Downward short wave incoming irradiances play a key role in the radiation budget at the earth surface. The monitoring of this parameter is essential for the understanding of the basic mechanisms involved in climate change, such as the greenhouse effect, the global dimming, the change in cloud cover and precipitations, etc. Unfortunately, the density of the ground measurement network is insufficient, especially on continents like Africa, or countries in the Near East. To circumvent this lack of measured data, the meteorological satellites are of great help and models converting the satellite images into the different radiation components become increasingly performing. If these converting models are well validated over the United States and Europe, it is not the case over the African continent. A previous study [1] conducted on data covering the year 2006 over 12 sites situated in Western Africa at latitudes from 17°N to 5°S show that the global irradiance retrieved from satellite images is highly dependent on the knowledge of the aerosol optical depth (aod) and the water vapor content of the atmosphere (w). The satellite derived irradiance components are obtained from two inputs: the clear sky irradiance obtained from the atmospheric parameters (aod and w), and the cloud properties obtained from the Meteosat images. In Skukuza, aerosol optical depth is acquired within the aeronet network simultaneously with the global irradiance from 2004 to 2007. The state of the art satellite irradiance deriving algorithms use as input the aerosol optical depth from the MACC-II project (Monitoring Atmosphere Composition and Climate [2, 3]) on a daily basis. The present paper analyses the performance of two clear sky models routinely used in the state of the art satellite algorithms, as well as a validation of the irradiance obtained from the satellite images. The first results show that the clear sky models stays within 4% for the global component acquired in Skukuza; the all-weather estimated irradiance is derived with a low bias, and a standard deviation of 8%, 24% and 32% for respectively the monthly, daily and hourly values.