Climate signals in the lower stratosphere and upper troposphere

Empirical orthogonal function (EOF) analysis and regression analysis are used to investigate zonally averaged seasonal temperature anomaly patterns and trends in the lower stratosphere and upper troposphere. The first four EOFs explain 64 percent of the temperature variance and can be related, respectively, to the solar flux (SF) and El Niño/Southern Oscillation (ENSO), to the quasi-biennial oscillation (QBO), to atmospheric carbon dioxide (CO2) and turbidity (TB), and to ENSO. The signal of the fourth EOF is modulated in January to March by the solar flux, with the sense of the modulation determined by the phase of the quasi-biennial oscillation.

Meteorological signals in primary productivity at two mountain lakes

Fluctuations in primary productivity at two subalpine lakes reveal both meteorological and biological influences. At Castle Lake, California, large-scale climate events such as the El Niño/Southern Oscillation affect total annual production and, combined with human fishing activity, modify the seasonal pattern of productivity. At Lake Tahoe, California-Nevada, local spring weather conditions modulate annual production and its seasonality by determining the depth of mixing and resulting internal nutrient load. Climatic conditions also contribute to deviations from the long-term trend in productivity by increasing the incidence of forest fires and through anomalous external nutrient loads during precipitation extremes. A 3-year cycle in productivity of as yet unknown origin has also been detected at Lake Tahoe.

Frequency-based interpolation of sampled signals with applications in audio restoration

A new interpolation technique has been developed for replacing missing samples in a sampled waveform drawn from a stationary stochastic process, given the power spectrum for the process. The method works with a finite block of data and is based on the assumption that components of the block DFT are Gaussian zero-mean independent random variables with variance proportional to the power spectrum at each frequency value. These assumptions make the interpolator particularly suitable for signals with a sharply-defined harmonic structure, such as audio waveforms recorded from music or voiced speech. Some results are presented and comparisons are made with existing techniques.

On the use of a decimative spectral estimation method based on eigenanalysis and SVD for formant and bandwidth tracking of speech signals

In this paper, a Decimative Spectral estimation method based on Eigenanalysis and SVD (Singular Value Decomposition) is presented and applied to speech signals in order to estimate Formant/Bandwidth values. The underlying model decomposes a signal into complex damped sinusoids. The algorithm is applied not only on speech samples but on a small amount of the autocorrelation coefficients of a speech frame as well, for finer estimation. Correct estimation of Formant/Bandwidth values depend on the model order thus, the requested number of poles. Overall, experimentation results indicate that the proposed methodology successfully estimates formant trajectories and their respective bandwidths.