|This project describes an integrated approach to modeling
atmospheric chemistry with trace gas data assimilation.
Specifically, we ran CMAQ from within DART to assimilate
both synthetic and real observations of CO for the period
of June 2001.
[link to more information]
Alexis Zubrow, firstname.lastname@example.org
Alexis Zubrow CISES, The University of Chicago, Chicago, IL, USA
Li Chen CISES, The University of Chicago, Chicago, IL, USA
V. R. Kotamarthi Argonne National Laboratory, Argonne, IL, USA
Michael L. Stein Statistics Department, The University of Chicago, Chicago, IL, USA
|Fractional bias between the mean of the ensembles without data assimilation and the original full CMAQ run.||Fractional bias between the mean of the ensembles after data assimilation and the original full CMAQ run.|
An integrated approach to modeling atmospheric chemistry with
trace gas data assimilation is a relatively new focus of the
atmospheric chemistry modeling community. It is expected that
the predictive capability of CTMs can be significantly improved
by assimilating measurements of key trace gases from
satellite-based platforms and surface monitors. Ensemble
adjustment Kalman filter (EAKF) methods are simple to implement,
don't need adjoints and backward integration, and are capable of
handling non-Gaussian model errors. These factors have led to the
adoption of EAKF methods for weather and climate simulations.
Additionally, EAKF provides a measure of error resulting from
the assimilation. We have combined EAKF data assimilation with
a single-tracer version of CMAQ. The Data Assimilation Research
Testbed (DART), developed by NCAR, was used to create an EAKF
enabled CMAQ for assimilating CO. DART provides a modular
environment that can integrate dynamical models with various
assimilation techniques. Specifically, we ran CMAQ in ensemble
adjustment Kalman filter mode to assimilate both synthetic and
real observations of CO for the period of June 2001.
We argue that it is a viable approach for further data assimilation
experiments and potentially for air quality forecasting.
Corresponding author: Alexis Zubrow, who has now moved to the University of North Carolina Institute for the Environment
The (dynamic) citation or link to the actual publication would go here.