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Winds from a Bayesian Hierarchical Model: Computation for Atmosphere-Ocean Research
Timothy J. Hoar, Ralph F. Milliff, Douglas Nychka, Christopher K. Wikle and L. Mark Berliner
Journal of Computational and Graphical Statistics
Vol. 12, No. 4, Statistical Analysis of Massive Data Streams (Dec., 2003), pp. 781-807
Published by: Taylor & Francis, Ltd. on behalf of the American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of America
Stable URL: http://www.jstor.org/stable/1390978
Page Count: 27
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Abstract
Advances in computing power are allowing researchers to use Bayesian hierarchical models (BHM) on problems previously considered computationally infeasible. This article discusses the procedure of migrating a BHM from a workstation-class implementation to a massively parallel architecture, indicative of the current direction of advances in computing hardware. The parallel implementation is nearly 500 times larger than the workstation-class implementation from the data perspective. The BHM in question combines the information from a scatterometer on board a polar-orbiting satellite and the result of a numerical weather prediction model and produces an ensemble of high-resolution tropical surface wind fields with physically realistic variability at all spatial scales.
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Journal of Computational and Graphical Statistics © 2003 American Statistical Association