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Forecasting Monthly Rainfall in the Bowen Basin of Queensland, Australia, Using Neural Networks with Niño Indices

  • John Abbot
  • Jennifer Marohasy
Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9992)

Abstract

For three decades there has been a significant global effort to improve El Niño-Southern Oscillation (ENSO) forecasts with the focus on using fully physical ocean-atmospheric coupled general circulation models (GCMs). Despite increasing sophistication of these models and the computational power of the computers that drive them, their predictive skill remains comparable with relatively simple statistical models. In this study, an artificial neural network (ANN) is used to forecast four indices that describe ENSO, namely Niño 1 + 2, 3, 3.4 and 4. The skill of the forecast for Niño 3.4 is compared with forecasts from GCMs and found to be more accurate particularly for forecasts with longer-lead times, and with no evidence of a Spring Predictability Barrier. The forecast values for Niño 1 + 2, 3, 3.4 and 4 were subsequently used as input to an ANN to forecast rainfall for Nebo, a locality in the Bowen Basin, a major coal-mining region of Queensland.

Keywords

ENSO Niño Sea surface temperature Artificial neural network General circulation model Rainfall Spring predictability barrier 
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Notes

Acknowledgements

This work was funded by the B. Macfie Family Foundation.

References

  1. 1.
    Queensland Government Flood Commission of Inquiry, Chap. 13 Mining (2012). http://www.floodcommission.qld.gov.au/publications/final-report
  2. 2.
    Sharma, V., van de Graaff, S., Loechel, B., Franks, D.: Extractive resource development in a changing climate: learning the lessons from extreme weather events in Queensland, Australia. In: National Climate Change Adaptation Research Facility, Gold Coast, p. 110 (2013)Google Scholar
  3. 3.
    Risbey, J.S., Pook, M.J., Mcintosh, P.C.: On the remote drivers of rain variability in Australia. Mon. Weather Rev. 137, 3233–3253 (2009)CrossRefGoogle Scholar
  4. 4.
    Cai, W., van Rensch, P.: The 2011 southeast Queensland extreme summer Rain: a confirmation of a negative Pacific Decadal Oscillation phase? Geophys. Res. Lett. 39, L08702 (2012)CrossRefGoogle Scholar
  5. 5.
    Anwar, M.R., Rodriguez, D., Liu, D.L., et al.: Quality and potential utility of ENSO-based forecasts of spring rainfall and wheat yield in south-eastern Australia. Aust. J. Agri. Res. 59, 112–126 (2008)CrossRefGoogle Scholar
  6. 6.
    Clarke, A.J., Van Gorder, S., Everingham, Y.: Forecasting long-lead rainfall probability with application to Australia’s Northeastern coast. J. Appl. Meteorol. Climatol. 49, 1443–1453 (2010)CrossRefGoogle Scholar
  7. 7.
    Hu, W., Clements, A., Williams, G., et al.: Dengue fever and El Nino/Southern Oscillation in Queensland, Australia: a time series predictive model. Occup. Environ. Med. 67, 307–311 (2010)CrossRefGoogle Scholar
  8. 8.
    Brigode, P., Mićović, Z., Bernardara, P., et al.: Linking ENSO and heavy rainfall events over coastal British Columbia through a weather pattern classification. Hydrol. Earth Syst. Sci. 17, 1455–1473 (2013)CrossRefGoogle Scholar
  9. 9.
    McCabe, G.J., Ault, T.R., Cook, B.I., et al.: Influences of the El Nino Southern Oscillation and the Pacific Decadal Oscillation on the timing of the North American spring. Int. J. Climatol. 32, 2301–2310 (2012)CrossRefGoogle Scholar
  10. 10.
    Bulic, I.H., Kucharski, F.: Delayed ENSO impact on spring precipitation over North Atlantic/European region. Clim Dynam. 38, 2593–2612 (2012)CrossRefGoogle Scholar
  11. 11.
    Xu, K., Zhu, C., He, J.: Two types of El Nino-related Southern Oscillation and their different impacts on global land precipitation. Adv. Atmos. Sci. 30(6), 1743–1757 (2013)CrossRefGoogle Scholar
  12. 12.
    Diatta, S., Fink, A.H.: Statistical relationship between remote climate indices and West African monsoon variability. Int. J. Climatol. 34(2), 3348–3367 (2014)CrossRefGoogle Scholar
  13. 13.
    Latif, M.T., Stockdale, J., Wolff, J., et al.: Climatology and variability in the ECHO coupled GCM. Tellus 46A, 351–366 (1994)CrossRefGoogle Scholar
  14. 14.
    Latif, M., Barnett, T.P., Cane, M.A., et al.: A review of ENSO prediction studies. Clim. Dynam. 9, 167–179 (1994)CrossRefGoogle Scholar
  15. 15.
    Tangang, F.T., Hsieh, W.W., Tang, B.: Forecasting the equatorial Pacific sea surface temperatures by neural network models. Clim. Dynam. 13, 135–147 (1997)CrossRefGoogle Scholar
  16. 16.
    Halide, H., Ridd, P.: Complicated ENSO models do not significantly outperform very simple ENSO models. Int. J. Climatol. 28, 219–233 (2008)CrossRefGoogle Scholar
  17. 17.
    Barnston, A.G., Tippett, M.K., L’Heureux, M.L., et al.: Skill of real-time seasonal ENSO model predictions during 2002–2011: is our capability increasing? B. Am. Meteorol. Soc. 93(5), 631–651 (2012)CrossRefGoogle Scholar
  18. 18.
    Chen, D., Cane, M.A.: El Nino prediction and predictability. J. Comput. Phys. 227, 3625–3640 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  19. 19.
    Zheng, F., Zhu, J., Wang, H., et al.: Ensemble hindcasts of ENSO events over the past 120 years using a large number of ensembles. Adv. Atmos. Sci. 26(2), 359–372 (2009)CrossRefGoogle Scholar
  20. 20.
    Peng, Y., Duan, W., Xiang, J.: Can the uncertainties of Madden–Jullian Oscillation cause a significant “Spring Predictability Barrier” for ENSO events? Acta Meteorol. Sin. 26(5), 566–578 (2012)CrossRefGoogle Scholar
  21. 21.
    Duan, W., Zhang, R.: Is model parameter error related to a significant spring predictability barrier for El Nino events? results from a theoretical model. Adv. Atmos. Sci. 27(5), 1003–1013 (2010)CrossRefGoogle Scholar
  22. 22.
    Kramer, W., Dijkstra, H.A.: Optimal localized observations for advancing beyond the ENSO predictability barrier. Nonlin. Processes Geophys. 20, 221–230 (2013)CrossRefGoogle Scholar
  23. 23.
    Yan, L., Yu, Y.: The spring prediction barrier in ENSO hindcast experiments using the FGOALS-g model. Chinese J. Oceanology Limnol. 30(6), 1093–1104 (2012)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Duan, W., Wei, C.: The ‘spring predictability barrier’ for ENSO predictions and its possible mechanism: results from a fully coupled model. Int. J. Climatol. 33, 1280–1292 (2013)CrossRefGoogle Scholar
  25. 25.
    Abbot, J., Marohasy, J.: Input selection and optimization for monthly rainfall forecasting in Queensland, Australia, using artificial neural networks. Atmos. Res. 128(3), 166–178 (2014)CrossRefGoogle Scholar
  26. 26.
    Abbot, J., Marohasy, J.: Application of artificial neural networks to rainfall forecasting in Queensland. Australia. Adv. Atmos. Sci. 29(4), 717–730 (2012)CrossRefGoogle Scholar
  27. 27.
    Abbot, J., Marohasy, J.: The application of artificial intelligence for monthly rainfall forecasting in the Brisbane Catchment, Queensland, Australia. WIT Trans. Ecol. Environ. 172, 1743–3541 (2013)Google Scholar
  28. 28.
    Abbot, J., Marohasy, J.: The potential benefits of using artificial intelligence for monthly rainfall forecasting for the Bowen Basin, Queensland, Australia. WIT Trans. Ecol. Environ. 171, 1743–3541 (2013)Google Scholar
  29. 29.
    ASCE Task Committee on Application of Artificial Neural Networks in Hydrology: Artificial neural networks in hydrology. I preliminary concepts. J. Hydrol. Eng. 5, 115–123 (2000)Google Scholar
  30. 30.
    Verdon, D.C., Franks, S.W.: Long-term behavior of ENSO: Interactions with the PDO over the past 400 years inferred from paleoclimate records. Geophys. Res. Lett. 33(6), L06712 (2006)CrossRefGoogle Scholar
  31. 31.
    Power, S., Casey, T., Folland, C., et al.: Interdecadal modulation of the impact of ENSO on Australia. Clim. Dynam. 15, 319–324 (1999)CrossRefGoogle Scholar
  32. 32.
    Izumo, T., Vialard, J., Lengaigne, M., et al.: Influence of the state of the Indian Ocean Dipole on the following year’s El Niño. Nat. Geosci. 3, 168–172 (2010)CrossRefGoogle Scholar
  33. 33.
    Singh, P., Borah, B.: Indian summer monsoon rainfall prediction using artificial neural network. Stoch. Environ. Res. Risk. Assess. 27, 1585–1599 (2013)CrossRefGoogle Scholar
  34. 34.
    Acharya, N., Chattopadhyay, S., Kulkarni, M.A., et al.: A neurocomputing approach to predict monsoon rainfall in monthly scale using SST anomaly as a predictor. Acta Geophys. 60(1), 260–279 (2012)CrossRefGoogle Scholar
  35. 35.
    Saigal, S., Mehrotra, D.: Performance comparison of time series data using predictive data mining techniques. Adv. Inf. Mining. 4(1), 57–66 (2012)Google Scholar
  36. 36.
    Willmott, C.J., Matsuura, K.: Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Clim. Res. 30, 79–82 (2005)CrossRefGoogle Scholar
  37. 37.
    Zhu, J., Zhou, G.Q., Zhang, R.H., et al.: Improving ENSO prediction in a hybrid coupled model with an embedded entrainment temperature parameterisation. Int. J. Climatol. 33, 343–355 (2013)CrossRefGoogle Scholar
  38. 38.
    Webster, P.J., Yang, S.: Monsoon and ENSO: selectively interactive systems. Quart. J. Roy. Meteor. Soc. 118, 877–926 (1992)CrossRefGoogle Scholar
  39. 39.
    Lau, K.M., Yang, S.: The Asian monsoon and predictability of the tropical ocean-atmosphere system. Quart. J. Roy. Meteor. Soc. 122, 945–957 (1996)Google Scholar
  40. 40.
    McPhaden, M.J.: Tropical Pacific Ocean heat content variations and ENSO persistence barriers. Geophys. Res. Lett. 30, 1480 (2003)CrossRefGoogle Scholar
  41. 41.
    Zheng, F., Zhu, J.: Spring predictability barrier of ENSO events from the perspective of an ensemble prediction system. Global Planet. Change 72, 108–117 (2010)CrossRefGoogle Scholar
  42. 42.
    Wu, A., Hsieh, W.W., Tang, B.: Neural network forecasts of the tropical Pacific sea surface temperatures. Neural Netw. 19, 145–154 (2006)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Climate Modelling LaboratoryNoosavilleAustralia
  2. 2.Department of EngineeringUniversity of TasmaniaHobartAustralia
  3. 3.Institute of Public AffairsMelbourneAustralia

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