Abstract
Measured differential phase shift ФDP is known to be a noisy unstable polarimetric radar variable, such that the quality of ФDP data has direct impact on specific differential phase shift KDP estimation, and subsequently, the KDP-based rainfall estimation. Over the past decades, many ФDP 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 ФDP 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 ФDP 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 ФDP 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 ФDP 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.
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Supported by the National Natural Science Foundation of China (41375038), China Meteorological Administration Special Public Welfare Research Fund (GYHY201306040 and GYHY201306075), and Jiangshu Province Meteorological Administration Beijige Open Research Fund (BJG201201).
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Hu, Z., Liu, L., Wu, L. et al. A comparison of de-noising methods for differential phase shift and associated rainfall estimation. J Meteorol Res 29, 315–327 (2015). https://doi.org/10.1007/s13351-015-4062-6
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DOI: https://doi.org/10.1007/s13351-015-4062-6