Abstract
ABSTRACT Due to high spatial and temporal resolution and near real-time accessibility of satellite precipitation data, the necessity of using these data in the hydrological application seems to be more pressing than ever. In this study, the skill of six post real-time (Climate Hazards Group Infrared Precipitation with Station data (CHIRPS); CPC MORPHing technique (CMORPH); Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN); PERSIANN Climate Data Record (PERSIANN-CDR); precipitation produced from the inversion of the satellite soil moisture (SM) observations derived from the European Space Agency (ESA) Climate Change Initiative (SM2RAIN); Tropical Rainfall Measuring Mission (TRMM 3B42-V7)) and two near real-time (PERSIANN Cloud Classification System (PERSIANN-CCS); TRMM real-time (TRMM 3B42-RT)) satellite daily precipitation products are evaluated by comparing with 28 rain gauges in Karkheh River Basin, located in the semi-arid region of Iran. The evaluation is performed for two types of quantiles (lower quantile (< Q10 and < Q25) and upper quantile (> Q50, > Q75, and > Q95)) and rainy seasons using categorical and quantitative metrics for the period March 2003 to December 2014. The spatial analysis indicated that there is not remarkable variation in the skill of satellite precipitation products across the study area. Results showed that the satellite precipitation estimates are more accurate in lower than upper quantile. The seasonal analysis presented that the skill of satellite precipitation products for fall and spring is slightly higher than winter. For post real-time satellite, in terms of POD (VHI), PERSIANN-CDR in spring (winter and spring), SM2RAIN in winter and spring (fall) shows the best skill, and according to FAR and CSI, CMORPH is the best in all seasons. In addition, VHI and POD of PERSIANN-CCS have better skill than 3B42-RT for near real-time satellite for all seasons. Generally, PERSIANN-CCS (PERSIANN-CDR and SM2RAIN) shows the best skill for near (post) real-time satellite precipitation estimations when whole data are included in the analysis.
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