Search

ABSTRACT This article studies a problem of predicting seasonal precipitation over East Asia from real observations and multi-model ensembles. Classical model output statistics approach based on principal component analysis ( PCA) has been widely used for climate prediction. However, it may not be efficient in predicting precipitation since PCA assumes that information of data should be retained by the second moment of them, which is too stringent to climate data that can be skewed or asymmetric. This article presents a method based on data-adaptive PCA ( DPCA) by Lim and Oh (2016) that can adapt to non-Gaussian distributed data. In addition to investigate the utility of DPCA for climate study, we propose a data-adaptive principal component regression for seasonal precipitation prediction, which consists of DPCA and a regularized regression technique that is able to handle high-dimensional data. We apply the proposed method to nine general circulation models for prediction of precipitations on the summer season (June, July, and August). The prediction ability of the proposed method is evaluated in comparison with observations and model outputs (prediction) of each constituent model. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper considers the problem of predicting the rainfall over East Asia from multimode outputs. For this purpose, we propose a new multimode ensemble method based on regularized regression approach, which consists of two steps, the pre-processing step and the ensemble step. In the pre-processing step, we improve prediction from each model output using regularized regression, and in the ensemble step, we apply regularization-based regression method to combine the result from the pre-processing step. The main benefits of the proposed method are that it improves prediction accuracy, and it is capable of solving the singularity problem so that it can integrate many climate variables from multimode outputs for a better prediction. The proposed method is applied to monthly outputs from nine general circulation models ( GCMs) on boreal summer (June, July, and August) over 20 years (1983-2002). The prediction ability of the proposed ensemble forecast is compared with the observations and the outputs (prediction) from each GCM. The results show that the proposed method is capable of improving forecast accuracy by adjusting each model before combining. [ABSTRACT FROM AUTHOR]

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 (1979-2007) 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 © 2011 Royal Meteorological Society [ABSTRACT FROM AUTHOR]