Abstract
The key factors, namely, the radar data quality, raindrop size distribution (RSD) variability, and the data integration method, which significantly affect radar-based quantitative precipitation estimation (QPE) are investigated using the RCWF (S-band) and NCU C-POL (C-band) dual-polarization radars in northern Taiwan. The radar data quality control (QC) procedures, including the corrections of attenuation, the systematic bias, and the wet-radome effect, have large impact on the QPE accuracy. With the proper QC procedures, the values of normalized root mean square error (NRMSE) decrease about 10~40% for R(ZHH) and about 5~15% for R(KDP). The QPE error from the RSD variability is mitigated by applying seasonal coefficients derived from eight-year disdrometer data. Instead of using discrete QPEs (D-QPE) from one radar, the synthetic QPEs are derived via discretely combined QPEs (DC-QPE) from S- and C-band radars. The improvements in DC-QPE compared to D-QPE are about 1.5–7.0% and 3.5–8.5% in R(KDP) and R(KDP, ZDR), respectively. A novel algorithm, Lagrangian-evolution adjustment (LEA), is proposed to compensate D-QPE from a single radar. The LEA-QPE shows 1–4% improvements in R(KDP, ZDR) at the C-band radar, which has a larger scanning temporal gap (up to 10 min). The synthetic LEA-QPEs by combining two radars have outperformed both D-QPEs and DC-QPEs.
Highlights
The radar-based quantitative precipitation estimation (QPE) is obtained by integrating each radar scan discretely, hereafter discrete QPEs (D-QPE) (∆Ti : time difference between two scans)
The results indicate that applying a proper attenuation correction can significantly cantly mitigate the impacts of the attenuation effect on the coefficients of α and β for S-band (C-band) QPEs
The most improvements are found in rainfall rates above the S-band Lagrangian-evolution adjustment (LEA)-QPEs. These results indicate that the LEA algorithm has more positive feedback in the case of large
Summary
Accurate radar-based quantitative precipitation estimation (QPE) has been one of the longstanding goals of meteorological radar. Marshall and Palmer [1] utilized horizontal reflectivity (ZHH , mm m−3 ) and a power-law relation, ZHH = aRb (Z-R) obtained from simulated radar variables based on measured raindrop size distribution (RSD), to estimate rainfall rate (R, mm h−1 ). The Z-R relation varies vastly in convective, stratiform precipitation, and different climatological regions due to the natural variability in RSD [2]. Additional dual-pol variables from a dual-pol radar, such as the differential reflectivity (ZDR ) and specific differential phase (KDP ), are utilized in QPE. The QPE has significantly been improved by the better quality of radar data and the inclusion of the RSD information [4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24]
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