Space-time data fusion under error in computer model output: an application to modeling air quality.
Space-time data fusion under error in computer model output: an application to modeling air quality.
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DOI：
10.1111/j.1541-0420.2011.01725.x
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发表时间：
2012-09
期刊：
影响因子：
1.9
通讯作者：
Holland DM
中科院分区：
文献类型：
--
作者：
Berrocal VJ;Gelfand AE;Holland DM
我们提供了可用于获得更准确的环境暴露评估的方法。特别是，我们提出了两种建模方法，结合联合收割机监测数据在点的水平与数值模型输出在网格单元格的水平，产生改进的预测环境暴露在点的水平。扩展我们先前的降尺度模型。数值模式输出的时空降尺度器。Journal of Agricultural，Biological and Environmental Statistics 15，176-197），这些新模型旨在解决模型输出的两个潜在问题。人们认识到，在作为位置所在的网格单元的邻居的网格单元的输出中可能存在有用的信息。第二种方法是确认一个站与其邻近的网格单元之间可能存在空间错位。第一个模型是一个高斯马尔可夫随机场平滑降尺度，涉及监测站数据和计算机模型输出通过引入一个潜在的高斯马尔可夫随机场链接到两个数据源。第二个模型是一个平滑的降尺度与空间变化的随机权重定义通过一个潜在的高斯过程和一个指数核函数，产生，在每个站点上，一个新的变量上的监测站数据回归空间线性模型。我们采用这两种方法，每日臭氧浓度数据为美国东部在夏季的6月，7月和2001年8月，分别获得了5%和15%的预测增益，在整体预测均方误差超过我们早期的降尺度模型。也许更重要的是，预测收益在远离监测点的保留点更大。
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. 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. Perhaps more importantly, the predictive gain is greater at hold-out sites that are far from monitoring sites.
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影响因子：
3.6
作者：
Lu, HL;Carlin, BP
通讯作者：
Carlin, BP
DOI：
10.1198/016214507000000031
发表时间：
2007-12-01
影响因子：
3.7
作者：
Sahu, Sujit K.;Gelfand, Alan E.;Holland, David M.
通讯作者：
Holland, David M.
DOI：
10.1198/016214504000000241
发表时间：
2004-03-01
影响因子：
3.7
作者：
Zhang, H
通讯作者：
Zhang, H
影响因子：
5.7
作者：
BESAG, J;GREEN, P;MENGERSEN, K
通讯作者：
MENGERSEN, K
DOI：
10.1111/j.1467-9868.2008.00663.x
发表时间：
2008-09-01
期刊：
Journal of the Royal Statistical Society. Series B, Statistical methodology
影响因子：
--
作者：
Banerjee S;Gelfand AE;Finley AO;Sang H
通讯作者：
Sang H