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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.
Highlights
Quantitative precipitation estimation (QPE) is one of the most important applications for weather radar
To quantitatively compare the performance of KDP when ΦDP is de-noised with alternative methods, a radar radial profile is simulated, which passes through two convective cloud cells that were assumed of a gamma drop size distribution (Ulbrich, 1983, Chandrasekar et al, 1990; Scarchilli et al, 1993) as follows: Nw(D) = N0Dμe−(3.67+μ)D/D0, (10)
− 2 ln 2 r − rmax/2 2, rmax where Dmax represents the maximum equivalent diameter of raindrops; rmax is the diameters of two cells, for which the first is set to 30 km and the 15 km; and r is the distance of a raindrop from the center of each cell
Summary
Quantitative precipitation estimation (QPE) is one of the most important applications for weather radar. A comprehensive review of the reliability of weather radar QPE products was conducted by Wilson and Brandes (1979), and it discussed in detail the sources of uncertainty associated with radar-based rainfall estimates. These include calibration, attenuation, anomalous propagation, bright band, beam blockage, ground clutter, spurious returns, and random errors. Polarimetric radar can measure multiple polarization parameters, including differential reflectivity ZDR, specific differential phase KDR, and cross-correlation coefficient ρHV between two orthogonal radar returns. KDR is immune to radar miscalibration, attenuation in precipitation, and beam blockage, while ρDR can significantly improve the radar data quality, distinguishing rain echoes from the radar signals caused by other scatters such as snow, ground clutter, insects, birds, chaff, etc.
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