Título:
Improving the prediction of the Madden–Julian Oscillation of the ECMWF model by post-processing
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Coordinador:
Director:
Compilador:
Autor:
Silini, Riccardo
Lerch, Sebastian
Mastrantonas, Nikolaos
Kantz, Holger
Barreiro, Marcelo
Masoller, Cristina
Lerch, Sebastian
Mastrantonas, Nikolaos
Kantz, Holger
Barreiro, Marcelo
Masoller, Cristina
Tutor:
Tipo de documento:
Artículo
Editor:
Palabras clave:
Madden–Julian Oscillation
Weather forecast
Climate models
Weather forecast
Climate models
Descriptores:
Año de publicación:
2022
Contenido:
Resumen:
The Madden–Julian Oscillation (MJO) is a major source of predictability on the sub-seasonal (10 to 90 d) timescale. 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-week 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 Forecasts (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.
Descripción:
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Editorial:
European Geosciences Union
EN:
Earth System Dynamics, 2022, 13: 1157–1165
Financiadores:
Citación:
Silini, R, Lerch, S, Mastrantonas, N, [y otros autores]. "Improving the prediction of the Madden–Julian Oscillation of the ECMWF model by post-processing". Earth System Dynamics. [en línea] 2022, 13: 1157–1165. 9 h. DOI: 10.5194/esd-13-1157-2022
Citación:
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ISBN:
e-ISBN:
ISSN:
2190-4979
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Licencia:
Licencia Creative Commons Atribución (CC - By 4.0)
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| Ficheros | Descripción | Tamaño | Formato | ||
|---|---|---|---|---|---|
| 10.5194esd-13-1157-2022.pdf | — | 2.58 MB | Adobe PDF |