Articles | Volume 21, issue 17
https://doi.org/10.5194/acp-21-12867-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-21-12867-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Technical note: Evaluation of profile retrievals of aerosols and trace gases for MAX-DOAS measurements under different aerosol scenarios based on radiative transfer simulations
Information Materials and Intelligent Sensing Laboratory of Anhui
Province, Institutes of Physical Science and Information Technology, Anhui
University, Hefei, 230601, China
Key Laboratory of Environmental Optical and Technology, Anhui Institute
of Optics and Fine Mechanics, Chinese Academy of Science, Hefei, 230031,
China
Max Planck Institute for Chemistry, 55128 Mainz, Germany
now at: EUMETSAT, Darmstadt, Germany
Steffen Beirle
Max Planck Institute for Chemistry, 55128 Mainz, Germany
Pinhua Xie
CORRESPONDING AUTHOR
Key Laboratory of Environmental Optical and Technology, Anhui Institute
of Optics and Fine Mechanics, Chinese Academy of Science, Hefei, 230031,
China
CAS Center for Excellence in Urban Atmospheric Environment, Institute of
Urban Environment, Chinese Academy of Sciences, Xiamen, 361021, China
University of Chinese Academy of Sciences, Beijing, 100049, China
School of Environmental Science and Optoelectronic Technology, University
of Science and Technology of China, Hefei, 230026, China
Thomas Wagner
Max Planck Institute for Chemistry, 55128 Mainz, Germany
Jin Xu
Key Laboratory of Environmental Optical and Technology, Anhui Institute
of Optics and Fine Mechanics, Chinese Academy of Science, Hefei, 230031,
China
Ang Li
Key Laboratory of Environmental Optical and Technology, Anhui Institute
of Optics and Fine Mechanics, Chinese Academy of Science, Hefei, 230031,
China
Steffen Dörner
Max Planck Institute for Chemistry, 55128 Mainz, Germany
Bo Ren
Key Laboratory of Environmental Optical and Technology, Anhui Institute
of Optics and Fine Mechanics, Chinese Academy of Science, Hefei, 230031,
China
School of Environmental Science and Optoelectronic Technology, University
of Science and Technology of China, Hefei, 230026, China
Xiaomei Li
Key Laboratory of Environmental Optical and Technology, Anhui Institute
of Optics and Fine Mechanics, Chinese Academy of Science, Hefei, 230031,
China
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Cited
9 citations as recorded by crossref.
- McPrA - A new gas profile inversion algorithm for MAX-DOAS and apply to 50 m vertical resolution J. Zheng et al.
- A CNN-SVR model for NO2 profile prediction based on MAX-DOAS observations: The influence of Chinese New Year overlapping the 2020 COVID-19 lockdown on vertical distributions of tropospheric NO2 in Nanjing, China X. Tian et al.
- Particle Size as a Key Driver of Black Carbon Wet Removal: Advances and Insights Y. Qiao et al.
- One-step retrieval of ground-level ozone concentrations from OMI hyperspectral observations using machine learning L. Sun et al.
- How can we trust TROPOMI based methane emissions estimation: calculating emissions over unidentified source regions B. Zheng et al.
- Research of NO2 vertical profiles with look-up table method based on MAX-DOAS Y. Guo et al.
- Ozone profiles without blind area retrieved from MAX-DOAS measurements and comprehensive validation with multi-platform observations X. Ji et al.
- Evaluation of MAX-DOAS Profile Retrievals under Different Vertical Resolutions of Aerosol and NO2 Profiles and Elevation Angles X. Tian et al.
- Exploring Aerosol Vertical Distributions and Their Influencing Factors: Insight from MAX-DOAS and Machine Learning S. Zhang et al.
9 citations as recorded by crossref.
- McPrA - A new gas profile inversion algorithm for MAX-DOAS and apply to 50 m vertical resolution J. Zheng et al.
- A CNN-SVR model for NO2 profile prediction based on MAX-DOAS observations: The influence of Chinese New Year overlapping the 2020 COVID-19 lockdown on vertical distributions of tropospheric NO2 in Nanjing, China X. Tian et al.
- Particle Size as a Key Driver of Black Carbon Wet Removal: Advances and Insights Y. Qiao et al.
- One-step retrieval of ground-level ozone concentrations from OMI hyperspectral observations using machine learning L. Sun et al.
- How can we trust TROPOMI based methane emissions estimation: calculating emissions over unidentified source regions B. Zheng et al.
- Research of NO2 vertical profiles with look-up table method based on MAX-DOAS Y. Guo et al.
- Ozone profiles without blind area retrieved from MAX-DOAS measurements and comprehensive validation with multi-platform observations X. Ji et al.
- Evaluation of MAX-DOAS Profile Retrievals under Different Vertical Resolutions of Aerosol and NO2 Profiles and Elevation Angles X. Tian et al.
- Exploring Aerosol Vertical Distributions and Their Influencing Factors: Insight from MAX-DOAS and Machine Learning S. Zhang et al.
Saved (final revised paper)
Latest update: 28 Apr 2026
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, three 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.
The performances of two MAX-DOAS inversion algorithms were evaluated for various aerosol...
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