Spatio-Temporal Calibration and Resolution Refinement of Output from Deterministic Models

• Activity Number: 182
• Type: Contributed
• Date/Time: Monday, August 10, 2015 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics and the Environment
• Author(s): Owais Gilani* and Lisa McKay and Timothy Gregoire and Yongtao Guan and Brian Leaderer and Theodore Holford
• Companies: University of Michigan School of Public Health and Yale School of Public Health and Yale School of Forestry and Environmental Studies and University of Miami and Yale School of Public Health and Yale University
• Keywords: Spatiotemporal ; Calibration ; Grid data ; Change of support ; Integrated exposure modeling ; Kalman filter

Abstract

Spatiotemporal calibration of output from deterministic models is an increasingly popular tool to more accurately and efficiently estimate the true distribution of spatiotemporal processes. Current calibration techniques require access to a single source of data on observed measurements of the process of interest that are both temporally and spatially dense. Additionally, these methods calibrate deterministic models available in grid-cell format with relatively small pixel sizes. We develop a modeling strategy that allows us to simultaneously incorporate information from two sources of data on observed measurements of the process that differ in their spatial and temporal resolutions to calibrate estimates from a deterministic model available on a regular grid that are temporally dense and have good spatial coverage, but with a large pixel size so that the centroid of the pixel does not provide a good estimate for most points within the pixel. This method improves the estimate of the pollutant at the grid centroids and also refines the spatial resolution of the grid data. The modeling strategy is applied to daily estimates of ambient nitrogen dioxide concentration over Connecticut.