Abstract. The proposed development is an attempt to enhance aerosol retrieval by emphasizing statistical optimization in inversion of advanced satellite observations. This optimization concept improves retrieval accuracy relying on the knowledge of measurement error distribution. Efficient application of such optimization requires pronounced data redundancy (excess of the measurements number over number of unknowns) that is not common in satellite observations. The POLDER imager on board the PARASOL microsatellite registers spectral polarimetric characteristics of the reflected atmospheric radiation at up to 16 viewing directions over each observed pixel. The completeness of such observations is notably higher than for most currently operating passive satellite aerosol sensors. This provides an opportunity for profound utilization of statistical optimization principles in satellite data inversion. The proposed retrieval scheme is designed as statistically optimized multi-variable fitting of all available angular observations obtained by the POLDER sensor in the window spectral channels where absorption by gas is minimal. The total number of such observations by PARASOL always exceeds a hundred over each pixel and the statistical optimization concept promises to be efficient even if the algorithm retrieves several tens of aerosol parameters. Based on this idea, the proposed algorithm uses a large number of unknowns and is aimed at retrieval of extended set of parameters affecting measured radiation.Correspondence to: O. Dubovik (dubovik@loa.univ-lille1.fr)The algorithm is designed to retrieve complete aerosol properties globally. Over land, the algorithm retrieves the parameters of underlying surface simultaneously with aerosol. In all situations, the approach is anticipated to achieve a robust retrieval of complete aerosol properties including information about aerosol particle sizes, shape, absorption and composition (refractive index). In order to achieve reliable retrieval from PARASOL observations even over very reflective desert surfaces, the algorithm was designed as simultaneous inversion of a large group of pixels within one or several images. Such multi-pixel retrieval regime takes advantage of known limitations on spatial and temporal variability in both aerosol and surface properties. Specifically the variations of the retrieved parameters horizontally from pixel-to-pixel and/or temporary from day-to-day are enforced to be smooth by additional a priori constraints. This concept is expected to provide satellite retrieval of higher consistency, because the retrieval over each single pixel will be benefiting from coincident aerosol information from neighboring pixels, as well, from the information about surface reflectance (over land) obtained in preceding and consequent observations over the same pixel.The paper provides in depth description of the proposed inversion concept, illustrates the algorithm performance by a series of numerical tests and presents the examples of preliminary retrieval results o...

Abstract. This paper presents the GARRLiC algorithm (Generalized Aerosol Retrieval from Radiometer and Lidar Combined data) that simultaneously inverts coincident lidar and radiometer observations and derives a united set of aerosol parameters. Such synergetic retrieval results in additional enhancements in derived aerosol properties because the back-scattering observations by lidar improve sensitivity to the columnar properties of aerosol, while radiometric observations provide sufficient constraints on aerosol amount and type that are generally missing in lidar signals. GARRLiC is based on the AERONET algorithm, improved to invert combined observations by radiometer and multi-wavelength elastic lidar observations. The algorithm is set to derive not only the vertical profile of total aerosol concentration but it also differentiates between the contributions of fine and coarse modes of aerosol. The detailed microphysical properties are assumed height independent and different for each mode and derived as a part of the retrieval. The GARRLiC inversion retrieves vertical distribution of both fine and coarse aerosol concentrations as well as the size distribution and complex refractive index for each mode. The potential and limitations of the method are demonstrated by the series of sensitivity tests. The effects of presence of lidar data and random noise on aerosol retrievals are studied. Limited sensitivity to the properties of the fine mode as well as dependence of retrieval accuracy on the aerosol optical thickness were found. The practical outcome of the approach is illustrated by applications of the algorithm to the real lidar and radiometer observations obtained over Minsk AERONET site.