## 2 Latest Developments  
Several studies have assessed the quality of the rainfall products of GPM missions when estimating global precipitation and the performance of precipitation nowcasting. At present, point measurements using a rain gauge and disdrometer [21; 22], ground-based weather radar [23] and satellite-based sensors [24] are the most widely used techniques to measure and estimate precipitation. In most regions, tipping bucket rain gauges are frequently utilized to measure precipitation. Several studies demonstrated that tipping bucket rain gauge measurements are the most reliable technique in terms of point ground measurements [25; 26; 27]. However, they cannot measure quantities of precipitation less than 0.1 or 0.2 mm, depending on the size of the bucket [28]. Thus, depending on the intensity of rainfall measured, the tipping bucket rain gauges perform a temporal integration over a more or less long period. By contrast, ground weather radars have a large volume sample but present an indirect observation [23]. In terms of radar observations (also known as a remote sensing instrument), the reflectivity of precipitation at a given altitude is measured to estimate precipitation. The ground weather radar is able to supply rainfall rate data with a fine spatiotemporal resolution, but it is hindered by several limitations. For instance, the poor data management and archiving, difficult image data processing and usage is limited for weather forecasting [29]. Although ground weather radar can provide sound spatial resolutions to relatively large precipitation areas, the accuracy of radar precipitation estimates is affected by several sources of uncertainties and errors. For example, the radar signal attenuation and stability of radar calibration can affect the radar reflectivity measurement because of contamination of heavy precipitation by range effects or non-meteorological echoes (e.g., ground clutter) [30]. For satellite precipitation estimates, sensors onboard low-orbiting satellites are the only instruments able to retrieve global and homogeneous measurements of precipitation [31]. After the satellite measurement of radiances, the approaches applied to derive precipitation from radiances have advanced from Infrared (IR)- and Visible (VIS)-based methods to active and passive Microwave (MW) techniques and merged MW and IR methods [31]. In terms of active microwave sensors, Ku-band TRMM Precipitation Radar (PR) was the first space-borne precipitation radar and has operated since 1997 [32]. The goal of TRMM satellite was to measure moderate to heavy precipitation to give a better understanding of the precipitation distribution on Earth and conduct near-real-time precipitation products. Since then, many methods such as [33; 34; 35] have built upon the success of the TRMM, culminating in the GPM mission. The Integrated Multi-Satellite Retrievals for GPM (IMERG) is the multi-satellite precipitation algorithm for GPM, which merges sparse precipitation estimates from IR-based geosynchronous satellites, all constellation microwave sensors and monthly gauge precipitation data [29]. NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC) produced the latest GPM IMERG version 5 (IMERG V5) precipitation dataset, which was employed in this study. Compared to TRMM, GPM has improved temporal and spatial resolution, as well as a more efficient minimum latency time [31; 36; 37; 38]. For example, [PERSON] et al. [36] compared both the TRMM TMPA (V7) and GPM IMERG (V03) with the rain gauge measurements over India. Their findings demonstrates that GPM provide more accurate information about extreme rainfall events compared to TRMM. The IMERG data also presented a notable improvement over TMPA during the comparison with the rain gauge data in mountainous areas. [PERSON] et al. [37] compared both the GPM IMERG V05 data and the TRMM 3B42 V7 data with the rain gauge measurements over Pakistan.