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全球再分析数据集已成为研究气候规律和数值模拟的重要工具,其中海面风场数据集是波浪模拟的重要资料,风场资料的准确性是影响海浪要素模拟结果的关键因素,不同的海面风场资料在中国各个海域的适用性具有不确定性。利用黄海、东海海域的12个观测点,选取了2006—2018年间的11场台风进行对比,验证了ERA5和NCEP风场在台风期间与常海况下的风速;模拟了中国近海海域的波浪场,与范围内15个测站的有效波高及谱峰周期进行了对比验证;分析了ERA5和NCEP风场在黄海、东海波浪模拟的适用性。主要结果如下:(1)风场质量是造成台风浪模拟误差的主要原因之一,研究区域内ERA5风场在台风期间的风速大小与实测资料具有较高一致性;长江口邻近海域内,ERA5风速相关性在0.8以上;江苏海域内,ERA5风速相关性在0.9以上;(2)分别采用ERA5和NCEP再分析风场资料作为驱动风场输入Mike21 SW模型,较好地模拟了黄海、东海海域在不同海况下的波浪变化情况;在江苏海域,ERA5资料模拟波高值与浮标测站观测波高资料相关性超过0.85,平均绝对误差不超过0.2 m;(3)两种风场在江苏海域、长江口及其邻海的适用性比黄海北部更好。结果表明,NCEP和ERA5在中国近海海域波浪模拟的适用性有差异,在江苏海域、长江口及其邻海,基于ERA5的数值模拟结果相对于NCEP模拟结果精度提高。 相似文献
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采用SWAN (Simulating Waves Nearshore)模型搭建了覆盖整个台湾海峡和台湾岛东部部分海域的波浪模型,并利用此模型计算了常风浪场、崇武海洋站设计波浪要素和西沙湾海域极值波浪场。计算结果显示,在常风浪模拟中,4个浮标站计算值与实测值有效波高绝对误差均在0.20 m以内,平均绝对误差值为0.13 m;崇武海洋站的设计波浪计算值与多年资料推算值平均绝对误差为0.14 m,平均相对误差为1.9%; SWAN和CGWAVE (Conjugate Gradient Wave Model)在西沙湾海域7个点的波浪极值计算值在S、SE、SSW三个方向上平均绝对误差分别为0.11、0.10、0.07 m,平均相对误差不足3%。以上计算结果表明,SWAN模型在常风浪模拟和设计波浪要素计算中具有良好的适应性。 相似文献
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黄海西北部浮游细菌生物量分布特征及其与环境因子的关系 总被引：3，自引：0，他引：3
分别研究了2006年7月(夏季)和2007年1月(冬季)、5月(春季)、10月(秋季)黄海西北部海域浮游细菌生物量的分布特征,探讨了它们与温度、盐度和Chl a含量之间的关系.结果表明,研究海区平均细菌生物量春、夏、秋和冬季分别为:41.083,8.34,16.68和6.20 mg/m3.4个季节表层细菌生物量分布均呈现出从辽东半岛近岸区域向外海逐渐降低的趋势.春、秋季除浮游植物Chl a外各生态因子与细菌生物量之间均无显著性相关关系.夏、冬季与温度、盐度和Chl a含量的相关性均不显著. 相似文献
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基于高分辨率MASNUM第三代海浪模式并根据Yuan等(2009)提出的波浪破碎统计物理模型,对乳山湾外海2008年各个季节波浪特征要素(有效波高和跨零周期等)与波浪破碎参数(白冠覆盖率、卷入深度和破碎能量损耗率)进行了数值模拟。利用Janson-1卫星高度计观测资料对有效波高模拟结果进行了检验,平均误差在0.17m左右。模拟结果显示,该海域波浪参数具有明显的季节变化特征,在2008-08乳山湾邻近海域受大风天气过程影响期间,有效波高在1.0~1.8m,破碎参数变化显著,白冠覆盖率最大达4.5%,卷入深度在1.5m以上,相应的破碎能量损耗率量值在6~11kg/s3。结果表明,波浪破碎过程对该区域海洋动力环境有着重要影响,是造成乳山湾口表层高溶解氧的可能机制之一。 相似文献
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文章通过BP神经网络模型,利用西沙站的实测潮位推算三亚站潮位,研究用一地点的潮位资料去推算另一地点(异地)潮位的方法。文章比较了不同隐含层节点数和输入因子对潮位推算结果的影响,采用预测时间(t)之前N个小时(t–N+1,…,t–1,t)西沙站的实测潮位数据作为输入因子,输入因子数目在2~10之间,隐含层分别采用节点数3、4、5、10和15建模,分多种情况进行推算。结果显示,对文中使用的特定情形,隐含层为4个节点的效果最好,隐含层为15个节点的效果最差;输入层为2个节点的效果最好,输入因子增多会使得推算效果变差。隐含层为4个节点、输入因子为t–1、t时刻潮位的仿真验证的结果最好,推算值和实测值之间的相关系数为0.9901,均方根误差为0.06m,误差在–0.16~0.15m之间。结果表明,如果两个地点的潮位具有物理上的关联,通过BP神经网络模型,用一地点的实测潮位推算另一地点潮位的方法是可行的。 相似文献
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基于SWAN模式的“灿鸿”台风浪数值模拟 总被引：1，自引：0，他引：1
以第三代海浪模式SWAN(simulating wave nearshore,近岸海浪数值模型)为基础,构建了东中国海海域波浪数值模式,并以高时间、空间分辨率的CCMP(cross calibrated multi-platform,多平台交叉校正)风场作为驱动风场进行波浪计算,模拟了1509号"灿鸿"台风的波浪过程。同时,对SWAN模式中的底摩擦参数化方案、波浪破碎参数、风能输入与白冠耗散、波-波非线性相互作用等因素对台风浪模拟的影响进行了分析,并对模式中的各影响因素给出了建议。模拟结果与浮标实测有效浪高数据(舟山朱家尖站、南麂岛站、舟山外海站、温州外海站)两者之间的偏差较小,表明本研究所建立的模式以及选择的参数合理,SWAN和CCMP风场的结合能满足海洋波浪数值模拟的需求。本研究对于台风浪数值预报具有参考意义。 相似文献
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基于CCMP卫星遥感海面风场数据的渤海风浪模拟研究 总被引：6，自引：0，他引：6
CCMP（Cross Calibrated Multi-Platform）风场数据是一种具有较高的时间、空间分辨率和全球海洋覆盖能力的新型卫星遥感资源。在充分分析CCMP海面风场数据可靠性的基础上,以该卫星遥感海面风场数据为强迫输入项,运用第三代浅水波浪模式SWAN对渤海一次风浪过程进行了模拟,将模拟的结果与T/P、Jason卫星高度计观测得到的有效浪高数据进行比较分析,发现两者相关性达到0.78,模拟结果平均偏高0.3 m。试验表明CCMP卫星遥感风场数据能满足海洋浪高预报需求,能在海洋数值预报和海洋环境研究中发挥重要作用。 相似文献
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为了研究欧洲北海海域的波高全区域概率分布情况,从而为海洋平台等海洋浮式结构物的选址和结构设计提供依据。首先基于Global Waves Statistics(GWS)提供的实测数据,确定典型计算工况的发生概率;同时考虑实测数据中极端波浪环境下的数据缺失导致大波高分布概率偏小的问题,利用三参数Weibull分布确定不同重现期下的极值风速,作为典型计算工况的补充。以不同风速、风向的定常风场为输入项,利用第三代海浪数值模型SWAN模型,对北海全区域波高进行数值模拟。将数值模拟的稳态形式依照各工况的发生概率进行归一化累加处理,认为其结果可以表征全区域的波高概率分布情况。以波高概率分布的计算结果为依据,分析北海海域波浪环境的统计学特征,发现有效波高为7 m以上的大波高频发区在北海北部区域有大范围分布;有效波高4～5 m为北海东北区域的多发海况,极端海况下的有效波高主要分布于7～14 m区间,在地形突变区域的波高发生显著变化。 相似文献
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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. 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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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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Interpolation of wave heights 总被引：1，自引：0，他引：1
Remote sensing of waves often necessitates presentation of data in the form of wave height values grouped over large time intervals. This restricts their use to long-term applications only. This paper describes how such data can be made suitable for short-term usage in the field. Weekly mean significant wave heights were derived from their monthly mean observations with the help of different alternative techniques. These include model-free neural network schemes as well as model-based statistical and numerical methods. Superiority of neural networks was noted when the estimations were compared with corresponding observations. The network was trained using three different training algorithms, viz., error back propagation, conjugate gradient and cascade correlation. The technique of cascade correlation took minimum training time and showed better coefficient of correlation between observations and network output. 相似文献
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《海洋技术学报》2023,(2)
本文基于唐山近海海域1#、2#浮标2017年4月至11 月实时海浪观测数据及部分风速风向数据， 对唐山近海海域波浪有效波高、有效波向、有效波周期等波参数特征进行了统计分析， 并利用origin 软件对波参数与风速、风向相关性进行了研究。研究结果表明： 1#、2# 浮标海域常浪向为SSW、SW、SSE， 常浪向有效波高均以0.2 ～ 0.4 m 小浪及3 ～ 4 s 短周期为主，有效波高1 m 以上较大波浪极少出现； 该海域波浪以风浪为主， 波浪破碎速度较快， 有效波高与风速相关性较强， 相关系数r 为0.71， 风向与波向、有效波高与周期基本无相关性， 该研究资料可为海上活动及防灾减灾提供技术依据。 相似文献
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The tremendous increase in offshore operational activities demands improved wave forecasting techniques. With the knowledge of accurate wave conditions, it is possible to carry out the marine activities such as offshore drilling, naval operations, merchant vessel routing, nearshore construction, etc. more efficiently and safely. This paper describes an artificial neural network, namely recurrent neural network with rprop update algorithm and is applied for wave forecasting. Measured ocean waves off Marmugao, west coast of India are used for this study. Here, the recurrent neural network of 3, 6 and 12 hourly wave forecasting yields the correlation coefficients of 0.95, 0.90 and 0.87, respectively. This shows that the wave forecasting using recurrent neural network yields better results than the previous neural network application. 相似文献
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ABSTRACTIn this research, group method of data handling (GMDH) as a one of the self-organized approaches is utilized to predict three-dimensional free span expansion rates around pipeline due to waves. The GMDH network is developed using gene-expression programming (GEP) algorithm. In this way, GEP was performed in each neuron of GMDH instead of polynomial quadratic neuron. Effective parameters on the three-dimensional scour rates include sediment size, pipeline geometry, and wave characteristics upstream of pipeline. Four-dimensionless parameters are considered as input variables by means of dimensional analysis technique. Furthermore, scour rates along the pipeline, vertical scour rate, and additionally scour rates in the left and right of pipeline are determined as output parameters. Results of the proposed GMDH-GEP models for the training stages and testing ones are evaluated using various statistical indices. Performances of the GMDH-GEP models are compared with artificial neural network (ANN), GEP, GMDH, and traditional equations-based regression models. Moreover, sensitivity analysis and parametric study are conducted to perceive influences of different input parameters on the three-dimensional scour rates. 相似文献
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Underwater ultrasonic acoustic transducers are frequently used in ocean wave measurements as they measure surface level using acoustic waves. However, their effectiveness can be severely affected in rough sea conditions, when bubbles generated by breaking waves interfere with their acoustic signals. When the seas are rough, one therefore often has to rely on a pressure transducer, which is generally used as a back-up for the acoustic wave gauge. A pressure transfer function is then used to obtain the surface wave information. Alternatively, the present study employed an artificial neural network to convert the pressure signal into significant wave height, significant wave period, maximum wave height, and spectral peakedness parameter using data obtained from various water depths. The results showed that, for water depths greater than 20 m, the wave parameters obtained from the artificial neural network were significantly closer to those obtained by the acoustic measurements than those obtained by using a linear pressure transfer function. Moreover, for a given water depth, the wave heights estimated by the network model from pressure data were not as good as those estimated by linear wave theory for large wave heights (above a 4 m significant wave height in this study). This can be improved if the training data set has more records with large wave heights. 相似文献
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为实现对海面风速精确的短期预测,提出了一种基于长短期记忆(LSTM,long short-term memory)神经网络的短期风速预测模型,选取OceanSITES数据库中单个浮标站点采集的风速历史数据作为模型输入,经过训练设置最佳参数等步骤,实现了以LSTM方法,对该站点所在海区海面风速在各季节性代表月份海面风速的24h短期预测。同时通过不同预测时长的实验以及与BP(back propagation)神经网络神经网络和径向基函数神经网络(radial basis function neural network,RBF)的预测效果对比实验,证明了LSTM预测方法相比上述两种神经网络预测方法,在海表面风速预测应用中的优越性。最后通过多个海域对应的站点风速数据预测实验,证明了LSTM神经网络模型的普遍适用性,由相关系数和预测误差的分析可知该方法具备应对急剧变化数据的预测稳定性,可以作为海洋表面风速短期预测的一种可靠方法。 相似文献