基于广义极值分布的设计波高推算

简介了广义极值分布函数及其3种参数估计方法,包括极大似然(ML)、线性矩(LM)和间隔最大积(MPS)估计的计算方法。使用广义极值分布函数推算了北部湾涠洲岛海域3个波向的年波高极值序列设计波高,并与Weibull分布、Gumbel分布和皮尔逊Ⅲ型分布的推算结果加以对比。分析表明,涠洲岛海域极值波高服从于广义极值Ⅲ型分布,拟合优度检验结果表明广义极值分布能更好地拟合极值波高;MPS方法是一种优良的参数估计法,推算的设计波高可作为海岸环境工程设计的首要参考值。     

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.     

Anisotropy of wind and wave regimes in the Baltic proper

The directional distribution of moderate and strong winds in the Baltic Sea region is shown to be strongly anisotropic. The dominating wind direction is south-west and a secondary peak corresponds to north winds. North-west storms are relatively infrequent and north-east storms are extremely rare. Angular distribution of extreme wind speed also has a two-peaked shape with maxima corresponding to south-west and north winds, and a deep minimum for easterly winds. The primary properties of the anisotropy such as prevailing winds, frequency of their occurrence, directional distribution of mean and maximum wind speeds coincide on both sides of the Baltic proper. The specific wind regime penetrates neither into the mainland nor into the Gulf of Finland or the Gulf of Riga.Properties of the saturated wave field in the neighbourhood of proposed sites of the Saaremaa (Ösel) deep harbour are analysed on the basis of the wave model WAM forced by steady winds. The directional distribution of wave heights in typical and extreme storms is highly anisotropic. Remarkable wave height anomalies may occur in the neighbourhood of the harbour sites.     

海洋重现期波高统一化计算方法

重现期波高是港口海岸及海洋工程设计中不可回避的一个重要设计参数,尤其对深水海港、海上平台、海底油气管道、沿海核电站等重大涉海工程设计具有巨大的经济价值和深远的社会效益。但是,现有重现期波高推算缺乏统一的计算方法,导致计算结果相差悬殊。研究重现期波高的统一化计算方法,分析重现期波高计算中存在的各种不确定因素,提出减少这些不确定因素的新方法,建立误差小、应用方便、方法统一的重现期波高计算方法。基于澳大利亚悉尼站的长期连续观测波浪数据,研究发现:广义帕累托函数(generalized Pareto distribution III,GPD-III)和威布尔(Weibull)是重现期波高计算的最佳候选极值分布函数,新推导的函数形状参数计算公式较好提高重现期波高的计算精度,极值波高数据的分析方法和样本大小是影响重现期波高计算精确度的两个重要因素,短期波浪资料和年极值法可能高估重现期波高值。逐个风暴的极值波高数据分析法及最佳候选极值分布函数GPD-III和Weibull建议应用于涉海工程设计的重现期波高推算。     

A probability distribution for depth-limited extreme wave heights in a sea state

A model for the depth-limited distribution of the highest wave in a sea state is presented. The distribution for the extreme wave height is based on a probability density function (pdf) for depth-limited wave height distribution for individual waves [Méndez, F.J., Losada, I.J., Medina, R. 2004. Transformation model of wave height distribution. Coastal Eng, Vol. 50, 97:115.] and considers the correlation between consecutive waves. The model is validated using field data showing a good representation of the extreme wave heights in the surf zone. Some important statistical wave heights are parameterized obtaining useful expressions that can be used in further calculations.     

Design sea state assessment using genetic algorithm approach

Two major statistical issues can be distinguished in the procedure of wave extreme prediction. The first issue is that predicted extreme values must be based on data collected in a relatively short time. The second issue is extrapolation of the observed data into its extreme region, typically lying well beyond from even the most extreme available observation. The process of extrapolation plays a fundamental role in this area of analysis and therefore it is essential to fit empirically a convenient probability distribution that describes the available data as closely as possible. Determination of extreme values probability distribution parameters by genetic algorithm is applied to improve the methodology of extreme sea state prediction.Illustrative applications of the method are given for a North Atlantic sea environment. The results are presented as crest height maximum values occurring with a given probability or in a design storm that has a specified return period.     

欧洲北海海域波高区间分布概率研究

为了研究欧洲北海海域的波高全区域概率分布情况,从而为海洋平台等海洋浮式结构物的选址和结构设计提供依据。首先基于Global Waves Statistics(GWS)提供的实测数据,确定典型计算工况的发生概率;同时考虑实测数据中极端波浪环境下的数据缺失导致大波高分布概率偏小的问题,利用三参数Weibull分布确定不同重现期下的极值风速,作为典型计算工况的补充。以不同风速、风向的定常风场为输入项,利用第三代海浪数值模型SWAN模型,对北海全区域波高进行数值模拟。将数值模拟的稳态形式依照各工况的发生概率进行归一化累加处理,认为其结果可以表征全区域的波高概率分布情况。以波高概率分布的计算结果为依据,分析北海海域波浪环境的统计学特征,发现有效波高为7 m以上的大波高频发区在北海北部区域有大范围分布;有效波高4～5 m为北海东北区域的多发海况,极端海况下的有效波高主要分布于7～14 m区间,在地形突变区域的波高发生显著变化。     

Application of double bounded probability density function for analysis of ocean waves

Ocean waves and forces induced by them on offshore structures are random in nature. Experience has shown that short term statistics of wave heights can be described by the Rayleigh distribution for narrow band spectra (Longuet-Higgins, 1952) and that the long term statistics or the evaluation of design wave is based on certain well known extreme value distribution such as mixed Frechet distribution (Thom, 1973a, b).This paper presents a new application of the double bounded probability density function to describe the ocean wave statistics. The prime importance is to estimate the most probable maximum wave height for offshore structural designs.     

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.     

Non-stationary extreme value models to account for trends and shifts in the extreme wave climate due to climate change

The extreme values of wave climate data are of great interest in a number of different ocean engineering applications, including the design and operation of ships and offshore structures, marine energy generation, aquaculture and coastal installations. Typically, the return values of certain met-ocean parameters such as significant wave height are of particular importance. There exist many methods for estimating such return values, including the initial distribution approach, the block maxima approach and the peaks-over threshold approach. In a climate change perspective, projections of such return values to a future climate are of great importance for risk management and adaptation purposes. However, many approaches to extreme value modelling assume stationary conditions and it is not straightforward how to include non-stationarity of the extremes due to for example climate change. In this paper, various non-stationary GEV-models for significant wave height are developed that account for trends and shifts in the extreme wave climate due to climate change. These models are fitted to block maxima in a particular set of wave data obtained for a historical control period and two future projections for a future period corresponding to different emission scenarios. These models are used to investigate whether there are trends in the data within each period that influence the extreme value analysis and need to be taken into account. Moreover, it will be investigated whether there are significant inter-period shifts or trends in the extreme wave climate from the historical period to the future periods. The results from this study suggest that the intra-period trends are not statistically significant and that it might be reasonable to ignore these in extreme value analyses within each period. However, when it comes to comparing the different data sets, i.e. the historical period and the future projections, statistical significant inter-period changes are detected. Hence, the accumulated effect of a climatic trend may not be negligible over longer time periods. Interestingly enough, such statistically significant shifts are not detected if stationary extreme value models are fitted to each period separately. Therefore, the non-stationary extreme value models with inter-period shifts in the parameters are proposed as an alternative for extreme value modelling in a climate change perspective, in situations where historical data and future projections are available.     

Short-term statistics of waves measured off Ratnagiri,eastern Arabian Sea

The short-term wave characteristics are required for design and operation of industrial facilities within the coastal areas. Water surface displacement measured using waverider buoy moored at 13 m water depth in the eastern Arabian Sea off the west coast of India have been analyzed to study the short-term statistics of waves covering full one year period. The study indicates that the values of the observed maximum wave height as a function of duration are not consistent with the theoretical expected value. There is significant variation (1.29–2.19) in the ratio between highest 1% wave and significant wave height compared to the theoretical value of 1.67. The data recorded at 13 m water depth indicates that the significant wave height is ∼8% lower than that predicted by the conventional Rayleigh distribution. The theoretical bivariate log-normal distribution represents the joint distributions of wave heights and periods for the study area.     

Bayesian inference for long-term prediction of significant wave height

This paper considers the problem of estimating long-term predictions of significant wave-height. A method which combines Bayesian methodology and extreme value techniques is adopted. Inferences are based on the Metropolis–Hastings algorithm implemented in an appropriate Markov Chain Monte Carlo scheme. The method is applied to obtain return values of extreme values of significant wave height collected on the northern North Sea. The results are compared with those obtained by Guedes Soares and Scotto [Guedes Soares, C. and Scotto, M.G., 2004. Application of the r-order statistics for long-term predictions of significant wave heights. Coastal Engineering, 51, 387–394].     

A maximum-entropy compound distribution model for extreme wave heights of typhoon-affected sea areas

A new compound distribution model for extreme wave heights of typhoon-affected sea areas is proposed on the basis of the maximum-entropy principle.The new model is formed by nesting a discrete distribution in a continuous one,having eight parameters which can be determined in terms of observed data of typhoon occurrence-frequency and extreme wave heights by numerically solving two sets of equations derived in this paper.The model is examined by using it to predict the N-year return-period wave height at two hydrology stations in the Yellow Sea,and the predicted results are compared with those predicted by use of some other compound distribution models.Examinations and comparisons show that the model has some advantages for predicting the N-year return-period wave height in typhoon-affected sea areas.     

Statistical properties of successive wave heights and successive wave periods

Two successive wave heights are modeled by a Gaussian copula, which is referred to as the Nataf model. Results with two initial distributions for the transformation are presented, the Næss model [Næss A. On the distribution of crest to trough wave heights. Ocean Engineering (1985);12(3):221–34] and a two-parameter Weibull distribution, where the latter is in best agreement with data. The results are compared with existing models. The Nataf model has also been used for modeling three successive wave heights.Results show that the Nataf transformation of three successive wave heights can be approximated by a first order autoregressive model. This means that the distribution of the wave height given the previous wave height is independent of the wave heights prior to the previous wave height. Thus, the joint distribution of three successive wave heights can be obtained by combining conditional bivariate distributions. The simulation of successive wave heights can be done directly without simulating the time series of the complete surface elevation.Successive wave periods with corresponding wave heights exceeding a certain threshold have also been studied. Results show that the distribution for successive wave periods when the corresponding wave heights exceed the root-mean-square value of the wave heights, can be approximated by a multivariate Gaussian distribution.The theoretical distributions are compared with observed wave data obtained from field measurements in the central North Sea and in the Japan Sea, with laboratory data and numerical simulations.     

Prediction of extreme wave heights using neuro wavelet technique

Precise prediction of extreme wave heights is still an evading problem whether it is done using physics based modeling or by extensively used data driven technique of Artificial Neural Network (ANN). In the present paper, Neuro Wavelet Technique (NWT) is used specifically to explore the possibility of prediction of extreme events for five major hurricanes Katrina 2005, Dean 2007, Gustav 2008, Ike 2008, Irene 2011 at four locations (NDBC wave buoys stations)¹ namely; 42040, 42039, 41004, 41041 in the Gulf of Mexico. Neuro Wavelet Technique is employed by combining Discrete Wavelet Transform and Artificial Neural Networks. Discrete wavelet transform analyzes frequency of signal with respect to time at different scales. It decomposes time series into low (approximate) and high (detail) frequency components. The decomposition of approximate components (extreme events in the ocean wave series) can be carried out up to the desired multiple levels in order to provide relatively smooth varying amplitude series. This feature of wavelet transforms make it plausible for predicting extreme events with a better accuracy. In the present study third, fifth and seventh level of decompositions are used which facilitates 3 to 7 times filtering of low frequency events and seems to pay the dividend in the form of better prediction accuracy at extreme events. To develop these Neuro wavelet models to forecast the waves with lead times of 12 hr to 36 hr in advance, previously measured significant wave heights at same locations were used. The results were judged by wave plots, scatter plots and other error measures. From the results it can be concluded that the Neuro Wavelet Technique can be employed to solve the ever eluding problem of accurate forecasting of the extreme events.     

Correlation between individual waves in a real sea state

The correlation between individual waves in a real sea state has a central role in existing theories of wave grouping. The attractive Kimura (1980) theory has two critical assumptions, that the sequence of individual wave heights follows a Markov process and that the joint distribution of consecutive wave heights follows a bivariate Rayleigh form. Analysis of measured water surface records suggests that sequences of individual waves can reasonably be described as a first order mixed autoregressive, moving-average or ARMA process, though a distinction among ARMA (1,0), ARMA (0,1) and ARMA (1,1) models was beyond the resolution of the data. These include the Markov or ARMA (1,0) model. The decisive detail, the joint distribution of consecutive wave heights in the sea state, was evaluated by a simulation methodology that is consistent with the Gaussian random wave model. The estimates are dependent on spectral shape and are consistently narrower and more sharply focussed at the peak than the corresponding bivariate Rayleigh estimate. The resulting predictions of run and group length statistics differ from the Kimura theory, though not by a sufficient margin to displace the Kimura theory as a pragmatic choice for wave grouping.     

Quantifying uncertainty in extreme values of design parameters with resampling techniques

In this paper, a methodology for the selection of statistical models for describing the extreme wave heights on the basis of resampling techniques is presented. Two such techniques are evaluated: the jackknife and the bootstrap. The methods are applied to two high-quality datasets of wave measurements in the Mediterranean and one from the East Coast of the USA. The robustness of the estimates of the extreme values of wave heights at return periods important for coastal engineering design is explored further. In particular, we demonstrate how an ensemble error norm can be used to select the most appropriate extreme probability model from a choice of cumulative distribution functions (CDFs). This error norm is based on the mean error norm of the optimised CDF for each resampled (replicate) data series. The resampling approach is also used to present confidence intervals of the CDF parameters. We provide a brief discussion of the sensitivity of these parameters and the suitability of each model in terms of uncertainty with resampling techniques. The advantages of resampling are outlined, and the superiority of the bootstrap over the jackknife in quantifying the uncertainty of extreme quantiles is demonstrated for these records.     

Wave characteristics and extreme parameters in the Bohai Sea

This paper is aimed at the whole Bohai Sea,as the complement and improvement of wave characteristics and extreme parameters.Wave fields were simulated in the Bohai Sea by using wave model SWAN from 1985 to 2004.The input data based on the hindcast of high-resolution wind fields from RAMS and water level fields from POM,which have been tested and verified well.Comparisons of significant wave heights between simulation and station observations show a good agreement in general.By statistical analysis,the wave characteristics such as significant wave heights, dominant wave directions and their seasonal variations are discussed.In addition,main wave extreme parameters and directional extreme values particularly for 100-year return period are investigated.     

Estimation of mean and extreme waves in the East China Seas

Accurately estimating the mean and extreme wave statistics and better understanding their directional and seasonal variations are of great importance in the planning and designing of ocean and coastal engineering works. Due to the lack of long-term wave measurement data, the analysis of extreme waves is often based on the numerical wave hind-casting results. In this study, the wave climate in the East China Seas (including the Bohai Sea, the Yellow Sea and the East China Sea) for the past 35 years (1979–2013) is hind-casted using a third generation wave model – WAMC4 (Cycle 4 version of WAM model). Two sets of reanalysis wind data from NCEP (National Centers for Environmental Prediction, USA) and ECMWF (European Centre for Medium-range Weather Forecasts) are used to drive the wave model to generate the long-term wave climate. The hind-casted waves are then analysed to study the mean and extreme wave statistics in the study area. The results show that the mean wave heights decrease from south to north and from sea to land in general. The extreme wave heights with return periods of 50 and 100 years in the summer and autumn seasons are significantly higher than those in the other two seasons, mainly due to the effect of typhoon events. The mean wave heights in the winter season have the highest values, mainly due to the effect of winter monsoon winds. The comparison of extreme wave statistics from both wind fields with the field measurements at several nearshore wave observation stations shows that the extreme waves generated by the ECMWF winds are better than those generated by the NCEP winds. The comparison also shows the extreme waves in deep waters are better reproduced than those in shallow waters, which is partly attributed to the limitations of the wave model used. The results presented in this paper provide useful insight into the wave climate in the area of the East China Seas, as well as the effect of wind data resolution on the simulation of long-term waves.     

Correlation of extreme waves and water levels using a third-generation wave model and a 3D flow model

A procedure to correlate extreme wave heights and extreme water levels in coastal waters using numerical models together with joint probability analysis has been proposed. A third-generation wave model for wave simulation and a three-dimensional flow model for water level simulation are coupled through the surface atmospheric boundary layer. The model has been calibrated and validated against wind, wave and water level data collected in the coastal waters of Hong Kong. The annual maximum wave height and the concomitant water level have been obtained by simulating the annual extreme typhoon event for 50 consecutive years. The results from bivariate extreme value analysis of the simulated data show that the commonly used empirical method may lead to underestimation of the design water level.