Identification and Removal of Non-meteorological Echoes in Dual-polarization Radar Data Based on a Fuzzy Logic Algorithm

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 non-meteorological 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.