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Machine learning prediction of the MJO extends beyond one month
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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.
Forward citations
Cited by 2 Pith papers
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Propagation of the Madden-Julian oscillation as a deterministic chaotic phenomenon
The Madden-Julian oscillation's eastward propagation can split into multiple, probabilistically selected timing regimes under one background state, with the seasonal equatorial asymmetry of sea surface temperature act...
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Improving the Predictability of the Madden-Julian Oscillation at Subseasonal Scales with Gaussian Process Models
A Gaussian process MJO forecaster with empirical correlations claims better skill than an ANN for the first five lead days and probabilistic coverage extending beyond three weeks after a covariance correction.
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