An Ensemble Kalman Filter for severe dust storm data assimilation over China
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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2008-06-01
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| Series: | Atmospheric Chemistry and Physics |
| Online Access: | http://www.atmos-chem-phys.net/8/2975/2008/acp-8-2975-2008.pdf |
| _version_ | 1811283213897695232 |
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| author | C. Lin Z. Wang J. Zhu |
| author_facet | C. Lin Z. Wang J. Zhu |
| author_sort | C. Lin |
| collection | DOAJ |
| description | 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 PM<sub>10</sub> 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. |
| first_indexed | 2024-04-13T02:07:19Z |
| format | Article |
| id | doaj.art-892f16e97a5141218b1b8cac99e91f06 |
| institution | Directory Open Access Journal |
| issn | 1680-7316 1680-7324 |
| language | English |
| last_indexed | 2024-04-13T02:07:19Z |
| publishDate | 2008-06-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Atmospheric Chemistry and Physics |
| spelling | doaj.art-892f16e97a5141218b1b8cac99e91f062022-12-22T03:07:25ZengCopernicus PublicationsAtmospheric Chemistry and Physics1680-73161680-73242008-06-0181129752983An Ensemble Kalman Filter for severe dust storm data assimilation over ChinaC. LinZ. WangJ. ZhuAn 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 PM<sub>10</sub> 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.http://www.atmos-chem-phys.net/8/2975/2008/acp-8-2975-2008.pdf |
| spellingShingle | C. Lin Z. Wang J. Zhu An Ensemble Kalman Filter for severe dust storm data assimilation over China Atmospheric Chemistry and Physics |
| title | An Ensemble Kalman Filter for severe dust storm data assimilation over China |
| title_full | An Ensemble Kalman Filter for severe dust storm data assimilation over China |
| title_fullStr | An Ensemble Kalman Filter for severe dust storm data assimilation over China |
| title_full_unstemmed | An Ensemble Kalman Filter for severe dust storm data assimilation over China |
| title_short | An Ensemble Kalman Filter for severe dust storm data assimilation over China |
| title_sort | ensemble kalman filter for severe dust storm data assimilation over china |
| url | http://www.atmos-chem-phys.net/8/2975/2008/acp-8-2975-2008.pdf |
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