基于SSA和AR模型的海面变化预测试验

以吴淞站1955-2001年月平均潮位序列为基础,采用奇异谱分析(SSA)与自回归模型(AR)相结合的方案(SSA AR),进行了月平均潮位预测试验。基本思路是对SSA分析的结果选择若干有意义的分量进行序列重建,借助于自回归模型进行分量预测,再对它们进行叠加,从而建立预测模型。本文以1955-1996年数据为基础建立模型,1997-2001年数据作为验证,检验结果表明,两种方法的结合使用显示了较好的效果。     

一种基于小波分解和模糊推理神经网络的潮位预测方法

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。     

太平洋月平均水位变化中的周期成分

为了研究太平洋月平均水位变化中的周期成分,本文根据作者改进的月平均水位周期信号分析方法,分析了太平洋236个水位站的月平均序列.对其中大于45a的序列进行二阶谱和二阶凝聚谱分析,表明这些序列中有的周期成分相位相关性显着.因此采用考虑非线性效应的混合谱周期分析方法,即重复进行最大熵谱分析及周期检验和最小二乘滤波,逐次检验并滤掉最显着的周期分量,求得序列中的显着周期.结果表明水位序列的振动周期因站而异,并非固定不变.讨论了准2a大气振动周期、与埃尔尼诺事件相关的3～7a周期、太阳黑子、双太阳黑子和25～30a超长振动周期的存在.周期振动的迟角和振幅的区域分布和变化对月平均水位的预测具有重要意义;为此对检验出的显着周期根据物理背景作进一步判别,从中选出较客观合理者作为月平均水位序列的拟合周期.用这样的周期分量加线性趋势项拟合月平均水位序列,求得相应的振幅和初相角及线性趋势项.     

基于小波分析的我国台风风暴潮直接经济损失周期分析及预测

本文选取1989-2021年我国台风风暴潮直接经济损失统计数据,依据线性趋势法和Mann-Kendall非参数检验法进行分析,结果表明,32年间我国风暴潮灾害经济损失呈现显著下降趋势,整体呈厚尾分布特征,采用对数化处理后呈显著的正态分布特征。采用Morlet小波变换对我国台风风暴潮直接经济损失的周期变化规律进行分析,t检验结果显示,全域存在准两次高频振荡,1～2年及7～8年的周期振荡,但随时间变化年际周期逐渐缩短为3～5年,说明风暴潮经济损失序列存在高频振荡和多周期嵌套的低频振荡规律。在此基础上,采用Daubechies小波分解分离高频信号和低频信号,均方根误差和信噪比精度分析结果表明,当小波基设置消失矩为7,分解层数为2时,我国台风风暴潮直接经济损失时间序列具有最优分解重构效果。对各分解层进行小波系数平稳性检验和白噪声检验,建立的小波分解-ARMA组合模型的模拟精度和预测精度均优于传统的自回归移动平均模型和Fourier级数拓展模型,证明了小波分解法在我国台风风暴潮经济损失快速评估中具有可靠性和优越性。     

区域平均海平面高度异常的统计预测

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。     

基于ARMA模型的潮位拟合及内插研究

在水深测量过程中,采集的潮位数据往往存在断缺现象,给水深测量内业工作者造成干扰。针对潮位数据断缺问题,对断缺数据简单预处理后,利用ARMA模型对潮位数据序列变化规律进行分析,拟合潮位时间序列数据变化趋势,然后将拟合的值内插到数据断缺处,以建立完整的潮位改正文件,最后对拟合模型进行精度评价分析。实验结果表明,拟合模型精度良好,可以快速获取符合要求的插值,提高工作效率。     

吴淞站潮位数据的小波检验和调整

上海吴淞站是我国沿海地区记录最长的验潮站之一。通过对吴淞站和黄浦公园站的潮位数据的对比分析,揭示出1952年之前数据存在着系统偏差。并通过小波分析方法获得了序列变化的突变点在1929年和1951年,进而对吴淞月均潮位数据分3个时段进行了调整。在此基础上,对吴淞站1937年9月~1943年12月间缺失数据采用回归模型进行了插补。这一检验定位和调整的思路可供吴淞站和其他存在数据瑕疵的长序列潮位数据分析利用研究借鉴。     

长江口潮差中长期变化对河口生态环境的影响

为了探讨长江口潮差的中长期变化对生态环境的影响,利用小波变换法对1972—2018年该河口代表性潮位站的潮差序列(共66336个数据)进行周期性分析。结果表明,该站潮差除了常见的15 d大、小潮周期外,还有变幅约19 cm(相当于多年平均潮差的7.5%)的0.5 a周期和变幅约16 cm(相当于多年平均潮差的6.3%)的18.5 a周期。月均潮差极大值出现在3月和9月,极小值出现在6月和12月。年均潮差极大值出现在1977,1996和2015年,极小值出现在1986年和2005年。上述潮差变化在时间上与长江口灾害性盐水入侵、悬沙浓度长周期变化以及水下三角洲冲淤转变等重大事件存在明显的对应关系。结论认为,上述中长期潮差周期变化对长江口生态环境具有不可忽视的潜在影响,在今后对河口生态环境的研究中应得到重视。     

基于谱分析的GPS浮标测量参数提取方法

介绍采用谱分析原理提取海浪信息的方法。根据GPS浮标测量得到的海浪高度数据,先通过滑动平均的方法分离海浪信息和潮流潮位信息,然后对海浪信息采用AR模型法进行功率谱估计,并对海浪信息进行连续小波变换,对AR模型法功率谱估计结果和小波变换结果进行综合研究,提取海浪特征参数———周期。最后通过一个GPS浮标试验对上述方法进行了验证。     

1956—2018年青海省黄河流域降水变化特征分析及预测

本研究基于1956—2018年31个气象站月值降水数据,采用M-K检验、Pettitt检验、Morlet小波分析、ARIMA模型等方法,分析青海省黄河流域近63年包括趋势、突变、周期在内的降水量特性以及验证未来5年降水趋势预测变化的合理性。结果表明:(1)近63年青海省黄河流域降水具有集中程度高、年内分配不均、丰枯季明显的特点;不同年代的月均降水量均集中在4—10月,且未来月均降水量有上升的变化趋势。(2)年降水量呈显著增长趋势,增长率为(9.07 mm/10 a),春季和冬季降水增长趋势显著,夏季和秋季降水无显著的增长趋势,2004年为该流域大的降水转折年。(3)年降水时间序列存在23~32 a,15~20 a,9~13 a以及4~6 a的周期变化规律,四级降水主周期分别对应30、15、11和6 a时间尺度。(4)ARIMA(2,1,4)模型能够较好地拟合1956—2018年降水序列并对2019—2023年降水数据合理预测;线性回归及M-K检验分析结果显示1956—2023年降水序列呈显著增长趋势,增长率为(9.22 mm/10 a),与趋势预测结果相一致;ARIMA(2,1,4)模型可以对青海省黄河流域进行短期年降水量预测,为当地水资源合理规划和管理提供参考。     

基于小波变换和LSTM神经网络的格陵兰岛近海海域海平面变化预测

海平面变化包含多种不同时间尺度信息，传统的预测方法仅对海平面变化趋势项、周期项进行拟合，难以利用海平面变化的不同时间尺度信号，使得预测精度不高。本文基于深度学习的预测模型，提出一种融合小波变换（wavelet transform，WT）与LSTM （long short-term memory，LSTM）神经网络的海平面异常组合预测模型。首先利用小波分解得到反映海平面变化总体趋势的低频分量和刻画主要细节信息的高频分量；然后通过LSTM神经网络对代表不同时间尺度的各个分量预测和重构，实现海平面变化的非线性预测。基于该模型的海平面变化预测的均方根误差、平均绝对误差和相关系数分别为12.76 mm、9.94 mm和0.937，预测精度均优于LSTM和EEMD-LSTM预测模型，WT-LSTM组合模型对区域海平面变化预测具有较好的应用价值。     

Foraminifera and tidal notches: Dating neotectonic events at Korphos,Greece

Fossil assemblages of foraminifera and thecamoebians from three salt-marsh cores recovered at Korphos, Greece, provided evidence for five transgression events since the mid Holocene. Marsh accretion rates based on radiocarbon-dated peat and geomorphic evidence from a series of discrete, v-shaped, submerged tidal notches indicated that these transgression events were rapid and episodic. Correlation of the tidal notches with the transgression horizons in the salt-marsh stratigraphy revealed a stepwise pattern of relative sea-level change at Korphos, which is best explained by coseismic subsidence related to fault displacement (earthquakes) associated with the Hellenic subduction zone. A comparison between the Korphos data and a model of Holocene sea-level change for the Peloponnesus reinforces this interpretation as sea-level rose in a series of jumps by amounts greater than accounted for by eustatic and glacio-hydro-isostatic factors (up to ~ 2.0 m). This study illustrates that by combining microfossil, sedimentary and geomorphic records of past sea-level change, problems frequently encountered with each record individually (e.g. dating submerged notches and autocompaction of marsh sediments) may be overcome.     

A comparison of the main methods for estimating probabilities of extreme still water levels

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.     

Prediction models for tidal level including strong meteorologic effects using a neural network

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.     

海面溢油轨迹的分析与预报

考虑主要分期（Ｍ２，Ｓ２，Ｋ１，Ｏ１，Ｍ４，ＭＳ４）的潮流以及Ｍ２分期的潮汐余流，建立预报潮流场，并考虑海面风力的作用以及油的蒸发和乳化，利用欧拉－拉格朗日方法跟踪油膜的中心点，提出预报油膜的轨迹及其扩散范围。该模型适用于近岸事故溢油的快速预报．     

Tidal level response to sea-level rise in the yangtze estuary

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.     

埕岛油田及附近地区的水位

在渤海内,M2分潮有两个无潮点,一个在秦皇岛东面,另一个位于渤海湾至莱州湾之间。此外,渤海海峡附近还有一个K1分潮的无潮点。埕岛油田位于M2分潮无潮点和K1分潮波的腹部附近。整个石油开发区半日潮差均较小,其中以东南部最小,西北部有明显增加。全日潮差由东北向西南增加,变化幅度较小。在一般天气条件下,海面高度主要决定于潮汐,潮差不大,但在台风影响下,可能引起异常的增水,寒潮可以引起大的减水或增水。国内不少单位在黄河口做过潮汐观测,但资料甚少。埕岛油田位于黄河口的西北部,海上水位资料十分缺乏。我们利用12井位和20井位进行了4个月的水位观测,使用Aanderaa水位计,取样间隔为15min。岸边站(3站)是在岸边打桩,使用滚筒式水位计,获得了一年的水位观测资料,这些资料十分宝贵。本文详细地分析了该区域的水位特征,并给出施工设计需要的各个参数。     

Exploring conventional tidal prediction schemes for improved coastal numerical forecast modeling

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.     

On the predictability of mode-1 internal tides

A frequency-wavenumber tidal analysis for deriving internal-tide harmonic constants from TOPEX/Poseidon (T/P) measurements of sea-surface height (SSH) has been developed, taking advantage of the evident temporal and spatial coherence and the weak dissipation of internal tides. Previous analyses consisted of simple tidal analysis at individual points, which gave inconsistent harmonic constants at altimeter track crossover points. Such analyses have difficulty in distinguishing between the effects of interference, incoherence, and dissipation. The frequency-wavenumber analysis provides an objective way to interpolate the internal tides measured along altimetry tracks to any arbitrary point, while leveraging all available data for optimal tidal estimates. Tidal analysis of T/P data from 2000 to 2007 is used to predict in situ time series measured during the 2001-2002 Hawaiian Ocean mixing experiment (HOME), the 1987 reciprocal tomography experiment (RTE87), and the 1991 acoustic mid-ocean dynamics experiment (AMODE), demonstrating both the temporal coherence and the lack of incoherent elements to this wave propagation. It has been conjectured that significant energy would be lost from mode-1 internal tides as they cross the 28.9°N critical latitude of parametric subharmonic instability (PSI). No apparent change in amplitude at 28.9°N was detected by this analysis, however. Further, after correcting for changes in background stratification, the amplitude of the mode-1 internal tide was found to decrease by less than 20% over the 2000 km between the Hawaiian Ridge and 40°N. A significant fraction of the variability of internal waves, that component associated with mode-1 internal tides, appears to be predictable over most of the world's oceans, using harmonic constants derived from satellite altimetry.