Abstract
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.
Published Version
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