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对海洋数据进行挖掘能够有效地预测海洋灾害事件。海洋监测数据具有时序长、间隔短、多要素间强关联的特点,对长时间序列进行直接分析挖掘速度慢、效率低,现有方法大多采用符号化时间序列方法,但可能导致部分信息丢失且破坏
要素间的关联性。本文定义了时间序列 motif,用于发现时间序列中重复出现的,先前未知的局部信息,解决了符号化导致的信息丢失的问题,实现了时间序列 motif 的精确快速提取。通过构建 motif 规则树,实现了海洋多要素时间序列间强关联规则的挖掘。最后,给出关联规则评价参数,同随机游走数据对比后,证明了本文方法的有效性。 相似文献
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南海海面风、浪场的EOF分析 总被引：3，自引：0，他引：3
在气候分析中常常借助自然正交展开方法分析气象要素的时空分布特征,而对于海洋研究,由于过去观测手段的限制,很难获得较长时间序列和较大空间系列的观测资料,因此像气候分析中常用的经验正交函数(EOF)方法在海洋要素的分析中较少使用.卫星遥感技术的发展为海洋学研究提供了丰富的空间分布均一、时间序列较长的观测资料.本文根据TOPEX/Poseidon(T/P)卫星高度计获得的海面风、浪信息,利用气候分析中常用的EOF分析方法,分析了南海海面风、浪场的时空模态特征.这一分析结果可以很好地反映南海海面风、浪场间的关系,从而为海浪场的经验预报和分析提供参考. 相似文献
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海洋事件离不开各要素环境数据的共同作用,获取要素之间的关联关系从而进行海洋事件的预报预测,是一个亟待解决的问题。为此,本文提出一种多视图协同的关联关系分析方法来度量海洋各要素数据间的关联关系。首先,在传统平行坐标技术的基础上增加刷技术、轴排序等功能对海洋多要素数据进行初步探索,同时引入散点矩阵图展示各要素的分布;其次,以平行坐标中数据线间的角度、面积以及散点图中要素分布的距离为差异度量方式,对计算得到的差异构建相似性矩阵;再次,采用多维标度法得到原始多要素数据在低维空间中的表达;最后,使用K-means算法对降维后的低维度数据进行聚类分析。本文提出的方法从视觉角度对数据进行分析和特征挖掘,并得到高维数据在低维空间上的可视化展示,实现了有效量化海洋数据不同要素间的相关关系。 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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ASAR波模式数据反演参数误差与海浪条纹清晰度的相关性分析 总被引：1，自引：0，他引：1
欧洲环境卫星-高级合成孔径雷达(EnvironmentalSatellite-AdvancedSyntheticAperture Radar,Envisat-ASAR)波模式数据提供了全球风、浪要素信息,在海浪模式预报与同化方面有重要作用。该数据合成孔径雷达(SyntheticApertureRadar,SAR)图像普遍存在海浪条纹清晰度不同的现象,但是否影响数据精度尚无定论。本文通过比较2010年NODC (the National Oceanographic Date Center)浮标观测数据和波模式数据,发现经过官方修正后的海浪参数反而具有更大误差。进而通过对比不同条纹清晰度的SAR图像反演参数误差,揭示了ASAR产品海浪参数与浮标测量值之间的误差与海浪条纹清晰度的关系。结果表明:海浪条纹清晰的SAR图像的主波波长和主波周期的反演误差更小,而条纹不清晰SAR图像的有效波高和风速的反演误差更小。通过分析海浪参数对海浪条纹清晰度的敏感性,证实了有效波高和方位向截断波长对SAR图像条纹清晰度的响应最好,波陡次之,与卫星飞行方位角和入射角无关。因此,在反演和修正SAR波模式数据时,考虑图像的条纹清晰度,将会有效提高反演数据的精度。该研究可为高分三号等卫星的波模式数据波浪要素反演精度的提升提供有价值的参考。 相似文献
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利用舟山海洋浮标站和附近海岛自动测风站环境风场多年资料,采用空间平滑和时间平滑方法做资料预处理,综合分析风力大小、风向、潮汐和浪高之间的关系,结果显示:平均风力等级大小和平均浪高之间呈准线性关系,风大浪也大;同一等级风力情形下,浪高是以平均值为峰值的多等级离散分布;且与风向密切相关,即北风和东风浪大,南风和西风浪小,其中东风浪最大,这在实践中得到证实;在潮汐和浪高的关系中,大潮汛期间浪高稍大于小潮汛浪高,差值很小,显示潮汐对浪高的影响并不明显。最后建立回归拟合方程,并通过应用检验。此研究结果对本地海浪经验预报提供了有力支撑和有益的修正,有较好的应用价值。 相似文献
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类型丰富、时空分辨率高的海洋探测数据,为信号分解和机器学习算法的应用提供了可能。本文针对如何建立有效的海温预测模型这一问题,使用高时空分辨率的海表温度(SST)融合产品,引入信号处理领域的集合经验模态分解(EEMD)和机器学习领域的自回归积分滑动平均模型(ARIMA)。首先利用最适于分解自然信号的EEMD方法,将海温数据分解成多个确定频率的序列;再利用ARIMA分别对各个频率的序列进行预测,最后将各个序列的预测结果进行组合。该方法在丰富数据的支撑下,比以往直接使用海温数据所建立的预测模型精度更高,为更好地进行海温预测提供了新方法。 相似文献
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对快速增长的海洋经济做出可靠的预测,可以深化对海洋经济发展规律的认识,对确定海南省海洋经济增长前景和目标、制定海洋开发战略具有重要的实践和参考意义。文章在对比不同预测方法的基础上,基于现有的数据基础,遴选出灰色系统模型与时间序列模型作为预测工具,对海南省海洋生产总值进行预测。研究结果表明:①与时间序列模型相比,灰色系统模型在海南省海洋经济预测方面更为有效。②目前海南省海洋经济正处于成长期,未来10年仍将保持快速增长。③根据预测结果,到2025年海南省海洋生产总值将达到3 340亿元,年均增长率约为12.5%。 相似文献
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LIU Guilin CHEN Baiyu WANG Liping ZHANG Shuaifang ZHANG Kuangyuan LEI Xi 《海洋湖沼学报（英文）》2019,(2):448-460
When exploring the temporal and spatial change law of ocean environment, the most common method used is using smaller-scale observed data to derive the change law for a larger-scale system. For instance, using 30-year observation data to derive 100-year return period design wave height. Therefore, the study of inherent self-similarity in ocean hydrological elements becomes increasingly important to the study of multi-year return period design wave height derivation. In this paper, we introduced multifractal to analyze the statistical characteristics of wave height series data observed from oceanic hydrological station. An improvement is made to address the existing problems of the multifractal detrended fluctuation analysis (MF-DFA) method, where trend function showed a discontinuity between intervals. The improved MFDFA method is based on signal mode decomposition, replacing piecewise polynomial fitting used in the original method. We applied the proposed method to the wave height data collected at Chaolian Island, Shandong, China, from 1963 to 1989 and was able to conclude the wave height sequence presented weak multi-fractality. This result provided strong support to the past research on the derivation of multi-year return period design wave height with observed data. Moreover, the new method proposed in this paper also provides a new perspective to explore the intrinsic characteristic of data. 相似文献
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台风是影响中国黄东海的强天气现象,其引起的强风、巨浪和台风增水严重威胁着沿海地区人民的生命与财产安全。本文以海浪模式SWAN（Simulating Waves Nearshore）与区域海洋模式ROMS（Regional Ocean Modeling System）为基础,构建了中国黄东海海域在201509号台风“灿鸿”影响下的海浪-海洋耦合模式。通过浮标与Jason-2高度计有效波高数据验证了模式结果的准确性。进行了敏感性实验分析,对比耦合（ROMS+SWAN）与非耦合（SWAN）下以及使用不同地形数据（ETOPO1、ETOPO2、GEBCO）、不同物理参数化方案（风能输入、白冠耗散、底摩擦耗散）下的模拟结果差异。结果发现在射阳与前三岛浮标处,使用GEBCO地形数据（15弧秒间隔）下的模拟效果更好且稳定。在空间分布上,台风中心附近的浪流相互作用显著,在其前进方向右侧表现为耦合的有效波高值低于非耦合有效波高值,差值最高可达1米。选择不同风输入与耗散项方案时的模拟差异主要发生在最大波高处,选择不同的风能输入与白冠耗散项方案带来的差异接近0.4米,而底摩擦项方案选择不同带来的差异接近1米。因而在模拟实际的海况时,需要综合考虑这些因素带来的影响,才能达到SWAN海浪模型最好的海浪模拟效果。 相似文献
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Robert E. Cheney Bruce C. Douglas David T. Sandwell James G. Marsh Thomas V. Martin John J. McCarthy 《Marine Geophysical Researches》1984,7(1-2):17-32
Satellite-borne altimeters have had a profound impact on geodesy, geophysics, and physical oceanography. To first order approximation,
profiles of sea surface height are equivalent to the geoid and are highly correlated with seafloor topography for wavelengths
less than 1000 km. Using all available Geos-3 and Seasat altimeter data, mean sea surfaces and geoid gradient maps have been
computed for the Bering Sea and the South Pacific. When enhanced using hill-shading techniques, these images reveal in graphic
detail the surface expression of seamounts, ridges, trenches, and fracture zones. Such maps are invaluable in oceanic regions
where bathymetric data are sparse. Superimposed on the static geoid topography is dynamic topography due to ocean circulation.
Temporal variability of dynamic height due to oceanic eddies can be determined from time series of repeated altimeter profiles.
Maps of sea height variability and eddy kinetic energy derived from Geos-3 and Seasat altimetry in some cases represent improvements
over those derived from standard oceanographic observations. Measurement of absolute dynamic height imposes stringent requirements
on geoid and orbit accuracies, although existing models and data have been used to derive surprisingly realistic global circulation
solutions. Further improvement will only be made when advances are made in geoid modeling and precision orbit determination.
In contrast, it appears that use of altimeter data to correct satellite orbits will enable observation of basin-scale sea
level variations of the type associated with climatic phenomena. 相似文献
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Significant wave height estimates are necessary for many applications in coastal and offshore engineering and therefore various estimation models are proposed in the literature for this purpose. Unfortunately, most of these models provide simultaneous wave height estimations from wind speed measurements. However, in practical studies, the prediction of significant wave height is necessary from previous time interval measurements. This paper presents a dynamic significant wave height prediction procedure based on the perceptron Kalman filtering concepts. Past measurements of significant wave height and wind speed variables are used for training the adaptive model and it is then employed to predict the significant wave height amounts for future time intervals from the wind speed measurements only. The verification of the proposed model is achieved through the dynamic significant wave height and wind speed time series plots, observed versus predicted values scatter diagram and the classical linear significant wave height models. The application of the proposed model is presented for a station in USA. 相似文献
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Maaria Nordman Jaakko Kuokkanen Mirjam Bilker-Koivula Hannu Koivula Pasi Häkli Sonja Lahtinen 《Marine Geodesy》2013,36(5):457-476
AbstractWe studied geoid validation using ship-borne global navigation satellite systems (GNSS) on the Baltic Sea. We obtained geoid heights by combining GNSS–inertial measurement unit observations, tide gauge data, and a physical sea model. We used two different geoid models available for the area. The ship route was divided into lines and the lines were processed separately. The GNSS results were reduced to the sea surface using attitude and draft parameters available from the vessel during the campaign. For these lines, the residual errors between ellipsoidal height versus geoid height and absolute dynamic topography varied between 0 and 15?cm, grand mean being 2?cm. The mean standard deviations of the original time series were approximately 11?cm and reduced to below 5?cm for the time series filtered with 10?min moving average. We showed that it is possible to recover geoid heights from the GNSS observations at sea and validate existing geoid models in a well-controlled area. 相似文献
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《Applied Ocean Research》2004,26(3-4):114-136
Two successive wave heights are modeled by a Gaussian copula, which is referred to as the Nataf model. Results with two initial distributions for the transformation are presented, the Næss model [Næss A. On the distribution of crest to trough wave heights. Ocean Engineering (1985);12(3):221–34] and a two-parameter Weibull distribution, where the latter is in best agreement with data. The results are compared with existing models. The Nataf model has also been used for modeling three successive wave heights.Results show that the Nataf transformation of three successive wave heights can be approximated by a first order autoregressive model. This means that the distribution of the wave height given the previous wave height is independent of the wave heights prior to the previous wave height. Thus, the joint distribution of three successive wave heights can be obtained by combining conditional bivariate distributions. The simulation of successive wave heights can be done directly without simulating the time series of the complete surface elevation.Successive wave periods with corresponding wave heights exceeding a certain threshold have also been studied. Results show that the distribution for successive wave periods when the corresponding wave heights exceed the root-mean-square value of the wave heights, can be approximated by a multivariate Gaussian distribution.The theoretical distributions are compared with observed wave data obtained from field measurements in the central North Sea and in the Japan Sea, with laboratory data and numerical simulations. 相似文献
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Prediction of Extreme Significant Wave Height from Daily Maxima 总被引：4，自引：0，他引：4
For prediction of the extreme significant wave height in the ocean areas where long term wave data are not available, the empirical method of extrapolating short term data (1-3 years) is used in design practice. In this paper two methods are proposed to predict extreme significant wave height based on short-term daily maxima. According to the da-a recorded by the Oceanographic Station of Liaodong Bay at the Bohai Sea, it is supposed that daily maximum wave heights are statistically independent. The data show that daily maximum wave heights obey log-normal distribution, and that the numbers of daily maxima vary from year to year, obeying binomial distribution. Based on these statistical characteristics, the binomial-log-normal compound extremum distribution is derived for prediction of extreme significant wave heights (50-100 years). For examination of its accuracy and validity, the prediction of extreme wave heights is based on 12 years' data at this station, and based on each 3 years' data respectively 相似文献
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Learning from data for wind-wave forecasting 总被引：1，自引：0，他引：1
Along with existing numerical process models describing the wind-wave interaction, the relatively recent development in the area of machine learning make the so-called data-driven models more and more popular. This paper presents a number of data-driven models for wind-wave process at the Caspian Sea. The problem associated with these models is to forecast significant wave heights for several hours ahead using buoy measurements. Models are based on artificial neural network (ANN) and instance-based learning (IBL) .To capture the wind-wave relationship at measurement sites, these models use the existing past time data describing the phenomenon in question. Three feed-forward ANN models have been built for time horizon of 1, 3 and 6 h with different inputs. The relevant inputs are selected by analyzing the average mutual information (AMI). The inputs consist of priori knowledge of wind and significant wave height. The other six models are based on IBL method for the same forecast horizons. Weighted k-nearest neighbors (k-NN) and locally weighted regression (LWR) with Gaussian kernel were used. In IBL-based models, forecast is made directly by combining instances from the training data that are close (in the input space) to the new incoming input vector. These methods are applied to two sets of data at the Caspian Sea. Experiments show that the ANNs yield slightly better agreement with the measured data than IBL. ANNs can also predict extreme wave conditions better than the other existing methods. 相似文献
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This paper generalises the application of univariate models of the long-term time series of significant wave height to the case of the bivariate series of significant wave height and mean period. A brief review of the basic features of multivariate autoregressive models is presented, and then applications are made to the wave time series of Figueira da Foz, in Portugal. It is demonstrated that the simulated series from these models exhibit the correlation between the two parameters a feature that univariate series cannot reproduce. An application to two series of significant wave height from two neighbouring stations shows the applicability of this type of models to other type of correlated data sets. 相似文献