Evaluating the performance of remote sensing precipitation products CMORPH, PERSIANN, and TMPA, in the arid region of northwest China

The arid region of northwest China is a large area with complex topography. Hydrological research is limited by scarcity and uneven distribution of rain gauges. Satellite precipitation products provide wide coverage and high spatial–temporal resolutions, but the accuracy needs to be evaluated before application. In this paper, the reliability of four satellite precipitation products (CMORPH [Climate Prediction Center’s morphing technique], PERSIANN [Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks], TRMM [Tropical Rainfall Measuring Mission] 3B42, and TRMM 3B43) were evaluated through comparison with ground data or reported values on daily, monthly, and annual scales from 2003 to 2010. Indices including frequency bias index, probability of detection, and false alarm ratio were used to evaluate recorded precipitation occurrences; relative mean bias, the correlation coefficient, and the Nash coefficient were used to assess precipitation amount. Satellite precipitation products were more accurate in the warm than in the cold season, and performed better in northern Xinjiang than in other regions during the cold season. CMORPH and PERSIANN tended to overestimate precipitation. TRMM 3B42 and TRMM 3B43 performed best because the former most accurately detected precipitation occurrences on a daily scale, and both produced accurate space–time distribution of precipitation and the best consistency with rain gauge observations. Only a few monthly precipitation values for TRMM 3B42 and TRMM 3B43, and annual precipitation values for TRMM 3B42 were with satisfactory precision. TRMM3B42 and TRMM 3B43 are therefore recommended, but correction will be needed before application. Factors including elevation, relative relief, longitude, and latitude had significant effects on the performance of satellite precipitation products, and these factors may be helpful in correcting satellite precipitation.