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WAVEWATCH和SWAN嵌套模拟台风浪场的结果分析 总被引：1，自引：0，他引：1
利用WAVEWATCH和SWAN嵌套模拟2007年8月墨西哥湾飓风迪安的波浪场.将QSCAT/NCEP混合风场与台风模型风场合成为背景风场.修改WAVEWATCH和SWAN嵌套接口以使WAVEWATCH和SWAN2种海浪预报模式能够有效地嵌套运行.利用WAVEWATCH和SWAN嵌套模拟飓风迪安的波浪场,采用浮标资料检验模拟结果,以验证WAVEWATCH和SWAN模拟的准确性及修改后嵌套接口的可用性.结果表明,修改嵌套接口之后模式运行平稳,2种模式的结果与浮标及高度计观测数据均基本吻合.嵌套模拟结果好于单纯使用WAVEWATCH模拟的结果,体现了利用2种模式嵌套模拟台风浪场的科学性. 相似文献
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建立基于四阶完全非线性Boussinesq水波方程的二维波浪传播数值模型。采用Kennedy等提出的涡粘方法模拟波浪破碎。在矩形网格上对控制方程进行离散,采用高精度的数值格式对离散方程进行数值求解。对规则波在具有三维特征地形上的传播过程进行了数值模拟,通过数值模拟结果与实验结果的对比,对所建立的波浪传播模型进行了验证。同时,为了考察非线性对波浪传播的影响,给出和上述模型具有同阶色散性、变浅作用性能但仅具有二阶完全非线性特征的波浪模型的数值结果。通过对比两个模型的数值结果以及实验数据,讨论非线性在波浪传播过程中的作用。研究结果表明,所建立的Boussinesq水波方程在深水范围内不但具有较精确的色散性和变浅作用性能,而且具有四阶完全非线性特征,适合模拟波浪在近岸水域的非线性运动。 相似文献
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海岸湿地是近海地区重要的生态系统,由于潮流、波浪尤其是非连续水流与植被的相互作用,导致该海域的水动力环境复杂多变。本文发展了一个深度平均二维波流耦合数学模型,模拟湿地海域波浪和波生沿岸流的运动特性。水动力模型中植物拖曳力作为源项放入动量方程中,在波浪作用量平衡方程增加波能耗散项用于解释水生植物对波浪产生的阻力作用。在动态耦合模型中,波浪模型为潮流模型提供波浪辐射应力、波高、波浪周期等数据信息,潮流模型为波浪模型提供计算的水位和流速,可以达到双向动态耦合。本文发展的波流耦合模型通过三个实验室试验数据加以验证,计算结果和实验数据吻合较好,在波浪、波生流和植物迭加条件下,所建模型能够有效地模拟波浪、沿岸流等不同现象。 相似文献
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利用时域高阶边界元方法建立了模拟极限波浪运动的完全非线性数值模型,其中自由水面满足完全非线性自由水面条件.采用半混合欧拉-拉格朗日方法追踪流体瞬时水面,运用四阶Runge-Kutta方法更新下一时间步的波面和速度势,同时应用镜像格林函数消除水槽两个侧面和底面上的积分.研究中利用波浪聚焦的方法产生极限波浪,并且在水槽中开展了物理模型实验,将测点试验数据与数值结果进行了对比,两者吻合得很好.对极限波浪运动的非线性和流域内速度分布进行了研究. 相似文献
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波浪漫滩是近岸波浪的小尺度运动,在实际海域的波生流数值计算中通常被忽略。本文基于Boussinesq方程的FUNWAVE模式,分别采用波浪漫滩边界、固壁边界、海绵边界进行Haller波浪港池物理模型实验的数值模拟,比较三种边界计算结果与实验观测数据的误差,检验波浪漫滩边界对波生流数值计算的影响;然后设计了多种周期、波高的波生流数值模拟试验,分析多种波浪入射条件下波浪漫滩边界对近岸波生流数值计算的影响。结果表明,波浪漫滩对邻近区域波生流有明显影响,漫滩边界下的波生流计算结果更接近实验观测值,在近岸波生流数值模型中引入波浪漫滩边界可以提高波生流计算精度。 相似文献
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为完善海洋观测体系,提高海洋观测数据在海洋预报和海洋防灾减灾中的适用性,文章以海洋经济较发达和遭受海洋灾害较多的温州市和台州市为例,选取潮位、波浪和水温3个重要海洋观测要素,分析海洋观测数据在海洋预报和海洋防灾减灾中的适用,并提出对策建议。研究结果表明:由于观测时间较短、地理位置特殊和数据代表性不足,海洋观测站的潮位数据未能在台风风暴潮的预报和防灾减灾中有效发挥作用;由于波浪观测仪器布设位置的地形阻挡和观测站少,波浪数据的预报准确性和实际应用不足;个别观测站的水温数据不适用于大面海洋环境和赤潮的预报,且缺少对低温灾害的观测。针对海洋观测数据的实际应用与相关业务脱节的问题,未来应提高观测数据质量、紧密结合当地海洋预报和海洋防灾减灾工作需求、开展重点目标保障预报工作以及加强海洋观测宣传教育。 相似文献
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基于NCEP CFSV2再分析风场驱动SWAN模型,对南海至北部湾为期1年的海浪逐时过程进行了数值模拟,利用Jason-2卫星和近岸浮标整年观测数据检验了模拟效果。在此基础上,评估了模型空间网格尺度对北部湾内波浪模拟的影响,分析了波浪的季节变化特征,辨析了局地风和南海传入浪对海湾波浪的驱动贡献。研究显示:（1）较Jason-2卫星观测值,有效波高模拟值的均方根误差和分散系数分别约为0.4 m和0.2;较北部湾湾顶近岸浮标逐时观测值,有效波高的均方根误差和分散系数分别约为0.2 m和0.4,平均波周期的均方根误差和分散系数分别约为0.6 s和0.2,平均波向的均方根误差约为30°;（2）空间网格分辨率为12'×12'的模型对北部湾20 m以深开敞海域波浪的模拟效果良好,模拟值较2'×2'模型的平均相对偏差在10%以下;（3）北部湾冬季盛行东北向波,夏季盛行偏南向浪,季风转换期盛行东南向浪,全年波浪在季风期强于季风转换期,冬季最强、冬夏转换期最弱;（4）局地风对北部湾波浪的驱动贡献自湾口向湾内增强,季风期强于季风转换期;南海传入浪的驱动贡献自湾口向湾内减弱,季风转换期强于季风期;海湾中部和北部的波浪以局地风为主控因素,海南岛南部和东部水域以传入浪的影响为主,海南岛西南水域受局地风和传入浪的共同控制。 相似文献
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Forecasting of ocean wave heights, with warning time of a few hours or days, is necessary in planning many operation-related activities in the ocean. Such information is currently derived by numerically solving the differential equation representing wave energy balance. The solution procedure involved is extremely complex and calls for very large amounts of meteorological and oceanographic data. This paper presents a complementary and simple method to make a point forecast of waves in real time sense based on the current observation of waves at a site. It incorporates the technique of neural networks. The network involved is first trained by different algorithms and then used to forecast waves with lead times varying from 3 to 24 h. The results of different training algorithms are compared with each other. The neural output is further compared with the statistical AR models. 相似文献
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《Ocean Engineering》1999,26(3):191-203
Forecasting of ocean wave heights, with warning time of a few hours or days, is necessary in planning many operation-related activities in the ocean. Such information is currently derived by numerically solving the differential equation representing wave energy balance. The solution procedure involved is extremely complex and calls for very large amounts of meteorological and oceanographic data. This paper presents a complementary and simple method to make a point forecast of waves in real time sense based on the current observation of waves at a site. It incorporates the technique of neural networks. The network involved is first trained by different algorithms and then used to forecast waves with lead times varying from 3 to 24 h. The results of different training algorithms are compared with each other. The neural output is further compared with the statistical AR models. 相似文献
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采用2010—2017年南海5个浮标波高观测资料和中国气象局热带气旋最佳路径集中的热带气旋参数, 基于前馈型误差反向传播(Forward Feedback Back Propagation, FFBP)神经网络(Artificial Neural Network, ANN)方法, 分别建立了各浮标站的台风浪高快速计算模型。研究显示, 基于热带气旋中心坐标、中心最低气压、近中心最大风速、热带气旋中心与浮标之间的距离和方位4个参数建立的神经网络模型经反复训练后, 模型输出结果可以很好地拟合观测数据, 各浮标有效波高计算值与观测值的均方根误差小于0.3m, 平均相对误差为5.78%~7.23%, 相关系数大于0.9, 属高度相关。独立测试结果显示, “山竹”( 国际编号: 1822)影响期间有效波高最大值的神经网络模型预报结果与观测值基本吻合, 相对误差为-31.06%~0.98%, 但计算的最大值出现时间和观测情况不完全一致。该计算方法可应用于热带气旋影响期间的有效波高最大值计算, 因而在海洋工程领域和海洋预报领域具有应用前景。 相似文献
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V. G. Polnikov V. I. Dymov T. A. Pasechnik I. V. Lavrenov Z. K. Abuzyarov S. A. Sannasiraj 《Oceanology》2008,48(1):7-14
With the purpose of revealing the actual advantages of the new source function that was earlier proposed in [5] for use in numerical wind wave models, its testing and verification was carried out by means of modification of the WAM (Cycle-4) model. The verification was performed on the basis of a comparison of the results of wave simulation for a given wind field with the buoy observation data obtained in three oceanic regions. In the Barents Sea, this kind of comparison was made for wave observations from a single buoy with an interval of 6 hours for a period of 3 years. In two regions of the North Atlantic, the comparison was performed for 3 buoys in both regions for observation periods of 30 days with an interval of 1 hour. Estimations of the simulation accuracy were obtained for a series of wind wave parameters, and they were compared with the original and modified WAM model. Advantages of the modified model consisting of the enhancement of the calculation speed by 20–25% and a 1.5- to 2-fold increase in the simulation accuracy for the significant wave height and the mean period were proved. 相似文献
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Neural networks for wave forecasting 总被引：1，自引：0，他引：1
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. 相似文献
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Eugen Rusu 《Ocean Engineering》2011,38(16):1763-1781
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. 相似文献
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Comparison of wind and wave measurements and models in the Western Mediterranean Sea 总被引：2，自引：0，他引：2
Fabrice Ardhuin Luciana Bertotti Jean-Raymond Bidlot Luigi Cavaleri Valentina Filipetto Jean-Michel Lefevre Paul Wittmann 《Ocean Engineering》2007,34(3-4):526-541
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. 相似文献
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海浪直接影响海上活动和航行安全,同时也蕴藏着巨大的可再生能源,对海浪核心参数之一波高预测至关重要。基于2015年7月～2022年6月山东小麦岛(36°N,120.6°E)站点实测的波高数据,利用反向传播神经网络(back-propagation neural network,BPNN)、长短记忆网络(long short-term memory, LSTM)和支持向量机回归(support vector regression, SVR)三种机器学习模型对波高进行预测,并分析了瑞利参数的引入对预测结果的影响。结果显示,模型输入项引入瑞利参数后,对1 h和6 h波高预测提升效果有限,预测值与测试集的相关性提升不超过0.02,均方根误差的降低不超过0.01 m;在12h和24h的预测中,BPNN和LSTM模型预测结果相关性提升0.03～0.07,均方根误差降低0.02～0.03m,而SVR模型预测结果变化不显著。说明瑞利参数有助改善BPNN和LSTM模型中长期海浪预报。此外,特征扰动方法(机器学习中特征重要性的计算方法之一)验证了瑞利参数在波高预测中的重要性,瑞利参数的引入为波高的机器学习预... 相似文献