干旱区生态环境知识资源中心

To investigate the long-term characteristics of precipitation in Xinjiang, China, two long-term monthly satellite precipitation datasets called CHIRPS (Climate Hazards Group Infrared Precipitation with Stations data) and PERSIANN-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Climate Data Record) are evaluated and compared with in situ measurements from 105 meteorological stations for the period 1983-2014. The evaluation is performed at multiple temporal and spatial scales. Results based on comparisons with in situ measurements show that PERSIANN-CDR and CHIRPS have similar correlations. However, both of the BIAS and RMSE, CHIRPS outperformed PERSIANN-CDR with the smaller errors and bias. In terms of the long time-series comparison at temporal scale, CHIRPS is more accurate with gauge observations at monthly and annual scales while PERSIANN-CDR tends to overestimate the precipitation in the rain season (from May to September). Furthermore, compared with PERSIANN-CDR, results show that CHIRPS is more accurate in reflecting the spatial distribution of average monthly and annual precipitation. In summary, the study shows that CHIRPS is a valuable complement to gauge precipitation data and provides useful guidance when choosing satellite precipitation product for hydrometeorological applications in Xinjiang.