Comprehensive evaluation of four high-resolution satellite precipitation products under diverse climate conditions in Iran

High-resolution, satellite-retrieved precipitation products are useful input data for hydrological predictions and water resources management, especially in developing countries where the availability of ground-based rainfall measurements with high spatial coverage is very limited. In this study, four widely-used satellite rainfall estimates (TMPA-3B42V7, TMPA-3B42RT, PERSIANN, and CMORPH) are evaluated on a dense raingauge network over six regions in Iran with various physiographic and climate conditions. Assessments are implemented at the daily scale for different seasons during the 5-year period 2003-2008. Overall, the results show that 3B42V7 leads to better performance than the other three products over different terrains. According to the relative bias (RBias), one of the verification metrics used in this study; 3B42V7, with an average value of 13.43% over all the regions, matches best with the raingauge observations, while PERSIANN and 3B42RT overestimate precipitation by 78.13% and 31%, respectively. But, CMORPH with RBias of -17.6% tends to underestimate the rainfall amount. Furthermore, the evaluations over different seasons indicate that the best performance for PERSIANN and both TMPA products is during the winter, while for CMORPH it is during the autumn season. From the critical success index (CSI), used to assess the rain detecting skill of satellite products, one can conclude that PERSIANN leads to better estimations during the winter and summer, 3B42RT during the spring, and CMORPH during the autumn. Generally, the analyses implemented in this research provide quantitative information of error characteristics associated with satellite precipitation products over different parts of Iran and thus will offer users a better understanding of satellite rainfall estimates' applicability in this area. [GRAPHICS]