Institute of Statistics and Decision Sciences
Duke University
presents:
Mark Handcock
New York University
"An Approach to Statistical Spatial-Temporal Modeling of Meteorological Fields: An Application to Global Warming Data"
Abstract: In this paper we develop a random field model for the mean temperature over the region in the northern United States covering eastern Montana through the Dakotas and northern Nebraska up to the Canadian border. The readings are the temperatures at the stations in the United States historical climatological network reported in Quinlan, Karl & Williams (1987).
The stochastic structure is modeled by a stationary spatial-temporal Gaussian random field. For this region, we find little evidence of temporal dependence while the spatial structure is temporally stable. The approach strives to incorporate the uncertainty in estimating the covariance structure into the predictive distributions and the final inference.
As an application of the model we derive posterior distributions of the areal mean over time. A posterior distribution for the static areal mean is presented as a basis for calibrating temperature shifts by the historical record. For this region and season, it indicates that under the scenario of a gradual increase of $5^{\circ}F$ over 50 years, it will take 30-40 winters of data before the change will be discernible from the natural variation in temperatures.}
Key Words: Gaussian random fields, Bayesian statistics; Climatic change.
Friday, October 6, 1995
11:45 - 12:45
116 Old Chemistry Building Any questions concerning the seminar may be addressed to Cheryl McGhee @ (919) 684-8029, e-mail cheryl@isds.duke.edu, or finger seminar@isds.duke.edu.