<![CDATA[Accuracy of rainfall data is very important in hydrological analysis, especially in areas with limited data such as Labuan Bajo City, Indonesia. Global climate data generated from satellite observations and assimilation products, which are freely available, have great potential for use in data scarce areas. However, this data needs to be evaluated to measure its accuracy in rainfall estimates. This research aims to evaluate the accuracy of eight global rainfall data products, namely Global Precipitation Measurement (GPM), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks – Cloud Classification System (PERSIANN-CCS), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks - Climate Data Record (PERSIANN-CDR), Precipitation Data Integration and Retrieval - Now (PDIR-Now), European Reanalysis for the 5th Generation (ERA5), European Reanalysis for the 5th Generation Land (ERA5-LAND), and Climate Hazards Group InfraRed Precipitation with Station Data (CHIRPS) at two temporal scales: monthly and 15-daily. Evaluation is carried out using an assessment matrix which includes Root Mean Square Error (RMSE), Nash-Sutcliffe Efficiency (NSE), correlation (r), and Relative Bias (RB). The evaluation results show that on a monthly scale, ERA5, PERSIANN, and GPM provide the best results, with ERA5 being the top. On a 15-daily scale, ERA5 also shows the best performance, followed by GPM and ERA5-LAND. These findings confirm that ERA5 is the main choice for monitoring rainfall in Labuan Bajo City, which is very important for water resource management in areas with limited direct observation data.]]>
