Journal of Meteorological Research

, Volume 32, Issue 4, pp 584–597 | Cite as

Improved Algorithms for Removing Isolated Non-Meteorological Echoes and Ground Clutters in CINRAD

  • Haibo Zou
  • Shuwen Zhang
  • Xudong Liang
  • Xueting Yi


Using China New Generation Weather Radar (CINRAD) level-II data, the original algorithms for removing isolated non-meteorological echoes and ground clutters in radar data, which have been applied to Weather Surveillance Radar-1988 Doppler (WSR-88D) in the USA and Severe Weather Automatic Nowcast (SWAN) system in China, are modified and improved. To remove isolated non-meteorological echoes, the new algorithm introduces a constraint parameter (Po) to distinguish whether a window of 5 × 5 points is isolated as external echoes. A statistical analysis of 150 radar scans (5 cases, with each case comprising 30 scans) under three different echo types (small-scale convection, typhoon, and large-scale synoptic system) shows that the constraint parameter Po ⩽ 0.167 is suitable for removing isolated non-meteorological echoes while preserving the edge of meteorological echoes. A new parameter, NDZ, which promotes the ability of the algorithm to identify the ground clutters appearing at two adjacent elevation angles, is constructed based on the vertical continuity of reflectivity. These improved algorithms are tested for four cases (three cases of isolated non-meteorological echoes and one case of ground clutters). Based on the statistics of 232 volume scans of radar data (on a temporal resolution of 1 h) measured at Nanchang station from 0000 UTC 5 to 1600 UTC 14 March 2015, it is found that the improved algorithms not only eliminate most (over 95% under clear-sky conditions) of the isolated non-meteorological echoes and ground clutters (including those appearing at two adjacent elevation angles), but also well preserve the structure of meteorological echoes (storms). Key words: radar, isolated non-meteorological echoes, ground clutter, quality control

Key words

radar isolated non-meteorological echoes ground clutter quality control 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.



We thank the two anonymous reviewers and the editor for their constructive comments, which have certainly helped improve the manuscript from its original version. Dr. Zhiqun Hu provided useful comments and suggestions regarding the received baseband complex signal at the signal-processing level.


  1. Alfieri, L., P. Claps, and F. Laio, 2010: Time-dependent Z–R relationships for estimating rainfall fields from radar measurements. Nat. Hazards Earth Sys., 10, 149–158, doi: 10.5194/nhess-10-149-2010.CrossRefGoogle Scholar
  2. Anagnostou, E. N., and W. F. Krajewski, 1999: Real-time radar rainfall estimation. Part I: Algorithm formulation. J. Atmos. Oceanic Technol., 16, 189–197, doi: 10.1175/1520-0426 (1999)016<0189:RTRREP>2.0.CO;2.Google Scholar
  3. Bedka, K. M., C. Wang, R. Rogers, et al., 2015: Examining deep convective cloud evolution using total lightning, WSR-88D, and GOES-14 super rapid scan datasets. Wea. Forecasting, 30, 271–590, doi: 10.1175/WAF-D-14-00062.1.CrossRefGoogle Scholar
  4. Bergen, W. R., and S. C. Albers, 1988: Two-and three-dimensional de-aliasing of Doppler radar velocities. J. Atmos. Oceanic Technol., 5, 305–319, doi: 10.1175/1520-0426(1988)005 <0305:TATDDA>2.0.CO;2.CrossRefGoogle Scholar
  5. Chrisman, J., D. Rinderknecht, and R. Hamilton, 1995: WSR-88D clutter suppression and its impact on meteorological data interpretation. Preprints, First WSR-88. User’s Conference, Norman, OK, WSR-88. Operational Support Facility, USA, 11–14 October, 9–20.Google Scholar
  6. Fabry, F., and J. Gadoury, 2009: Another method for ground target identification and filtering using spectral processing of dual-polarization returns. 34th Conference on Radar Meteorology, Williamsburg, VA, 6 October, Amer. Meteor. Soc.Google Scholar
  7. Friedrich, K., M. Hagen and T. Einfalt, 2006: A quality control concept for radar reflectivity, polarimetric parameters, and Doppler velocity. J. Atmos. Oceanic Technol., 23, 865–887, doi: 10.1175/jtech1920.1.CrossRefGoogle Scholar
  8. Fulton, R. A., J. P. Breidenbach, D. J. Seo, et al., 1998: The WSR-88D rainfall algorithm. Wea. Forecasting, 13, 377–395, doi: 10.1175/1520-0434(1998)013<0377:TWRA>2.0.CO;2.CrossRefGoogle Scholar
  9. Gourley, J. J., P. Tabary, and J. Parent du Chatelet, 2007: A fuzzy logic algorithm for the separation of precipitating from nonprecipitating echoes using polarimetric radar observations. J. Atmos. Oceanic Technol., 24, 1439–1451, doi: 10.1175/jtech2035.1.CrossRefGoogle Scholar
  10. Hubbert, J. C., M. Dixon, S. M. Ellis, et al., 2009a: Weather radar ground clutter. Part I: Identification, modeling, and simulation. J. Atmos. Oceanic Technol., 26, 1165–1180, doi: 10.1175/2009jtecha1159.1.Google Scholar
  11. Hubbert, J. C., M. Dixon, and S. M. Ellis, 2009b: Weather radar ground clutter. Part II: Real-time identification and filtering. J. Atmos. Oceanic Technol., 26, 1181–1197, doi: 10.1175/2009jtecha1160.1.Google Scholar
  12. Jones, T. A., D. Stensrud, L. Wicker, et al., 2015: Simultaneous radar and satellite data storm-scale assimilation using an ensemble Kalman filter approach for 24 May 2011. Mon. Wea. Rev., 143, 165–194, doi: 10.1175/mwr-d-14-00180.1.CrossRefGoogle Scholar
  13. Kessinger, C. K., S. Ellis, J. Van Andel, et al., 2003: The AP clutter mitigation scheme for the WSR-88D. Preprints, 31st Conference on Radar Meteorology, Seattle WA, 6–12 August, Amer. Meteor. Soc., 526–529.Google Scholar
  14. Krishnapuram, R., and J. M. Keller, 1993: A possibilistic approach to clustering. IEEE Transactions on Fuzzy Systems, 1, 98–110, doi: 10.1109/91.227387.CrossRefGoogle Scholar
  15. Lakshmanan, V., C. Karstens, J. Krause, et al., 2014: Quality control of weather radar data using polarimetric variables. J. Atmos. Oceanic Technol., 31, 1234–1249, doi: 10.1175/jtech-d-13-00073.1.CrossRefGoogle Scholar
  16. Liu, L., M. X. Chen, and Y. C. Wang, 2016: Numerical nowcasting experiments for the simulation of a mesoscale convective system using a cloud model and radar data assimilation with 4DVar. Acta Meteor. Sinica, 74, 213–228, doi: 10.11676/qxxb2016.021. (in Chinese)Google Scholar
  17. Liu, L. P., L. L. Wu, and Y. M. Yang, 2007: Development of fuzzy-logical two-step ground clutter detection algorithm. Acta Meteor. Sinica, 65, 252–260, doi: 10.3321/j.issn:0577-6619.2007.02.011. (in Chinese)Google Scholar
  18. Liu, S.-Y., L.-F. Yan, and J. Sun, 2008: Retrieval of horizontal wind and quality controlling of single Doppler radar data on landing typhoon. J. Trop. Meteor., 24, 105–110, doi: 10.3969/j.issn.1004-4965.2008.02.001. (in Chinese)Google Scholar
  19. Pamment, J. A., and B. J. Conway, 1998: Objective identification of echoes due to anomalous propagation in weather radar data. J. Atmos. Oceanic Technol., 15, 98–113, doi: 10.1175/1520-0426(1998)015<0098:oioedt>;2.CrossRefGoogle Scholar
  20. 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 Meteor. Sinica, 24, 50–65.Google Scholar
  21. Qi, Y. C., and J. Zhang, 2017: A physically based two-dimensional seamless reflectivity mosaic for radar QPE in the MRMS system. J. Hydrometeor., 18, 1327–1340, doi: 10.1175/jhm-d-16-0197.1.CrossRefGoogle Scholar
  22. Qin, Y. Y., J. D. Gong, Z. C. Li, et al, 2014: Assimilation of Doppler radar observations with an ensemble square root filter: A squall line case study. J. Meteor. Res., 28, 230–251, doi: 10.1007/s13351-014-2046-6.CrossRefGoogle Scholar
  23. Rico-Ramirez, M. A., and I. D. Cluckie, 2008: Classification of ground clutter and anomalous propagation using dual-polarization weather radar. IEEE Trans. Geosci. Remote Sens., 46, 1892–1904, doi: 10.1109/tgrs.2008.916979.CrossRefGoogle Scholar
  24. Ridal, M., and M. Dahlbom, 2017: Assimilation of multinational radar reflectivity data in a mesoscale model: A proof of concept. J. Appl. Meteor. Climatol., 56, 1739–1751, doi: 10.1175/jamc-d-16-0247.1.CrossRefGoogle Scholar
  25. Serafin, R. J., and J. W. Wilson, 2000: Operational weather radar in the United States: Progress and opportunity. Bull. Amer. Meteor. Soc., 81, 501–518, doi: 10.1175/1520-0477(2000) 081<0501:owritu>2.3.CO;2.CrossRefGoogle Scholar
  26. Steenburgh, W. J., S. F. Halvorson, and D. J. Onton, 2000: Climatology of lake-effect snowstorms of the Great Salt Lake. Mon. Wea. Rev., 128, 709–727, doi: 10.1175/1520-0493(2000)128 <0709:coleso>;2.CrossRefGoogle Scholar
  27. Steiner, M., and J. A. Smith, 2002: Use of three-dimensional reflectivity structure for automated detection and removal of nonprecipitating echoes in radar data. J. Atmos. Oceanic Technol., 19, 673–686, doi: 10.1175/1520-0426(2002)019 <0673:uotdrs>;2.CrossRefGoogle Scholar
  28. Su, T. J., J. X. Ge, and H. B. Zhang, 2018: Review of dual polarization weather radar system development in China. J. Mar. Meteor., 38, 62–68, doi: 10.19513/j.cnki.issn2096-3599.2018. 01.008. (in Chinese)Google Scholar
  29. Tang, F., 2011: Quality control of radar reflectivity data and its application to GRAPES 3DVAR. Master dissertation, Nanjing University of Information Science & Technology, China, 48 pp. (in Chinese)Google Scholar
  30. Torres, S. M., and D. A. Warde, 2014: Ground clutter mitigation for weather radars using the autocorrelation spectral density. J. Atmos. Oceanic Technol., 31, 2049–2066, doi: 10.1175/jtech-d-13-00117.1.CrossRefGoogle Scholar
  31. Wang, Y. B., and Y. F. Wan, 2006: Automatic quality control for radar volume-scanning reflectivity fields. Meteor. Sci. Technol., 34, 615–619, doi: 10.3969/j.issn.1671-6345.2006.05. 021. (in Chinese)Google Scholar
  32. Wu, T., Y. F. Wan, W. F. Wo, et al., 2013: Design and application of radar reflectivity quality control algorithm in SWAN. Meteor. Sci. Technol., 41, 809–817, doi: 10.3969/j.issn.1671-6345.2013.05.004. (in Chinese)Google Scholar
  33. Xiao, Y. J., L. P. Liu, and Y. Shi, 2008: Study of methods for three-dimensional multiple-radar reflectivity mosaics. Acta Meteor. Sinica, 22, 351–361.Google Scholar
  34. Xu, M. Y., F. Li, Y. C. Xia, et al., 2017: Analysis of CINRAD radar operation status during 2009–2014. Meteor. Mon., 43, 365–372, doi: 10.7519/j.issn.1000-0526.2017.03.013. (in Chinese)Google Scholar
  35. Yang, M. L., L. P. Liu, D. B. Su, et al., 2011: The application of an automated 2-D multi-pass Doppler radar velocity dealiasing and the research of its effect. Meteor. Mon., 37, 203–212, doi: 10.7519/j.issn.1000-0526.2011.2.010. (in Chinese)Google Scholar
  36. Yu, X. D., X. P. Yao, T. N. Xiong, et al., 2006. Doppler Weather Radar Principles and Operational Applications. China Meteorological Press, Beijing, 25 pp. (in Chinese)Google Scholar
  37. Zhang, J., S. Wang, and B. Clarke, 2004: WSR-88D reflectivity quality control using horizontal and vertical reflectivity structure. Preprints, 11th Conference on Aviation, Range and Aerospace Meteorology, Hyannis, MA, 4–8 October, Amer. Meteor. Soc.Google Scholar
  38. Zhang, J., K. Howard, and J. J. Gourley, 2005: Constructing threedimensional multiple-radar reflectivity mosaics: Examples of convective storms and stratiform rain echoes. J. Atmos. Oceanic Technol., 22, 30–42, doi: 10.1175/jtech-1689.1.CrossRefGoogle Scholar
  39. Zheng, Y. Y., X. D. Yu, C. Fang, et al., 2004: Analysis of a strong classic supercell storm with Doppler weather radar data. Acta Meteor. Sinica, 62, 317–328, doi: 10.3321/j.issn:0577-6619. 2004.03.006. (in Chinese)Google Scholar

Copyright information

© The Chinese Meteorological Society and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Haibo Zou
    • 1
    • 2
    • 3
  • Shuwen Zhang
    • 1
  • Xudong Liang
    • 2
  • Xueting Yi
    • 3
  1. 1.College of Atmosphere SciencesLanzhou UniversityLanzhouChina
  2. 2.State Key Laboratory of Severe WeatherChinese Academy of Meteorological SciencesBeijingChina
  3. 3.Meteorological Disaster Emergency Warning Center of Jiangxi ProvinceNanchangChina

Personalised recommendations