Accuracy of Daily Satellite Rainfall Estimates over Kenya: A Multi-Product Evaluation

Accurate and reliable rainfall data is crucial for climate sensitive applications in Kenya. This study evaluates the performance of nine satellite rainfall estimates (SREs) datasets. The study aims to identify the most suitable dataset for various applications in the country. The datasets include Africa Rainfall Climatology (ARC2), NOAA’s Rainfall Estimation Version 2 (RFEv2), Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), Integrated Multi-satellitE Retrievals for GPM (IMERG), Climate Prediction Center Morphing Technique (CMORPH), Tropical Applications of Meteorology using SATellite and ground-based observations (TAMSAT), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), Climate Hazards Infrared Precipitation (CHIRP), and Multi-Source Weighted-Ensemble Precipitation (MSWEP). The datasets are assessed based on their spatial and temporal accuracy against ground-based observations in Kenya. The daily rainfall station data is obtained from the Kenya Meteorological Department which maintains an updated archive of quality-controlled observed rainfall datasets across the country. Metrics are evaluated on how well satellite estimates capture ground-based observed rainfall. The metrics used include correlation coefficient (r), root mean square error, mean error, mean absolute error, bias, probability of detection (POD), false alarm ratio (FAR), and Heidke Skill Score (HSS). Results indicate that the RFEv2 is the best-performing satellite rainfall estimate dataset with r=0.58, HSS=0.64 and FAR=0.26. Therefore, it can be applied especially in cases of scarce data. In terms of performance, RFEv2 is followed closely by CMORPH with r=0.63, HSS=0.64 and FAR=0.32.