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Advances in Atmospheric Sciences

, Volume 32, Issue 9, pp 1217–1230 | Cite as

Identification and removal of non-meteorological echoes in dual-polarization radar data based on a fuzzy logic algorithm

  • Bo-Young Ye
  • GyuWon Lee
  • Hong-Mok Park
Article

Abstract

A major issue in radar quantitative precipitation estimation is the contamination of radar echoes by non-meteorological targets such as ground clutter, chaff, clear air echoes etc. In this study, a fuzzy logic algorithm for the identification of non-meteorological echoes is developed using optimized membership functions and weights for the dual-polarization radar located at Mount Sobaek. For selected precipitation and non-meteorological events, the characteristics of the precipitation and non-meteorological echo are derived by the probability density functions of five fuzzy parameters as functions of reflectivity values. The membership functions and weights are then determined by these density functions. Finally, the nonmeteorological echoes are identified by combining the membership functions and weights. The performance is qualitatively evaluated by long-term rain accumulation. The detection accuracy of the fuzzy logic algorithm is calculated using the probability of detection (POD), false alarm rate (FAR), and clutter-signal ratio (CSR). In addition, the issues in using filtered dual-polarization data are alleviated.

Key words

dual-polarization radar non-meteorological echo quality control fuzzy logic algorithm 

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References

  1. Berenguer, M., D. Sempere-Torres, C. Corral, and R. Sánchez-Diezma, 2006: A fuzzy logic technique for identifying nonprecipitating echoes in radar scans. J. Atmos. Oceanic Technol., 23, 1157–1180.CrossRefGoogle Scholar
  2. Cao, Q., G. F. Zhang, R. D. Palmer, M. Knight, R. May, and R. J. Stafford, 2012: Spectrum-Time Estimation and Processing (STEP) for imprving weather radar data quality. IEEE Trans. Geosci. Remote Sens., 50, 4670–4683.CrossRefGoogle Scholar
  3. Cho, Y. H., G. W. Lee, K. E. Kim, and I. Zawadzki, 2006: Identification and removal of ground echoes and anomalous propagation using the characteristics of radar echoes. J. Atmos. Oceanic Technol., 23, 1206–1222.CrossRefGoogle Scholar
  4. Gourley, J. J., P. Tabary, and J. P. 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.CrossRefGoogle Scholar
  5. Grecu, M., and W. F. Krajewski, 2000: An efficient methodology for detection of anomalous propagation echoes in radar reflectivity data using neural networks. J. Atmos. Oceanic Technol., 17, 121–129.CrossRefGoogle Scholar
  6. Haykin, S., and C. Deng, 1991: Classification of radar clutter using neural networks. IEEE Transactions on Neural Networks, 2, 589–600.CrossRefGoogle Scholar
  7. Hubbert, J. C., M. Dixon, S. Ellis, and G. Meymaris, 2009a: Weather radar ground clutter. Part I: Identification, modeling, and simulation. J. Atmos. Oceanic Technol., 26, 1165–1180.CrossRefGoogle Scholar
  8. Hubbert, J. C., M. Dixon, and S. Ellis, 2009b: Weather radar ground clutter. Part II: Real-time identification and filtering. J. Atmos. Oceanic Technol., 26, 1181–1197.CrossRefGoogle Scholar
  9. Krajewski, W. F., and B. Vignal, 2001: Evaluation of anomalous propagation echo detection inWSR-88D data: A large sample case study. J. Atmos. Oceanic Technol., 18, 807–814.CrossRefGoogle Scholar
  10. Lakshmanan, V., A. Fritz, T. Smith, K. Hondl, and G. Stumpf, 2007: An automated technique to quality control radar reflectivity data. J. Appl. Meteor. Climatol., 46, 288–305.CrossRefGoogle Scholar
  11. Li, Y., G. Zhang, R. J. Doviak, L. Lei, and Q. Cao, 2013: A new approach to detect ground clutter mixed with weather signals. IEEE Trans. Geosci. Remote Sens., 51, 2373–2387.CrossRefGoogle Scholar
  12. Li, Y. G., G. F. Zhang, and R. J. Doviak, 2014: Ground clutter detection using the statistical properties of signals received with a polarimetric radar. IEEE Transactions on Signal Processing, 62, 597–606.CrossRefGoogle Scholar
  13. Liu, H. P., and 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.CrossRefGoogle Scholar
  14. Mahale, V. N., G. F. Zhang, and M. Xue, 2014: Fuzzy logic classification of S-band polarimetric radar echoes to identify threebody scattering and improve data quality. J. Appl.Meteor. Climatol., 53, 2017–2033.CrossRefGoogle Scholar
  15. Moszkowicz, S., G. J. Ciach, and W. F. Krajewski, 1994: Statistical detection of anomalous propagation in radar reflectivity patterns. J. Atmos. Oceanic Technol., 11, 1026–1034.CrossRefGoogle Scholar
  16. Nicol, J. C., A. J. Illingworth, T. Darlington, and J. Sugier, 2011: Techniques for improving ground clutter identification. Proc. Symp. Weather Radar Hydrol., IAHS Press, 351 pp.Google Scholar
  17. 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.CrossRefGoogle Scholar
  18. Rico-Ramirez, M. A., and I. D. Cluckie, 2008: Classification of ground clutter and anomalous propagation using dualpolarization weather radar. IEEE Trans. Geosci. Remote Sens., 46, 1892–1904.CrossRefGoogle Scholar
  19. Siggia, A. D., and R. E. Passarelli Jr., 2004: Gaussian model adaptive processing (GMAP) for improved ground clutter cancellation and moment calculation. Proc. 3rd European Conf. on Radar in Meteorol. and Hydrology, Visby, Sweden, 67–73.Google Scholar
  20. Silverman, B. W., 1981: Using kernel density estimates to investigate multimodality. Journal of the Royal Statistical Society: Series B, 43, 97–99.Google Scholar
  21. Smith, J. A., D. J. Seo, M. L. Baeck, and M. D. Hudlow, 1996: An intercomparison study of NEXRAD precipitation estimates. Water Resour. Res., 32, 2035–2045.CrossRefGoogle Scholar
  22. 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.CrossRefGoogle Scholar
  23. Torres, S., D. Warde, and D. Zrnic, 2012: Signal Design and Processing Techniques for WSR-88D Ambiguity Resolution: Part 15. The CLEAN-AP Filter, National Severe Storms Lab., Norman, OK, 65 pp.Google Scholar
  24. Warde, D. A., and S. M. Torres, 2014: The autocorrelation spectral density for Doppler-weather-radar signal analysis. IEEE Trans. Geosci. Remote Sens., 52, 508–518.CrossRefGoogle Scholar

Copyright information

© Chinese National Committee for International Association of Meteorology and Atmospheric Sciences, Institute of Atmospheric Physics, Science Press and Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Department of Astronomy and Atmospheric Sciences, Research and Training Team for Future Creative Astrophysicists and CosmologistsKyungpook National UniversityDaeguKorea
  2. 2.Center for Atmospheric REmote sensing (CARE)Kyungpook National UniversityDaeguKorea

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