Evaluation of accuracy of daily rainfall values TRMM, GPM, ERA5, and PERSIANN in Razavi Khorasan Province

Remote sensing, as a powerful tool in meteorology, is able to cover the gaps in ground measurements and provide a uniform platform for spatial analysis. However, due to the different results obtained from the accuracy and performance of sensors in different regions, it is necessary to evaluate and validate their products in each region independently. This study, therefore, aims to evaluate the performance of the satellite precipitation products (SPPs) of PERSIANN, GPM, ERA5, and TRMM in a daily rainfall scale over the Razavi Khorasan Province, against the ground observations collected from the 19 rain gauges. In this regard, 113,880 images were called to extract daily rainfall data from the above four databases. To evaluate the SPPs, statistical indices including correlation coefficient (CC), root mean square of the errors (RMSE), and percentage of bias (PBias), Kline Gupta Efficiency (KGE), and Agreement Index (d) were used. Moreover, POD, FAR, and CSI classification indices were used to evaluate the accuracy of the data presented in the indication of days with rainfall events. The climate effect was also investigated by incorporating the change in latitude. Our results revealed better performance for the ERA5 dataset with 0.2≤ CC ≤ 0.68, 2≤ RMSE ≤ 3.7, and its performance indicators have KGE ≥ 0.26 and d ≥ 0.68. Regarding classification indices, ERA5 has POD and CSI higher than 0.68 and 0.3, respectively, and TRMM has relative superiority only in the FAR index. At the level of the studied area, the index analysis shows the better performance of the ERA5 database compared to other studied data. Based on the results of this research, the increase in elevation has improved some statistical indicators in some products, whereas, in some other cases, changes in elevation have a negative effect on the accuracy. In general, the ERA5 database at the point and regional scale are evaluated to have a more appropriate performance in estimating daily precipitation data, and its data can be used in meteorological and hydrological analysis.