Articles | Volume 14, issue 6
https://doi.org/10.5194/amt-14-4403-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-14-4403-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
15 Jun 2021
Research article |
| 15 Jun 2021
| 15 Jun 2021
On the estimation of boundary layer heights: a machine learning approach
Pacific Northwest National Laboratory, ASGC Division, Richland, 99354, USA
Rob K. Newsom
Pacific Northwest National Laboratory, ASGC Division, Richland, 99354, USA
Larry K. Berg
Pacific Northwest National Laboratory, ASGC Division, Richland, 99354, USA
Heng Xiao
Pacific Northwest National Laboratory, ASGC Division, Richland, 99354, USA
Po-Lun Ma
Pacific Northwest National Laboratory, ASGC Division, Richland, 99354, USA
David D. Turner
National Oceanic and Atmospheric Administration/Global Systems
Laboratory, Boulder, 80305, USA
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Cited
35 citations as recorded by crossref.
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- Estimating Boundary Layer Height from LiDAR Data under Complex Atmospheric Conditions Using Machine Learning Z. Liu et al.
- Atmospheric boundary layer height from ground-based remote sensing: a review of capabilities and limitations S. Kotthaus et al.
- Best estimate of the planetary boundary layer height from multiple remote sensing measurements D. Zhang et al.
- A Comprehensive Ensemble Model for Marine Atmospheric Boundary-Layer Prediction in Meteorologically Sparse and Complex Regions: A Case Study in the South China Sea Y. Chen et al.
- Evaluation of a Method for Calculating the Height of the Stable Boundary Layer Based on Wind Profile Lidar and Turbulent Fluxes H. Sun et al.
- 基于温湿拉曼激光雷达反演大气边界层高度的方法与验证 黄. Huang Yisen et al.
- Evaluation of retrieval methods for planetary boundary layer height based on radiosonde data H. Li et al.
- Deep-learning-derived planetary boundary layer height from conventional meteorological measurements T. Su & Y. Zhang
- A deep learning method for predicting lower troposphere temperature using surface reanalysis H. Fan et al.
- Boundary Layer Height Characteristics in Mexico City from Two Remote Sensing Techniques A. Burgos-Cuevas et al.
- Estimation of Planetary Boundary Layer Height From Lidar by Combining Gradient Method and Machine Learning Algorithms H. Li et al.
- 基于深度森林的遥感融合反演大气边界层高度方法 赵. Zhao Zhongxing et al.
- Review of urban computing in air quality management as smart city service: An integrated IoT, AI, and cloud technology perspective A. Kaginalkar et al.
- Using aircraft measurements to characterize subgrid-scale variability of aerosol properties near the Atmospheric Radiation Measurement Southern Great Plains site J. Fast et al.
- Technical note: Towards atmospheric compound identification in chemical ionization mass spectrometry with pesticide standards and machine learning F. Bortolussi et al.
- Simulation model of Reactive Nitrogen Species in an Urban Atmosphere using a Deep Neural Network: RNDv1.0 J. Gil et al.
- Estimating the urban atmospheric boundary layer height from remote sensing applying machine learning techniques G. de Arruda Moreira et al.
- Characterizing warm atmospheric boundary layer over land by combining Raman and Doppler lidar measurements Y. Chu et al.
- On the drivers of ice nucleating particle diurnal variability in Eastern Mediterranean clouds K. Gao et al.
- Improved estimation of diurnal variations in near-global PBLH through a hybrid WCT and transfer learning approach Y. Li et al.
- Machine learning-enabled real-time detection of cloud and aerosol layers using airborne lidar M. McGill et al.
- Machine learning model to accurately estimate the planetary boundary layer height of Beijing urban area with ERA5 data K. Peng et al.
- Atmospheric turbulence inflow effect on the aerodynamics and aeroacoustics of side-by-side urban air mobility aircraft M. Araghizadeh et al.
- Evaluation and Applications of Multi-Instrument Boundary-Layer Thermodynamic Retrievals E. Smith et al.
- Observations of wind farm wake recovery at an operating wind farm R. Krishnamurthy et al.
- Edge AI cosmos blockchain distributed network for precise ablh detection S. Shivadekar et al.
- Mixed layer height retrievals using MicroPulse Differential Absorption Lidar L. Colberg et al.
- Thermodynamics-guided machine learning model for predicting convective boundary layer height and its multi-site applicability Y. Chu et al.
- Evaluating convective planetary boundary layer height estimations resolved by both active and passive remote sensing instruments during the CHEESEHEAD19 field campaign J. Duncan Jr. et al.
- 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.
- Densely Connected Neural Networks for Nonlinear Regression C. Jiang et al.
- Improved method for temporally interpolating radiosonde profiles in the convective boundary layer L. von Klitzing et al.
- Deep-Pathfinder: a boundary layer height detection algorithm based on image segmentation J. Wijnands et al.
35 citations as recorded by crossref.
- The Development and Application of Machine Learning in Atmospheric Environment Studies L. Zheng et al.
- AI model to improve the mountain boundary layer height of ERA5 J. Xin et al.
- Estimating Boundary Layer Height from LiDAR Data under Complex Atmospheric Conditions Using Machine Learning Z. Liu et al.
- Atmospheric boundary layer height from ground-based remote sensing: a review of capabilities and limitations S. Kotthaus et al.
- Best estimate of the planetary boundary layer height from multiple remote sensing measurements D. Zhang et al.
- A Comprehensive Ensemble Model for Marine Atmospheric Boundary-Layer Prediction in Meteorologically Sparse and Complex Regions: A Case Study in the South China Sea Y. Chen et al.
- Evaluation of a Method for Calculating the Height of the Stable Boundary Layer Based on Wind Profile Lidar and Turbulent Fluxes H. Sun et al.
- 基于温湿拉曼激光雷达反演大气边界层高度的方法与验证 黄. Huang Yisen et al.
- Evaluation of retrieval methods for planetary boundary layer height based on radiosonde data H. Li et al.
- Deep-learning-derived planetary boundary layer height from conventional meteorological measurements T. Su & Y. Zhang
- A deep learning method for predicting lower troposphere temperature using surface reanalysis H. Fan et al.
- Boundary Layer Height Characteristics in Mexico City from Two Remote Sensing Techniques A. Burgos-Cuevas et al.
- Estimation of Planetary Boundary Layer Height From Lidar by Combining Gradient Method and Machine Learning Algorithms H. Li et al.
- 基于深度森林的遥感融合反演大气边界层高度方法 赵. Zhao Zhongxing et al.
- Review of urban computing in air quality management as smart city service: An integrated IoT, AI, and cloud technology perspective A. Kaginalkar et al.
- Using aircraft measurements to characterize subgrid-scale variability of aerosol properties near the Atmospheric Radiation Measurement Southern Great Plains site J. Fast et al.
- Technical note: Towards atmospheric compound identification in chemical ionization mass spectrometry with pesticide standards and machine learning F. Bortolussi et al.
- Simulation model of Reactive Nitrogen Species in an Urban Atmosphere using a Deep Neural Network: RNDv1.0 J. Gil et al.
- Estimating the urban atmospheric boundary layer height from remote sensing applying machine learning techniques G. de Arruda Moreira et al.
- Characterizing warm atmospheric boundary layer over land by combining Raman and Doppler lidar measurements Y. Chu et al.
- On the drivers of ice nucleating particle diurnal variability in Eastern Mediterranean clouds K. Gao et al.
- Improved estimation of diurnal variations in near-global PBLH through a hybrid WCT and transfer learning approach Y. Li et al.
- Machine learning-enabled real-time detection of cloud and aerosol layers using airborne lidar M. McGill et al.
- Machine learning model to accurately estimate the planetary boundary layer height of Beijing urban area with ERA5 data K. Peng et al.
- Atmospheric turbulence inflow effect on the aerodynamics and aeroacoustics of side-by-side urban air mobility aircraft M. Araghizadeh et al.
- Evaluation and Applications of Multi-Instrument Boundary-Layer Thermodynamic Retrievals E. Smith et al.
- Observations of wind farm wake recovery at an operating wind farm R. Krishnamurthy et al.
- Edge AI cosmos blockchain distributed network for precise ablh detection S. Shivadekar et al.
- Mixed layer height retrievals using MicroPulse Differential Absorption Lidar L. Colberg et al.
- Thermodynamics-guided machine learning model for predicting convective boundary layer height and its multi-site applicability Y. Chu et al.
- Evaluating convective planetary boundary layer height estimations resolved by both active and passive remote sensing instruments during the CHEESEHEAD19 field campaign J. Duncan Jr. et al.
- 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.
- Densely Connected Neural Networks for Nonlinear Regression C. Jiang et al.
- Improved method for temporally interpolating radiosonde profiles in the convective boundary layer L. von Klitzing et al.
- Deep-Pathfinder: a boundary layer height detection algorithm based on image segmentation J. Wijnands et al.
Saved (final revised paper)
Latest update: 11 May 2026
Short summary
Planetary boundary layer (PBL) height is a critical parameter in atmospheric models. Continuous PBL height measurements from remote sensing measurements are important to understand various boundary layer mechanisms, especially during daytime and evening transition periods. Due to several limitations in existing methodologies to detect PBL height from a Doppler lidar, in this study, a machine learning (ML) approach is tested. The ML model is observed to improve the accuracy by over 50 %.
Planetary boundary layer (PBL) height is a critical parameter in atmospheric models. Continuous...
