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Carboni, E; Thomas, GE; Sayer, AM; Siddans, R; Poulsen, CA; Grainger, RG; Ahn, C; Antoine, D; Bevan, S; Braak, R; Brindley, H; DeSouza-Machado, S; Deuze, JL; Diner, D; Ducos, F; Grey, W; Hsu, C; Kalashnikova, OV; Kahn, R; North, PRJ; Salustro, C; Smith, A; Tanre, D; Torres, O; Veihelmann, B (2012). Intercomparison of desert dust optical depth from satellite measurements. ATMOSPHERIC MEASUREMENT TECHNIQUES, 5(8), 1973-2002.

This work provides a comparison of satellite retrievals of Saharan desert dust aerosol optical depth (AOD) during a strong dust event through March 2006. In this event, a large dust plume was transported over desert, vegetated, and ocean surfaces. The aim is to identify the differences between current datasets. The satellite instruments considered are AATSR, AIRS, MERIS, MISR, MODIS, OMI, POLDER, and SEVIRI. An interesting aspect is that the different algorithms make use of different instrument characteristics to obtain retrievals over bright surfaces. These include multi-angle approaches (MISR, AATSR), polarisation measurements (POLDER), single-view approaches using solar wavelengths (OMI, MODIS), and the thermal infrared spectral region (SEVIRI, AIRS). Differences between instruments, together with the comparison of different retrieval algorithms applied to measurements from the same instrument, provide a unique insight into the performance and characteristics of the various techniques employed. As well as the intercomparison between different satellite products, the AODs have also been compared to co-located AERONET data. Despite the fact that the agreement between satellite and AERONET AODs is reasonably good for all of the datasets, there are significant differences between them when compared to each other, especially over land. These differences are partially due to differences in the algorithms, such as assumptions about aerosol model and surface properties. However, in this comparison of spatially and temporally averaged data, it is important to note that differences in sampling, related to the actual footprint of each instrument on the heterogeneous aerosol field, cloud identification and the quality control flags of each dataset can be an important issue.



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