Articles | Volume 8, issue 13
Atmos. Chem. Phys., 8, 3473–3482, 2008
https://doi.org/10.5194/acp-8-3473-2008

Special issue: Dust storm forecast and early warning in East Asia

Atmos. Chem. Phys., 8, 3473–3482, 2008
https://doi.org/10.5194/acp-8-3473-2008

  02 Jul 2008

02 Jul 2008

Data assimilation of dust aerosol observations for the CUACE/dust forecasting system

T. Niu1, S. L. Gong2,1, G. F. Zhu3, H. L. Liu1, X. Q. Hu4, C. H. Zhou1, and Y. Q. Wang1 T. Niu et al.
  • 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 (phenomena) and dust loading retrieval from the Chinese geostationary satellite FY-2C. By a number of case studies, the DAS was found to provide corrections to both under- and over-estimates of SDS, presenting a major improvement to the forecasting capability of CUACE/Dust in the short-term variability in the spatial distribution and intensity of dust concentrations in both source regions and downwind areas. 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 forecast results with DAS 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 by the unification of observation and numerical model to improve the performance of forecast model.

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