McHenry John N, Vukovich Jeffery M, Hsu N Christina
a Baron Services , Raleigh , NC , USA.
b NASA/Goddard Space Flight Center , Greenbelt , MD , USA.
J Air Waste Manag Assoc. 2015 Dec;65(12):1395-412. doi: 10.1080/10962247.2015.1096862.
This two-part paper reports on the development, implementation, and improvement of a version of the Community Multi-Scale Air Quality (CMAQ) model that assimilates real-time remotely-sensed aerosol optical depth (AOD) information and ground-based PM2.5 monitor data in routine prognostic application. The model is being used by operational air quality forecasters to help guide their daily issuance of state or local-agency-based air quality alerts (e.g. action days, health advisories). Part 1 describes the development and testing of the initial assimilation capability, which was implemented offline in partnership with NASA and the Visibility Improvement State and Tribal Association of the Southeast (VISTAS) Regional Planning Organization (RPO). In the initial effort, MODIS-derived aerosol optical depth (AOD) data are input into a variational data-assimilation scheme using both the traditional Dark Target and relatively new "Deep Blue" retrieval methods. Evaluation of the developmental offline version, reported in Part 1 here, showed sufficient promise to implement the capability within the online, prognostic operational model described in Part 2. In Part 2, the addition of real-time surface PM2.5 monitoring data to improve the assimilation and an initial evaluation of the prognostic modeling system across the continental United States (CONUS) is presented.
Air quality forecasts are now routinely used to understand when air pollution may reach unhealthy levels. For the first time, an operational air quality forecast model that includes the assimilation of remotely-sensed aerosol optical depth and ground based PM2.5 observations is being used. The assimilation enables quantifiable improvements in model forecast skill, which improves confidence in the accuracy of the officially-issued forecasts. This helps air quality stakeholders be more effective in taking mitigating actions (reducing power consumption, ride-sharing, etc.) and avoiding exposures that could otherwise result in more serious air quality episodes or more deleterious health effects.
这篇分为两部分的论文报告了一种社区多尺度空气质量（CMAQ）模型版本的开发、实施和改进情况，该模型在常规预测应用中同化实时遥感气溶胶光学厚度（AOD）信息和地面PM2.5监测数据。该模型正被业务空气质量预报员用于帮助指导他们每日发布基于州或地方机构的空气质量警报（例如行动日、健康建议）。第1部分描述了初始同化能力的开发和测试，该能力是与美国国家航空航天局（NASA）以及东南部能见度改善州和部落协会（VISTAS）区域规划组织（RPO）合作离线实施的。在最初的工作中，利用中分辨率成像光谱仪（MODIS）获取的气溶胶光学厚度（AOD）数据，采用传统的暗目标和相对较新的“深蓝”反演方法，将其输入到变分数据同化方案中。此处第1部分报告的对开发中的离线版本的评估显示出足够的前景，可在第2部分所述的在线预测业务模型中实施该能力。在第2部分中，介绍了添加实时地面PM2.5监测数据以改进同化，并对美国大陆（CONUS）的预测建模系统进行了初步评估。
现在空气质量预报经常被用于了解空气污染何时可能达到不健康水平。首次使用了一种业务空气质量预报模型，该模型包括同化遥感气溶胶光学厚度和地面PM2.5观测数据。这种同化能够在模型预测技能上实现可量化的改进，从而提高对官方发布预报准确性的信心。这有助于空气质量相关利益方更有效地采取缓解行动（减少电力消耗、拼车等），并避免可能导致更严重空气质量事件或更有害健康影响的暴露。
