  Storm surge, the onshore rush of sea water caused by the high winds and low
pressure associated with a hurricane, can compound the effects of inland
flooding caused by rainfall, leading to loss of property and loss of life for
residents of coastal areas. Numerical ocean models are essential for creating
storm surge forecasts for coastal areas. These models are driven primarily by
the surface wind forcings. Currently, the gridded wind fields used by ocean
models are specified by deterministic formulas that are based on the central
pressure and location of the storm center. While these equations incorporate
important physical knowledge about the structure of hurricane surface wind
fields, they cannot always capture the asymmetric and dynamic nature of a
hurricane. A new Bayesian multivariate spatial statistical modeling framework
is introduced combining data with physical knowledge about the wind fields to
improve the estimation of the wind vectors. Many spatial models assume the data
follow a Gaussian distribution. However, this may be overly-restrictive for
wind fields data which often display erratic behavior, such as sudden changes
in time or space. In this paper we develop a semiparametric multivariate
spatial model for these data. Our model builds on the stick-breaking prior,
which is frequently used in Bayesian modeling to capture uncertainty in the
parametric form of an outcome. The stick-breaking prior is extended to the
spatial setting by assigning each location a different, unknown distribution,
and smoothing the distributions in space with a series of kernel functions.
This semiparametric spatial model is shown to improve prediction compared to
usual Bayesian Kriging methods for the wind field of Hurricane Ivan.
