Strengths and limitations of the NATALI code for aerosol typing from multiwavelength Raman lidar observations
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-l...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2018-01-01
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| Series: | EPJ Web of Conferences |
| Online Access: | https://doi.org/10.1051/epjconf/201817605005 |
| Summary: | 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. |
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| ISSN: | 2100-014X |