## 4 Discussion  
The management of satellite data poses significant challenges due to their voluminous nature, necessitating the implementation of efficient step-by-step protocols. Proper data storage is also crucial to accommodate large datasets. Streamlining data processing can optimize researcher time and productivity. Furthermore, visualizing the results may the enhance data analysis and facilitate informed decision making. In this study, we propose a sequential approach for data storage and processing utilizing Python, pHp, and MariaDB. Our methodology offers improved organization and structure to the data management including data processing, comparing and enhancing the satellite data with rain gauge observations. Apart from that, our results demonstrate the program's effectiveness in managing large datasets, thus providing a valuable tool for researchers dealing with data-intensive applications. In their recent study, [PERSON], [PERSON] et al. [84] evaluated the eight satellite precipitation products using similar performance metrics as those presented herein. The utilization of the proposed pHp programs could significantly enhance the visualization of the output. The findings of this study highlight the potential of visualization to access the accuracy of IMERG estimations. This provides a framework for correcting and adjusting IMERG estimations based on the rain gauge measurements. Furthermore, the examination of different IMERG grid resolutions and time period provides a clearer picture of the impact of spatial-temporal factors on the observed relationships. The absence of rain gauges within mountainous areas may result in the misinterpretation of rainfall patterns. The spatial distribution of rain gauges outside the mountainous areas could impact the hydrological modeling, climate studies, and water resource management in mountainous areas. The reasons behind the uneven distribution of rain gauges in mountainous areas might be attributed to factors such as logistical challenges, the limitation of access, and high cost for installation and maintenance. Although the LSTM model was able to enhance the SPEs, it is noted that the overall performance was not uniform. The non-uniform performance highlights the complexity of satellite precipitation estimates. Additionally, this study did not consider other geographical data as input such as climatic data such as wind speed, humidity, etc. Future research  
Figure 15: pHp program that shows the performance of \(KGE\) before and after LSTM training for the period 2011–2020. should focus on identifying and addressing these factors to improve the uniformity of the performance. In terms of \(CC\) and \(KGE\), those rainfall grids along the coastal region outperformed those in the central region. Conversely, the rainfall grids in the central region showed better performances in terms of \(MAE\) and \(RMSE\). Perhaps a different model should be elaborated for those grids for these two regions with different training parameters, otherwise further study is required for performing rule-based optimization. Apart from that, the model for training the SPE data should introduce relevant auxiliary factors such as altitude, brightness, temperature, humidity, etc., so that the model would represent the exact condition of the study area, and a more accurate output can be obtained. In the LSTM modeling program developed, the sliding windows derived from the time series data are used to perform short-term prediction with a fixed length of sequence. The power of LSTM lies in the recurrence of this process with the continuous sliding of the data windows to reduce the errors and bias between IMERG satellite rainfall estimates and rain-gauged observation data. With sufficient time series data (10 years of rainfall data), LSTM modeling with the ADAM optimizer has shown the great correction of IMERG satellite rainfall estimates that are trained with rain gauge observation data.