SE Australia springtime rainfall forecast using July data

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. ICE1 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.