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

为进一步分析2006年MILAGRO （Megacity Initiative: Local Global and Research Observations）实地活动的数据，将开发墨西哥城大都市区（MCMA）的改进气象模拟。目的是更好地了解MCMA的气象和空气污染以及MCMA羽流的区域影响。卫星遥感数据将用于评价和改进数值天气模拟，重点是城市对地方气象的影响，如城市热岛和城市诱发环流。数据同化技术将用于改进模拟和更好地量化不确定性。基于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来源区域，并评估来自已知来源（包括工业、火山和生物来源）的影响强度。该项目将协助墨西哥城制定基于科学的环境政策，并为全球其他城市地区提供见解。可视化工具将用于展示空气质量问题，并通过与政府机构和本地学术及研究机构的研讨会和外展活动，增加利益相关者的参与。该项目将为本科生提供研究机会，并为一名研究生和一名博士后研究员提供研究和指导机会，他们将参与分析和建模。

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.