Application of a bias estimator for the improved assimilation of Measurements of Pollution in the Troposphere (MOPITT) carbon monoxide retrievals

This study discusses an improved technique for the assimilation of carbon monoxide retrievals from the Measurements of Pollution in the Troposphere (MOPITT) instrument in a chemistry‐transport model using a suboptimal Kalman filter. An online bias estimator algorithm is employed to identify systemat...

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Bibliographic Details
Published in:Journal of Geophysical Research. D. Atmospheres 2004-08, Vol.109 (D16), p.D16304.1-n/a
Main Authors: Lamarque, J.-F., Khattatov, B., Yudin, V., Edwards, D. P., Gille, J. C., Emmons, L. K., Deeter, M. N., Warner, J., Ziskin, D. C., Francis, G. L., Ho, S., Mao, D., Chen, J., Drummond, J. R.
Format: Article
Language:English
Subjects:
Air pollution
Algorithms
Atmospheric chemistry
Bias
Carbon monoxide
data assimilation
Data retrieval
Diagnostic systems
Earth, ocean, space
Exact sciences and technology
Geophysics
Kalman filters
Mathematical models
MOPITT
On-line systems
Pollution abatement
Pollution monitoring
Pollution transport
Retrieval
Troposphere
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Summary:This study discusses an improved technique for the assimilation of carbon monoxide retrievals from the Measurements of Pollution in the Troposphere (MOPITT) instrument in a chemistry‐transport model using a suboptimal Kalman filter. An online bias estimator algorithm is employed to identify systematic biases in the model and account for them during the assimilation. Results suggest a large decline (both locally and globally) in the observation minus forecast diagnostics and provide insights about possible model deficiencies by enabling explicit examination of model biases.
ISSN:0148-0227
2156-2202
DOI:10.1029/2003JD004466