We introduce a Bayesian multivariate hierarchical framework to estimate a space-time model for a joint series of monthly extreme temperatures and amounts of precipitation. Data are available for 360 monitoring stations over 60 years, with missing data affecting almost all series. Model components account for spatio-temporal correlation and annual cycles, dependence on covariates and between responses. Spatio-temporal dependence is modeled by the nearest neighbor Gaussian process (GP), response multivariate depen- dencies are represented by the linear model of coregionalization and effects of annual cycles are included by a circular representation of time. The proposed approach allows imputation of missing values and interpolation of climate surfaces at the national level. It also provides a characterization of the so called Italian ecoregions, namely broad and discrete ecologically homogeneous areas of similar potential as regards the climate, physiography, hydrography, vegetation, and wildlife. To now, Italian ecoregions are hierarchically classified into 4 tiers that go from 2 Divisions to 35 Subsections and are defined by informed expert judgments. The current climatic characterization of Italian ecoregions is based on bioclimatic indices for the period 1955–2000.

A hierarchical multivariate spatio-temporal model for clustered climate data with annual cycles / Mastrantonio, Gianluca; Jona Lasinio, Giovanna; Pollice, Alessio; Capotorti, Giulia; Teodonio, Lorenzo; Genova, Giulio; Blasi, Carlo. - In: THE ANNALS OF APPLIED STATISTICS. - ISSN 1932-6157. - 13:2(2019), pp. 797-823. [10.1214/18-AOAS1212]

A hierarchical multivariate spatio-temporal model for clustered climate data with annual cycles

Jona Lasinio, Giovanna
Membro del Collaboration Group
;
Capotorti, Giulia
Membro del Collaboration Group
;
Genova, Giulio
Membro del Collaboration Group
;
Blasi, Carlo
Ultimo
Membro del Collaboration Group
2019

Abstract

We introduce a Bayesian multivariate hierarchical framework to estimate a space-time model for a joint series of monthly extreme temperatures and amounts of precipitation. Data are available for 360 monitoring stations over 60 years, with missing data affecting almost all series. Model components account for spatio-temporal correlation and annual cycles, dependence on covariates and between responses. Spatio-temporal dependence is modeled by the nearest neighbor Gaussian process (GP), response multivariate depen- dencies are represented by the linear model of coregionalization and effects of annual cycles are included by a circular representation of time. The proposed approach allows imputation of missing values and interpolation of climate surfaces at the national level. It also provides a characterization of the so called Italian ecoregions, namely broad and discrete ecologically homogeneous areas of similar potential as regards the climate, physiography, hydrography, vegetation, and wildlife. To now, Italian ecoregions are hierarchically classified into 4 tiers that go from 2 Divisions to 35 Subsections and are defined by informed expert judgments. The current climatic characterization of Italian ecoregions is based on bioclimatic indices for the period 1955–2000.
2019
multivariate process; cyclic effect; coregionalization; NNGP
01 Pubblicazione su rivista::01a Articolo in rivista
A hierarchical multivariate spatio-temporal model for clustered climate data with annual cycles / Mastrantonio, Gianluca; Jona Lasinio, Giovanna; Pollice, Alessio; Capotorti, Giulia; Teodonio, Lorenzo; Genova, Giulio; Blasi, Carlo. - In: THE ANNALS OF APPLIED STATISTICS. - ISSN 1932-6157. - 13:2(2019), pp. 797-823. [10.1214/18-AOAS1212]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1288020
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