CAJ | 학술논문

Measured diff erential phase shift Φ DP is known to be a noisy unstable polarimetric radar variable, such that the quality of Φ DP data has direct impact on specifi c diff erential phase shift KDP estimation, and subsequently, the KDP-based rainfall estimation. Over the past decades, many Φ DP de-noising methods have been developed; however, the de-noising eff ects in these methods and their impact on KDP-based rainfall estimation lack comprehensive comparative analysis. In this study, simulated noisy Φ DP data were generated and de-noised by using several methods such as fi nite-impulse response (FIR), Kalman, wavelet, traditional mean, and median fi lters. The biases were compared between KDP from simulated and observedΦ DP radial profi les after de-noising by these methods. The results suggest that the complicated FIR, Kalman, and wavelet methods have a better de-noising eff ect than the traditional methods. AfterΦ DP was de-noised, the accuracy of the KDP-based rainfall estimation increased signifi cantly based on the analysis of three actual rainfall events. The improvement in estimation was more obvious when KDP was estimated withΦ DP de-noised by Kalman, FIR, and wavelet methods when the average rainfall was heavier than 5 mm h?1. However, the improved estimation was not signifi cant when the precipitation intensity further increased to a rainfall rate beyond 10 mm h?1. The performance of wavelet analysis was found to be the most stable of these fi lters.