Spatio-temporal accuracy evaluation of MSWEP daily precipitation over the Huaihe River Basin, China: A comparison study with representative satellite- and reanalysis-based products

Abstract

Multi-source weighted-ensemble precipitation (MSWEP) is one of the most popular merged global precipitation products with long-term spanning and high spatial resolution. While various studies have acknowledged its ability to accurately estimate precipitation in terms of temporal dynamics, its performance regarding spatial pattern and extreme rainfall is overlooked. To fill this knowledge gap, the daily precipitation of two versions of MSWEP (MSWEP V2.1 & V2.2) are compared with that of three representative satellite- and reanalysis-based products, namely the Tropical Rainfall Measuring Mission (TRMM 3B42 V7), the climate prediction center morphing technique satellite-gauge merged product (CMORPH BLD), and the fifth-generation reanalysis product of the European Centre for Medium Range Weather Forecasts (ERA5). The comparison is made according to the dense daily rainfall observations from 539 rain gauges over the Huaihe River Basin in China during 2006–2015. The results show that MSWEP V2.1, MSWEP V2.2 and CMORPH BLD have better performance on temporal accuracy of precipitation estimation, followed by ERA5 and TRMM 3B42 V7. MSWEPs yield the most even spatial distribution across the basin since it takes full advantage of the multi datasets. As the weighted-ensemble method is independently carried out on each grid in MSWEPs, the spatial distribution of local precipitation is changed by different source data, which results in that MSWEPs perform worse than CMORPH BLD in terms of the representation of precipitation spatial pattern. In addition, the capability of MSWEPs to describe the spatial structure in the rainy season is lower than that in the dry season. Strong precipitation (⩾100 mm/d) events are better represented in TRMM 3B42 V7 products than in MSWEPs. Finally, based on the comparison results, we suggest to improve the merging algorithm of MSWEP by considering the precipitation spatial self-correlation and adjusting the merging weights based on the performance of the source datasets under different precipitation intensities.