Advertisement

The Impact of Rotary Joint on Deviations of Amplitude and Phase and Its Calibration for Dual-Polarization Weather Radar

  • Shao Nan
  • Han Xu
  • Bu ZhichaoEmail author
  • Chen Yubao
  • Pan Xinmin
  • Qin Jianfeng
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 516)

Abstract

The variation of rotary joint is one of the primary reasons that result in dynamic deviation of ZDR and ΦDP as polarimetric parameters, and external instruments are needed to detect and calibrate to ensure the amplitude and phase consistency for dual-polarization weather radar. This paper analyzes the impact of rotary joint of dual-polarization weather radar on ZDR and ΦDP, and it proposes a detection and calibration method using external instrument based on baseline curve of deviation. Then this method is used to test and calibrate a S-band dual-polarization weather radar produced by Beijing Metstar Radar Co., Ltd., and the results are analyzed. It is shown that ZDR and ΦDP deviation introduced by rotary joint can satisfy requirement of relevant technical specifications, and this method can reduce deviation of amplitude and phase through calibration to enhance the reliability of radar observation data effectively.

Keywords

Rotary joint ZDR ΦDP Detection Calibration 

References

  1. 1.
    Doviak RJ, Zrnic DS, Schotland RM. Doppler radar and weather observations. Appl Opt. 1994;33(21):4531.Google Scholar
  2. 2.
    Laroche S. Polarimetric doppler weather radar: principles and applications: V.N. Bringi and V. Chandrasekar. Cambridge University Press, 2001, 636 pp. Atmos Res. 2002;63(1–2):159–160.Google Scholar
  3. 3.
    Wang H, Ran Y, Deng Y, et al. Study on deep-learning-based identification of hydrometeors observed by dual polarization doppler weather radars. Eurasip J Wirel Commun Networking. 2017;2017(1):173.CrossRefGoogle Scholar
  4. 4.
    Galati G, Pavan G. Estimation techniques for rainfall rate using differential phase shift in X-band weather radar. In: Geoscience and remote sensing symposium proceedings, 1998. IGARSS’98. 1998 IEEE International. IEEE; 1998, vol. 1. p. 138–40.Google Scholar
  5. 5.
    Liu H, Chandrasekar V. An adaptive neural network scheme for precipitation estimation from radar observations. In: Geoscience and remote sensing symposium proceedings, 1998. IGARSS ’98. 1998 IEEE International. IEEE; 1998, vol. 4. p. 1895–7.Google Scholar
  6. 6.
    Schuur T, Ryzhkov A, Heinselman P, et al. Observations and classification of echoes with the polarimetric WSR-88D radar; 2003.Google Scholar
  7. 7.
    Bringi VN, Thurai M, Hannesen R. Dual-polarization weather radar handbook. AMS-Gematronik GmbH; 2007.Google Scholar
  8. 8.
    Liu H, Chandrasekar V. Classification of hydrometeors based on polarimetric radar measurements: development of fuzzy logic and neuro-fuzzy systems, and in situ verification. J Atmos Oceanic Technol. 2000;17(2):140–64.CrossRefGoogle Scholar
  9. 9.
    Marks DA, Wolff DB, Carey LD, et al. Quality control and calibration of the dual-polarization radar at Kwajalein, RMI. J Atmos Oceanic Technol. 2011;28(28):181–96.CrossRefGoogle Scholar
  10. 10.
    Kwon S, Lee GW, Kim G. Rainfall estimation from an operational S-band dual-polarization radar: effect of radar calibration. J Meteorol Soc Jpn. 2015;93(1):65–79.CrossRefGoogle Scholar
  11. 11.
    Ryzhkov AV, Giangrande SE, Melnikov VM, et al. Calibration issues of dual-polarization radar measurements. J Atmos Oceanic Technol. 2005;22(8):1138–55.CrossRefGoogle Scholar
  12. 12.
    Gorgucci E, Scarchilli G, Chandrasekar V. A procedure to calibrate multiparameter weather radar using properties of the rain medium. IEEE Trans Geosci Remote Sens. 1999;37(1):269–76.CrossRefGoogle Scholar
  13. 13.
    Hubbert JC, Bringi VN, Brunkow D. Studies of the polarimetric covariance matrix. Part I: calibration methodology. J Atmos Oceanic Technol. 2003;20(5):696–706.CrossRefGoogle Scholar
  14. 14.
    Williams ER, Cho JYN, Smalley DJ, et al. End-to-end calibration of NEXRAD differential reflectivity with metal spheres. In: Conference on radar meteorology. American Meteorological Society; 2013.Google Scholar
  15. 15.
    Scarchilli G, Gorgucci E, Chandrasekar V, et al. Self-consistency of polarization diversity measurement of rainfall. IEEE Trans Geosci Remote Sens. 1996;34(1):22–6.CrossRefGoogle Scholar
  16. 16.
    Vivekanandan J, Zhang G, Ellis SM, et al. Radar reflectivity calibration using differential propagation phase measurement. Radio Sci. 2016;38(3):14-1-14-14.CrossRefGoogle Scholar
  17. 17.
    Lim S, Chandrasekar V, Bringi VN. Hydrometeor classification system using dual-polarization radar measurements: model improvements and in situ verification. IEEE Trans Geosci Remote Sens. 2005;43(4):792–801.CrossRefGoogle Scholar
  18. 18.
    Baldini L, Gorgucci E. Identification of the melting layer through dual-polarization radar measurements at vertical incidence. J Atmos Oceanic Technol. 2006;23(6):829–39.CrossRefGoogle Scholar
  19. 19.
    Zrnić D, Doviak R, Zhang GF, et al. Bias in differential reflectivity due to cross coupling through the radiation patterns of polarimetric weather radars. J Atmos Oceanic Technol. 2009;27(10):1624–37.CrossRefGoogle Scholar
  20. 20.
    Melnikov VM, Zrnic DS, et al. ZDR calibration issues in the WSR-88Ds. Report on 2013-MOU. p. 1–55.Google Scholar
  21. 21.
    Wang Y, Chandrasekar V. Polarization isolation requirements for linear dual-polarization weather Radar in simultaneous transmission mode of operation. IEEE Trans Geosci Remote Sens. 2006;44(8):2019–28.CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Shao Nan
    • 1
  • Han Xu
    • 1
  • Bu Zhichao
    • 1
    Email author
  • Chen Yubao
    • 1
  • Pan Xinmin
    • 2
  • Qin Jianfeng
    • 3
  1. 1.CMA Meteorological Observation CentreBeijingChina
  2. 2.Henan Meteorological Observation Data CenterZhengzhouChina
  3. 3.Wuhan Meteorological AgencyWuhanChina

Personalised recommendations