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.

跨季节降雨预报仍属极具挑战性的研究课题，当前此类预报通常以概率形式输出，而非基于雨量站实测数据得到的确定性降雨预估量。本文中ICE1证实，在适宜的预报场景下，针对选定的时段与地理区域，通过一系列相对简便的步骤，即可获得具备实用价值的定量降雨预报。本研究探索的典型应用案例之一，便是主要依托冬季周边海域的海表温度，对澳大利亚东南部区域的南半球春季降雨进行预报。

第一阶段，本研究通过探究目标降雨与前期海表温度之间的相关性，筛选预报因子。除厄尔尼诺、印度洋偶极子等经典海洋气候指标外，选定海域的海表温度还可捕捉其他典型的气候变异模态。当特征相关模态被识别后，将以经验正交函数（empirical orthogonal functions, EOFs）展开的形式纳入预报因子筛选流程。EOF展开能够提取极强的气候信号。例如，在冬季印度洋海域，主模态EOF与未来降雨的相关性，要优于当前常用的印度洋偶极子指标。

本预报模型的核心技术模块采用深度学习人工神经网络，以与历史降雨数据相关性最强的信息源作为模型输入。神经网络基于历史降雨数据完成训练，最终输出基于当前预报因子状态的定量降雨预报结果。所得后报结果对9月、10月的预报精度较高，对11月的预报可靠性则有所降低。此外，本研究还给出了选定澳大利亚东南部区域2024年南半球春季降雨的模型预报结果。