Analysis of Rainfall and Temperature Data Using Ensemble Empirical Mode Decomposition

Willard Zvarevashe; Symala Krishnannair; Venkataraman Sivakumar

doi:10.5334/dsj-2019-046

Abstract

Climatic variables such as rainfall and temperature have nonlinear and non-stationary characteristics such that analysing them using linear methods inconclusive results are found. Ensemble empirical mode decomposition (EEMD) is a data-adaptive method that is best suitable for data with nonlinear and non-stationary characteristics. The average monthly rainfall and temperature data for a selected region in South Africa are decomposed into intrinsic mode functions (IMFs) at different time scales using EEMD. The IMFs exhibit an inter-annual to inter-decadal variability. The influence of climatic oscillations such as El-Niño Southern Oscillation (ENSO) and quasi-biennial oscillation (QBO) is identified. The influence of temperature variability on rainfall is also shown at different time scales. Based on the results obtained, the EEMD method is found to be suitable to identify different oscillations in the rainfall and temperature data.

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Analysis of Rainfall and Temperature Data Using Ensemble Empirical Mode Decomposition

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