A bivariate space-time downscaler under space and time misalignment

Berrocal, VJ; Gelfand, AE; Holland, DM

HERO ID

807079

Reference Type

Journal Article

Year

2010

Language

English

PMID

21853015

HERO ID 807079
In Press No
Year 2010
Title A bivariate space-time downscaler under space and time misalignment
Authors Berrocal, VJ; Gelfand, AE; Holland, DM
Journal Annals of Applied Statistics
Volume 4
Issue 4
Page Numbers 1942-1975
Abstract Ozone and particulate matter PM(2.5) are co-pollutants that have long been associated with increased public health risks. Information on concentration levels for both pollutants come from two sources: monitoring sites and output from complex numerical models that produce concentration surfaces over large spatial regions. In this paper, we offer a fully-model based approach for fusing these two sources of information for the pair of co-pollutants which is computationally feasible over large spatial regions and long periods of time. Due to the association between concentration levels of the two environmental contaminants, it is expected that information regarding one will help to improve prediction of the other. Misalignment is an obvious issue since the monitoring networks for the two contaminants only partly intersect and because the collection rate for PM(2.5) is typically less frequent than that for ozone.Extending previous work in Berrocal et al. (2009), we introduce a bivariate downscaler that provides a flexible class of bivariate space-time assimilation models. We discuss computational issues for model fitting and analyze a dataset for ozone and PM(2.5) for the ozone season during year 2002. We show a modest improvement in predictive performance, not surprising in a setting where we can anticipate only a small gain.
Doi 10.1214/10-AOAS351
Pmid 21853015
Wosid WOS:000295451000021
Is Certified Translation No
Dupe Override No
Comments Source: Web of Science WOS:000295451000021
Is Public Yes
Language Text English
Keyword Co-kriging; coregionalization; dynamic model; kriging; multivariate spatial process; spatially varying coefficients
Is Qa No