High resolution satellite-based rainfall estimates (SREs) have enormouspotential for use in hydrological applications, particularly in thedeveloping world as an alternative to conventional rain gauges which aretypically sparse. In this study, three SREs have been evaluated againstcollocated rain gauge measurements in Ethiopia across six river basins thatrepresent different rainfall regimes and topography. The comparison is madeusing five-year (2003–2007) averages, and results are stratified by riverbasin, elevation and season. The SREs considered are: the Climate PredictionCenter morphing method (CMORPH), Precipitation Estimation from RemotelySensed Information Using Neural Networks (PERSIANN) and the real-timeversion of the Tropical Rainfall Measuring Mission (TRMM) MultisatellitePrecipitation Analysis (TMPA) 3B42RT. Overall, the microwave-based productsTMPA 3B42RT and CMORPH outperform the infrared-based product PERSIANN:PERSIANN tends to underestimate rainfall by 43 %, while CMORPH tends tounderestimate by 11 % and TMPA 3B42RT tends to overestimate by 5 %. Thebias in the satellite rainfall estimates depends on the rainfall regime,and, in some regimes, the elevation. In the northwest region, which ischaracterized mainly by highland topography, a humid climate and a strongIntertropical Convergence Zone (ITCZ) effect, elevation has a stronginfluence on the accuracy of the SREs: TMPA 3B42RT and CMORPH tend tooverestimate at low elevations but give reasonably accurate results at highelevations, whereas PERSIANN gives reasonably accurate values at lowelevations but underestimates at high elevations. In the southeast region,which is characterized mainly by lowland topography, a semi-arid climate andsoutherly winds, elevation does not have a significant influence on theaccuracy of the SREs, and all the SREs underestimate rainfall across almostall elevations.
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