Identifying periodic components in atmospheric data using a family of minimum variance spectral estimatorsJournal of Climate
Publication VersionPublished Version
AbstractThis 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 Islands in the equatorial Pacific.
Copyright OwnerAmerican Meteorological Society
Citation InformationChristopher Kim Wikle, Peter J. Sherman and Tsing-Chang Chen. "Identifying periodic components in atmospheric data using a family of minimum variance spectral estimators" Journal of Climate Vol. 8 Iss. 10 (1995) p. 2352 - 2363
Available at: http://works.bepress.com/peter_sherman/2/