Gilani Owais, McKay Lisa A, Gregoire Timothy G, Guan Yongtao, Leaderer Brian P, Holford Theodore R
School of Public Health, University of Michigan, Ann Arbor, MI 48109, U.S.A.
Yale School of Public Health, Yale University, New Haven, CT 06520, U.S.A.
Stat Med. 2016 Jun 30;35(14):2422-40. doi: 10.1002/sim.6867. Epub 2016 Jan 21.
Spatiotemporal calibration of output from deterministic models is an increasingly popular tool to more accurately and efficiently estimate the true distribution of spatial and temporal processes. Current calibration techniques have focused on a single source of data on observed measurements of the process of interest that are both temporally and spatially dense. Additionally, these methods often calibrate deterministic models available in grid-cell format with pixel sizes small enough that the centroid of the pixel closely approximates the measurement for other points within the pixel. 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. This method not only improves estimates of the pollutant at the grid centroids but also refines the spatial resolution of the grid data. The modeling strategy is illustrated by calibrating and spatially refining daily estimates of ambient nitrogen dioxide concentration over Connecticut for 1994 from the Community Multiscale Air Quality model (temporally dense grid-cell estimates on a large pixel size) using observations from an epidemiologic study (spatially dense and temporally sparse) and Environmental Protection Agency monitoring stations (temporally dense and spatially sparse). Copyright © 2016 John Wiley & Sons, Ltd.
确定性模型输出的时空校准是一种越来越流行的工具，用于更准确、高效地估计空间和时间过程的真实分布。当前的校准技术主要集中于感兴趣过程的观测测量的单一数据源，这些数据在时间和空间上都是密集的。此外，这些方法通常校准以网格单元格式提供的确定性模型，其像素大小足够小，以至于像素的质心非常接近该像素内其他点的测量值。我们开发了一种建模策略，使我们能够同时纳入来自过程观测测量的两个数据源（在空间和时间分辨率上有所不同）的信息，以校准来自规则网格上可用的确定性模型的估计值。这种方法不仅改进了网格质心处污染物的估计，还提高了网格数据的空间分辨率。通过使用来自一项流行病学研究（空间密集但时间稀疏）和环境保护局监测站（时间密集但空间稀疏）的观测数据，对1994年康涅狄格州社区多尺度空气质量模型（大像素大小的时间密集网格单元估计值）的环境二氧化氮浓度每日估计值进行校准和空间细化，说明了该建模策略。版权所有© 2016约翰·威利父子有限公司。
