## 3 Results
### Daily Analysis
Also in this section, continuous and categorical statistics are used to describe the IMERG characteristics with respect to gauge locations. This analysis gives a spatial perspective of the IMERG behavior for the daily analysis. To this end, Figure 8 shows correlations statistics in the same way it was presented in Figure 3 for the hourly analysis. In Figure 8a correlations are good for most meteorological stations. _COR_ values between 0.4 and 1.0 dominate, while only a few stations show correlations below 0.4. A flat regression line in Figure 8c indicates no relation between _COR_ and the elevation of the meteorological station. The scatter plot in Figure 8b shows that daily precipitation intensities below 75 mm/day are the most common. The shape of the plot also suggests that IMERG tends to overestimate rain gauges for small precipitation amounts and underestimates them for large rainfall. It is not clear here what threshold to select, for IMERG switches from underestimating to overestimate daily rain gauges but it should be between 10 and 50 mm/day. In a manner similar to that of Figure 4, Figure 9 shows the _RBIAS_ and _RMSE_ statistics for the daily analysis. Figure 9a shows that the bulk of the stations have _RBIAS_ ranging between \(-\)50 and 50%; while most of the rest of the stations significantly overestimate observations. The trend of the _RBIAS_ in Figure 9c indicates that the overestimation decreases with elevation. _RMSE_ values in Figure 9b are found to increase toward south of the region where more precipitation occurs. Also, _RMSE_ tends to decrease from the coast toward inlands. These trends seem to be dominant over any relationship that may exist between the _RMSE_ and the elevations of the meteorological stations. Note that Figure 9d shows only a slight trend for _RMSE_ to decrease with terrain elevation. Categorical statistics (_FAR_, _POD_, _CSI_ and _ACC_) for daily precipitation data are shown in Figure 10 in the same manner _RBIAS_ and _RMSE_ were shown in Figure 9. Therefore, each categorical statistic is represented in a map to analyze spatial behavior and in a trend plot to analyze its relationship with respect to the elevation of the meteorological stations. Figure 10a,b show that _FAR_ values are generally smaller than _POD_ values which means that daily IMERG precipitation data are more accurate than the hourly data. _FAR_ values are usually smaller than  
Figure 7: Absolute errors as a function of meteorological stations elevation. Absolute errors were calculated using the IMERG and gauge daily data from 1 April 2014 to 31 October 2015. Lines show the absolute errors for precipitation intensities ranging between 0 and 10 mm/day (cyan), 10 and 25 mm/day (blue) and over 25 mm/day (green). 0.6 while most _POD_ values overreach this threshold. Also, _FAR_ tends to decrease while _POD_ increases with increasing terrain elevations; see Figure 10c,d. These results are consistent with the fact that _CSI_ in Figure 10g also increases with elevation. Figure 10e shows most meteorological stations having a _CSI_ between 0.4 and 0.8 which represents an improvement over the hourly analysis. Figure 8: (**a**) Correlation (_COR_) map; (**b**) scatter plot for daily precipitation events and (**c**) correlation as a function of meteorological stations elevations (red line); the black line shows the trend and the elevation of the meteorological stations is represented by the gray, shaded area. IMERG estimates and gauge daily data from 1 April 2014 to 31 October 2015 have been used for these calculations. Figure 9: (**a**) Relative bias (_RBIAS_) map; (**b**) Root mean squared error (_RMSE_) map; (**c**) and (**d**) are _RBIAS_ and _RMSE_, respectively, shown as a function of meteorological stations elevations (red lines); the black lines show the trends and the elevation of the meteorological stations is represented by the gray, shaded area. IMERG estimates and gauge daily data from 1 April 2014 to 31 October 2015 have been used for these calculations.