Enhancement of aerosol characterization using synergy of lidar and sun-photometer coincident observations: the GARRLiC algorithm

[摘要] This paper presents the GARRLiC algorithm (Generalized Aerosol Retrieval fromRadiometer and Lidar Combined data) that simultaneously inverts coincidentlidar and radiometer observations and derives a united set of aerosolparameters. Such synergetic retrieval results in additional enhancements inderived aerosol properties because the back-scattering observations by lidarimprove sensitivity to the columnar properties of aerosol, while radiometricobservations provide sufficient constraints on aerosol amount and type thatare generally missing in lidar signals.

GARRLiC is based on the AERONET algorithm, improved to invert combinedobservations by radiometer and multi-wavelength elastic lidar observations.The algorithm is set to derive not only the vertical profile of total aerosolconcentration but it also differentiates between the contributions of fineand coarse modes of aerosol. The detailed microphysical properties areassumed height independent and different for each mode and derived as a partof the retrieval. The GARRLiC inversion retrieves vertical distribution ofboth fine and coarse aerosol concentrations as well as the size distributionand complex refractive index for each mode.

The potential and limitations of the method are demonstrated by the series ofsensitivity tests. The effects of presence of lidar data and random noise onaerosol retrievals are studied. Limited sensitivity to the properties of thefine mode as well as dependence of retrieval accuracy on the aerosol opticalthickness were found. The practical outcome of the approach is illustrated byapplications of the algorithm to the real lidar and radiometer observationsobtained over Minsk AERONET site.

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