Merging multisatellite precipitation products using stacking method and the censored-shifted gamma ensemble model output statistics in china's Beimiaoji basin

Abstract

High-quality precipitation data are crucial for hydrological analyses, water resource management, and drought monitoring. Merging precipitation products from different sensors and algorithms provides more reliable spatial information and can obtain high-quality precipitation data. To obtain high-spatiotemporal-resolution precipitation, in this study, we proposed a method to merge multisource precipitation products by downscaling coarse resolution products(geographically weighted regression (GWR) algorithm) and then eliminating biases in individual datasets(stacking algorithm), and finally merging the bias-adjusted precipitation by weighted average(the ensemble model output statistics-censored, shifted gamma (EMOS-CSG) algorithm). The multisource precipitation products included the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation 3B42 in Real-Time (TMPA-3B42RT), Climate Precipitation Center Morphing Technique (CMORPH), Global Satellite Mapping of Precipitation Near-Real-Time (GSMaP_NRT), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) products). The merging method was applied to the Beimiaoji basin from April to October in the 2016–2019 period. The precipitation observations at 38 gauges were spatially and randomly divided into two parts, 28 gauges were used for the training, while the other for the performance evaluations. The results showed that (1) the daily merged precipitation product had a better performance than the original satellite products in terms of the six considered statistical indexes, with the lowest root mean square error (RMSE) at 4.33 mm and the highest correlation coefficient (CC) at 0.64; (2) the utilized merging method not only increased the spatial resolution to 1 km but also captured more detailed precipitation distribution information; and (3) considering the influence of the gauge density, the performance of the merged product was still improving after the number of gauge stations exceeds 16, but the improvement was not significant.