Statistical Downscaling Output GCM Modeling with Continuum Regression and Pre-Processing PCA Approach

Abstract

One of the climate models used to predict the climatic conditions is Global Circulation Models (GCM). GCM is a computer-based model that consists of different equations. It uses numerical and deterministic equation which follows the physics rules. GCM is a main tool to predict climate and weather, also it uses as primary information source to review the climate change effect. Statistical Downscaling (SD) technique is used to bridge the large-scale GCM with a small scale (the study area). GCM data is spatial and temporal data most likely to occur where the spatial correlation between different data on the grid in a single domain. Multicollinearity problems require the need for pre-processing of variable data X. Continuum Regression (CR) and pre-processing with Principal Component Analysis (PCA) methods is an alternative to SD modelling. CR is one method which was developed by Stone and Brooks (1990). This method is a generalization from Ordinary Least Square (OLS), Principal Component Regression (PCR) and Partial Least Square method (PLS) methods, used to overcome multicollinearity problems. Data processing for the station in Ambon, Pontianak, Losarang, Indramayu and Yuntinyuat show that the RMSEP values and R2 predict in the domain 8x8 and 12x12 by uses CR method produces results better than by PCR and PLS.