A three stage ‘Matching-Retrieval-Optimization’ method for radar–rainfall retrieval: a case study in the Yiluo River Basin, China

In modern rainfall monitoring, forecasting, and early warning systems, radar serves as a critical technological foundation, profoundly transforming approaches to flood management and disaster mitigation. To improve the accuracy of radar-based rainfall retrievals and effectively assess the reliability of the results, this study introduces an innovative, transferable three-stage radar–rainfall retrieval framework (TS-RR), consisting of three essential components: matching, retrieval, and optimization. Additionally, a novel point-real-process (PAP) three-dimensional evaluation method is proposed, enabling a comprehensive and quantitative assessment of retrieval accuracy from three perspectives: stations, spatial distribution, and rainfall processes. The results demonstrate that (1) the TS-RR framework effectively guides radar–rainfall retrieval, with the optimization phase (Stage III) significantly enhancing the capture of rainfall peaks, improving spatial distribution accuracy (KLD < 0.1), and demonstrating strong adaptability to dynamic rainfall processes (CC > 0.9). (2) Under limited sample conditions, the PMM in Stage II exhibits strong robustness, maintaining stable correlation performance (correlation coefficient, CC = 0.7–0.9). (3) The PAP approach effectively evaluates the reliability of the retrieval results. The proposed method offers practical value by providing scientifically grounded data support and decision-making references for water resource management, flood forecasting, and early warning systems.