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. A practical challenge facing the US Environmental Protection Agency (USEPA) is to provide real-time forecasting of current 8 h average ozone exposure over the entire conterminous United States. Such real-time forecasting is now provided as spatial forecast maps of current 8 h average ozone defined as the average of the previous four hours, current hour, and predictions for the next three hours. Current patterns are updated hourly throughout the day on the EPA-AIRNow web site.

Our contribution is to show how we can substantially improve upon current real-time forecasting systems. 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 with an efficient computational strategy to fit model parameters. This strategy can be viewed as hybrid in that it blends offline model fitting with online predictions followed by fast spatial interpolation to produce the desired real-time forecast maps. Model validation for the eastern US shows consequential improvement of our fully inferential approach compared with the existing implementations.

准确评估暴露于环境臭氧浓度对于告知公众和污染监测机构可能导致不利健康影响的臭氧水平非常重要。美国环境保护署 (USEPA) 面临的一个实际挑战是提供对整个美国本土当前 8 小时平均臭氧暴露的实时预测。这种实时预测现在作为当前8 小时平均臭氧的空间预测图提供，定义为前四小时、当前小时和接下来三小时预测的平均值。当前模式在 EPA-AIRNow 网站上全天每小时更新一次。

我们的贡献是展示我们如何大幅改进当前的实时预测系统。我们引入了基于实时监测数据和数值模型输出的一阶差分的降尺度融合模型。该模型具有灵活的系数结构和有效的计算策略来拟合模型参数。这种策略可以被视为混合策略，因为它将离线模型拟合与在线预测结合起来，然后进行快速空间插值以生成所需的实时预测图。美国东部的模型验证表明，与现有实现相比，我们的完全推理方法得到了相应的改进。