时空错位下的二元时空降尺度器
A bivariate space-time downscaler under space and time misalignment
摘要：
臭氧和颗粒物PM2.5是长期以来与公共健康风险增加有关的共同污染物。关于这两种污染物浓度水平的信息来自两个来源：监测站点和复杂数值模型的输出，这些模型在大的空间区域产生浓度表面。在本文中，我们提供了一个完全基于模型的方法，用于融合这两个来源的信息对共同污染物，这是计算上可行的大空间区域和长时间。由于这两种环境污染物的浓度水平之间的关联，预计有关一种污染物的信息将有助于改善对另一种污染物的预测。错位是一个明显的问题，因为这两种污染物的监测网络只有部分交叉，而且PM2.5的收集率通常低于臭氧。扩展Berrocal等人（2010）的先前工作，我们引入了一个双变量降尺度器，它提供了一类灵活的双变量时空同化模型。我们讨论了模型拟合的计算问题，并分析了2002年臭氧季节的臭氧和PM2.5的数据集。我们在预测性能方面表现出适度的改善，在我们只能预期很小增益的情况下并不奇怪。
英文摘要：
Ozone and particulate matter PM2.5 are co-pollutants that have long been associated with increased public health risks. Information on concentration levels for both pollutants come from two sources: monitoring sites and output from complex numerical models that produce concentration surfaces over large spatial regions. In this paper, we offer a fully-model based approach for fusing these two sources of information for the pair of co-pollutants which is computationally feasible over large spatial regions and long periods of time. Due to the association between concentration levels of the two environmental contaminants, it is expected that information regarding one will help to improve prediction of the other. Misalignment is an obvious issue since the monitoring networks for the two contaminants only partly intersect and because the collection rate for PM2.5 is typically less frequent than that for ozone. Extending previous work in Berrocal et al. (2010), we introduce a bivariate downscaler that provides a flexible class of bivariate space-time assimilation models. We discuss computational issues for model fitting and analyze a dataset for ozone and PM2.5 for the ozone season during year 2002. We show a modest improvement in predictive performance, not surprising in a setting where we can anticipate only a small gain.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
