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
Viewed
Total article views: 4,872 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 07 Jun 2018)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 3,552 | 1,247 | 73 | 4,872 | 99 | 71 |
- HTML: 3,552
- PDF: 1,247
- XML: 73
- Total: 4,872
- BibTeX: 99
- EndNote: 71
Total article views: 4,129 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 10 Oct 2018)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 3,119 | 955 | 55 | 4,129 | 86 | 61 |
- HTML: 3,119
- PDF: 955
- XML: 55
- Total: 4,129
- BibTeX: 86
- EndNote: 61
Total article views: 743 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 07 Jun 2018)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 433 | 292 | 18 | 743 | 13 | 10 |
- HTML: 433
- PDF: 292
- XML: 18
- Total: 743
- BibTeX: 13
- EndNote: 10
Viewed (geographical distribution)
Total article views: 4,872 (including HTML, PDF, and XML)
Thereof 4,841 with geography defined
and 31 with unknown origin.
Total article views: 4,129 (including HTML, PDF, and XML)
Thereof 4,093 with geography defined
and 36 with unknown origin.
Total article views: 743 (including HTML, PDF, and XML)
Thereof 748 with geography defined
and -5 with unknown origin.
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
Cited
46 citations as recorded by crossref.
- Consistency of the Single Calculus Chain Optical Products with Archived Measurements from an EARLINET Lidar Station K. Voudouri et al. 10.3390/rs12233969
- Achieve accurate recognition of 3D point cloud images by studying the scattering characteristics of typical targets Q. Li et al. 10.1016/j.infrared.2021.103852
- Cellular Self-Structuring and Turbulent Behaviors in Atmospheric Laminar Channels I. Roșu et al. 10.3389/feart.2021.801020
- SAMIRA-SAtellite Based Monitoring Initiative for Regional Air Quality K. Stebel et al. 10.3390/rs13112219
- An aerosol classification scheme for global simulations using the K-means machine learning method J. Li et al. 10.5194/gmd-15-509-2022
- Optical and geometrical aerosol particle properties over the United Arab Emirates M. Filioglou et al. 10.5194/acp-20-8909-2020
- 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
- Combined sun-photometer–lidar inversion: lessons learned during the EARLINET/ACTRIS COVID-19 campaign A. Tsekeri et al. 10.5194/amt-16-6025-2023
- Pollen observations at four EARLINET stations during the ACTRIS-COVID-19 campaign X. Shang et al. 10.5194/acp-22-3931-2022
- Comparison of two automated aerosol typing methods and their application to an EARLINET station K. Voudouri et al. 10.5194/acp-19-10961-2019
- An EARLINET early warning system for atmospheric aerosol aviation hazards N. Papagiannopoulos et al. 10.5194/acp-20-10775-2020
- 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
- Geometrical and Microphysical Properties of Clouds Formed in the Presence of Dust above the Eastern Mediterranean E. Marinou et al. 10.3390/rs13245001
- 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
- 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
- Analysis of sulfate aerosols over Austria: a case study C. Talianu & P. Seibert 10.5194/acp-19-6235-2019
- 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
- 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
- ALICENET – an Italian network of automated lidar ceilometers for four-dimensional aerosol monitoring: infrastructure, data processing, and applications A. Bellini et al. 10.5194/amt-17-6119-2024
- 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
- Characterization of forest fire and Saharan desert dust aerosols over south-western Europe using a multi-wavelength Raman lidar and Sun-photometer V. Salgueiro et al. 10.1016/j.atmosenv.2021.118346
- Retrieval and analysis of the composition of an aerosol mixture through Mie–Raman–fluorescence lidar observations I. Veselovskii et al. 10.5194/amt-17-4137-2024
- Automated Aerosol Classification from Spectral UV Measurements Using Machine Learning Clustering N. Siomos et al. 10.3390/rs12060965
- Optical properties of Central Asian aerosol relevant for spaceborne lidar applications and aerosol typing at 355 and 532 nm J. Hofer et al. 10.5194/acp-20-9265-2020
- Ground-Based Measurements of Cloud Properties at the Bucharest–Măgurele Cloudnet Station: First Results R. Pîrloagă et al. 10.3390/atmos13091445
46 citations as recorded by crossref.
- Consistency of the Single Calculus Chain Optical Products with Archived Measurements from an EARLINET Lidar Station K. Voudouri et al. 10.3390/rs12233969
- Achieve accurate recognition of 3D point cloud images by studying the scattering characteristics of typical targets Q. Li et al. 10.1016/j.infrared.2021.103852
- Cellular Self-Structuring and Turbulent Behaviors in Atmospheric Laminar Channels I. Roșu et al. 10.3389/feart.2021.801020
- SAMIRA-SAtellite Based Monitoring Initiative for Regional Air Quality K. Stebel et al. 10.3390/rs13112219
- An aerosol classification scheme for global simulations using the K-means machine learning method J. Li et al. 10.5194/gmd-15-509-2022
- Optical and geometrical aerosol particle properties over the United Arab Emirates M. Filioglou et al. 10.5194/acp-20-8909-2020
- 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
- Combined sun-photometer–lidar inversion: lessons learned during the EARLINET/ACTRIS COVID-19 campaign A. Tsekeri et al. 10.5194/amt-16-6025-2023
- Pollen observations at four EARLINET stations during the ACTRIS-COVID-19 campaign X. Shang et al. 10.5194/acp-22-3931-2022
- Comparison of two automated aerosol typing methods and their application to an EARLINET station K. Voudouri et al. 10.5194/acp-19-10961-2019
- An EARLINET early warning system for atmospheric aerosol aviation hazards N. Papagiannopoulos et al. 10.5194/acp-20-10775-2020
- 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
- Geometrical and Microphysical Properties of Clouds Formed in the Presence of Dust above the Eastern Mediterranean E. Marinou et al. 10.3390/rs13245001
- 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
- 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
- Analysis of sulfate aerosols over Austria: a case study C. Talianu & P. Seibert 10.5194/acp-19-6235-2019
- 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
- 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
- ALICENET – an Italian network of automated lidar ceilometers for four-dimensional aerosol monitoring: infrastructure, data processing, and applications A. Bellini et al. 10.5194/amt-17-6119-2024
- 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
- Characterization of forest fire and Saharan desert dust aerosols over south-western Europe using a multi-wavelength Raman lidar and Sun-photometer V. Salgueiro et al. 10.1016/j.atmosenv.2021.118346
- Retrieval and analysis of the composition of an aerosol mixture through Mie–Raman–fluorescence lidar observations I. Veselovskii et al. 10.5194/amt-17-4137-2024
- Automated Aerosol Classification from Spectral UV Measurements Using Machine Learning Clustering N. Siomos et al. 10.3390/rs12060965
- Optical properties of Central Asian aerosol relevant for spaceborne lidar applications and aerosol typing at 355 and 532 nm J. Hofer et al. 10.5194/acp-20-9265-2020
- Ground-Based Measurements of Cloud Properties at the Bucharest–Măgurele Cloudnet Station: First Results R. Pîrloagă et al. 10.3390/atmos13091445
Latest update: 22 Nov 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. ...
Altmetrics
Final-revised paper
Preprint