## 3 Results  
By considering IMERG grids and time period selections in the first pHP program, researchers gain a comprehensive understanding of the factors influencing the observed results. From the terrain view, it is noticed that most of the rain gauges are positioned outside the mountainous areas, as shown in Figure 7. The terrain map clearly illustrates the spatial relationship between rain gauge locations and the mountainous terrain. This finding suggests that the rainfall measurements may not adequately capture the variability and intensity of the rainfall within the mountainous areas. Based on the regression result produced by the program, it is noted that the IMERG satellite rainfall estimations overestimated the actual rainfall by about 100% and have a high \(RMSE\) (15.25 mm/day). The analysis of the scatter plot reveals a noticeable relationship between IMERG overestimation with reference to the rain gauge data. The linear equation generated provides insights into the nature and magnitude of the overestimation. Referring to the scatter plot (Figure 8), the data points become more scattered as the rainfall intensity increases. In other words, the performance of the satellite estimations decreased as the rainfall became heavier. Figure 7: Selected IMERG grids that contain rain gauge stations. Referring to the time series graph generated (Figure 9), high peak rainfall was found in the month of December every year, which is also part of the Northeast monsoon. This is common as the prevailing north-easterly winds flow across the South China Sea, bringing in more moisture to the land surface. [PERSON], [PERSON] [67] conducted a test to evaluate the capturing storm capabilities of satellites by increasing the rainfall threshold. They found that, regardless of any SPEs, their accuracy decreased as the rainfall threshold increased, which can be considered consistent with our present study. Figure 8: Scatter plot that shows the comparison of the daily areal rainfall between IMERG estimations and rain gauge observations for the period 2011–2020. From the second pHp program, it is noted that the hardest hit regions are along the northeast part of Peninsular Malaysia, as shown in Figure 10. Overall, IMERG satellite rainfall overestimates the actual rainfall for the whole study site. The extracted rainfall estimations from the SPEs were then trained using the LSTM model programmed in Python with the rain gauge observations as the reference data. As a result, it was found that all of the statistics metrics improved after performing the LSTM training, indicating that the proposed LSTM methodology has good adaptability in enhancing the SPEs. The \(CC\) was raised by approximately 14.19%, \(P\)_Bias_ was reduced by 81-118%, \(MAE\) was reduced by 18-59%, \(RMSE\) was reduced by 1-66%, and the \(KGE\) was raised by approximately 200%. Table 1 summarizes the highest and lowest value of each metric for evaluating the performance of original and improved SPEs. Figure 9: Time series graph for rain gauge observations and IMERG rainfall for the period 2011–2020. The last pHp program, as shown in Figures 11-15, presents the performance metrics of all IMERG grids before and after LSTM training in the map format. Originally, the IMERG is able to capture the rainfall along the coastal region in the northeast part of Kelantan and Terengganu states with a \(CC\) ranging from 0.50 to 0.70, and poorer performance was found for the central region of Peninsula Malaysia (Pahang state) with a \(CC\) less than 0.50.