Preprints
https://doi.org/10.5194/egusphere-2022-2
https://doi.org/10.5194/egusphere-2022-2
 
07 Mar 2022
07 Mar 2022

Improving the prediction of the Madden-Julian Oscillation of the ECMWF model by post-processing

Riccardo Silini1, Sebastian Lerch2, Nikolaos Mastrantonas3,4, Holger Kantz5, Marcelo Barreiro6, and Cristina Masoller1 Riccardo Silini et al.
  • 1Departament de Física, Universitat Politècnica de Catalunya, Sant Nebridi 22, 08222 Terrassa, Barcelona, Spain
  • 2Institute of Economics, Karlsruhe Institute of Technology, Blücherstr. 17, 76185 Karlsruhe, Germany
  • 3European Centre for Medium-Range Weather Forecast (ECMWF), Reading, UK
  • 4Technische Universität Bergakademie Freiberg (TUBAF), Freiberg, Germany
  • 5Max-Planck Institute for the Physics of Complex Systems, 01187 Dresden, Germany
  • 6Departamento de Ciencias de la Atmósfera, Facultad de Ciencias, Universidad de la República, Igua 4225, 11400 Montevideo, Uruguay

Abstract. 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 (MLR) and a Machine Learning (ML) algorithm as post-processing methods to improve the forecast of the model that currently holds the best MJO forecasting performance, the European Centre for Medium-Range Weather Forecast (ECMWF) model. We find that both MLR and ML improve the MJO prediction and that ML outperforms MLR. The largest improvement is in the prediction of the MJO geographical location and intensity.

Journal article(s) based on this preprint

23 Aug 2022
Improving the prediction of the Madden–Julian Oscillation of the ECMWF model by post-processing
Riccardo Silini, Sebastian Lerch, Nikolaos Mastrantonas, Holger Kantz, Marcelo Barreiro, and Cristina Masoller
Earth Syst. Dynam., 13, 1157–1165, https://doi.org/10.5194/esd-13-1157-2022,https://doi.org/10.5194/esd-13-1157-2022, 2022
Short summary

Riccardo Silini et al.

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-2', Anonymous Referee #1, 21 Mar 2022
    • AC1: 'Reply on RC1', Riccardo Silini, 22 Mar 2022
  • RC2: 'Comment on egusphere-2022-2', Anonymous Referee #2, 19 Jul 2022
    • AC2: 'Reply on RC2', Riccardo Silini, 22 Jul 2022

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-2', Anonymous Referee #1, 21 Mar 2022
    • AC1: 'Reply on RC1', Riccardo Silini, 22 Mar 2022
  • RC2: 'Comment on egusphere-2022-2', Anonymous Referee #2, 19 Jul 2022
    • AC2: 'Reply on RC2', Riccardo Silini, 22 Jul 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish subject to minor revisions (review by editor) (25 Jul 2022) by Andrey Gritsun
AR by Riccardo Silini on behalf of the Authors (31 Jul 2022)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (01 Aug 2022) by Andrey Gritsun

Journal article(s) based on this preprint

23 Aug 2022
Improving the prediction of the Madden–Julian Oscillation of the ECMWF model by post-processing
Riccardo Silini, Sebastian Lerch, Nikolaos Mastrantonas, Holger Kantz, Marcelo Barreiro, and Cristina Masoller
Earth Syst. Dynam., 13, 1157–1165, https://doi.org/10.5194/esd-13-1157-2022,https://doi.org/10.5194/esd-13-1157-2022, 2022
Short summary

Riccardo Silini et al.

Riccardo Silini et al.

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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.

Short summary
The Madden-Julian Oscillation (MJO) has important socio-economic impacts due to its influence on both, tropical and extratropical weather extremes. In this study, we use Machine Learning (ML) to correct the predictions of the weather model holding the best performance, developed by the European Centre for Medium-Range Weather Forecast (ECMWF). We show that the ML post-processing leads to an improved prediction of the MJO geographical location and intensity.