A dipole mode at thermocline layer in the tropical Indian Ocean

Temperature data at different layers of the past 45 years were studied and we found adiploe mode in the thermocline layer (DMT): anomalously cold sea temperature off the coast of Sumatra and warm sea temperature in the western Indian Ocean. First, we analyzed the temperature and the temperature anomaly (TA) along the equatorial Indian Ocean in different layers. This shows that stronger cold and warm TA signals appeared at subsurface than at the surface in the tropical Indian O-cean. This result shows that there may be a strong dipole mode pattern in the subsurface tropical Indian Ocean. Secondly we used Empirical Orthogonal Functions (EOF) to analyze the TA at thermocline layer. The first EOF pattern was a dipole mode pattern. Finally we analyzed the correlations between DMT and surface tropical dipole mode (SDM), DMT and Nino 3 SSTA, etc. and these correlations are strong.     

Parabolic Wave Propagation Modelling in Orthogonal Coordinate Systems

This paper presents a numerical model study of the propagation of water waves using the parabolic approximation of the mild-slope equation in the orthogonal coordinate system. Two types of coordinate systems are studied: (a) a general form of orthogonal coordinate system and (b) the conformal system, a special form of orthogonal coordinate system. Two typical examples, namely, expanded breakwaters and a circular channel, are studied to validate the model. First, the examples are studied by use of the general orthogonal coordinates. Then the same examples are computed by use of the confonnal system. The computational results show that the confonnal coordinate system generally gives better predictions than the general orthogonal system. A numerical technique for generating the conformal grid is combined with the numerical model to improve the practicability of the model. The comparison between the result from the numerical grid system and that from the analytical grid system shows that reliable computational results can be obtained by use of the numerical confonnal grid system.     

Inverse reconstructions of ecosystem flows in investigating regime shifts: impact of the choice of objective function

Inverse analysis is increasingly used in ecosystem modelling to objectively reconstruct a large number of unknown flows or interactions from a small number of observations. This type of analysis may be useful in relating observed regime shifts in ecosystem structure to underlying processes. Inversions of ecosystem flow networks currently use a constrained least-squares solution which at the same time minimizes the squared norm (the sum of squares) of the reconstructed flows. This minimum norm (MN) inversion is thought to be a parsimonious solution to the ecosystem flow inverse problem, but it may well not reflect how ecosystems are organised. It has been proposed instead that ecosystems evolve to maximize energy/mass flows or that they maximize the information content of the network weighted by ecosystem flows (ascendancy). We used simulated inverse experiments, where inverse analyses are applied to simulations of flow networks, to explore objective functions different than the MN generally used. We could not compute inverse solutions that maximize ascendancy because the objective function is unbounded. We could calculate inversions that maximize flows; however, these generally overestimated the simulated flows, even though the simulations were designed to maximize flows. It appears that the ecosystem flow inverse problem is too under-determined (too few data relative to the number of unknowns) to allow the use of these maximizing goal functions. We introduce a new minimization that simultaneously minimizes the squared flows and the squared differences between flows. This smoothing minimization makes the inverse flows as even as possible and it helps with some technical issues with MN inversions. The simulated inverse experiments indicated that this smoothed norm (SM) is the most robust in comparative analyses of contrasting ecosystem states, such as those that can be associated with regime shifts. Like the MN inversion, the SM inversion has no ecological basis. However, it is a conservative norm that is less likely to produce false differences between the dynamics of regimes.     

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。     

Transfer-function modelling between environmental variation and mesozooplankton in the Baltic Sea

Time series of freshwater runoff, seawater salinity, temperature and oxygen were used in transfer functions (TF) to model changes of mesozooplankton taxa in the Baltic Sea from the 1960’s to the 1990’s. The models were then compared with long term zooplankton monitoring data from the same period. The TF models for all taxa over the whole Baltic proper and at different depth layers showed statistically significant estimates in t-tests. TF models were further compared using parsimony as a criterion. We present models showing 1) r² > 0.4, 2) the smallest residual standard error with the combination of exploratory variables, 3) the lowest number of parameters and 4) the highest proportional decrease in error term when the TF model residual standard error was compared with those of the univariate ARIMA model of the same response variable. Most often (7 taxa out of a total of 8), zooplankton taxa were dependent on freshwater runoff and/or seawater salinity. Cladocerans and estuarine copepods were more conveniently modelled through the inclusion of seawater temperature and oxygen data as independent variables. Our modelling, however, explains neither the overall increase in zooplankton abundance nor a simultaneous decrease found in the neritic copepod, Temora longicornis. Therefore, biotic controlling agents (e.g. nutrients, primary production and planktivore diets) are suggested as independent variables for further TF modelling. TF modelling enabled us to put the controlling factors in a time frame. It was then possible, despite the inherent multiple correlation among parameters studied to deduce a chain-of-events from the environmental controls and biotic feedback mechanisms to changes in zooplankton species. We suggest that the documented long-term changes in zooplankton could have been driven by climatic regulation only. The control by climate could be mediated to zooplankton through marine chemical and physical factors, as well as biotic factors if all of these were responding to the same external control, such as changes in the freshwater runoff. Increased runoff would explain both the increasing eutrophication, causing the overall increase of zooplankton, and the changes in selective predation, contributing to decline of Temora.     

Nonparametric Estimates of the Sea State Bias for the Jason-1 Radar Altimeter

The Jason-1 sea state bias (SSB) is analyzed in depth from the first year of GDR products. Compared to previous missions, this work benefits from two aspects of the empirical determination of the SSB from the altimetric data themselves. First, from a methodological point of view, a nonparametric technique (NP) has been developed and largely tested on TOPEX/Poseidon 1, GFO and Envisat data. The NP estimator has proven to be a useful tool in the SSB estimation, and it is now mature enough to be used for a refined analysis. On the other hand, the SSB can be extracted from three different data sets (crossovers, collinear data, and residuals) with different characteristics. It is then possible to cross calibrate various estimations of the SSB models and to determine the most accurate one. A systematic comparison is made between these different estimates for the Jason-1 altimeter. The collinear and crossover data sets yield very similar estimates despite their difference of spatial and temporal distributions. These SSB models assure consistency with the TOPEX mission when comparing Jason-1 and TOPEX residuals during the tandem phase. Thanks to the present work, the impact of the short wavelengths filtering on the SSB estimation is evidenced. More generally, our understanding of potential errors affecting the sea surface height and their impact onto the SSB estimation is also improved.     

Shoreline variability via empirical orthogonal function analysis: Part I temporal and spatial characteristics

Empirical orthogonal functions (EOFs) or principal components were used to extract the significant modes of shoreline variability from several data sets collected at three very different locations. Although EOFs have proven to be a valuable tool in the analysis of nearshore data, most applications have focused on the ability of the technique to describe cross-shore or profile variability. Here however, EOFs were used to help identify the dominant modes of longshore shoreline variability at Duck, North Carolina, the Gold Coast, Australia, and at several locations within the Columbia River Littoral Cell in the U.S. Pacific Northwest. In part one of this analysis, characteristic patterns of shoreline variability identified by the EOF analysis are described in detail. At each site, the dominant modes consisting of the first four eigenfunctions were found to describe nearly 95% of the total shoreline variability. At both Duck and the Gold Coast, several interesting longshore periodic features suggestive of sand waves were identified, while boundary effects related to natural headlands and navigational structures/entrances dominated the Pacific Northwest data sets.