## 4 Discussion  
Estimating rainfall in Indonesia using a combination of rain gauge, radar, and weather satellite data is critical because the country comprises a large and varied geographic area with relatively high rainfall, with many regions often experiencing floods and landslides. Accurate rainfall estimation can aid in water resource management, reduce natural disaster risks, and improve the quality of life for communities [140, 141]. Rain gauges directly measure rainfall in a specific area over a certain period, while a weather radar obtains information on the speed and direction of water particles in rain clouds, allowing for the estimation of rainfall location and intensity. Weather satellites are used to collect data on weather conditions in larger areas, even on a global scale. Weather satellites can also provide a broad picture of clouds and rainfall in a particular area. Rain gauges, although simple and low in cost, have the disadvantage of collecting data that depend on the accuracy of the measurements and the inaccuracy of observation times [142, 143, 144]. Weather radar has several types, with their respective characteristics, advantages, and disadvantages. Although S-band-based radars offer better coverage, they are susceptible to attenuation, especially in areas with heavy rain such as Indonesia. On the other hand, C-band-based radar is more suitable for use in Indonesia because it is effective in detecting the type of rainfall that commonly occurs in the region [35]. However, the use of X-band-based radar in Indonesia is limited due to its shorter range and susceptibility to attenuation. X-band produces higher spatial-temporal resolution products [145]. This type is suitable for urban hydrology applications in Indonesia. In addition, the use of data from weather satellites such as Himawari, GSMaP, and TRMM has been widely adopted for estimating rainfall in Indonesia. Nevertheless, challenges such as parallax effects need to be taken into account to ensure the accuracy of observations [146]. In facing this challenge, integrating data from various sources and understanding the characteristics of each tool is the key to increasing the accuracy of rainfall estimates in Indonesia. The approach to estimating rainfall in Indonesia through the use of rain gauge, weather radar, and weather satellite data shows a variety of methods and techniques that have been applied. The traditional approach of using rain gauge data faces challenges in overcoming the inability of a single rain gauge to represent rainfall in an area. Therefore, spatial interpolation methods are needed to estimate rainfall around known points, using various methods such as Thiessen polygon, inverse distance weighting, linear spline, and kriging. The choice of interpolation method must take into account the unique characteristics of each region [147, 148]. The distribution of rain gauges shown in Figure 1 highlights a concerning disparity in gauge density in Indonesia. Papua Island, in the eastern region of Indonesia, has fewer rain gauges even though the region has local rain patterns with its diverse topography. In contrast, Java Island, in the southern region of Indonesia, has a monsoon pattern and non-complex topography, but the rain gauge distribution there is very dense. The rain gauge density in Indonesia needs attention, as it can impact the selection of statistical models and methods. In addition, statistical models do not always have accurate results [149, 26], depending on the linear relationships between variables. Furthermore, the relationship between reflectivity weather radar data and rainfall intensity data from rain gauges produces an empirical equation for the Z-R relationship. Constant adjustments to empirical equations have been applied to estimate rainfall in several regions of Indonesia, with each region having a different Z-R equation.