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

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Cited articles

Alvarez, M. S., Vera, C. S., and Kiladis, G. N.: MJO Modulating the Activity of the Leading Mode of Intraseasonal Variability in South America, Atmosphere, 8, 232,, 2017. a
Barrett, B. S., Densmore, C. R., Ray, P., and Sanabia, E. R.: Active and weakening MJO events in the Maritime Continent, Clim. Dynam., 57, 157–172,, 2021. a
Bergman, J. W., Hendon, H. H., and Weickmann, K. M.: Intraseasonal Air–Sea Interactions at the Onset of El Niño, J. Climate, 14, 1702–1719, 2001. a
Camargo, S. J., Wheeler, M. C., and Sobel, A. H.: Diagnosis of the MJO Modulation of Tropical Cyclogenesis Using an Empirical Index, J. Atmos. Sci., 66, 3061–3074,, 2009. a
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