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

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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.