Title page for ETD etd-01212010-174148

Type of Document Master's Thesis
Author Gou, Tianyi
Author's Email Address tygou@vt.edu
URN etd-01212010-174148
Title Computational Tools for Chemical Data Assimilation with CMAQ
Degree Master of Science
Department Computer Science
Advisory Committee
Advisor Name Title
Sandu, Adrian Committee Chair
Cao, Yang Committee Member
Marr, Linsey C. Committee Member
  • Data Assimilation
  • Chemical Transport Models
  • Adjoint Sensitivity Analysis
Date of Defense 2010-01-11
Availability unrestricted
The Community Multiscale Air Quality (CMAQ) system is the Environmental Protection Agency's main modeling tool for atmospheric pollution studies. CMAQ-ADJ, the adjoint model of CMAQ, offers new analysis capabilities such as receptor-oriented sensitivity analysis and chemical data assimilation.

This thesis presents the construction, validation, and properties of new adjoint modules in CMAQ, and illustrates their use

in sensitivity analyses and data assimilation experiments. The new module of discrete adjoint of advection is implemented with the aid of automatic differentiation tool (TAMC) and is fully validated by comparing the adjoint sensitivities with finite difference values.

In addition, adjoint sensitivity with respect to boundary conditions and boundary condition scaling factors are developed and validated in CMAQ.

To investigate numerically the impact of the continuous and discrete advection adjoints on data assimilation, various four dimensional variational (4D-Var) data assimilation experiments are carried out with the 1D advection PDE, and with CMAQ advection using synthetic and real observation data. The results show that optimization procedure gives better estimates of the reference initial condition and converges faster when using gradients computed by the continuous adjoint approach. This counter-intuitive result is explained using the nonlinearity properties of the piecewise parabolic method (the numerical discretization of advection in CMAQ).

Data assimilation experiments are carried out using real observation data. The simulation domain encompasses Texas and the simulation period

is August 30 to September 1, 2006. Data assimilation is used to improve both initial and boundary conditions. These experiments further

validate the tools developed in this thesis.

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