###### Abstract  
This study introduces a systematic methodology whereby different technologies were utilized to download, pre-process, and interactively compare the rainfall datasets from the Integrated Multi-Satellite Retrievals for Global Precipitation Mission (IMERG) satellite and rain gauges. To efficiently handle the large volume of data, we developed automated shell scripts for downloading IMERG data and storing it, along with rain gauge data, in a relational database system. Hypertext pre-processor (pHP) programs were built to visualize the result for better analysis. In this study, the performance of IMERG estimations over the east coast of Peninsula Malaysia for the duration of 10 years (2011-2020) against rain gauge observation data is evaluated. Moreover, this study aimed to improve the daily IMERG estimations with long short-term memory (LSTM) developed with Python. Findings show that the LSTM with Adaptive Moment Estimation (ADAM) optimizer trained against the mean square error (\(MSE\)) loss enhances the accuracy of satellite estimations. At the point-to-pixel scale, the correlation between satellite estimations and ground observations was increased by about 15%. The bias was reduced by 81-118%, \(MAE\) was reduced by 18-59%, the root-mean-square error (\(RMSE\)) was reduced by 1-66%, and the Kling-Gupta efficiency (\(KGE\)) was increased by approximately 200%. The approach developed in this study establishes a comprehensive and scalable data processing and analysis pipeline that can be applied to diverse datasets and regions encountering similar domain-specific challenges. IMERG satellite rainfall; SQL relational database; LSTM; ADAM; Python; pHp 2023  
1 2023  
S. Ho, Y.-Y. Y. (CMORPH) [13], the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) [14; 15], the Global Satellite Mapping of Precipitation (GSMaP) [16], Climate Hazards Group Infrared Precipitation with Station (CHIRPS) data [17], and the Integrated Multi-Satellite Retrievals Global (IMERG) precipitation mission [18]. Many studies on assessing the performance and accuracy of these SPEs have been made over the world [19; 20; 21; 22; 23; 24; 25]. Although these spatial-temporal datasets are now freely available, employing them is a great challenge as they are too large and complex. Although research has been performed to design data series management [26], researchers and hydrologists are still facing great challenges such as computational requirements as well as software and data storage issues when handling this huge amount of data, which is hindering its accessibility [27]. It is challenging to deal with a bigger scale of data in satellite and gauge precipitation research [28]. In addition, the acquisition, searching, transfer, analysis, and visualization of the data in many areas, such as geoscience, remote sensing, hydrology, and environmental research, are also challenging. Google Earth Engine's cloud computing platform has effectively addressed the challenges of big data analysis. However, there are still several main limitations, such as privacy, tool restrictions, selected data mining models limitations, features limitations, computational restrictions, third-party software causing time-consuming processes, and others [29]. The accuracy and reliability of SPEs are also among the concerns of hydrologists [30]. The high spatial-temporal datasets can be biased depending on the target terrain, elevation, and season. Therefore, many researchers have dedicated their efforts to exploring the application of machine learning (ML) and deep learning (DL) in improving the accuracy and reliability of SPEs [31; 32; 33]. Recurrent neural network (RNN) is a deep learning approach for modeling sequential data and has been widely used in different types of applications such as speech recognition, natural language processing, energy management, and time series analysis [34; 35; 36; 37].