# Evaluating the Latest IMERG Products in a Subtropical Climate: The Case of Parana State, Brazil
## 4 Discussion
### Daily and Monthly Evaluation of IMERG Products  
Intensity and volume on a daily scale provide important information in hydrological applications, such as frequency analysis, daily precipitation event detection, and irrigation planning. The overestimation by IMERG daily products compared to ground data was observed by [29] when evaluating IMERG, GSMaP, and PERSIANN products over the whole globe (~26, 44, and 23% of bias, respectively). However, the authors observed higher correlation for IMERG products (~0.6) compared to GSMaP and PERSIANN (~0.5 and 0.4, respectively). Also, [28] related the overestimation of IMPA, IMERG-F, and GSMaP over South Brazil, but a slightly lower values of RMSE and mean error (0.86 and 0.09 mm day\({}^{-1}\), respectively) for GSMaP compared to IMPA (1.31 and 0.99 mm day\({}^{-1}\), respectively) and IMERG (1.31 and 1.01 mm day\({}^{-1}\), respectively). Regarding the underestimation of light precipitation and overestimation of heavy precipitation by IMERG presented in this study, [22] observed a tendency of IMERG daily products underestimate the frequency of rainfall events (<1 mm day\({}^{-1}\)) and overestimate the frequency of intense rainfall events (>10 mm day\({}^{-1}\)) across the Tibetan Plateau region. In this way, due to the limited performance of the daily products, the use of this data requires attention by the user and previous calibrations of the products at this temporal scale. Figure 12: Anomalies for 9 stations selected at random. The dashed lines represent the values of \(-1.96\) and \(1.96\)\(\sigma\) (monthly standard deviation). Regarding the comparison of the precipitation estimate on a monthly and daily scale, [18] also observed better performance of the monthly estimates of the precipitation when analyzing the TRMM products in Brazil. According to the authors, the monthly estimates are less affected by systematic errors than daily estimates. The IMERG products are calibrated using monthly data of in-situ gauging stations of the Global Precipitation Climatology Centre (GPCC) network [52], which can also explain the better performance of products on a monthly basis as compared to the daily scale. The better performance of the monthly precipitation product throughout Brazil also was confirmed by [20]. The authors observed an average of CC of 0.93 and RMSE of 23.20 mm when comparing IMERG version 5 in the state of Parana. The better performance observed by [20], compared to that found in this study, may be due to the methods used for interpolation of observed capture data when assimilating them to the IMERG forecast fields. Typically, interpolation methods do not capture a large spatial variability of rain and the estimation is complex due to the spatial discontinuity [53]. The interpolation leads to the smoothing of high and low peaks of precipitation in a region, improving the relationship between observed and modeled values. With respect to the performance of IMERG for estimating seasonal precipitation, [22] showed different performance of the estimative in dry and wet seasons over sub-regions of Mainland China. The authors showed greater correlation of IMERG's products and ground gauges over the dry season for two sub-regions, as observed in this study, and the opposite for the other six. Thus, the investigation of the performance of satellites products in a regional scale is very relevant. However, the IMERG overestimated the rainfall over regions of heavy rainfall as well as moderate rainfall. In coastal areas, even the difference of a short distance between the ocean and coastal mountains can induce failure of the satellite sensor to discriminate between the adjacent pixels of land and water, generating signal contamination and resulting in poor performance in estimating precipitation [17]. The limitation of the precipitation estimates by satellites (Global Precipitation Climatology Project-GPCP) in orographic regions was also observed in the Andes by [54].