Abstract We assess the capability of fast wave models to deterministically predict nonlinear ocean surface waves from non-uniformly distributed data such as sampled from an optical ocean sensor. Linear and weakly nonlinear prediction algorithms are applied to long-crested irregular waves based on a set of laboratory experiments and corresponding numerical simulations. An array of wave gauges is used for data acquisition, representing the typical spatial sampling an optical sensor (e.g., LIDAR) would make at grazing incidence. Predictions of the weakly nonlinear Improved Choppy Wave Model are compared to those of the Linear Wave Theory with and without a nonlinear dispersion relationship correction. Wave models are first inverted based on gauge data which provides the initial model parameters, then propagated to issue a prediction. We find that the wave prediction accuracy converges with the amount of input data used in the inversion. When waves are propagated in the models, correctly modeling the nonlinear wave phase velocity provides the main improvement in accuracy, while including nonlinear wave shape effects only improves surface elevation representation in the spatio-temporal region where input data are acquired. Surface slope prediction accuracy, however, strongly depends on the appropriate nonlinear wave shape modeling.

中文翻译：

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使用非均匀采样波浪仪的确定性非线性海浪预测算法的实验和数值评估

摘要 我们评估了快波模型从非均匀分布的数据（例如从光学海洋传感器采样）中确定性地预测非线性海面波的能力。基于一组实验室实验和相应的数值模拟，将线性和弱非线性预测算法应用于长波峰不规则波。一组波计用于数据采集，代表光学传感器（例如，LIDAR）在掠入射时进行的典型空间采样。将弱非线性改进波涛模型的预测与具有和不具有非线性色散关系校正的线性波理论的预测进行比较。波浪模型首先根据提供初始模型参数的规范数据进行反演，然后传播以发布预测。我们发现波浪预测精度与反演中使用的输入数据量收敛。当波在模型中传播时，对非线性波相速度的正确建模主要提高了精度，而包含非线性波形效应只会改善获取输入数据的时空区域的表面高程表示。然而，表面坡度预测精度在很大程度上取决于适当的非线性波形建模。而包括非线性波形效应只会改善获取输入数据的时空区域中的表面高程表示。然而，表面坡度预测精度在很大程度上取决于适当的非线性波形建模。而包括非线性波形效应只会改善获取输入数据的时空区域中的表面高程表示。然而，表面坡度预测精度在很大程度上取决于适当的非线性波形建模。