PROPAGATION OF 30—60 DAY LOW FREQUENCY OSCILLATIONS AND THEIR INFLUENCE UPON THE SUBTROPICAL WESTERLIES JET STREAM DURING NORTHERN HEMISPHERE WINTER

Based on daily ECMWF gridpoint data of two winters during 1981—1983 including an ENSOyear,propagation of low frequency oscillations(LFO)during Northern Hemisphere winters andtheir influences upon 30—60 day oscillations of the subtropical jet stream are studied with the sta-tistical methods as complex empirical orthogonal function(CEOF)and so on.Results show that inthe winter of a normal year(1981—1982),30—60 day oscillations in the subtropical zone aremainly in the northern and southern flanks of exit region of jet stream.In the ENSO year(1982—1983),they are mainly in the vicinity of entrance and exit regions of jet stream.Intraseasonalchanges of subtropical jet stream manifested themselves as latitudinal fluctuation or longitudinalprogression or regression of about 40 day period.There are marked differences between propagat-ing passages of low frequency modes responsible for changes of subtropical jet stream in the normalyear(1981—1982)and in the ENSO year(1982—1983).Changes of oscillation amplitude showobvious phases.In general,the one in late winter is stronger than that in early winter,strongestone occurs in February.     

REMOTE SENSING FOREST BIOMASS: AN EVALUATION USING HIGH RESOLUTION REMOTE SENSOR DATA AND LOBLOLLY PINE PLOTS

Eight-year-old Loblolly pine (Pinus taeda) plots located in South Carolina were evaluated using high resolution aircraft multispectral scanner data to determine if biomass could be accurately measured. Sixteen pine plots located on both sandy and clay soils were treated with 0, 180, or 360 kg. (approximately 0, 400, or 800 lb.) of nitrogen per plot. Indices of biomass from remote sensing data were significantly correlated with in situ biomass measurements made in each plot. A ratio of infrared (.9–1.1 μm) and red (.65–.70 μm) channels yielded the best correlation. The indices were not sensitive to differences in soil type (sandy or clay).     

On least squares estimation of generalized covariance functions

Least squares estimation (LSE) is theoretically related to quadratic unbiased estimation of variance components. It is argued that these methods of estimation of variance components essentially generalize LSE though they are not formally equivalent.     

基于EMD与神经网络的机械故障诊断技术

经验模式分解 (EMD)是分析非线性、非平稳信号的有力工具 ,它将信号分解为突出了原信号的不同时间尺度的局部特征信息的内在模函数 (IMF)分量。本文通过将各 IMF分量输入到 BP网络中进行训练学习和故障诊断 ,比直接输入原信号可以提高 BP网络对故障诊断的准确率 ,而且减少了训练时间。     

A Reconstruction of Observed Profiles in the Sea East of Japan Using Vertical Coupled Temperature-Salinity EOF Modes

It is important to estimate hard-to-observe parameters in the ocean interior from easy-to-observe parameters. This study therefore demostrates a reconstruction of observed temperature and salinity profiles of the sea east of Japan (30°≈40°N, 140°≈150°E). The reconstruction was done by estimating suboptimal state from several values of the observed profiles and/or sea surface dynamic height (SDH) calculated from the profiles. The estimation used a variational method with vertical coupled temperature-salinity empirical orthogonal function (EOF) modes. Profiles of temperature and salinity in the subtropical region are effectively reconstructed from in situ temperature profile data, or sea surface temperature (SST) and SDH. For example, the analyzed temperature field from SST and SDH has an accuracy to within 1°C in the subtropical region. Salinity in the sea north of Kuroshio, however, is difficult to estimate because of its complex variability which is less correlated with temperature than in the subtropical region. Sea surface salinity is useful to estimate the subsurface structure. We also show the possibility that the estimation is improved by considering nonlinearity in the equation calculating SDH from temperature and salinity analysis values in order to examine the misfit between analysis and observation. Analysis using TOPEX/POSEIDON altimetry data instead of SDH was also performed. This revised version was published online in July 2006 with corrections to the Cover Date.     

Estimation of Water Particle Velocities by Empirical Transfer Function

-In previous and this studies it appears that the linear and nonlinear wave theory can notaccurately and easily predict the water particle velocities.Therefore,different from the theoretical consider-ations,in this study we have attempted to determine the transfer function empirically.Laboratory experi-ments were performed under various wave conditions.The empirical formulas of the transfer function ofthe wave height,angular frequency and water particle velocity were obtained on the basis of these test databy dimensional analysis and regression analysis.In intermediate and deep water depth conditions,thetransfer function was only a function of a nondimensional parameter which is composed of the angular fre-quency,the depth of the velocity gauge under the still water level,water depth and the acceleration of grav-ity.Finally,the empirical formulas were compared with experimental data and observational data formpresent and Cavaleri's(1978)studies.The empirical formulas were found to be in sufficient correl     

Income distribution dynamics and cross-region convergence in Europe

This paper presents a continuous version of the model of distribution dynamics to analyse the transition dynamics and implied long-run behaviour of the EU-27 NUTS-2 regions over the period 1995–2003. It departs from previous research in two respects: first, by introducing kernel estimation and three-dimensional stacked conditional density plots as well as highest density regions plots for the visualisation of the transition function, based on Hyndman et al. (J Comput Graph Stat 5(4):315–336, 1996), and second, by combining Getis’ spatial filtering view with kernel estimation to explicitly account for the spatial dimension of the growth process. The results of the analysis indicate a very slow catching-up of the poorest regions with the richer ones, a process of shifting away of a small group of very rich regions, and highlight the importance of geography in understanding regional income distribution dynamics.