Articles | Volume 14, issue 6
Atmos. Meas. Tech., 14, 4335–4353, 2021

Special issue: Tropospheric profiling (ISTP11) (AMT/ACP inter-journal SI)

Atmos. Meas. Tech., 14, 4335–4353, 2021

Research article 11 Jun 2021

Research article | 11 Jun 2021

Deriving boundary layer height from aerosol lidar using machine learning: KABL and ADABL algorithms

Thomas Rieutord et al.

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Cited articles

Arciszewska, C. and McClatchey, J.: The importance of meteorological data for modelling air pollution using ADMS-Urban, Meteorol. Appl., 8, 345–350, 2001. a
Arthur, D. and Vassilvitskii, S.: k-means++: The advantages of careful seeding, in: Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms, Society for Industrial and Applied Mathematics, 1027–1035, 2007. a
Besse, P., Guillouet, B., and Laurent, B.: Wikistat 2.0: Educational Resources for Artificial Intelligence, arXiv: preprint, arXiv:1810.02688, 2018. a
Breiman, L., Friedman, J., Olshen, R., and Stone, C.: Classification and Regression Trees, Wadsworth, 1984. a, b
Brilouet, P.-E., Durand, P., and Canut, G.: The marine atmospheric boundary layer under strong wind conditions: Organized turbulence structure and flux estimates by airborne measurements, J. Geophys. Res.-Atmos., 122, 2115–2130, 2017. a
Short summary
This article describes two methods to estimate the height of the very first layer of the atmosphere. It is measured with aerosol lidars, and the two new methods are based on machine learning. Both are open source and available under free licenses. A sensitivity analysis and a 2-year evaluation against meteorological balloons were carried out. One method has a good agreement with balloons but is limited by training, and the other has less good agreement with balloons but is more flexible.