Strengths and limitations of the NATALI code for aerosol typing from multiwavelength Raman lidar observations
National Institute of R&D for Optoelectronics, Romania
2 University of Natural Resources and Life Science, Institute of Meteorology, Austria
3 University of Warsaw, Faculty of Physics, Institute of Geophysics, Poland
Published online: 13 April 2018
A Python code was developed to automatically retrieve the aerosol type (and its predominant component in the mixture) from EARLINET’s 3 backscatter and 2 extinction data. The typing relies on Artificial Neural Networks which are trained to identify the most probable aerosol type from a set of mean-layer intensive optical parameters. This paper presents the use and limitations of the code with respect to the quality of the inputed lidar profiles, as well as with the assumptions made in the aerosol model.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (http://creativecommons.org/licenses/by/4.0/).