Practical implementation of Hilbert-Huang Transform algorithm

Hilbert-Huang Transform (HHT) is a newly developed powerful method for nonlinear and non-stationary time series analysis. The empirical mode decomposition is the key part of HHT, while its algorithm was protected by NASA as a US patent, which limits the wide application among the scientific community. Two approaches, mirror periodic and extrema extending methods, have been developed for handling the end effects of empirical mode decomposition. The implementation of the HHT is realized in detail to widen the application. The detailed comparison of the results from two methods with that from Huang et al. (1998, 1999), and the comparison between two methods are presented. Generally, both methods reproduce faithful results as those of Huang et al. For mirror periodic method (MPM), the data are extended once forever. Ideally, it is a way for handling the end effects of the HHT, especially for the signal that has symmetric waveform. The extrema extending method (EEM) behaves as good as MPM, and it is better t     

The Formation and Circulation of the Intermediate Water in the Japan Sea

In order to clarify the formation and circulation of the Japan/East Sea Intermediate Water (JESIW) and the Upper portion of the Japan Sea Proper Water (UJSPW), numerical experiments have been carried out using a 3-D ocean circulation model. The UJSPW is formed in the region southeast off Vladivostok between 41°N and 42°N west of 136°E. Taking the coastal orography near Vladivostok into account, the formation of the UJSPW results from the deep water convection in winter which is generated by the orchestration of fresh water supplied from the Amur River and saline water from the Tsushima Warm Current under very cold conditions. The UJSPW formed is advected by the current at depth near the bottom of the convection and penetrates into the layer below the JESIW. The origin of the JESIW is the low salinity coastal water along the Russian coast originated by the fresh water from the Amur River. The coastal low salinity water is advected by the current system in the northwestern Japan Sea and penetrates into the subsurface below the Tsushima Warm Current region forming a subsurface salinity minimum layer. This revised version was published online in August 2006 with corrections to the Cover Date.     

提取分潮调和常数的新方法——正交方法

给出了提取潮汐调和常数的一种新方法--正交方法,并应用1992～1997年的TOPEX/POSEIDON卫星高度计遥感资料,提取中国海M2分潮调和常数.同时,利用最小二乘法来提取中国海M2分潮调和常数,两种方法结果比较渤海、黄海、东海海域M2分潮振幅、迟角的均方差分别是3.3 cm,3.6°;南中国海海域M2分潮振幅、迟角均方差分别是1.1 cm,1.7°,结果表明正交方法是一种可信的具有实用性的方法.     

近45 a东北地区春季降水异常的气候特征

利用1959-2003年我国东北地区93站春季降水资料，将降水场分成5个区域，并在此基础之上分析了春季降水的时空变化特征，发现：降水量年际变化及长期趋势有明显的区域差异，呈东多西少的分布特征；西部是旱涝易发生区，近45a来降水量略有增多；降水量的周期振荡存在明显的区域差异。     

中国重力异常的小波变换与多尺度分析

阐述了小波变换及多尺度分析方法的原理，利用二维小波多尺度分析方法对中国布格重力异常进行了分解，并从其中划分出有意义的剩余异常．模型结果证明方法有效．由于本文方法可以将重力异常分解成各种几何尺度意义下的不同成分，这为重力解释和研究地壳的结构提供更多更新的依据．     

Simulation of normal distributed smooth fields by Karhunen-Loéve expansion in combination with kriging

Simulation of multigaussian stochastic fields can be made after a Karhunen-Loéve expansion of a given covariance function. This method is also called simulation by Empirical Orthogonal Functions. The simulations are made by drawing stochastic coefficients from a random generator. These numbers are multiplied with eigenfunctions and eigenvalues derived from the predefined covariance model. The number of eigenfunctions necessary to reproduce the stochastic process within a predefined variance error, turns out to be a cardinal question. Some ordinary analytical covariance functions are used to evaluate how quickly the series of eigenfunctions can be truncated. This analysis demonstrates extremely quick convergence to 99.5% of total variance for the 2nd order exponential (‘gaussian’) covariance function, while the opposite is true for the 1st order exponential covariance function. Due to these convergence characteristics, the Karhunen-Loéve method is most suitable for simulating smooth fields with ‘gaussian’ shaped covariance functions. Practical applications of Karhunen-Loéve simulations can be improved by spatial interpolation of the eigenfunctions. In this paper, we suggest interpolation by kriging and limits for reproduction of the predefined covariance functions are evaluated.     

Simulation of normal distributed smooth fields by Karhunen-Loéve expansion in combination with kriging

Simulation of multigaussian stochastic fields can be made after a Karhunen-Loéve expansion of a given covariance function. This method is also called simulation by Empirical Orthogonal Functions. The simulations are made by drawing stochastic coefficients from a random generator. These numbers are multiplied with eigenfunctions and eigenvalues derived from the predefined covariance model. The number of eigenfunctions necessary to reproduce the stochastic process within a predefined variance error, turns out to be a cardinal question. Some ordinary analytical covariance functions are used to evaluate how quickly the series of eigenfunctions can be truncated. This analysis demonstrates extremely quick convergence to 99.5% of total variance for the 2nd order exponential (‘gaussian’) covariance function, while the opposite is true for the 1st order exponential covariance function. Due to these convergence characteristics, the Karhunen-Loéve method is most suitable for simulating smooth fields with ‘gaussian’ shaped covariance functions. Practical applications of Karhunen-Loéve simulations can be improved by spatial interpolation of the eigenfunctions. In this paper, we suggest interpolation by kriging and limits for reproduction of the predefined covariance functions are evaluated.     

腐解9个月后杉木枯枝落叶化感物质对杉木的化感作用

采用有机溶剂与蒸馏水混和浸提杉木枯枝落叶腐解9个月后化感物质,应用杉木种子发芽试验进行生物检测.结果表明:杉木枯枝落叶腐解9个月后,杉木枯枝落叶中的弱极性化感物质对绝对发芽率、绝对发芽势、胚根长、胚轴长及干物质量等指标表现为抑制作用,对鲜物质量则表现为轻微的促进作用;极性化感物质对杉木种子发芽各指标均等表现为抑制作用.腐解土中的弱极性化感物质对杉木种子发芽各指标均表现为抑制作用,极性化感物质对绝对发芽率、绝对发芽势、胚轴长及干物质量等表现为抑制作用,但对胚根长、鲜物质量表现为促进作用.     

基于EEMD和ARIMA的海温预测模型研究

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。     

Research on the Method of De-noising the Short-wave Time Signal at Low SNR

A voice enhancement algorithm based on the Empirical Mode Decomposition (EMD) and the improved spectral subtraction is proposed for the low-SNR (Signal Noise Ratio) shortwave time signal. This method is proposed to solve the problem that the shortwave time signal cannot be used for timing in complex noisy environments. The core idea of this method is to use the Hilbert-Huang Transform (HHT) algorithm to make the empirical mode decomposition on the noisy shortwave signal, and to select the intrinsic mode functions containing the shortwave signal information for the signal reconstruction by through the maximum correlation. Then, to make the spectral subtraction on the reconstructed signal to achieve the purpose of noise reduction. The experimental result shows that this method has a better noise reduction than the traditional methods.