Spatiotemporal hierarchical Bayesian modeling of tropical ocean surface winds
Published in Journal of the American Statistical Association, 2001
Recommended citation: *As bibtex* http://dnychka.github.io/files/wikle2001spatiotemporal.pdf
@article{wikle2001spatiotemporal,
title={Spatiotemporal hierarchical {B}ayesian modeling of tropical ocean surface winds},
author={Wikle, Christopher K and Milliff, Ralph F and Nychka, Douglas and Berliner, L Mark},
journal={Journal of the American Statistical Association},
volume={96},
number={454},
pages={382--397},
year={2001},
publisher={Taylor \& Francis}
}
Spatiotemporal processes are ubiquitous in the environmental and physical sciences. This is certainly true of atmospheric and oceanic processes, which typically exhibit many different scales of spatial and temporal variability. The complexity of these processes and the large number of observation/prediction locations preclude the use of traditional covariance-based spatiotemporal statistical methods. Alternatively, we focus on conditionally specified (i.e., hierarchical) spatiotemporal models. These methods offer several advantages over traditional approaches. Primarily, physical and dynamical constraints can be easily incorporated into the conditional formulation, so that the series of relatively simple yet physically realistic conditional models leads to a much more complicated spatiotemporal covariance structure than can be specified directly.
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