论文标题
相分辨的海浪预测，基于整体的数据同化
Phase-resolved ocean wave forecast with ensemble-based data assimilation
论文作者
论文摘要
通过基于整体的数据同化（DA），我们解决了相位分辨的海浪预测中最臭名昭著的困难之一，这是关于由于非线性波模型中的混乱性质和代表性不足的物理性质的混乱性和代表性不足而导致的真实表面升高。特别是，我们使用集合卡尔曼滤波器（ENKF）开发了高阶光谱（HOS）方法的耦合方法，可以将测量数据纳入模拟中以改善预测性能。该耦合中的一个独特功能是可预测区域和测量区域之间的不匹配，该区域通过特殊算法来修改ENKF中的分析方程。我们使用合成数据和实际雷达测量值测试了新的ENKF-HOS方法的性能。对于这两种情况（尽管详细信息有所不同），这表明新方法的准确性比仅HOS方法的方法更高，并且可以使用依次同化的数据保留不规则波场的相位信息，以任意长时间的预测。
Through ensemble-based data assimilation (DA), we address one of the most notorious difficulties in phase-resolved ocean wave forecast, regarding the deviation of numerical solution from the true surface elevation due to the chaotic nature of and underrepresented physics in the nonlinear wave models. In particular, we develop a coupled approach of the high-order spectral (HOS) method with the ensemble Kalman filter (EnKF), through which the measurement data can be incorporated into the simulation to improve the forecast performance. A unique feature in this coupling is the mismatch between the predictable zone and measurement region, which is accounted for through a special algorithm to modify the analysis equation in EnKF. We test the performance of the new EnKF-HOS method using both synthetic data and real radar measurements. For both cases (though differing in details), it is shown that the new method achieves much higher accuracy than the HOS-only method, and can retain the phase information of an irregular wave field for arbitrarily long forecast time with sequentially assimilated data.
