HR: 0830h
AN: GC31B-0194    [PDF]
TI: Visual Representations Of Non-Separable Spatiotemporal Covariance Models
AU: Kolovos, A
EM: kolovos@email.unc.edu
AF: Center for the Advanced Study of the Environment (CASE),The University of North Carolina at Chapel Hill, 114 Rosenau Hall, CB\# 7431, Chapel Hill, NC 27599 United States
AU: * Christakos, G
EM: george_christakos@unc.edu
AF: Center for the Advanced Study of the Environment (CASE),The University of North Carolina at Chapel Hill, 114 Rosenau Hall, CB\# 7431, Chapel Hill, NC 27599 United States
AU: Hristopulos, D T
EM: dionisi@mred.tuc.gr
AF: Technical University of Crete, Department of Mineral Resources Engineering, Chania, 73100 Greece
AU: Serre, M L
EM: marc_serre@unc.edu
AF: Center for the Advanced Study of the Environment (CASE),The University of North Carolina at Chapel Hill, 114 Rosenau Hall, CB\# 7431, Chapel Hill, NC 27599 United States
AB: Natural processes that relate to climatic variability (such as air circulation, air-water and air-soil energy exchanges) contain inherently stochastic components. Spatiotemporal random fields are frequently employed to model such processes and deal with the uncertainty involved. Covariance functions are statistical tools that are used to express correlations between process values across space and time. This work focuses on a review and visual representation of a series of useful covariance models that have been introduced in the Modern Spatiotemporal Geostatistics literature. Some of their important features are examined and their application can significantly improve the interpretation of space/time correlations that affect the long-term climatic evolution both on a local or a global scale.
DE: 0910 Data processing
DE: 1694 Instruments and techniques
DE: 1869 Stochastic processes
DE: 3210 Modeling
DE: 9820 Techniques applicable in three or more fields
SC: Global Climate Change [GC]
MN: 2003 Fall Meeting