一个中国边缘海的风-浪-流预报系统

海洋预报是进行海上活动的安全保障,海洋预报系统技术已经成为现代海洋气象业务的技术支撑。海洋观测、数据同化、数值模拟和高性能计算机等技术的进步极大地推动着海洋业务化预报的发展。采用大气数值模式（WRF）、海洋数值模式（CROCO）和海浪数值模式（SWAN）的多模式高分辨率离线耦合方式,添加南京信息工程大学“海洋数值模拟与观测实验室”团队自主研发的一系列海洋模式参数化方案,包括浪致混合参数化方案、亚中尺度参数化方案、海山诱导混合参数化方案以及涡旋诱导的沿等密度面和跨等密度面混合参数化方案,并通过同化技术和最新的人工智能技术与观测资料相结合,构建一种面向中国边缘海的风浪流多参数耦合预报系统,用于海上风电功率的预报和其他海洋灾害预警。实际观测资料的验证表明,该预报系统能较准确地模拟海上风场、海流、海温、波浪、潮汐等海洋气象要素。同时实现了按需实时可视化全景展示。     

Wave height forecasting in Dayyer, the Persian Gulf

Forecasting of wave parameters is necessary for many marine and coastal operations. Different forecasting methodologies have been developed using the wind and wave characteristics. In this paper, artificial neural network (ANN) as a robust data learning method is used to forecast the wave height for the next 3, 6, 12 and 24 h in the Persian Gulf. To determine the effective parameters, different models with various combinations of input parameters were considered. Parameters such as wind speed, direction and wave height of the previous 3 h, were found to be the best inputs. Furthermore, using the difference between wave and wind directions showed better performance. The results also indicated that if only the wind parameters are used as model inputs the accuracy of the forecasting increases as the time horizon increases up to 6 h. This can be due to the lower influence of previous wave heights on larger lead time forecasting and the existing lag between the wind and wave growth. It was also found that in short lead times, the forecasted wave heights primarily depend on the previous wave heights, while in larger lead times there is a greater dependence on previous wind speeds.     

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。     

A simple new shoreline change model

A simple new shoreline change model has been developed and calibrated/evaluated with several sets of high quality field data. The model is based upon the general observation that the shoreline tends to approach an equilibrium position exponentially with time when subjected to constant forcing. The model represents the shoreline response to cross-shore processes only and is extremely efficient, requiring only readily obtainable wave and water-level data as input. Shoreline changes are forced by changes in the local water surface elevation due to a combination of local tide, storm surge and wave-induced setup. The model contains three adjustable parameters, representing a baseline condition from which equilibrium shoreline displacements are calculated, and two rate constants, all of which are evaluated by minimizing the error between model hindcasts and several historical shoreline data sets. Several possible forms for the rate parameters, incorporating local wave and sediment properties, were considered and evaluated. At most sites, the model has proven successful in predicting large-scale shoreline response to local water level and wave forcing. The combination of model accuracy and efficiency, along with the minimal data required to drive the model, make it a potentially useful tool in many coastal engineering applications. As more high-quality shoreline, wave and water-level data sets become available, significant improvements can be made in the determination of the rate parameter governing the time scale of the beach response.     

Forecasting and hindcasting waves with the SWAN model in the Southern California Bight

The Naval Research Laboratory created a wave forecasting system in support of the Nearshore Canyon Experiment (NCEX) field program. The outer nest of this prediction system encompassed the Southern California Bight. This forecasting system is described in this paper, with analysis of results via comparison to the extensive buoy network in the region. There are a number of potential errors, two of which are poor resolution of islands in the Bight—which have a strong impact on nearshore wave climate—and the use of the stationary assumption for computations. These two problems have straightforward solutions, but the solutions are computationally expensive, so an operational user must carefully consider their cost. The authors study the impact of these two types of error (relative to other errors, such as error in boundary forcing) using several hindcasts performed after the completion of NCEX. It is found that, with buoy observations as ground truth, the stationary assumption leads to a modest increase in root-mean-square error; this is due to relatively poor prediction of the timing of swell arrivals and local sea growth/decay. The model results are found to be sensitive to the resolution of islands; however, coarse resolution does not incur an appreciable penalty in terms of error statistics computed via comparison to buoy observations, suggesting that other errors dominate. Inaccuracy in representation of the local atmospheric forcing likely has a significant impact on wave model error. Perhaps most importantly, the accuracy of directional distribution of wave energy at the open ocean boundaries appears to be a critical limitation on the accuracy of the model-data comparisons inside the Bight.     

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.     

A comparison of weakly and fully non-linear models of the shoaling of a solitary internal wave

This study investigates the behaviour of internal solitary waves crossing a continental slope in the presence of a seasonal thermocline. Comparisons are made between a fully non-linear computational fluid dynamics (CFD) model, and weakly non-linear theory. Previous observations suggested that the amplitudes of solitary waves are capped as they pass across the continental slope, which may be due to laminar dynamics, or due to the effect of turbulence. Across the continental slope, CFD and second order variable depth KdV (vEKdV) predictions agree well with observations of a limited change in solitary wave amplitude. First order variable depth KdV theory overpredicts the final amplitude significantly. In terms of the wave shape, the CFD modeled wave changes from a KdV shape in deep water towards an EkdV solution in shallow water, as observations suggest. The phase speed of the CFD and vEKdV waves are similar to that observed in waters of 400–500 m deep, but are slightly lower than observed in 140 m depth. CFD predictions using a standard k, turbulence model showed that turbulence had little effect on the amplitude. These preliminary results indicate that in this situation wave capping is due to laminar, large amplitude solitary wave dynamics and is independent of turbulent mixing.     

Verification of an operational ocean circulation-surface wave coupled forecasting system for the China's seas

An operational ocean circulation-surface wave coupled forecasting system for the seas off China and adjacent areas(OCFS-C) is developed based on parallelized circulation and wave models. It has been in operation since November 1, 2007. In this paper we comprehensively present the simulation and verification of the system, whose distinguishing feature is that the wave-induced mixing is coupled in the circulation model. In particular, with nested technique the resolution in the China's seas has been updated to(1/24)° from the global model with(1/2)°resolution. Besides, daily remote sensing sea surface temperature(SST) data have been assimilated into the model to generate a hot restart field for OCFS-C. Moreover, inter-comparisons between forecasting and independent observational data are performed to evaluate the effectiveness of OCFS-C in upper-ocean quantities predictions, including SST, mixed layer depth(MLD) and subsurface temperature. Except in conventional statistical metrics, non-dimensional skill scores(SS) is also used to evaluate forecast skill. Observations from buoys and Argo profiles are used for lead time and real time validations, which give a large SS value(more than 0.90). Besides, prediction skill for the seasonal variation of SST is confirmed. Comparisons of subsurface temperatures with Argo profiles data indicate that OCFS-C has low skill in predicting subsurface temperatures between 100 m and 150 m. Nevertheless, inter-comparisons of MLD reveal that the MLD from model is shallower than that from Argo profiles by about 12 m, i.e., OCFS-C is successful and steady in MLD predictions. Validation of 1-d, 2-d and 3-d forecasting SST shows that our operational ocean circulation-surface wave coupled forecasting model has reasonable accuracy in the upper ocean.     

Soft computing approach for real-time estimation of missing wave heights

This paper presents soft computing approach for estimation of missing wave heights at a particular location on a real-time basis using wave heights at other locations. Six such buoy networks are developed in Eastern Gulf of Mexico using soft computing techniques of Artificial Neural Networks (ANN) and Genetic Programming (GP). Wave heights at five stations are used to estimate wave height at the sixth station. Though ANN is now an established tool in time series analysis, use of GP in the field of time series forecasting/analysis particularly in the area of Ocean Engineering is relatively new and needs to be explored further. Both ANN and GP approach perform well in terms of accuracy of estimation as evident from values of various statistical parameters employed. The GP models work better in case of extreme events. Results of both approaches are also compared with the performance of large-scale continuous wave modeling/forecasting system WAVEWATCH III. The models are also applied on real time basis for 3 months in the year 2007. A software is developed using evolved GP codes (C++) as back end with Visual Basic as the Front End tool for real-time application of wave estimation model.     

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.     

Forecast of wave run-up on coastal structure using offshore wave forecast data

In this paper, first we introduce the wave run-up scale which describes the degree of wave run-up based on observed sea conditions near and on a coastal structure. Then, we introduce a simple method which can be used for daily forecast of wave run-up on a coastal structure. The method derives a multiple linear regression equation between wave run-up scale and offshore wind and wave parameters using long-term photographical observation of wave run-up and offshore wave forecasting model results. The derived regression equation then can be used for forecasting the run-up scale using the offshore wave forecasting model results. To test the implementation of the method, wave run-up scales were observed at four breakwaters in the East Coast of Korea for 9 consecutive months in 2008. The data for the first 6 months were used to derive multiple linear regression equations, which were then validated using the run-up scale data for the remaining 3 months and the corresponding offshore wave forecasting model results. A comparison with an engineering formula for wave run-up is also made. It is found that this method can be used for daily forecast and warning of wave run-up on a coastal structure with reasonable accuracy.     

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.     

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。     

Development of a skill assessment tool for the Korea operational oceanographic system

A standard skill assessment(SA) tool was developed and implemented to evaluate the performance of operational forecast models in the Korea operational oceanographic system.The SA tool provided a robust way to assess model skill in the system by comparing predictions and observations,and involved the computation of multiple skill metrics including correlation and error skills.User-and system-based acceptance criteria of skill metrics were applied to determine whether predictions were acceptable for the system.To achieve this,the tool produced a time series comparison plot,a skill score table,and an advanced summarized diagram to effectively demonstrate the multiple skill scores.Moreover,the SA was conducted to evaluate both atmospheric and hydrodynamic forecast variables.For the atmospheric variables,acceptable error criteria were preferable to acceptable correlation criteria over short timescales,since the mean square error overwhelmed the observation variance.Conversely,for the hydrodynamic variables,acceptable root mean square percentage error(e.g.,p erms) criteria were preferable to acceptable error(e.g.,erms) criteria owing to the spatially variable tidal intensity around the Korean Peninsula.Furthermore,the SA indicated that predetermined acceptance error criteria were appropriate to satisfy a target central frequency(fc) for which errors fell within the specified limits(i.e.,the fc equals 70%).     

Estimation of Longshore Sediment Transport Rate for a Typical Pocket Beach Along West Coast of India

Estimation of Longshore Sediment Transport Rate (LSTR) in the littoral zone is essential for managing and developing any coastal zone. Numerical models are powerful tool to understand and investigate various processes responsible for LSTR in a systematic manner since direct measurement of LSTR is a difficult task. A one dimensional LITDRIFT model was implemented along the Vengurla coast for estimation of LSTR and to analyze the sensitivity of each input parameters towards the computation of LSTR. Major input parameters required for the estimation of LSTR are nearshore wave climate, bathymetry, and sediment characteristics. The nearshore wave data at a depth of 15 m were collected using wave rider buoy in 2015 is used in the present study. Field observations were carried out to survey the regional bathymetry and sediment characteristics. Annual net LSTR along Vengurala coast is relatively less and varies from ?7778 to ?9015 m³ with an average of ?8511 m³. The net direction of LSTR is towards south and gross LSTR is 1.18 × 10⁵ m³/year. The LSTR reveals more sensitivity to coastline orientation and wave direction. A 1° changes in coastline angle and wave direction causes approximately 3000 m³/month difference in LSTR. Moreover, wave height, wave period, and bed roughness has an important role for controlling LSTR. The model results help to identify the role of each parameter contributing towards LSTR estimation and a similar model approach can be applied to other coasts for estimating LSTR in an accurate way.     

卫星高度计波高数据同化对西北太平洋海浪数值预报的影响评估

通过数值试验验证卫星高度计波高数据同化对西北太平洋3 d海浪预报的改进效果。驱动海浪模式的强迫场采用国家海洋环境预报中心基于MM5模式预报的风场,波高数据同化使用的观测数据是Jason-1卫星高度计有效波高。用最优插值数据同化方法获得海浪有效波高的最优估计并重构相应的海浪方向谱,以此为初始场进行为期3 d的数值预报试验。与没有同化的预报进行了比较和分析,结果表明卫星高度计海浪数据同化对0~72 h预报有不同程度的明显改善,改进程度随预报时效的增加而减少。     

Assimilating temperature and salinity profile observations using an anisotropic recursive filter in a coastal ocean model

In order to improve the ocean forecasting in the North Sea and Baltic Sea, an assimilation scheme based on a bottom-topography-dependent anisotropic recursive filter has been used in this study. This scheme can stretch or flatten the shape of a local representative contour surface of the background error covariance function into the form of an ellipse. Furthermore, the computing efficiency has been largely improved due to implicit computation of the background error covariance. A two-month experiment has been used for verifying the impact of assimilating ocean profile observations on ocean forecasting. The results indicate that the use of temperature and salinity profiles can largely improve the oceanic forecasting. The root mean square differences between the forecasts and observations for temperature and salinity have been reduced by 36% and 18% in the experiment period, respectively. Moreover, it is found that the anisotropic recursive filter approach is especially efficient in areas with complex coastlines and sharp fronts, e.g., inner Danish waters. The results also show that the propagation of observation information from an observation position to its neighboring grid points is closely related to currents.     

An evaluation of model estimates of ocean wave directions

The potential accuracy of local models is investigated to determine the mean direction of waves from the time history of locally observed significant wave height (or peak frequency) and locally observed wind. This is done by comparing results of such models with observations at a location in the southern North Sea for a period of six weeks. The model results are also compared with results of two synoptic models which require large scale wind information to estimate the local mean wave direction.For significant wave heights larger than 1.5 m the rms-error of the estimated mean wave direction was about 30° for the best performing local model and about 15° for the best performing synoptic model.     

南海及邻近海域大气-海浪-海洋耦合精细化数值预报系统研制

本文主要介绍了南海及邻近海域大气-海浪-海洋耦合精细化数值预报系统的研制概况。预报区域为99°E~135°E,15°S~45°N,包括渤海、黄海、东海和南海及其周边海域。为了给耦合预报模式提供较准确的预报初始场,在预报开始之前,分别进行了海浪模式和海洋模式的前24小时同化后报模拟。海浪模式和海洋模式都采用了集合调整Kalman滤波同化方法,海浪模式同化了Jason-2有效波高数据;海洋模式同化了SST数据、MADT数据和ARGO剖面数据。为了改进海洋温度和盐度的模拟,我们在海洋模式的垂向混合方案中引入波致混合和内波致混合的作用。预报系统的运行主要包括两个阶段,首先海浪模式和海洋模式进行了2014年1月至2015年10月底的同化后报模拟,强迫场源自欧洲气象中心的六小时的再分析数据产品。然后耦合预报系统将同化后报模拟的结果作为初始场进行了14个月的耦合预报。预报产品包括大气产品（气温、风速风向、气压等）、海浪产品（有效波高和波向等）、海流产品（温度、盐度和海流等）。一系列观测资料的检验比较表明该大气-海浪-海洋耦合精细化数值预报系统的预报结果较为可靠,可以为南海及周边海洋资源开发和安全保障提供数据和信息产品服务。     

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.