AN APPROACH TO STATISTICAL SPATIAL-TEMPORAL MODELING OF METEOROLOGICAL FIELDS

• 成果类型: Article
• 署名作者: HANDCOCK, MS; WALLIS, JR
• 署名单位: International Business Machines (IBM); IBM USA
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.2307/2290832
• 发表日期: 1994
• 页码: 368-378
• 关键词: UNITED-STATES; covariance; temperatures; climate; maximum

摘要

In this article 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 nor-them Nebraska up to the Canadian border. The readings are temperatures at the stations in the U.S. historical climatological network. 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, the distribution indicates that under the scenario of a gradual increase of 5-degrees-F over 50 years, it will take 30-40 winters of data before the change will be discernible from the natural variation in temperatures.