Enhanced remote sensing of atmospheric aerosol by joint inversion of active and passive remote sensing observations

Doctorant: A. Lopatin

This thesis presents the GARRLiC algorithm (Generalized Aerosol Retrieval from Ra- diometer and Lidar Combined data) that simultaneously inverts co-incident lidar and sun-photometer observations and derives a united set of aerosol parameters that describe both columnar and vertical aerosol properties.

GARRLiC searches for the best fit of the multi-source measurements together with a priori constraints on aerosol characteristics through the continuous space of all possible solutions under statistically formulated criteria. It retrieves height independent size distribution, complex refractive index and fraction of spherical particles together with vertically resolved aerosol concentration, all differentiated between fine and coarse aerosol modes.

The potential and limitations of the method are demonstrated by sensitivity tests. The tests showed that the complete set of aerosol parameters for each aerosol component can be robustly derived with acceptable accuracy in all considered situations. Limited sensitivity to the properties of the fine mode and dependence of retrieval accuracy on the aerosol optical thickness for both modes were found. It was shown that sensitivity to fine mode refractive index could be improved by accounting for polarization data provided by passive instruments. The effects of the presence of lidar data and random noise on aerosol retrievals were studied.

The algorithm was also applied to the real lidar and radiometer observations obtained over Minsk (Belarus) and Lille (France) AERONET sites. Suggested approach could be easily modified to retrieve aerosol properties from all possible combinations of existing passive and active remote sensing instruments.

Keywords : atmospheric aerosol, remote sensing, inverse problem, multi-instrument observations, LIDAR; sun-photometer

Laboratories: co-tutelle LOA / LOSM