1. Analysis of TraceP Observations Using a 4D-Var Technique
Tianfeng Chai, Greg R. Carmichael
Center for Global and Regional Environmental Research, University of Iowa
Dacian N. Daescu
Portland State University
Adrian Sandu
Virginia Ploytechnic Institute and State University

2. Background
Significant advances have been made in Chemical Transport Models
Large amounts of atmospheric chemistry observations are becoming available, but sometimes difficult for the conventional methods to use
Data assimilation has shown its capability in providing optimal analysis by integrating model analysis and measurements in meteorology, oceanography, and other fields
Why not apply data assimilation to atmospheric Chemistry?
Number of variables
Stiff system

3. Chemical Transport Model
3D atmospheric transport-chemistry model (STEM-III)
where chemical reactions are modeled by nonlinear stiff terms
Use operator splitting to solve CTM

4. TraceP field experiment
Shown are measured CO along the aircraft flight path, the brown isosurface represents modeled dust (100 ug/m3), and the blue isosurface is CO (150 ppb)shaded by the fraction due to biomass burning (green is more than 50%).

5. Basic idea of 4D-Var Define a cost functional
which measures the distance between model output and observations, as well as the deviation of the solution from the background state
Derive adjoint of tangent linear model
Where  is the forcing term, which is chosen so that the adjoint variables are the sensitivities of the cost functional with respect to state variables (concentrations), i.e.
Use adjoint variables for sensitivity analysis, as well as data assimilation

6. 4D-Var application Forward CTM model evolution
Update control variables
Checkpointing files
Cost function
Observations
Optimization
Gradients
Backward adjoint model integration

7. Computational aspects
Parallel Implementation using our PAQMSG library
The parallel adjoint STEM implements a distributed checkpointing scheme

8. Sensitivity analysis
In sensitivity analysis, the cost functional is chosen as
The adjoint variables then give the sensitivities of ozone concentration at Cheju at the final time step to different chemical species at different time steps,

9. Influence functions (over Cheju O3 concentration at 0:0:00 UT, 3/07/01) of O3, NO2, HCHO at -48, -24 hr

10. Data assimilation test
Assimilation window
6 hours starting from 0:0:0 GMT on March 1st
Observations
O3 and/or NO2 concentrations at the end of the assimilation window at all grid points from the reference run
Control variables
initial concentrations of O3 or NO2
Initial guess
reference initial values increased by 20%

11. Data assimilation results
The evolutions of cost function and RMS error of the control variable during the optimization procedure. The results are normalized by their pre-assimilation values. Several tests are shown using different control (CTRL) and observed (OBS) variables.
Timing : Assimilation/Forward = 2.2

12. Conclusions and future work
The current 4D-Var system is able to give detailed sensitivity analysis
The 4D-Var system can successfully reduce the cost function to recover the initial condition using Twin experiments
Using observations to adjust emissions (choosing emissions as control variables) is undergoing
We plan to use the current system in air quality forecast applications
This work is supported by NSF Grant ITR/AP&IM

13. Thank you!