Artificial neural network to translate offshore satellite wave data to coastal locations

Owing to the spatial averaging involved in satellite sensing, use of observations so collected is often restricted to offshore regions. This paper discusses a technique to obtain significant wave heights at a specified coastal site from their values gathered by a satellite at deeper offshore locations. The technique is based on the approach of Artificial Neural Network (ANN) of Radial Basis Function (RBF) and Feed-forward Back-propagation (FFBP) type. The satellite-sensed data of significant wave height; average wave period and the wind speed were given as input to the network in order to obtain significant wave heights at a coastal site situated along the west coast of India. Qualitative as well as quantitative comparison of the network output with target observations showed usefulness of the selected networks in such an application vis-à-vis simpler techniques like statistical regression. The basic FFBP network predicted the higher waves more correctly although such a network was less attractive from the point of overall accuracy. Unlike satellite observations collection of buoy data is costly and hence, it is generally resorted to fewer locations and for a smaller period of time. As shown in this study the network can be trained with samples of buoy data and can be further used for routine wave forecasting at coastal locations based on more permanent flow of satellite observations.     

基于海浪谱分解与重构的资料同化试验

针对有效波高资料提出一种海浪谱分解与重构的资料同化方案:利用历史时段内的有效波高观测资料和模式计算波高场,采用最优插值方法得到分析波高场;在WAVEWATCH-Ⅲ模式的波浪能量密度谱和有效波高分析值之间引入一个变异系数矩阵,描述模式的误差,以此为状态向量构建卡尔曼滤波系统,对分解过的海浪谱进行修正和重构,得到同化后的海浪谱初始场。利用美国阿拉斯加湾北部海域的7个浮标站进行同化和72 h预报试验,对连续1个月的预报结果进行统计表明:采用该同化方案后24 h预报结果的有效波高均方根误差比未同化的结果降低了0.13 m;同化方案对预报效果的影响可持续36 h左右,随着预报时效延长,同化的效果减弱。     

数值模式与统计模型相耦合的近岸海浪预报方法

针对数值模式和统计模型预报近岸海浪存在的局限性,构建了数值模式和统计模型相耦合的近岸海浪预报框架,在模式计算格点和近岸预报目标点之间定义一个海浪能量密度谱传递系数,通过经验正交函数分解和卡尔曼滤波方法建立传递系数的统计预报模型并与数值模式进行耦合。经过对近岸波浪观测站1a的预报试验表明:该方法能够提高近岸海浪有效波高预报精度,有效波高的均方根误差降低了约0.16m,平均相对误差降低约9%。进一步试验和分析发现,该方法的预报有效时间小于24h,将海浪能量密度谱经过分解后得到的基本模态反映了近岸波侯的主要特征,海浪能量密度谱传递系数的变化体现了波侯的季节变化特点。     

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.     

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.     

海南岛近岸养殖区台风浪预报技术研究

随着海南深水网箱养殖规模的不断扩大,海浪精细化预报的需求越来越紧迫。以海南岛周边海域为目标区域,基于近岸海洋模式ADCIRC(Advancedcirculationmodel)和海浪模式SWAN(Simulating WavesNearshore),建立了海南岛近岸养殖区台风浪数值预报系统。该系统采用非结构高分辨率网格,近岸分辨率达到了100m。选取2014年第9号超强台风"威马逊"(RAMMASUN)进行针对海南岛近岸养殖区的台风浪数值模拟后报。模拟结果与实测数据较为吻合。采用全球预报系统GFS(Global Forecast System)风场和气压场数据作为驱动场对2018年7月的一次热带风暴过程进行预报,48小时、24小时预报的有效波高和实测结果比较平均相对误差分别为20.75%和17.0%。总体来说,该模型的预报精度可以满足近岸养殖区台风浪预报业务的需求。     

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.     

Hindcasting wave spectra in finite‐depth waters

A model is described for hindcasting or forecasting waves in finite‐depth waters. The model is particularly applicable to coastal sites where the water is depth‐limited. The wave energy density spectrum is modelled in the frequency‐directional domain. For each spectral component a ray is defined along which wave energy propagates to reach the site. For sites exposed to the open ocean a background spectral wave model is required to provide input to the ray endpoints. Further growth and dissipation is then effected along the rays according to the local wind and water depth. The model was used to hindcast wave spectra over a period of 9 months for a site in the Canterbury Bight, New Zealand. The results were compared with measurements from a Waveridcr buoy at the site. The model succeeds in explaining about 40% of the variance in measured significant wave heights. However, the present application is handicapped by errors inherent in the background spectral model and in specifying the local wind.     

Hindcast of the wave conditions along the west Iberian coast

This paper describes the development of a wave prediction system for the west Iberian coast. The implemented wave prediction system is based on two state-of-the-art spectral wave models, WAM for the ocean area and SWAN for the nearshore. However, because of its extended geographical space the SWAN model will include some generation effects in the coarse SWAN simulations, complemented by wave transformation effects near the coast. The system was validated by means of extended hindcast runs in various regions belonging to the continental Portuguese coastal environment, which were compared with buoy data, focusing on the extreme energetic events and both direct comparisons and statistical results are presented.     

Evaluation of the wave transformation in an open bay with two spectral models

An evaluation of two state of the art phase averaged wave models for the transformation scale, SWAN and STWAVE, is carried out in the present work. The target area is the Obidos Bay located in the central part of the Portuguese continental nearshore. The wave input for the two models is provided by an offshore buoy. In order to compare the nearshore outputs of the wave models against in-situ measurements, a directional buoy and an ADCP, operating in intermediate water depth, are used. The wave parameters considered for comparisons are significant wave height, peak period and wave direction. Sensitivity analyses studies and evaluations in the spectral and geographical spaces concerning the results of the two models are also carried out in both intermediate and shallow water. The present study provides some information on the performances of the two wave models in different forcing conditions as well as on their sensitivity in relationship with various input parameters and some physical processes. STWAVE appears to be faster and more robust than SWAN, which on the other hand has more options and flexibility. In statistical terms the results are comparable.     

在Zebiak-Cane模式中观测MJO强迫对ENSO可预报性的影响, 第1部分:对最大预报误差的影响

With the observational wind data and the Zebiak-Cane model, the impact of Madden-Julian Oscillation (MJO) as external forcing on El Ni(n)o–Southern Oscillation (ENSO) predictability is studied. The obs...     

Evaluation of a high-resolution wave hindcast model SWAN for the West Mediterranean basin

This study aims to present an evaluation and implementation of a high-resolution SWAN wind wave hindcast model forced by the CFSR wind fields in the west Mediterranean basin, taking into account the recent developments in wave modelling as the new source terms package ST6. For this purpose, the SWAN model was calibrated based on one-year wave observations of Azeffoune buoy (Algerian coast) and validated against eleven wave buoys measurements through the West Mediterranean basin. For the calibration process, we focused on the whitecapping dissipation coefficient C_ds and on the exponential wind wave growth and whitecapping dissipation source terms. The statistical error analysis of the calibration results led to conclude that the SWAN model calibration corrected the underestimation of the significant wave height hindcasts in the default mode and improved its accuracy in the West Mediterranean basin. The exponential wind wave growth of Komen et al (1984) and the whitecapping dissipation source terms of Janssen (1991) with C_ds = 1.0 have been thus recommended for the western Mediterranean basin. The comparison of the simulation results obtained using this calibrated parameters against eleven measurement buoys showed a high performance of the calibrated SWAN model with an average scatter index of 30% for the significant wave heights and 19% for the mean wave period. This calibrated SWAN model will constitute a practical wave hindcast model with high spatial resolution (˜3 km) and high accuracy in the Algerian basin, which will allow us to proceed to a finer mesh size using the SWAN nested grid system in this area.     

Modeling of tropical cyclone winds and waves for emergency management

This paper compares three commonly used parametric models of tropical cyclone winds and evaluates their application in the wave model WAM. The parametric models provide surface wind fields based on best tracks of tropical cyclones and WAM simulates wave growth based on the wind energy input. The model package is applied to hindcast the wind and wave conditions of Hurricane Iniki, which directly hit the Hawaiian Island of Kauai in 1992. The parametric wind fields are evaluated against buoy and aircraft measurements made during the storm. A sensitivity analysis determines the spatial and spectral resolution needed to model the wave field of Hurricane Iniki. Comparisons of the modeled waves with buoy measurements indicate good agreement within the core of the storm and demonstrate the capability of the model package as a forecasting tool for emergency management.     

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.     

Wave prediction using wave rider position measurements and NARX network in wave energy conversion

Several control methods of wave energy converters (WECs) need prediction in the future of wave surface elevation. Prediction of wave surface elevation can be performed using measurements of surface elevation at a location ahead of the controlled WEC in the upcoming wave. Artificial neural network (ANN) is a robust data-learning tool, and is proposed in this study to predict the surface elevation at the WEC location using measurements of wave elevation at ahead located sensor (a wave rider buoy). The nonlinear autoregressive with exogenous input network (NARX NN) is utilized in this study as the prediction method. Simulations show promising results for predicting the wave surface elevation. Challenges of using real measurements data are also discussed in this paper.     

Use of advanced directional wave spectra analysis methods

Frequency-dependent cross-spectral parameters for pitch-roll buoy data and parameters that describe directional wave spectra based on a directional Fourier series are developed by the National Data Buoy Center (NDBC) and other organizations that collect wave data. The World Meteorological Organization (WMO) specifies wave data product formats in its Wave Observation (FM 65 WAVEOB) code. Other organizations, such as the US Army Corps of Engineers Field Wave Gaging Program (FWGP), have similar specifications. A directional Fourier series has poor directional resolution compared with advanced methods such as those based on maximum likelihood and maximum entropy. Transformations are developed for applying the advanced methods and working with directional wave information stored in the WMO's FM 65 WAVEOB code, the FWGP's Wave Data Analysis Standard (WDAS) format, and similar codes and formats. Using the transformations, a directional Fourier series expansion method, a Maximum Likelihood Method (MLM), and a Maximum Entropy Method (MEM) are each applied to 115 sets of NDBC directional wave data. The MEM and MLM provide better directional resolution, but the MEM produces artificial double peaks. There are considerable differences for the three used methods. The developed transformation equations enable wave data users to apply the method that best suits their applications.     

Validation of the WAMC4 wave model for the Black Sea

The present paper describes the set-up and application of the third-generation wave model — WAM Cycle 4 to the Black Sea. The wind fields are calculated by a regional atmosphere model (REMO), which was driven with the conditions from the global NCEP re-analysis project. These atmospheric data are used to force the state-of-the-art WAM model. The validation is done by comparison of wave model output against directional buoy measurements registered at three deep-water locations and wave gauge data taken at a point in intermediate depth near the Black Sea coast. The results reveal that agreement between modeled and measured data is satisfactory and the quality of the simulations increases under more energetic and severer wind and wave conditions. Following the validation, a 41-year wave hindcast was implemented spanning the period 1958–1998.     

Wave prediction in a port using a fully nonlinear Boussinesq wave model

A wave forecasting system using FUNWAVE-TVD which is based on the fully nonlinear Boussinesq equations by Chen(2006) was developed to provide an accurate wave prediction in the Port of Busan, South Korea. This system is linked to the Korea Operational Oceanographic System(KOOS) developed by Park et al.(2015). The computational domain covers a region of 9.6 km×7.0 km with a grid size of 2 m in both directions, which is sufficient to resolve short waves and dominant sea states. The total number of grid points exceeds 16 millions,making the model computational expensive. To provide real-time forecasting, an interpolation method, which is based on pre-calculated results of FUNWAVE-TVD and SWAN forecasting results at the FUNWAVE-TVD offshore boundary, was used. A total of 45 cases were pre-calculated, which took 71 days on 924 computational cores of a Linux cluster system. Wind wave generation and propagation from the deep water were computed using the SWAN in KOOS. SWAN results provided a boundary condition for the FUNWAVE-TVD forecasting system. To verify the model, wave observations were conducted at three locations inside the port in a time period of more than 7 months. A model/model comparison between FUNWAVE-TVD and SWAN was also carried out. It is found that, FUNWAVE-TVD improves the forecasting results significantly compared to SWAN which underestimates wave heights in sheltered areas due to incorrect physical mechanism of wave diffraction, as well as large wave heights caused by wave reflections inside the port.     

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。