The possibility of improving aerosol prediction with ensemble-based data assimilation method

Airborne aerosol particles impact not only air quality but also climate through their direct and indirect effects. It is also well known that the influence of aerosols on the air quality is directly linked to the human health. Thus an aerosol prediction system with better accuracy on the temporal and spatial distributions is necessary to provide their information with the public. It is thought that an aerosol prediction system with data assimilation may provide better aerosol forecast than ever. Data assimilation integrates observations and numerical simulations to obtain the optimal solution and reduce the uncertainty. Yumimoto and Takemura (2011) have developed an ensemble based data assimilation system with the local ensemble transformed Kalman filter (LETKF; Hunt et al., 2007) based on a global aerosol climate model (SPRINTARS; Takemura et al., 2002, 2005). In their result, it has been shown that the global distribution of aerosol can be improved. They also indicated that the LETKF data assimilation method may be a good tool to improve the accuracy of the aerosol prediction system. In this study, we try to develop an aerosol prediction system with the data assimilation system shown by Yumimoto and Takemura (2011). We assimilate semi-real time data of the aerosol optical thickness measured by MODIS onboard TERRA and AQUA in the East Asian region into SPRINTARS and use the assimilated data as the initial condition to forecast the temporal and spatial distribution of aerosol particles. Differences between predicted results with/without data assimilation will be revealed in the presentation to show whether the data assimilation with the retrieved satellite data is useful to improve prediction of the aerosol distribution in the East Asian region. Ground-based semi-real time observation data in East Asia from sunphotometer and lidar will be used in the aerosol forecast system with assimilation. This study is supported by the Funding Program for Next Generation World-Leading Researchers (GR079)