Identifying Periodic Components in Atmospheric Data Using a Family of Minimum Variance Spectral Estimators

Abstract

This work describes the application of a recently developed signal processing technique for identifying periodic components in the presence of unknown colored noise. Specifically, the application of this technique to the identification of strongly periodic components in meteorological time series is examined. The technique is based on the unique convergence properties of the family of minimum variance (MV) spectral estimators. The MV convergence methodology and computational procedures are described and are illustrated with a theoretical example. The utility of this method to atmospheric signals is demonstrated with a 26-year (1964?1989) time series of 70-mb wind components at Truk Island in the equatorial Pacific. The MV method clearly shows that although equatorial disturbances with periods of 3?5 days have a strong signal, they do not show a strong periodic component. As expected, MV convergence illustrates that the 70-mb zonal wind series at this location has a significant periodic component at the frequency of the annual cycle. In addition, the MV technique provides evidence for a strong periodic component at the frequency of the semiannual cycle and at a frequency within the commonly accepted range of the QBO. Although the QBO is clearly not a strictly periodic phenomenon (since its period is known to vary), the available data suggest that it can be modeled as a periodic component of the zonal wind. This is substantiated by a simple three-sinusoid plus autoregressive order 1 noise model of the 70-mb Truk zonal wind. This parsimonious model provides a very good fit to the observed data.