CPC CCA (Canonical Correlation Analysis)    Statistical Model

NOAA Climate Prediction Center
Camp Springs, Maryland, U.S.

CCA predictions are based on anomaly patterns of SST, depth of the 20°C isotherm, and sea level pressure, all for the most recent four consecutive 3-month periods to detect evolution. CCA is a multivariate regression that relates patterns in the predictor fields (across fields and across temporal snapshots) to patterns in the predictand field. Following model building, the tool determines the projections of the current or recent climate state onto the main modes, and generates a forecast using the implications of the historically related predictand patterns.

On one of the pages of CPC's monthly issued Climate Diagnostics Bulletin, the latest CCA forecast is available.

Contacts: Xuxiang (Luke) He: Luke.He@noaa.gov

References:
Barnston, A. G., and C. F. Ropelewski, 1992: Prediction of ENSO episodes using canonical correlation analysis. J. Climate, 5, 1316-1345.
Barnett, T. P., and R. Preisendorfer, 1987: Origins and levels of monthly and seasonal forecast skill for United States surface air temperatures determined by canonical correlation analysis. Mon. Wea. Rev., 115, 1825-1850.