Articles | Volume 24, issue 7
https://doi.org/10.5194/acp-24-4047-2024
https://doi.org/10.5194/acp-24-4047-2024
Research article
 | 
04 Apr 2024
Research article |  | 04 Apr 2024

Extending the wind profile beyond the surface layer by combining physical and machine learning approaches

Boming Liu, Xin Ma, Jianping Guo, Renqiang Wen, Hui Li, Shikuan Jin, Yingying Ma, Xiaoran Guo, and Wei Gong

Related authors

Global-ABLWind: a global atmospheric boundary layer wind speed profile dataset derived from Aeolus and surface ancillary information
Zhe Tong, Boming Liu, Xin Ma, Jianping Guo, Haowei Zhang, Haoyu Dong, Ge Han, Yingying Ma, and Wei Gong
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-73,https://doi.org/10.5194/essd-2026-73, 2026
Preprint under review for ESSD
Short summary
A comprehensive reappraisal of long-term aerosol characteristics, trends, and variability in Asia
Shikuan Jin, Yingying Ma, Zhongwei Huang, Jianping Huang, Wei Gong, Boming Liu, Weiyan Wang, Ruonan Fan, and Hui Li
Atmos. Chem. Phys., 23, 8187–8210, https://doi.org/10.5194/acp-23-8187-2023,https://doi.org/10.5194/acp-23-8187-2023, 2023
Short summary
Estimating hub-height wind speed based on a machine learning algorithm: implications for wind energy assessment
Boming Liu, Xin Ma, Jianping Guo, Hui Li, Shikuan Jin, Yingying Ma, and Wei Gong
Atmos. Chem. Phys., 23, 3181–3193, https://doi.org/10.5194/acp-23-3181-2023,https://doi.org/10.5194/acp-23-3181-2023, 2023
Short summary
Performance evaluation for retrieving aerosol optical depth from the Directional Polarimetric Camera (DPC) based on the GRASP algorithm
Shikuan Jin, Yingying Ma, Cheng Chen, Oleg Dubovik, Jin Hong, Boming Liu, and Wei Gong
Atmos. Meas. Tech., 15, 4323–4337, https://doi.org/10.5194/amt-15-4323-2022,https://doi.org/10.5194/amt-15-4323-2022, 2022
Short summary
Carbon dioxide cover: carbon dioxide column concentration seamlessly distributed globally during 2009–2020
Haowei Zhang, Boming Liu, Xin Ma, Ge Han, Qinglin Yang, Yichi Zhang, Tianqi Shi, Jianye Yuan, Wanqi Zhong, Yanran Peng, Jingjing Xu, and Wei Gong
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-215,https://doi.org/10.5194/essd-2022-215, 2022
Preprint withdrawn
Short summary

Cited articles

Atmospheric Radiation Measurement (ARM) user facility data: Doppler Lidar Horizontal Wind Profiles, ARM [data set], https://adc.arm.gov/discovery/#/results/instrument_class_code::dlprof-wind, (last access: 18 September 2023), 2023. 
Anderson, J. D.: Ludwig Prandtl's boundary layer, Phys. Today, 58, 42–48, https://doi.org/10.1063/1.2169443, 2005. 
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/a:1010933404324, 2001. 
Barthelmie, R. J., Shepherd, T. J., Aird, J. A., and Pryor, S. C.: Power and wind shear implications of large wind turbine scenarios in the US Central Plains, Energies, 13, 4269, https://doi.org/10.3390/en13164269, 2020. 
Coleman, T. A., Knupp K. R., and Pangle P. T.: The effects of heterogeneous surface roughness on boundary-layer kinematics and wind shear, Electronic J. Severe Storms Meteor., 16, 1–29, https://doi.org/10.55599/ejssm.v16i3.80, 2021. 
Download
Short summary
Accurate wind profile estimation, especially for the lowest few hundred meters of the atmosphere, is of great significance for the weather, climate, and renewable energy sector. We propose a novel method that combines the power-law method with the random forest algorithm to extend wind profiles beyond the surface layer. Compared with the traditional algorithm, this method has better stability and spatial applicability and can be used to obtain the wind profiles on different land cover types.
Share
Altmetrics
Final-revised paper
Preprint