Preprints
https://doi.org/10.5194/amt-2022-165
https://doi.org/10.5194/amt-2022-165
 
06 Jul 2022
06 Jul 2022
Status: this preprint is currently under review for the journal AMT.

Algorithm for vertical distribution of boundary layer aerosol components in remote sensing data

Futing Wang1,2, Ting Yang1,3, Zifa Wang1,2,3, Haibo Wang1,2, and Xi Chen1,2 Futing Wang et al.
  • 1State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
  • 2College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
  • 3Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021, China

Abstract. The vertical distribution of atmospheric aerosol components is vital to the estimation of radiation forcing and the catalysis of atmospheric photochemical processes. Based on the synergy of ground-based lidar and sun-photometer, this paper developed a new algorithm to get the vertical mass concentration profiles of fine aerosol components for the first time. The sky radiance at multiple scatter angles, the total optical depth (TOD) at 440, 675, 870, and 1020 nm, and the lidar signals at 532 nm and 1064 nm were applied to retrieve the aerosol properties. Besides, the internal mixing model and normalized volume size distribution model were constructed, according to the absorption and water-solubility of aerosol components, to separate the profiles of black carbon (BC), water-insoluble organic matter (WIOM), water-soluble organic matter (WSOM), ammonium nitrate-like (AN), and fine aerosol water content (AW). The results showed a reasonable vertical distribution of aerosol components compared with in situ observations and reanalysis data. The estimated and observed BC concentration matched well with a correlation coefficient up to 0.91, while there was an evident overestimation of OM (NMB=0.98). And the retrieved AN concentrations were closer to the simulated results (the correlation coefficient of 0.85), especially in the polluted condition. The correlations of BC and OM were weaker relatively, with a correlation coefficient of about 0.5. Besides, the uncertainties caused by input parameters (i.e. RH, volume concentration, and extinction coefficients) were assessed by Monte Carlo method. AN and AW had smaller uncertainties at higher RH. In this paper, the algorithm was also applied to the remote sensing measurements of Beijing and two typical cases were presented. Under the clean condition with low RH, there were comparable AN and WIOM but peaking at different altitudes. While in the polluted case, AN was dominant and the maximum mass concentration occurred near the surface. We expected the algorithm can provide a new idea for lidar inversion and promote the development of aerosol components profiles.

Futing Wang et al.

Status: open (until 10 Aug 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2022-165', Francisco Molero, 11 Jul 2022 reply
  • RC2: 'Comment on amt-2022-165', Anonymous Referee #1, 25 Jul 2022 reply

Futing Wang et al.

Futing Wang et al.

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Short summary
We develop a new algorithm to get the vertical mass concentration profiles of fine aerosol components based on the synergy of ground-based remote sensing for the first time. The comparisons with in situ observations and chemistry transport model validate the performance of algorithm. Uncertainties caused by input parameters are also assessed in this paper. We expected that the algorithm can provide a new idea for lidar inversion and promote the development of aerosol components profiles.