A Synthetic Quantitative Precipitation Estimation by Integrating S- and C-Band Dual-Polarization Radars over Northern Taiwan

The key factors, namely, the radar data quality, raindrop size distribution (RSD) variability, and integration method, which significantly affect radar-based quantitative precipitation estimation (QPE) are investigated using RCWF (S-band) NCU C-POL (C-band) dual-polarization radars in northern Taiwan. quality control (QC) procedures, including corrections of attenuation, systematic bias, wet-radome effect, have large impact on QPE accuracy. With proper QC values normalized root mean square error (NRMSE) decrease about 10~40% for R(ZHH) 5~15% R(KDP). from RSD variability is mitigated by applying seasonal coefficients derived eight-year disdrometer data. Instead discrete QPEs (D-QPE) one radar, synthetic via discretely combined (DC-QPE) S- C-band radars. improvements DC-QPE compared to D-QPE 1.5–7.0% 3.5–8.5% R(KDP) R(KDP, ZDR), respectively. A novel algorithm, Lagrangian-evolution adjustment (LEA), proposed compensate a single radar. LEA-QPE shows 1–4% ZDR) at has larger scanning temporal gap (up 10 min). LEA-QPEs combining two outperformed both D-QPEs DC-QPEs.