Wind‐wave development across a large shallow intertidal estuary: A case study of Manukau Harbour,New Zealand

Locally generated wind‐waves in estuaries play an important role in the sediment dynamics and the transport of biota. Wave growth in estuaries is complicated by tidally varying depth, fetch, and currents. Wave development was studied at six sites along a transect across Manukau Harbour, New Zealand, which is a large intertidal estuary with a tidal range of up to 4 m. Three meteorological masts were also deployed across the measurement transect to measure wave forcing by the wind. A spatial variation in wind speed by up to a factor of 2 was observed which has a significant effect on wave development at short fetches. The wind variation can be explained by the extreme change in surface roughness at the upwind land‐water boundary. The tidally varying depth results in non‐stationary wave development. At the long fetch sites wave development is dictated by the tidally varying depth with peak frequencies continuing to decrease after high water, whereas wave height is attenuated by bottom friction. The non‐dimensional energy and peak frequency parameters commonly used to describe wave growth, clearly exhibit depth limiting effects, but with wider scatter than in previous studies in simpler environments. The peak frequency predictions of Young & Verhagen (1996a) fit our data well. However, the wide variability of energy limits the usefulness of standard growth prediction curves in such situations, and highlights the requirement for a validated, shallow‐water numerical model.     

Prediction of significant wave height using regressive support vector machines

Wave parameters prediction is an important issue in coastal and offshore engineering. In this literature, several models and methods are introduced. In the recent years, the well-known soft computing approaches, such as artificial neural networks, fuzzy and adaptive neuro-fuzzy inference systems and etc., have been known as novel methods to form intelligent systems, these approaches has also been used to predict wave parameters, as well. It is not a long time that support vector machine (SVM) is introduced as a strong machine learning and data mining tool. In this paper, it is used to predict significant wave height (H_s). The data set used in this study comprises wave wind data gathered from deep water locations in Lake Michigan. Current wind speed (u) and those belonging up to six previous hours are given as input variables, while the significant wave height is the output parameter. The SVM results are compared with those of artificial neural networks, multi-layer perceptron (MLP) and radial basis function (RBF) models. The results show that SVM can be successfully used for prediction of H_s. Furthermore, comparisons indicate that the error statistics of SVM model marginally outperforms ANN even with much less computational time required.     

Real time wave forecasting using wind time history and numerical model

Operational activities in the ocean like planning for structural repairs or fishing expeditions require real time prediction of waves over typical time duration of say a few hours. Such predictions can be made by using a numerical model or a time series model employing continuously recorded waves. This paper presents another option to do so and it is based on a different time series approach in which the input is in the form of preceding wind speed and wind direction observations. This would be useful for those stations where the costly wave buoys are not deployed and instead only meteorological buoys measuring wind are moored. The technique employs alternative artificial intelligence approaches of an artificial neural network (ANN), genetic programming (GP) and model tree (MT) to carry out the time series modeling of wind to obtain waves. Wind observations at four offshore sites along the east coast of India were used. For calibration purpose the wave data was generated using a numerical model. The predicted waves obtained using the proposed time series models when compared with the numerically generated waves showed good resemblance in terms of the selected error criteria. Large differences across the chosen techniques of ANN, GP, MT were not noticed. Wave hindcasting at the same time step and the predictions over shorter lead times were better than the predictions over longer lead times. The proposed method is a cost effective and convenient option when a site-specific information is desired.     

Has wind–wave modeling reached its limit?

This article uses a comparison of four different numerical wave prediction models for hindcast wave conditions in Lake Michigan during a 10-day episode in October 1988 to illustrate that typical wave prediction models based on the concept of a wave energy spectrum may have reached a limit in the accuracy with which they can simulate realistic wave generation and growth conditions. In the hindcast study we compared the model results to observed wave height and period measurements from two deep water NOAA/NDBC weather buoys and from a nearshore Waverider buoy. Hourly wind fields interpolated from a large number of coastal and overlake observations were used to drive the models. The same numerical grid was used for all the models. The results show that while the individual model predictions deviate from the measurements by various amounts, they all tend to reflect the general trend and patterns of the wave measurements. The differences between the model results are often similar in magnitude to differences between model results and observations. Although the four models tested represent a wide range of sophistication in their treatment of wave growth dynamics, they are all based on the assumption that the sea state can be represented by a wave energy spectrum. Because there are more similarities among the model results than significant differences, we believe that this assumption may be the limiting factor for substantial improvements in wave modeling.     

利用SWAN波浪模式研究北部湾海域台风浪场的波浪方向谱特征

以高精度再分析风场为驱动,利用SWAN模式模拟了台风“达维”Damrey（2005）经过北部湾海域时的波浪场。通过与实测的风和波浪实测对比发现,波浪后报结果与实测结果符合较好。文章给出了台风浪期间波高、周期、波长和波向等要素的分布特征,讨论了以台风眼为中心不同海域的波浪方向谱特征。本文最后分析了台风期间实测波浪能谱的变化特征。     

Triple diagram method for the prediction of wave height and period

Many formulations have been developed so far to predict the wave height and period from fetch length and wind blowing duration for a constant wind speed. This study aimed to predict wave parameters from fetch length and meteorological factors by using triple diagram methodology based on Kriging principles. Proposed model results were compared with Joint North Sea Wave Project (JONSWAP) model which is used so commonly in the ocean and coastal engineering studies. For the implementation of the methodology hourly wave and wind data were obtained from a buoy located in Lake Ontario. Numerical and graphical comparisons demonstrated that the proposed method outperforms the classical formulation.     

MASNUM-WAM海浪模式集合Kalman滤波同化研究-I.风场扰动对海浪模拟影响

模式集合样本的代表性和观测信息的可靠性是制约数据同化效果的重要因素,而前者对海浪模式同化的影响尤为显著。由于海浪模式对初始场的敏感性较弱,来自大气的风输入源函数是海浪的重要能量输入,如何合理地对风输入进行扰动,构造海浪的集合模式运行,是实现和改进海浪模式集合Kalman滤波同化的关键问题。为了实现海浪模式集合运行,本文提出了风场的三种集合扰动方案,分别为：纯随机数、随机场和时间滞后的风场扰动方法。本研究利用2014年1月ECMWF全球风场,基于这三种风场扰动方法开展了集合海浪模式的集合运行实验,并统计分析了海浪特征要素（有效波高）和二维波数谱对风场扰动的响应。结果表明,随机场集合扰动方案所构造的风场集合效果最佳,所得海浪模拟结果的集合样本发散度适中,能够较为合理地反映背景误差的统计特征,可用于进一步的集合Kalman滤波海浪数据同化实验。     

耦合模式中海浪参数对台风浪预报的影响研究

以西北太平洋一次"双台风"共同影响下的台风浪为例,针对模式中风摄入和白帽耗散、底摩擦、波破碎、波-波非线性相互作用等海浪物理过程对台风浪预报的影响进行了敏感性试验分析。在此基础上,基于各物理过程最优参数化方案探讨了耦合模式和单独海浪模式的海浪预报性能,分析了耦合模式的海浪预报场分布特征。结果表明:不同海浪物理过程参数化对于波高预报的准确性是有所差异的。在相对最优的海浪各参数化方案组合下,无论耦合模式还是单独海浪模式都能较好地反映波高的变化和分布趋势。相比而言,耦合模式对于台风浪大值区的浪高预报要比单独海浪模式的更接近观测,且可以很好地刻画出双台风影响下浪的分布演变特征,对于西太平洋台风浪的预报具有很好的适用性。     

Evaluating the efficacy of SVMs,BNs, ANNs and ANFIS in wave height prediction

Wave Height (WH) is one of the most important factors in design and operation of maritime projects. Different methods such as semi-empirical, numerical and soft computing-based approaches have been developed for WH forecasting. The soft computing-based methods have the ability to approximate nonlinear wind–wave and wave–wave interactions without a prior knowledge about them. In the present study, several soft computing-based models, namely Support Vector Machines (SVMs), Bayesian Networks (BNs), Artificial Neural Networks (ANNs) and Adaptive Neuro-Fuzzy Inference System (ANFIS) are used for mapping wind data to wave height. The data set used for training and testing the simulation models comprises the WH and wind data gathered by National Data Buoy Center (NDBC) in Lake Superior, USA. Several statistical indices are used to evaluate the efficacy of the aforementioned methods. The results show that the ANN, ANFIS and SVM can provide acceptable predictions for wave heights, while the BNs results are unreliable.     

Improvement and application of a saturation based wave dissipation function in SWAN model

Wave dissipation characteristics in SWAN (Simulating Waves Nearshore) model are investigated through numerical experiments. It is found that neither the fully developed integral parameters of wind waves (significant wave height and peak frequency) nor the high frequency spectral tail can be well reproduced by the default wave dissipation source terms. A new spectral dissipation source term is proposed, which comprises saturation based dissipation above two times of peak frequency and improved whitecapping dissipation at lower frequency spectrum. The reciprocal wave age (u /c p ) is involved into the whitecapping model to adjust dissipation rate at different wind speed. The Phillips higher frequency saturation parameter in the saturation-based dissipation is no longer taken as a constant, but varies with wave age. Numerical validations demonstrate that both the wind wave generation process and higher frequency spectrum of wind waves can be well simulated by the new wave dissipation term.     

永暑海区波浪要素变化特征分析

利用永暑礁测站1988-2009年共22a的波浪实测资料,对永暑海区的波浪要素的基本特征、变化规律、风与浪的相关规律进行分析研究,阐明了海区海浪的特点及其年变化规律。该区是热带季风气候区,海区的波浪主要受季风影响,季风时期的风向、风浪传播方向、涌浪传播方向基本一致。波高以轻浪和中浪为主,小波分析表明波高在6-9月具有3-6年的变化周期。提供了较详实的海浪资料及变化规律。     

A forecasting model for wave heights based on a long short-term memory neural network

To explore new operational forecasting methods of waves, a forecasting model for wave heights at three stations in the Bohai Sea has been developed. This model is based on long short-term memory(LSTM) neural network with sea surface wind and wave heights as training samples. The prediction performance of the model is evaluated,and the error analysis shows that when using the same set of numerically predicted sea surface wind as input, the prediction error produced by the proposed LSTM model at Sta. N01 is 20%, 18% and 23% lower than the conventional numerical wave models in terms of the total root mean square error(RMSE), scatter index(SI) and mean absolute error(MAE), respectively. Particularly, for significant wave height in the range of 3–5 m, the prediction accuracy of the LSTM model is improved the most remarkably, with RMSE, SI and MAE all decreasing by 24%. It is also evident that the numbers of hidden neurons, the numbers of buoys used and the time length of training samples all have impact on the prediction accuracy. However, the prediction does not necessary improve with the increase of number of hidden neurons or number of buoys used. The experiment trained by data with the longest time length is found to perform the best overall compared to other experiments with a shorter time length for training. Overall, long short-term memory neural network was proved to be a very promising method for future development and applications in wave forecasting.     

GA-BP神经网络在罗源湾口波浪模拟研究中的应用

由于BP神经网络存在收敛速度慢和易于陷入极小值等缺点,引入遗传算法(GA)对网络的权值和阈值加以优化,并采用不同组合的输入因子和样本数,对福建省罗源湾口的波浪进行模拟研究.对输入因子的分析结果表明,研究区域的波浪主要受台湾海峡波浪传播影响,与局地气象因子(风速、风向、海气温差)的相关性较弱.训练样本试验表明,30 d以上的波浪历史数据可使GA-BP神经网络充分学习研究区域的波浪特征,从而实现对波浪要素的高精度模拟.模拟结果显示,对春、夏季实测波浪数据的模拟效果均很好,其中相关性分别为0.967和0.938,均方根误差分别为0.112 m和0.107 m,表明GA-BP神经网络在近岸波浪模拟预报中有较广阔的应用前景.     

Wave Numerical Model for Shallow Water

The history of forecasting wind waves by wave energy conservation equation is briefly des-cribed.Several currently used wave numerical models for shallow water based on different wave theoriesare discussed.Wave energy conservation models for the simulation of shallow water waves are introduced,with emphasis placed on the SWAN model,which takes use of the most advanced wave research achieve-ments and has been applied to several theoretical and field conditions.The characteristics and applicabilityof the model,the finite difference numerical scheme of the action balance equation and its source termscomputing methods are described in detail.The model has been verified with the propagation refractionnumerical experiments for waves propagating in following and opposing currents;finally.the model is ap-plied to the Haian Gulf area to simulate the wave height and wave period field there,and the results arecompared with observed data.     

Comparative Experimental and Numerical Study of Wave Loads on A Monopile Structure Using Different Turbulence Models

This study numerically and experimentally investigates the effects of wave loads on a monopile-type offshore wind turbine placed on a 1: 25 slope at different water depths as well as the effect of choosing different turbulence models on the efficiency of the numerical model. The numerical model adopts a two-phase flow by solving Unsteady Reynolds-Averaged Navier-Stokes(URANS) equations using the Volume Of Fluid(VOF) method and three different turbulence models. Typical environmental conditions from the East China Sea are studied. The wave run-up and the wave loads applied on the monopile are investigated and compared with relevant experimental data as well as with mathematical predictions based on relevant theories. The numerical model is well validated against the experimental data at model scale. The use of different turbulence models results in different predictions on the wave height but less differences on the wave period. The baseline turbulence model and Shear-Stress Transport(SST) turbulence model exhibit better performance on the prediction of hydrodynamic load, at a model-scale water depth of 0.42 m, while the laminar model provides better results for large water depths. The SST turbulence model performs better in predicting wave run-up for water depth 0.42 m, while the laminar model and standard model perform better at water depth 0.52 m and 0.62 m, respectively.     

Experimental and numerical investigations of monopile ringing in irregular finite-depth water waves

In storm conditions, nonlinear wave loads on monopile offshore wind turbines can induce resonant ringing-type responses. Efficient, validated methods which capture such events in irregular waves in intermediate or shallow water depth conditions are needed for design. Dedicated experiments and numerical studies were performed toward this goal. The extensive experimental campaign at 1:48 scale was carried out for Statoil related to the development of the Dudgeon wind farm, and included both a rigid model and a flexible, pitching-type, single degree-of-freedom model. Twenty 3-hour duration realizations for 4 sea states and 2 water depths were tested for each model. A high level of repeatability in ringing events was observed. Uncertainties in the experimental results were critically examined. The stochastic variation in the 3-hour maximum bending moment at the sea bed was significantly larger than the random variation in repetition tests, and highlighted the need for a good statistical basis in design. Numerical simulations using a beam element model with a modified Morison wave load model and second order wave kinematics gave reasonable prediction of the ringing response of the flexible model, and of the measured excitation forces on the rigid model in the absence of slamming. The numerical model was also used to investigate the sensitivity of the responses with respect to damping and natural period. A simple single degree-of-freedom model was shown to behave similarly to a fully flexible model when considering changes in natural frequency and damping.     

Prediction and assimilation of surf-zone processes using a Bayesian network: Part I: Forward models

Prediction of coastal processes, including waves, currents, and sediment transport, can be obtained from a variety of detailed geophysical-process models with many simulations showing significant skill. This capability supports a wide range of research and applied efforts that can benefit from accurate numerical predictions. However, the predictions are only as accurate as the data used to drive the models and, given the large temporal and spatial variability of the surf zone, inaccuracies in data are unavoidable such that useful predictions require corresponding estimates of uncertainty. We demonstrate how a Bayesian-network model can be used to provide accurate predictions of wave-height evolution in the surf zone given very sparse and/or inaccurate boundary-condition data. The approach is based on a formal treatment of a data-assimilation problem that takes advantage of significant reduction of the dimensionality of the model system. We demonstrate that predictions of a detailed geophysical model of the wave evolution are reproduced accurately using a Bayesian approach. In this surf-zone application, forward prediction skill was 83%, and uncertainties in the model inputs were accurately transferred to uncertainty in output variables. We also demonstrate that if modeling uncertainties were not conveyed to the Bayesian network (i.e., perfect data or model were assumed), then overly optimistic prediction uncertainties were computed. More consistent predictions and uncertainties were obtained by including model-parameter errors as a source of input uncertainty. Improved predictions (skill of 90%) were achieved because the Bayesian network simultaneously estimated optimal parameters while predicting wave heights.     

An evaluation of model estimates of ocean wave directions

The potential accuracy of local models is investigated to determine the mean direction of waves from the time history of locally observed significant wave height (or peak frequency) and locally observed wind. This is done by comparing results of such models with observations at a location in the southern North Sea for a period of six weeks. The model results are also compared with results of two synoptic models which require large scale wind information to estimate the local mean wave direction.For significant wave heights larger than 1.5 m the rms-error of the estimated mean wave direction was about 30° for the best performing local model and about 15° for the best performing synoptic model.     

Wave set-up in the storm surge along open coasts during Typhoon Anita

Wave set-up in storm surges is studied using a numerical model for coasts in Tosa Bay, Japan, open to the Pacific Ocean. Simulation models employing only atmospheric pressures and winds as external forces are unable to properly simulate open coast storm surge heights, such as those due to Typhoon Anita (1970). However, the present study shows that a numerical model incorporating wave-induced radiation stresses, as well as wind stresses and pressure gradients, is able to account for the open coast surge heights. There is a maximum contribution of 40% by the radiation stresses to the peak sea level rises. This study also evaluates the effects of the tides; including the tides improves the agreement between the predicted water surface elevations and the observations. The difference in predictions between one-way coupling from wave to surge models and two-way coupling of the surge and wave models is found to be small.     

基于物理关联深度学习的海浪浪高预测方法

精确的海浪有效波高（简称浪高）预测对于海上生产生活具有重要意义。针对现有海浪浪高预测模型对不同海洋要素间关联信息考虑不足,以及长时序浪高数据本身存在非平稳性的问题,本文设计了一种考虑物理约束与差值约束的海浪浪高时间序列预测方法。该方法基于风速与浪高之间的物理关联,设计物理约束,并通过提取差分信息设计差值约束,结合现有基于深度学习的时间序列预测模型,实现浪高预测。采用黄海和东海的6个不同站点浮标数据进行了大量实验。实验结果表明,本文提出的方法可以利用海洋要素间的物理关联,有效提高浪高预测精度,并避免因不同要素间融合造成的信息间干扰;同时,利用差值约束,限制时间序列预测结果的变动范围。本文方法可以与不同类型的时间序列预测模型相结合,显著提升原有模型的性能,并在长时间序列的预测中体现出很好的鲁棒性,为海洋要素预测中物理与数据驱动模型的有效结合提供了思路和验证。