Long-term prediction of El Niño-Southern Oscillation using reservoir computing with data-driven realtime filter
This work addresses climate forecasting for meteorology and environmental science, but it is incremental as it builds on existing reservoir computing and filtering techniques.
The authors tackled long-term prediction of El Niño-Southern Oscillation by introducing a new realtime band-pass filter combined with reservoir computing, achieving a prediction horizon of 24 months using only past time series.
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.