Open AccessModeling the heterogeneous spatial structure of temperature observations from a meteorological network in Spain
Author(s) -
Capilla Carmen,
Capilla Raquel
Publication year - 2002
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.67
H-Index - 298
eISSN - 2156-2202
pISSN - 0148-0227
DOI - 10.1029/2001jd000860
Subject(s) - covariance , interpolation (computer graphics) , covariance function , multivariate interpolation , kriging , spatial variability , spatial dependence , spatial analysis , representation (politics) , geostatistics , field (mathematics) , random field , statistical physics , meteorology , mathematics , computer science , statistics , geography , physics , animation , computer graphics (images) , politics , law , political science , pure mathematics , bilinear interpolation
At some stage, investigations of regional variability in climatological information require the analysis of spatial structure and interpolation between data points. In this paper we study both spatial analysis and interpolation using the example of temperature observations from a fixed surface meteorological network. Initial analyses indicate that the mean field can be modeled with a deterministic term plus a nonstationary stochastic component. The deterministic component models the influence of site location in terms of geographic coordinates, elevation, and local environment. The stochastic component presents anisotropy and nonstationarity through second‐order moments and accounts for high variability between subareas in the studied region. The heterogeneous spatial structure of the region is modeled with a technique that transforms the site geographic map into a new site representation with an isotropic and stationary covariance structure. The transformed representation provides valuable information on the influence of microclimatic conditions in the spatial covariance structure. The model captures the main spatial structure characteristics over the region and allows optimal prediction of the spatial mean field at unobserved locations.