用实时数据滤波提升机器学习预测厄尔尼诺能力，可提前两年预报。
Long-term prediction of El Niño-Southern Oscillation using reservoir computing with data-driven realtime filter
- 设计基于历史数据的实时带通滤波器，适配长期气候预测流程。
- 利用储层计算模型，实现对厄尔尼诺现象长达24个月的精准预测。
- 方法可直接用于业务化预测，适合气候建模与灾害预警研究者。
近年来，机器学习在气候动力现象时间序列预测中的应用日益活跃。已知对时间序列数据施加带通滤波是构建高质量数据驱动模型的关键步骤。为提升机器学习模型的长期可预测性，本文提出一种新型带通滤波器，其仅依赖历史时间序列，可直接应用于实时业务预测流程。将该滤波器与储层计算（reservoir computing）结合，后者是一种基于数据驱动动力系统的机器学习技术。以厄尔尼诺-南方涛动（El Niño-Southern Oscillation, ENSO）为例，仅使用历史数据，实现了对未来24个月多周期动态的预测。
原文摘要 · Abstract (English)
In recent years, the application of machine learning approaches to time-series forecasting of climate dynamical phenomena has become increasingly active. It is known that applying a band-pass filter to a time-series data is a key to obtaining a high-quality data-driven model. Here, to obtain longer-term predictability of machine learning models, we introduce a new type of band-pass filter. It can be applied to realtime operational prediction workflows since it relies solely on past time series. We combine the filter with reservoir computing, which is a machine-learning technique that employs a data-driven dynamical system. As an application, we predict the multi-year dynamics of the El Niño-Southern Oscillation with the prediction horizon of 24 months using only past time series.
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