Joint Spatio-Temporal Analysis of a Linear and a Directional Variable: Space-Time Modeling of Wave Heights and Wave Directions

• Activity Number: 182
• Type: Contributed
• Date/Time: Monday, August 10, 2015 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics and the Environment
• Author(s): Fangpo Wang* and Alan Gelfand and Giovanna Jona-Lasinio
• Companies: Adobe Systems, Inc. and Duke University and Sapienza University of Rome
• Keywords: Angular variable ; Bayesian kriging ; hierarchical model ; latent variables ; projected Gaussian process ; Markov chain Monte Carlo

Abstract

It is valuable to have a better understanding of factors that influence sea motion and to provide more accurate forecasts. In particular, we are motivated by data on wave heights and outgoing wave directions over a region in the Adriatic sea during the time of a storm, with the overarching goal of understanding the association between wave directions and wave heights to enable improved prediction of wave behavior. Our contribution is to develop a fully model-based approach to capture joint structured spatial and temporal dependence between a linear and an angular variable. Model fitting is carried out using a suitable data augmented Markov chain Monte Carlo (MCMC) algorithm. We illustrate with data outputs from a deterministic wave model for a region in the Adriatic Sea. The proposed joint model framework enables both spatial interpolation and temporal forecasting.