The Comparison of T-Mode and Pearson Correlation Matrices in Classfication of DailyRainfall Patterns in Peninsular Malaysia

The aim of this study is to identify daily rainfall patterns of wet days linked to thetopography of Peninsular Malaysia using two different configurations of points in the data.The data used in this study were obtained from 75 rain gauge stations in Peninsular Malaysiafrom the year 1975-2007. We only consider data for the period in which southwest monsoonoccur from June until September yielding a total of 153 days.A typical classificationapproach in identifying daily rainfall patterns requires the use of configuration points ofentities between the rows and column of the data based on correlation matrices. In this study,we compare effect on the cluster of daily rainfall patterns on two types of correlationmatrices: T-mode correlation matrix and Pearson correlation matrix. These matrices are thenused as inputs for Principal Component Analysis (PCA) to reduce the dimension of thedataset before clustering the rainfall patterns of wet days. We have found that although Tmodecorrelation matrix is popularly used in subtropical climate studies, it is unable to showclear classification in defining daily rainfall patterns in tropical climate data. Using Calinskiand Harabasz Index, only two-rainfall pattern cluster can be identified on T-mode correlationmatrix. On the other hand, Pearson correlation matrix showed three different rainfall patternsand each cluster are identified to be linked to certain topographic characteristics. These threeclusters indicate that the rainfall pattern during the southwest monsoon experiencing themost heavy rain in the western part of the Peninsula, particularly in characterizing therainfall pattern of the northwestern and western region of Peninsular Malaysia. Theseclusters are mapped out using ARCGIS software.