## 6 Conclusions  
An evaluation of the two recent post real time satellite datasets (IMERG and 3B42) and a common precipitation forecast model dataset (ERA-Interim) in daily, monthly and seasonal scales against rain-gauges for four different topography and climate conditions in Iran from mid-March 2014 to February 2015 shows that IMERG, 3B42 and ERA-Interim lead to underestimation over Iran. Overall, in daily scale (at days with observed precipitation), the results reveal that all three products lead to underestimation but IMERG underestimates precipitation slightly in all four regions. The correlation coefficient between IMERG and the rain-gauge data in daily scale is mostly far superior to those of for Era-Interim and 3B42. On the basis of daily timescale of bias in comparison with the ground data, the IMERG product is better than ERA-Interim and 3B42 products. Based on monthly and seasonal scales (all days), in Guilan (G8) all products in Bushehr (G4) and Kermanshah (G5) ERA-Interim and in Tehran (G2) IMERG and ERA-Interim tend to underestimate. According to the categorical verification technique used in this study, IMERG yields better results for detection of precipitation events on the basis of Probability of Detection (POD), Critical Success Index (CSI) and False Alarm Ratio (FAR) in those areas with stratiform and orographic precipitation such as Tehran and Kermanshah compared with other satellite/model data sets. Moreover, in case of precipitation below 15 mm/day, ERA-Interim and IMERG indicate better results in all study areas, particularly ERA-Interim in Guilan, which shows reasonable results of POD, FAR and CSI. Meanwhile, for heavy precipitation (>15 mm/day), IMERG is far superior to the other products in all study areas which could be used in the future for early warning of floods _etc._  
Relative to TRMM, GPM is designed to make more accurate and frequent observations of global precipitation, especially over middle and high latitudes [39]. Although the newly introduced sensors and upgraded calibration algorithms have undoubtedly improved the GPM constellation satellites' accuracy, some challenging issues in satellite retrieval processes will continue to remain open for the satellite community, providing the impetus for more research and development. With respect to the current monitoring skills, it is almost certain that regions characterized by complex terrain and snowy/ice cover will remain to be problematic for multi-satellite retrieval in GPM [15]. These results will better guide those users who are taking advantage of these satellite-based precipitation data to accommodate their various research and operational applications. The following conclusions are drawn based on this study:  
1. Located in North of Iran, Guilan region enjoys a humid and subtropical climate. Under this climate condition, ERA-Interim performed reasonably on the basis of POD, FAR, CSI, RMSE and MAE indices on the daily scale. Additionally, the GPM constellation satellites' product (IMERG) was superior to the TRMM Multi-satellite Precipitation Satellites (TMPA) product (3B42). Moreover, all three satellite/model products underestimated the precipitation in this region, a similar conclusion by [PERSON] _et al._[13] and [PERSON] _et al._[11] who tested 3B42 in this region. Such findings may be attributed to local wind and convective precipitation in this region so that satellites could not detect precipitation properly. 2. Along the Zagros Mountains in the West of Iran, the Kermanshah region has a hot Mediterranean subtropical climate. All products, including IMERG, TRMM and ERA-Interim, underestimate the