Comparison of hidden and observed regime-switching autoregressive models for (u,v)-components of wind fields in the Northeast Atlantic
International audience Several multisite stochastic generators of zonal and meridional components of wind are proposed in this paper. A regime-switching framework is introduced to account for the alternation of intensity and variability that is observed on wind conditions due to the existence of dif...
Published in: | Advances in Statistical Climatology, Meteorology and Oceanography |
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Main Authors: | , , , |
Other Authors: | , , , , , , , , , , , , , , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
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HAL CCSD
2016
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Online Access: | https://hal.science/hal-01250353 https://hal.science/hal-01250353/document https://hal.science/hal-01250353/file/mainMSVAR_templateASCMO.pdf https://doi.org/10.5194/ascmo-2-1-2016 |
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Institut national des sciences de l'Univers: HAL-INSU |
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[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST] |
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[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST] Bessac, Julie Ailliot, Pierre Cattiaux, Julien Monbet, Valérie Comparison of hidden and observed regime-switching autoregressive models for (u,v)-components of wind fields in the Northeast Atlantic |
topic_facet |
[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST] |
description |
International audience Several multisite stochastic generators of zonal and meridional components of wind are proposed in this paper. A regime-switching framework is introduced to account for the alternation of intensity and variability that is observed on wind conditions due to the existence of different weather types. This modeling blocks time series into periods in which the series is described by a single model. The regime-switching is modeled by a discrete variable that can be introduced as a latent (or 5 hidden) variable or as an observed variable. In the latter case a clustering algorithm is used before fitting the model to extract the regime. Conditionally to the regimes, the observed wind conditions are assumed to evolve as a linear Gaussian vector autoregressive (VAR) model. Various questions are explored, such as the modeling of the regime in a multisite context, the extraction of relevant cluster-ings from extra-variables or from the local wind data, and the link between weather types extracted 10 from wind data and large-scale weather regimes derived from a descriptor of the atmospheric circulation. We also discuss relative advantages of hidden and observed regime-switching models. For artificial stochastic generation of wind sequences, we show that the proposed models reproduce the average space-time motions of wind conditions; and we highlight the advantage of regime-switching models in reproducing the alternation of intensity and variability in wind conditions. |
author2 |
Argonne National Laboratory Lemont (ANL) Institut de Recherche Mathématique de Rennes (IRMAR) Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes) Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-École normale supérieure - Rennes (ENS Rennes)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-INSTITUT AGRO Agrocampus Ouest Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro) Laboratoire de Mathématiques de Bretagne Atlantique (LMBA) Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS) Centre national de recherches météorologiques (CNRM) Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP) Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3) Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales Toulouse (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France-Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3) Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales Toulouse (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France-Centre National de la Recherche Scientifique (CNRS) Applications of interacting particle systems to statistics (ASPI) Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes) Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Inria Rennes – Bretagne Atlantique Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria) ANR-11-LABX-0020,LEBESGUE,Centre de Mathématiques Henri Lebesgue : fondements, interactions, applications et Formation(2011) |
format |
Article in Journal/Newspaper |
author |
Bessac, Julie Ailliot, Pierre Cattiaux, Julien Monbet, Valérie |
author_facet |
Bessac, Julie Ailliot, Pierre Cattiaux, Julien Monbet, Valérie |
author_sort |
Bessac, Julie |
title |
Comparison of hidden and observed regime-switching autoregressive models for (u,v)-components of wind fields in the Northeast Atlantic |
title_short |
Comparison of hidden and observed regime-switching autoregressive models for (u,v)-components of wind fields in the Northeast Atlantic |
title_full |
Comparison of hidden and observed regime-switching autoregressive models for (u,v)-components of wind fields in the Northeast Atlantic |
title_fullStr |
Comparison of hidden and observed regime-switching autoregressive models for (u,v)-components of wind fields in the Northeast Atlantic |
title_full_unstemmed |
Comparison of hidden and observed regime-switching autoregressive models for (u,v)-components of wind fields in the Northeast Atlantic |
title_sort |
comparison of hidden and observed regime-switching autoregressive models for (u,v)-components of wind fields in the northeast atlantic |
publisher |
HAL CCSD |
publishDate |
2016 |
url |
https://hal.science/hal-01250353 https://hal.science/hal-01250353/document https://hal.science/hal-01250353/file/mainMSVAR_templateASCMO.pdf https://doi.org/10.5194/ascmo-2-1-2016 |
genre |
Northeast Atlantic |
genre_facet |
Northeast Atlantic |
op_source |
ISSN: 2364-3579 EISSN: 2364-3587 Advances in Statistical Climatology, Meteorology and Oceanography https://hal.science/hal-01250353 Advances in Statistical Climatology, Meteorology and Oceanography, 2016, 2 (1), pp.1-16. ⟨10.5194/ascmo-2-1-2016⟩ |
op_relation |
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op_rights |
info:eu-repo/semantics/OpenAccess |
op_doi |
https://doi.org/10.5194/ascmo-2-1-2016 |
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Advances in Statistical Climatology, Meteorology and Oceanography |
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container_issue |
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container_start_page |
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16 |
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ftinsu:oai:HAL:hal-01250353v1 2024-04-14T08:16:29+00:00 Comparison of hidden and observed regime-switching autoregressive models for (u,v)-components of wind fields in the Northeast Atlantic Bessac, Julie Ailliot, Pierre Cattiaux, Julien Monbet, Valérie Argonne National Laboratory Lemont (ANL) Institut de Recherche Mathématique de Rennes (IRMAR) Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes) Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-École normale supérieure - Rennes (ENS Rennes)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-INSTITUT AGRO Agrocampus Ouest Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro) Laboratoire de Mathématiques de Bretagne Atlantique (LMBA) Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS) Centre national de recherches météorologiques (CNRM) Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP) Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3) Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales Toulouse (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France-Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3) Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales Toulouse (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France-Centre National de la Recherche Scientifique (CNRS) Applications of interacting particle systems to statistics (ASPI) Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes) Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Inria Rennes – Bretagne Atlantique Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria) ANR-11-LABX-0020,LEBESGUE,Centre de Mathématiques Henri Lebesgue : fondements, interactions, applications et Formation(2011) 2016 https://hal.science/hal-01250353 https://hal.science/hal-01250353/document https://hal.science/hal-01250353/file/mainMSVAR_templateASCMO.pdf https://doi.org/10.5194/ascmo-2-1-2016 en eng HAL CCSD Copernicus Publications info:eu-repo/semantics/altIdentifier/doi/10.5194/ascmo-2-1-2016 hal-01250353 https://hal.science/hal-01250353 https://hal.science/hal-01250353/document https://hal.science/hal-01250353/file/mainMSVAR_templateASCMO.pdf doi:10.5194/ascmo-2-1-2016 info:eu-repo/semantics/OpenAccess ISSN: 2364-3579 EISSN: 2364-3587 Advances in Statistical Climatology, Meteorology and Oceanography https://hal.science/hal-01250353 Advances in Statistical Climatology, Meteorology and Oceanography, 2016, 2 (1), pp.1-16. ⟨10.5194/ascmo-2-1-2016⟩ [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST] info:eu-repo/semantics/article Journal articles 2016 ftinsu https://doi.org/10.5194/ascmo-2-1-2016 2024-03-21T17:24:28Z International audience Several multisite stochastic generators of zonal and meridional components of wind are proposed in this paper. A regime-switching framework is introduced to account for the alternation of intensity and variability that is observed on wind conditions due to the existence of different weather types. This modeling blocks time series into periods in which the series is described by a single model. The regime-switching is modeled by a discrete variable that can be introduced as a latent (or 5 hidden) variable or as an observed variable. In the latter case a clustering algorithm is used before fitting the model to extract the regime. Conditionally to the regimes, the observed wind conditions are assumed to evolve as a linear Gaussian vector autoregressive (VAR) model. Various questions are explored, such as the modeling of the regime in a multisite context, the extraction of relevant cluster-ings from extra-variables or from the local wind data, and the link between weather types extracted 10 from wind data and large-scale weather regimes derived from a descriptor of the atmospheric circulation. We also discuss relative advantages of hidden and observed regime-switching models. For artificial stochastic generation of wind sequences, we show that the proposed models reproduce the average space-time motions of wind conditions; and we highlight the advantage of regime-switching models in reproducing the alternation of intensity and variability in wind conditions. Article in Journal/Newspaper Northeast Atlantic Institut national des sciences de l'Univers: HAL-INSU Advances in Statistical Climatology, Meteorology and Oceanography 2 1 1 16 |