Space.time interpolation of daily air temperatures
Impacto
Scholar |
Otros documentos de la autoría: Sáez Zafra, Marc; Barceló, M.A.; Tobías, Aurelio; Varga, Diego; Ocaña-Riola, R.; Juan, Pablo; Mateu, Jorge
Metadatos
Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
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comunitat-uji-handle3:10234/43643
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INVESTIGACIONMetadatos
Título
Space.time interpolation of daily air temperaturesAutoría
Fecha de publicación
2012Editor
UCLA Department of StatisticsISSN
1945-1296Cita bibliográfica
Journal of Environmental Statistics July 2012, Volume 3, Issue 5Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
http://www.jenvstat.org/v03/i05/paperVersión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
We propose a model to describe the mean function as well as the spatio-temporal
covariance structure of 15 years of both maximum and minimum daily temperature data
from 190 stations throughout the region of Catalonia ... [+]
We propose a model to describe the mean function as well as the spatio-temporal
covariance structure of 15 years of both maximum and minimum daily temperature data
from 190 stations throughout the region of Catalonia (Spain), with daily data covering
the period 1994-2008. Our aim is threefold: (a) estimation of the long-term trend of
maximum and minimum temperatures; (b) assessing the spatial and temporal variability
of temperatures, and (c) interpolation of the spatial temperatures at any given time.
Long-term trend, annual harmonics and winds were considered as explanatory vari-
ables of the mean function. The parameters associated with these variables were allowed
to vary between stations and within each year. We controlled temporal autocorrelation
by means of ARMA models. For the spatial covariance structure we used the Mat ern
family of covariance functions and a nugget term. Spatio-temporal models were built as
Bayesian hierarchical models with two stages following the integrated nested place Laplace
approximation (INLA) for Bayesian inference. For the nal model estimation we used a
two-stage approach, in which we rst assumed the stations were spatially independent,
and then we modeled the spatio-temporal covariance using the interim posterior from the
residuals of the model in the rst-stage as prior distributions of replications of a spatial
process. We allowed all spatial parameters to also vary with time. [-]
Publicado en
Journal of Environmental Statistics, 2012, vol. 3, num. 5Derechos de acceso
info:eu-repo/semantics/openAccess
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