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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- An EARLINET early warning system for atmospheric aerosol aviation hazards N. Papagiannopoulos et al. 10.5194/acp-20-10775-2020
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- Long term observations of biomass burning aerosol over Warsaw by means of multiwavelength lidar L. Janicka et al. 10.1364/OE.496794
- Reducing Aerosol Forcing Uncertainty by Combining Models With Satellite and Within‐The‐Atmosphere Observations: A Three‐Way Street R. Kahn et al. 10.1029/2022RG000796
- Characterization of aerosol over the eastern Mediterranean by polarization-sensitive Raman lidar measurements during A-LIFE – aerosol type classification and type separation S. Groß et al. 10.5194/acp-25-3191-2025
- Comprehensive thematic T-matrix reference database: a 2017–2019 update M. Mishchenko 10.1016/j.jqsrt.2019.106692
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- Properties of Saharan Dust Versus Local Urban Dust—A Case Study D. Szczepanik et al. 10.1029/2021EA001816
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- Aerosol Typing Based on Multiwavelength Lidar Observations and Meteorological Model Data M. Mylonaki et al. 10.1051/epjconf/202023708003
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Latest update: 02 Apr 2025
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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