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Multiscale characterization and prediction of monsoon rainfall in India using Hilbert–Huang transform and time-dependent intrinsic correlation analysis

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Abstract

In this paper, the Hilbert–Huang transform (HHT) approach is used for the multiscale characterization of All India Summer Monsoon Rainfall (AISMR) time series and monsoon rainfall time series from five homogeneous regions in India. The study employs the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) for multiscale decomposition of monsoon rainfall in India and uses the Normalized Hilbert Transform and Direct Quadrature (NHT-DQ) scheme for the time–frequency characterization. The cross-correlation analysis between orthogonal modes of All India monthly monsoon rainfall time series and that of five climate indices such as Quasi Biennial Oscillation (QBO), El Niño Southern Oscillation (ENSO), Sunspot Number (SN), Atlantic Multi Decadal Oscillation (AMO), and Equatorial Indian Ocean Oscillation (EQUINOO) in the time domain showed that the links of different climate indices with monsoon rainfall are expressed well only for few low-frequency modes and for the trend component. Furthermore, this paper investigated the hydro-climatic teleconnection of ISMR in multiple time scales using the HHT-based running correlation analysis technique called time-dependent intrinsic correlation (TDIC). The results showed that both the strength and nature of association between different climate indices and ISMR vary with time scale. Stemming from this finding, a methodology employing Multivariate extension of EMD and Stepwise Linear Regression (MEMD-SLR) is proposed for prediction of monsoon rainfall in India. The proposed MEMD-SLR method clearly exhibited superior performance over the IMD operational forecast, M5 Model Tree (MT), and multiple linear regression methods in ISMR predictions and displayed excellent predictive skill during 1989–2012 including the four extreme events that have occurred during this period.

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References

  • Adarsh S, Janga Reddy M (2016) Analysing the hydroclimatic 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 

  • Anctil F, Coulibaly P (2004) Wavelet analysis of the inter-annual variability in southern Québec streamflow. J Clim 17:163–173

    Article  Google Scholar 

  • Antico A, Schlotthauer G, Torres ME (2014) Analysis of hydro-climatic variability and trends using a novel empirical mode decomposition: application to Parana river basin. J Geophys Res Atmos 119(3):1219–1233. doi:10.1002/2013DJD020420

    Article  Google Scholar 

  • Azad S (2011) Extreme Indian monsoon rainfall years and the sunspot cycle. Adv Sci Lett 4(1):1–5

    Article  Google Scholar 

  • Azad S, Narasimha R, Sett SK (2008) A wavelet based significance test for periodicities in Indian monsoon rainfall. Int J Wavelets Multi-Resolut Inf Process 6(2):291–304

    Article  Google Scholar 

  • Barnhart BL, Eichinger WE (2011) Empirical mode decomposition applied to solar irradiance, global temperature, sunspot number and CO2 concentration data. J Atmos Solar Terr Phys 73(2011):1771–1779

    Article  Google Scholar 

  • Bedrosian E (1963) A product theorem for Hilbert transforms. Proc IEEE Trans 51:868–869

    Google Scholar 

  • Bhalme HN, Jadhav SK (1984) The double (Hale) sunspot cycle and floods and droughts in India. Weather 39:112

    Article  Google Scholar 

  • Bhattacharya S, Narasimha R (2007) Regional differentiation in multi-decadal connections between Indian monsoon rainfall and solar activity. J Geophys Res 112:D24103. doi:10.1029/2006JD008353

    Article  Google Scholar 

  • Campbell WH, Blechman JB, Bryson RA (1983) Long period tidal forcing of Indian monsoon rainfall: a hypothesis. J Clim Appl Meteorol 22:287–296

    Article  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(2):233–265

    Article  Google Scholar 

  • Claud C, Pascal T (2007) Revisiting the possible links between the quasi-biennial oscillation and the Indian summer monsoon using NCEP R-2 and CMAP fields. J Clim 20:773–787

    Article  Google Scholar 

  • Claud C, Duchiron B, Terray P (2008) On associations between the 11-year solar cycle and the Indian summer monsoon system. J Geophys Res 113:D09105. doi:10.1029/2007JD008996

    Article  Google Scholar 

  • Cong Z, Chetouani M (2009) Hilbert-Huang transform based physiological signals analysis for emotion recognition. In: International symposium on in signal processing and information technology (ISSPIT), pp 334–339

  • DelSole T, Shukla J (2009) Artificial skill due to predictor screening. J Clim 22:331–345

    Article  Google Scholar 

  • DelSole T, Shukla J (2012) Climate models produce skillful predictions of Indian summer monsoon rainfall. Geophys Res Lett 39(L09703):28

    Google Scholar 

  • Dhanya CT, Nagesh Kumar D (2010) Nonlinear ensemble prediction of chaotic daily rainfall. Adv Water Resour 33(2010):327–347

    Article  Google Scholar 

  • Dong B, Sutton RT, Scaife AA (2006) Multidecadal modulation of El Niño-Southern Oscillation (ENSO) variance by Atlantic Ocean sea surface temperatures. Geophys Res Lett 33:L08705. doi:10.1029/2006GL025766

    Article  Google Scholar 

  • Draper NR, Smith H (1998) Applied regression analysis. Wiley, Hoboken, pp 307–312

    Google Scholar 

  • Duffy DG (2004) The application of Hilbert Huang transforms to meteorological datasets. J Atmos Oceanic Technol 21:599–611

    Article  Google Scholar 

  • Fan J, Yao Q (2003) Non-linear time series: non parametric and parametric methods. Springer, New York

    Book  Google Scholar 

  • Feng S, Hu Q (2008) How the North Atlantic multidecadal oscillation may have influenced the Indian summer monsoon during the past two millennia. Geophys Res Lett 35:L01707. doi:10.1029/2007GL032484

    Article  Google Scholar 

  • Franceschini S, Tsai CW (2010) Application of Hilbert-Huang transform method for analyzing toxic concentrations in the Niagara river. J Hydrol Eng 15(2):90–96

    Article  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. doi: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(2):L02706. doi:10.1029/2005GL024803

    Article  Google Scholar 

  • Grinsted A, Moore JC, Jevrejeva S (2004) Application of the cross wavelet transform and wavelet coherence to geophysical time series. Nonlinear Process Geophys 11(5/6):561–566

    Article  Google Scholar 

  • Hahn DG, Shukla J (1976) An apparent relationship between Eurasian snow cover and Indian monsoon rainfall. J Atmos Sci 33:2461–2462

    Article  Google Scholar 

  • Hu W, Si BC (2013) Soil water prediction based on its scale-specific control using multivariate empirical mode decomposition. Geoderma 193-194:180–188

    Article  Google Scholar 

  • Huang Y-X (2013) Hilbert-Huang transform in ocean turbulence. In: EGU general assembly 2013, held 7–12 April 2013, Vienna, ID EGU2013-9900

  • 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(2014):90–100

    Article  Google Scholar 

  • Huang NE, Wu Z (2008) A review on Hilbert Huang transform: method and its applications to geophysical studies. Rev Geophys 46(2):RG2006. doi:10.1029/2007RG000228

    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 nonlinear and non-stationary time series analysis. Proc R Soc Lond Ser A 454:903–995

    Article  Google Scholar 

  • Huang Y, Schmitt FG, Lu Z, Liu Y (2009a) Analysis of daily river flow fluctuations using empirical mode decomposition and arbitrary order Hilbert spectral analysis. J Hydrol 454:103–111

    Article  Google Scholar 

  • Huang NE, Wu Z, Long SR, Arnold KC, Blank K, Liu TW (2009b) On instantaneous frequency. Adv Adapt Data Anal 1(2):177–229

    Article  Google Scholar 

  • Huang G, Su Y, Kareem A, Liao H (2016) Time-frequency analysis of non-stationary process based on multivariate empirical mode decomposition. J Eng Mech 142. doi:10.1061/(ASCE)EM.1943-7889.0000975

    Article  Google Scholar 

  • Ismail DKB, Lazure P, Puillat I (2015) Advanced spectral analysis and cross correlation based on empirical mode decomposition: application to the environmental time series. Geosci Remote Sens Lett 12(9):1968–1972

    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 

  • Janga Reddy M, Adarsh S (2016) Time frequency characterization of subdivisional scale seasonal rainfall in India using Hilbert Huang transform. Stoch Environ Res Risk Assess 30(4):1063–1085

    Article  Google Scholar 

  • Jothiprakash V, Kote AS (2011) Effect of pruning and smoothing while using M5 model tree technique for reservoir inflow prediction. J Hydrol Eng 16(7):563–574

    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(2012):26–41

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Kripalani RH, Kulkarni A (1997b) 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 (1999) Climatology and variability of historical Soviet snow depth data: some new perspectives in snow—Indian monsoon teleconnections. Clim Dyn 15:475–489

    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 

  • Kuai KZ, Tsai CW (2012) Identification of varying time scales in sediment transport using the Hilbert-Huang transform method. J Hydrol 420–421:245–254

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Labat D (2005) Recent advances in wavelet analyses: Part 1. A review of concepts. J Hydrol 314:275–288

    Article  Google Scholar 

  • Liu PC (1994) Wavelet spectrum analysis and ocean wind waves. In: Foufoula-Georgiou E, Praveen Kumar M (eds) Wavelets in geophysics, pp 151–166

    Chapter  Google Scholar 

  • Lu R, Dong B, Ding H (2006) Impact of the Atlantic multidecadal oscillation on the Asian summer monsoon. Geophys Res Lett 33:L24701. doi:10.1029/2006GL027655

    Article  Google Scholar 

  • Maity R, Nagesh Kumar D (2006a) Bayesian dynamic modeling for monthly Indian summer monsoon rainfall using El Niño-Southern Oscillation (ENSO) and Equatorial Indian Ocean Oscillation (EQUINOO). J Geophys Res 111:D07104. doi:10.1029/2005JD006539

    Article  Google Scholar 

  • Maity R, Nagesh Kumar D (2006b) Hydro-climatic association of monthly summer monsoon rainfall over India with large-scale atmospheric circulation from tropical Pacific Ocean and Indian Ocean. Atmos Sci Lett 7(4):101–107

    Article  Google Scholar 

  • Mamgain A, Dash SK, Parth sarthi P (2010) Characteristics of Eurasian snow depth with respect to Indian summer monsoon rainfall. Meteorol Atmos Phys 110:71–83

    Article  Google Scholar 

  • Massei N, Fournier M (2012) Assessing the expression of large scale climatic fluctuations in the hydrologic variability of daily Seine river flow (France) between 1950–2008 using Hilbert Huang Transform. J Hydrol 448(2012):119–128

    Article  Google Scholar 

  • Massei N, Durand A, Deloffre J, Dupont J, Valdes D, Laignel B (2007) Investigating possible links between the North Atlantic oscillation and rainfall variability in north western France over the past 35 years. J Geophys Res Atmos 112:1–10

    Article  Google Scholar 

  • Mooley DA, Parthasarathy B (1983) Indian summer monsoon and El Niño. Pageoph 121:339–352

    Article  Google Scholar 

  • Murphy AH (1988) Skill scores based on the mean square error and their relationships to the correlation coefficient. Mon Weather Rev 116:2417–2424

    Article  Google Scholar 

  • Nagesh Kumar D, Janga Reddy M, Maity R (2007) Regional rainfall forecasting using large scale climate teleconnections and artificial intelligence techniques. J Intell Syst 16(4):307–322

    Google Scholar 

  • Narasimha R, Bhattacharyya S (2010) A wavelet cross-spectral analysis of Solar-ENSO-rainfall connections in the Indian monsoons. Appl Comput Harmon Anal 28:285–295

    Article  Google Scholar 

  • Narasimha R, Kailas SV (2001) A wavelet map of monsoon variability. Proc Indian Natl Sci Acad 67(3):327–341

    Google Scholar 

  • Nuttall AH (1966) On the quadrature approximation to the Hilbert transform of modulated signals. Proc IEEE 54:1458–1459

    Article  Google Scholar 

  • Papadimitriou S, Sun J, Yu PS (2006) Local correlation tracking in time series. In: Proceedings of IEEE sixth international conference on data mining, 18–22 December 2006, Hong Kong, pp 456–465

  • Parthasarathy B, Pant GB (1984) The spatial and temporal relationships between the Indian summer monsoon rainfall and the Southern oscillation. Tellus 36A:269–277

    Google Scholar 

  • Quinlan JR (1992) Learning with continuous classes. In: Proceedings of Australian joint conference on artificial intelligence. World Scientific Press, Singapore, pp 343–348

  • Rahman Md. A, Chetty M, Bulach D, Wangikar PP (2015) Frequency decomposition based gene clustering. neural information processing. In: Lecture notes in computer science, pp 170–181

    Chapter  Google Scholar 

  • Rao RKS, Lakhole NJ (1978) Quasi-biennial oscillation and summer southwest monsoon. Indian J Meteorol Hydrol Geophys 29:403–411

    Google Scholar 

  • Rehman N, Mandic DP (2010) Multivariate empirical mode decomposition. Proc R Soc 466:1291–1302

    Article  Google Scholar 

  • Rilling G, Fladrin P, Goncalves P (2003) On empirical mode decomposition and its algorithms. In: Proceedings of IEEE-EURASIP workshop on nonlinear signal and image processing NSIP-03, Grado, pp 8–11

  • Rodo X, Rodriguez-Arias MA (2006) A new method to detect transitory signatures and local time/space variability structures in the climate system: the scale-dependent correlation analysis. Clim Dyn 27:441–458

    Article  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 

  • Scafetta N (2014) Multi-scale dynamical analysis (MSDA) of sea level records versus PDO, AMO, and NAO indexes. Clim Dyn 43:175–192

    Article  Google Scholar 

  • Shukla J, Paolino DA (1983) The southern oscillation and long-range forecasting of the summer monsoon rainfall over India. Monsoon Weather Rev 111:1830–1837

    Article  Google Scholar 

  • Singh P, Borah B (2012) Indian summer monsoon rainfall prediction using neural network. Stoch Environ Res Risk Assess 27(7):1585–1599

    Article  Google Scholar 

  • Singh KK, Pal M, Singh VP (2010) Estimation of mean annual flood in Indian catchments using back-propagation neural network and M5 model tree. Water Resour Manag 24:2007–2019

    Article  Google Scholar 

  • Torrence C, Webster PJ (1999) Inter-decadal changes in the ENSO-monsoon system. J Clim 12:2679–2690

    Article  Google Scholar 

  • Torres ME, Colominas MA, Schlotthauer G, Fladrin P (2011) A complete ensemble empirical mode decomposition with adaptive noise. IEEE Int Conf Acoust Speech Signal Process Prague 22–27:4144–4147

    Google Scholar 

  • Usoskin IG, Mursula K (2003) Long-term solar cycle evolution: review of recent developments. Sol Phys 218:319–343

    Article  Google Scholar 

  • Vijayakumar R, Kulkarni R (1995) The variability of the inter-annual oscillations of the Indian summer monsoon rainfall. Adv Atmos Sci 12(1):95–102

    Article  Google Scholar 

  • Walker G (1923) Correlation in seasonal variations of weather, VII: a preliminary study of world weather. Mem India Meteorol Dep 24(4):75–131

    Google Scholar 

  • Wang C, Picaut J (2004) Understanding ENSO physics—a review. In: Wang C, Xie S-P, Carton JA (eds) Earth’s climate: the ocean–atmosphere interaction. Geophysical monograph series, vol 147. AGU, Washington, D.C., pp 21–48

    Chapter  Google Scholar 

  • Wang B, Xiang B, Li J, Webster PJ, Rajeevan MN, Liu J, Kuung-Ja H (2015) Rethinking Indian monsoon rainfall prediction in the context of recent global warming. Nat Commun 6 (article number 7154). doi:10.1038/ncomms8154

  • Zhang R, Delworth TL (2006) Impact of Atlantic multidecadal oscillations on India/Sahel rainfall and Atlantic hurricanes. Geophys Res Lett 33:L17712. doi:10.1029/2006GL02626

    Article  Google Scholar 

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Adarsh, S., Reddy, M.J. Multiscale characterization and prediction of monsoon rainfall in India using Hilbert–Huang transform and time-dependent intrinsic correlation analysis. Meteorol Atmos Phys 130, 667–688 (2018). https://doi.org/10.1007/s00703-017-0545-6

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