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

ITR：开发通用计算框架，用于使用伴随函数优化大气化学物质传输模型和测量的集成

• 批准号: 0205198
• 负责人: Gregory Carmichael
• 金额: $ 230万
• 依托单位: University of Iowa
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-09-01 至 2008-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0205198&HistoricalAwards=false
• 关键词: ITR; Development; General; Computational; Framework

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.

该项目的总体目标是开发通用计算工具和相关软件，将大气化学和光学测量结果同化到化学传输模型（CTM）中。这些工具的开发使用户无需成为伴随建模和优化理论方面的专家。这些发展将促进对以下方面的更深入理解：(1) CTM 的不准确性； (2) CTM输入的敏感性和参数不确定性； (3) 模式预测与大气测量的比较。这些计算工具有望将大气化学建模领域推向下一个阶段，即理解模型预测在多大程度上涵盖了可用的测量结果，而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应用于观测数据的解释和预测活动。研究方法将包括：（1）开发用于 CTM 中 4 维 Var 数据同化的新颖且有效的算法； (2) 开发通用软件支持工具，以方便构造可在任何 CTM 中使用的离散伴随函数； (3) 将这些技术应用于重要应用，包括： (a) 洛杉矶排放控制策略分析； (b) 整合测量和模型，为 ACE-Asia 强化现场实验产生一致/最佳的分析数据集； (c) 进行逆向分析以更好地估计排放量； (d) 设计观测策略以提高化学预报能力。该项目的目标是开发和利用信息技术研究（ITR）工具来集成测量和建模分析，目标是提供最佳的大气分析状态，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关键化学成分与其源和汇相关的空间和时间场。这些信息对于设计具有成本效益的排放控制策略以改善空气质量、解释观测数据（例如在密集的现场活动中获得的数据）以及执行空气质量预报至关重要。开发集成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至关重要，这些数据现在已经可用，并且在未来几年将变得更加普遍。除了在信息技术、大气化学、空气质量和全球变化领域产生更广泛的影响外，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。