Modified Weibull distribution for maximum and significant wave height simulation and prediction

Calibration coefficients incorporated in the modified Weibull distribution are more effective for maximum wave height simulation. The parametric relations are derived there from to estimate various wave height statistics including extreme wave heights. The characteristic function of the Weibull distribution is derived. The Weibull distribution is suggested for the newly defined significant wave height simulation by the method of characteristic function. The statistical tools suggested and developed here for predicting the required wave height statistics are validated against the wave data (both deep and shallow) of eastern Arabian Sea comprising rough monsoon conditions also, giving reasonable accuracy.     

Modelling the long-term distribution of significant wave height with the Beta and Gamma models

The paper suggests modelling the long-term distribution of significant wave height with the Gamma, Beta of the first and second kind models. The three models are interrelated, flexible and cover the three different tail types of Extreme Value Theory. They can be used simultaneously as a means of assessing the uncertainty effects that result from choosing equally plausible models with different tail types. This procedure is intended for those applications that require the long-term distribution of significant wave height as input rather than the prediction of extreme values. The models are fitted to some significant wave data as an illustration. Details about maximum likelihood estimation are given in A.     

The influence of seasonality on estimating return values of significant wave height

A time-dependent generalized extreme value (GEV) model for monthly significant wave heights maxima is developed. The model is applied to several 3-hour time series from the Spanish buoy network. Monthly maxima show a clear non-stationary behavior within a year, suggesting that the location, scale and shape parameters of the GEV distribution can be parameterized using harmonic functions. To avoid a possible over-parameterization, an automatic selection model, based on the Akaike Information Criterion, is carried out. Results show that the non-stationary behavior of monthly maxima significant wave height is adequately modeled, drastically increasing the significance of the parameters involved and reducing the uncertainty in the return level estimation. The model provides new information to analyze the seasonal behavior of wave height extremes affecting different natural coastal processes.     

Modelling the long-term time series of significant wave height with non-linear threshold models

Linear autoregressive models and non-linear threshold autoregressive (TAR) models are used in the present work to describe the time series of the significant wave height of sea-states at Figueira da Foz, located in the Portuguese coast. The seasonal components of this series are identified and a TAR model with two regimes is proposed. A simulation study was carried out with the purpose of verifying if both the non-linear and linear models are suited to describe the probabilistic structure of the process. It is shown that both methods are adequate to describe the lower statistical moments of the original data, but the non-linear model represents better the skewness and the kurtosis of the data.     

Seasonality and duration in extreme value distributions of significant wave height

This paper presents a statistical model to characterize the long-term extreme value distribution of significant wave height, conditioning to the duration of the storm and accounting for seasonality. A time-dependent version of the peak over threshold (POT) approach is used to build the model, which is then applied to specific reanalysis time series and NOAA buoy records. The model considers the annual and semiannual cycles which are parameterized in terms of harmonic functions. The inclusion of seasonal variabilities substantially reduces the residuals of the fitted model. The information obtained in this study can be useful to design maritime works, because (a) the model improves the understanding of the variability of extreme wave climate along a year and (b) the model accounts for the duration of the storm, which is a key parameter in several formulations for rubble mound breakwater design.     

一种利用浮标站资料改进海浪模式有效波高预报的方法

基于浮标站海浪历史数据,利用回归分析方法建立了海浪数值模式有效波高预报产品的一元二次回归方程订正统计模型。通过2017年7月1日-2018年10月10日期间业务试运行结果发现:订正方程能有效改善有效波高数值预报产品的预报精度,且预报时效越短订正效果越显著。其中,第6~11 h预报时效内的订正前后平均绝对误差值减小0.17~0. 241 m,第6~18 h预报时效内订正前后均方根误差减小幅度为0.103~0. 28 m。这说明应用订正统计模型对海浪模式输出产品进行订正,也是改进海浪模式预报准确率的一种有效途径。     

A joint distribution of significant wave height and characteristic surf parameter

The paper provides a joint distribution of significant wave height and characteristic surf parameter. The characteristic surf parameter is given by the ratio between the slope of a beach or a structure and the square root of the characteristic wave steepness in deep water defined in terms of the significant wave height and the spectral peak period. The characteristic surf parameter is used to characterize surf zone processes and is relevant for e.g. wave run-up on beaches and coastal structures. The paper presents statistical properties of the wave parameters as well as an example of results corresponding to typical field conditions.     

Bivariate autoregressive models for the time series of significant wave height and mean period

This paper generalises the application of univariate models of the long-term time series of significant wave height to the case of the bivariate series of significant wave height and mean period. A brief review of the basic features of multivariate autoregressive models is presented, and then applications are made to the wave time series of Figueira da Foz, in Portugal. It is demonstrated that the simulated series from these models exhibit the correlation between the two parameters a feature that univariate series cannot reproduce. An application to two series of significant wave height from two neighbouring stations shows the applicability of this type of models to other type of correlated data sets.     

Extreme response prediction for nonlinear floating offshore structures by Monte Carlo simulation

The paper describes a method for the prediction of extreme response statistics of floating offshore structures subjected to random seas by Monte Carlo simulation. The particular case of the horizontal surge motions of a tension leg platform is considered, taking into account both the first order, wave frequency and the second order, slow-drift motions. The advantage of the Monte Carlo method is its simplicity and versatility, which allows us to account for the effect of time-variant wave-drift damping, as well as nonlinear mooring characteristics without noticeable increase in the computational complexity. It is demonstrated in this paper that the commonly assumed obstacle against using the Monte Carlo method for estimating extreme responses, i.e. excessive CPU time, can be circumvented, bringing the computation time down to quite acceptable levels.     

基于随机集合的非传统型有效波极值模型

The analysis and design of offshore structures necessitates the consideration of wave loads. Realistic modeling of wave loads is particularly important to ensure reliable performance of these structures. Among the available methods for the modeling of the extreme significant wave height on a statistical basis, the peak over threshold method has attracted most attention. This method employs Poisson process to character- ize time-varying properties in the parameters of an extreme value distribution. In this paper, the peak over threshold method is reviewed and extended to account for subjectivity in the modeling. The freedom in selecting the threshold and the time span to separate extremes from the original time series data is incorpo- rated as imprecision in the model. This leads to an extension from random variables to random sets in the probabilistic model for the extreme significant wave height. The extended model is also applied to different periods of the sampled data to evaluate the significance of the climatic conditions on the uncertainties of the parameters.     

On the choice of data transformation for modelling time series of significant wave height

Autoregressive models have been shown to adequately model the time series of significant wave height. However, since this series exhibits a seasonal component and has a non-gaussian nature, it is necessary to transform the series before a model can be fit to the data. Two different transformations that have been used in earlier work are shown not to be appropriate for all types of applications. A third transformation is proposed here, which combines the better features of the two earlier ones and which is appropriate for simulation work. This is demonstrated with an example of a series from Figueira da Foz, a location of the Portuguese Coast.     

Effect of sampling rate on wave height statistics

This paper examines the effects of digital processing, and therefore discretisation or sampling, of sea surface elevations that are, in reality, continuous. Using random linear wave theory, probability distributions for the measured (as opposed to actual) wave amplitudes and heights have been obtained which are conditional on the sampling rate. It is shown that with low sampling rates there are significant departures from the usual Rayleigh distribution. Rates of 1 Hz or less may lead to significant underestimation of the probability of very large waves. An analysis of full-scale measurements obtained from a platform in the North Sea supports these results.     

HY-2A卫星高度计不同重跟踪方案对反演海浪有效波高的影响

本文基于Amarouche的二阶理论回波模型,导出了带有偏度系数的二阶理论回波模型;针对HY-2A卫星高度计波形特点,引入了奇异值分解滤波,并根据最大似然估计算法反演参数的不同得到6种重跟踪方案;利用其中的五参数方案处理该波形数据,获得海面散射点高度概率密度函数中偏度的合理取值为0.15;将结果分别与浮标、Jason-1和HY-2AIDR有效波高对比,分析6种方案反演有效波高的准确度,确定了MLE4_SVD(波形重跟踪之前进行滤波)对HY-2A高度计重跟踪更适合反演有效波高。     

A generalized approach for long-term modelling of extreme crest-to-trough wave heights

This paper deals with the long-term modelling of high sea waves. The solution is given for the return period of sea storms during which an arbitrary chosen number of waves, with crest-to-trough heights exceeding a fixed threshold, occur. This return period is derived starting from the Equivalent Triangular Storm (ETS) model, which associates a triangle to each actual storm and thus represents a significant wave height time series at a fixed location by means of a sequence of triangular storms. The short-term statistics is then applied to investigate the occurrence of large crest-to-trough wave heights during a given storm. Finally, by combining the statistical distribution of significant wave heights, the ETS model and the short-term wave statistics, the solution is given for the return periods R_N and R_N of a sea storm in which N or at least N waves higher than a fixed threshold occur. The values of R_N are then calculated, starting from data of two buoys moored in the Pacific Ocean and in the Mediterranean Sea.     

Wave energy assessment based on trivariate distribution of significant wave height,mean period and direction

Based on the Vine copula theory, a trivariate statistical model of significant wave height, characterized wave period and mean wave direction was constructed. To maintain the properties of the different types of variables, a special copula function was derived from the model developed by Johnson and Wehrly based on the maximum entropy principle. It was then combined with the Archimedean copulas to construct the proposed model. An effective algorithm for generating corresponding joint pseudo-random numbers was also developed. Statistical analysis of hindcast data for the significant wave height, mean wave period, and direction, which were collected from an observation point in the North Atlantic every three hours from 1997 to 2001, was performed. The marginal distributions of the significant wave height and mean wave period were fitted by a modified maximum entropy distribution, and the mean wave direction was fitted by a mixture of von Mises distributions. It was shown that the proposed model is a good fit for the data. The seasonal wave energy resources in the target area were assessed using the model estimates. Histograms of the directional wave energy, wave energy roses, and scatter and energy diagrams were presented.     

Application of a fuzzy inference system for the prediction of longshore sediment transport

A fuzzy inference system (FIS) and a hybrid adaptive network-based fuzzy inference system (ANFIS), which combines a fuzzy inference system and a neural network, are used to predict and model longshore sediment transport (LST). The measurement data (field and experimental data) obtained from Kamphuis [1] and Smith et al. [2] were used to develop the model. The FIS and ANFIS models employ five inputs (breaking wave height, breaking wave angle, slope at the breaking point, peak wave period and median grain size) and one output (longshore sediment transport rate). The criteria used to measure the performances of the models include the bias, the root mean square error, the scatter index and the coefficients of determination and correlation. The results indicate that the ANFIS model is superior to the FIS model for predicting LST rates. To verify the ANFIS model, the model was applied to the Karaburun coastal region, which is located along the southwestern coast of the Black Sea. The LST rates obtained from the ANFIS model were compared with the field measurements, the CERC [3] formula, the Kamphuis [1] formula and the numerical model (LITPACK). The percentages of error between the measured rates and the calculated LST rates based on the ANFIS method, the CERC formula (K_sig = 0.39), the calibrated CERC formula (K_sig = 0.08), the Kamphuis [1] formula and the numerical model (LITPACK) are 6.5%, 413.9%, 6.9%, 15.3% and 18.1%, respectively. The comparison of the results suggests that the ANFIS model is superior to the FIS model for predicting LST rates and performs significantly better than the tested empirical formulas and the numerical model.     

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.     

Stochastic inversion for the roll gyradius second moment mass property in ships at full-scale and model-scale

In current Naval Architecture practice, employing static considerations is an important and necessary step in assessing ship stability and seakeeping properties (e.g. inclining experiments, load line regulations, range of stability calculations). However, damaged vessels and vessels operating in heavy weather or in conditions where topside icing is a concern may require an additional assessment of stability that considers dynamic effects. Within such contexts, the actual (i.e. current) second moment properties of the vessel mass become very important in the associated equations of motion for a given ship. One such critical second mass moment property is the roll gyradius, as it is closely related to the occurrence of capsizing. The present paper furnishes a means for reckoning the actual roll gyradius of a given ship operating within a seaway. The approach hinges on the formulation and solution of a stochastic inverse problem that leverages existing seakeeping software against the shipboard inertial measurement unit (IMU) telemetry. The method is demonstrated at full-scale and validated at model scale.