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A non‐stationary spatial model for temperature interpolation applied to the state of Rio de Janeiro
Author(s) -
Cunha Marcelo,
Gamerman Dani,
Fuentes Montserrat,
Paez Marina
Publication year - 2017
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.1111/rssc.12207
Subject(s) - interpolation (computer graphics) , convolution (computer science) , generalization , representation (politics) , term (time) , multivariate interpolation , spatial dependence , mathematics , computer science , statistics , artificial intelligence , artificial neural network , mathematical analysis , motion (physics) , physics , quantum mechanics , politics , political science , law , bilinear interpolation
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.