Application of partial least squares regression to the diagnosis of year-to-year variations in Pacific Northwest snowpack and Atlantic hurricanes

Application of the method of partial least squares (PLS) regression to geophysical data is illustrated with two cases: (1) finding sea level pressure patterns over the North Pacific associated with dynamically‐induced winter‐to‐winter variations in snowpack in the Cascade mountains of western Washin...

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Bibliographic Details
Published in:Geophysical research letters 2010-02, Vol.37 (3), p.n/a
Main Authors: Smoliak, Brian V., Wallace, John M., Stoelinga, Mark T., Mitchell, Todd P.
Format: Article
Language:English
Subjects:
Atlantic basin hurricane activity
Atmospheric sciences
Cascade mountain range snowpack
Climate change
Computer science
Earth sciences
Earth, ocean, space
Exact sciences and technology
Geophysics
Hurricanes
Least squares method
Marine
Mountains
Oceans
partial least squares regression
Regression
Sea surface temperature
Snowpack
Statistical methods
Time series
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Summary:Application of the method of partial least squares (PLS) regression to geophysical data is illustrated with two cases: (1) finding sea level pressure patterns over the North Pacific associated with dynamically‐induced winter‐to‐winter variations in snowpack in the Cascade mountains of western Washington state and (2) finding patterns of sea surface temperature over the tropical oceans that modulate Atlantic hurricane activity on a year‐to‐year basis. In both examples two robust patterns in the “predictor field” are identified that, in combination, account for over half the variance in the target time series.
ISSN:0094-8276
1944-8007
DOI:10.1029/2009GL041478