Abstract
Based on the long series of gauge rainfall data from 1979 to 2015, the performance of Multi-Source Weighted-Ensemble Precipitation (MSWEP) precipitation dataset in the Zhoushan Archipelago and its surrounding sea area in Southeast China was evaluated from a variety of perspectives, and then the Cressman scheme was used to merge MSWEP with surface gauge measurements. It was found that at the spatial scale of 0.1° × 0.1°, MSWEP correctly detected most of the daily rainfall events in the study area. The surface precipitation was generally underestimated, with a relative deviation no more than 10%, but there was a fairly high miss reporting on heavy precipitation. The performance of MSWEP is also obviously characterized with seasonal fluctuation. Compared with the gauge records interpolation results, the accuracy statistics of rainfall dataset generated by merging MSWEP with gauge observations is improved to a certain degree. Especially its comprehensive identification ability of the dry and wet state for daily precipitation has been obviously raised. In addition, the merged data has the mixed characteristics of rain gauge observations and MSWEP in spatial structure. This paper has deepened the understanding of the performance of MSWEP in islands and sea areas, and also strengthened the understanding of the marginal effect of merging gauge data with MSWEP, even other global precipitation datasets.
Highlights
Precipitation is one of the most basic meteorological and hydrological elements
Using the Cressman scheme, at three different time scales of daily, monthly, and yearly, in the study area precipitation dataset based on gauges and Multi-Source Weighted-Ensemble Precipitation (MSWEP) rainfall combination
Discussion the accuracy evaluation results of MSWEP and CMSWEP at different time scales are given from different perspectives in the above sections
Summary
Under the combined influenced by atmospheric motion and underlying surface, precipitation has complex spatial variability. Acquisition of detailed precipitation spatial distribution is of great significance for a series of applications including natural disaster prevention and control, water resources management and regulation, infrastructure operation and maintenance, and ecological environment protection. Rain gauge networks have been the main way to obtain the spatial distribution of precipitation. In order to make up for the deficiency of the rain gauge network in spatial coverage, representativeness and timeliness, weather radar, meteorological satellite, atmospheric numerical model, and other precipitation acquisition technologies were developed. The existing precipitation acquisition methods are different in the principle of observation way, accuracy, spatial and time resolution, and coverage, but they are theoretically complementary to some extent [1]. Integration and merging of precipitation information from different sources and with different nature
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