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

Rieutord, Thomas; Aubert, Sylvain; Machado, Tiago

doi:https://doi.org/10.5194/amt-14-4335-2021

Articles | Volume 14, issue 6

https://doi.org/10.5194/amt-14-4335-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue:

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

https://doi.org/10.5194/amt-14-4335-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 14, issue 6

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, Sylvain Aubert, and Tiago Machado

Model code and software

KABL T. Rieutord, S. Aubert, and T. Machado https://github.com/ThomasRieutord/kabl

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.