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Article
Identifying periodic components in atmospheric data using a family of minimum variance spectral estimators
Journal of Climate
  • Christopher Kim Wikle, Iowa State University
  • Peter J. Sherman, Iowa State University
  • Tsing-Chang Chen, Iowa State University
Document Type
Article
Publication Version
Published Version
Publication Date
10-1-1995
DOI
10.1175/1520-0442(1995)008<2352:IPCIAD>2.0.CO;2
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 Islands in the equatorial Pacific.
Comments

This article is from Journal of Climate 8 (1995): 2352, doi: 10.1175/1520-0442(1995)008<2352:IPCIAD>2.0.CO;2. Posted with permission.

Copyright Owner
American Meteorological Society
Language
en
Date Available
2015-09-01
File Format
application/pdf
Citation Information
Christopher 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/