# Evaluating the Latest IMERG Products in a Subtropical Climate: The Case of Parana State, Brazil
## 3 Results
### Daily and Monthly Evaluation of IMERG Products
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Region**} & **RMSE** & **MAE** & **MBE** & **RMSE** & **MAE** & **MBE** \\ \cline{2-7}  & & **(mm day\({}^{-1}\))** & & & **(mm month\({}^{-1}\))** & \\ \hline Central-South & 13.70 & 6.19 & 0.02 & 48.50 & 36.30 & 0.83 \\ Central-West & 13.40 & 5.79 & 0.14 & 51.20 & 37.80 & 4.44 \\ Central-East & 11.90 & 5.24 & 0.18 & 43.20 & 31.90 & 5.52 \\ Metropolitan & 13.10 & 5.99 & 0.44 & 56.90 & 41.80 & 13.20 \\ Northwest & 12.40 & 5.14 & 0.31 & 50.50 & 36.90 & 9.37 \\ Central-North & 12.20 & 5.17 & 0.14 & 44.40 & 32.10 & 4.25 \\ Pioneer-North & 11.30 & 4.66 & 0.26 & 46.90 & 33.40 & 7.75 \\ West & 14.30 & 6.12 & 0.08 & 55.40 & 39.50 & 2.38 \\ Southeast & 12.60 & 5.58 & 0.31 & 41.60 & 31.10 & 9.41 \\ Southwest & 14.80 & 6.50 & 0.17 & 48.20 & 35.80 & 5.54 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of error metrics on a daily and monthly scale of the satellite precipitation products in Parana regions. The metrics were calculated based on mean areal precipitation. Figure 7: Confusion matrix for different precipitation ranges at daily and monthly scales. The spatial distribution of the error metrics for estimating daily and monthly rainfall by IMERG, for each gauge in the state of Parana, are shown in Figure 8. Corroborating the results presented in Table 2, the MBE and MAE values are spatially well distributed in the study area with close values to the averages shown in Figure 5, for daily and monthly data. However, less accurate metrics were observed in the coastal areas (eastern part of the state), where IMERG presents, greater disagreements in some stations. Since the IMERG pixel covers an extensive area, large variability in precipitation is masked in areas where orographic effects are prevalent. This is evident near the coastal region for the daily monthly products and over the southwest region for daily products where there are abrupt changes in elevation (Figure 1). The SK presented homogeneous spatial distribution in a daily scale, and a greater number of negative values over the west region (ranging between 0 and \(-0.5\)), which suggest an overestimation of precipitation in this area. The KGE was also homogeneous across Parana, with values close to 0.43 for the daily and 0.81 for the monthly products, as shown in Figure 8.  
The distribution of POD, CSI, and FAR (Figure 9) showed good performance of IMERG's products in detecting monthly rain events throughout Parana, with values very close to ideals in the entire area. The performance of IMERG's products in estimating daily rainfall also was homogeneous across the state area, with the worst performance on the coast, in agreement with the statistical metrics. Figure 8: Spatial distribution of performance metrics on a daily and monthly scale. Concerning the daily and monthly RMSE, higher values are found in the southwest and coastal areas of Parana, which correspond to areas with high volumes of rain in the autumn and summer, respectively, and the highest volumes of annual rainfall in Parana (Figure 4). Thus, the precipitation estimates by IMERG performed better in the drier areas of the state. This behavior is better confirmed by a seasonal assessment of the errors. In Figure 10 the performance metrics for the monthly IMERG product grouped by seasons are presented. For example, IMERG showed better metrics in the winter (JJ)A = June, July, and August), which is the period with lowest rainfall rates. Likewise, the monthly predictions during autumn (MAM = March, April, and May) and spring (SON = September, October, and November) showed better scores than during summer (DJF = December, January, and February). The orographic effect of the coastal region is not captured in any of the seasons. Only in winter (JJA) the errors were smaller because of the lower rainfall rates. Higher RMSEs were already expected during the wet season, because of the higher magnitude in rainfall rates.