Prediction of ENSO Episodes Using Canonical Correlation Analysis

@inproceedings{Barnston1992PredictionOE,
  title={Prediction of ENSO Episodes Using Canonical Correlation Analysis},
  author={Anthony G. Barnston and C. F. Ropelewski},
  year={1992}
}
Abstract Canonical correlation analysis (CCA) is explored as a multivariate linear statistical methodology with which to forecast fluctuations of the El Nino/Southern Oscillation (ENSO) in real time. CCA is capable of identifying critical sequence of predictor patterns that tend to evolve into subsequent patterns that can be used to form a forecast. The CCA model is used to forecast the 3-month mean sea surface temperature (SST) in several regions of the tropical Pacific and Indian oceans for… CONTINUE READING

Citations

Publications citing this paper.
SHOWING 1-10 OF 109 CITATIONS

Model Tuning with Canonical Correlation 1 Analysis 2

VIEW 4 EXCERPTS
CITES METHODS & BACKGROUND
HIGHLY INFLUENCED

ENSO Prediction with Markov Models: The Impact of Sea Level

VIEW 11 EXCERPTS
CITES BACKGROUND & METHODS
HIGHLY INFLUENCED

Forecasting Sea Surface Temperature in the Kiribati Region

Tokaua B. Tekabu, Dinesh K. Rao, Ravinesh Chand, Mohammed G.M. Khan
  • 2018 5th Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE)
  • 2018
VIEW 4 EXCERPTS
HIGHLY INFLUENCED

FILTER CITATIONS BY YEAR

1994
2019

CITATION STATISTICS

  • 13 Highly Influenced Citations