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.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/amt-14-4335-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
11 Jun 2021
Research article |
| 11 Jun 2021
| 11 Jun 2021
Deriving boundary layer height from aerosol lidar using machine learning: KABL and ADABL algorithms
CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France
Sylvain Aubert
Météo-France, Direction des Systèmes d'Observation, Toulouse, France
Tiago Machado
CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France
Météo-France, Direction des Systèmes d'Observation, Toulouse, France
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Cited
19 citations as recorded by crossref.
- Bagged Tree Model to Retrieve Planetary Boundary Layer Heights by Integrating Lidar Backscatter Profiles and Meteorological Parameters W. Wei et al. 10.3390/rs14071597
- Observations of the Boundary Layer in the Cape Grim Coastal Region: Interaction with Wind and the Influences of Continental Sources Z. Chen et al. 10.3390/rs15020461
- The atmospheric boundary layer: a review of current challenges and a new generation of machine learning techniques L. Canché-Cab et al. 10.1007/s10462-024-10962-5
- Estimating Boundary Layer Height from LiDAR Data under Complex Atmospheric Conditions Using Machine Learning Z. Liu et al. 10.3390/rs14020418
- Machine Learning Techniques for Vertical Lidar-Based Detection, Characterization, and Classification of Aerosols and Clouds: A Comprehensive Survey S. Lolli 10.3390/rs15174318
- Toward instrument combination for boundary layer classification T. Rieutord et al. 10.1002/asl.1144
- Deep-Pathfinder: a boundary layer height detection algorithm based on image segmentation J. Wijnands et al. 10.5194/amt-17-3029-2024
- The Development and Application of Machine Learning in Atmospheric Environment Studies L. Zheng et al. 10.3390/rs13234839
- Atmospheric boundary layer height from ground-based remote sensing: a review of capabilities and limitations S. Kotthaus et al. 10.5194/amt-16-433-2023
- Machine learning model to accurately estimate the planetary boundary layer height of Beijing urban area with ERA5 data K. Peng et al. 10.1016/j.atmosres.2023.106925
- Comparison of Radiosonde Measurements of Meteorological Variables with Drone, Satellite Products, and WRF Simulations in the Tropical Andes: The Case of Quito, Ecuador L. Muñoz et al. 10.3390/atmos14020264
- AI model to improve the mountain boundary layer height of ERA5 J. Xin et al. 10.1016/j.atmosres.2024.107352
- 融合K-means和熵权法的高鲁棒性大气边界层高度估计方法 刘. Liu Zhenxing et al. 10.3788/AOS221534
- Study of Planetary Boundary Layer, Air Pollution, Air Quality Models and Aerosol Transport Using Ceilometers in New South Wales (NSW), Australia H. Duc et al. 10.3390/atmos13020176
- Deep-learning-derived planetary boundary layer height from conventional meteorological measurements T. Su & Y. Zhang 10.5194/acp-24-6477-2024
- Evaluation of retrieval methods for planetary boundary layer height based on radiosonde data H. Li et al. 10.5194/amt-14-5977-2021
- Validation and calibration of aerosol optical depth and classification of aerosol types based on multi-source data over China J. Wang et al. 10.1016/j.scitotenv.2023.166603
- A method to assess the cloud-aerosol transition zone from ceilometer measurements J. Ruiz de Morales et al. 10.1016/j.atmosres.2024.107623
- Characterizing warm atmospheric boundary layer over land by combining Raman and Doppler lidar measurements Y. Chu et al. 10.1364/OE.451728
18 citations as recorded by crossref.
- Bagged Tree Model to Retrieve Planetary Boundary Layer Heights by Integrating Lidar Backscatter Profiles and Meteorological Parameters W. Wei et al. 10.3390/rs14071597
- Observations of the Boundary Layer in the Cape Grim Coastal Region: Interaction with Wind and the Influences of Continental Sources Z. Chen et al. 10.3390/rs15020461
- The atmospheric boundary layer: a review of current challenges and a new generation of machine learning techniques L. Canché-Cab et al. 10.1007/s10462-024-10962-5
- Estimating Boundary Layer Height from LiDAR Data under Complex Atmospheric Conditions Using Machine Learning Z. Liu et al. 10.3390/rs14020418
- Machine Learning Techniques for Vertical Lidar-Based Detection, Characterization, and Classification of Aerosols and Clouds: A Comprehensive Survey S. Lolli 10.3390/rs15174318
- Toward instrument combination for boundary layer classification T. Rieutord et al. 10.1002/asl.1144
- Deep-Pathfinder: a boundary layer height detection algorithm based on image segmentation J. Wijnands et al. 10.5194/amt-17-3029-2024
- The Development and Application of Machine Learning in Atmospheric Environment Studies L. Zheng et al. 10.3390/rs13234839
- Atmospheric boundary layer height from ground-based remote sensing: a review of capabilities and limitations S. Kotthaus et al. 10.5194/amt-16-433-2023
- Machine learning model to accurately estimate the planetary boundary layer height of Beijing urban area with ERA5 data K. Peng et al. 10.1016/j.atmosres.2023.106925
- Comparison of Radiosonde Measurements of Meteorological Variables with Drone, Satellite Products, and WRF Simulations in the Tropical Andes: The Case of Quito, Ecuador L. Muñoz et al. 10.3390/atmos14020264
- AI model to improve the mountain boundary layer height of ERA5 J. Xin et al. 10.1016/j.atmosres.2024.107352
- 融合K-means和熵权法的高鲁棒性大气边界层高度估计方法 刘. Liu Zhenxing et al. 10.3788/AOS221534
- Study of Planetary Boundary Layer, Air Pollution, Air Quality Models and Aerosol Transport Using Ceilometers in New South Wales (NSW), Australia H. Duc et al. 10.3390/atmos13020176
- Deep-learning-derived planetary boundary layer height from conventional meteorological measurements T. Su & Y. Zhang 10.5194/acp-24-6477-2024
- Evaluation of retrieval methods for planetary boundary layer height based on radiosonde data H. Li et al. 10.5194/amt-14-5977-2021
- Validation and calibration of aerosol optical depth and classification of aerosol types based on multi-source data over China J. Wang et al. 10.1016/j.scitotenv.2023.166603
- A method to assess the cloud-aerosol transition zone from ceilometer measurements J. Ruiz de Morales et al. 10.1016/j.atmosres.2024.107623
1 citations as recorded by crossref.
Latest update: 20 Nov 2024
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.
This article describes two methods to estimate the height of the very first layer of the...
