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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- 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
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- SAMIRA-SAtellite Based Monitoring Initiative for Regional Air Quality K. Stebel et al. 10.3390/rs13112219
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- DeLiAn – a growing collection of depolarization ratio, lidar ratio and Ångström exponent for different aerosol types and mixtures from ground-based lidar observations A. Floutsi et al. 10.5194/amt-16-2353-2023
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- Combined sun-photometer–lidar inversion: lessons learned during the EARLINET/ACTRIS COVID-19 campaign A. Tsekeri et al. 10.5194/amt-16-6025-2023
- Multiyear Typology of Long-Range Transported Aerosols over Europe V. Nicolae et al. 10.3390/atmos10090482
- Pollen observations at four EARLINET stations during the ACTRIS-COVID-19 campaign X. Shang et al. 10.5194/acp-22-3931-2022
- 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
- Comparison of two automated aerosol typing methods and their application to an EARLINET station K. Voudouri et al. 10.5194/acp-19-10961-2019
- 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
- Determination of Lidar Ratio for Major Aerosol Types over Western North Pacific Based on Long-Term MPLNET Data S. Wang et al. 10.3390/rs12172769
- 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
- Analysis of sulfate aerosols over Austria: a case study C. Talianu & P. Seibert 10.5194/acp-19-6235-2019
- 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
- Classification of lidar measurements using supervised and unsupervised machine learning methods G. Farhani et al. 10.5194/amt-14-391-2021
- Retrieval of Aerosol Microphysical Properties from Multi-Wavelength Mie–Raman Lidar Using Maximum Likelihood Estimation: Algorithm, Performance, and Application Y. Chang et al. 10.3390/rs14246208
- 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
- Towards Early Detection of Tropospheric Aerosol Layers Using Monitoring with Ceilometer, Photometer, and Air Mass Trajectories M. Adam et al. 10.3390/rs14051217
- ALiDAn: Spatiotemporal and Multiwavelength Atmospheric Lidar Data Augmentation A. Vainiger et al. 10.1109/TGRS.2022.3201436
- The Search for Atmospheric Laminar Channels: Experimental Results and Method Dissemination I. Roșu et al. 10.3390/s22010158
- Optical properties of aerosol and cloud particles measured by a single-line-extracted pure rotational Raman lidar L. Peng et al. 10.1364/OE.427864
- Automated time–height-resolved air mass source attribution for profiling remote sensing applications M. Radenz et al. 10.5194/acp-21-3015-2021
- Smoke of extreme Australian bushfires observed in the stratosphere over Punta Arenas, Chile, in January 2020: optical thickness, lidar ratios, and depolarization ratios at 355 and 532 nm K. Ohneiser et al. 10.5194/acp-20-8003-2020
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- Geometrical and Microphysical Properties of Clouds Formed in the Presence of Dust above the Eastern Mediterranean E. Marinou et al. 10.3390/rs13245001
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Latest update: 16 Jul 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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