Improved seasonal rainfall prediction for grain growers using farm level data and novel modelling

成功的粮食生产是澳大利亚的一种关键出口商品，在很大程度上取决于可靠的季节性预报。然而，高度多变的气候意味着，对于澳大利亚的2.5万名谷物种植者来说，目前的预测在空间和时间上都缺乏细节。该项目将结合模糊分类和人工神经网络，利用17,000个谷物种植者协会成员的高密度气候数据和气象局的海洋表面温度等气候驱动因素，开发一个本地详细的持续更新数据驱动的季节预报系统。在根据观测数据进行验证后，预报将通过基于网络的门户网站提供给用户。

Successful grain production, a key export commodity for Australia, depends heavily on reliable seasonal forecasts. However, the highly variable climate means that for Australia’s 25,000 grain growers current forecasts lack detail in space and time. Using a combination of fuzzy classification and artificial neural networks, this project will develop a locally detailed continuously updating data-driven seasonal forecast system using high density climate data from the 17,000 Grain Growers Association members and climate drivers such as sea surface temperature from the Bureau of Meteorology. After validation against observed data, the forecasts will be delivered via a web-based portal to users.