Derivation of coastal wind and wave parameters from offshore measurements of TOPEX satellite using ANN

The paper discusses an artificial neural network (ANN) approach to project information on wind speed and waves collected by the TOPEX satellite at deeper locations to a specified coastal site. The observations of significant wave heights, average wave period and wind speed at a number of locations over a satellite track parallel to a coastline are used to estimate corresponding values of these three parameters at the coastal site of interest. A combined network involving an input and output of all the three parameters, viz., wave height, period and wind speed instead of separate networks for each one of these variables was found to be necessary in order to train the network with sufficient flexibility. It was also found that network training based on statistical homogeneity of data sets is essential to obtain accurate results. The problem of modeling wind speeds that are always associated with very high variations in their magnitudes was tackled in this study by imparting training in an innovated manner.     

Stochastic time-series simulation of wave parameters using ship observations

A stochastic simulation technique was used with ship wave observations, which form the largest world-wide data base of wave information. Twenty years of wave parameter (height, period, and direction) observations from the Comprehensive Ocean–Atmosphere Data Set (COADS) were used as the input data. Simulations were compared to four years of wave parameters from a National Data Buoy Center (NDBC) data buoy near Monterey Bay, CA. The comparisons are satisfactory with differences mainly caused by biases between ship observations and buoy data. The stochastic simulation technique is attractive because it is computationally efficient and few decisions are required for its application. The applied techniques can be employed with global COADS data to simulate wave conditions at many world-wide locations where measurements and hindcasts by computer models do not exist.     

Real-time wave forecasting using genetic programming

The forecasting of ocean waves on real-time or online basis is necessary while carrying out any operational activity in the ocean. In order to obtain forecasts that are station-specific a time-series-based approach like stochastic modeling or artificial neural network was attempted by some investigators in the past. This paper presents an application of a relatively new soft computing tool called genetic programming for this purpose. Genetic programming is an extension of genetic algorithm and it is suited to explore dependency between input and output data sets. The wave rider buoy measurements available at two locations in the Gulf of Mexico are analyzed. The forecasts of significant wave heights are made over lead times of 3, 6, 12 and 24 h. The sample size belonged to a period of 15 years and it included an extensive testing period of 5 years. The forecasts made by the approach of genetic programming indicated that it can be regarded as a promising tool for future applications to ocean predictions.     

推算波浪多年一遇波高的新方法

采用卫星遥感推算多年一遇波高的新方法，对中国近海多年一遇波高进行推算研究。运用卫星遥感散射计得到的风资料推算了整个渤海区域波浪多年一遇的波高分布。通过与有实测资料4个点推算的结果比较表明：卫星资料和实测计算的误差随着重现期的增大而减小，最大误差百年和50年一遇波高为20cm；而百年和50年一遇波高正是工程中最为关心的，说明运用卫星遥感散射计风资料推算渤海多年一遇波高可行且结果合理。本研究方法为进一步运用和完善卫星遥感资料推算中国近海多年一波高场提供了一条新途径。     

Use of nautical radar as a wave monitoring instrument

Common marine X-Band radars can be used as a sensor to survey ocean wave fields. The wave field images provided by the radars are sampled and analysed by a wave monitoring system (called WaMoS II) developed by the German research institute GKSS. This measuring system can be mounted on a ship, on offshore stations or at coastal locations. The measurement is based on the backscatter of microwaves from the ocean surface, which is visible as ‘sea clutter' on the radar screen. From this observable sea clutter, a numerical analysis is carried out. The unambiguous directional wave spectrum, the surface currents and sea state parameters such as wave periods, wave lengths, and wave directions can be derived. To provide absolute wave heights, the response of the nautical radar must be calibrated. Similar to the wave height estimations for Synthetic Aperture Radars, the so-called ‘Signal to Noise Ratio' leads to the determination of the significant wave height (H_S). In this paper, WaMoS II results are compared with directional buoy data to show the capabilities of nautical microwave radars for sea state measurements.     

基于10 米浮标载体的锚链式剖面观测系统实践之一——自容式采集方式

为了对我国近海海域进行剖面观测,获取到多层位水体的多要素观测数据,尝试依托中国近海海洋观测研究网络东海站10 m浮标系统的锚系,采用在锚系上挂载自容式组合传感器方式,获取到8个观测周期共412 d的有效剖面观测数据,观测层位分布于10,20,30 m 3个水层;通过结合浮标获取的表层温度数据与3个水层的对比分析表明,锚链式剖面观测方案在实用性和可推广性方面具有显著优势,是基于海面浮标载体进行剖面观测的有益尝试,可在我国构建的海洋浮标观测网络上进行广泛应用,将为我国近海海洋科学研究提供弥足珍贵的水面-水体全序列系统观测数据资料,从而为近海海洋科学研究取得突破性进展提供重要的数据支撑。     

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.     

Sensitivity of wave model predictions to wind fields in the Western Mediterranean sea

The paper compares the wave hindcast in the Western Mediterranean sea using the reanalysis wind fields from HIPOCAS and ERA-40 from ECMWF for November 2001. The study has concentrated on the Mediterranean coast of Spain where there are known difficulties with the wind and wave modelling. Two winter storms have been compared. The main differences between the significant wave heights using the ERA-40 reanalysis (ECMWF) and HIPOCAS reanalysis winds were observed to increase when moving southwards in the geographical domain at the offshore locations. Systematic negative biases of Hs were obtained with the ERA-40 data at all the coastal locations analyzed, whereas positive biases are typical for the HIPOCAS reanalysis. For offshore and coastal locations when using the ERA-40 data the Hs biases increased moving to South, while this pattern was not so clear for the HIPOCAS data. The inconsistencies in the comparisons of modelled waves against measurements seem to be associated with the quality of the wind fields.     

Nearshore directional wave measurements by surface-following buoy and acoustic Doppler current profiler

Directional energy spectra of nearshore surface waves were measured for a 3-year period (2004–2007) at a site with mean depth 14 m and mean tidal range 2.1 m. Triaxys surface-following wave buoys reported hourly directional wave energy spectra and wave parameters near the offshore end of the Savannah River Entrance Channel, Georgia, USA. An acoustic Doppler current profiler (ADCP) was located beside the wave buoy for 3 months. Directional and non-directional surface wave energy spectra and the corresponding bulk wave parameters (height, period, and direction) are compared for the two systems. Most parameters derived from the spectra agree closely; the most significant differences were found at the upper and lower frequency measurement limits, where signal-to-noise ratios were lower. The wave buoy consistently reports a small amount of energy below 0.05 Hz that does not appear in the ADCP-derived spectra and does not appear to be related to the mooring system. This leads to larger mean and peak periods reported by the buoy. All directional spectra were computed using the Maximum Entropy Method for both instruments, but the buoy, with spectra derived from six independent time series, provides lower directional resolving power than the ADCP, which utilizes twelve time series. Both systems gave similar results defining mean and peak wave directions, with the primary difference being that the ADCP indicates energy to be more tightly concentrated around the peak direction.     

Prediction and assimilation of surf-zone processes using a Bayesian network: Part II: Inverse models

A Bayesian network model has been developed to simulate a relatively simple problem of wave propagation in the surf zone (detailed in Part I). Here, we demonstrate that this Bayesian model can provide both inverse modeling and data-assimilation solutions for predicting offshore wave heights and depth estimates given limited wave-height and depth information from an onshore location. The inverse method is extended to allow data assimilation using observational inputs that are not compatible with deterministic solutions of the problem. These inputs include sand bar positions (instead of bathymetry) and estimates of the intensity of wave breaking (instead of wave-height observations). Our results indicate that wave breaking information is essential to reduce prediction errors. In many practical situations, this information could be provided from a shore-based observer or from remote-sensing systems. We show that various combinations of the assimilated inputs significantly reduce the uncertainty in the estimates of water depths and wave heights in the model domain. Application of the Bayesian network model to new field data demonstrated significant predictive skill (R² = 0.7) for the inverse estimate of a month-long time series of offshore wave heights. The Bayesian inverse results include uncertainty estimates that were shown to be most accurate when given uncertainty in the inputs (e.g., depth and tuning parameters). Furthermore, the inverse modeling was extended to directly estimate tuning parameters associated with the underlying wave-process model. The inverse estimates of the model parameters not only showed an offshore wave height dependence consistent with results of previous studies but the uncertainty estimates of the tuning parameters also explain previously reported variations in the model parameters.     

Near-shore swell estimation from a global wind-wave model: Spectral process,linear, and artificial neural network models

Estimation of swell conditions in coastal regions is important for a variety of public, government, and research applications. Driving a model of the near-shore wave transformation from an offshore global swell model such as NOAA WaveWatch3 is an economical means to arrive at swell size estimates at particular locations of interest. Recently, some work (e.g. Browne et al. [Browne, M., Strauss, D., Castelle, B., Blumenstein, M., Tomlinson, R., 2006. Local swell estimation and prediction from a global wind-wave model. IEEE Geoscience and Remote Sensing Letters 3 (4), 462–466.]) has examined an artificial neural network (ANN) based, empirical approach to wave estimation. Here, we provide a comprehensive evaluation of two data driven approaches to estimating waves near-shore (linear and ANN), and also contrast these with a more traditional spectral wave simulation model (SWAN). Performance was assessed on data gathered from a total of 17 near-shore locations, with heterogenous geography and bathymetry, around the continent of Australia over a 7 month period. It was found that the ANNs out-performed SWAN and the non-linear architecture consistently out-performed the linear method. Variability in performance and differential performance with regard to geographical location could largely be explained in terms of the underlying complexity of the local wave transformation.     

Comparison of wind and wave measurements and models in the Western Mediterranean Sea

We have hindcast the wind and wave conditions in the Mediterranean Sea for two one month periods. Four different meteorological models and three different wave models have been used. The results have been compared with satellite and buoy wind and wave observations.Several conclusions concerning both the instruments and the models have been derived. The quality of both wind and wave results has been assessed. Close to the coasts high resolution, nested wave models are required for sufficient reliability.A wave threshold analysis suggests a sufficient reliability only off the coast, with a substantial decrease for low wave heights.     

Estimation of extreme wave heights using GEOSAT measurements

Satellite technology has yielded a large database of global ocean wave heights which may be used for engineering applications. However, the sampling protocol used by the satellite leads to some difficulties in making use of these data for practical applications. These difficulties and techniques to estimate extreme wave heights using satellite measurements are discussed. Significant wave heights for a 50-year return period are estimated using GEOSAT measurements for several regions around North America. Techniques described here may be used for estimation of wave heights associated with any specified return interval in regions where buoy data are not readily available.     

Estimation of extreme wave heights using GEOSAT measurements

Satellite technology has yielded a large database of global ocean wave heights which may be used for engineering applications. However, the sampling protocol used by the satellite leads to some difficulties in making use of these data for practical applications. These difficulties and techniques to estimate extreme wave heights using satellite measurements are discussed. Significant wave heights for a 50-year return period are estimated using GEOSAT measurements for several regions around North America. Techniques described here may be used for estimation of wave heights associated with any specified return interval in regions where buoy data are not readily available.     

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.     

Data assimilation of partitioned HF radar wave data into Wavewatch III

In this study the assimilation of HF radar data into a high resolution, coastal Wavewatch III model is investigated. An optimal interpolation scheme is used to assimilate the data and the design of a background error covariance matrix which reflects the local conditions and difficulties associated with a coastal domain is discussed. Two assimilation schemes are trialled; a scheme which assimilates mean parameters from the HF radar data and a scheme which assimilates partitioned spectral HF radar data. This study demonstrates the feasibility of assimilating partitioned wave data into a coastal domain. The results show that the assimilation schemes provide satisfactory improvements to significant wave heights but more mixed results for mean periods. The best improvements are seen during a stormy period with turning winds. During this period the model is deficient at capturing the change in wave directions and the peak in the waveheights, while the high sea state ensures good quality HF radar data for assimilation. The study also suggests that there are both physical and practical advantages to assimilating partitioned wave data compared to assimilating mean parameters for the whole spectrum.     

Neural networks for wave forecasting

The physical process of generation of waves by wind is extremely complex, uncertain and not yet fully understood. Despite a variety of deterministic models presented to predict the heights and periods of waves from the characteristics of the generating wind, a large scope still exists to improve on the existing models or to provide alternatives to them. This paper explores the possibility of employing the relatively recent technique of neural networks for this purpose. A simple 3-layered feed forward type of network is developed to obtain the output of significant wave heights and average wave periods from the input of generating wind speeds. The network is trained with different algorithms and using three sets of data. The results show that an appropriately trained network could provide satisfactory results in open wider areas, in deep water and also when the sampling and prediction interval is large, such as a week. A proper choice of training patterns is found to be crucial in achieving adequate training.     

Wave model predictions in the Black Sea: Sensitivity to wind fields

This paper evaluates the impact of using different wind field products on the performance of the third generation wave model SWAN in the Black Sea and its capability for predicting both normal and extreme wave conditions during 1996. Wind data were obtained from NCEP CFSR, NASA MERRA, JRA-25, ECMWF Operational, ECMWF ERA40, and ECMWF ERA-Interim. Wave data were obtained in 1996 at three locations in the Black Sea within the NATO TU-WAVES project. The quality of wind fields was assessed by comparing them with satellite data. These wind data were used as forcing fields for the generation of wind waves. Time series of predicted significant wave height (H_mo), mean wave period (T_m02), and mean wave direction (DIR) were compared with observations at three offshore buoys in the Black Sea and its performance was quantified in terms of statistical parameters. In addition, wave model performance in terms of significant wave height was also assessed by comparing them against satellite data.The main scope of this work is the impact of the different available wind field products on the wave hindcast performance. In addition, the sensitivity of wave model forecasts due to variations in spatial and temporal resolutions of the wind field products was investigated. Finally, the impact of using various wind field products on predicting extreme wave events was analyzed by focussing on storm peaks and on an individual storm event in October 1996. The numerical results revealed that the CFSR winds are more suitable in comparison with the others for modelling both normal and extreme events in the Black Sea. The results also show that wave model output is critically sensitive to the choice of the wind field product, such that the quality of the wind fields is reflected in the quality of the wave predictions. A finer wind spatial resolution leads to an improvement of the wave model predictions, while a finer temporal resolution in the wind fields generally does not significantly improve agreement between observed and simulated wave data.     

2016年苏北近海风场和波浪场多时间尺度变化的数值模拟研究

利用高分辨率的大气和波浪数值模式,模拟了2016年苏北近海的风场和波浪场,并与卫星高度计资料、散射计风场、再分析资料以及实测浮标资料进行了比较,验证了模式的准确性。基于这套模式结果,系统地分析了江苏近海的风场和波浪场的多时间尺度变化:季节变化、日变化以及季节内变化(台风、寒潮)。分析结果表明:苏北近海海域的风速、有效波高和涌浪在冬季和秋季较大、春季和夏季较小;冬季盛行西北风,常浪向为西北向,夏季盛行东南风,常浪向为东南向。风场和波浪场还具有显著的日变化特征,且日变化存在季节变化规律,离岸越近海域日变化特征越明显。同时,江苏近海还会经历季节内尺度的强天气过程的影响,比如台风和寒潮。     

Real-time wave forecasts off the western Indian coast

The wave observations at three locations off the west coast of India have been analyzed using artificial neural network (ANN) to obtain forecasts of significant wave heights at intervals of 3, 6, 12 and 24 h. The most appropriate training method requiring an input of four observations spread over previous 24 h has been selected after considerable trials. Further, the networks are trained after filling in the missing information. Larger gaps in data are filled in using spatial mapping involving observations at nearby locations, while relatively smaller gaps are accounted for by the statistical technique of multiple regressions in temporal mode. It is found that by doing so the long-interval forecasting is tremendously improved, with corresponding accuracy levels becoming close to those of the short-interval forecasts. If the amount of gaps is restricted to around 2% per year or so it is possible to obtain 12 h ahead forecasts with 0.08 m accuracy on an average and 24 h ahead forecast with a mean accuracy of 0.13 m. However, in harsher environments the prediction accuracy can change.