Atmos. Chem. Phys. Discuss., 7, 8309-8332, 2007
www.atmos-chem-phys-discuss.net/7/8309/2007/
doi:10.5194/acpd-7-8309-2007
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Review Status
This discussion paper has been under review for the journal Atmospheric Chemistry and Physics (ACP). Please refer to the corresponding final paper in ACP.
Data assimilation of dust aerosol observations for CUACE/Dust forecasting system
T. Niu1, S. L. Gong1,2, G. F. Zhu3, H. L. Liu1, X. Q. Hu4, C. H. Zhou1, and Y. Q. Wang1
1Center for Atmosphere Watch & Services (CAWAS), Chinese Academy of Meteorological Sciences, China Meteorological Administration (CMA), Beijing 100081, China
2Air Quality Research Division, Science & Technology Branch, Environment Canada, 4905 Dufferin Street, Toronto, Ontario M3H 5T4, Canada
3State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, China Meteorological Administration (CMA), Beijing 100081, China
4National Satellite Meteorological Center, China Meteorological Administration (NSMC/CMA), Beijing 100081, China

Abstract. A data assimilation system (DAS) was developed for the Chinese Unified Atmospheric Chemistry Environment – Dust (CUACE/Dust) forecast system and applied in the operational forecasts of sand and dust storm (SDS) in spring 2006. The system is based on a three dimensional variational method (3D-Var) and uses extensively the measurements of surface visibility and dust loading retrieval from the Chinese geostationary satellite FY-2C. The results show that a major improvement to the capability of CUACE/Dust in forecasting the short-term variability in the spatial distribution and intensity of dust concentrations has been achieved, especially in those areas far from the source regions. The seasonal mean Threat Score (TS) over the East Asia in spring 2006 increased from 0.22 to 0.31 by using the data assimilation system, a 41% enhancement. The assimilation results usually agree with the dust loading retrieved from FY-2C and visibility distribution from surface meteorological stations, which indicates that the 3D-Var method is very powerful for the unification of observation and numerical modeling results.

Citation: Niu, T., Gong, S. L., Zhu, G. F., Liu, H. L., Hu, X. Q., Zhou, C. H., and Wang, Y. Q.: Data assimilation of dust aerosol observations for CUACE/Dust forecasting system, Atmos. Chem. Phys. Discuss., 7, 8309-8332, doi:10.5194/acpd-7-8309-2007, 2007.
 
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