Collaborative Research: Simulations of MILAGRO Meteorology and Transport for Analysis of Atmospheric Chemistry Data

将改进墨西哥城大都市区的气象模拟，以进一步分析2006年MILAGRO（特大城市倡议：地方全球和研究观测）实地活动的数据。其目的是更好地了解气象和空气污染的MCMA和区域影响的MCMA羽。卫星遥感数据将用于评价和改进数值天气模拟，重点是城市对当地气象的影响，如城市热岛和城市引起的环流。数据同化技术将用于改进模拟和更好地量化不确定性。根据气象分析的前向和后向轨迹将用于评估墨西哥城盆地的混合过程以及不同来源区域对空气质量的影响。关于气体和气溶胶相浓度的数据将与轨迹模拟相结合，以进一步确定可能的源区，并评估已知来源的影响强度，包括工业、火山和生物。该项目将协助墨西哥城制定以科学为基础的环境政策，并为世界其他城市地区提供深入的见解。将使用可视化工具来展示空气质量问题，并通过与政府机构和当地学术和研究机构举办讲习班和外联活动，增加利益攸关方的参与。该项目将为本科生提供研究机会，并为将参与分析和建模的研究生和博士后研究人员提供研究和指导机会。

Improved meteorological simulations for the Mexico City Metropolitan Area (MCMA) will be developed for further analysis of data from the 2006 MILAGRO (Megacity Initiative: Local Global and Research Observations) field campaign. The objective is to better understand meteorology and air pollution in the MCMA and regional effects of the MCMA plume. Satellite remote sensing data will be used to evaluate and improve numerical weather simulations, with a focus on urban impacts on local meteorology such as the urban heat island and urban-induced circulations. Data assimilation techniques will be used to improve the simulations and to better quantify uncertainties. Forward and backward trajectories based on the meteorological analyses will be used to evaluate mixing processes in the Mexico City basin and the impact of different source regions on air quality. Data on gas and aerosol phase concentrations will be combined with trajectory simulations to further identify possible source regions and to evaluate the strength of impacts from known sources, including industrial, volcanic, and biological. This project will assist in the development of science-based environmental policies for Mexico City as well as provide insights into other urban areas worldwide. Visualization tools will be used to demonstrate air quality issues and to increase stakeholder participation through workshops and outreach activities with both government agencies and local academic and research institutions. The project will provide research opportunities for undergraduate students as well as research and mentoring opportunities for a graduate student and a post-doctoral researcher who will be involved in the analysis and modeling.