Articles | Volume 13, issue 10
https://doi.org/10.5194/amt-13-5537-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-13-5537-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
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
Department of Electrical and Computer Engineering, Virginia Tech,
Blacksburg, VA 24060, USA
Elena Spinei
CORRESPONDING AUTHOR
Department of Electrical and Computer Engineering, Virginia Tech,
Blacksburg, VA 24060, USA
Anuj Karpatne
Department of Computer Science, Virginia Tech, Blacksburg, VA 24060, USA
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Multi-axis differential optical absorption spectroscopy (MAX-DOAS) is a ground-based remote sensing measurement technique that derives atmospheric aerosol and trace gas vertical profiles from skylight spectra. In this study, consistency and reliability of MAX-DOAS profiles are assessed by applying nine different evaluation algorithms to spectral data recorded during an intercomparison campaign in the Netherlands and by comparing the results to colocated supporting observations.
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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...