## 2 Rainfall Data Source
### Weather Satellite  
Weather satellites are used to study and monitor the Earth's weather and climate [32]. Weather satellites can observe cloud tops, including rain-producing clouds, far from the Earth's surface [67; 68; 69]. Some weather satellites can also provide rainfall estimates. Several types of weather satellites are used in Indonesia, including Himawari, Global Satellite Mapping of Precipitation (GSMaP), Global Precipitation Measurement (GPM), and Tropical Rainfall Measuring Mission (TRMM).  
The Himawari satellite is the most commonly used because it has several products directly related to weather analysis, such as the infrared-enhanced product for analyzing cloud-top temperature [70; 71; 72], further aiding in rainfall estimation [73]. Moreover, it produces enhanced water vapor data for displaying atmospheric humidity conditions [74; 75], and several other usable channels [76]. By analyzing the IR brightness temperature (BT) at 10.4 microns and multiple IR BT differences (BTD), researchers have developed rainfall probability maps in Indonesia [77]. RGB values for cloud-color appearance [76; 78], such as during monitoring of the mesoscale convective complex (MCC) around the Indonesian capital have been used, whereby the Himawari-8 satellite detected dense clouds with small ice particles and cloud top temperatures below \(-50\)\({}^{\circ}\)C, which were visualized as red and yellow dots, indicating conditions of severe storm [76]. Figure 4 shows Himawari satellite imagery, satellite temperature graphs, and rainfall graphs when high rainfall intensity occurs in one region in Indonesia.  
The GSMaP is also widely used for rainfall estimation mapping in Indonesia [79; 80]. GSMap shows a good correlation with rain gauge observations at daily and monthly scales, although it tends to overestimate rainfall and exceptionally light rainfall events [81]. GPM satellites in Indonesia have shown varying effectiveness levels depending on the region and timescale. They generally show good correlation with rain gauge data on annual, seasonal and monthly scales, although performance is less reliable on daily and hourly scales [82]. Their performance varies with topography, showing better accuracy in flat and moderately high areas [83]. Lastly, the TRMM satellite is extensively used to analyze rainfall with tropical region characteristics [84]. Specifically in Indonesia, the effectiveness of TRMM satellite rainfall estimates has been studied extensively, revealing its potential and limitations. The linear correction model significantly improves the validity of TRMM data for daily rainfall estimation [85]. The TRMM tends to underestimate rainfall intensity, requiring correction to improve performance [86]. TRMM data require careful correction to improve accuracy.  
One of the challenges of weather satellite observation is the parallax effect, which causes the observed object's position to differ from its actual position. This can be corrected by comparing cloud data from weather satellite observations with reflectivity data from weather radar observations [87]. Figure 5 shows satellite parallax correction for the entire territory of Indonesia.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Weather Radar Product** & **Advantages** & **Disadvantages** \\ \hline PPI & Easy to interpret and used for fast analysis [65], suitable for regions in Indonesia with local rain patterns, such as islands with quick and dynamic rain cycles. & Limited to one elevation angle. \\ \hline CAPPI & It is more accurate for estimating rainfall at a certain height [66]. It is helpful for several users in Indonesia, such as those in the aviation sector. & Requires more time for data processing. \\ \hline CMAX & It provides information on maximum rainfall intensity [66], which is very useful for detecting extreme rainfall, which often occurs in Indonesia. & Potentially overestimates. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Weather radar products and their advantages and disadvantages.