An improvement of seasonal climate prediction by regularized canonical correlation analysis

Abstract

This article proposes a statistical method based on the regularized canonical correlation analysis (RCCA) to improve on the conventional canonical correlation analysis (CCA) method for seasonal climate prediction. The fundamental idea of this method is to combine the regularization principle with the classical CCA to handle high-dimensional data in which the number of variables is larger than the number of observations. This study focuses on prediction of future precipitation for the boreal summer (June-July-August, JJA) on both global and regional scales. We apply the RCCA method to the JJA hindcast/forecast archives for 29 years (19792007) obtained from the operational seasonal prediction system at Korea Meteorological Administration (KMA) in order to correct the model biases and provide a more accurate climate prediction. It is observed that the results from the RCCA method demonstrate a more accurate seasonal climate prediction as compared to the results from the general circulation model (GCM) and the CCA method coupled with empirical orthogonal functions (EOF) analysis, which is the modified CCA technique widely used, in terms of both correlation and mean square error. Copyright (c) 2011 Royal Meteorological Society