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Variations in precipitation on the Tibetan Plateau (TP) significantly impact regional water resources and global climate systems; however, precise estimation is hindered by limited ground observations. This study performed a multidimensional evaluation of eight precipitation datasets, including satellite-based datesets (TRMM, GPM, CMORPH, and PERSIANN) and reanalysis-based datasets(ERA5-Land, GLDAS, CMFD, and TPHiPr), across the TP. Bias correction considering wind speed and temperature was implemented on observational data from 87 meteorological stations and 5 flux stations. Performance assessment utilized meteorological and flux stations through statistical indices, while grid uncertainties were evaluated with the generalized three-cornered hat method. Comprehensive performance was analyzed across various temporal and spatial scales using the Distance between Indices of Simulation and Observation (DISO) index. The results indicated that TPHiPr exhibited superior performance across spatiotemporal scales, demonstrating significant robustness. CMFD demonstrated commendable overall performance, while GPM excelled in flux station evaluations and grid uncertainty assessments. In contrast, CMORPH, PERSIANN, ERA5-Land, and GLDAS exhibited notable biases. Selecting or integrating more suitable precipitation products is advisable, contingent upon research requirements and the relevant spatial and temporal scales. These findings offer essential insights for hydrological modeling and climate research in the TP and similar alpine regions.
Cheng et al. (Mon,) studied this question.