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| Titel |
Evaluating model performance of an ensemble-based chemical data assimilation system during INTEX-B field mission |
| VerfasserIn |
A. F. Arellano, K. Raeder, J. L. Anderson, P. G. Hess, L. K. Emmons, D. P. Edwards, G. G. Pfister, T. L. Campos, G. W. Sachse |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1680-7316
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| Digitales Dokument |
URL |
| Erschienen |
In: Atmospheric Chemistry and Physics ; 7, no. 21 ; Nr. 7, no. 21 (2007-11-16), S.5695-5710 |
| Datensatznummer |
250005254
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| Publikation (Nr.) |
 copernicus.org/acp-7-5695-2007.pdf |
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| Zusammenfassung |
| We present a global chemical data assimilation system using a global
atmosphere model, the Community Atmosphere Model (CAM3) with simplified
chemistry and the Data Assimilation Research Testbed (DART) assimilation
package. DART is a community software facility for assimilation studies
using the ensemble Kalman filter approach. Here, we apply the assimilation
system to constrain global tropospheric carbon monoxide (CO) by assimilating
meteorological observations of temperature and horizontal wind velocity and
satellite CO retrievals from the Measurement of Pollution in the Troposphere
(MOPITT) satellite instrument. We verify the system performance using
independent CO observations taken on board the NSF/NCAR C-130 and NASA DC-8
aircrafts during the April 2006 part of the Intercontinental Chemical
Transport Experiment (INTEX-B). Our evaluations show that MOPITT data
assimilation provides significant improvements in terms of capturing the
observed CO variability relative to no MOPITT assimilation (i.e. the
correlation improves from 0.62 to 0.71, significant at 99% confidence).
The assimilation provides evidence of median CO loading of about 150 ppbv at
700 hPa over the NE Pacific during April 2006. This is marginally higher
than the modeled CO with no MOPITT assimilation (~140 ppbv). Our
ensemble-based estimates of model uncertainty also show model overprediction
over the source region (i.e. China) and underprediction over the NE Pacific,
suggesting model errors that cannot be readily explained by emissions alone.
These results have important implications for improving regional chemical
forecasts and for inverse modeling of CO sources and further demonstrate the
utility of the assimilation system in comparing non-coincident measurements,
e.g. comparing satellite retrievals of CO with in-situ aircraft
measurements. |
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