This study focuses on the importance of initial conditions to air-quality predictions. We ran assimilation experiments using the WRF-Chem model and grid-point statistical interpolation （GSI）, for a 9-day severe part...This study focuses on the importance of initial conditions to air-quality predictions. We ran assimilation experiments using the WRF-Chem model and grid-point statistical interpolation （GSI）, for a 9-day severe particulate matter pollution event that occurred in Shanghai in December 2013. In this application, GSI used a three-dimensional variational approach to assimilate ground-based PM2.s observations into the chemical model, to obtain initial fields for the aerosol species. In our results, data assimilation significantly reduced the errors when compared to a simulation without assimilation, and improved forecasts of PM2.5 concentrations. Despite a drop in skill directly after the assimilation, a positive effect was present in forecasts for at least 12-2413, and there was a slight improvement in the 48-h forecasts. In addition to performing well in Shanghai, the verification statistics for this assimilation experiment are encouraging for most of the surface stations in China.展开更多
基金supported by the National Natural Science Foundation of China under Grant no.41375014the Project of Science and Technology Commission of Shanghai Municipality under Grant nos.12DZ1202702 and 14DZ1202904+1 种基金the Project of Scientific and Technological Development of the Shanghai Meteorological Bureau under Grant no.YJ201407the Project of National Science & Technology Pillar ProgramProject of National Science & Technology Pillar Program under Grant no. 2014BAC16B05
文摘This study focuses on the importance of initial conditions to air-quality predictions. We ran assimilation experiments using the WRF-Chem model and grid-point statistical interpolation （GSI）, for a 9-day severe particulate matter pollution event that occurred in Shanghai in December 2013. In this application, GSI used a three-dimensional variational approach to assimilate ground-based PM2.s observations into the chemical model, to obtain initial fields for the aerosol species. In our results, data assimilation significantly reduced the errors when compared to a simulation without assimilation, and improved forecasts of PM2.5 concentrations. Despite a drop in skill directly after the assimilation, a positive effect was present in forecasts for at least 12-2413, and there was a slight improvement in the 48-h forecasts. In addition to performing well in Shanghai, the verification statistics for this assimilation experiment are encouraging for most of the surface stations in China.
