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ACP | Articles | Volume 18, issue 19
Atmos. Chem. Phys., 18, 14511–14537, 2018
https://doi.org/10.5194/acp-18-14511-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue: EARLINET aerosol profiling: contributions to atmospheric and...

Atmos. Chem. Phys., 18, 14511–14537, 2018
https://doi.org/10.5194/acp-18-14511-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 10 Oct 2018

Research article | 10 Oct 2018

A neural network aerosol-typing algorithm based on lidar data

Doina Nicolae et al.
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Status: closed
Status: closed
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 Bogdan Antonescu on behalf of the Authors (19 Sep 2018)  Author's response    Manuscript
ED: Publish as is (20 Sep 2018) by Vassilis Amiridis
Publications Copernicus
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Short summary
A new aerosol typing algorithm based on artificial neural networks (ANNs) has been developed. The algorithm is providing the most probable aerosol type based on EARLINET LIDAR profiles. The ANNs used by the algorithm were trained using synthetic data, for which a new aerosol model has been developed. Blind tests on EARLINET data samples showed the capability of the algorithm to retrieve the aerosol type from a large variety of data, with different quality and physical content.
A new aerosol typing algorithm based on artificial neural networks (ANNs) has been developed. ...
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