A neural network aerosol-typing algorithm based on lidar data

• Published in: Atmospheric chemistry and physics Vol. 18; no. 19; pp. 14511 - 14537
• Main Authors: Nicolae, Doina, Vasilescu, Jeni, Talianu, Camelia, Binietoglou, Ioannis, Nicolae, Victor, Andrei, Simona, Antonescu, Bogdan
• Format: Journal Article
• Language: English
• Published: Katlenburg-Lindau Copernicus GmbH 10.10.2018; Copernicus Publications
• Subjects: Aerosols; Algorithms; Artificial neural networks; Atmospheric aerosols; Atmospheric research; Data; Data processing; Depolarization; Earth; Identification; Identification and classification; Lidar; Methods; Neural networks; Optical properties; Parameter identification; Profiles; Properties; Ratios; Resolution; Typing
• ISSN: 1680-7324; 1680-7316; 1680-7324
• DOI: 10.5194/acp-18-14511-2018

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.