Articles | Volume 24, issue 7
https://doi.org/10.5194/acp-24-4047-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-24-4047-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
04 Apr 2024
Research article |
| 04 Apr 2024
| 04 Apr 2024
Extending the wind profile beyond the surface layer by combining physical and machine learning approaches
Boming Liu
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430072, China
Xin Ma
CORRESPONDING AUTHOR
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430072, China
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China
Renqiang Wen
CTG Science and Technology Research Institute, China Three Gorges Corporation, Beijing, 101100, China
Hui Li
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430072, China
Shikuan Jin
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430072, China
Yingying Ma
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430072, China
Xiaoran Guo
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China
Wei Gong
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430072, China
Wuhan Institute of Quantum Technology, Wuhan 430206, China
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- 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
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12 citations as recorded by crossref.
- Influence of clouds on planetary boundary layer height: A comparative study and factors analysis H. Li et al. 10.1016/j.atmosres.2024.107784
- Validation of Mainland Water Level Elevation Products From SWOT Satellite L. Yu et al. 10.1109/JSTARS.2024.3435363
- Advanced method for compiling a high-resolution gridded anthropogenic CO 2 emission inventory at a regional scale M. Xu et al. 10.1080/10095020.2024.2425182
- Monitoring Methane Concentrations with High Spatial Resolution over China by Using Random Forest Model Z. Jin et al. 10.3390/rs16142525
- 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
- Estimation of Boundary Layer Height From Radar Wind Profiler by Deep Learning Algorithms Z. Tong et al. 10.1109/TGRS.2024.3434403
- Insights into global visibility patterns: Spatiotemporal distributions revealed by satellite remote sensing J. He et al. 10.1016/j.jclepro.2024.143069
- Validation Method for Spaceborne IPDA LIDAR ${{\mathrm{X}}_{\mathrm{C}{{\mathrm{O}}_2}}}$ Products via TCCON H. Zhang et al. 10.1109/JSTARS.2024.3418028
- Seamless reconstruction and spatiotemporal analysis of satellite-based XCO2 incorporating temporal characteristics: A case study in China during 2015–2020 J. He et al. 10.1016/j.asr.2024.07.007
- Analysis of Spatiotemporal Changes in Energy Consumption Carbon Emissions at District and County Levels Based on Nighttime Light Data—A Case Study of Jiangsu Province in China C. Xiang et al. 10.3390/rs16183514
- Retrieving Hourly Aerosol Optical Depth for Geostationary Satellite FY-4B/AGRI by Surface-related Dynamic Spectral Reflectance Ratio Method W. Wang et al. 10.1016/j.asr.2024.10.057
- A methane monitoring station siting method based on WRF-STILT and genetic algorithm L. Fan et al. 10.3389/fenvs.2024.1394281
Latest update: 22 Nov 2024
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.
Accurate wind profile estimation, especially for the lowest few hundred meters of the...
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