Long-term prediction of El Niño-Southern Oscillation using reservoir computing with data-driven realtime filter
arXiv:2501.17781 · doi:10.1063/5.0261124
Abstract
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.
arXiv commentThis article may be downloaded for personal use only. Any other use requires prior permission of the author and AIP Publishing. This article appeared in T. Jinno et al., Chaos 1 May 2025; 35 (5): 053149 and may be found at https://doi.org/10.1063/5.0261124
References in corpus (7)
- Attractor Reconstruction by Machine Learning
- Improved El Niño-Forecasting by Cooperativity Detection
- Machine-learning inference of fluid variables from data using reservoir computing
- Complexity based approach for El Nino magnitude forecasting before the "spring predictability barrier"
- Machine learning prediction of the MJO extends beyond one month
- RcTorch: a PyTorch Reservoir Computing Package with Automated Hyper-Parameter Optimization
- Data-driven modeling from biased small training data using periodic orbits