A Comparison of De-noising Methods for Differential Phase Shift and Associated Rainfall Estimation

Measured differential phase shift DP is known to be a noisy unstable polarimetric radar variable, such that the quality of DP data has direct impact on specific differential phase shift KDP estimation, and subsequently, the KDP -based rainfall estimation. Over the past decades, many DP de-noising methods have been developed; however, the de-noising effects in these methods and their impact on KDP -based rainfall estimation lack comprehensive comparative analysis. In this study, simulated noisy DP data were generated and de-noised by using several methods such as finite-impulse response (FIR), Kalman, wavelet, traditional mean, and median filters. The biases were compared between KDP from simulated and observed DP radial profiles after de-noising by these methods. The results suggest that the complicated FIR, Kalman, and wavelet methods have a better de-noising effect than the traditional methods. After DP was de-noised, the accuracy of the KDP -based rainfall estimation increased significantly based on the analysis of three actual rainfall events. The improvement in estimation was more obvious when KDP was estimated with 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 significant 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 filters.