Exploring rainfall variabilities using statistical functional data analysis

Abstract

Abstract Functional data analysis (FDA) has been widely applied in various scientific fields, including climatological, hydrological, environmental, and biomedical. The flexibility of the FDA in incorporating temporal elements into the statistical analysis makes the method highly demanded compared to the conventional statistical approach. This study introduces FDA methods to investigate the variations and patterns of rainfall throughout Peninsular Malaysia, which includes 16 rain gauge stations in Peninsular Malaysia from 1999 to 2019. A descriptive statistic of the functional data depicted the mean and variation of the rainfall curve over time, while the functional principal component analysis measured the temporal variability of the rainfall curve. According to the findings, the first and second principal components accounted for 87.4% of all variations. The first principal component was highly characterised by the stations over the eastern region during the northeast monsoon since the highest variability was observed from November to January. On the other hand, the stations impacted by the inter-monsoon season were best described by the second principal component. Based on the factor scores derived from the functional principal component, those rain gauge stations with comparable features were then clustered. Overall, the results showed that the rainfall pattern is strongly influenced by their geographical and topographical features and the seasonal monsoon effect.