Advancing High-Mountain Precipitation Reconstruction Through Merging of Multiple Data Sources: Triple Collocation Versus Signal-to-Noise Ratio Optimization

Merging multisource satellite precipitation data through triple collocation (TC) is an established method for enhancing precipitation estimates without ground observations. However, the effectiveness of TC is limited by three constraints: the need for three data sources, the sum of weights equalling one, and no error cross correlation (ECC) among datasets. These limitations restrict applications to areas with three distinct data sources, may result in suboptimal combinations, and can introduce biases. We explored a new framework, signal-to-noise ratio optimization (SNR-opt), which alleviates these restrictions. Applying TC and SNR-opt to datasets in the Himalayas, including three satellite-based (IMERG Final, CMORPH, and PERSIANN), one soil moisture-based (SM2RAIN-ASCAT), and one reanalysis dataset (ERA5), we assessed their performance. The flexibility of SNR-opt allowed for creating products suited to varying rainfall conditions and reconstructing extreme precipitation products for high-mountain Asia (HMA). The results show that: 1) the data triplet containing IMERG-final was the best performing among all merging products; 2) the merging method significantly enhanced dataset accuracy compared to the parent datasets, with SNR-opt proving superior to TC in all selected precipitation indices evaluated; and 3) SNR-opt had the flexibility of estimating both low and heavy rainfall, with a reduced bias for extreme precipitation, using fewer inputs (two) compared to TC. These advantages were found to be especially notable for the high-mountain region in focus, given the significant variability and limited observational data available.