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.

中文翻译：

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使用深度学习从波谱估计风速和风向

摘要。海浪频谱的高频部分与当地风强烈耦合。海浪谱的测量可用于估计海面风。在这项研究中，两个深度神经网络 (DNN) 用于根据浮标的前五个傅立叶系数估计风速和风向。DNN 是通过 2014-2018 年期间 100 多个气象浮标的风和波浪测量值进行训练的。发现波浪测量可以最好地代表大约 1 小时前的风信息，因为波浪谱包含了短时间内的风信息。估计风速的总体均方根误差（RMSE）为~1.1 m/s，风速为7~25 m/s时风向的RMSE为~14°。