Paci Lucia, Gelfand Alan E, Holland David M
Department of Statistical Science at Duke University, Box 90251, Durham NC 27708-0251, USA.
Spat Stat. 2013 May 1;4:79-93. doi: 10.1016/j.spasta.2013.04.003.
The accurate assessment of exposure to ambient ozone concentrations is important for informing the public and pollution monitoring agencies about ozone levels that may lead to adverse health effects. High-resolution air quality information can offer significant health benefits by leading to improved environmental decisions. A practical challenge facing the U.S. Environmental Protection Agency (USEPA) is to provide real-time forecasting of current 8-hour average ozone exposure over the entire conterminous United States. Such forecasting is now provided as spatial forecast maps of 8-hour average ozone defined as the average of the previous four hours, current hour, and for the next three hours. Current 8-hour average patterns are updated hourly throughout the day on the EPA-AIRNow web site. The contribution here is to show how we can substantially improve upon current real-time forecasting systems. To enable such forecasting, we introduce a downscaler fusion model based on first differences of real-time monitoring data and numerical model output. The model has a flexible coefficient structure and uses an efficient computational strategy to fit model parameters. Our hybrid computational strategy blends continuous background updated model fitting with real-time predictions. Model validation analyses show that we are achieving very accurate and precise ozone forecasts.
准确评估环境臭氧浓度暴露情况对于向公众和污染监测机构通报可能导致健康不良影响的臭氧水平至关重要。高分辨率空气质量信息可通过改善环境决策带来显著的健康益处。美国环境保护局（USEPA）面临的一个实际挑战是提供整个美国本土当前8小时平均臭氧暴露的实时预测。现在，这种预测是以8小时平均臭氧的空间预测图形式提供的，该平均臭氧定义为前四小时、当前小时以及接下来三小时的平均值。当前8小时平均模式全天每小时在EPA - AIRNow网站上更新。本文的贡献在于展示如何能够大幅改进当前的实时预测系统。为实现这种预测，我们引入了一种基于实时监测数据和数值模型输出一阶差分的降尺度融合模型。该模型具有灵活的系数结构，并使用高效的计算策略来拟合模型参数。我们的混合计算策略将连续的背景更新模型拟合与实时预测相结合。模型验证分析表明，我们正在实现非常准确和精确的臭氧预测。
