Articles | Volume 11, issue 2
https://doi.org/10.5194/ascmo-11-123-2025
https://doi.org/10.5194/ascmo-11-123-2025
26 Aug 2025
 | 26 Aug 2025

Forecasting springtime rainfall in southeastern Australia using empirical orthogonal functions and neural networks

Stjepan Marčelja

Cited articles

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Cai, W. and Cowan, T.: Dynamics of late autumn rainfall reduction over southeastern Australia, Geophys. Res. Lett., 35, L09708, https://doi.org/10.1029/2008GL033727, 2008. 
Cai, W., van Rensch, P., Cowan, T., and Hendon, H. H.: Teleconnection pathways of ENSO and the IOD and the mechanisms for impacts on Australian rainfall, J. Climate, 24, 3910–3923, https://doi.org/10.1175/2011JCLI4129.1, 2011a. 
Cai, W., van Rensch, P., and Cowan, T.: Influence of Global-Scale Variability on the Subtropical Ridge over Southeast Australia, J. Climate, 24, 6035–6053, https://doi.org/10.1175/2011JCLI4149.1, 2011b. 
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Short summary
Southeasterm Australia, including the Murray–Darling Basin, is a highly productive agricultural region largely dependent on adequate rainfall, providing irrigation water needed for crops.

The Australian Bureau of Meteorology uses linear methods and provides seasonal forecasts expressed as the probability of exceeding median rainfall. I use expanded methods, including more ocean data and deep learning neural networks that provide nonlinear estimates of the rainfall as measured by rain gauges.

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