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Time-dependent intrinsic cross-correlation approach for multi-scale teleconnection analysis for monthly rainfall of India

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Abstract

This study proposes an efficient framework employing Ensemble Empirical Mode Decomposition (EEMD) algorithm coupled with Time-Dependent Intrinsic Cross Correlation (TDICC) method to detect the teleconnection between large scale climatic oscillations and monthly rainfall of India. Indian Ocean Dipole (IOD), El Niño Southern Oscillation (ENSO) and the enhanced convective phases of Madden Julian Oscillation (MJO) have taken into consideration as climatic oscillations for the teleconnection study. EEMD method decomposed each signal to a set of zero mean oscillatory modes namely Intrinsic Mode Functions (IMFs)with definite periodicity. The time-dependent running correlation analysis of the IMFs of ENSO and IOD showed strong negative correlation with the modes of rainfall, at inter annual scales. All the IMFs of MJO indices 8–10 showed very strong positive correlation while MJO indices 2 and 3 showed very strong negative correlations with the corresponding IMFs of rainfall. TDICC analysis found the most influential antecedent information of climatic oscillation in the prediction of IMFs of rainfall at different time scales. On considering the different lags in TDICC analysis, the high frequency modes are associated with transition in correlation from positive to negative and vice versa while low frequency modes display a stable pattern in the teleconnections of rainfall with climatic oscillations. The TDIC-based identification of relevant modes and TDICC-based identification of significant lags will substantially alleviate the computational complexities and help in improved predictions of monthly rainfall over India.

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Availability of data and material

The datasets analysed during the current study are publicly available in the website of Indian Institute of Tropical Meteorology (IITM) (http://www.tropmet.res.in). Climate Prediction Center (CPC), NOAA data sets (https://www.cpc.ncep.noaa.gov. and https://www.esrl.noaa.gov).

Code availability

The code generated during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The authors are grateful to the Indian Institute of Tropical Meteorology Pune for providing the relevant data regarding rainfall in the public domain. The authors also thank National Oceanic and Atmospheric Administration for making available data regarding ENSO, MJO and IOD indexes for research. The authors also thank Department of Computer Science and IT, Amrita School of Arts and Sciences for the support provided.

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No funding was received for conducting this study.

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Authors and Affiliations

  1. Department of Computer Science and IT, Amrita School of Arts and Sciences, Amrita Vishwa Vidyapeetham, Kochi Campus, India

    Kavya Johny & Maya L. Pai

  2. Department of Civil Engineering, TKM College of Engineering, Kollam, India

    S. Adarsh

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  1. Kavya Johny
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  2. Maya L. Pai
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  3. S. Adarsh
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Correspondence to Kavya Johny.

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Johny, K., Pai, M.L. & Adarsh, S. Time-dependent intrinsic cross-correlation approach for multi-scale teleconnection analysis for monthly rainfall of India. Meteorol Atmos Phys 134, 73 (2022). https://doi.org/10.1007/s00703-022-00910-9

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