The availability of accurate precipitation data is crucial for water resources management, disaster prevention, and related research. While gridded products offer precipitation information at high spatial resolution, they still exhibit significant errors in precipitation estimation. The merging of multi-source gridded products has become a mainstream approach for improving precipitation estimation. However, many existing frameworks rely on gauge observations to estimate the merging weights, which limits their applicability in data-scarce regions. Moreover, these frameworks predominantly focus on enhancing precipitation estimation rather than on precipitation events. This study proposes a novel Double Triple Collocation-based (DTC) merging framework, which combines time–space TC (TC_2D)-based precipitation rate merging with categorical triple collocation (CTC)-based rain/no-rain merging. The objective is to minimize errors in precipitation estimation and enhance the detection capability for precipitation events without relying on rain gauge observations. Given that the TC_2D-based method is initially applied to precipitation merging, its effectiveness must be verified by comparing it with classic TC-based merging approaches (TC_Space and TC_Time). Taking the Jiulong River Basin (JRB) as a case study, the performance of the DTC and its comparative objects was evaluated with three triplets composed of independent precipitation products. The results indicated that all merged precipitation products outperform their parent products. Furthermore, the merged precipitation datasets, after being corrected using rain/no-rain time series generated by CTC-based merging, showed enhanced capability in detecting precipitation events. The performance of merged precipitation products was found to be highly dependent on the quality of the satellite precipitation products (SPPs) within the triplets. This study provides a promising approach for generating high-quality precipitation datasets, particularly in regions with limited observation data availability.

中文翻译：

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使用基于双三重配置（DTC）的合并框架改进日降水估计


准确的降水数据的可用性对于水资源管理、灾害预防和相关研究至关重要。虽然格网化产品以高空间分辨率提供降雨信息，但它们在降雨量估计中仍然表现出显著的误差。多源网格化产品的合并已成为改进降水估计的主流方法。然而，许多现有框架依赖于仪表观测来估计合并权重，这限制了它们在数据稀缺地区的适用性。此外，这些框架主要侧重于增强降水估计，而不是降水事件。本研究提出了一种新的基于双重三重搭配 （DTC） 的合并框架，该框架将基于时空 TC （TC_2D） 的降水率合并与基于分类三重搭配 （CTC） 的雨/无雨合并相结合。目标是在不依赖雨量计观测的情况下最大限度地减少降水估计中的误差并提高降水事件的检测能力。鉴于基于 TC_2D 的方法最初应用于降水合并，必须通过将其与经典的基于 TC 的合并方法（TC_Space 和 TC_Time）进行比较来验证其有效性。以九龙河流域 （JRB） 为例，用由独立降水产物组成的 3 个三元组评价了 DTC 及其比较对象的性能。结果表明，所有合并的沉淀产物都优于其母产品。此外，合并的降水数据集在使用基于 CTC 的合并生成的下雨/无雨时间序列进行校正后，显示出增强的降水事件检测能力。 发现合并沉淀产物的性能高度依赖于三联体内卫星沉淀产物 （SPP） 的质量。本研究为生成高质量的降水数据集提供了一种有前途的方法，特别是在观测数据可用性有限的地区。