Articles | Volume 15, issue 1
https://doi.org/10.5194/amt-15-1-2022
© Author(s) 2022. 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-15-1-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
03 Jan 2022
Research article |
| 03 Jan 2022
| 03 Jan 2022
Wind speed and direction estimation from wave spectra using deep learning
Hubei Key Laboratory of Marine Geological Resources, China University of Geosciences, Wuhan, 430000, China
Laboratory for Regional Oceanography and Numerical Modeling, Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao,
266000, China
Southern Marine Science and Engineering Guangdong Laboratory
(Guangzhou), Guangzhou, 511458, China
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Cited
18 citations as recorded by crossref.
- MoWe: Motion Observation for Wind Estimation of Sailing Robots Q. Sun et al. https://doi.org/10.1002/rob.22512
- Wind-sea and swell separation of 1D wave spectrum by deep learning W. Meng et al. https://doi.org/10.1016/j.oceaneng.2023.113672
- Wind velocity estimates from wave observing platforms K. Mudd et al. https://doi.org/10.1080/21664250.2024.2321660
- Statistical downscaling of coastal directional wave spectra using deep learning T. Gao & H. Jiang https://doi.org/10.1016/j.coastaleng.2024.104557
- Regional Wave Spectra Prediction Method Based on Deep Learning Y. Liu et al. https://doi.org/10.3390/jmse13081461
- Wind inflow observation from load harmonics via neural networks: A simulation and field study K. Kim et al. https://doi.org/10.1016/j.renene.2022.12.051
- Ocean Surface Wind Estimation From Waves Based on Small GPS Buoy Observations in a Bay and the Open Ocean T. Shimura et al. https://doi.org/10.1029/2022JC018786
- Graph network design for weather routing using spatiotemporal homogeneity analysis of weather data J. Kim et al. https://doi.org/10.1016/j.oceaneng.2025.123999
- Differences in Ozone and Particulate Matter Between Ground Level and 20 m Aloft are Frequent During Wintertime Surface‐Based Temperature Inversions in Fairbanks, Alaska M. Cesler‐Maloney et al. https://doi.org/10.1029/2021JD036215
- Hybrid numerical-AI modeling of directional wave spectra on an idealized one-dimensional nearshore slope G. Yang et al. https://doi.org/10.1016/j.oceaneng.2026.127556
- Wavelength Cut-Off Error of Spectral Density from MTF3 of SWIM Instrument Onboard CFOSAT: An Investigation from Buoy Data Y. Luo et al. https://doi.org/10.3390/rs16163092
- Point-by-point data-driven approach for statistical downscaling of integrated wave parameters in coastal regions T. Du et al. https://doi.org/10.1016/j.oceaneng.2025.121174
- Physics-Guided Deep Learning Model for Ocean Waves and Storm Surges Induced by Tropical Cyclones with Unified Underlying Logic A. Li et al. https://doi.org/10.1007/s13344-026-0010-8
- Sea surface wind speed retrieval based on ICESat-2 ocean signal vertical distribution J. Xu et al. https://doi.org/10.1016/j.rse.2025.114686
- The method of single points wave spectrum generation for regional sea based on multimodal model in the Bohai sea, China X. Cao et al. https://doi.org/10.1016/j.dynatmoce.2025.101586
- Enhancing offshore wind Resource assessment through neural network-based HF radar data analysis N. Martzikos et al. https://doi.org/10.1016/j.renene.2025.124547
- Sea Surface Wind Speed From ICESat-2 Along-Track Wave Spectra S. Li et al. https://doi.org/10.1109/TGRS.2026.3699428
- Nearshore wind estimation from buoy wave spectra via physics-guided machine learning: A framework for offshore wind applications J. Chen et al. https://doi.org/10.1016/j.renene.2025.125000
18 citations as recorded by crossref.
- MoWe: Motion Observation for Wind Estimation of Sailing Robots Q. Sun et al. https://doi.org/10.1002/rob.22512
- Wind-sea and swell separation of 1D wave spectrum by deep learning W. Meng et al. https://doi.org/10.1016/j.oceaneng.2023.113672
- Wind velocity estimates from wave observing platforms K. Mudd et al. https://doi.org/10.1080/21664250.2024.2321660
- Statistical downscaling of coastal directional wave spectra using deep learning T. Gao & H. Jiang https://doi.org/10.1016/j.coastaleng.2024.104557
- Regional Wave Spectra Prediction Method Based on Deep Learning Y. Liu et al. https://doi.org/10.3390/jmse13081461
- Wind inflow observation from load harmonics via neural networks: A simulation and field study K. Kim et al. https://doi.org/10.1016/j.renene.2022.12.051
- Ocean Surface Wind Estimation From Waves Based on Small GPS Buoy Observations in a Bay and the Open Ocean T. Shimura et al. https://doi.org/10.1029/2022JC018786
- Graph network design for weather routing using spatiotemporal homogeneity analysis of weather data J. Kim et al. https://doi.org/10.1016/j.oceaneng.2025.123999
- Differences in Ozone and Particulate Matter Between Ground Level and 20 m Aloft are Frequent During Wintertime Surface‐Based Temperature Inversions in Fairbanks, Alaska M. Cesler‐Maloney et al. https://doi.org/10.1029/2021JD036215
- Hybrid numerical-AI modeling of directional wave spectra on an idealized one-dimensional nearshore slope G. Yang et al. https://doi.org/10.1016/j.oceaneng.2026.127556
- Wavelength Cut-Off Error of Spectral Density from MTF3 of SWIM Instrument Onboard CFOSAT: An Investigation from Buoy Data Y. Luo et al. https://doi.org/10.3390/rs16163092
- Point-by-point data-driven approach for statistical downscaling of integrated wave parameters in coastal regions T. Du et al. https://doi.org/10.1016/j.oceaneng.2025.121174
- Physics-Guided Deep Learning Model for Ocean Waves and Storm Surges Induced by Tropical Cyclones with Unified Underlying Logic A. Li et al. https://doi.org/10.1007/s13344-026-0010-8
- Sea surface wind speed retrieval based on ICESat-2 ocean signal vertical distribution J. Xu et al. https://doi.org/10.1016/j.rse.2025.114686
- The method of single points wave spectrum generation for regional sea based on multimodal model in the Bohai sea, China X. Cao et al. https://doi.org/10.1016/j.dynatmoce.2025.101586
- Enhancing offshore wind Resource assessment through neural network-based HF radar data analysis N. Martzikos et al. https://doi.org/10.1016/j.renene.2025.124547
- Sea Surface Wind Speed From ICESat-2 Along-Track Wave Spectra S. Li et al. https://doi.org/10.1109/TGRS.2026.3699428
- Nearshore wind estimation from buoy wave spectra via physics-guided machine learning: A framework for offshore wind applications J. Chen et al. https://doi.org/10.1016/j.renene.2025.125000
Saved (final revised paper)
Latest update: 24 Oct 2026
Short summary
Sea surface wind and waves are important ocean parameters that can be continuously observed by meteorological buoys. Meteorological buoys are sparse in the ocean due to their high cost of deployment and maintenance. In contrast, low-cost compact wave buoys are suited for deployment in large numbers. Although wave buoys are not designed for wind measurement, we found that deep learning can estimate wind from wave measurements accurately, making wave buoys a good-quality data source for sea wind.
Sea surface wind and waves are important ocean parameters that can be continuously observed by...
