Articles | Volume 18, issue 19
https://doi.org/10.5194/acp-18-14511-2018
https://doi.org/10.5194/acp-18-14511-2018
Research article
 | 
10 Oct 2018
Research article |  | 10 Oct 2018

A neural network aerosol-typing algorithm based on lidar data

Doina Nicolae, Jeni Vasilescu, Camelia Talianu, Ioannis Binietoglou, Victor Nicolae, Simona Andrei, and Bogdan Antonescu

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Cited articles

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
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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.
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