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
Viewed
Total article views: 3,444 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 17 Dec 2020)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 2,298 | 1,067 | 79 | 3,444 | 62 | 99 |
- HTML: 2,298
- PDF: 1,067
- XML: 79
- Total: 3,444
- BibTeX: 62
- EndNote: 99
Total article views: 2,775 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 15 Jun 2021)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 1,926 | 776 | 73 | 2,775 | 57 | 91 |
- HTML: 1,926
- PDF: 776
- XML: 73
- Total: 2,775
- BibTeX: 57
- EndNote: 91
Total article views: 669 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 17 Dec 2020)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 372 | 291 | 6 | 669 | 5 | 8 |
- HTML: 372
- PDF: 291
- XML: 6
- Total: 669
- BibTeX: 5
- EndNote: 8
Viewed (geographical distribution)
Total article views: 3,444 (including HTML, PDF, and XML)
Thereof 3,381 with geography defined
and 63 with unknown origin.
Total article views: 2,775 (including HTML, PDF, and XML)
Thereof 2,708 with geography defined
and 67 with unknown origin.
Total article views: 669 (including HTML, PDF, and XML)
Thereof 673 with geography defined
and -4 with unknown origin.
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
Cited
23 citations as recorded by crossref.
- The Development and Application of Machine Learning in Atmospheric Environment Studies L. Zheng et al. 10.3390/rs13234839
- AI model to improve the mountain boundary layer height of ERA5 J. Xin et al. 10.1016/j.atmosres.2024.107352
- Estimating Boundary Layer Height from LiDAR Data under Complex Atmospheric Conditions Using Machine Learning Z. Liu et al. 10.3390/rs14020418
- 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
- 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. 10.3390/rs13183596
- Evaluation of retrieval methods for planetary boundary layer height based on radiosonde data H. Li et al. 10.5194/amt-14-5977-2021
- Deep-learning-derived planetary boundary layer height from conventional meteorological measurements T. Su & Y. Zhang 10.5194/acp-24-6477-2024
- A deep learning method for predicting lower troposphere temperature using surface reanalysis H. Fan et al. 10.1016/j.atmosres.2022.106542
- Boundary Layer Height Characteristics in Mexico City from Two Remote Sensing Techniques A. Burgos-Cuevas et al. 10.1007/s10546-022-00759-w
- Estimation of Planetary Boundary Layer Height From Lidar by Combining Gradient Method and Machine Learning Algorithms H. Li et al. 10.1109/TGRS.2023.3329122
- Review of urban computing in air quality management as smart city service: An integrated IoT, AI, and cloud technology perspective A. Kaginalkar et al. 10.1016/j.uclim.2021.100972
- Using aircraft measurements to characterize subgrid-scale variability of aerosol properties near the Atmospheric Radiation Measurement Southern Great Plains site J. Fast et al. 10.5194/acp-22-11217-2022
- Simulation model of Reactive Nitrogen Species in an Urban Atmosphere using a Deep Neural Network: RNDv1.0 J. Gil et al. 10.5194/gmd-16-5251-2023
- Estimating the urban atmospheric boundary layer height from remote sensing applying machine learning techniques G. de Arruda Moreira et al. 10.1016/j.atmosres.2021.105962
- Characterizing warm atmospheric boundary layer over land by combining Raman and Doppler lidar measurements Y. Chu et al. 10.1364/OE.451728
- Machine learning-enabled real-time detection of cloud and aerosol layers using airborne lidar M. McGill et al. 10.3389/frsen.2023.1116817
- 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
- Evaluation and Applications of Multi-Instrument Boundary-Layer Thermodynamic Retrievals E. Smith et al. 10.1007/s10546-021-00640-2
- Edge AI cosmos blockchain distributed network for precise ablh detection S. Shivadekar et al. 10.1007/s11042-024-18128-9
- 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. 10.5194/amt-15-2479-2022
- 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
- Densely Connected Neural Networks for Nonlinear Regression C. Jiang et al. 10.3390/e24070876
- Deep-Pathfinder: a boundary layer height detection algorithm based on image segmentation J. Wijnands et al. 10.5194/amt-17-3029-2024
23 citations as recorded by crossref.
- The Development and Application of Machine Learning in Atmospheric Environment Studies L. Zheng et al. 10.3390/rs13234839
- AI model to improve the mountain boundary layer height of ERA5 J. Xin et al. 10.1016/j.atmosres.2024.107352
- Estimating Boundary Layer Height from LiDAR Data under Complex Atmospheric Conditions Using Machine Learning Z. Liu et al. 10.3390/rs14020418
- 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
- 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. 10.3390/rs13183596
- Evaluation of retrieval methods for planetary boundary layer height based on radiosonde data H. Li et al. 10.5194/amt-14-5977-2021
- Deep-learning-derived planetary boundary layer height from conventional meteorological measurements T. Su & Y. Zhang 10.5194/acp-24-6477-2024
- A deep learning method for predicting lower troposphere temperature using surface reanalysis H. Fan et al. 10.1016/j.atmosres.2022.106542
- Boundary Layer Height Characteristics in Mexico City from Two Remote Sensing Techniques A. Burgos-Cuevas et al. 10.1007/s10546-022-00759-w
- Estimation of Planetary Boundary Layer Height From Lidar by Combining Gradient Method and Machine Learning Algorithms H. Li et al. 10.1109/TGRS.2023.3329122
- Review of urban computing in air quality management as smart city service: An integrated IoT, AI, and cloud technology perspective A. Kaginalkar et al. 10.1016/j.uclim.2021.100972
- Using aircraft measurements to characterize subgrid-scale variability of aerosol properties near the Atmospheric Radiation Measurement Southern Great Plains site J. Fast et al. 10.5194/acp-22-11217-2022
- Simulation model of Reactive Nitrogen Species in an Urban Atmosphere using a Deep Neural Network: RNDv1.0 J. Gil et al. 10.5194/gmd-16-5251-2023
- Estimating the urban atmospheric boundary layer height from remote sensing applying machine learning techniques G. de Arruda Moreira et al. 10.1016/j.atmosres.2021.105962
- Characterizing warm atmospheric boundary layer over land by combining Raman and Doppler lidar measurements Y. Chu et al. 10.1364/OE.451728
- Machine learning-enabled real-time detection of cloud and aerosol layers using airborne lidar M. McGill et al. 10.3389/frsen.2023.1116817
- 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
- Evaluation and Applications of Multi-Instrument Boundary-Layer Thermodynamic Retrievals E. Smith et al. 10.1007/s10546-021-00640-2
- Edge AI cosmos blockchain distributed network for precise ablh detection S. Shivadekar et al. 10.1007/s11042-024-18128-9
- 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. 10.5194/amt-15-2479-2022
- 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
- Densely Connected Neural Networks for Nonlinear Regression C. Jiang et al. 10.3390/e24070876
- Deep-Pathfinder: a boundary layer height detection algorithm based on image segmentation J. Wijnands et al. 10.5194/amt-17-3029-2024
Latest update: 17 Jul 2024
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...
