Spatio-temporal Modelling of Temperature Fields in the Pacific Northwest
Camila M. Casquilho-Resende, Nhu D. Le, James V. Zidek

TL;DR
This paper introduces a flexible Bayesian spatio-temporal model for daily temperatures in the Pacific Northwest, accounting for complex topography and nonstationarity, enabling better understanding and prediction of temperature variations.
Contribution
It extends existing models by incorporating site-specific features and spatio-temporal interactions, capturing nonstationary spatial structures without assuming a fixed covariance structure.
Findings
Model captures rapid temperature changes in the region.
Residuals are approximately stationary, facilitating higher-level analysis.
Spatio-temporal interactions are essential for accurate temperature modeling.
Abstract
The importance of modelling temperature fields goes beyond the need to understand a region's climate and serves too as a starting point for understanding their socioeconomic, and health consequences. The topography of the study region contributes much to the complexity of modelling these fields and demands flexible spatio-temporal models that are able to handle nonstationarity and changes in trend. In this paper, we develop a flexible stochastic spatio-temporal model for daily temperatures in the Pacific Northwest, and describe a methodology for performing Bayesian spatial prediction. A novel aspect of this model, an extension of the spatio-temporal model proposed in Le and Zidek (1992), is its incorporation of site-specific features of a spatio-temporal field in its spatio-temporal mean. Due to the often surprising Pacific Northwestern weather, the analysis reported in the paper shows…
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Taxonomy
TopicsClimate Change and Health Impacts · Air Quality and Health Impacts · demographic modeling and climate adaptation
