Forecasting seasonal rainfall in SE Australia using Empirical Orthogonal Functions and Neural Networks

TL;DR

This study combines Empirical Orthogonal Functions and neural networks to improve seasonal rainfall forecasts in southeastern Australia, showing promising results for early spring predictions based on ocean surface temperatures.

Contribution

It introduces a novel approach integrating EOF analysis with neural networks for seasonal rainfall prediction in SE Australia, outperforming traditional methods in certain months.

Findings

Accurate hindcasts for September and October rainfall.

EOF analysis identifies key ocean indicators for prediction.

Neural networks effectively utilize correlated ocean data.

Abstract

Quantitative forecasting of average rainfall into the next season remains highly challenging, but in some favourable isolated cases may be possible with a series of relatively simple steps. We chose to explore predictions of austral springtime rainfall in SE Australia regions based on the surrounding ocean surface temperatures during the winter. In the first stage, we search for correlations between the target rainfall and both the standard ocean climate indicators as well as the time series of surface temperature data expanded in terms of Empirical Orthogonal Functions (EOFs). In the case of the Indian Ocean, during the winter the dominant EOF shows stronger correlation with the future rainfall than the commonly used Indian Ocean Dipole. Information sources with the strongest correlation to the historical rainfall data are then used as inputs into deep learning artificial neural networks. The resulting hindcasts appear accurate for September and October and less reliable for November. We also attempt to forecast the rainfall in several regions for the coming austral spring.