Preprints
https://doi.org/10.5194/acp-2021-45
https://doi.org/10.5194/acp-2021-45

  22 Feb 2021

22 Feb 2021

Review status: this preprint is currently under review for the journal ACP.

Technical note:Evaluation of profile retrievals of aerosols and trace gases for MAX-DOAS measurements under different aerosol scenarios based on radiative transfer simulations

Xin Tian1,2, Yang Wang3,a, Steffen Beirle3, Pinhua Xie2,4,5,6, Thomas Wagner3, Jin Xu2, Ang Li2, Steffen Dörner3, Bo Ren2,6, and Xiaomei Li2 Xin Tian et al.
  • 1Information Materials and Intelligent Sensing Laboratory of Anhui Province, Institutes of Physical Science and Information Technology, Anhui University, Hefei, 230601, China
  • 2Key laboratory of Environmental Optical and Technology, Anhui Institute of optics and Fine Mechanics, Chinese Academy of Science, Hefei, 230031, China
  • 3Max Planck Institute for Chemistry, Mainz, 55128, Germany
  • 4CAS Center for Excellence in Urban Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021, China
  • 5University of Chinese Academy of Sciences, Beijing, 100049, China
  • 6School of Environmental Science and Optoelectronic Technology, University of Science and Technology of China, Hefei, 230026, China
  • anow at: EUMETSAT, Darmstadt. Germany

Abstract. Ground-based Multi-AXis Differential Optical Absorption Spectroscopy (MAX-DOAS) is a state of the art remote sensing technique for deriving vertical profiles of trace gases and aerosols. However, MAX-DOAS profile inversions under aerosol pollution scenarios are challenging because of the complex radiative transfer and limited information content of the measurements. In this study, the performances of two inversion algorithms were evaluated for various aerosol pollution scenarios based on synthetic slant column densities (SCDs) derived from radiative transfer simulations. One inversion algorithm is based on optimal estimation, the other uses a parameterized approach. In this analysis, 3 types of profile shapes for aerosols and NO2 were considered: exponential, Boltzmann, and Gaussian. First, the systematic deviations of the retrieved aerosol profiles from the input profiles were investigated. For most cases, the AODs of the retrieved profiles were found to be systematically lower than the input values, and the deviations increased with increasing AOD. Especially for the optimal estimation algorithm and for high AOD, these findings might explain part of the deviations between the AOD retrieved from MAX-DOAS and sun photometers in previous studies. For the optimal estimation algorithm the agreement with the input values can be improved by optimizing the covariance matrix of the a priori uncertainties. Second, the aerosol effects on the NO2 profile retrieval were tested. Here, especially for the optimal estimation algorithm, a systematic dependence on the NO2 VCD was found with a strong relative overestimation of the retrieved results for low NO2 VCDs and an underestimation for high NO2 VCDs. In contrast, the dependence on the aerosol profiles was found to be rather low. In general, both inversion schemes can well retrieve the near-surface values of aerosol extinction and trace gases concentrations.

Xin Tian et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Referee comment on acp-2021-45', Anonymous Referee #1, 28 Apr 2021
  • RC2: 'Comment on acp-2021-45', Anonymous Referee #2, 01 May 2021

Xin Tian et al.

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Short summary
The performances of two MAX-DOAS inversion algorithms were evaluated for various aerosol pollution scenarios. One inversion algorithm is based on optimal estimation, the other uses a parameterized approach. In this analysis, 3 types of profile shapes for aerosols and NO2 were considered: exponential, Boltzmann, and Gaussian. The evaluation results can effectively guide the application of the two inversion algorithms in the actual atmosphere and improve the accuracy of the actual inversion.
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