Modelling of aerosol optical depth variability at regional scale

Monitoring of aerosol optical depth (AOD) is of particular importance due to the significant role of aerosols in the atmospheric radiative budget. AOD is a key parameter in studies related to global climatology, atmospheric pollutants, forest fires, and for performing atmospheric corrections on remotely sensed imagery of surface scenes. Up to now the two standard techniques used for retrieving AOD are; (i) sun photometry which provides measurements of high temporal frequency and sparse spatial frequency, and (ii) approaches such as DDV (Dense Dark Vegetation) inversion algorithms which use dark targets in remotely sensed imagery. Although the latter techniques allow AOD retrieval over appreciable spatial domains, the irregular spatial pattern of dark targets and the typically low repeat frequencies of imaging satellites exclude the acquisition of AOD databases on a continuous spatio-temporal basis. In order to overcome this drawback, the authors adopted a modelling approach based on a passive atmospheric circulation model and an AOD data assimilation technique, which ties AOD computations from the circulation model to available AOD measurements. The authors present in this paper the first validation results for this new model applied to mid-latitude eastern North America during June 1997. The results show the potential of this approach especially when used with remotely sensed AOD