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| Titel |
An Ensemble Kalman Filter for severe dust storm data assimilation over China |
| VerfasserIn |
C. Lin, Z. Wang, J. Zhu |
| 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 ; 8, no. 11 ; Nr. 8, no. 11 (2008-06-17), S.2975-2983 |
| Datensatznummer |
250006186
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| Publikation (Nr.) |
 copernicus.org/acp-8-2975-2008.pdf |
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| Zusammenfassung |
| An Ensemble Kalman Filter (EnKF) data assimilation system was developed for
a regional dust transport model. This paper applied the EnKF method to
investigate modeling of severe dust storm episodes occurring in March 2002
over China based on surface observations of dust concentrations to explore
the impact of the EnKF data assimilation systems on forecast improvement. A
series of sensitivity experiments using our system demonstrates the ability
of the advanced EnKF assimilation method using surface observed PM10 in
North China to correct initial conditions, which leads to improved forecasts
of dust storms. However, large errors in the forecast may arise from model
errors (uncertainties in meteorological fields, dust emissions, dry
deposition velocity, etc.). This result illustrates that the EnKF requires
identification and correction model errors during the assimilation procedure
in order to significantly improve forecasts. Results also show that the EnKF
should use a large inflation parameter to obtain better model performance
and forecast potential. Furthermore, the ensemble perturbations generated at
the initial time should include enough ensemble spreads to represent the
background error after several assimilation cycles. |
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