Wind Speed and Direction Estimation from Wave Spectra using Deep Learning
Abstract: . High-frequency parts of ocean wave spectra are strongly coupled to the local wind. Measurements of ocean wave spectra can be used to estimate sea surface winds. In this study, two deep neural networks (DNNs) were used to estimate the wind speed and direction from the first five Fourier coefficients from buoys. The DNNs were trained by wind and wave measurements from more than 100 meteorological buoys during 2014–2018. It is found that the wave measurements can best represent the wind information ~1 h ago, because the wave spectra contain wind information a short period before. The overall root-mean-square error (RMSE) of estimated wind speed is ~1.1 m/s, and the RMSE of wind direction is ~14° when wind speed is 7~25 m/s. This model can not only be used for the wind estimation for compact wave buoys but also for the quality control of wind and wave measurements from meteorological buoys.
Summary (1 min read)
- Sea surface wind and waves are important parameters for the marine environment and ocean dynamics.
- Many meteorological buoys can provide comprehensive wind and wave information including surface wind speeds, wind directions, and wave spectra with high accuracy.
- Scatterometers can retrieve both wind speed and direction with a wide swath and the best overall accuracy, but wave information is not available from them.
- Moreover, most satellites have limited 35 temporal resolutions and often perform worse in nearshore regions than in the open ocean due to the land contamination of backscatter.
- Their model can estimate wind speed with a root-mean-square error (RMSE) of 2 m/s and wind directions with an RMSE of ~20° when wind speed is higher than 10 m/s.
2.1 Collocated Wind and Wave Data
- Many buoys from the National Data Buoy Center (NDBC) coastal-marine automated network can provide quality-60 controlled in-situ wave and wind measurements.
- The authors will 85 show in Section 4 that the input layer of the DNNs can be refined after obtaining the basic knowledge of how these models work.
- For wind directions, the RMSE is larger than 25° when U10 < 5 m/s but decreases fast with the increase of U10.
- The wind information estimated from wave spectra achieves good accuracy, but the DNN model uses all available wave 195 spectral information as the input.
- By blocking some of the inputs (setting the values of normalized input into zeros), one can know which input is more important for the DNN model.
- This indicates that E and α1 are the most important parameters for estimating U10 and wind directions, respectively.
- Hz and its two neighboring bins, 0.19 and 0.21 Hz, are blocked (set to zero after normalization).
5 Concluding Remarks
- Ocean wave spectra can be used to sea surface winds.
- The two models can also be used as a quality control tool for wind and wave measurements from meteorological buoys.
- This can compensate for some of the errors induced by strong surface currents or wind-induced drifts.
- Finally, the authors hope to point out that such DNN models need not to be trained from the beginning using a large amount of data.
- This work is jointly supported by the Key-Area Research and Development Program of Guangdong Province (2020B1111020005), the Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory (GML2019ZD0604), and the National Natural Science Foundation of China (U2006210, 41806010).
- The NDBC data are available from the website of the National Centers for Environmental 255 Information (https://www.ncei.noaa.gov/data/oceans/ndbc/cmanwx/).
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