南海反气旋涡旋温度细结构特征分析

基于水下滑翔机观测资料,对南海北部一个反气旋涡旋的温度细结构进行了特征分析.温度细结构强度由温度的脉动值确定,并随着尺度的增加呈指数衰减.在垂直方向上,细结构强度随着深度的加深而减弱,细结构特征在海洋表层(0～100m)和表层以下(＞100 m)存在显著区别.表层内,垂向混合和水平混合对细结构强度均有贡献,细结构强度大...     

用CTD资料分析东海温、盐、密度垂向细结构初步尝试——Ⅰ.浅海内波的垂向结构

用时间间隔为1小时的12次CTD观测短记录分析了东海浅水区温、盐、密度脉动的垂向特性.平均温、盐、密度剖面可以分成明显的3层:跃层及其上、下层.温、盐、密脉动基本上由内波引起,但在跃层上、下界也存在一定的入侵卷挟.跃层中心的深度起伏大于6.35米,厚度变化在3.5-8米之间.它们随时间的变化是间歇性的,不规则的.用最大熵谱估计方法估计得温度脉动的垂向波数谱,表明能量主要集中在低波数段(<0.13周/米),其间有两突出的谱峰,估计谱线斜率低于-3次方.在中(0.13-0.64周/米)、高(>0.64周/米)波数段谱线分别有-3与-5次方波数依从关系.可能低波数段是内波运动;中波数段是细结构过程或内波与细结构的联合作用;而高波数段的细结构则很弱.     

论风浪的局域结构──Ⅰ.风浪的局域结构与局域小波能谱

基于小波变换,引入了能刻画风浪局域结构的局域小波能谱。论述了风浪的整体结构与局域结构。指出了在不同时间尺度上,风浪具有不同的局域化特征。提出了风场演化过程中整体的共振在线性相互作用是否存在的质疑。     

海滩碎波带波能振荡的多尺度分析

对不同波况条件下实测的一岬间海滩碎波带两个测站的波面数据 ,通过小波变换方法计算了小波能谱 ,并作了显著性检验。结果表明 :( 1 )碎波带波能存在典型的多尺度振荡特性 ;( 2 )在正常波况下 ,波能主要分布于风浪频域 ,入射波自外测站向内测站传播过程中 ,能量具有向低频方向转移的趋势 ;( 3)在高能波况下 ,能量均向长重力波频带增加 ,在长重力波显著尺度 ,二测站之间存在良好的位相一致关系     

南海西南海域的内波和细结构

根据１９９０年初夏南海西南部水域的两个连续站ＣＴＤ和多层海流计资料及５０多个大面站ＣＴＤ资料的分析研究得到一些关于此海域的内波和细结构的特性。它包括等温、盐、密度面起伏的特性，盐度双跃层和逆盐层等长存性细结构、温度Ｃｏｘ数的概率密度函数及流速频谱的特性，Ｃｏｘ数和温度脉动垂向波数谱的一般品性及其随水层和地域的变化规律等诸方面。     

南沙海区温度细结构正态性检验

对南沙海区观测得的部分温度脉动一深度序列作了正态检验。检验时先用根据序列的自回归模型构成的滤波器滤去序列元素间之相关性产生白噪声序列 ,而后用 χ2一拟合优度检验法对此白噪声序列作正态性独立同分布检验并对偏度与峰度作了检验。检验结果表明 ,除少数样本近似正态分布外 ,大多数样本的分布为非正态的 ,这是在样本内小于内波尺度的不可逆细结构所起的作用     

海州湾湾顶浅海区ADCP测流中垂向流速分量分析

根据2012年6月在海州湾湾顶浅海区利用ADCP定点测流以及CTD等观测资料,采用经验模态分解(EMD)和Hilbert变换方法,对该海区海流的垂向流速分量及其影响因素进行了分析,研究了垂向流速分量的频率结构及其随时间的变化。结果表明,海州湾湾顶浅海区垂向流速分量变化不规则,以团块状分布为主。经验模态分解显示垂向流速分量存在不同的模态,各模态对整个波动的贡献差异明显,其中心频率构成具有垂向变频特征。垂向流速分量与水平流速分量在低频模态和趋势项具有较好的相关性,表明垂向流速分量与水平流速分量具有一定的联系。     

2008年夏季南黄海叶绿素a垂向分布格局初探

根据2008年8月南黄海海域的温度、盐度、光照和叶绿素a的观测资料,分析了叶绿素a垂向分布特征及其与温度、盐度、光照的关系.结果表明,叶绿素a垂向有1个、2个或多个峰区,按其所在水团和垂向特征可分为10个类型;叶绿素a垂向分布格局与海水层化、光照和营养盐等环境因子有关.     

夏季烟台—威海北部近海溶解氧浓度垂向分布的最小值

基于2020年夏季的大面航次观测数据,分析了烟台—威海北部海洋牧场及邻近海域海水溶解氧浓度垂向分布最小值（氧最小值层）的空间分布特征,并探讨了影响因素。从6月至8月,海水溶解氧浓度不断减小,垂向结构亦存在显著变化。海水溶解氧浓度垂向分布的最小值主要集中于7月的近岸海域,最小值大致从外海向近岸方向减小,其距离海底高度及与底层溶解氧浓度之差的绝对值均于双岛湾邻近海域为最大。海水溶解氧浓度垂向分布的最小值位于最强密度层结以下。但是海水溶解氧浓度垂向分布最小值的强度向北减小,而密度层结向北增大,两者的空间分布基本相反,说明密度层结抑制垂向湍流扩散可极大减少深层海水溶解氧的来源,是海水溶解氧浓度垂向分布最小值形成的必要条件,但不是主导因素。在海水溶解氧浓度垂向分布的最小值层,表观耗氧量存在垂向分布的最大值,大部分站点的pH存在垂向分布的最小值,说明局地增强、持续的生物地球化学耗氧是控制海水溶解氧浓度垂向分布最小值形成和空间分布的一个重要过程。研究结果表明氧最小值层是夏季烟台—威海北部近岸海水溶解氧垂向结构的典型特征之一。     

南海中尺度涡温盐结构的季节特征及形成机制

本文利用最新的涡旋数据集和ARMOR3D数据,研究了南海中尺度涡温盐结构的季节特征及形成机制。合成分析的结果表明,在冬季,涡旋引起温度异常的水平分布在50米以浅表现为类似偶极型分布,而在50m以深则趋向于中心对称分布;在夏季,温度异常的水平分布均表现为中心对称的特征。涡旋引起盐度异常的水平分布也具有类似的季节特征,但是偶极型中的不对称性相对较弱。在垂向上,涡旋所致的温度异常表现为单层结构,而盐度异常则为三层结构。进一步的分析表明,涡旋所致温盐异常的垂向分布特征与背景温盐的垂向分层有关;而在50m以浅,温盐异常的水平分布的不对称特征主要由背景温盐场的水平平流所致。     

Study of fine structure of temperature in the equatorial westernPacific Ocean with wavelet transform

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Wavelet analysis for processing of ocean surface wave records

Wavelet analysis is a relatively new technique and in the recent years enormous interest in application of wavelets has been observed. This modern technique is particularly suitable for non-stationary processes as in contrast to the Fourier transform, (FT), the wavelet transform (WT) allows exceptional localization, both in time and frequency domains. The wavelet transform has been successfully implemented in signal and image processing, ordinary and partial differential equation theory, numerical analysis, communication theory and other fields. On the other hand, the application of the WT to ocean engineering and oceanography is rare. In this paper the WTs capability to give a full time–frequency representation of the wave signals is demonstrated. The processing of the time series of the non-stationary deep water waves, waves breaking at the tropical coral reefs and mechanically generated waves in the wave flume demonstrates the ability of the wavelet transform technique to detect a complex variability of these signals in the time–frequency domain. Various spectral representations resulting from the wavelet transform are discussed and their application for wave signals is shown.     

基于现场实测的渤海风速特性研究

风荷载是影响海洋结构物设计和安全服役最为显著的环境因素之一。利用在役海洋平台监测系统对渤海风速场展开长期监测,获得了长期的风速信息。对极值风速进行分析,利用极值Ⅰ、Ⅱ、Ⅲ型分布对风速极值的概率密度进行拟合。基于极值Ⅰ型概率分布,获得了渤海海域重现期为5 a、10 a、30 a和50 a的最大风速值。对年最大风速的脉动风分量、湍流强度、阵风因子进行了分析。考虑高频分量的影响,利用小波变换,得到了脉动风速的近似分量和细节分量,计算了实测风速的纵向和横向的空间积分尺度。对比经验脉动风谱与实测脉动风谱,证明了Davenport风速谱能够较好地拟合渤海现场风速场。     

Wave grouping characteristics in nearshore Great Lakes

The recently advanced approach of wavelet transform is applied to the analysis of wave data measured in the nearshore areas of the Great Lakes. The conventional spectrum analysis of wave time series in the frequency domain can be readily generalized to the frequency and time domain using the wavelet transform. The traditional Fourier transform approach has not been able to directly assess the time localized nature of wave groups. With the application of wavelet transformation, the relatively unexplored wave grouping characteristics come to light as the predominant feature of wave processes.     

由模拟波面分析双峰谱型海浪的统计特征

采用目前国际上最新的随机波分析方法,由协方差矩阵的循环嵌套技术,对美国国家浮标44008站2002年6月一典型的双峰海浪谱资料进行谱分析.以实测平均谱为靶谱,对随机波面进行模拟.得到模拟波面估计谱与实测谱极为相近,谱峰及谱峰频率都基本一致.说明利用模拟波面研究海浪具有代表性,它可以反映实测海浪的特征.利用实测海浪谱密度,统计波特征量的周期概率分布,得到理论周期概率密度与估计周期概率密度分布相符较好,且与模拟波面的波周期分布也较好的一致.利用Longuet-Higgins(1983)模型计算了波高-周期联合概率密度分布.得到变换高斯过程计算的波高与周期联合分布与实测情况基本相同,更好地描述了波高-周期联合概率密度分布.     

The influence of long tides on ecosystem dynamics in the Barents Sea

The Barents Sea ecosystem has been associated with large biomass fluctuations. If there is a hidden deterministic process behind the Barents Sea ecosystem, we may forecast the biomass in order to control it. This presentation concludes, for the first time, investigations of a long data series from North Atlantic water and the Barents Sea ecosystem. The analysis is based on a wavelet spectrum analysis from the data series of annual mean Atlantic sea level, North Atlantic water temperature, the Kola section water temperature, and species from the Barents Sea ecosystem.The investigation has identified dominant fluctuations correlated with the 9.3-yr phase tide, the 18.6-yr amplitude tide, and a 74-yr superharmonic cycle in the North Atlantic water, Barents Sea water, and Arctic data series. The correlation between the tidal cycles and dominant Barents Sea ecosystem cycles is estimated to be R=0.6 or better. The long-term mean fluctuations correlate with the 74-yr superharmonic cycle. The wavelets analysis shows that the long-term 74-yr cycle may introduce a phase reversal in the identified 18-yr periods of temperature and salinity. The present analysis suggests that forced vertical and horizontal nodal tides influence the ocean's thermohaline circulation, and that they behave as a coupled non-linear oscillation system.The Barents Sea ecosystem analysis shows that the biomass life cycle and the long-term fluctuations correlate better than R=0.5 to the lunar nodal tide spectrum. Barents Sea capelin has a life cycle related to a third harmonic of the 9.3-yr tide. The life cycles of shrimp, cod, herring, and haddock are related to a third harmonic of the 18.6-yr tide. Biomass growth was synchronized to the lunar nodal tide. The biomass growth of zooplankton and shrimp correlates with the current aspect of lunar nodal tidal inflow to the Barents Sea. The long-term biomass fluctuation of cod and herring is correlated with a cycle period of about 3×18.6=55.8 yr. This analysis suggests that we may understand the Barents Sea ecosystem dynamic as a free-coupled oscillating system to the forced lunar nodal tides. This free-coupled oscillating system has a resonance related to the oscillating long tides and the third harmonic and superharmonic cycles.     

Case study of the transformation of swells propagating into Sendai Bay

A high-resolution two-dimensional map of swell wavelength and propagating direction was retrieved from a phased array type L-band synthetic aperture radar scene observed on 24 September 2006 over Sendai Bay in the Tohoku region of Japan. Features of the wavelength map were compared with a wavelength map calculated from in situ measurements of the swell period using infinitesimal amplitude surface wave theory. The maps corresponded well in most areas, although large differences appeared in the near-shore area (area I) and an area of complicated bottom topography (area II). Because the spatial resolution of swell parameters was too coarse to investigate the surface waves in area I, wavelet transform (WT) was applied along a swell ray to improve the spatial resolution. In the resulting WT wavelength map, the large difference in area I disappeared. For area II, perturbation theory was introduced for surface waves propagating obliquely on a gentle slope. The large wavelength difference in area II was well explained by the second-order solution of perturbation theory.     

Low dimensional chaos in the AT and GC skew profiles of DNA sequences

This paper investigates the existence of low-dimensional deterministic chaos in the AT and GC skew profiles of DNA sequences. It has taken DNA sequences from eight organisms as samples. The skew profiles are analysed using continuous wavelet transform and then nonlinear time series methods. The invariant measures of correlation dimension and the largest Lyapunov exponent are calculated. It is demonstrated that the AT and GC skew profiles of these DNA sequences all exhibit low dimensional chaotic behaviour. It suggests that chaotic properties may be ubiquitous in the DNA sequences of all organisms.     

Wavelet and artificial neural network analyses of tide forecasting and supplement of tides around Taiwan and South China Sea

In multi-resolution analysis (MRA) by wavelet function Daubechies (db), we decompose the signal in two parts, the low and high-frequency contents. We remove the high-frequency content and reconstruct a new “de-noise” signal by using inverse wavelet transform. The calculation of tidal constituent phase-lags was made to determine the input and output data patterns used in building network structure of Artificial Neuron-Network (ANN) model. The “de-noise” signal was, then, used as the input data to improve the forecasting accuracy of the ANN model. The wavelet spectrum, conventional energy spectrum (fast Fourier transform, FFT), and harmonic analysis were used to analyze the characteristics of tidal data.Using only a very short-period data as a training data set in Artificial Neuron-Network Back-Propagate (ANN-BP) model, the developed ANN+Wavelet model can accurately predict or supply the missing tide data for a long period (1–5 years). The results also show that the concept of tidal constituent phase-lags can improve ANN model of tidal forecasting and data supplement. The addition of the wavelet analysis to ANN method can prominently improve the prediction quality.