Seasonal precipitation prediction via data‐adaptive principal component regression

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.

A Recursive Approach to Long-Term Prediction of Monthly Precipitation Using Genetic Programming

“…There are difficulties in the accurate prediction of precipitation because of the complexity of physical processes (Chow et al 1988;Kulligowski and Barros 1998), especially for long-term prediction. As a result, many efforts have been made to develop appropriate methods to predict precipitation, which can be classified into the following types, e.g., dynamical methods (Claußnitzer and Névir 2009;Landman et al 2014), statistical methods (Barnston and Smith 1996;Lee and Ouarda 2010;Kim et al 2017;Chardon et al 2018), soft computing methods (Silverman and Dracup 2000;Partal and Cigizoglu 2009;Ortiz-García et al 2014), and numerical weather prediction methods (Richardson 2005;Park et al 2008). Overall, the above-mentioned methods have been widely used and can be efficient in precipitation prediction; however, most of them rely on utilizing other climatic indices (e.g., El Niño-Southern Oscillation, ENSO) and climatic variables (e.g., sea surface temperature, SST).…”

An Alternative to PCA for Estimating Dominant Patterns of Climate Variability and Extremes, with Application to U.S. and China Seasonal Rainfall

“…This led to the development of rotated EOF analysis, as studied in, for instance, Mestas-Nunez [3] and Lian and Chen [4], and used in Chen and Sun [5]. In another extension, known as extended EOF analysis, PCA has been used to understand developments of patterns in time [6], and in yet another has been adapted to better handle skewed data [7]. PCA and related methods have been discussed in text books such as Wilks [8], von Storch and Zwiers [9] and Jolliffe [10] and in the review paper Hannachi et al [11].…”

An Alternative to PCA for Estimating Dominant Patterns of Climate Variability and Extremes, with Application to US Rainfall

“…This led to the development of rotated EOF analysis, as studied in, for instance, Mestas-Nunez [3] and Lian and Chen [4], and used in Chen and Sun [5]. In another extension, known as extended EOF analysis, PCA has been used to understand developments of patterns in time [6], and in yet another 2 of 15 has been adapted to better handle skewed data [7]. PCA and related methods have been discussed in text books such as Wilks [8], von Storch and Zwiers [9] and Jolliffe [10] and in the review paper Hannachi et al [11].…”