## 4 Discussion  
In this study, multiple radar and satellite precipitation products with different spatial and temporal characteristics were compared to a rain-gauge network (RADOME) over mainland France during two rainfall case studies over 3- and 4-day periods. Every area of interest studied concerned mountainous regions of the south and the east of France, such as the French Alps (the SAL and DUR areas), the Jura (the JUR area), and the Massif Central (the CEV, WMC, and NMC areas) massif. Hence, the study was conducted over complex topography. The precipitation products were compared using the point-to-pixel method, which provided absolute bias between the product's estimations and observations from the rain-gauge network RADOME. Figure 20 represents the distributions of the absolute bias computed for each product against the rain-gauge observations for every specific area of interest. Concerning the ground-based radar estimations, the results show that both PANTHERE and OPERA provided a good spatial representation of the events and PANTHERE had the lowest biases regardless of the case studies. Figure 20 shows that the median of the absolute biases was very small (close to 0 mm) over the different areas of interest,  
Figure 20: Distribution of the absolute biases computed at the stations by using the point-to-pixel method inside the specific areas presented in Figure 2c for the April (**a–e**) and June cases (**f**). The corresponding areas are as follows: WMC (**a**), SAL (**b**), DUR (**c**), CEV (**d**), JUR (**e**), and NMC (**f**). The boxes correspond to the interquartile range of the absolute bias distribution, with the lower and upper end of the boxes being, respectively, the 1 st and 3 rd quartiles of the distribution. The orange lines are the medians of the distributions. The whiskers go to the last value point within 1.5 times the interquartile range. Outliers are identified with a small circle. The solid red line represents the equation y = 0.  
confirming the performance of the product seen in Section 3.1.2. Figure 20 also shows the tendency of OPERA to slightly overestimate the precipitation amounts compared to RADOME, with notable exceptions over the areas within the Alps where a slight underestimation was recorded as seen in Section 3.1.3. This underestimation experienced by OPERA over the Alps area can be attributed to the lack of radar coverage in this specific area. Due to differences in sampling volume, it was possible to find biases between radar and rain gauge in one direction or the other. For example, [PERSON] et al. [69] found a underestimation of radar data compared to rain gauge of 10% in a study over northern Europe. [PERSON] et al. [70] also found a general underestimation in Abruzzo and Marsica regions in Italy. In contrast, the overestimation signal estimated over the JUR area tended to be linked with the contribution of the German radars. The strongest differences experienced by both products were observed over the mountainous areas of the south of France, revealing the difficulties for radar products to correctly estimate precipitation over area with complex topography. The OPERA overestimation trend was also observed during the June case. As for the satellite-based products, we can see in Figure 20 that they generally tend to underestimate precipitations amounts. This type of event was not well represented by PDIR, which can be explained because IR-based products tend to fail to correctly estimate low-intensity rainfall and orographic events [12]. GHE tends to overestimate the precipitation over multiple areas, which could be explained because of the fixed algorithm, based on brightness temperature, to retrieve rain amounts (Section 2.1.3). Both IMERG products showed similar results and, thus, showed the small effects of the forward and backward morphing of LATE (see Section 3.2.2). Figure 20 shows that the bias distributions of LATE were larger than EARLY in most of the areas of interest.