LAUSR

ABSTRACT A novel multiple precipitation assimilation framework based on Bayesian merging with Gamma distribution (BMG) is proposed in this study. Thirty-four rainfall events are collected from 2010 to 2015 over Jinhua River watershed, China. Seven assimilating scenarios consisted of three satellite rainfall products, namely Tropical Rainfall Measuring Mission Multi-Satellite Precipitation Analysis 3B42 (TMPA 3B42), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and Climate Prediction Centre Morphing technique (CMORPH) and the interpolation of gauge products are compared with the ground measurements at spatial resolutions of 0.250° × 0.250°, 0.100° × 0.100°, 0.050° × 0.050°, 0.025° × 0.025°, and 0.010° × 0.010°. The results show that (1) the performance of all merging scenarios gradually decreases with the increase of spatial resolutions, however all scenarios perform consistently at different spatial scales, indicating that the BMG is robust and insensitive to spatial resolutions; (2) the merging product synthesized from PERSIANN, CMORPH, and gauge measurements outperforms other merging scenarios, suggesting their compatibility is higher than that of TMPA 3B42 and others; and (3) the contribution of an individual satellite product on different merging scenarios becomes larger as the spatial resolution increases. Overall, the proposed BMG has great potential to assimilate various rainfall products for continuous rainfall events even at large scales.