Spatiotemporal Assessment of Reanalysis and Remotely-Sensed Precipitation Datasets

Among the meteorological components, because of having considerable changes in time and space, precipitation is one of the complicated issue in hydrological process. By increasing of satellite-based technologies over the past decades, that’s easy to access to high resolution precipitation products in most parts of the world. This research addressed the accuracy of European Center for Medium Range Weather Forecasts (ECMWF) reanalysis datasets for estimation of daily and monthly precipitation over the SefidRood catchment. Moreover, in order to better evaluating the performance of ECMWF dataset, the PERSIAN and TRMM datasets are used. Findings on daily time scale, show that the correlation coefficient between ground observation and ECMWF, PERSIAN and TRMM products is about 0.8, 0.47 and 0.32 and this completely prove the superiority of ECMWF for estimation of rainfall at daily time scale. At monthly time scale, both of ECMWF and PERSIAN products correlate very well with gauges measurements (CC statistic is more than 0.9), but TRMM owing to have CC equal to 0.57 correlates moderately with observations. According to the categorical verification statistics, ECMWF yields better results for detection of precipitation events on the basis of Probability of Detection (POD), Critical Success Index (CSI) and False Alarm Ratio (FAR) in SefidRood catchment, compared with other satellite data sets. Therefore, in ungauged catchments or hydrological modeling that require an accurate precipitation datasets, using ECMWF dataset is suggested.