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Space-time data fusion under error in computer model output: an application to modeling air quality.

Publication ,  Journal Article
Berrocal, VJ; Gelfand, AE; Holland, DM
Published in: Biometrics
September 2012

We provide methods that can be used to obtain more accurate environmental exposure assessment. In particular, we propose two modeling approaches to combine monitoring data at point level with numerical model output at grid cell level, yielding improved prediction of ambient exposure at point level. Extending our earlier downscaler model (Berrocal, V. J., Gelfand, A. E., and Holland, D. M. (2010b). A spatio-temporal downscaler for outputs from numerical models. Journal of Agricultural, Biological and Environmental Statistics 15, 176-197), these new models are intended to address two potential concerns with the model output. One recognizes that there may be useful information in the outputs for grid cells that are neighbors of the one in which the location lies. The second acknowledges potential spatial misalignment between a station and its putatively associated grid cell. The first model is a Gaussian Markov random field smoothed downscaler that relates monitoring station data and computer model output via the introduction of a latent Gaussian Markov random field linked to both sources of data. The second model is a smoothed downscaler with spatially varying random weights defined through a latent Gaussian process and an exponential kernel function, that yields, at each site, a new variable on which the monitoring station data is regressed with a spatial linear model. We applied both methods to daily ozone concentration data for the Eastern US during the summer months of June, July and August 2001, obtaining, respectively, a 5% and a 15% predictive gain in overall predictive mean square error over our earlier downscaler model (Berrocal et al., 2010b). Perhaps more importantly, the predictive gain is greater at hold-out sites that are far from monitoring sites.

Duke Scholars

Published In

Biometrics

DOI

EISSN

1541-0420

ISSN

0006-341X

Publication Date

September 2012

Volume

68

Issue

3

Start / End Page

837 / 848

Related Subject Headings

  • United States
  • Time Factors
  • Statistics & Probability
  • Ozone
  • Normal Distribution
  • Models, Statistical
  • Markov Chains
  • Humans
  • Environmental Exposure
  • Data Interpretation, Statistical
 

Citation

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ICMJE
MLA
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Berrocal, V. J., Gelfand, A. E., & Holland, D. M. (2012). Space-time data fusion under error in computer model output: an application to modeling air quality. Biometrics, 68(3), 837–848. https://doi.org/10.1111/j.1541-0420.2011.01725.x
Berrocal, Veronica J., Alan E. Gelfand, and David M. Holland. “Space-time data fusion under error in computer model output: an application to modeling air quality.Biometrics 68, no. 3 (September 2012): 837–48. https://doi.org/10.1111/j.1541-0420.2011.01725.x.
Berrocal VJ, Gelfand AE, Holland DM. Space-time data fusion under error in computer model output: an application to modeling air quality. Biometrics. 2012 Sep;68(3):837–48.
Berrocal, Veronica J., et al. “Space-time data fusion under error in computer model output: an application to modeling air quality.Biometrics, vol. 68, no. 3, Sept. 2012, pp. 837–48. Epmc, doi:10.1111/j.1541-0420.2011.01725.x.
Berrocal VJ, Gelfand AE, Holland DM. Space-time data fusion under error in computer model output: an application to modeling air quality. Biometrics. 2012 Sep;68(3):837–848.
Journal cover image

Published In

Biometrics

DOI

EISSN

1541-0420

ISSN

0006-341X

Publication Date

September 2012

Volume

68

Issue

3

Start / End Page

837 / 848

Related Subject Headings

  • United States
  • Time Factors
  • Statistics & Probability
  • Ozone
  • Normal Distribution
  • Models, Statistical
  • Markov Chains
  • Humans
  • Environmental Exposure
  • Data Interpretation, Statistical