Articles | Volume 13, issue 2
https://doi.org/10.5194/amt-13-537-2020
https://doi.org/10.5194/amt-13-537-2020
Research article
 | 
07 Feb 2020
Research article |  | 07 Feb 2020

Improved fuzzy logic method to distinguish between meteorological and non-meteorological echoes using C-band polarimetric radar data

Shuai Zhang, Xingyou Huang, Jinzhong Min, Zhigang Chu, Xiaoran Zhuang, and Hengheng Zhang

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Cited articles

Bringi, V. N. and Chandrasekar, V.: Polarimetric Doppler Weather Radar, Cambridge University Press, Cambridge, UK, 2001. 
Cho, Y. H., Lee, G., Kim, K. E., and Zawadzki, I.: Identification and Removal of Ground Echoes and Anomalous Propagation Using the Characteristics of Radar Echoes, J. Atmos. Ocean. Tech., 23, 1206–1222, 2006. 
Crum, T. D. and Alberty, R. L.: The WSR-88D and the WSR-88D Operational Support Facility, B. Am. Meteorol. Soc., 74, 1669–1688, 1993. 
Doswell III, C. A., Davies-Jones, R., and Keller, D. L.: On summary measures of skill in rare event forecasting based on contingency tables, Weather Forecast., 5, 576–585, 1990. 
Doviak, R. J. and Zrnić, D. S.: Doppler radar and weather observations, Academic Press, Mineola, NY, 2nd edn., Dover Publications, Mineola, N.Y., 2006. 
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Short summary
The discrimination between meteorological and non-meteorological echoes is necessary to obtain better meteorological application performance. However, the widely used algorithms have high expectations for polarimetric data, which have similar characteristics between meteorological and non-meteorological echoes in the weak-signal regions. Therefore, an improved fuzzy logic method is proposed in this paper to improve the classification performance in weak-signal regions.