## 3 Results and Discussion  
### Case Study for Southern Brazil--R1  
The first case of extreme rain that caused a natural disaster occurred from 24 June 2014 to 28 June 2014 in R1. There were more than 300 mm of rain in five days, which caused several disasters in the region. The municipality of Irai (Figure 3), in the northern region of Rio Grande do Sul, reported the highest accumulated rainfall (467 mm/month) in the state in June 2014. According to the Civil Defence report ([https://s2 id.mi.gov.br/](https://s2 id.mi.gov.br/), accessed on 2 September 2022), the elevation of the Uruguay River resulted in more than 600 people being homeless and more than 1000 displaced. The event caused substantial road damage and affected the supply of essential services, including water, electricity, transportation, and medical assistance. About 36 houses were completely submerged and 400 houses flooded. More than 400 km of secondary roads were damaged, and eight dams were destroyed. The flood also destroyed several bridges, leaving entire communities isolated and only reachable by boat. The financial losses for the agriculture and livestock sectors associated with this extreme rainfall were incalculable. Large amounts of crops were washed away by the floods, and fertile soils were destroyed, including severe erosion. Diary farming was critically affected because roads and bridges were destroyed, making it impossible to collect or distribute milk on time. Substantial efforts by the government and civil society were necessary to restore the damages caused by this extreme event. Figure 4 shows the influence of the MCS on the extreme rain event recorded between 24 June 2014 and 28 June 2014 in the southern region of Brazil (R1). The total rain accumulated over the five days is represented by Figure 4a. The most intense rain (\(\geq\)120 mm/5 days) occurred between the states of Rio Grande do Sul and Santa Catarina. The contribution of MCS is above 90% for most grid points analyzed (Figure 4b), suggesting a substantial influence of the deep convection represented by MCS. Figure 4c represents the number of MCS events recorded by the TOOCAN algorithm over the five days analyzed. More than 20 MCS were registered during these five days. MCSs were present more than 80% of the time in this region (Figure 4d). According to [PERSON] and [PERSON] [49], the dipole structure of geopotential anomalies in the mid-level atmosphere significantly potentiates the occurrence of extreme precipitation events on a daily scale in this region. This kind of structure, combined with an intensification of the low-level jet of South America, usually provides favorable instability conditions for the development of extreme precipitation \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Statistical Measure** & **Equation** & **Range** & **Perfect Score** \\ \hline POD & A/(A + C) & 0 to 1 & 1 \\ MR & C/(A + C) & 0 to 1 & 0 \\ FAR & B/(A + B) & 0 to 1 & 0 \\ TS & A/(A + B + C) & 0 to 1 & 1 \\ FBI & (A + B)/(A + C) & 0 to \(\infty\) & 1 \\ PC & (A + D)/(A + B + C + D) & 0 to 1 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Statistical measures obtained by a contingency table. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Gauge \(\geq\) Threshold** & **Gauge \textless{} Threshold** \\ \hline Satellite \(\geq\) Threshold & Hits (A) & False Alarms (B) \\ Satellite \textless{} Threshold & Misses (C) & Correct Negatives (D) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Contingency table considering the P99 th threshold by grid point. systems such as MCS events [50; 51]. These conditions would explain the high number of MCS observed. To verify the performance of satellite rain estimation products, we chose a specific case of extreme rain in R1. We selected the case based on the days with a higher number of grid points with intense rainfall values. Figure 5 shows the spatial distribution of this precipitation event from different satellite products.