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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- Airborne Lidar Observations of a Spring Phytoplankton Bloom in the Western Arctic Ocean J. Churnside et al. 10.3390/rs13132512
- Spatio-temporal discrimination of molecular, aerosol and cloud scattering and polarization using a combination of a Raman lidar, Doppler cloud radar and microwave radiometer D. Wang et al. 10.1364/OE.393625
- 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
- Comprehensive thematic T-matrix reference database: a 2017–2019 update M. Mishchenko 10.1016/j.jqsrt.2019.106692
- Biomass burning events measured by lidars in EARLINET – Part 1: Data analysis methodology M. Adam et al. 10.5194/acp-20-13905-2020
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- Properties of Saharan Dust Versus Local Urban Dust—A Case Study D. Szczepanik et al. 10.1029/2021EA001816
- Combining Mie–Raman and fluorescence observations: a step forward in aerosol classification with lidar technology I. Veselovskii et al. 10.5194/amt-15-4881-2022
- Machine Learning Techniques for Vertical Lidar-Based Detection, Characterization, and Classification of Aerosols and Clouds: A Comprehensive Survey S. Lolli 10.3390/rs15174318
- HETEAC – the Hybrid End-To-End Aerosol Classification model for EarthCARE U. Wandinger et al. 10.5194/amt-16-2485-2023
- Multiyear Typology of Long-Range Transported Aerosols over Europe V. Nicolae et al. 10.3390/atmos10090482
- Aerosol Typing Based on Multiwavelength Lidar Observations and Meteorological Model Data M. Mylonaki et al. 10.1051/epjconf/202023708003
- Aerosol type classification analysis using EARLINET multiwavelength and depolarization lidar observations M. Mylonaki et al. 10.5194/acp-21-2211-2021
- Global aerosol-type classification using a new hybrid algorithm and Aerosol Robotic Network data X. Wei et al. 10.5194/acp-24-5025-2024
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- 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
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Latest update: 24 Dec 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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