Climate-based Monte Carlo simulation of trivariate sea states

Accurate wave climate characterization, which is vital to understand wave-driven coastal processes and to design coastal and offshore structures, requires the availability of long term data series. Where existing data are sparse, synthetically generated time series offer a practical alternative. The main purpose of this paper is to propose a methodology to simulate multivariate hourly sea state time series that preserve the statistical characteristics of the existing empirical data. This methodology combines different techniques such as univariate ARMAs, autoregressive logistic regression and K-means clusterization algorithms, and is able to take into account different time and space scales. The proposed methodology can be broken down into three interrelated steps: i) simulation of sea level pressure fields, ii) simulation of daily mean sea conditions time series and iii) simulation of hourly sea state time series. Its effectiveness is demonstrated by synthetically generating multivariate hourly sea states from a specific location near the Spanish Coast. The direct comparison between simulated and empirical time series confirms the ability of the developed methodology to generate multivariate hourly time series of sea states. Finally, the potential of the proposed methodology to simulate multivariate time series at multiple locations and incorporate climate change issues is discussed.     

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.     

时间不连续压力序列的傅立叶分析方法

提出了一种用于对不连续压力采样序列的傅立叶分析方法.此方法将周期函数展开成傅立叶级数,但在数值积分时取函数周期内有采样值的区间作为积分域,然后求解线性方程组得到傅立叶级数的系数值.为了检验本方法的有效性,利用此方法对解析函数进行了拟合,当一个周期内的取样时间大于1/2周期时,利用此方法能够得到满意的结果.利用实验方法研究了波浪作用下截断圆柱表面的压力分布.在波浪作用下静水面附近的测点在露出水面时没有压力值.利用所提出的傅立叶分析方法对略低于静水面位置的实测压力进行了分析,拟合结果与实测结果吻合很好,说明此方法在处理物理模型实验中间断采样得到的数据是有效的.利用数值方法对波浪压力进行了计算,并将一阶和二阶波压力的数值结果与实测值进行了比较.     

Wave hindcasting by coupling numerical model and artificial neural networks

By coupling numerical wave model (NWM) and artificial neural networks (ANNs), a new procedure for wave prediction is proposed. In many situations, numerical wave modeling is not justified due to economical consideration. Although incorporation of an ANN model is inexpensive, such a model needs a long time period of wave data for training, which is generally inconvenient to achieve. A proper combination of these two methods could carry the potentials of both. Based on the proposed approach, wave data are generated by a NWM by means of a short period of assumed winds at a concerned point. Then, an ANN is designed and trained using the above-mentioned generated wind-wave data. This ANN model is capable of mapping wind-velocity time series to wave height and period time series with low cost and acceptable accuracy. The method was applied for wave hindcasting to two different sites; Lake Superior and the Pacific Ocean. Simulation results show the superiority of the proposed approach.     

Analytical convolution model for shipping water evolution on a fixed structure

The shipping of water is a problem that affects naval and offshore structures. Estimating its propagation on the decks of these structures by using analytical methods has been a main concern of projects. However, classical approaches disregard the decay tendency of water elevation time series and tend to overestimate the resultant water on deck. This paper is concerned with estimating the evolution of water along the deck of a fixed structure due to shipping water events. An analytical convolution model is proposed to estimate water elevations. The model considers the freeboard exceedance time series and the mean shipping flow velocity as inputs and the frictional effects of the bottom by resistance coefficients, which enables an approximated representation of the water elevation time series over the deck. It was validated with experiments of isolated shipping water events that were generated with the wet dam-break approach. The results obtained with the proposed model captured the experimental results, approximating the peak values and the decay trend of time series. Improvement of the proposed approach over classical models to represent shipping water elevations was demonstrated by comparing the results obtained with those of the dam-break model of Stoker.     

极值波高建模中不确定性的模糊量化方法

A non-traditional fuzzy quantification method is presented in the modeling of an extreme significant wave height. First, a set of parametric models are selected to fit time series data for the significant wave height and the extrapolation for extremes are obtained based on high quantile estimations. The quality of these results is compared and discussed. Then, the proposed fuzzy model, which combines Poisson process and generalized Pareto distribution(GPD) model, is applied to characterizing the wave extremes in the time series data. The estimations for a long-term return value are considered as time-varying as a threshold is regarded as non-stationary. The estimated intervals coupled with the fuzzy theory are then introduced to construct the probability bounds for the return values. This nontraditional model is analyzed in comparison with the traditional model in the degree of conservatism for the long-term estimate. The impact on the fuzzy bounds of extreme estimations from the non stationary effect in the proposed model is also investigated.     

A simplified hindcast method for the estimation of extreme storm surge events in semi-enclosed basins

Human presence, coastal erosion, and tourism activities are increasing the attention to coastal flooding risk. To perform risk assessments, long time series of observed or hindcast wave parameters and tide levels are then necessary. In some cases, only a few years of observation are available, so that observed extreme data are not always representative and reliable. A hindcast system aimed to reconstruct long time series of total tide levels may be of great help to perform robust extreme events analysis and then to protect human life, activities as well as to counteract coastal erosion by means of risk assessments. This work aims to propose a simplified method to hindcast storm surge levels time series in semi-enclosed basins with low computational costs. The method is an extension of a previous work of some of the authors and consists of a mixed approach in which the estimation of storm surge obtained by using the theory of linear dynamic system is corrected by using a statistical method. Both steps are characterized by low computational costs. Nevertheless, the results may be considered reliable enough also in view of the simplicity of the approach. The proposed method has been applied to the Manfredonia case study, a small village located in the Southern Adriatic Italian coast and often prone to coastal flooding events. The comparison of extreme events estimated on the basis of hindcast levels time series is satisfactorily similar to those estimated on the basis of observed tide series.     

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.     

Refined autoregressive moving average modeling of underwater heaveprocess

Earlier treatments of the underwater dynamic motion effects including the heave, or vertical dynamic motion, phenomenon relied on frequency response methods and Kalman filtering for the compensation task. An alternative model of the heave process is proposed. The model is based on transforming the underlying time series using exponential operations and then finding autoregressive integrated moving-average (ARIMA) representations of the time series. A refined ARMA model based on modeling of a series of innovations is also proposed. A computational comparison of the performance of two estimators is conducted using a real heave record as a base case. The refined ARMA model gives better results than the other alternative models investigated     

Weak-mode identification and time-series reconstruction from high-level noisy measured data of offshore structures

The identification of true weak modes buried in high-level, noisy, measured data from offshore structures is a practical but challenging problem because weak modes are typically eliminated as noise and rarely, yield a discrete time series. This study proposes a weak-mode identification and time-series reconstruction method for offshore structures when high-level noise is present. A theoretical development proposed in this study extends the traditional modal analysis to reconstructing the discrete time series of weak modes, thereby removing its previous limitations to only frequencies, damping ratios and mode shapes. Additionally, a second development proposed in this study makes the reconstructed time series not simply a combination of harmonic components from a Fourier transform but rather complex exponentials; the damping of the test structure is thus estimated with a better accuracy. A third theoretical development avoids variations in the results from different original signals by handling multiple signals simultaneously. The proposed approach primarily includes three steps: (1) estimate the poles and corresponding residues of high-level, noisy, measured data by converting high-order difference equations to first-order difference equations; (2) isolate the poles of weak modes by assigning multiple rough-pole windows, and subsequently extract the corresponding residues based on the row number of the isolated pole vector; and (3) identify and reconstruct the time series of the weak modes of interest in the form of complex exponentials. The most primary advantage of the proposed process in engineering applications is that the pole windows can be easily obtained and assigned from the relationship between the frequencies and their poles. Three numerical examples are studied: the first presents the detailed numerical operation of the proposed method, the second extends the proposed method from managing one signal to managing multiple signals, and the third demonstrates the advantage of the approach compared with traditional methods. The numerical results indicate that the original signals can be decomposed into multiple complex exponentials with representative poles and corresponding residues, and that the new signals representing weak modes could be reconstructed by assigning a range of frequencies in terms of their relations with the poles. To study the performance of the proposed method when applied to offshore structures such as offshore platforms and marine risers, the experimental data from the high mode VIV experiments sponsored by the Norwegian Deepwater Programme (NDP) are used firstly. The results show that two dominant frequencies corresponding to the in-line and cross-flow directions can be identified simultaneously even one mode is very weak compared with the other, and the time series of the weak mode could be reconstructed with a rough frequency window. Then sea-test data of two offshore platforms are used: one was collected from the JZ20-2MUQ offshore platform when it was excited by ice, and the other was collected from the WZ11-4D platform when it was excited by waves. The results further demonstrated that a large model order is required to estimate all poles and residues of the original noisy signals, and that the row number corresponding to a weak mode of the isolated pole matrix could be easily determined via finite element analysis or engineering experiences. Therefore, the proposed approach provides not only modal parameters, such as frequencies and damping ratios of true weak modes buried in high-level noise, but also the discrete time series of the weak mode.     

Temporal significant wave height estimation from wind speed by perceptron Kalman filtering

Significant wave height estimates are necessary for many applications in coastal and offshore engineering and therefore various estimation models are proposed in the literature for this purpose. Unfortunately, most of these models provide simultaneous wave height estimations from wind speed measurements. However, in practical studies, the prediction of significant wave height is necessary from previous time interval measurements. This paper presents a dynamic significant wave height prediction procedure based on the perceptron Kalman filtering concepts. Past measurements of significant wave height and wind speed variables are used for training the adaptive model and it is then employed to predict the significant wave height amounts for future time intervals from the wind speed measurements only. The verification of the proposed model is achieved through the dynamic significant wave height and wind speed time series plots, observed versus predicted values scatter diagram and the classical linear significant wave height models. The application of the proposed model is presented for a station in USA.     

Long-range dependence in the runoff time series of the most important Patagonian river draining to the Pacific Ocean

The Baker River is the largest free-flowing river in Chilean Patagonia. Long-range dependence (LRD), a recognised hydrological property of river runoff worldwide, was detected for the Baker River runoff time series. Analyses were conducted on a monthly scale between 1961 and 2015 using the fractal and multifractal detrended fluctuation analysis methodology. A long-range-dependent Hurst coefficient (H) equal to 0.94 was obtained. A scaling range, which is the signature of LRD, was detected for the Baker River runoff time series between 1 and 5.25 years. Baker River runoff showed a strong correlation (r?=?0.96) with the Antarctic Oscillation (AAO) Index during the 2007–2015 period. The high storage capacity of Lake General Carrera, the size of the Baker River basin area and the dynamics of AAO are proposed as main factors that contribute to the emergence of LRD in the Baker River runoff time series.     

Spark平台下的海表面温度并行预测算法

面对海量的海表面温度数据,如何使用大数据处理平台和新的处理技术来实时处理、分析并预测海表面温度数据,是一个亟待解决的问题。本文基于现阶段的时间序列方法和专家意见,首先,将类比合成方法引入到海表面温度预测应用中;其次,基于Spark平台提出了一种改进的快速DTW算法SparkDTW;最后,为了充分利用通过时间序列挖掘得到的信息,将SparkDTW与SVM相结合,提出了SparkDTW+SVM混合模型,为海表面温度预测的应用研究提供了较好的理论基础和技术支持。实验结果表明,SparkDTW算法预测精度优于SVM,提高了海表面温度预测效率,验证了将类比合成方法应用在海表面温度预测的可行性;SparkDTW+SVM在精度方面要优于SparkDTW和SVM,表明SVM模型能充分利用时间序列挖掘的信息,验证了SparkDTW+SVM在海表面温度预测的有效性。     

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.     

多向不规则波浪的确定性模拟

波浪波动时间过程及波列的模拟，对于开展实际波浪对于工程建筑物的作用具有重要的意义。本文采用线性叠加的单叠加模型，建立了多向不规则波浪的确定性模拟方法。基于理论模拟的规则波、单向不规则波和多向不规则波，验证了波浪确定性模拟方法的有效性。定性地对比分析了模拟波列和已知波列的一致性；定量地研究了模拟波浪在空间范围rr/L_s的误差分布情况(rr表示指定位置与给定位置的空间距离，L_s为有效波长)。并且建议，采用本文方法进行波浪确定性模拟时，最佳的浪高仪间距应小于0.12L_s。     

Forecasting of chlorophyll-a concentrations in South San Francisco Bay using five different models

Accurate and reliable eutrophication level forecasting models are necessary for characterizing complicated water quality processes in bays. In this study, the ability of coupled discrete wavelet transform (DWT) with artificial neural network (ANN) and multi linear regression (MLR) (WANN and WMLR), ANN, MLR and genetic algorithm-support vector regression (GA-SVR) models for chlorophyll-a level forecasting applications were considered. The data used to develop and validate the models were monthly chlorophyll-a (Chl-a) data recorded from January 1994 to December 2013 were obtained from the NO.36 station located in the South San Francisco bay, USA. In the proposed WANN and WMLR models, the observed time series of Chl-a were decomposed to sub time series at different scales by DWT. Afterwards, the sub time series were used as input data to the ANN and MLR systems to predict the 1 month ahead Chl-a. Also the genetic algorithm was linked to SVR models to search for the optimal SVR parameters. The relative performance of the proposed models was compared together and the results showed that the WANN models were found to provide more accurate monthly Chl-a forecasts compared to the other models. The determination coefficient was 0.87, −0.04, 0.31, −2.36 and 0.24 for the WANN, WMLR, ANN, MLR and GA-SVR models, respectively. In addition, the WANN model predicted extreme Chl-a values precisely. The results indicate that the WANN models are a promising new method for eutrophication level forecasting in bays such as those found in South San Francisco Bay.     

The Continuous Plankton Recorder survey and the North Atlantic Oscillation: Interannual- to Multidecadal-scale patterns of phytoplankton variability in the North Atlantic Ocean

At interannual to multidecadal time scales, much of the oceanographic and climatic variability in the North Atlantic Ocean can be associated with the North Atlantic Oscillation (NAO). While evidence suggests that there is a relationship between the NAO and zooplankton dynamics in the North Atlantic Ocean, the phytoplankton response to NAO-induced changes in the environment is less clear. Time series of monthly mean phytoplankton colour values, as compiled by the Continuous Plankton Recorder (CPR) survey, are analysed to infer relationships between the NAO and phytoplankton dynamics throughout the North Atlantic Ocean. While a few areas display highly significant (p < 0.05) trends in the CPR colour time series during the period 1948–2000, nominally significant (p < 0.20) positive trends are widespread across the basin, particularly on the continental shelves and in a transition zone stretching across the Central North Atlantic. When long-term trends are removed from both the NAO index and CPR colour time series, the correlation between them ceases to be significant. Several hypotheses are proposed to explain the observed variability in the CPR colour and its relationship with climate in the North Atlantic.     

一种改进的水下声学定位算法

通过分析讨论泰勒级数展开法和Chan算法的优缺点,提出了一种先由Chan算法解算求得初始参考值,再由泰勒级数展开法在该初始值处展开进行迭代求解的协同定位算法。通过实验,将本方法与传统定位算法(泰勒法)、Chan算法进行对比。结果表明,该方法无需额外提供初始值,解算出较好的结果的同时且能保持良好的时间效率。通过此方法,在实际生产作业中可节约定位时间,改善定位精度。     

基于图相似性匹配的海水溶解氧辅助评价方法

海水溶解氧评价的正确性对于维护海洋生态系统的稳定性具有重要意义。海水溶解氧数据属于非线性时间序列,可视图方法（时间序列转化为图）是分析这类数据较有效的方法,但仍存在未同时考虑数据的时间演变特性以及变量间相互影响的问题。针对现存问题,本研究提出基于图相似性匹配的海水溶解氧辅助评价方法。首先,同时考虑海温、盐度对海水溶解氧的影响以及数据的不可逆性,提出溶氧温盐 转移概率(dissolved oxygen temperature salinity-transition probability,DOTS-TP)有向可视图方法,实现了多变量时间序列到单变量溶解氧图的转化;然后,在将墨西哥湾溶解氧图作为评价参照的基础上,综合利用图的多层次信息,提出子图节点全局(subtree node global, SNG)图相似性匹配方法,通过计算SNG评价指数实现对海水溶解氧的辅助评价。实验结果表明DOTS-TP有向可视图方法能更准确地表达海水溶解氧信息,SNG图相似性匹配方法适用于所有海水溶解氧数据,并能得出正确的辅助评价结果。