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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/amt-2019-368
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-2019-368
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  21 Oct 2019

21 Oct 2019

Review status
A revised version of this preprint was accepted for the journal AMT and is expected to appear here in due course.

Machine learning as an inversion algorithm for aerosol profile and property retrieval from multi-axis differential absorption spectroscopy measurements: a feasibility study

Yun Dong1, Elena Spinei1, and Anuj Karpatne2 Yun Dong et al.
  • 1Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA 24060, USA
  • 2Department of Computer Science, Virginia Tech, Blacksburg, VA 24060, USA

Abstract. In this study, we explore a new approach based on machine learning (ML) for deriving aerosol extinction coefficient profiles, single scattering albedo and asymmetry parameter at 360 nm from a single MAX-DOAS sky scan. Our method relies on a multi-output sequence-to-sequence model combining Convolutional Neural Networks (CNN) for feature extraction and Long Short-Term Memory networks (LSTM) for profile prediction. The model was trained and evaluated using data simulated by VLIDORT v2.7, which contains 1 459 200 unique mappings. 75 % randomly selected simulations were used for training and the remaining 25 % for validation. The overall error of estimated aerosol properties for (1) total AOD is −1.4 ± 10.1 %, (2) for single scattering albedo is 0.1 ± 3.6 %; and (3) asymmetry factor is −0.1 ± 2.1 %. The resulting model is capable of retrieving aerosol extinction coefficient profiles with degrading accuracy as a function of height. The uncertainty due to the randomness in ML training is also discussed.

Yun Dong et al.

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Yun Dong et al.

Yun Dong et al.

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