Abstract
Based on the high-quality homogenized precipitation data from all 2,419 national weather stations in China, the climatology and anomaly percentage fields are derived, and then the digital elevation model (DEM) is employed to reduce the influence of elevation on the spatial interpolation accuracy of precipitation due to the unique topography in China. Then, the gradient plus inverse distance squared (GIDS) method and the inverse distance squared (IDS) method are used to grid the climatology field and the anomaly percentage field, respectively, and the 0.5 × 0.5° gridded datasets during 1961–2018 in China are obtained by combining them together. The evaluation shows that the mean absolute error (MAE) between the analysis value and the observation is 15.8 mm/month. The MAE in South China is generally higher than that in North China, and the MAE is obviously larger in summer than in other seasons. Specifically, 94.6, 54.4, 4.6, and 53.8% of the MAE are below 10 mm/month in winter (DJF), spring (MAM), summer (JJA), and autumn (SON), respectively, and 99.5, 79.9, 22.8, and 82.1% of them are less than 20 mm/month. The MAE over China in four seasons is 3.8, 13.2, 33.5, and 12.7 mm/month, respectively. This dataset has the potential of broad application prospects in the evaluations of weather and climate models and satellite products.
Highlights
In the study of global or regional large-scale climate change, it is necessary to grid the climate series to effectively reduce or avoid spatial sampling errors (Shen et al, 2010; Wu and Gao, 2013; Zhao et al, 2014; Zhao and Zhu, 2015; Cheng et al, 2020)
The mean absolute error (MAE) in southern regions is generally higher than that in northern regions in each season, which is closely related to the abundance degree of precipitation
The digital elevation model (DEM) data are employed to reduce the influence of elevation on the spatial interpolation accuracy of precipitation due to the unique topography in China
Summary
In the study of global or regional large-scale climate change, it is necessary to grid the climate series to effectively reduce or avoid spatial sampling errors (Shen et al, 2010; Wu and Gao, 2013; Zhao et al, 2014; Zhao and Zhu, 2015; Cheng et al, 2020). Precipitation is one of the most important meteorological elements. High-resolution gridded precipitation data are important input parameters for atmospheric, climatic, hydrological, and ecological models, and they are necessary for the evaluations of numerical forecast products. Several daily or monthly precipitation series have been developed on regional scales in China in the past 30–100 years (Xie et al, 2007; Shen et al, 2010; Li et al, 2012; Wu and Gao, 2013; Zhao et al, 2014). A series of problems, such as the low density of stations, the uneven distribution of stations, the quality of raw data, and the inadequacy of interpolation methods, lead to the systematic evaluation on the gridded precipitation datasets still being open to discussion, especially using higher quality observation and more state-of-the-art interpolation methods
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