Long-term prediction of El Niño-Southern Oscillation using reservoir computing with data-driven realtime filter
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915299444391936 |
|---|---|
| author | Jinno, Takuya Mitsui, Takahito Nakai, Kengo Saiki, Yoshitaka Yoneda, Tsuyoshi |
| author_facet | Jinno, Takuya Mitsui, Takahito Nakai, Kengo Saiki, Yoshitaka Yoneda, Tsuyoshi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_17781 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Long-term prediction of El Niño-Southern Oscillation using reservoir computing with data-driven realtime filter Jinno, Takuya Mitsui, Takahito Nakai, Kengo Saiki, Yoshitaka Yoneda, Tsuyoshi Computational Physics Machine Learning Atmospheric and Oceanic Physics 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. |
| title | Long-term prediction of El Niño-Southern Oscillation using reservoir computing with data-driven realtime filter |
| topic | Computational Physics Machine Learning Atmospheric and Oceanic Physics |
| url | https://arxiv.org/abs/2501.17781 |