Multivariate analysis of the energy cycle of the South American rainy season

The South American (SA) rainy season is studied in this paper through the application of a multivariate Empirical Orthogonal Function (EOF) analysis to a SA gridded precipitation analysis and to the components of Lorenz Energy Cycle (LEC) derived from the National Centers for Environmental Prediction (NCEP) reanalysis. The EOF analysis leads to the identification of patterns of the rainy season and the associated mechanisms in terms of their energetics. The first combined EOF represents the northwest-southeast dipole of the precipitation between South and Central America, the South American Monsoon System (SAMS). The second combined EOF represents a synoptic pattern associated with the SACZ (South Atlantic convergence zone) and the third EOF is in spatial quadrature to the second EOF. The phase relationship of the EOFs, as computed from the principal components (PCs), suggests a nonlinear transition from the SACZ to the fully developed SAMS mode by November and between both components describing the SACZ by September-October (the rainy season onset). According to the LEC, the first mode is dominated by the eddy generation term at its maximum, the second by both baroclinic and eddy generation terms and the third by barotropic instability previous to the connection to the second mode by September-October. The predominance of the different LEC components at each phase of the SAMS can be used as an indicator of the onset of the rainy season in terms of physical processes, while the existence of the outstanding spectral peaks in the time dependence of the EOFs at the intraseasonal time scale could be used for monitoring purposes. Copyright (C) 2009 Royal Meteorological Society

The influence of ARIMA-GARCH parameters in feed forward neural networks prediction

The objective of this article is to find out the influence of the parameters of the ARIMA-GARCH models in the prediction of artificial neural networks (ANN) of the feed forward type, trained with the Levenberg-Marquardt algorithm, through Monte Carlo simulations. The paper presents a study of the relationship between ANN performance and ARIMA-GARCH model parameters, i.e. the fact that depending on the stationarity and other parameters of the time series, the ANN structure should be selected differently. Neural networks have been widely used to predict time series and their capacity for dealing with non-linearities is a normally outstanding advantage. However, the values of the parameters of the models of generalized autoregressive conditional heteroscedasticity have an influence on ANN prediction performance. The combination of the values of the GARCH parameters with the ARIMA autoregressive terms also implies in ANN performance variation. Combining the parameters of the ARIMA-GARCH models and changing the ANN`s topologies, we used the Theil inequality coefficient to measure the prediction of the feed forward ANN.

A New Insight on the Preparation of Stabilized Alpha-Nickel Hydroxide Nanoparticles

Nickel hydroxide can provide an outstanding cathode material in alkaline secondary batteries, however the progressive decrease of the charge capacity as a function of the number of oxidation/reduction cycles is a challenging problem to be solved. New improvements on the electrochemical properties of electrode materials can be achieved by exploiting the much better performance of alpha-nickel hydroxide. Such materials were obtained in a stable form by sol-gel method and characterized by thermogravimetric analyses, UV-Vis spectroscopy, X-ray diffractometry, scanning and transmission electron microscopy, cyclic voltammetry and electrochemical quartz crystal microbalance techniques. The results revealed not only the formation of the alpha-Ni(OH)(2) phase, but also a much better electrochemical reversibility and stability as compared with similar materials obtained by electrochemical precipitation method.

La(2-x)Ce(x)Cu(1-y)Zn(y)O(4) perovskites for high temperature water-gas shift reaction

The performance of La(2-x)Ce(x)Cu(1-y)Zn(y)O(4) perovskites as catalysts for the high temperature water-gas shift reaction (H T-W G S R) was investigated. The catalysts were characterized by EDS, XRD, BET surface area, TPR, and XANES. The results showed that all the perovskites exhibited the La(2)CuO(4) orthorhombic structure, so the Pechini method is suitable for the preparation of pure perovskite. However, the La(1.90)Ce(0.10)CuO(4) perovskite alone, when calcined at 350/700 degrees C, also showed a (La(0.935)Ce(0.065))(2)CuO(4) perovskite with tetragonal structure, which produced a surface area higher than the other perovskites. The perovskites that exhibited the best catalytic performance were those calcined at 350/700 degrees C and, among these, La(1.90)Ce(0.10)CuO(4) was outstanding, probably because of the high surface area associated with the presence of the (La(0.935)Ce(0.065))(2)CuO(4) perovskite with tetragonal structure and orthorhombic La(2)CuO(4) phase.