A Neural Network Aerosol Typing Algorithm Based on Lidar Data

Abstract. Atmospheric aerosolsplay 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 aerosolsources. Recently developed lidar-based techniques were able to retrieve the height distributions of optical and microphysicalproperties 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 AerosolTyping Algorithm Based on Lidar Data (NATALI) was developed to estimate the most probable aerosoltype from a set of multispectral lidar data. The algorithm was adjusted to run on the EARLINET 3 β + 2 α ( + 1 δ ) 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 aerosolmodel 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 aerosolmixtures (high-resolution typing) if the LPDR is available in the input data files, and (b) identification of five predominant aerosoltypes (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 aerosoltype. 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 aerosoltype from a large variety of data, with different levels of quality and physical content.