Joint segmentation of wind speed and direction using a hierarchical model

Nicolas Dobigeon (1, 2, 3) , Jean-Yves Tourneret (1, 2)

1 IRIT-SC - Signal et Communications
2 Toulouse INP - Institut National Polytechnique (Toulouse)
3 IUF - Institut universitaire de France

The problem of detecting changes in wind speed and direction is considered. Bayesian priors, with various degrees of certainty, are used to represent relationships between the two time series. Segmentation is then conducted using a hierarchical Bayesian model that accounts for correlations between the wind speed and direction. A Gibbs sampling strategy overcomes the computational complexity of the hierarchical model and is used to estimate the unknown parameters and hyperparameters. Extensions to other statistical models are also discussed. These models allow us to study other joint segmentation problems including segmentation of wave amplitude and direction. The performance of the proposed algorithms is illustrated with results obtained with synthetic and real data.

Mots clés: Wind direction; Segmentation; Bayesian analysis; Gibbs sampler; Wind speed

Domaines: Autre [cs.OH]

Nicolas Dobigeon, Jean-Yves Tourneret. Joint segmentation of wind speed and direction using a hierarchical model. Computational Statistics and Data Analysis, 2007, 51 (12), pp.5603--5621. ⟨10.1016/j.csda.2007.04.016⟩. ⟨hal-00474912⟩