A generalized canonical mixed regression model for ENSO prediction with its experiment

A scheme is proposed for predicting NINO-region SST in terms of a generalized canonical mixed regression model based on principal component canonical correlation analysis (PC-CCA), and into the scheme are introduced such techniques as EEOF, PRESS criterion and consensus prediction. By optimizing physical factors and selecting optimal model parameters, experiments were made successful in predicting the NINO SST index for 1 to 4 seasons to follow. The scheme is shown to be stable in operation and its total technical level compares well with that of the model published in NOAA/NWS/NCEP CPC Climate Diagnostics Bulletin but the number of factors needed in our scheme is much fewer than that for the CPC°¯s model in dealing with the same problems. This makes it possible to establish an operational ENSO monitoring system in China.

Keywords: ENSO prediction, canonical regression, predictive scheme