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Empirical forecasting and Indian Ocean dipole teleconnections of south–west monsoon rainfall in Kerala

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Abstract

Rainfall is a vital hydrologic variable that has a direct and significant impact on the economic development of monsoon-dominated state of Kerala in southern India. An effective approach providing accurate prediction of rainfall makes it possible to take preventive and mitigation measures against natural disasters. In this study, the ensemble empirical mode decomposition (EEMD)–artificial neural networks (ANN)–multiple linear regression (MLR) hybrid approach is used to forecast the south–west monsoon (SWM) rainfall of Kerala. The EEMD of SWM rainfall of Kerala resulted in a set of orthogonal components of specific periodic scale. The non-linear components are identified and separately modeled using ANN and rest of the components are modeled using linear regression to get their values at a specific time t. Finally, the predicted modes are recombined to get the forecasts of a generic time t. The SWM rainfalls of 1871–1972 are used for model calibration and forecasts are made sequentially for 1973–2014 period, which clearly demonstrated its efficacy in handling non-linear part of SWM rainfall data with a predictive skill of 0.65 for validation data. Further, by considering a dataset of 1961–2014 period, this study has investigated the possible teleconnection of SWM rainfall of Kerala with the Indian Ocean dipole (IOD) using the cross-correlation and EEMD-based time-dependent intrinsic correlation (TDIC) analyses. Apart from the strong correlation in the trend component, the analysis has proved the dominancy of negative association of IOD with SWM of Kerala in different process scales with strong positive association at localized time spells. The forecasting strategy demonstrated in the study and the evidence of IOD–SWM rainfall link are an amendment to the efforts for improving the predictability of SWM rainfall in Kerala.

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References

  • Abraham A, Steinberg D, Philip NS (2001) Rainfall forecasting using soft computing models and multivariate adaptive regression splines. IEEE SMC Trans Spec Issue Fusion Soft Comput Hard Comput Ind Appl 1:1–6

    Google Scholar 

  • Adarsh S, Janga Reddy M (2016) Analyzing the hydro-climatic teleconnections of summer monsoon rainfall in Kerala, India using multivariate empirical mode decomposition and time dependent intrinsic correlation. IEEE Geosci Remote Sens Lett 13(9):1221–1225

    Article  Google Scholar 

  • Adarsh S, Janga Reddy M (2017) Multiscale characterization and prediction of monsoon rainfall in India using Hilbert Huang transform and time dependent intrinsic correlation analysis. Meteorol Atmos Phys. https://doi.org/10.1007/s00703-017-0545-6

    Google Scholar 

  • Afshar NR, Fahimi H (2012) Rainfall forecasting using Fourier series. J Civil Eng Archit 6(9):1258–1262

    Google Scholar 

  • Bala I, Singh OP (2008) Relationship between Indian Ocean dipole and summer monsoon. Mausem 59(2):167–172

    Google Scholar 

  • Beltran-Castro J, Valencia-Aguirre J, Orozco-Alzate M, et al (2013) Rainfall forecasting based on ensemble empirical mode decomposition and neural networks. In: Rojas I, Joya G, Gabestany J (eds) Advances in computational intelligence. IWANN 2013. Lecture Notes in Computer Science, vol 7902. Springer, Berlin, Heidelberg, pp 471–480. https://doi.org/10.1007/978-3-642-38679-4_47

    Chapter  Google Scholar 

  • Chen X, Wu Z, Huang NE (2010) The time-dependent intrinsic correlation based on the empirical mode decomposition. Adv Adapt Data Anal 2:233–265

    Article  Google Scholar 

  • Fan J, Yao Q (2003) Nonlinear time series: nonparametric and parametric methods. Springer, New York

    Book  Google Scholar 

  • Gadgil S, Vinayachandran PN, Francis PA, Gadgil S (2004) Extremes of the Indian summer monsoon rainfall, ENSO and equatorial Indian Ocean oscillation. Geophys Res Lett 31:L12213. https://doi.org/10.1029/2004GL019733

    Article  Google Scholar 

  • Goswami BN, Madhusoodanan MS, Neema CP, Sengupta D (2006) A physical mechanism for North Atlantic SST influence on the Indian summer monsoon. Geophys Res Lett 33:L02706. https://doi.org/10.1029/2005GL024803

    Google Scholar 

  • Guhathakurta P (2006) Long-range monsoon rainfall prediction of 2005 for the districts and sub-division Kerala with artificial neural network. Curr Sci 90(6):773–779

    Google Scholar 

  • Huang Y, Schmitt FG (2014) Time dependent intrinsic correlation analysis of temperature and dissolved oxygen time series using empirical mode decomposition. J Mar Syst 130:90–100

    Article  Google Scholar 

  • Huang S, Chang J, Huang Q, Chen Y (2014) Monthly streamflow prediction using modified EMD-based support vector machine. J Hydrol 511:764–775

    Article  Google Scholar 

  • Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, Yen NC, Tung CC, Liu HH (1998) The empirical mode decomposition and the Hilbert spectrum for non-linear and non-stationary time series analysis. Proc R Soc Lond Ser A454:903–995

    Article  Google Scholar 

  • Iyengar RN, Raghu Kanth TSG (2005) Intrinsic mode functions and a strategy for forecasting Indian monsoon rainfall. Meteorol Atmos Phys 90:17–36

    Article  Google Scholar 

  • Iyengar RN, Raghu Kanth TSG (2006) Forecasting of seasonal monsoon rainfall at subdivision level. Curr Sci 91(3):350–356

    Google Scholar 

  • Karthikeyan L, Nagesh Kumar D (2013) Predictability of non-stationary time series using wavelet and EMD based ARMA models. J Hydrol 502:103–119

    Article  Google Scholar 

  • Kashid SS, Maity R (2012) Prediction of monthly rainfall on homogeneous monsoon regions of India based on large scale circulation patterns using genetic programming. J Hydrol 454:26–41

    Article  Google Scholar 

  • Kripalani RH, Kulkarni A (1997a) Climatic impacts of El Niño/La Nina on the Indian monsoon: a new perspective. Weather 52:39–46

    Article  Google Scholar 

  • Kripalani RH, Kulkarni A (1997b) Rainfall variability over south East Asia—connections with Indian monsoon and ENSO extremes: new perspectives. Int J Climatol 17:1155–1168

    Article  Google Scholar 

  • Krishna Kumar KB, Rajagopalan B, Cane MA (1999) On the weakening relationship between the Indian monsoon and ENSO. Science 284:2156–2159

    Article  Google Scholar 

  • Krishnamurthy V, Kinter JL (2003) The Indian monsoon and its relation to global climate variability. In: Rodó X, Comín FA (eds) Global climate. Springer, Berlin, Heidelberg, pp 186–236

    Chapter  Google Scholar 

  • Krishnaswamy J, Vaidyanathan S, Rajagopalan B, Bonell M, Sankaran M, Bhalla RS, Badiger S (2015) Non-stationary and non-linear influence of ENSO and Indian Ocean dipole on the variability of Indian monsoon rainfall and extreme rain events. Clim Dyn 45(1–2):175–184

    Article  Google Scholar 

  • Kumar KK, Rajagopalan B, Hoerling M, Bates G, Cane M (2006) Un raveling the mystery of Indian monsoon failure during ElNiño. Science 314:115–119

    Article  Google Scholar 

  • Mini VK, Pushpa VL, Manoj KB (2016) Inter-annual and long term variability of rainfall in Kerala. Vayu Mandal 42(1):30–42

    Google Scholar 

  • Pai ML, Pramod KV, Balchand AN (2014) Long range forecast on south west monsoon rainfall using artificial neural networks based on clustering approach. Int J Inf Technol Comput Sci 6(7):1–8

    Google Scholar 

  • Parthasarathy B, Munot AA, Kothawale DR (1994) All-India monthly and seasonal rainfall series: 1871–1993. Theor Appl Climatol 49(4):217–224

    Article  Google Scholar 

  • Rao AR, Hsu EC (2008) Hilbert-Huang transform analysis of hydrological and environmental time series, vol 60. Springer Science & Business Media, Berlin

    Google Scholar 

  • Sahai AK, Soman MK, Satyan V (2000) All India summer monsoon rainfall prediction using an artificial neural network. Clim Dyn 16:291–302

    Article  Google Scholar 

  • Saji NH, Goswami BN, Vinayachandran Yamagata T (1999) A dipole mode in the tropical Indian Ocean. Nature 401:360–363

    Google Scholar 

  • Singh P, Borah B (2013) Indian summer monsoon rainfall prediction using artificial neural network. Stoch Environ Res Risk Assess 27(7):1585–1599

    Article  Google Scholar 

  • Varikoden H, Preethi B (2013) Wet and dry years of Indian summer monsoon and its relation with Indo-Pacific sea surface temperatures. Int J Climatol 33:1761–1771

    Article  Google Scholar 

  • Walker GT (1923) Correlation in seasonal variations of weather VIII, a preliminary study of world-weather. Mem Indian Meteorol Dep 24(4):75–131

    Google Scholar 

  • Wang B, Xiang B, Li J, Webster PJ, Rajeevan MN, Liu J, Kuung-Ja Ha (2015) Rethinking Indian monsoon rainfall prediction in the context of recent global warming. Nat Commun. https://doi.org/10.1038/ncomms8154 (Article number: 7154 )

    Google Scholar 

  • Wu Z, Huang NE (2005) Ensemble empirical mode decomposition: a noise-assisted data analysis method. Centre for ocean-land-atmospheric studies Tech. Rep. 193, Center for Ocean-Land-Atmosphere Studies, Calverton, MD, pp 1–51. ftp://grads.iges.org/pub/ctr/ctr_193.pdf

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Acknowledgements

The authors thank the Indian Institute of Tropical Meteorology Pune and Climate Data Library for the providing the necessary data in the public domain with the intention of promotion of non-commercial scientific research. The authors also thank Amrita School of Arts and Sciences for the facilities provided.

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Authors and Affiliations

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

    Kavya Johny & Maya L. Pai

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

    S. Adarsh

Authors
  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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Responsible Editor: A.-P. Dimri.

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Johny, K., Pai, M.L. & Adarsh, S. Empirical forecasting and Indian Ocean dipole teleconnections of south–west monsoon rainfall in Kerala. Meteorol Atmos Phys 131, 1055–1065 (2019). https://doi.org/10.1007/s00703-018-0620-7

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  • DOI: https://doi.org/10.1007/s00703-018-0620-7

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