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arXiv · 2301.01254

Machine learning prediction of the MJO extends beyond one month

Tamaki Suematsu·Kengo Nakai·Tsuyoshi Yoneda·Daisuke Takasuka·Takuya Jinno·Yoshitaka Saiki·Hiroaki Miura
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Abstract

The prediction of the Madden-Julian Oscillation (MJO), a massive tropical weather event with vast global socio-economic impacts, has been infamously difficult with physics-based weather prediction models. Here we construct a machine learning model using reservoir computing technique that forecasts the real-time multivariate MJO index (RMM), a macroscopic variable that represents the state of the MJO. The training data was refined by developing a novel filter that extracts the recurrency of MJO signals from the raw atmospheric data and selecting a suitable time-delay coordinate of the RMM. The model demonstrated the skill to forecast the state of MJO events for a month from the pre-developmental stages. Best-performing cases predicted the RMM sequence over two months, which exceeds the expected inherent predictability limit of the MJO.

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Tamaki Suematsu, Kengo Nakai, Tsuyoshi Yoneda, Daisuke Takasuka, Takuya Jinno, Yoshitaka Saiki, Hiroaki Miura. 2022-12-29. Machine learning prediction of the MJO extends beyond one month. https://arxiv.org/abs/2301.01254

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