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

Abstract. Forecasting rainfall into the next season remains highly challenging and is normally presented in terms of probabilities rather than the expected rainfall as measured by rain gauges. I show here that, in favourable cases, for the selected times of the year and selected geographical regions, it is possible to obtain useful quantitative forecasts of rainfall with a series of relatively simple steps. One such instance explored in this work is the prediction of austral springtime rainfall in SE Australia regions predominantly based on the surrounding ocean surface temperatures during the winter. In the first stage, I search for predictors by exploring correlations between the target rainfall and ocean surface temperatures at earlier times. In addition to standard ocean climate indicators such as El Niño or the Indian Ocean Dipole, other typical patterns of variation are captured in terms of the temperatures of selected ocean areas. When characteristic patterns of correlation are discovered, they are included in the predictor selection in the form of expansion in terms of the empirical orthogonal functions (EOFs). EOF expansions can provide very strong signals. For example, in the case of the Indian Ocean, during the winter, the dominant EOF shows a stronger correlation with future rainfall than the commonly used Indian Ocean Dipole. The technical part of the forecast model is provided by deep learning artificial neural networks, where I use the information sources with the strongest correlation in relation to the historical rainfall data as the inputs. The networks are trained on past rainfall data, and the output is a quantitative forecast based on the current state of the predictors. The resulting hindcasts appear to be accurate for September and October and less reliable for November. I also present model forecasts for rainfall during the 2024 austral spring in the selected SE Australia regions.