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同化为化学传输模式（CTM）。这些工具的开发，使用户不需要在伴随建模和优化理论的专家。这些发展将促进对以下问题的更深入理解：（1）CTM的不准确性;（2）CTM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。这些计算工具有希望将大气化学建模领域推向下一个平台，即理解模型预测涵盖可用测量的程度，这种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）开发新的和有效的算法，用于CTM中的4维变分数据同化;（2）开发通用软件支持工具，以促进任何CTM中使用的离散伴随的构建;和（3）将这些技术应用于重要应用，包括：（a）分析洛杉矶的排放控制策略;（B）将测量和模型结合起来，为亚洲环境中心密集实地试验提供一致/最佳的分析数据集;及（d）设计观测策略以提高化学预报能力。本项目的目标是发展和利用信息技术研究（ITR）工具，以提供大气的最佳分析状态为目标，将测量和建模分析集成在一起，即建模量和测量量的紧密结合。这种改进的状态估计更好地定义了与其源和汇有关的关键化学成分的空间和时间领域。这一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、解释诸如在密集实地活动期间获得的观测数据以及进行空气质量预报至关重要。开发综合测量和模型的工具，对于充分利用目前可获得的、今后几年将更加普遍的大量对流层卫星化学数据这一挑战也至关重要。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.