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ACP | Articles | Volume 18, issue 19
Atmos. Chem. Phys., 18, 14511–14537, 2018
https://doi.org/10.5194/acp-18-14511-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue: EARLINET aerosol profiling: contributions to atmospheric and...

Atmos. Chem. Phys., 18, 14511–14537, 2018
https://doi.org/10.5194/acp-18-14511-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 10 Oct 2018

Research article | 10 Oct 2018

A neural network aerosol-typing algorithm based on lidar data

Doina Nicolae et al.

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Alados-Arboledas, L., Müller, D., Guerrero-Rascado, J. L., Navas-Guzman, F., Perez-Ramirez, D., and Olmo, F. J.: Optical and microphysical properties of fresh biomass burning aerosol retrieved by Raman lidar, and star-and sun-photometry, Geophys. Res. Lett., 38, 1–5, https://doi.org/10.1029/2010GL045999, 2011. a, b
Ali, A., Amin, S. E., Ramadan, H. H., and Tolba, M. F.: Enhancement of OMI aerosol optical depth data assimilation using artificial neural network, Neural Comput. Appl., 23, 2267–2279, https://doi.org/10.1007/s00521-012-1178-9, 2012. a
Amiridis, V., Giannakaki, E., Balis, D. S., Gerasopoulos, E., Pytharoulis, I., Zanis, P., Kazadzis, S., Melas, D., and Zerefos, C.: Smoke injection heights from agricultural burning in Eastern Europe as seen by CALIPSO, Atmos. Chem. Phys., 10, 11567–11576, https://doi.org/10.5194/acp-10-11567-2010, 2010. a
Ansmann, A., Wagner, F., Müller, D., Althausen, D., Herber, A., von Hoyningen-Huene, W., and Wandinger, U.: European pollution outbreaks during ACE 2: Optical particle properties inferred from multiwavelength lidar and star-Sun photometry, J. Geophys. Res., 107, 4259, https://doi.org/10.1029/2001JD001109, 2002. a, b
Ansmann, A., Tesche, M., Groß, S., Freudenthaler, V., Seifert, P., Hiebsch, A., Schmidt, J., Wandinger, U., Mattis, I., Müller, D., and Wiegner, M.: The 16 April 2010 major volcanic ash plume over central Europe: EARLINET lidar and AERONET photometer observations at Leipzig and Munich, Germany, Geophys. Res. Lett., 37, L13810, https://doi.org/10.1029/2010GL043809, 2010. a, b, c, d, e, f, g
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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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