| Summary: | This work focuses on characterising various aspects of clean-background and polluted aerosol, mainly focusing on the North East Atlantic and Europe, using a range of in-situ and remote sensing instrumentation. Prior to achieving the objectives of aerosol characterisation, effort was invested in characterising a new, near-real-time satellite aerosol profiling products. The near-real-time Level 1.5 Cloud-Aerosol Light Detection and Ranging (lidar) with Orthogonal Polarization (CALIOP) products were evaluated against data from ground-based European Aerosol Research Lidar Network (EARLINET). A statistical study was performed on 48 CALIOP overpasses with ground tracks within a 100 km distance from operating stations over three years period. For the whole data set, the correlation coefficient (R) was 0.86. The correlation was reduced somewhat to R=0.6 when the analysis was repeated for the planetary boundary layer (PBL) on its own. Further filtering to remove free troposphere (FT) layers with high attenuated backscatter did not improve the agreement either and suggests that the considerable variability across the data set, leading to the low correlation, is due to spatial inhomogeneities in the PBL. Additional evaluation between CALIOP and ground-based lidar close to Atlanta (United States (US)) also showed poor agreement. This poor agreement led us to consider the whole feasibility of a Calibration & Validation (CALVAL) exercise using a ground-based reference stations for space-borne CALVAL platforms. The required accuracy necessitates uncertainties of the order of 2% to be meaningful. To achieve this level of uncertainty with a polar-orbiting satellite would require averaging the ground-based lidar profiles along the ground track of CALIOP for distances of at least 1,500 km, which, clearly, cannot be achieved with ground-based lidars. Satellite remote sensing is useful for characterising sources and extent of long range aerosol transport and, in particular, quantifying its contribution to transboundary air ...
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