# Evaluating the Latest IMERG Products in a Subtropical Climate: The Case of Parana State, Brazil
## 3 Results
### Daily and Monthly Evaluation of IMERG Products  
The relationship between IMERG daily and monthly precipitation and the data observed by gauges are presented in Figure 5. For the daily values, it is observed that IMERG  
Figure 4: Average monthly rainfall observed during the study period for gauges and by IMERG, and density curve of monthly observations. overestimates the values, with a low coefficient of determination (R\({}^{2}\) = 0.19). At the monthly scale, precipitation values are closer to those observed, with R\({}^{2}\) of 0.73. Based on the general summaries of the metrics used in this study, presented in Figure 6, IMERG showed better performance for estimating monthly precipitation, compared to the daily product. The high KGE value (0.81) indicates a strong correlation between the monthly products of IMERG and the precipitation gauge data, which shows its ability to quantify monthly precipitation in the humid subtropical region. A lower accuracy is observed for daily products (KGE = 0.43), indicating a lower correlation between satellite precipitation data and pluviometers. This is due mainly to the high variability of precipitation over small areas on a daily scale. Our findings indicate that both IMERG products (daily and monthly) overestimate the observed precipitation, with a bias (MBE) of 0.19 and 5.98 mm, in the daily and monthly products, respectively. Regarding the errors, IMERG presented MAE of 5.64 and 35.90 mm and accuracy (RMSE) of 13.10 and 50.12 mm for daily and monthly products, respectively. Figure 5: Regressions between daily and monthly data observed by gauges and IMERG. Figure 6: Error distribution and performance criteria of the IMERG products at daily and at monthly time scales. Regarding the ability to detect rain at the monthly resolution, IMERG had a nearly perfect performance, with POD equal to 1, CSI of 0.99, and FAR of 0.01 (very close to ideal, 0). In a daily resolution, IMERG demonstrated a limited performance to detect rain events, with CSI of 0.39, a detection probability of 64%, and the risk of false alarm of 50%. To better understand the accuracy of the IMERG products in different ranges of precipitation observations, a confusion matrix is shown in Figure 7. At the daily scale, the IMERG generally underestimate rainfall events, as seen by a large number of rainfall events greater than 1 mm) predicted in the range 0-1. This agrees with the positive SK value at daily scale (Figure 6). Considering the positive values of the error metrics showed in Figure 6 (MBE, MAE and RMSE), the positive errors of IMERG daily products could offset its negative errors. In contrast, monthly precipitation estimates of the IMERG generally overestimate events as observed in gauge stations (negative SK in Figure 6). Only for months with higher rainfall rates (>200 mm) the IMERG product underestimates observed data. The underprediction at both daily and monthly scale can be attributed to the IMERG spatial and temporal resolution. Heavy rainfall events usually occur in shorter time intervals and small areas. Thus, sparse heavy rainfall events can be underestimated in the daily scale and not be accounted for in the monthly scale, even though the \"Final run\" relies on the calibration using monthly observations. The spatial distribution of the metrics for the Parana regions (Figure 2) at daily and monthly scale are summarized in Table 2. Less accuracy was observed in the Southwest, West, Central-West, and Metropolitan regions in daily and monthly products.