A neural network aerosol-typing algorithm based on lidar data

Nicolae, Doina; Vasilescu, Jeni; Talianu, Camelia; Binietoglou, Ioannis; Nicolae, Victor; Andrei, Simona; Antonescu, Bogdan

doi:https://doi.org/10.5194/acp-18-14511-2018

Articles | Volume 18, issue 19

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...

https://doi.org/10.5194/acp-18-14511-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 18, issue 19

Research article

|

10 Oct 2018

Research article |

| 10 Oct 2018

A neural network aerosol-typing algorithm based on lidar data

Doina Nicolae, Jeni Vasilescu, Camelia Talianu, Ioannis Binietoglou, Victor Nicolae, Simona Andrei, and Bogdan Antonescu

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Final revised paper (published on 10 Oct 2018)
Preprint (discussion started on 07 Jun 2018)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC1: 'review', Anonymous Referee #2, 03 Jul 2018
- AC1: 'Response to Anonymous Referee #2', Bogdan Antonescu, 19 Sep 2018
RC2: 'Review #2 on Nicolae et al. A Neural Network Aerosol Typing Algorithm Based on Lidar Data.', Anonymous Referee #1, 09 Aug 2018
- AC2: 'Response to Anonymous Referee #1', Bogdan Antonescu, 19 Sep 2018

Peer-review completion

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

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.