Hierarchical Bayesian Spatio-Temporal Modeling for Wind Data
Li Chen
Department of Statistics
North Carolina State University

Wednesday, 11 February 2004
3:10 PM
E202 Engineering Building

Classical geostatistics and time series methods are powerful tools for
stationary and separable space-time processes. However, it is well
recognized that in real applications spatio-temporal processes are rarely
stationary and separable. In this work, some new approaches to model and estimate nonstationarity and nonseparability are presented. We present new classes of nonseparable and nonstationary models for spacetime processes.
We also propose a test for separability to better understand the space-time dependence. We apply the above statistical methods to model spatio-temporal structures of wind fields and assess the performance of numerical models for wind prediction. Consequently, improved wind field maps can be obtained by combining observed wind data with numerical model output.

Keywords: stationary, separability, spatio-temporal, hierarchical, Bayesian

Refreshments will be served at 2:45 p.m. in Room 008 of the Statistics



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