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

T. Niu, S. L. Gong, G. F. Zhu, H. L. Liu, X. Q. Hu, C. H. Zhou, Y. Q. Wang
2007 Atmospheric Chemistry and Physics Discussions  
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 mea-5 surements 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
more » ... 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 10 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. 15 schemes, surface boundary data (e.g. soil texture, soil wetness, and land-use data 8310 ACPD EGU as they are quite expensive for computation and not easy for system upgrade due to their algorithm depending closely on a model. 3D-Var method was first applied to the assimilation of observational data in 1981 (Bengtsson, 1981) . Up to now 3D-Var method plays an important role in studies and operations on weather and climate. Considering the situation of our computer resource and operational requirement, a 3D-Var
doi:10.5194/acpd-7-8309-2007 fatcat:tzlvjf7odzgn5fhloazusrmgge