Fusing point and areal level space-time data with application to wet deposition
Fusing point and areal level space-time data with application to wet deposition
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DOI：
10.1111/j.1467-9876.2009.00685.x
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发表时间：
2010-01-01
影响因子：
1.6
通讯作者：
Holland, David M.
中科院分区：
文献类型：
--
作者：
Sahu, Sujit K.;Gelfand, Alan E.;Holland, David M.
出于在美国东部的化学沉积预测在每周，季节和年度尺度的问题，本文开发了一个框架，在这种情况下，点和网格参考时空数据的联合建模。分层模型可以提供准确的空间插值和时间聚合相结合的信息，从观察点参考监测数据和网格输出的数值模拟模型称为“社区多尺度空气质量模型”。该技术避免了支持的变化问题，出现在其他层次模型的数据融合设置结合联合收割机点和网格参考数据。层次时空模型拟合每周湿硫酸盐和硝酸盐沉积数据在美国东部。该模型是验证预留数据从一些监测站点。预测贝叶斯方法的开发和说明推断汇总摘要，如季度和年度硫酸盐和硝酸盐沉积地图。最高的湿硫酸盐沉积发生在主要排放源附近，如化石燃料发电厂，而较低的值发生在本底监测点附近。
Motivated by the problem of predicting chemical deposition in eastern USA at weekly, seasonal and annual scales, the paper develops a framework for joint modelling of point- and grid-referenced spatiotemporal data in this context. The hierarchical model proposed can provide accurate spatial interpolation and temporal aggregation by combining information from observed point-referenced monitoring data and gridded output from a numerical simulation model known as the 'community multi-scale air quality model'. The technique avoids the change-of-support problem which arises in other hierarchical models for data fusion settings to combine point- and grid-referenced data. The hierarchical space-time model is fitted to weekly wet sulphate and nitrate deposition data over eastern USA. The model is validated with set-aside data from a number of monitoring sites. Predictive Bayesian methods are developed and illustrated for inference on aggregated summaries such as quarterly and annual sulphate and nitrate deposition maps. The highest wet sulphate deposition occurs near major emissions sources such as fossil-fuelled power plants whereas lower values occur near background monitoring sites.