Articles | Volume 13, issue 3
Research article
23 Aug 2022
Research article |  | 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


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 | EF: Editorial file upload
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
AR by Riccardo Silini on behalf of the Authors (01 Aug 2022)
Short summary
The Madden–Julian Oscillation (MJO) has important socioeconomic 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 Forecasts (ECMWF). We show that the ML post-processing leads to an improved prediction of the MJO geographical location and intensity.
Final-revised paper