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
                                        
                                    Viewed
                        
                            Total article views: 2,418 (including HTML, PDF, and XML)
                        
                            
                                
                                
                            
                                
                                
                            
                        
                        
                            Cumulative views and downloads 
                                         (calculated since 11 Dec 2023)
                        
                        
                            
                                
                            
                    
        
                    
                    | HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 1,885 | 449 | 84 | 2,418 | 125 | 85 | 119 | 
- HTML: 1,885
- PDF: 449
- XML: 84
- Total: 2,418
- Supplement: 125
- BibTeX: 85
- EndNote: 119
                        
                            Total article views: 2,101 (including HTML, PDF, and XML)
                        
                            
                                
                                
                            
                                
                                
                            
                        
                        
                            Cumulative views and downloads 
                                         (calculated since 04 Apr 2024)
                        
                        
                            
                                
                            
                    
                    
                    | HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 1,667 | 372 | 62 | 2,101 | 92 | 70 | 102 | 
- HTML: 1,667
- PDF: 372
- XML: 62
- Total: 2,101
- Supplement: 92
- BibTeX: 70
- EndNote: 102
                        
                            Total article views: 317 (including HTML, PDF, and XML)
                        
                            
                                
                                
                            
                                
                                
                            
                        
                        
                            Cumulative views and downloads 
                                         (calculated since 11 Dec 2023)
                        
                        
                            
                                
                            
                    
        
                
            | HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 218 | 77 | 22 | 317 | 33 | 15 | 17 | 
- HTML: 218
- PDF: 77
- XML: 22
- Total: 317
- Supplement: 33
- BibTeX: 15
- EndNote: 17
Viewed (geographical distribution)
                                Total article views: 2,418 (including HTML, PDF, and XML)
                                
                                Thereof 2,418 with geography defined
                                    and 0 with unknown origin. 
                            
        
                            
                                Total article views: 2,101 (including HTML, PDF, and XML)
                                
                                Thereof 2,101 with geography defined
                                    and 0 with unknown origin. 
                            
        
                            
                                Total article views: 317 (including HTML, PDF, and XML)
                                
                                Thereof 315 with geography defined
                                    and 2 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
21 citations as recorded by crossref.
- Characterization of local wind profiles: a random forest approach for enhanced wind profile extrapolation F. Rouholahnejad & J. Gottschall 10.5194/wes-10-143-2025
- Passive atmospheric wind profile retrieval via multi-region microwave radiometer network S. Jiang et al. 10.1016/j.atmosres.2025.108474
- 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
- 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
- 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
- Insights into global visibility patterns: Spatiotemporal distributions revealed by satellite remote sensing J. He et al. 10.1016/j.jclepro.2024.143069
- Space-ground integration system of methane emission monitoring and quantification: cases in Dongying, China H. He et al. 10.3389/feart.2025.1577961
- 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
- Influence of clouds on planetary boundary layer height: A comparative study and factors analysis H. Li et al. 10.1016/j.atmosres.2024.107784
- Monitoring Methane Concentrations with High Spatial Resolution over China by Using Random Forest Model Z. Jin et al. 10.3390/rs16142525
- Estimates of Wind Speed Profiles from Surface Observations: Machine Learning Versus Monin–Obukhov Approach S. Wan et al. 10.1007/s10546-025-00903-2
- Estimation of Boundary Layer Height From Radar Wind Profiler by Deep Learning Algorithms Z. Tong et al. 10.1109/TGRS.2024.3434403
- The effect of onshore winds on the atmospheric boundary layer height under clear sky conditions in the coastal region of Southeast China B. Liu et al. 10.1016/j.atmosres.2025.108328
- 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
- A multilevel downscaling model for enhancing nocturnal aerosol optical depth reanalysis from CAMS over the Beijing-Tianjin-Hebei region, China S. Wang et al. 10.1016/j.eti.2025.104238
21 citations as recorded by crossref.
- Characterization of local wind profiles: a random forest approach for enhanced wind profile extrapolation F. Rouholahnejad & J. Gottschall 10.5194/wes-10-143-2025
- Passive atmospheric wind profile retrieval via multi-region microwave radiometer network S. Jiang et al. 10.1016/j.atmosres.2025.108474
- 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
- 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
- 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
- Insights into global visibility patterns: Spatiotemporal distributions revealed by satellite remote sensing J. He et al. 10.1016/j.jclepro.2024.143069
- Space-ground integration system of methane emission monitoring and quantification: cases in Dongying, China H. He et al. 10.3389/feart.2025.1577961
- 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
- Influence of clouds on planetary boundary layer height: A comparative study and factors analysis H. Li et al. 10.1016/j.atmosres.2024.107784
- Monitoring Methane Concentrations with High Spatial Resolution over China by Using Random Forest Model Z. Jin et al. 10.3390/rs16142525
- Estimates of Wind Speed Profiles from Surface Observations: Machine Learning Versus Monin–Obukhov Approach S. Wan et al. 10.1007/s10546-025-00903-2
- Estimation of Boundary Layer Height From Radar Wind Profiler by Deep Learning Algorithms Z. Tong et al. 10.1109/TGRS.2024.3434403
- The effect of onshore winds on the atmospheric boundary layer height under clear sky conditions in the coastal region of Southeast China B. Liu et al. 10.1016/j.atmosres.2025.108328
- 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
- A multilevel downscaling model for enhancing nocturnal aerosol optical depth reanalysis from CAMS over the Beijing-Tianjin-Hebei region, China S. Wang et al. 10.1016/j.eti.2025.104238
Latest update: 30 Oct 2025
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...
                    
                Altmetrics
                
                Final-revised paper
            
            
                    Preprint
                
                     
 
                             
                             
                     
                     
                    