Improving Precipitation Merging: A Generalized Two-Stage Framework Using the Signal-to-Noise Ratio Optimization (SNR-opt)

Gauge-independent, multi-source precipitation merging methods are well-established approach for improving precipitation estimates. These methods predominantly aim to minimise uncertainty in precipitation magnitude, yet they frequently neglect errors in distinguishing between rain and no-rain events. This oversight often leads to biased merging weights and suboptimal precipitation estimates. In this study, we introduce an innovative two-stage framework called the Generalised Signal-to-Noise Ratio Optimisation (G-SNR) framework, specifically designed to address these limitations. The first stage employs the Categorical Triple Collocation-Merging (CTC-M) method for binary merging, effectively mitigating errors in rain/no-rain classification. The second stage applies Signal-to-Noise Ratio Optimisation (SNR-opt) to enhance precipitation magnitude estimates, leveraging the improved classification outcomes. Evaluation results demonstrate that G-SNR consistently surpasses both input data and existing methods in terms of binary classification and magnitude estimation. Importantly, it achieves error reductions across all percentiles, delivering robust performance for both low and extreme precipitation events. This framework provides a comprehensive and reliable solution to longstanding challenges in precipitation merging, significantly enhancing both accuracy and dependability.