Forecasting Monthly Rainfall in the Bowen Basin of Queensland, Australia, Using Neural Networks with Niño Indices
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 barrierNotes
Acknowledgements
This work was funded by the B. Macfie Family Foundation.
References
- 1.Queensland Government Flood Commission of Inquiry, Chap. 13 Mining (2012). http://www.floodcommission.qld.gov.au/publications/final-report
- 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.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.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.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.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.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.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.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.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.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.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.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.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.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.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.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.Chen, D., Cane, M.A.: El Nino prediction and predictability. J. Comput. Phys. 227, 3625–3640 (2008)MathSciNetCrossRefMATHGoogle Scholar
- 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.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.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.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.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.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.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.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.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.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.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.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.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.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.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.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.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.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.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.Webster, P.J., Yang, S.: Monsoon and ENSO: selectively interactive systems. Quart. J. Roy. Meteor. Soc. 118, 877–926 (1992)CrossRefGoogle Scholar
- 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.McPhaden, M.J.: Tropical Pacific Ocean heat content variations and ENSO persistence barriers. Geophys. Res. Lett. 30, 1480 (2003)CrossRefGoogle Scholar
- 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.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