During the Cairo Aerosol Characterization Experiment an automated Sun photometer belonging to the NASA Aerosol Robotic Network has been implemented for the first time in the megacity of Cairo, Egypt. The inversion of the measurements performed by this instrument several times a day and over a duration of more than 1 year (from the end of October 2004 to the end of January 2006) provides a way of determining the temporal variability of aerosol characteristics such as size distribution, complex refractive index, single‐scattering albedo, and asymmetry parameter. The analysis of the results reveals that Cairo's aerosol is a mixture of three individual components produced by different mechanisms: “background pollution” aerosol produced by local urban activities, “pollution‐like” aerosol resulting from biomass burning in the Nile delta, and “dust‐like” aerosol released by wind erosion in the Sahara. It is also shown that the variations in the overall aerosol properties are in fact due to changes in the proportions of this mixture. In particular, short‐duration dust storms and biomass‐burning episodes explain the largest observed aerosol optical depths (AOD) (AOD > 0.7) through the extreme enhancements of concentrations in dust‐like aerosols characterized by low Ångström's exponent values (α < 0.5) and in “biomass‐burning” aerosols (1.0 < α < 1.5). When averaged over longer (monthly and yearly) time periods, the effects of these high‐frequency modifications are smoothed. In particular, an average “mixed aerosol” type is defined for the whole duration of the measurements period. The low single‐scattering albedo (SSA) of this average aerosol and its marked spectral dependence clearly indicate that, at least on a yearly basis, the aerosol is dominated by its two light absorbing pollution components (background pollution and pollution‐like) and to such an extent that it compares well with values obtained in other polluted megacities (e.g., Mexico City). This general dominance of the absorbing components can be challenged at shorter timescales. Indeed, the occurrence of several dust storms in springtime, and particularly in April, causes a significant increase in SSA and a parallel decrease in spectral dependence during this month. Conversely, the October biomass‐burning events are not able to cause such important deviations from the yearly averaged mixed aerosol model that its optical properties can no longer be used for this month.

HelioClim-3 (HC3) is a database providing time series of the surface downwelling solar irradiance that are computed from images of the Meteosat satellites. This paper presents the validation results of the hourly global horizontal irradiance (GHI) and direct normal irradiance (DNI), i.e., beam irradiance at normal incidence, of versions four and five of HC3 at seven Egyptian sites. The validation is performed for all-sky conditions, as well as cloud-free conditions. Both versions of HC3 provide similar performances whatever the conditions. Another comparison is made with the estimates provided by the McClear database that is restricted to cloud-free conditions. All databases capture well the temporal variability of the GHI in all conditions, McClear being superior for cloud-free cases. In cloud-free conditions for the GHI, the relative root mean square error (RMSE) are fairly similar, ranging from 6% to 15%; both HC3 databases exhibit a smaller bias than McClear. McClear offers an overall better performance for the cloud-free DNI estimates. For all-sky conditions, the relative RMSE for GHI ranges from 10% to 22%, except one station, while, for the DNI, the results are not so good for the two stations with DNI measurements.

Two databases of solar surface irradiance (SSI) derived from satellites were compared to ground measurements in Algeria, Egypt, Libya and Tunisia. We found that it was possible to accurately derive the SSI from geostationary meteorological satellites, even with a coarse spatial resolution. The two databases HelioClim-1 (HC1) and SSE exhibited similar and good performances. The bias was generally lower for SSE than for HC1; however, HC1 exhibited a smaller scattering of data compared to ground measurements (smaller standard deviation) than the SSE, allowing better performance when mapping the long-term variations in SSI. The long-term variations in SSI from 1985 to 2005 show that these four countries as a whole experienced dimming. Detailed analyses of the range of dimming at sites with long-term records and of its spatial distribution were performed. We found that the analysis of SSI from HC1 supports the findings for the individual sites. Dimming may be explained by: (1) transportation of sand dust northwards from the Sahel, (2) an increase in urbanization, and (3) an increase in cloud cover and aerosol loading.