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。     

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.     

Seasonal variations of wind and waves over Taiwan waters

We here investigate the frequency and intensity of oscillations in oceanographic data within intraseasonal time scales using spectral analysis of surface wind and wave time-series data collected at off-island weather stations or moored buoys around Taiwan. Data from marine weather stations were used to trace atmospheric conditions, while we used buoy data to examine sea states. The spectra and wavelet scalogram of the wind fields revealed oscillations with a period of around 20–33 days, and the energy density of the wind field at the off-island stations was stronger than that at the data buoy stations. However, the wavelet scalogram of the wave height measured at the buoy stations was stronger than its associated wind field. This long-period oscillation is consistent with the wavelet scalogram of the wind field calculated from the off-island weather stations. About 20–33 day oscillations exist within intraseasonal variations, which are closely linked to the atmospheric environment and to wind and ocean wave fields. Oscillations with a period of 5–10 days are a pronounced feature over northeastern Taiwan waters during the winter season and can be interpreted as the wave pattern following synoptic weather systems.     

Development of wavelet network model for accurate water levels prediction with meteorological effects

Accurate water levels modeling and prediction is essential for safety of coastal navigation and other maritime applications. Water levels modeling and prediction is traditionally developed using the least-squares-based harmonic analysis method that estimates the harmonic constituents from the measured water levels. If long water level measurements are not obtained from the tide gauge, accurate water levels prediction cannot be estimated. To overcome the above limitations, the current state-of-the-art artificial neural network has recently been developed for water levels prediction from short water level measurements. However, a highly nonlinear and efficient wavelet network model is proposed and developed in this paper for water levels modeling and prediction using short water level measurements. Water level measurements (about one month and a week) from six different tide gauges are employed to develop the proposed model and investigate the atmospheric changes effect. It is shown that the majority of error values, the differences between water level measurements and the modeled and predicted values, fall within the −5 cm and +5 cm range and root-mean-squared (RMS) errors fall within 1–6 cm range. A comparison between the developed highly nonlinear wavelet network model and the harmonic analysis method and the artificial neural networks shows that the RMS of the developed wavelet network model when compared with the RMS of the harmonic analysis method is reduced by about 70% and when compared with the RMS of the artificial neural networks is reduced by about 22%. It is also worth noting that if the atmospheric changes effect (meteorological effect) of the air pressure, the air temperature, the relative humidity, wind speed and wind direction are considered, the performance accuracy of the developed wavelet network model is improved by about 20% (based on the estimated RMS values).     

赣江流域旱涝时空变化特征研究

基于赣江流域40个气象站点1966—2005年的月降水、气温资料, 采用多时间尺度的标准化降水蒸散指数反映不同时间尺度的旱涝状况。再应用小波分析和GIS空间分析技术, 对其旱涝特征进行定量化分析, 阐述其时空变化格局。结果表明:40 a共出现6次中旱以上级别的干旱事件, 7次中涝以上级别的雨涝事件。在流域旱涝演变过程中主要存在3类尺度的周期变化规律。通过小波方差检验得出10 a时间尺度的周期振荡最强烈, 为该指数时间变化的第一主周期。重涝主要分布在赣江流域南部, 而重旱主要分布在西南地区。季节性旱涝变化以夏涝最为严重, 主要分布在流域总出口, 即南昌、丰城一带, 而伏旱、秋旱波及范围较大, 持续时间较长。     

遗传小波神经网络在变形预报中的应用

目前常用的变形预报方法有BP人工神经网络和小波神经网络,但是都存在收敛速度慢且易受局部极值的影响。针对这两种算法的不足,本文利用遗传算法的全局寻优特性,将遗传算法与小波神经网络结合,形成遗传小波神经网,将其应用于变形预报,取得了良好的效果;并将算法的预报精度、稳定性、有效区间及运算时间作为评价算法优劣的4个标准,对BP神经网络、小波神经网络及遗传小波神经网络进行对比,结果表明遗传小波神经网络具有明显的优势。     

Influence of the 11-Year Solar Cycle on Variations of Cosmic Ray Intensity

The monthly cosmic ray intensity (CRI) time series from Climax, Huancayo, Moscow, Kiel, and Calgary are used to investigate the presence of the 11-year periodic component with special attention paid to the solar influence on these variations. The results show obvious 11-year temporal characteristics in CRI variations. We also find a close anticorrelation between the 11-year solar cycle and CRI variations and time delays of the CRI relative to solar activity.     

1979~2013年西北太平洋地区海平面气压场季节转换时间的变化分析

采用1979~2013年NCEP/NCAR再分析资料,借助线性趋势、距平、累积距平、Mann-Kendall突变检验及Morlet小波等方法分析了西北太平洋地区海平面气压场季节转换时间的长期趋势和多尺度周期变化特征。结果表明:海平面气压场一年中存在两次季节转变,20°N~50°N海平面气压场冬夏季节转变的时间在第20候左右,而夏冬季节转变发生在第51候。并且海平面气压场的季节转换时间存在纬度差异与经度差异。通过趋势分析,发现海平面气压场由冬季型转变到夏季型的时间存在显著的趋势变化,并且在近35年内是趋于提前,其气候倾向率为-0.33候/10 a;夏季型转换为冬季型的时间趋于延后,气候倾向率为0.25候/10 a。季节转换时间的Mann-Kendall突变检测结果表明,海平面气压场由冬向夏的转换时间在1997~1998年间发生了突变;夏季型转换为冬季型的时间尚未发现显著突变。最后通过对季节转换时间的小波分析与小波功率谱的显著性检验得出,冬夏季节转换的时间具有显著的15 a周期变化;夏冬季节转换时间8 a周期振荡最为剧烈。     

粤西北霜冻天气的气候统计特征

统计分析粤西北1964～2006年霜日、初终霜期的多年平均特征,以及霜、冰日形成期间的典型气象要素场特征。结果表明:粤西北年霜日从高纬向低纬呈逐渐减少的趋势,年霜日主要集中在12月和1月;初霜日由北向南推迟,而终霜日则相反;霜形成过程中有90%以上02:00晴空无云,地表最低温度为-1.9～2.0℃;结冰过程,地表最低温度为-5.9～0℃;霜、冰形成过程中,静风情况下,08:00相对湿度高于非静风情况。     

Thousand-Year Cycle Signals in Solar Activity

Long-term variations of solar activity closely relate to terrestrial phenomena. More and more people attach importance to studies of long-term fluctuations of solar variation. However, because direct observations of solar activity are available only for the past four centuries, such studies are few. In this work, using the wavelet technique, the author investigates long-term fluctuations of reconstructed sunspot number series covering the past 11 400 years, with emphasis on the thousand-year cycle signals of solar variation. The results show a thousand-year cycle in solar activity.