Articles | Volume 15, issue 1
Atmos. Meas. Tech., 15, 1–9, 2022
Atmos. Meas. Tech., 15, 1–9, 2022

Research article 03 Jan 2022

Research article | 03 Jan 2022

Wind speed and direction estimation from wave spectra using deep learning

Haoyu Jiang

Related subject area

Subject: Others (Wind, Precipitation, Temperature, etc.) | Technique: In Situ Measurement | Topic: Data Processing and Information Retrieval
Options to correct local turbulent flux measurements for large-scale fluxes using an approach based on large-eddy simulation
Matthias Mauder, Andreas Ibrom, Luise Wanner, Frederik De Roo, Peter Brugger, Ralf Kiese, and Kim Pilegaard
Atmos. Meas. Tech., 14, 7835–7850,,, 2021
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Global ensemble of temperatures over 1850–2018: quantification of uncertainties in observations, coverage, and spatial modeling (GETQUOCS)
Maryam Ilyas, Douglas Nychka, Chris Brierley, and Serge Guillas
Atmos. Meas. Tech., 14, 7103–7121,,, 2021
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Reconstruction of the mass and geometry of snowfall particles from multi-angle snowflake camera (MASC) images
Jussi Leinonen, Jacopo Grazioli, and Alexis Berne
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A new zenith hydrostatic delay model for real-time retrievals of GNSS-PWV
Longjiang Li, Suqin Wu, Kefei Zhang, Xiaoming Wang, Wang Li, Zhen Shen, Dantong Zhu, Qimin He, and Moufeng Wan
Atmos. Meas. Tech., 14, 6379–6394,,, 2021
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Air temperature equation derived from sonic temperature and water vapor mixing ratio for air flow sampled through closed-path eddy-covariance flux systems
Xinhua Zhou, Tian Gao, Eugene S. Takle, Xiaojie Zhen, Andrew E. Suyker, Tala Awada, Jane Okalebo, and Jiaojun Zhu
Atmos. Meas. Tech. Discuss.,,, 2021
Revised manuscript accepted for AMT
Short summary

Cited articles

Babanin, A. V., McConochie, J., and Chalikov, D.: Winds near the surface of waves: Observations and modeling. J. Phys. Oceanogr., 48, 1079–1088,, 2018. 
Hsu, S. A., Meindl, E. A., and Gilhousen, D. B.: Determining the power-law wind-profile exponent under near-neutral stability conditions at sea, J. Appl. Meteorol., 33, 757–765,<0757:DTPLWP>2.0.CO;2 1994. 
Jiang, H.: Indirect validation of ocean remote sensing data via numerical model: An example of wave heights from altimeter, Remote Sens., 13, 2627,, 2020. 
Jiang, H. and Mu, L.: Wave Climate from Spectra and Its Connections with Local and Remote Wind Climate, J. Phys. Oceanogr., 49, 543–559, 2019. 
Jiang, H., Babanin, A. V., and Chen, G.: Event-based validation of swell arrival time, J. Phys. Oceanogr., 46, 3563–3569,, 2016. 
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.