共查询到18条相似文献，搜索用时 125 毫秒
1.
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。 相似文献
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基于第三代海浪模式WaveWatch III,采用集合最优插值(EnOI)方法对北印度洋海浪进行同化数值实验研究。在集合样本选取方案上,针对不同的实验分别选取有效波高(SWH)的历史后报场(样本A)、24h变化(样本B)以及以同一时刻72h预报时效和24h预报时效的差异(样本C)用于估计背景误差协方差。样本A和样本B是为海浪模拟而设计,样本C是为海浪预报而设计;通过与由高度计数据确定的模式背景误差进行比较,认为样本B优于样本A。采用样本B对2011年北印度洋海浪场进行同化模拟,结果表明2011-03-11相对误差改进都在5%及以上,其中7月份改进效果最佳。采用样本C对2013-07的有效波高进行0~72h预报,发现同化使0~24h预报改进最明显:均方根误差改进0.12m,相对误差改进5%。浮标检验结果支持上述结论。 相似文献
3.
数值模式与统计模型相耦合的近岸海浪预报方法 总被引：2，自引：2，他引：0
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。 相似文献
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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。 相似文献
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基于第三代海浪模式WaveWatchⅢ和Swan,采用四重网格嵌套建立了黄海、南海近海海浪的高精度数值预报系统,以及青岛第一海水浴场、广西北海银滩浴场、海南三亚亚龙湾海水浴场3个示范区近岸定点海浪的精细化数值预报系统。通过后报和预报试验对所建立的数值预报系统进行了系统的检验,后报波高与实测值吻合较好。准业务化预报试验表明有效波高的预报精度随预报时效的增加而降低,近海海浪大于2 m的平均预报相对误差小于30%。浴场海浪的平均预报绝对误差为0.35 m左右。预报精度可以满足业务化预报的要求。 相似文献
7.
为检验南海海浪业务化数值预报系统的预报效果,利用2010年和2011年3-11月的观测资料,通过计算预报值和观测值的绝对误差、相对误差等统计参数和线性回归分析对南海海浪业务化数值预报系统进行检验.统计结果显示有效波高和平均周期的预报误差24 h＜48 h＜72 h,有效波高的24 h、48 h、72 h预报平均绝对误差小于0.5 m,平均周期的24 h、48 h、72 h预报平均绝对误差小于0.8 s;预报误差有明显的季节变化,10月和11月的预报误差显著小于其它各月;回归分析结果显示预报值与观测值存在中度高度线性相关关系,随着预报时效的增长相关度逐渐递减,预报值较观测值偏大.总体来说,该系统的预报误差在可接受的范围之内,满足业务化预报的要求,但与欧洲气象中心等发达国家的预报系统比较来看,该系统还存在较大差距. 相似文献
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MASNUM海浪数值模式业务化预报与检验 总被引：2，自引：2，他引：0
介绍了MASNUM(Key Laboratory of Marine Science and Numerical Modeling)海浪数值预报系统,并利用全球和西北太平洋的Jason-1卫星数据和NDBC浮标数据中的海浪波高观测,对该预报系统进行了自2007年8月1日-2007年12月31日5个月的24,48和72 h预报结果的比较检验.模式校验结果表明,有效波高预报与观测的绝均差在0.5 m左右,从夏季到冬季,预报精度不断提高,与风场冬季预报精度较高吻合. 相似文献
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HY-2卫星高度计波高资料在集合最优插值同化中的应用研究——以台风“Lipee”为例 总被引：2，自引：2，他引：0
基于海浪模式SWAN（Simulating Waves Nearshore）,以台风“Lipee”为例,开展了集合最优插值（EnOI）同化HY-2卫星高度计有效波高（SWH）资料的台风浪数值预报影响研究。结果表明,利用HY-2卫星高度计波高资料结合EnOI方法进行同化,可有效改善海浪初始场质量,同化对绝对误差的改进可达15%,均方根误差改进14%。同化对预报误差、均方根误差都有一定程度的改进,其中在0~24 h预报时效内的改进最为明显,绝对误差可改进12%,均方根误差改进13%。研究结果不仅可为海洋预报、同化提供参考,而且可为进一步加强HY-2卫星高度计资料的应用提供技术支持。 相似文献
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基于支持向量回归(SVR)方法,建立了渤海海域近岸海浪有效波高短期预测模型,并设计了多组风浪信息组合输入方案,开展了有效波高预测敏感性试验。研究发现:综合考虑当前风浪信息作为模型的输入,对3 h和6 h有效波高预测具有较高的预报技巧,但随着预测时效的延长其预测准确性迅速降低;若此时引入未来预测风速信息作为模型输入,则可极大提高对12 h和24 h有效波高的预测能力;此外,若输入信息与预测对象之间不存在显著相关,多个信息的输入对有效波高预测效果提高无显著作用。建立的机器学习模型对小样本数据集具有良好的适应能力,能够有效解决海浪预报中的非线性问题,可为近岸海浪有效波高短期预测提供合理的技术参考。 相似文献
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The significant wave representation method is the simplest method for computing the transformation of significant wave height across-shore. However, many engineers are reluctant to use this method because many researchers have pointed out that the method possibly contains a large estimation error. Nevertheless, Rattanapitikon et al. [Rattanapitikon, W., Karunchintadit, R., Shibayama, T., 2003. Irregular wave height transformation using representative wave approach. Coastal Engineering Journal, JSCE 45(3), 489–510.] showed that the wave representation method could be used to compute the transformation of root mean square wave heights. It may also be possible to use it for computing the significant wave height transformation. Therefore, this study was carried out to examine the possibility of simulating significant wave height transformation across-shore by using the significant wave representation method. Laboratory data from small- and large-scale wave flumes were used to calibrate and examine the models. Six regular wave models were applied directly to irregular waves by using the significant wave height and spectral peak period. The examination showed that three regular wave models (with new coefficients) could be used to compute the significant wave height transformation with very good accuracy. On the strength of both accuracy and simplicity of the three models, a suitable model is recommended for computing the significant wave height transformation. The suitable model was also modified for better predictions. The modified model (with different coefficients) can be used to compute either regular wave height or significant wave height transformation across-shore. 相似文献
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基于海浪模式WAVEWATCH Ⅲ模拟北太平洋海浪要素,结合NDBC浮标资料进行验证,发现模拟出的有效波高与浮标测量值具有很好的一致性。基于改进型白冠覆盖率耗散模型,利用海浪模式模拟出的有效波高、有效波周期和摩擦速度等海浪要素计算出单位面积水柱内因海浪破碎产生的湍动能通量。通过改变环流模式sbPOM湍动能方程的上边界条件,引入海浪破碎产生的湍动能通量,并探究海浪破碎对北太平洋海表面温度模拟的影响。研究表明,由于海浪破碎的引入,环流模式sbPOM对北太平洋海表面温度模拟的准确程度得到提升,这为大气模式提供一个准确的北太平洋下边界条件具有重要意义。 相似文献
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To explore new operational forecasting methods of waves, a forecasting model for wave heights at three stations in the Bohai Sea has been developed. This model is based on long short-term memory(LSTM) neural network with sea surface wind and wave heights as training samples. The prediction performance of the model is evaluated,and the error analysis shows that when using the same set of numerically predicted sea surface wind as input, the prediction error produced by the proposed LSTM model at Sta. N01 is 20%, 18% and 23% lower than the conventional numerical wave models in terms of the total root mean square error(RMSE), scatter index(SI) and mean absolute error(MAE), respectively. Particularly, for significant wave height in the range of 3–5 m, the prediction accuracy of the LSTM model is improved the most remarkably, with RMSE, SI and MAE all decreasing by 24%. It is also evident that the numbers of hidden neurons, the numbers of buoys used and the time length of training samples all have impact on the prediction accuracy. However, the prediction does not necessary improve with the increase of number of hidden neurons or number of buoys used. The experiment trained by data with the longest time length is found to perform the best overall compared to other experiments with a shorter time length for training. Overall, long short-term memory neural network was proved to be a very promising method for future development and applications in wave forecasting. 相似文献
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非线性波浪波面追踪的一种新模式 总被引：1，自引：0，他引：1
基于Laplace方程的Green积分表达式和波面BemouUi方程所建立的非线性波动数学模型,是一个时域上具有初始值的边值问题,而精确地追踪自由表面的波动位置,给出波面运动瞬时的波面高度和波面势函数,是建立时域内非线性波浪数值模式的基础。本文采用0-1混合型边界元剖分计算域边界并离散Laplace方程的Green积分表达式,采用有限元剖分自由水面并推导满足自由表面非线性边界条件的波面有限元方程,联立计算域内以节点波势函数和波面位置高度的时间增量为未知量的线性方程组,通过时步内的循环迭代,给出每个时步上的波面位置和波面势函数,从而建立了一种新的非线性波浪波面追踪模式。数值造波水槽内的波浪试验表明,其数值模拟结果具有良好的计算精度。 相似文献
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《Applied Ocean Research》2007,29(1-2):72-79
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. 相似文献
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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. 相似文献
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The accuracy of predicting wave transformation in the nearshore is very important to wave hydrodynamics, sediment transport and design of coastal structures. An efficient numerical model based on the time-dependent mild-slope equation is presented in this paper for the estimation of wave deformation across the surf zone. This model incorporates an approximate nonlinear shoaling formula and an energy dissipation factor due to wave breaking to improve the accuracy of the calculation of wave height deformation prior to wave breaking and also in the surf zone. The model also computes the location of first wave breaking, wave recovery and second wave breaking, if physical condition permits. Good agreement is found upon comparison with experimental data over several one-dimensional beach profiles, including uniform slope, bar and step profiles. 相似文献
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Recently, the technology has been developed to make wave farms commercially viable. Since electricity is perishable, utilities will be interested in forecasting ocean wave energy. The horizons involved in short-term management of power grids range from as little as a few hours to as long as several days. In selecting a method, the forecaster has a choice between physics-based models and statistical techniques. A further idea is to combine both types of models. This paper analyzes the forecasting properties of a well-known physics-based model, the European Center for Medium-Range Weather Forecasts (ECMWF) Wave Model, and two statistical techniques, time-varying parameter regressions and neural networks. Thirteen data sets at locations in the Atlantic and Pacific Oceans and the Gulf of Mexico are tested. The quantities to be predicted are the significant wave height, the wave period, and the wave energy flux. In the initial tests, the ECMWF model and the statistical models are compared directly. The statistical models do better at short horizons, producing more accurate forecasts in the 1-5 h range. The ECMWF model is superior at longer horizons. The convergence point, at which the two methods achieve comparable degrees of accuracy, is in the area of 6 h. By implication, the physics-based model captures the underlying signals at lower frequencies, while the statistical models capture relationships over shorter intervals. Further tests are run in which the forecasts from the ECMWF model are used as inputs in regressions and neural networks. The combined models yield more accurate forecasts than either one individually. 相似文献