Synergetic technique combining elastic backscatter lidar data and sunphotometer AERONET inversion for retrieval by layer of aerosol optical and microphysical properties
Juan Cuesta, Pierre H. Flamant, and Cyrille Flamant
Juan Cuesta, Pierre H. Flamant, and Cyrille Flamant, "Synergetic technique combining elastic backscatter lidar data and sunphotometer AERONET inversion for retrieval by layer of aerosol optical and microphysical properties," Appl. Opt. 47, 4598-4611 (2008)
We present a so-called lidar and almucantar (LidAlm) algorithm that combines information provided by standard elastic backscatter lidar (i.e., calibrated attenuated backscatter coefficient profile at one or two wavelengths) and sunphotometer AERONET inversion of almucantar like measurements (i.e., column-integrated aerosol size distribution and refractive index). The purpose of the LidAlm technique is to characterize the atmospheric column by its different aerosol layers. These layers may be distinct or partially mixed, and they may contain different aerosol species (e.g., urban, desert, or biomass burning aerosols). The LidAlm synergetic technique provides the extinction and backscatter coefficient profiles, particle size distributions, and backscatter-to-extinction ratios for each aerosol layer. We present the LidAlm procedure and sensitivity studies. The applications are illustrated with examples of actual atmospheric conditions encountered in the Paris area.
Detlef Müller, Frank Wagner, Ulla Wandinger, Albert Ansmann, Manfred Wendisch, Dietrich Althausen, and Wolfgang von Hoyningen-Huene Appl. Opt. 39(12) 1879-1892 (2000)
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Dispersion in the LNM Parameters Used in the LidAlm Algorithm Associated with Uncertainties in the AERONET Inversion [Monte Carlo Simulations Used the Example of Fig. 4a as Noise-Free Data]
Monte Carlo simulations consider SEBL profiles at 532 and and the noise-free input data displayed in Fig. 4 (urban aerosols in the ABL, modes 1 and 3, and desert dust in the FT, modes 2 and 4).
Noise-free values are indicated in brackets.
Also presented in Fig. 5.
Table 3
Errors on LidAlm-retrieved Optical Properties Associated with Uncertainties in Auxiliary Input Data (Indicated in Parenthesis) as the First-Guess BER for Refractive Index Calculations , the Climatological Refractive Index for the FT Aerosol Layer in the FT, RH, and the Hygroscopic Growth Parameter ε (the Results Consider One Variable at a Time)
RH varies from 50% at the ground to 85% at the ABL top. stands for the variation of
Same as above but RH varies from 40% to 95%.
The refractive index is constant. RH from 40% to 95%.
Tables (3)
Table 1
Dispersion in the LNM Parameters Used in the LidAlm Algorithm Associated with Uncertainties in the AERONET Inversion [Monte Carlo Simulations Used the Example of Fig. 4a as Noise-Free Data]
Monte Carlo simulations consider SEBL profiles at 532 and and the noise-free input data displayed in Fig. 4 (urban aerosols in the ABL, modes 1 and 3, and desert dust in the FT, modes 2 and 4).
Noise-free values are indicated in brackets.
Also presented in Fig. 5.
Table 3
Errors on LidAlm-retrieved Optical Properties Associated with Uncertainties in Auxiliary Input Data (Indicated in Parenthesis) as the First-Guess BER for Refractive Index Calculations , the Climatological Refractive Index for the FT Aerosol Layer in the FT, RH, and the Hygroscopic Growth Parameter ε (the Results Consider One Variable at a Time)
RH varies from 50% at the ground to 85% at the ABL top. stands for the variation of
Same as above but RH varies from 40% to 95%.
The refractive index is constant. RH from 40% to 95%.