## 3 Results and Discussion
According to the results of the Student \(t\)-test, the averages of the distributions observed by the rain gauges and estimated by the satellite are statistically different over these municipalities (valor-\(p<0.05\)). When observing the estimates of the parameters of the linear regression model, we also found that Agua Branca, Propria and Pao de Acucar showed the worst results, with r \(\leq\) 0.68 and R\({}^{2}\leq\) 0.46 (Table 2). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Municipalities** & \(\beta_{0}\) & \(\beta_{1}\) & \(\mathbf{R^{2}}\) & \(\mathbf{r}\) & \(p\)**-Value** \\ \hline Agua Branca & 59.00 & 0.70 & 0.17 & 0.42 & \textless{}0.05 \\ Barra & \(-1.60\) & 0.86 & 0.86 & 0.93 & \textless{}0.05 \\ Barreiras & \(-1.60\) & 0.94 & 0.88 & 0.94 & \textless{}0.05 \\ Belo Horizonte & 1.50 & 1.10 & 0.88 & 0.94 & \textless{}0.05 \\ Bom Jesus da Lapa & 0.26 & 0.90 & 0.87 & 0.93 & \textless{}0.05 \\ Cabrobó & 4.70 & 0.81 & 0.76 & 0.87 & \textless{}0.05 \\ Carinhanha & \(-3.00\) & 0.99 & 0.84 & 0.92 & \textless{}0.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Estimates of the parameters (\(\beta_{0}\) and \(\beta_{1}\)) of the linear regression model, the determination coefficient (R\({}^{2}\)), correlation coefficient (r), the corresponding \(p\)-value. Figure 5: Precipitation density curves observed by rain gauges and IMERG at each gauge (**a**) Agua Branca, (**b**) Barra, (**c**) Barreiras, (**d**) Belo Horizonte, (**e**) Bom Jesus da Lapa, (**f**) Cabrobó, (**g**) Carinhanha, (**h**) Curvelo, (**i**) Formosa, (**j**) Ibipetuba, (**l**) Januaria, (**m**) Montes Claros, (**n**) Pao de acücar, (**o**) Paracatu, (**p**) Petrolina, (**q**) Pirapora, (**r**) Propria, (**s**) Remanso. It stands out that the greatest accumulation of precipitation is concentrated between the months of October and April, a period during the rainy season in much of the region, consistent with previous analyses obtained from rain gauge and satellite data [5; 6; 51]. A possible explanation for this underestimation of precipitation in the NEB east coast is the type of weather systems which usually affect this region. Previous studies have suggested the formation of warm clouds over this region [50; 52]. This hypothesis is consistent with the low frequency of electrical discharges in practically the entire NEB east coast [53]. Warm clouds have little vertical development and low ice content, which is essential for the electrification process of clouds. These conditions contribute to a deficiency on estimating precipitation by satellites [5; 54; 18; 15]. In addition, it is reasonable assume that IMERG's resolution can be not sufficient to represent(mainly) the local convection in the region, thus the rainfall is underestimated. In general, the IMERG provided satisfactory results to estimate the precipitation in the BSF. Data from this product is precious as it covers a more detailed spatial coverage that is not always supplied by the network of rainfall stations, in addition to being important for the most diverse applications that demand knowledge about the quantity, duration and distribution of the rainfall regime in time and space [55; 56; 57; 58; 59; 60]. It is also reinforced that the IMERG presented limitations for estimating precipitation in the lower Sao Francisco and that future studies are important to elucidate remaining gaps. For example, the results found in this work corroborate the results of [61], these authors analyzed the following satellite-derived precipitation estimate products: Multi-Satellite Precipitation Analysis (TMPA); Integrated Multi-satellite Retrievals (IMERG-F, version V05) and Global Satellite Mapping of Precipitation (GSMaP); in all of them there was an underestimate when compared with data measured by pluviometers in the coastal portion of Northeastern Brazil. [62] analyzed the daily precipitation estimated by IMERG for the year 2016 and found similar results.