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

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

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

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%).

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

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

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

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,

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

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%

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

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 0205198.

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