## 3 Results and Discussion
### Case Study for Northeastern--R3  
The heaviest rain in the northeast (R3) during our study period, much higher than the climatological average, occurred between 20 January 2016 and 24 January 2016, influenced by large areas of instability associated with SACZ. The average precipitation in this region during January is about 200 mm/month [30]. In contrast, some areas recorded approximately 600 mm during January 2016. At least one-third of the total rain recorded accumulated in just five days. Figure 11 shows one of the affected municipalities west of the state of Bahia. Using satellite data, [PERSON] et al. [54] observed that January is among the rainiest months in western Bahia. According to those authors, rainfall above 65 mm/day is expected to occur at least once every two years in northeastern Brazil during the austral summer (December, January, and February). Figure 10: Bar graphs of (**a**) Probability Of Detection (POD); **(b)** Miss Rate (MR); **(c)** False Alarm Ratio (FAR); **(d)** Threat Score (TS); **(e)** Frequency Bias Index (FBI); and **(f)** Proportion Correct (PC) for five days of accumulated precipitation over the central region of Brazil from 1 January 2012 to 5 January 2012. Figure 12 shows the influence of the MCS on the extreme rain observed in North-eastern Brazil (R3). Note that some points of the grid show a high accumulation of rain during the five days of the event (Figure 12a); however, only 60% to 70% of this rain was influenced by MCS (Figure 12b). The remaining 40% or 30% may have been generated by shallow convection, not detected by the TOOCAN algorithm or by other systems with more stratiform characteristics. One possible explanation is that the convective precipitation observed in parts of this region contributed to maintaining substantial divergence at high levels. The atmospheric circulation pattern associated with SACZ episodes remained active and persisted approximately between three and ten days with the characteristics of convective and stratiform precipitation [55]. The number of MCS varied considerably in this region; the most frequent recordings showed between 15 to 25 MCS per grid point during the five days analyzed (Figure 12c). The MCS identified by TOOCAN was present between 80% and 100% of the time during the five days of observation (Figure 12d). These results are consistent with previous analyses, such as [PERSON] [42] and [PERSON] et al. [54; 56]. According to those authors, stratiform and shallow convective clouds are the most frequent in this region; however, the rainfall associated with these clouds is not as abundant as precipitation caused by deep convective clouds. Figure 13 shows the spatial distribution of this extreme precipitation event based on different satellite products. Products with rain gauge adjustments (Figure 13e-j) performed better than products without adjustments (Figure 13a-d,l), identifying the highest rainfall values when they occurred in the reference data (Figure 13m). Figure 14 shows the results for categorical metrics, such as POD, MR, FAR, TS, FBI, and PC, for this event based on accumulated rain over five days. COSCHg and CMORPHg presented a perfect score for POD, TS, and PC (Figure 14a,d,f) for the complementary metrics MR and FAR (Figures 14b and 6c). The FBI (Figure 14e) shows a perfect score for CMORPHg. 3B42g, PERSIANNg, and COSCHg overestimated rainfall in about 50 mm and the remaining products underestimated rainfall by about 80 mm. In summary, COSCHg and CMORPHg performed better than other products in this case of extreme rainfall in R3, with a low FAR (0.35; 0.39), a high POD (0.81; 0.61), and a high PC (0.73; 0.65), respectively. Figure 11: Municipalities affected by the extreme rain. (**a**) flooding in municipality of São Gabriel in Bahia state; (**b**) flooding in municipality of Santa Maria da Vitoria in Bahia state. Photographs: [PERSON]/Fotografe o tempo and Divulgacão/Correios.