论文标题
相分辨的海浪预测，并通过数据同时估计数据同时估计
Phase-resolved ocean wave forecast with simultaneous current estimation through data assimilation
论文作者
论文摘要
在Wang＆Pan（J. Fluid Mech。，第918卷，A19，2021）中，作者开发了首个基于集合的数据同化（DA）的能力（DA），用于重建和预测海面波，即ENKF-HOS方法将ENKF-HOS方法耦合到Ensember Kalman Filter（ENKF）和高订单和高订单（Hos）方法。在这项工作中，我们通过允许其同时估计海洋电场来继续丰富该方法，这通常是先验的，并且在时空和时间上都可以（缓慢）变化。为了实现这一目标，我们将海洋电流（作为未知参数）对波的影响纳入了HOS-C方法作为正向预测模型，并通过迭代ENKF（IENKF）方法同时估算（电流）参数和（Wave）参数，这是在此DA问题中处理复杂性所必需的。新算法，称为IENKF-HOS-C方法，首先在电流的各种形式（稳定/不稳定，均匀/不均匀）的合成问题中进行了测试。结果表明，IENKF-HOS-C方法不仅能够准确估计当前场，还可以提高波场（偶数）相对于最新的ENKF-HOS方法的预测准确性。最后，使用来自船上雷达的真实数据，我们表明IENKF-HOS-C方法成功地恢复了与浮动浮标相匹配的当前速度。
In Wang & Pan (J. Fluid Mech., vol. 918, A19, 2021), the authors developed the first ensemble-based data assimilation (DA) capability for the reconstruction and forecast of ocean surface waves, namely the EnKF-HOS method coupling an ensemble Kalman filter (EnKF) and the high-order spectral (HOS) method. In this work, we continue to enrich the method by allowing it to simultaneously estimate the ocean current field, which is in general not known a priori and can (slowly) vary in both space and time. To achieve this goal, we incorporate the effect of ocean current (as unknown parameters) on waves to build the HOS-C method as the forward prediction model, and obtain a simultaneous estimation of (current) parameters and (wave) states via an iterative EnKF (IEnKF) method that is necessary to handle the complexity in this DA problem. The new algorithm, named IEnKF-HOS-C method, is first tested in synthetic problems with various forms (steady/unsteady, uniform/non-uniform) of current. It is shown that the IEnKF-HOS-C method is able to not only estimate the current field accurately, but also boost the prediction accuracy of the wave field (even) relative to the state-of-the-art EnKF-HOS method. Finally, using real data from a shipborne radar, we show that the IEnKF-HOS-C method successfully recovers the current speed that matches the in situ measurement by a floating buoy.
