Skip to main content

Analysis of Inter-Annual Climate Variability Using Discrete Wavelet Transform

  • 795 Accesses

Abstract

This chapter presents a data adaptive filtering technique to extract annual cycles and the analysis of inter-annual climate variability based on different climate signals using discrete wavelet transform (DWT). The annual cycle is considered as higher energy trend in a climate signal and separated by implementing a threshold-driven filtering technique. The fractional Gaussian noise (fGn) is used here as a reference signal to determine adaptive threshold without any prior training constraint. The climate signal and fGn are decomposed into a finite number of subband signals using the DWT. The subband energy of the fGn and its confidence intervals are computed. The upper bound of the confidence interval is set as the threshold level. The energy of individual subband of a climate signal is compared with the threshold. The lowest order subband of which the energy is greater than the threshold level is selected yielding the upper frequency limit of the trend representing annual cycle. All the lower frequency subbands starting from the selected one are used to reconstruct the annual cycle of the corresponding climate signal. The distance between adjacent peaks in the extracted cycles refers to the inter-annual variation of the climate condition. The experimental results illustrate the efficiency of the proposed data adaptive approach to separate the annual cycle and the quantitative analysis of climate variability.

Keywords

  • Climate signal
  • Discrete wavelet transform
  • Fractional gaussian noise
  • Multiband decomposition
  • Time domain filtering

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-94-017-8642-3_9
  • Chapter length: 17 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   99.00
Price excludes VAT (USA)
  • ISBN: 978-94-017-8642-3
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Hardcover Book
USD   129.00
Price excludes VAT (USA)
Fig. 9.1
Fig. 9.2
Fig. 9.3
Fig. 9.4
Fig. 9.5
Fig. 9.6
Fig. 9.7
Fig. 9.8
Fig. 9.9
Fig. 9.10
Fig. 9.11
Fig. 9.12

References

  • Bates BC, Charles SP, Hughes JP (1998) Stochastic downscaling of numerical climate model simulations. Environ Model Softw 13(3–4):325–333

    CrossRef  Google Scholar 

  • Broughton SA, Bryan KM (2008) Discrete Fourier analysis and wavelets: applications to signal and image processing, 1st edn. John Wiley & Sons, Inc., Hoboken, New Jersey

    Google Scholar 

  • Dairaku K, Emori S, Nozawa T, Yamazaki N, Hara M, Kawase H (2004) Hydrological change under the global warming in Asia with a regional climate model nested in a general circulation model. In: Proceedings of the third international workshop on monsoons (IWM-III), Hangzhou, China

    Google Scholar 

  • Flandrin P, Rilling G, Goncalves P (2004) Empirical mode decomposition as a filter bank. IEEE Signal Process Lett 11(2):112–114

    CrossRef  Google Scholar 

  • Harrison DE, Larkin NK (1997) Darwin sea level pressure, 1876–1996: evidence for climate change? Geophys Res Lett 24(14):1779–1782

    CrossRef  Google Scholar 

  • Mak M (1995) Orthogonal wavelet analysis: inter-annual variability in the sea surface temperature. Bull Am Meteorol Soc 76:2179–2186

    CrossRef  Google Scholar 

  • Mallat S (2008) A wavelet tour of signal processing, 3rd edn. Academic Press, Orlando

    Google Scholar 

  • Molla MKI, Rahman MS, Sumi A, Banik P (2006) Empirical model decomposition analysis of climate changes with special reference to rainfall data. Discrete Dyn Nat Soc 2006:1–17

    CrossRef  Google Scholar 

  • Molla MKI, Ghosh PR, Hirose K (2011) Bivariate EMD-based data adaptive approach to the analysis of climate variability. Discrete Dyn Nat Soc 2011:1–21

    CrossRef  Google Scholar 

  • Mpelasoka FS, Mullan AB, Heerdegen RG (2001) New Zealand climate change information derived by multivariate statistical and artificial neural networks approaches. Int J Climatol 21(11):1415–1433

    CrossRef  Google Scholar 

  • Oh HS, Ammann CM, Naveau P, Nychka D, Otto-Bliesner BL (2003) Multi-resolution time series analysis applied to solar irradiance and climate reconstructions. J Atmos Solar Terr Phys 65:191–201

    CrossRef  Google Scholar 

  • Rajagopalan B, Lall U, Cane MA (1997) Anomalous ENSO occurrences: an alternate view. J Climate 10(9):2351–2357

    CrossRef  Google Scholar 

  • Rajagopalan B, Lall U, Cane MA (1999) Comment on reply to the comments of Trenberth and Hurrell. Bull Am Meteorol Soc 80(12):2724–2726

    Google Scholar 

  • Strang G, Nquyen T (1996) Wavelets and filter banks. Willesley-Cambridge University Press, Willesley

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Md. Khademul Islam Molla .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2014 Springer Science+Business Media Dordrecht

About this chapter

Cite this chapter

Molla, M.K.I., Alam, A.T.M.J., Akter, M., Siddique, A.R.S.A., Rahman, M.S. (2014). Analysis of Inter-Annual Climate Variability Using Discrete Wavelet Transform. In: Islam, T., Srivastava, P., Gupta, M., Zhu, X., Mukherjee, S. (eds) Computational Intelligence Techniques in Earth and Environmental Sciences. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-8642-3_9

Download citation