## 6 Concluding Remarks  
With an increasing spatiotemporal resolution of the satellite-based rainfall datasets, more emphasis is given worldwide in using these sources of rainfall analyses for a wide range of applications. Two such datasets have been utilized in the present study, namely, TMPA and IMERG. These datasets were compared between them and again a local rain gauge network in El-Qaa Plain, Sinai Peninsula. The IMERG dataset now includes TRMM-era data extending back to 2000, rendering this dataset a valuable tool in many hydrological applications. Research in the application of the IMERG database in several sectors that need rainfall records will certainly continue in the years to come and this study is a contribution toward better assessing this valuable data source. The statistical metrics used demonstrate the low correlation and significant differences between the pixel values of the TMPA and IMERG datasets in the moderate and heavy-intensity 2015 and 2016 events. Datasets from the light-intensity events, namely, 2017 and 2018, were more highly correlated. Additionally, the values recorded over the lowland region were more uniform than those of the highland region, where a greater variation was observed. When the two satellite-based rainfall datasets were compared to the rain gauge data, it was noted that their performance was best during the light-intensity events, particularly around the event onset (3 h and 6 h). In contrast, poorer performance was noted during intense events and at the later precipitation stages in such events (12 h and 24 h). These findings are in good agreement with the findings by [PERSON] et al. (2019) who performed a similar comparative analysis over China. However, the two geographical areas have different climatic characteristics. The same authors found that both satellite-based products overestimate light rain, whereas both underestimate moderate to heavy rainfall. Further  
Figure 5: Bar plots of Probability of Detection (POD), False Alarm Rate (FAR), and Critical Success Index (CSI) results of the TMPA and IMERG at three different thresholds (0.1, 1, and 10) using data from all mentioned events. (**a**), (**b**), and (**c**) represent the thresholds of 0.1, 1, and 10 for the TMPA data, respectively, (**d**), (**e**), and (**f**) represent the thresholds of 0.1, 1, and 10 for the IMERG data. more, data coherence and uniformity were lower in the highland region (referenced to the Saint-Catherine station) when compared to the lowland region data (derived from the Ras-Sudr, Abu-Rudies, El-Tor, and Sharm El-Sheikh rain gauge stations). TMPA and IMERG were compared to the limited rain gauge records, using various statistical metrics to evaluate their effectiveness in replicating in-situ observations. Performance varied, with the IMERG data demonstrating the best performance, producing the lowest RMSE, BIAS, and MAE values. This was followed by the 0.1\({}^{\circ}\) resolution TMPA, and, lastly, the 0.25\({}^{\circ}\) resolution TMPA data, with the latter exhibiting the weakest performance. In the present study, categorical statistics have indicated high performance by both the TMPA and IMERG, during the light-intensity events. However, a low certainty was observed for the high-intensity events. Overall, the IMERG datasets performed better than the TMPA in all thresholds. The findings of this study could be used to support the postulation on the superior performance of IMERG over TMPA in arid and semi-arid areas, but this cannot be generalized. Several comparative studies confirm the superiority of the IMERG product over the TRMM-era one. For example, [PERSON] et al. [17] compared the performance of TRMM (3B42v7) and GPM (IMERG) over Far-East Asia during the pre-monsoon and monsoon seasons. Their results showed that GPM-3 IMERGHH performed better than TRMM3B42V7. However, both satellite-based products had several drawbacks with regard to topographical factors, especially for orographic regions.