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Advances in Statistical Climatology, Meteorology and Oceanography An international open-access journal on applied statistics
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Volume 2, issue 1
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 1–16, 2016
https://doi.org/10.5194/ascmo-2-1-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 1–16, 2016
https://doi.org/10.5194/ascmo-2-1-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

  29 Feb 2016

29 Feb 2016

Comparison of hidden and observed regime-switching autoregressive models for (u, v)-components of wind fields in the northeastern Atlantic

Julie Bessac1,2, Pierre Ailliot3, Julien Cattiaux4, and Valerie Monbet1,5 Julie Bessac et al.
  • 1Institut de Recherche Mathématiques de Rennes, UMR 6625, Université de Rennes 1, Rennes, France
  • 2Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL, USA
  • 3Laboratoire de Mathématiques de Bretagne Atlantique, UMR 6205, Université de Brest, Brest, France
  • 4CNRM-GAME, UMR 3589, CNRS/Météo France, Toulouse, France
  • 5INRIA Rennes, ASPI, Rennes, France

Abstract. 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.

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Several multi-site stochastic generators of zonal and meridional components of wind are proposed in this paper. 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. We also discuss the relative advantages of hidden and observed regime-switching models.
Several multi-site stochastic generators of zonal and meridional components of wind are proposed...
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