Quantitative Precipitation Estimation Based on S-Band Dual-Polarized Radar Observations over Coastal Eastern China during the Meiyu Season

This study established an S-band dual-polarization radar quantitative precipitation estimation system (NDRQPES) using raindrop size distribution (DSD) data observed during the Meiyu seasons of 2021–2023 in Ningbo, a coastal city in eastern China. The NDRQPES was evaluated by comparing its quantitative precipitation estimation (QPE) accuracy with the national radar mosaic operational product (CHN_QPE) during the Meiyu periods of 2023 and 2024. The results show that NDRQPES, incorporating linear rainfall relationships from localized DSD data, has significantly improved hourly precipitation estimate accuracy, with a correlation coefficient (CC) > 0.90 and relative bias (RB) within ±10%. Furthermore, it outperforms CHN_QPE in critical success index (CSI) across various rainfall rates. However, it exhibits biases in accumulated rainfall estimates, overestimating by 8.89% in 2023 and underestimating by 13.87% in 2024. NDRQPES is also less effective than CHN_QPE in detecting extreme heavy rainfall (> 64 mm h⁻¹). These deficiencies may be attributed to the microphysical complexity of Meiyu rainfall, including variabi-lity in hydrometeor morphology and noise interference in weak rainfall, as well as the limited representativeness of single-point DSD data. While CHN_QPE effectively captures intense precipitation events, it exhibits significant systematic overestimation, with an RB of 161.77% in 2024. This study provides valuable insights for refining and localizing dual-polarization radar precipitation algorithms for regional applications.