Uncertainties in remotely sensed precipitation data over Africa

Quantifying the amount of precipitation and its uncertainty is a challenging task all over the world, particularly over the African continent, where rain gauge (RG) networks are poorly distributed. In recent decades, several satellite remote sensing (SRS)-based precipitation products have become available with reasonable spatial and temporal resolutions to be applied in hydrological and climate studies. However, uncertainties of these products over Africa are largely unknown. In this study, the generalized ‘three-cornered-hat’ (TCH) method is applied to estimate uncertainties of gridded precipitation products over the entire African continent, without being dependent to the choice of a reference dataset. Six widely used SRS-based precipitation products (at monthly scales) were evaluated over the entire continent during the period of 2003–2010. The TCH results are further compared to those of the classical evaluation using the Global Precipitation Climatology Centre (GPCC) over entire Africa, as well as to the RG observations over the Greater Horn of Africa (GHA). Overall, for the study period (2003–2010), the TCH results indicate that the RG-merged products contain smaller error amplitudes compared to the satellite-only products, consistent with the GPCC-based evaluation. A multiple comparison procedure ranking, which was applied based on signal-to-noise ratios (SNRs), indicated that PERSIANN contains the highest SNR and thus suitable over most of Africa, followed by ARCv2, TRMM, CMORPH, TAMSAT, and GSMaP. To extract the main spatio-temporal variability of rainfall over Africa, complex empirical orthogonal function technique was applied, from which the extracted patterns of GPCC, TRMM, PERSIANN, and ARCv2 were found to be similar but different from those of TAMSAT, CMORPH, and GSMaP. Finally, the TCH and RG-based validation methods were found to provide similar evaluations for the SRS-only products (CMORPH and GSMaP) over GHA, with CMORPH emerging to be the most suitable product, consistent with previous studies.