Quality Control of Differential Propagation Phase Shift for Dual Linear Polarization Radar

Data processing and quality control is the foundation of the application of dual-linear polarization Doppler radar. Based on the observation in field experiments by a C-band Polarization Doppler Radar on Wheel (CPDRW), the difference of differential propagation phase shift Φ_DP between precipitation and ground clutter and its relationship with signal-to-noise ratio SNR are analyzed and a new data analyzing and processing methodology is suggested. According to this new method, the useless Φ_DP data can be given up and the K_DP data with higher accuracy can be acquired. Analysis indicates that Φ_DP data are vulnerable to the influence of the non-meteorological target like ground clutter and usually appears large fluctuations. Φ_DP data are also sensitive to the variability of SNR and cross-correlation coefficient ρ_HV(0), especially the latter. It appears abnormal fluctuations with the quality of related SNR and ρ_HV(0) becomes poor and that will affect the quality of the estimation of K_DP data if no appropriate quality control scheme is adopted. Using this kind of K_DP data, obvious errors in the quantitative application of precipitation estimation and precipitation particle morphology recognition can be obtained. In this new method, the abnormal volatility of Φ_DP data combining with reflectivity factor Z_H and radial velocity V_r information is used to isolate the ground clutter, and then improper data are eliminated in the quantitative application such as quantitative precipitation estimation or attenuation correction. According to SNR and ρ_HV(0), the meteorological data is divided into good, poor and bad categories. For the good data, the fluctuation is smaller, the increasing trend with distances which accords with theoretical expectations is evident, so the preprocessing algorithms and estimate K_DP data can be used directly; for the poor data, although the fluctuation is more pronounced than the good data, the data continuity begins to become poor and there are some Φ_DP data "pile" and "depression", however, much weather information remains and the variation trend is also obvious, so the data correction algorithm are applied so as not to affect the estimated K_DP data; and for the bad data, it not only has the large fluctuation, the overall variation trend is also difficult to identify, sometimes even negative growth phenomenon appears which is contrary to the theory, so they are eliminated to ensure the overall quality of Φ_DP data. After a large number of actual data validation, it reveals that the suggested method can keep the meteorological information to the greatest extent and ensure the overall quality of Φ_DP data at the same time, and it can also estimate the high quality of K_DP data.