CMAQ CO assimilation result 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, azubrow@unc.edu



EAKF-CMAQ: Development and Initial Evaluation of an Ensemble Adjustment Kalman Filter Based Data Assimilation for CO

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

DART/CMAQ CO assimilation result
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
azubrow@unc.edu

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