Comparison of hidden and observed regime-switching autoregressive models for (<i>u</i>, <i>v</i>)-components of wind fields in the northeastern Atlantic
Several multi-site 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 in wind conditions due to the existence of different weather types....
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2016-02-01
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| Series: | Advances in Statistical Climatology, Meteorology and Oceanography |
| Online Access: | http://www.adv-stat-clim-meteorol-oceanogr.net/2/1/2016/ascmo-2-1-2016.pdf |
| _version_ | 1818518716104572928 |
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| author | J. Bessac P. Ailliot J. Cattiaux V. Monbet |
| author_facet | J. Bessac P. Ailliot J. Cattiaux V. Monbet |
| author_sort | J. Bessac |
| collection | DOAJ |
| description | Several multi-site 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 in 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 hidden) variable or as an
observed variable. In the latter case a clustering algorithm is used before
fitting the model to extract the regime. Conditional on 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 multi-site context, the extraction of relevant
clusterings from extra variables or from the local wind data, and the link
between weather types extracted from wind data and large-scale weather
regimes derived from a descriptor of the atmospheric circulation. We also
discuss the 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. |
| first_indexed | 2024-12-11T01:14:07Z |
| format | Article |
| id | doaj.art-d761f7d96a9f45709136d57fec7ba0eb |
| institution | Directory Open Access Journal |
| issn | 2364-3579 2364-3587 |
| language | English |
| last_indexed | 2024-12-11T01:14:07Z |
| publishDate | 2016-02-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Advances in Statistical Climatology, Meteorology and Oceanography |
| spelling | doaj.art-d761f7d96a9f45709136d57fec7ba0eb2022-12-22T01:25:57ZengCopernicus PublicationsAdvances in Statistical Climatology, Meteorology and Oceanography2364-35792364-35872016-02-012111610.5194/ascmo-2-1-2016Comparison of hidden and observed regime-switching autoregressive models for (<i>u</i>, <i>v</i>)-components of wind fields in the northeastern AtlanticJ. Bessac0P. Ailliot1J. Cattiaux2V. Monbet3Institut de Recherche Mathématiques de Rennes, UMR 6625, Université de Rennes 1, Rennes, FranceLaboratoire de Mathématiques de Bretagne Atlantique, UMR 6205, Université de Brest, Brest, FranceCNRM-GAME, UMR 3589, CNRS/Météo France, Toulouse, FranceInstitut de Recherche Mathématiques de Rennes, UMR 6625, Université de Rennes 1, Rennes, FranceSeveral multi-site 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 in 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 hidden) variable or as an observed variable. In the latter case a clustering algorithm is used before fitting the model to extract the regime. Conditional on 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 multi-site context, the extraction of relevant clusterings from extra variables or from the local wind data, and the link between weather types extracted from wind data and large-scale weather regimes derived from a descriptor of the atmospheric circulation. We also discuss the 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.http://www.adv-stat-clim-meteorol-oceanogr.net/2/1/2016/ascmo-2-1-2016.pdf |
| spellingShingle | J. Bessac P. Ailliot J. Cattiaux V. Monbet Comparison of hidden and observed regime-switching autoregressive models for (<i>u</i>, <i>v</i>)-components of wind fields in the northeastern Atlantic Advances in Statistical Climatology, Meteorology and Oceanography |
| title | Comparison of hidden and observed regime-switching autoregressive models for (<i>u</i>, <i>v</i>)-components of wind fields in the northeastern Atlantic |
| title_full | Comparison of hidden and observed regime-switching autoregressive models for (<i>u</i>, <i>v</i>)-components of wind fields in the northeastern Atlantic |
| title_fullStr | Comparison of hidden and observed regime-switching autoregressive models for (<i>u</i>, <i>v</i>)-components of wind fields in the northeastern Atlantic |
| title_full_unstemmed | Comparison of hidden and observed regime-switching autoregressive models for (<i>u</i>, <i>v</i>)-components of wind fields in the northeastern Atlantic |
| title_short | Comparison of hidden and observed regime-switching autoregressive models for (<i>u</i>, <i>v</i>)-components of wind fields in the northeastern Atlantic |
| title_sort | comparison of hidden and observed regime switching autoregressive models for i u i i v i components of wind fields in the northeastern atlantic |
| url | http://www.adv-stat-clim-meteorol-oceanogr.net/2/1/2016/ascmo-2-1-2016.pdf |
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