivity and velocity differences and radar sensitivity of XPAR is presented. The reflectivity and velocity biases of XPAR are examined with SA and XDR. Reflectivity sensitivities, the horizontal and vertical structures of reflectivity by the three radars are compared and analyzed. The results indicated that while the XPRA with different operating modes can capture the main characteristic of 3D structures of precipitation, and the averaged reflectivity differences between XPAR and XDR, and XDR and SA, were 0.4 dB and 6.6 dB on 13 July and -4.5 dB and 5.1 dB on 2 August 2012, respectively. The minimum observed reflectivities at a range of 50 km for XPAR, XDR and SA were about 15.4 dBZ, 13.5 dBZ and -3.5 dBZ, respectively. The bias of velocity between XPAR and XDR was negligible. This study provides a possible method for the quantitative comparison of the XPAR data, as well as the sensitivity of reflectivity, calibration, gain and bias introduced by pulse compression.

摘要: An X-band phased-array meteorological radar (XPAR) was developed in China and will be installed in an airplane to observe precipitation systems for research purposes. In order to examine the observational capability of the XPAR and to test the operating mode and calibration before installation in the airplane, a mobile X-band Doppler radar (XDR) and XPAR were installed at the same site to observe convective precipitation events. Nearby S-band operational radar (SA) data were also collected to examine the reflectivity bias of XPAR. An algorithm for quantitative analysis of reflectivity and velocity differences and radar sensitivity of XPAR is presented. The reflectivity and velocity biases of XPAR are examined with SA and XDR. Reflectivity sensitivities, the horizontal and vertical structures of reflectivity by the three radars are compared and analyzed. The results indicated that while the XPRA with different operating modes can capture the main characteristic of 3D structures of precipitation, and the averaged reflectivity differences between XPAR and XDR, and XDR and SA, were 0.4 dB and 6.6 dB on 13 July and -4.5 dB and 5.1 dB on 2 August 2012, respectively. The minimum observed reflectivities at a range of 50 km for XPAR, XDR and SA were about 15.4 dBZ, 13.5 dBZ and -3.5 dBZ, respectively. The bias of velocity between XPAR and XDR was negligible. This study provides a possible method for the quantitative comparison of the XPAR data, as well as the sensitivity of reflectivity, calibration, gain and bias introduced by pulse compression.

Reference

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint