## 3 Results
### Hourly Analysis
Figure 3c confirms this slight trend. The scatter plot in Figure 3b confirms one of the main characteristics of the precipitation: its mode is not to precipitate. The shape of the scatter plot suggests that the probability for IMERG to overestimate the observed precipitation increases for precipitation intensities below 5 mm/h. On the contrary, IMERG \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Number of Events** & **Min (mm/h)** & **Max (mm/h)** & **COR** & **BIAS (mm/h)** & **RMSE (mm/h)** & **RBIAS (\%)** \\ \hline
27,670 & 1.20 & 35.79 & 0.18 & –2.70 & 5.82 & \(-61.00\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Extreme events statistics. Hourly analysis. Figure 3: (**a**) Correlation (_COR_) map; (**b**) scatter plot for hourly 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 hourly data from 1 April 2014 to 31 October 2015 have been used for these calculations. most likely underestimates rain gauges for precipitation intensities above this value. Above 5 mm/h, the probability of underestimation by IMERG increases with increasing observed rainfall intensities. Figure 4a,c for the _RBIAS_ and Figure 4b,d for the _RMSE_ are homologous to Figure 3a,c respectively. Figure 4a shows that for more than the half of the meteorological stations IMERG overestimates hourly rain gauges. Also, it can be noticed that overestimations values are higher than the underestimation values. For some meteorological stations the overestimation can be two or three times higher than their mean hourly rain gauges. In addition, Figure 4c shows that the relative bias clearly decreases with elevation. Figure 4b shows that the _RMSE_ values are below 2.5 mm/h. However, the majority of the stations show _RMSE_ values between 0.5 and 1.0 mm/h. In Figure 4d, the trend for _RMSE_ with respect to topography is flatter than that for _RBIAS_ (Figure 4c) and only a very slight trend of the _RMSE_ values to decrease as the elevation increases can be observed. Categorical statistics (_FAR_, _POD_, _CSI_ and _ACC_) for hourly precipitation data are shown in Figure 5 in the same manner that the _RBIAS_ and _RMSE_ have been shown in Figure 3. 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. For hourly precipitation data it can be noticed that the _FAR_ values (Figure 5a) are higher than the _POD_ values (Figure 5b) for all meteorological stations. Note that accurate IMERG estimates imply that the _FAR_ values are close to 0 while the _POD_ values are close to 1. However, values for the _FAR_ are greater than 0.4 and values between 0.6 and 0.8 are more common. Meanwhile, values for the _POD_ are below 0.8 and the most common values range from 0.4 to 0.6. The _FAR_ in Figure 5c shows more sensitivity to elevation than the _POD_ and the general trend of these values is to decrease with increasing elevations. Figure 4: (**a**) Relative bias (_RBIAS_) map; (**b**) root-mean-square error (_RMSE_) map; (**c**) and (**d**) are the _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 hourly data from 1 April 2014 to 31 October 2015 have been used for these calculations. Figure 5e,f also show two categorical measures that characterize the degree of accuracy by which IMERG represent hourly rain gauges. However, both measures seem to differ in how accurate IMERG is. _CSI_ values in Figure 5e range between 0 and 0.4 and a slight trend to increase with elevation can be observed in Figure 5g.