Improving the prediction of the Madden–Julian Oscillation of the ECMWF model by post-processing
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.
| 2022 | |
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Madden–Julian Oscillation Weather forecast Climate models |
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| Inglés | |
| Universidad de la República | |
| COLIBRI | |
| https://hdl.handle.net/20.500.12008/41312 | |
| Acceso abierto | |
| Licencia Creative Commons Atribución (CC - By 4.0) |
| _version_ | 1807522802286198784 |
|---|---|
| author | Silini, Riccardo |
| author2 | Lerch, Sebastian Mastrantonas, Nikolaos Kantz, Holger Barreiro, Marcelo Masoller, Cristina |
| author2_role | author author author author author |
| author_facet | Silini, Riccardo Lerch, Sebastian Mastrantonas, Nikolaos Kantz, Holger Barreiro, Marcelo Masoller, Cristina |
| author_role | author |
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| bitstream.checksumAlgorithm.fl_str_mv | MD5 MD5 MD5 MD5 MD5 |
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| collection | COLIBRI |
| dc.contributor.filiacion.none.fl_str_mv | Silini Riccardo Lerch Sebastian Mastrantonas Nikolaos Kantz Holger Barreiro Marcelo, Universidad de la República (Uruguay). Facultad de Ciencias. Instituto de Física. Masoller Cristina |
| dc.creator.none.fl_str_mv | Silini, Riccardo Lerch, Sebastian Mastrantonas, Nikolaos Kantz, Holger Barreiro, Marcelo Masoller, Cristina |
| dc.date.accessioned.none.fl_str_mv | 2023-11-20T13:18:50Z |
| dc.date.available.none.fl_str_mv | 2023-11-20T13:18:50Z |
| dc.date.issued.none.fl_str_mv | 2022 |
| dc.description.abstract.none.fl_txt_mv | 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. |
| dc.format.extent.es.fl_str_mv | 9 h. |
| dc.format.mimetype.es.fl_str_mv | application/pdf |
| dc.identifier.citation.es.fl_str_mv | 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 |
| dc.identifier.doi.none.fl_str_mv | 10.5194/esd-13-1157-2022 |
| dc.identifier.issn.none.fl_str_mv | 2190-4979 |
| dc.identifier.uri.none.fl_str_mv | https://hdl.handle.net/20.500.12008/41312 |
| dc.language.iso.none.fl_str_mv | en eng |
| dc.publisher.es.fl_str_mv | European Geosciences Union |
| dc.relation.ispartof.es.fl_str_mv | Earth System Dynamics, 2022, 13: 1157–1165 |
| dc.rights.license.none.fl_str_mv | Licencia Creative Commons Atribución (CC - By 4.0) |
| dc.rights.none.fl_str_mv | info:eu-repo/semantics/openAccess |
| dc.source.none.fl_str_mv | reponame:COLIBRI instname:Universidad de la República instacron:Universidad de la República |
| dc.subject.es.fl_str_mv | Madden–Julian Oscillation Weather forecast Climate models |
| dc.title.none.fl_str_mv | Improving the prediction of the Madden–Julian Oscillation of the ECMWF model by post-processing |
| dc.type.es.fl_str_mv | Artículo |
| dc.type.none.fl_str_mv | info:eu-repo/semantics/article |
| dc.type.version.none.fl_str_mv | info:eu-repo/semantics/publishedVersion |
| description | 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. |
| eu_rights_str_mv | openAccess |
| format | article |
| id | COLIBRI_4757929fc029d1e5ac290d20c1e4bd94 |
| identifier_str_mv | 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 2190-4979 10.5194/esd-13-1157-2022 |
| instacron_str | Universidad de la República |
| institution | Universidad de la República |
| instname_str | Universidad de la República |
| language | eng |
| language_invalid_str_mv | en |
| network_acronym_str | COLIBRI |
| network_name_str | COLIBRI |
| oai_identifier_str | oai:colibri.udelar.edu.uy:20.500.12008/41312 |
| publishDate | 2022 |
| reponame_str | COLIBRI |
| repository.mail.fl_str_mv | mabel.seroubian@seciu.edu.uy |
| repository.name.fl_str_mv | COLIBRI - Universidad de la República |
| repository_id_str | 4771 |
| rights_invalid_str_mv | Licencia Creative Commons Atribución (CC - By 4.0) |
| spelling | Silini RiccardoLerch SebastianMastrantonas NikolaosKantz HolgerBarreiro Marcelo, Universidad de la República (Uruguay). Facultad de Ciencias. Instituto de Física.Masoller Cristina2023-11-20T13:18:50Z2023-11-20T13:18:50Z2022Silini, 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-13-1157-20222190-4979https://hdl.handle.net/20.500.12008/4131210.5194/esd-13-1157-2022The 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.Submitted by Festari Camila (camifestari@gmail.com) on 2023-11-19T18:33:39Z No. of bitstreams: 2 license_rdf: 24251 bytes, checksum: 71ed42ef0a0b648670f707320be37b90 (MD5) 10.5194esd-13-1157-2022.pdf: 2703998 bytes, checksum: f280418ed6886f19fc0f1a5d99f8e42f (MD5)Approved for entry into archive by Faget Cecilia (lfaget@fcien.edu.uy) on 2023-11-20T12:22:23Z (GMT) No. of bitstreams: 2 license_rdf: 24251 bytes, checksum: 71ed42ef0a0b648670f707320be37b90 (MD5) 10.5194esd-13-1157-2022.pdf: 2703998 bytes, checksum: f280418ed6886f19fc0f1a5d99f8e42f (MD5)Made available in DSpace by Luna Fabiana (fabiana.luna@seciu.edu.uy) on 2023-11-20T13:18:50Z (GMT). No. of bitstreams: 2 license_rdf: 24251 bytes, checksum: 71ed42ef0a0b648670f707320be37b90 (MD5) 10.5194esd-13-1157-2022.pdf: 2703998 bytes, checksum: f280418ed6886f19fc0f1a5d99f8e42f (MD5) Previous issue date: 20229 h.application/pdfenengEuropean Geosciences UnionEarth System Dynamics, 2022, 13: 1157–1165Las obras depositadas en el Repositorio se rigen por la Ordenanza de los Derechos de la Propiedad Intelectual de la Universidad de la República.(Res. Nº 91 de C.D.C. de 8/III/1994 – D.O. 7/IV/1994) y por la Ordenanza del Repositorio Abierto de la Universidad de la República (Res. Nº 16 de C.D.C. de 07/10/2014)info:eu-repo/semantics/openAccessLicencia Creative Commons Atribución (CC - By 4.0)Madden–Julian OscillationWeather forecastClimate modelsImproving the prediction of the Madden–Julian Oscillation of the ECMWF model by post-processingArtículoinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionreponame:COLIBRIinstname:Universidad de la Repúblicainstacron:Universidad de la RepúblicaSilini, RiccardoLerch, SebastianMastrantonas, NikolaosKantz, HolgerBarreiro, MarceloMasoller, CristinaLICENSElicense.txtlicense.txttext/plain; charset=utf-84267http://localhost:8080/xmlui/bitstream/20.500.12008/41312/5/license.txt6429389a7df7277b72b7924fdc7d47a9MD55CC-LICENSElicense_urllicense_urltext/plain; charset=utf-844http://localhost:8080/xmlui/bitstream/20.500.12008/41312/2/license_urla0ebbeafb9d2ec7cbb19d7137ebc392cMD52license_textlicense_texttext/html; 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- Universidad de la Repúblicafalse |
| spellingShingle | Improving the prediction of the Madden–Julian Oscillation of the ECMWF model by post-processing Silini, Riccardo Madden–Julian Oscillation Weather forecast Climate models |
| status_str | publishedVersion |
| 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 |
| topic | Madden–Julian Oscillation Weather forecast Climate models |
| url | https://hdl.handle.net/20.500.12008/41312 |
