The socioeconomic impact of weather extremes draws the attention of researchers to the development of novel methodologies to make more accurate weather predictions. The Madden–Julian oscillation (MJO) is the dominant mode of variability in the tropical atmosphere on sub-seasonal time scales, and can promote or enhance extreme events in both, the tropics and the extratropics. Forecasting extreme events on the sub-seasonal time scale (from 10 days to about 3 months) is very challenging due to a poor understanding of the phenomena that can increase predictability on this time scale. Here we show that two artificial neural networks (ANNs), a feed-forward neural network and a recurrent neural network, allow a very competitive MJO prediction. While our average prediction skill is about 26–27 days (which competes with that obtained with most computationally demanding state-of-the-art climate models), for some initial phases and seasons the ANNs have a prediction skill of 60 days or longer. Furthermore, we show that the ANNs have a good ability to predict the MJO phase, but the amplitude is underestimated.

极端天气的社会经济影响引起了研究人员的注意，以开发新的方法来进行更准确的天气预测。马登-朱利安振荡 (MJO) 是亚季节时间尺度上热带大气变率的主要模式，可以促进或增强热带和温带地区的极端事件。由于对可以提高该时间尺度的可预测性的现象了解不足，因此在亚季节性时间尺度（从 10 天到大约 3 个月）上预测极端事件非常具有挑战性。在这里，我们展示了两个人工神经网络 (ANN)，一个前馈神经网络和一个循环神经网络，可以进行非常有竞争力的 MJO 预测。虽然我们的平均预测技能约为 26-27 天（这与使用最需要计算的最先进气候模型获得的预测技能相竞争），但对于某些初始阶段和季节，人工神经网络具有 60 天或更长的预测技能。此外，我们表明 ANN 具有很好的预测 MJO 相位的能力，但幅度被低估了。