A Non-Stationary Spatial Model for Temperature Interpolation Applied to the State of Rio De Janeiro. Issue 5 (11th January 2017)

• Record Type: Journal Article
• Authors: Cunha, Marcelo; Gamerman, Dani; Fuentes, Montserrat; Paez, Marina
• Is Part Of: Journal of the Royal Statistical Society. Volume 66:Issue 5(2017:Nov.)
• Page Start: 919
• Page End: 939
• Publication Date: 2017-01-11
• Subjects: Bayesian inference -- Convolution -- Non-stationarity in space -- Spatial interpolation -- Surface temperature; Statistics -- Periodicals; 519.5
• DOI: 10.1111/rssc.12207
• Languages: English
• ISSNs: 0035-9254

Abstract

Summary: We propose a model for spatial and temporal interpolation and prediction for a set of monthly minimum temperature measurements from 37 stations in the state of Rio de Janeiro, Brazil, from 1961 to 2010. The model is based on a hierarchical specification where data are modelled with a regression structure for the mean and a non-stationary spatial representation for the spatially structured noise term. The regression term is also allowed to contain spatial dependence through coefficients and its covariates. A novel structure is assumed for the spatial non-stationarity, based on a generalization of a currently proposed convolution of stationary models. It allows more flexibility in the model specification and automatically selects the number of model components. This model provides superior performance when compared with two special cases: one stationary, and one non-stationary, provided by a sum of locally stationary processes. The results show a growth in the spread of temperature, with largely urbanized, warmest areas growing warmer, and largely forested, coolest areas growing cooler.