A geostatistics-assisted approach to the deterministic approximation of climate data. (April 2015)

• Record Type: Journal Article
• Authors: Lanfredi, Maria; Coppola, Rosa; D'Emilio, Mariagrazia; Imbrenda, Vito; Macchiato, Maria; Simoniello, Tiziana
• Is Part Of: Environmental modelling & software. Volume 66(2015:Apr.)
• Page Start: 69
• Page End: 77
• Publication Date: 2015-04
• Subjects: Geostatistics -- Geographical model -- Climatic surface; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2014.12.009
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: We propose a nonconventional application of variogram analysis to support climate data modelling with analytical functions. This geostatistical technique is applied in the theoretical domain defined by each model variable to detect the systematic behaviours buried in the fluctuations determined by other driving factors and to verify the ability of candidate fits to remove correlations from the data. The climatic average of the atmospheric temperature measured at 387 European meteorological stations has been analysed as a function of geographical parameters by a step-wise procedure. Our final model accounts for non-linearity in latitude with a local-scale residual correlation that decays in approximately ten kilometres. The variance of the residuals from the fitted model (approximately 3% of the total) is mostly determined by local heterogeneity in transitional climates and by urban islands. Our approach is user-friendly, and the support of statistical inference makes the modelling self-consistent. Highlights: Variogram analysis is adapted to optimize deterministic modelling of climate data. The explanatory variables define the domain where our analysis is performed. Variograms support model identification and diagnostic checking. The analysis identifies scales where simple functions approximate complex patterns. The approach is suited when local and global scales are separable.