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.
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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- Impact Assessment of Flood Events Based on Multisource Satellite Remote Sensing: The Case of Kahovka Dam C. Zuo et al. 10.1109/JSTARS.2024.3490756
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- 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
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- Monitoring Methane Concentrations with High Spatial Resolution over China by Using Random Forest Model Z. Jin et al. 10.3390/rs16142525
- Estimation of Boundary Layer Height From Radar Wind Profiler by Deep Learning Algorithms Z. Tong et al. 10.1109/TGRS.2024.3434403
- Development of a Multi-Source Satellite Fusion Method for XCH4 Product Generation in Oil and Gas Production Areas L. Fan et al. 10.3390/app142311100
- 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
- FI-SCAPE: A Divergence Theorem Based Emission Quantification Model for Air/Spaceborne Imaging Spectrometer Derived XCH4 Observations Y. Huang et al. 10.1109/JSTARS.2024.3490896
Latest update: 23 Dec 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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