共查询到19条相似文献，搜索用时 171 毫秒
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Kalman滤波技术在海表温度预测中的应用 总被引：1，自引：0，他引：1
以EOF分解方法为基础,把AR模型和Kalman滤波方法相结合,建立了海表温度的预报模型。首先对历史时间序列资料进行EOF分解,在此基础上,利用时间权重系数建立AR(2)模型,并对此模型参数进行了改进,作为Kalman滤波的状态方程。然后用Kalman滤波方法对时间权重系数进行了滤波预测,并引入集合预报的思想对SST预测结果进行了重构,并与实况资料进行了相关性分析。以太平洋、印度洋、大西洋三大洋的热带海域为个例进行了预测试验。试验结果表明,预测效果较好,相关系数平均达到了98%以上,而残差方差在0.5以内。 相似文献
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本研究基于SODA(Simple Ocean Data Assimilation)的月平均海洋数据,提取出南海区域平均海平面高度异常(SSHA)的时间序列,并基于该时间序列开展了统计预测工作。研究中使用时间序列分解方法,将南海区域平均逐月SSHA时间序列分解为3个部分:年际变化项、季节项和扰动项。根据分解出的这3项时间序列变化特征,分别使用指数平滑法和自回归移动平均法去拟合时间序列中的年际变化项和扰动项,季节项将作为循环变化项叠加到前两项上。由此,建立了适用于该时间序列的预测模型,并且测试了该模型的预测能力。结果显示,研究建立的南海平均海平面高度异常模型的平均有效预报时间约为7个月,预报能力在春季和秋季较其余季节要强一些。另外,该模型在模拟时段内的预报技巧具有显著的十年际变化特征。 相似文献
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文章对1996—2015年江苏省海洋经济数据进行预处理,运用时间序列分析方法中的自回归移动平均(ARMA)模型,按序列平稳性检验、模型阶数识别、最优模型选择、模型显著性检验以及模型建立和预测5个步骤,最终选取自回归求和移动平均(ARIMA)模型(2,2,1),对"十三五"时期江苏省海洋生产总值进行预测。预测结果表明:"十三五"时期江苏省海洋生产总值将保持较快增长趋势,至2020年海洋生产总值将达9 407亿元。结合江苏省海洋经济发展现状和趋势,为促进海洋经济可持续发展,提出加强海洋生态环境保护、培育壮大优势海洋产业、加大海洋科技创新投入和提升港口综合实力的建议。 相似文献
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潮位预测严重影响沿海区域,尤其是近海浅水沿岸地区居民的生产生活和涉海活动。谐波分析是长周期潮位预测的传统方法,但无法预测非周期性气象过程发生时的水位变化。与数据处理方法相结合,人工智能的方法通过拟合输入与输出数据的历史数值关系,能够有效预测高度非线性和非平稳的流模式,因而在时间序列数据预测领域得到了广泛的应用。本文结合自适应模糊推理系统（Adaptive Neuro-Fuzzy Inference System, ANFIS）和小波分解方法,利用水位异常和风切变分量作为输入数据,实现了一种综合的多时效潮位预测方法。文中测试了多种输入变量组合和小波-ANFIS（WANFIS）模型,并与人工神经网络（Artificial Neural Network, ANN）、小波-ANN（WANN）和ANFIS模型进行了预测结果对比。通过不同指数的误差分析来看,相比ANN模型,ANFIS模型能够更准确的预测潮位变化,小波分解对ANFIS预测精度有一定的提高,且模型中水位异常和风切变分量数据的加入比单一的潮位数据输入能取得更好的预测结果。 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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以1977-2012年中国海赤潮的年发生频率及2001-2012年赤潮的月发生频率数据资料为基础,建立赤潮事件的年发生频率和月发生频率时间序列。赤潮的年发生频率与时间的分段回归拟合效果较好,月频率的季节性最大值在5月(约18.22),随机波动的大小随时间序列逐步增加,波动峰值主要出现在5-7月。利用Holt指数平滑法和Holt-Winter指数平滑法分别对赤潮事件的年发生频率和月发生频率进行预测,结果表明2013-2020年赤潮的年发生频率呈年平均增加1次的缓慢趋势上升,2013-2016年5-7月份为赤潮高发期,峰值出现在5月,基本稳定在25次左右。 相似文献
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《海洋通报(英文版)》2015,(1)
以1977-2012年中国海赤潮的年发生频率及2001-2012年赤潮的月发生频率数据资料为基础,建立赤潮事件的年发生频率和月发生频率时间序列。赤潮的年发生频率与时间的分段回归拟合效果较好,月频率的季节性最大值在5月(约18.22),随机波动的大小随时间序列逐步增加,波动峰值主要出现在5-7月。利用Holt指数平滑法和Holt-Winter指数平滑法分别对赤潮事件的年发生频率和月发生频率进行预测,结果表明2013-2020年赤潮的年发生频率呈年平均增加1次的缓慢趋势上升,2013-2016年5-7月份为赤潮高发期,峰值出现在5月,基本稳定在25次左右。 相似文献
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文章以山东省警戒潮位核定为基础,对其沿岸验潮站的实测数据情况进行分类;根据不同类别,分别采用相关分析、数值模拟等方法补充实测数据,获得年极值水位序列,并采用极值Ⅰ型方法计算重现期高潮位。在警戒潮位核定中建立年极值水位序列所使用方法的顺序是,有实测数据优先采用实测数据、没有实测数据利用相关关系、没有相关关系再使用数值模拟和调和分析的方法进行。值得注意的是,在使用相关关系建立年极值水位序列中,计算重现期高潮位时一定要满足潮汐性质相同、所受风暴潮过程相似等条件;在使用数值模拟建立年极值水位序列中,须与其全年天文潮最大值进行对比。 相似文献
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Accurate prediction of tidal level including strong meteorologic effects is very important for human activities in oceanic and coastal areas. The contribution of non-astronomical components to tidal level may be as significant as that of astronomical components under the weather, such as typhoon and storm surge. The traditional harmonic analysis method and other models based on the analysis of astronomical components do not work well in these situations. This paper describes the Back-Propagation Neural Network (BPNN) approach, and proposes a method of iterative multi-step prediction and the concept of periodical analysis. The prediction among stations shows that the BPNN model can predict the tidal level with great precision regardless of different tide types in different regions. Based on the non-stationary characteristic of hourly tidal record including strong meteorologic effects, three Back-Propagation Neural Network models were developed in order to improve the accuracy of prediction and supplement of tidal records: (1) Difference Neural Network model (DNN) for the supplementing of tidal record; (2) Minus-Mean-Value Neural Network model (MMVNN) for the corresponding prediction between tidal gauge stations; (3) Weather-Data-based Neural Networks model (WDNN) for set up and set down.The results show that the above models perform well in the prediction of tidal level or supplement of tidal record including strong meteorologic effects. 相似文献
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The rise of tidal level in tidal reaches induced by sea-level rise has a large impact on flood control and water supply for the regions around the estuary.This paper focuses on the variations of tidal level response along the tidal reaches in the Yangtze Estuary,as well as the impacts of upstream discharge on tidal level response,due to the sea-level rise of the East China Sea.Based on the Topex/Poseidon altimeter data obtained during the period 1993～2005,a stochastic dynamic analysis was performed and a forecast model was run to predict the sea-level rise of the East China Sea.Two-dimensional hydrodynamic numerical models downscaling from the East China Sea to estuarine areas were implemented to analyze the rise of tidal level along the tidal reaches.In response to the sea-level rise,the tidal wave characteristics change slightly in nearshore areas outside the estuaries,involving the tidal range and the duration of flood and ebb tide.The results show that the rise of tidal level in the tidal reaches due to the sea-level rise has upstream decreasing trends.The step between the stations of Zhangjiagang and Shiyiwei divides the tidal reaches into two parts,in which the tidal level response declines slightly.The rise of tidal level is 1～2.5 mm/a in the upper part,and 4～6 mm/a in the lower part.The stations of Jiangyin and Yanglin,as an example of the upper part and the lower part respectively,are extracted to analyze the impacts of upstream discharge on tidal level response to the sea-level rise.The relation between the rise of tidal level and the upstream discharge can be fitted well with a quadratic function in the upper part.However,the relation is too complicated to be fitted in the lower part because of the tide dominance.For comparison purposes,hourly tidal level observations at the stations of Xuliujing and Yanglin during the period 1993～2009 are adopted.In order to uniform the influence of upstream discharge on tidal level for a certain day each year,the hourly tidal level observations are corrected by the correlation between the increment of tidal level and the increment of daily mean upstream discharge.The rise of annual mean tidal level is evaluated.The resulting rise of tidal level at the stations of Xuliujing and Yanglin is 3.0 mm/a and 6.6 mm/a respectively,close to the rise of 5 mm/a according to the proposed relation between the rise of tidal level and the upstream discharge. 相似文献
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河口潮汐过程受上游径流、外海潮波等因素影响,动力机制复杂,潮位预报难度大。本文提出了一种基于非稳态调和分析（NS＿TIDE）和长短时记忆（LSTM）神经网络的混合模型,对河口潮位进行12～48 h短期预报。该模型首先对河口实测潮汐数据进行非稳态调和分析,通过与实测资料对比得到分析误差的时序序列,并以此作为LSTM神经网络的输入数据,通过网络学习并预测未来12～48 h潮位预报误差,据此对NS＿TIDE的预测结果进行实时校正。利用该模型对2020年长江口潮位过程进行了预报检验,结果表明混合模型12 h、24 h、36 h和48 h短期水位预报的均方根误差（RMSE）相比NS＿TIDE模型至多分别降低了0.16 m、0.15 m、0.14 m和0.12 m;针对2020年南京站最高水位预测,NS＿TIDE模型预报误差为0.64 m,而混合模型预报误差仅为0.10 m。 相似文献
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Annual variation rate of global sea-level rise and the prediction for the 21st century 总被引：1，自引：0，他引：1
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This study provides a practical guide to the use of classical tidal prediction algorithms in coastal numerical forecasting models such as tide and tide-storm-surge models. Understanding tidal prediction parameter formulas and their limitations is key to successfully modifying and upgrading tidal prediction modules in order to increase the accuracy of perpetual interannual simulations and, in particular, storm-surge modeling studies for tide-dominated coastal environments. The algorithms for the fundamental prediction parameters, the five astronomical variables, used in tidal prediction are collated and tested. Comparisons between their estimation using different parameterizations shows that these methods yield essentially the same results for the period 1900–2099, revealing all are applicable for tidal forecasting simulation. Through experiments using a numerical model and a harmonic prediction program, the effects of nodal modulation correction and its update period on prediction accuracy and sensitivity are examined and discussed using a case study of the tidally-dominated coastal regime off the west coast of Korea. Results indicate that this correction needs updating within <30 days for accurate perpetual interannual tidal and mean sea-level predictions, and storm-surge model predictions requiring centimeter accuracy, for tidally-dominated coastal regimes. Otherwise, unacceptable systematic errors occur. 相似文献
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Sea-level return periods are estimated at 18 sites around the English Channel using: (i) the annual maxima method; (ii) the r-largest method; (iii) the joint probability method; and (iv) the revised joint probability method. Tests are undertaken to determine how sensitive these four methods are to three factors which may significantly influence the results; (a) the treatment of the long-term trends in extreme sea level; (b) the relative magnitudes of the tidal and non-tidal components of sea level; and (c) the frequency, length and completeness of the available data. Results show that unless sea-level records with lengths of at least 50 years are used, the way in which the long-term trends is handled in the different methods can lead to significant differences in the estimated return levels. The direct methods (i.e. methods i and ii) underestimate the long (> 20 years) period return levels when the astronomical tidal variations of sea level (relative to a mean of zero) are about twice that of the non-tidal variations. The performance of each of the four methods is assessed using prediction errors (the difference between the return periods of the observed maximum level at each site and the corresponding data range). Finally, return periods, estimated using the four methods, are compared with estimates from the spatial revised joint probability method along the UK south coast and are found to be significantly larger at most sites along this coast, due to the comparatively short records originally used to calibrate the model in this area. The revised joint probability method is found to have the lowest prediction errors at most sites analysed and this method is recommended for application wherever possible. However, no method can compensate for poor data. 相似文献
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为满足当前港口工程对精细化潮流预报的需求,通过比较准调和分析、流体动力——数学模型、最小二乘法三种潮流预报方法,认为最小二乘法的调和分析方法最为适用于小尺度水域的潮流预报。该方法选择以定点及漂流观测获取码头前沿水域的实测流况资料,通过分析实测资料,了解所在水域的潮流特征,再结合最小二乘法调和分析,对前沿水域进行定点的潮流预报。 相似文献