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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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AMT | Articles | Volume 13, issue 10
Atmos. Meas. Tech., 13, 5537–5550, 2020
https://doi.org/10.5194/amt-13-5537-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Atmos. Meas. Tech., 13, 5537–5550, 2020
https://doi.org/10.5194/amt-13-5537-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 16 Oct 2020

Research article | 16 Oct 2020

A feasibility study to use machine learning as an inversion algorithm for aerosol profile and property retrieval from multi-axis differential absorption spectroscopy measurements

Yun Dong et al.

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AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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AR: Author's response | RR: Referee report | ED: Editor decision
AR by Svenja Lange on behalf of the Authors (22 Jun 2020)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (02 Jul 2020) by Omar Torres
RR by Anonymous Referee #2 (07 Jul 2020)
ED: Publish as is (07 Jul 2020) by Omar Torres
Publications Copernicus
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Short summary
This paper is about a feasibility study of applying a machine learning technique to derive aerosol properties from a single MAX-DOAS sky scan, which detects sky-scattered UV–visible photons at multiple elevation angles. Evaluation of retrieved aerosol properties shows good performance of the ML algorithm, suggesting several advantages of a ML-based inversion algorithm such as fast data inversion, simple implementation and the ability to extract information not available using other algorithms.
This paper is about a feasibility study of applying a machine learning technique to derive...
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