用于斯托克斯漂移剖面预测的参数化波浪谱模型
Parametric wave spectrum models for prediction of Stokes drift profiles
AI总结：
本文综述9种非定向海洋波浪谱参数化模型的15种变体，对比其与观测谱的斯托克斯漂移计算结果，评估无需动力波浪模型的简单模型，发现部分模型可准确预测表面斯托克斯漂移，但无模型能准确预测斯托克斯漂移剖面。
摘要：
本报告综述了9种非定向海洋波浪谱参数化模型的15种变体，将它们与基于观测的波浪谱进行对比，该观测谱由系泊浮标测得的波浪谱按风速分组并取平均得到。利用参数化模型和基于观测的波浪谱分别计算表面斯托克斯漂移（SSD）及斯托克斯漂移剖面，并对二者进行比较。将这种用于估算斯托克斯漂移的“参数化波浪模型”策略与其他估算方法（如使用动力波浪模型输出的波浪谱）进行对比，综述了各类方法的优缺点。斯托克斯漂移剖面的一项实际应用是海洋环流建模，相关信息用于计算波浪增强混合（即“朗缪尔湍流”）。本文在全球海洋模型的框架下展示了此类应用案例，该模型在两种模式（耦合与非耦合）及两种强迫条件（用风速近似的斯托克斯漂移、用完整海洋波浪模型计算的斯托克斯漂移）下进行测试。关于使用完整海洋波浪模型的非耦合测试的更全面综述，可参见另一篇报告Fan等人（2026）的成果。本文评估了仅使用风速和/或风应力、无需动力波浪模型的简单斯托克斯漂移模型，发现部分模型对SSD的常规预测足够准确；其中一部分模型还可通过参数化波浪谱预测斯托克斯漂移剖面，但无任何模型在此任务上达到准确。将从大型漂流浮标数据集计算得到的SSD值与同一浮标位置变化算出的总漂移进行对比，结果显示SSD约为总漂移的25%，符合“经验法则”的预期，但离散度较大，表明此类法则仅在平均意义上具有预测能力。
英文摘要：
In this report, 15 variants of nine parametric models for non-directional ocean wave spectra are reviewed. They are compared against observation-based spectra created by taking spectra measured by moored buoys, binning by wind speed, and averaging. Surface Stokes drift (SSD) and Stokes drift profiles are computed using the model and observation-based spectra, and these are compared. This 'parametric wave model' strategy for estimating Stokes drift is contrasted with other methods for estimating Stokes drift, such as using spectra from a dynamical wave model. Advantages and shortcomings are reviewed. One practical application for Stokes drift profiles is in ocean circulation modeling. Here, the information is used to compute wave-enhanced mixing ('Langmuir turbulence'). We present examples of such cases, in the context of a global ocean model, tested in two modes (coupled and uncoupled) and with two types of forcing (Stokes drift approximated using wind speed and Stokes drift computed using a full ocean wave model). A more comprehensive review of the uncoupled tests with the full ocean wave model can be found in a separate report, Fan et al. (2026). We evaluate simple Stokes drift models that use wind speed and/or wind stress but do not require a dynamical wave model. We find some are sufficiently accurate for routine prediction of SSD. A subset of these models can also predict Stokes drift profiles (via parametric spectra), but none are accurate in this role. The SSD values computed from a very large drifting buoy dataset are compared with the total drift calculated using positional change of the same buoys. The SSD is roughly 25% of the total drift, consistent with expectations based on 'rules of thumb'. However, the scatter is large, indicating that such rules have predictive skill only in the mean sense.
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