ITR: Development of a General Computational Framework for the Optimal Integration of Atmospheric Chemical Transport Models and Measurements Using Adjoints

该项目的总体目标是开发通用计算工具和相关软件，用于将大气化学和光学测量数据同化为化学传输模型(CTMS)。这些工具的开发使用户不需要是伴随建模和优化理论方面的专家。这些进展将有助于加深对：(1)CTMS的不准确性；(2)CTMS输入和参数不确定性的敏感性；以及(3)模式预测和大气测量的比较的理解。这些计算工具有望将大气化学模拟领域推向了解模型预测在多大程度上涵盖现有测量的下一个平台，这一理解目前因缺乏系统理论和通用分析工具而受到阻碍。这些技术和分析工具将用于解释观测数据和预报活动。研究方法将包括：(1)开发CTMS中四维Var数据同化的新的有效算法；(2)开发通用软件支持工具，以促进构建可用于任何CTM的离散伴随项；以及(3)将这些技术应用于重要的应用，包括：(A)分析洛杉矶的排放控制战略；(B)综合测量和模型，为ACE-Asia密集的实地试验产生一致/最佳的分析数据集；(C)反分析，以产生更好的排放量估计；这个项目的目标是开发和利用信息技术研究(ITR)工具，将测量和模拟分析结合起来，目的是提供大气的最佳分析状态，即模拟和测量量的密切和密切的结合。这一改进的状态估计更好地确定了关键化学成分相对于其源和汇的空间和时间场。这些信息对于设计具有成本效益的排放控制战略以改善空气质量、解释观测数据(如在密集的实地活动中获得的数据)以及执行空气质量预报至关重要。开发综合测量和模型的工具对于充分利用对流层中大量卫星化学数据的挑战也是至关重要的，这些数据现已可用，并将在今后几年变得更加普遍。除了在信息技术、大气化学、空气质量和全球变化领域的这些更广泛的影响外，该项目还将为学生和博士后提供参与高度跨学科和协作活动的机会。

The overall goal of this project is to develop general computational tools, and associated software, for assimilation of atmospheric chemical and optical measurements into chemical transport models (CTMs). These tools are to be developed so that users need not be experts in adjoint modeling and optimization theory. These developments will foster a deeper understanding of: (1) inaccuracies in CTMs; (2) sensitivities of CTMs input and parameter uncertainties; and (3) the comparison of model predictions and atmospheric measurements. These computational tools have the promise to move the field of atmospheric chemical modeling to the next plateau of understanding the extent to which model predictions encompass available measurements, an understanding that is currently hampered by the absence of systematic theory and general analysis tools. These techniques and analysis tools will be applied both to the interpretation of observational data and to forecasting activities. The research approach will entail: (1) Development of novel and efficient algorithms for 4-dimensional-Var data assimilation in CTMs; (2) Development of general software support tools to facilitate the construction of discrete adjoints to be used in any CTM; and (3) Application of these techniques to important applications including: (a) analysis of emission control strategies for Los Angeles; (b) the integration of measurements and models to produce a consistent/optimal analysis data set for the ACE-Asia intensive field experiment; (c) the inverse analysis to produce a better estimate of emissions; and (d) the design of observation strategies to improve chemical forecasting capabilities.The objective of this project is the development and utilization of Information Technology Research (ITR) tools to integrate measurement and modeling analysis with the goal of providing an optimal analysis state of the atmosphere, that is an intimate and close integration of modeled and measured quantities. This improved estimate of the state better defines the spatial and temporal fields of key chemical components in relation to their sources and sinks. This information is critical in designing cost-effective emission control strategies for improved air quality, for the interpretation of observational data such as those obtained during intensive field campaigns, and to the execution of air-quality forecasting. The development of the tools to integrate measurements and models is also critical to the challenge of a full utilization of the vast amounts of satellite chemical data in the troposphere that are now becoming available, and which will become more prevalent in the coming years. In addition to these broader impacts in the fields of information technology, atmospheric chemistry, air quality, and global change, this project will provide opportunities for students and post-docs to participate in a highly interdisciplinary and collaborative activity.