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Improving the prediction of the Madden-Julian Oscillation of the ECMWF model by post-processing
  • +3
  • Riccardo Silini,
  • Sebastian Lerch,
  • Nikolaos Mastrantonas,
  • Holger Kantz,
  • Marcelo Barreiro,
  • Cristina Masoller
Riccardo Silini
UPC Universitat Politècnica de Catalunya

Corresponding Author:riccardo.silini@outlook.com

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Sebastian Lerch
Karlsruhe Institute of Technology
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Nikolaos Mastrantonas
European Centre for Medium Range Weather Forecasts
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Holger Kantz
Max Planck Institute for the Physics of Complex Systems, Dresden, Germany
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Marcelo Barreiro
Facultad de Ciencias, Universidad de la Republica, Uruguay
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Cristina Masoller
Universitat Politecnica de Catalunya
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The Madden-Julian Oscillation (MJO) is a major source of predictability on the sub-seasonal (10- to 90-days) time scale. An improved forecast of the MJO, may have important socioeconomic impacts due to the influence of MJO on both, tropical and extratropical weather extremes. Although in the last decades state-of-the-art climate models have proved their capability for forecasting the MJO exceeding the 5 weeks prediction skill, there is still room for improving the prediction. In this study we use Multiple Linear Regression and an Artificial Neural Network as post-processing methods to improve one of the currently best dynamical models developed by the European Centre for Medium-Range Weather Forecast (ECMWF). We show that the post-processing with the machine learning algorithm employed leads to an improvement of the MJO prediction. The largest improvement is in the prediction of the MJO geographical location and intensity.