Articles | Volume 2, issue 1
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 Bessac, Pierre Ailliot, Julien Cattiaux, and Valerie Monbet

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Cited articles

Ailliot, P. and Monbet, V.: Markov-switching autoregressive models for wind time series, Environ. Modell. Softw., 30, 92–101, 2012.
Ailliot, P., Monbet, V., and Prevosto, M.: An autoregressive model with time-varying coefficients for wind fields, Environmetrics, 17, 107–117, 2006.
Ailliot, P., Thompson, C., and Thomson, P.: Space time modeling of precipitation using a hidden Markov model and censored Gaussian distributions, J. Roy. Stat. Soc. C-App., 58, 405–426, 2009.
Ailliot, P., Bessac, J., Monbet, V., and Pene, F.: Non-homogeneous hidden Markov-switching models for wind time series, J. Stat. Plan. Infer., 160, 75–88, 2015.
Bardossy, A. and Plate, E. J.: Space-time model for daily rainfall using atmospheric circulation patterns, Water Resour. Res., 28, 1247–1259, 1992.
Short summary
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.