Improving the prediction of the Madden–Julian Oscillation of the ECMWF model by post-processing
<p>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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2022-08-01
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| Series: | Earth System Dynamics |
| Online Access: | https://esd.copernicus.org/articles/13/1157/2022/esd-13-1157-2022.pdf |
| _version_ | 1828433846483288064 |
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| author | R. Silini S. Lerch N. Mastrantonas N. Mastrantonas H. Kantz M. Barreiro C. Masoller |
| author_facet | R. Silini S. Lerch N. Mastrantonas N. Mastrantonas H. Kantz M. Barreiro C. Masoller |
| author_sort | R. Silini |
| collection | DOAJ |
| description | <p>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.</p> |
| first_indexed | 2024-12-10T18:41:22Z |
| format | Article |
| id | doaj.art-fdafe5ceaeec4960a7514b80bf01cede |
| institution | Directory Open Access Journal |
| issn | 2190-4979 2190-4987 |
| language | English |
| last_indexed | 2024-12-10T18:41:22Z |
| publishDate | 2022-08-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Earth System Dynamics |
| spelling | doaj.art-fdafe5ceaeec4960a7514b80bf01cede2022-12-22T01:37:39ZengCopernicus PublicationsEarth System Dynamics2190-49792190-49872022-08-01131157116510.5194/esd-13-1157-2022Improving the prediction of the Madden–Julian Oscillation of the ECMWF model by post-processingR. Silini0S. Lerch1N. Mastrantonas2N. Mastrantonas3H. Kantz4M. Barreiro5C. Masoller6Departament de Física, Universitat Politècnica de Catalunya, Sant Nebridi 22, 08222 Terrassa, Barcelona, SpainInstitute of Economics, Karlsruhe Institute of Technology, Blücherstr. 17, 76185 Karlsruhe, GermanyEuropean Centre for Medium-Range Weather Forecasts (ECMWF), Reading, UKInterdisciplinary Environmental Research Centre, Technische Universität Bergakademie Freiberg (TUBAF), Freiberg, GermanyMax Planck Institute for the Physics of Complex Systems, 01187 Dresden, GermanyDepartamento de Ciencias de la Atmósfera, Facultad de Ciencias, Universidad de la República, Igua 4225, 11400 Montevideo, UruguayDepartament de Física, Universitat Politècnica de Catalunya, Sant Nebridi 22, 08222 Terrassa, Barcelona, Spain<p>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.</p>https://esd.copernicus.org/articles/13/1157/2022/esd-13-1157-2022.pdf |
| spellingShingle | R. Silini S. Lerch N. Mastrantonas N. Mastrantonas H. Kantz M. Barreiro C. Masoller Improving the prediction of the Madden–Julian Oscillation of the ECMWF model by post-processing Earth System Dynamics |
| title | Improving the prediction of the Madden–Julian Oscillation of the ECMWF model by post-processing |
| title_full | Improving the prediction of the Madden–Julian Oscillation of the ECMWF model by post-processing |
| title_fullStr | Improving the prediction of the Madden–Julian Oscillation of the ECMWF model by post-processing |
| title_full_unstemmed | Improving the prediction of the Madden–Julian Oscillation of the ECMWF model by post-processing |
| title_short | Improving the prediction of the Madden–Julian Oscillation of the ECMWF model by post-processing |
| title_sort | improving the prediction of the madden julian oscillation of the ecmwf model by post processing |
| url | https://esd.copernicus.org/articles/13/1157/2022/esd-13-1157-2022.pdf |
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