Christine Maswi

Accurate and reliable rainfall data is crucial for climate-sensitive applications in Kenya. This study evaluates the performance of nine satellite rainfall estimate (SRE) 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-Early), 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-CDR), 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. 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 based on how well satellite estimates capture ground-based observed rainfall. The metrics used include the correlation coefficient (r), root mean square error, mean error, mean absolute error, bias, probability of detection (POD), false alarm ratio (FAR), and the Heidke skill score (HSS). Comparing the different products, RFEv2, CMORPH and ARC2 provided the best overall compromise across the metrics utilized in the study, while CHIRP did not perform very well, highlighting the importance of merging with ground stations.