Abstract
Evaluating the accuracy of the spatial and temporal dimensions of satellite precipitation products (SPPs) can effectively grasp product performance and potential application. Three high spatial resolution SPPs, CHIRPS, GPM-IMERG, and PERSIANN-CCS, were collected from 2015 to 2018 in this study. Observation data rain gauges were used to analyze and evaluate the spatio-temporal accuracy features of these SPPs. First, based on statistical and categorical indices, the accuracy of the SPPs temporal dimension in different months and seasons was evaluated, and the accuracy of the SPPs' spatial dimension in different basins was analyzed. Then, targeting the precipitation and process of the rainstorm events, observation capabilities of these SPPs were explored. In addition, effects of rain gauge irregular distribution, autocorrelation in SPPs, and elevation gradients on the performance of products in detecting precipitation were discussed. The main conclusions are: (1) the performance of SPPs in summer is relatively good, and the bias (relative bias, frequency bias) in spring is large. The performance of SPPs in winter is slightly weak, the probability of detection is low and the false alarm rate is high. (2) there are performance differences in the precipitation spatial expression of SPPs in different basins. The gradient features are obvious from the southeast coast to the northwest inland. (3) the products have certain capabilities to express extreme precipitation events, but the performance is slightly lower compared with rain gauges. Among the three SPPs, GPM-IMERG shows best expression of precipitation with correlation in 0.79 and probability of detection in 0.70. (4) in the range of 200–2000 m, the detection performance of SPPs is closely affected by elevation. Autocorrelation can cause overestimation of evaluation indices at product calibration rain gauges. This study clarifies the spatial and temporal features and accuracy performance of three high-resolution SPPs in China area, which can provide references for its application in the fields of hydrological process, meteorology and climate.
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