RAND Statistics Seminar Series
A Multivariate Nongaussian Bayesian Spatial Modeling Framework for Hurricane Surface Wind Fields
Presented by Montserrat Fuentes, North Carolina State University
Thursday, April 12, 2007
RAND Corporation, Santa Monica, CA
Please contact Nancee Inouye if you would like to attend this seminar.
Modelling surface wind fields is essential for hurricane forecasting. A wind field gives the direction and velocity of the wind at any location in the vicinity of the hurricane. The numerical ocean models used to predict the storm surge for coastal areas rely heavily on wind field inputs. Currently, deterministic formulas are used to generate the wind fields for the storm surge model based on a few meteorological inputs such as the radius and central pressure of the storm. This paper develops a statistical framework that uses both the deterministic wind model and observed satellite and buoy wind data to improve estimates of the wind field. Since wind fields often display erratic behavior, such as sudden changes in time or space, the multivariate spatial model for the true wind field avoids assuming the wind fields are normally distributed. Instead, the multivariate spatial prior for the underlying wind field is nonGaussian, and an extension of the nonparametric Bayesian stick-breaking prior. The spatial stick-breaking prior represents the distribution at each location as a potentially infinite mixture of normals, with the mixture probabilities varying spatially according kernel functions. This nonparametric spatial model is shown to improve prediction compared the deterministic model alone and to usual Bayesian Kriging methods for the wind field of Hurricane Ivan in September, 2004.
Attending a Seminar
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