Enhanced Quantitative Precipitation Estimation During Typhoon Storms Based on Optimized Dual-Polarimetric Radar Quality Control

Accurate radar-based quantitative precipitation estimation (QPE) plays a critical role in disaster prevention and reduction. In mountainous terrain, blockage of low-elevation radar beams can lead to missing low-level precipitation observations, while errors in specific differential phase KDP retrieval further limit QPE accuracy. To address these issues, this study uses data from two S-band dual-polarization weather radars located in Fangchenggang and Beihai, together with ground rain gauges, local raindrop size distribution (DSD) data, and digital elevation model (DEM) data. Hybrid-scan elevation construction and dual-radar mosaicking are adopted to improve the effective coverage of low-elevation observations, and a threshold-fusion strategy for KDP, denoted as KDP_TF, is proposed. The strategy selects the larger of the conventionally quality-controlled KDP_QC and variationally reconstructed KDP_VR at each valid gate. Based on the CSU-HIDRO algorithm, three QPE products, namely the quality-control QPE (QC_QPE), the variational-reconstruction QPE (VR_QPE), and the threshold-fusion QPE (TF_QPE), are generated. The precipitation events associated with Typhoon Yagi in 2024 and Typhoon Matmo in 2025 are selected for evaluation. The results indicate that TF_QPE most effectively mitigates the underestimation of 24 h accumulated precipitation. Compared with QC_QPE, the magnitude of the negative relative bias (RB) of TF_QPE is reduced, from −27.30% to −20.07% and from −16.56% to −9.78%, respectively, for the Yagi and Matmo events; the root-mean-square error (RMSE) decreases from 46.76 to 43.79 mm and from 43.27 to 43.13 mm, respectively. The hourly precipitation results further show that TF_QPE better captures the peak intensity of heavy precipitation and exhibits no evident temporal lag relative to rain gauge observations. For the heavy precipitation category, TF_QPE also yields the highest probability of detection (POD) and critical success index (CSI). These results suggest that the QPE framework developed in this study, which integrates hybrid-scan elevation construction, dual-radar mosaicking, and threshold-fusion-based KDP optimization, can improve the effective low-elevation observational coverage in mountainous terrain and mitigate the underestimation of typhoon-induced heavy precipitation in radar QPE. These results provide technical references for typhoon-related heavy-rainfall monitoring and flood early-warning as well as radar-QPE applications over coastal Guangxi.