Winds from a Bayesian Hierarchical Model: Computation for Atmosphere-Ocean Research

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.