1. Investigating the multiscale teleconnections of Madden–Julian oscillation and monthly rainfall using time-dependent intrinsic cross-correlation.
- Author
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Johny, Kavya, Pai, Maya L., and Adarsh, S.
- Subjects
MADDEN-Julian oscillation ,HILBERT-Huang transform ,STATISTICAL correlation ,TIME series analysis - Abstract
This study proposes a novel ensemble empirical mode decomposition (EEMD) time-dependent intrinsic cross-correlation (TDICC)-coupled framework to investigate the correlation between monthly rainfall over India and Madden–Julian oscillation (MJO) in different timescales. EEMD first decomposes the monthly rainfall and MJO time series into different orthogonal modes namely intrinsic mode functions (IMFs) and a residue with specific periodicity representing the physical processes governing, independently. Then, the significant modes that can be used for rainfall predictions are extracted by executing time-dependent intrinsic correlation (TDIC) which follows the concept of running correlation analysis. Finally, the lags significant for rainfall predictions at different scales are identified by invoking the TDICC analysis considering different time lags up to 12 months. Among the ten MJO indices considered in the study, indices 2 to 5 (longitudes 100° E, 120° E, 140° E, 160° E) are strongly negatively correlated while MJO indices 8 to 10 (longitudes 10° W, 20° E and 70° E) are found to be strongly positively correlated with the rainfall at all the timescales. Contrary to this similarity in the nature of correlations, the correlation patterns can differ with lags both in the nature and strength of associations. The mode of rainfall at annual scale for all the indices can be predicted with lags 1, 2, 5–8 while the mode of highest frequency can be predicted with lag 1 information alone. For the prediction of the low-frequency IMFs (mostly 5th IMF onward) of all the indices, all the 12 lags are found to be significant, implied by the unchanging and stable lagged correlation pattern. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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