AT655 Objective Analysis in the Atmospheric Sciences Spring 2012 Instructor

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Objective Analysis in the Atmospheric Sciences

Spring 2012

David W. J. Thompson

Room 430, Atmospheric Science


Allyson Clark

Room 212, ATS West

Office hours:

Meeting times:

Tuesday, Thursday 10:00-11:15 PM, ATS West 121
Rescheduled classes:

No class Feb. 7, 9.

We will have a few make up classes on Fridays, 10:00-11:15 PM, ATS West 121.

Dates TBD.

Course credits:

Course website:

Primary sources:

- Class notes.

- Notes from Professors Dennis Hartmann and John M. Wallace

(Department of Atmospheric Sciences, University of Washington).

See links via class website.

Possible reference texts (available via the library or purchase):
Statistical Analysis in Climate Research. von Storch and Zwiers, Cambridge U. Press, 1999

Statistical Methods in the Atmospheric Sciences Wilks, Academic Press, 1995

Schaum’s Outline Series: Statistics Murray R. Spiegel McGraw-Hill.

The course provides an overview of the statistical methods used to interpret data sets in the atmospheric and oceanic sciences. This is a tools class: the objective is to provide a working knowledge of the statistical tools most commonly used in the literature. Topics include the application of basic statistics (superposed epoch analysis; significance testing; curve fitting; correlation and regression theory), matrix methods (principal component analysis; MCA; CCA), and time series analysis (harmonic analysis; power spectra; filtering; cross-spectrum analysis; SSA; wavelet analysis). Emphasis is placed on the application of the tools discussed in class to the analysis of atmospheric and oceanic data.

Course outline:

1. Review and application of elementary statistics

• Review of basic statistics

Correlation theory; regression; multiple-regression; sampling theory of correlation

• Superposed epoch analysis

• Significance testing

• Application of regression/compositing/etc. to data
2. Matrix methods

• Review of linear algebra; vector spaces; rank; orthogonalization

• Matrix decomposition; singular value decomposition (SVD)

• Application of SVD to data compression and filtering

• Empirical orthogonal functions; principal-component analysis

• Maximum covariance analysis (MCA); canonical correlation analysis (CCA)

Application of EOF, MCA, CCA to atmospheric data
3. Time series analysis

• Autocorrelation; red noise white noise

• Harmonic analysis; power spectra; methods of computing power spectra

• Significance testing of spectral peaks; data windows

Cross spectrum analysis

• Filtering; filter design; recursive/nonrecursive filters; response functions

• Application of time series analysis to data sets
4. As time allows...

• Review of more fancy (but not necessarily more useful) methods...

Grading and exams:

Grades are based on roughly 5-6 evenly weighted homework assignments. The homework emphasize the application of the tools discussed in class to real-world data. There are no exams.

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