## 1 Introduction
For a more effective management of the limited water resources in the area, it is clear that it is critical to acquire sufficient data on the spatiotemporal distribution of the rainfall events with special emphasis on light events [26]. The existing coarse rain gauge network is not sufficient to shed light on this aspect and it seemed that the knowledge gap can be filled by exploiting rainfall estimates from satellite missions that are capable of providing data on spatiotemporal distributions of rainfall. In order to demonstrate that satellite-derived data can meet this need, two sets of satellite-based rainfall data are tested and compared in this study. The first dataset refers to the most commonly used dataset related to the Tropical Rainfall Measuring Mission (TRMM). This dataset is the Multi-Satellite Precipitation Analysis, Version7 (3B42V7), hereafter, denoted as TMPA (Tropical Rainfall Measurement Mission Multi-satellite Precipitation Analysis) [27; 28; 29; 30; 31]. The second dataset refers to another more recent satellite rainfall measuring effort, the Global Precipitation Mission (GPM [7]). This dataset is the Integrated Multi-satellite Retrievals for GPM, hereafter, denoted as IMERG (Integrated Multi-satellite Retrievals for Global Precipitation Measurement) [32]. The performance of TMPA and IMERG has been investigated in several studies over different parts of the world and it is still an ongoing topic of study [33; 34; 35; 36]. Bearing in mind that the availability of the GPM-related dataset started after the launch and operational functioning of the core observatory in 2015. Studies that make use of IMERG products have only been published recently. [PERSON] et al. [37] compared IMERG and TMPA in the tropical Andes and [PERSON] and [PERSON] [38] assessed them over Singapore. [PERSON] et al. [39] evaluated the two datasets against rain gauge records in the Tibetan Plateau. The study was followed by another study over the same area by [PERSON] et al. [40]. A similar study was carried out by [PERSON] et al. [41] over another mountainous region in Pakistan. [PERSON] and [PERSON] [42] have used the two datasets in their study over Malaysia. The performance of the satellite-based analyses was also tested over the mountainous region of Northwest China [43]. [PERSON] et al. [44] compared reference and satellite-based mean daily precipitations over Northwestern South America. In addition, [PERSON] et al. [45] have assessed the two datasets over a humid basin in China. From the above brief listing of the recently published research on the comparative assessment of TMPA and IMERG, it is evident that the respective investigators have been focusing mainly on areas where rainfall is not scarce, and a sufficient network for ground measurements is in place. However, it is challenging to investigate the performance of these two datasets in an arid environment with the employment of a rather inadequate rain gauge network where rainfall estimations are highly desirable. Bearing in mind the above, the objective of the present study is to compare TMPA and IMERG analyses against ground measurements of precipitation over an arid area covered with a coarse rain-gauge network. The targeted area is the El-Qaa Plain in the Sinai Peninsula. The present study will form the basis for making recommendations on improving and expanding the current rain gauge network. The utilization and contrasting of the two precipitation data sets against the existing in situ data set was performed in support of a double-sided study aiming to optimize the design of a new rain gauge network over the test site. In addition to other decisive factors and the adoption of suitable statistical metrics, the better performance of the two satellite-based data sets may be used as providing an objective criterion for site selection of a future denser rain-gauge network. Several authors have investigated the groundwater localization in the area under study [46; 47; 48; 49; 24].