Abstract
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.
Highlights
Simulation and forecasts of sand and dust storms (SDS) have been progressed significantly in the last decay to address the issues related to climate changes and air quality impacts (Levin et al, 1996; Tegen and Fung, 1995; Zender et al, 2003)
This paper presents the development of the SDS-data assimilation system (DAS) and its application in spring 2006 SDS operational forecasts in East Asia
This study extends the assimilation scheme in GRAPES to assimilate visibility and satellite retrieval dust loading data for a SDS forecast system –CUACE/Dust
Summary
Operational forecast results tal Ozone Mapping Spectrometer (TOMS) aerosol index (AI) data as the initial dust loading input for a dust prediction system, Alpert et al (2002) has found a positive improvement for the model performance. This demonstrates the importance of an accurate initial dust concentration even though no data assimilation was used. Up to now 3D-Var method plays an important role in weather and climate studies and operational forecast since it was first applied to the assimilation of observational data in 1981 (Bengtsson, 1981).
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