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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 Nicolae1, Jeni Vasilescu1, Camelia Talianu1,2, Ioannis Binietoglou1, Victor Nicolae1,3, Simona Andrei1, and Bogdan Antonescu1 Doina Nicolae et al.
  • 1National Institute of R&D for Optoelectronics, 409 Atomiştilor Str., Măgurele, Ilfov, Romania
  • 2Institute of Meteorology, University of Natural Resources and Life Sciences, 33 Gregor-Mendel Str., 1180, Vienna, Austria
  • 3Faculty of Physics, University of Bucharest, Atomiştilor 405, Măgurele, Ilfov, Romania

Abstract. Atmospheric aerosols play a crucial role in the Earth's system, but their role is not completely understood, partly because of the large variability in their properties resulting from a large number of possible aerosol sources. Recently developed lidar-based techniques were able to retrieve the height distributions of optical and microphysical properties of fine-mode and coarse-mode particles, providing the types of the aerosols. One such technique is based on artificial neural networks (ANNs). In this article, a Neural Network Aerosol Typing Algorithm Based on Lidar Data (NATALI) was developed to estimate the most probable aerosol type from a set of multispectral lidar data. The algorithm was adjusted to run on the EARLINET 3β + 2α( + 1δ) profiles. The NATALI algorithm is based on the ability of specialized ANNs to resolve the overlapping values of the intensive optical parameters, calculated for each identified layer in the multiwavelength Raman lidar profiles. The ANNs were trained using synthetic data, for which a new aerosol model was developed. Two parallel typing schemes were implemented in order to accommodate data sets containing (or not) the measured linear particle depolarization ratios (LPDRs): (a) identification of 14 aerosol mixtures (high-resolution typing) if the LPDR is available in the input data files, and (b) identification of five predominant aerosol types (low-resolution typing) if the LPDR is not provided. For each scheme, three ANNs were run simultaneously, and a voting procedure selects the most probable aerosol type. The whole algorithm has been integrated into a Python application. The limitation of NATALI is that the results are strongly dependent on the input data, and thus the outputs should be understood accordingly. Additional applications of NATALI are feasible, e.g. testing the quality of the optical data and identifying incorrect calibration or insufficient cloud screening. Blind tests on EARLINET data samples showed the capability of NATALI to retrieve the aerosol type from a large variety of data, with different levels of quality and physical content.

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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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