Improving the accuracy of satellite and reanalysis precipitation data by their ensemble usage

This study evaluated the outputs of five precipitation (PCP) datasets. These models are ECMWF reanalysis 5th generation (ERA5), precipitation estimation from remotely sensed information using artificial neural networks-climate data record (PERSIANN-CDR), Asian precipitation-highly resolved observational data integration toward evaluation (APHRODITE), The national centers for environmental prediction climate forecast system reanalysis (NCEP CFSR) and climatic research unit (CRU). The PCP outputs of these models were compared with data of nine synoptic stations in the Khuzestan province. The results indicated a better match between the APHRODITE outputs and the PCP data at most stations (R-2 > 0.85, root-mean-square error (RMSE) < 17.049 mm and - 4.25 < Bias < 2.633 mm). However, CRU model has the highest critical success index (more than 0.711) and the lowest false alarm ratio (less than 0.2) and ERA5 has the highest probability of detection (more than 0.967) at most stations. Then, PCP outputs of five reanalysis (ERA5), interpolated (APHRODITE, NCEP CFSR and CRU) and satellite (PERSIANN-CDR) PCP datasets were combined to reduce the PCP estimation error. The multivariate adaptive regression splines models were employed for this purpose. The results show that the RMSE of all the stations, except Ahvaz station, decreased and the BIAS decreased too. Given the results, using ensemble data methods is a suitable way for reducing the error and increasing the accuracy of these models.