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Several climatic parameters are observed at the campus of the University of Ilorin, Ilorin, Nigeria in sub-Sahel Africa. This climatically important region is in the desert transition zone between the Sahara and the savanna of upper Nigeria and is influenced by the dusty Harmattan wind. It is characterized by persistent conditions with high aerosol loading as well as intense dust outbreaks that affect the local climate. The region is under the influence of the West African Monsoon (WAM) which has exhibited a dramatic change from wet conditions in the 50s and 60s to much drier conditions in the 70s, 80s and 90s. Observations at the Ilorin site were established to provide the Earth Observing System (EOS) Community with high quality climatic data, in particular, related to radiative fluxes and aerosols. The station is located in a region with extreme environmental conditions that are not common at other sites around the globe. For example, at Ilorin observed is the highest aerosol optical depth in the AErosol RObotic NETwork (AERONET) and the site is frequented by biomass burning during the dry season, thus adding new radiative effects to the dust aerosols (Pinker et al., 2001; Pandithurai et al., 2001; Holben et al., 2001, Smirnov et al., 2002). Biomass burning in savanna and forest ecosystems over Africa is believed to contribute about 20% to the global biomass burning. Dynamical models to predict WAM are not accurate in West Africa and tropical Atlantic regions, and are unable to simulate fundamental characteristics of rainfall ( diurnal, seasonal and annual cycles). In this study we present an analysis of the diurnal and seasonal variability for several years of rainfall observations. Comparison is made with the output of a numerical model and satellite based estimates. It was found that two observed relative peaks in the annual rainfall distribution are seen in the satellite data but are not detected by the numerical model. It was also shown that the satellite data are able to correctly depict diurnal variability. Both the model predictions and the satellite estimates tend to overestimate the rainfall at this site.