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

• Published in: Advances in Statistical Climatology, Meteorology and Oceanography
• Main Authors: Bessac, Julie, Ailliot, Pierre, Cattiaux, Julien, Monbet, Valérie
• Other Authors: Argonne National Laboratory Lemont (ANL), Institut de Recherche Mathématique de Rennes (IRMAR), AGROCAMPUS OUEST-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA), Laboratoire de Mathématiques de Bretagne Atlantique (LMBA), Université de Brest (UBO)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS), Groupe d'étude de l'atmosphère météorologique (CNRM-GAME), Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS), Applications of interacting particle systems to statistics (ASPI), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-AGROCAMPUS OUEST-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)
• Format: Article in Journal/Newspaper
• Language: English
• Published: HAL CCSD 2016
• Subjects: [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST]; Northeast Atlantic
• Online Access: https://hal.archives-ouvertes.fr/hal-01250353; https://hal.archives-ouvertes.fr/hal-01250353/document; https://hal.archives-ouvertes.fr/hal-01250353/file/mainMSVAR_templateASCMO.pdf; https://doi.org/10.5194/ascmo-2-1-2016

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.