A Bayesian Machine Learning Algorithm for Predicting ENSO Using Short Observational Time Series

  title={A Bayesian Machine Learning Algorithm for Predicting ENSO Using Short Observational Time Series},
  author={Nan Chen and Faheem Gilani and John Harlim},
  journal={Geophysical Research Letters},
A simple and efficient Bayesian machine learning (BML) training algorithm, which exploits only a 20‐year short observational time series and an approximate prior model, is developed to predict the Niño 3 sea surface temperature (SST) index. The BML forecast significantly outperforms model‐based ensemble predictions and standard machine learning forecasts. Even with a simple feedforward neural network (NN), the BML forecast is skillful for 9.5 months. Remarkably, the BML forecast overcomes the… 

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