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.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-18-14511-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
10 Oct 2018
Research article |
| 10 Oct 2018
| 10 Oct 2018
A neural network aerosol-typing algorithm based on lidar data
Doina Nicolae
CORRESPONDING AUTHOR
National Institute of R&D for Optoelectronics,
409 Atomiştilor Str., Măgurele, Ilfov, Romania
Jeni Vasilescu
National Institute of R&D for Optoelectronics,
409 Atomiştilor Str., Măgurele, Ilfov, Romania
Camelia Talianu
National Institute of R&D for Optoelectronics,
409 Atomiştilor Str., Măgurele, Ilfov, Romania
Institute of Meteorology, University of Natural
Resources and Life Sciences, 33 Gregor-Mendel Str., 1180, Vienna, Austria
Ioannis Binietoglou
National Institute of R&D for Optoelectronics,
409 Atomiştilor Str., Măgurele, Ilfov, Romania
Victor Nicolae
National Institute of R&D for Optoelectronics,
409 Atomiştilor Str., Măgurele, Ilfov, Romania
Faculty of Physics, University of Bucharest,
Atomiştilor 405, Măgurele, Ilfov, Romania
Simona Andrei
National Institute of R&D for Optoelectronics,
409 Atomiştilor Str., Măgurele, Ilfov, Romania
Bogdan Antonescu
National Institute of R&D for Optoelectronics,
409 Atomiştilor Str., Măgurele, Ilfov, Romania
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- Interrelations between surface, boundary layer, and columnar aerosol properties derived in summer and early autumn over a continental urban site in Warsaw, Poland D. Wang et al. 10.5194/acp-19-13097-2019
- Multiwavelength fluorescence lidar observations of smoke plumes I. Veselovskii et al. 10.5194/amt-16-2055-2023
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- HETEAC-Flex: an optimal estimation method for aerosol typing based on lidar-derived intensive optical properties A. Floutsi et al. 10.5194/amt-17-693-2024
- Investigation of artificial neural network performance in the aerosol properties retrieval N. Srivastava et al. 10.2166/wcc.2021.336
- An EARLINET early warning system for atmospheric aerosol aviation hazards N. Papagiannopoulos et al. 10.5194/acp-20-10775-2020
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Latest update: 18 Apr 2024
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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