EMD Analysis of Annual Rainfall in Chennai

Abstract

This paper presents an in-depth Empirical Mode Decomposition (EMD) analysis of the annual rainfall in Chennai, India, covering the period from 1901 to 2021. EMD is employed to decompose the complex and non-linear rainfall time series into intrinsic mode functions (IMFs) and a residual trend component, thereby revealing underlying patterns and periodicities in the data. Six IMFs and one residue were extracted, each representing different frequency components and long-term trends. The results highlight significant oscillatory modes linked to various climatic phenomena, such as the El Niño-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD), which impact Chennai's rainfall. Furthermore, the long-term residue indicates notable trends that have implications for understanding climate change effects in the region. The insights derived from this EMD analysis can inform water resource management, agricultural planning, and urban development strategies in Chennai, providing a foundation for improving resilience to climatic variability and extreme weather events. The study also underscores the potential of integrating EMD with advanced machine learning techniques to enhance rainfall prediction accuracy, paving the way for more effective climate adaptation measures.