PROJECT SUMMARY:

The overall goal of the proposed 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 (just as users
of CTMs need not be experts in numerical solutions of the
partial differential equations that underlie such models).
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. We plan to apply these techniques
and analysis tools both to the interpretation of
observational data and to forecasting activities. Our
research approach is to:

"Develop novel and efficient algorithms for 4D-Var data
assimilation in CTMs;

"Develop general software support tools to facilitate the
construction of discrete adjoints to be used in any CTM;

"Apply 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
AceAsia 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. Significant advances in our
fundamental understanding of atmospheric chemistry and our
ability to anticipate and manage change requires as
accurate a representation of the chemical state of the
atmosphere as possible. This proposal has as its objective
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. By optimal analysis state we mean
an intimate and close integration of modeled and measured
quantities, with the two merged together to provide a
consistent and best estimate of the chemical state of the
atmosphere. This improved estimate 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. A closer integration of measurements and
models will require significant advances in: (i) data
assimilation techniques; (ii) numerical algorithms; (iii)
software and application-specific data mining strategies;
and (iv) interfaces between measurement data and model
data. These topics, along with science applications,
represent the major research elements of this proposal. The
critical requirement for advancing the state of the science
is a focused, multidisciplinary effort, as envisioned
herein.