气象资料同化对PM2.5预报影响的模拟分析

基于GSI（网格点统计插值）同化系统和WRF-Chem模式，利用高分辨率的气象自动站观测资料和天气雷达资料进行同化和模拟预报，针对2017年11月4~5日发生在我国京津冀地区的一次污染过程，对比研究了气象资料同化对PM_2.5模拟效果的影响.结果表明，WRF-Chem模式能较为准确地预报出北京-石家庄-邯郸的污染带分布和演变，低层风场辐合是污染带形成的主要气象因素；无同化的控制试验由于地层风场辐合较强，高估了污染带上的PM_2.5浓度，同化试验减小了低层的风场辐合，同时增高了地面温度并抬升了边界层高度，从而降低了污染带上PM_2.5的浓度；预报检验分析表明，同化试验的预报效果整体好于控制试验，0~36h的平均BIAS（标准偏差）和RMSE（均方根误差）分别降低了7.55和5.42μg/m³，MFB（平均相对偏差）和MFE（平均相对误差）分别降低了28.8%和9.4%，同化试验在预报的第10~30h时段上的改善效果最为显著.

Research on the effects of assimilation meteorological observation data on aerosol concentration

Influence of meteorological data assimilation on aerosol simulation during an air pollution event occurred in 4~5 November 2017 over Beijing-Tianjin-Hebei was investigated, using the Weather Research and Forecasting Model with Chemistry (WRF-Chem) coupled with the Gridpoint Statistical Interpolation (GSI) data assimilation system. Two pairs of experiments were carried out to compare the differences in PM_2.5 with and without assimilating high-resolution meteorological observation data and radar data. It was shown that the WRF-Chem model can successfully simulate the spatial pattern and its evolution in the pollution zone of Beijing-Shijiazhuang-Handan. The convergence of low-level wind was an important factor for the pollution zone. But, the experiment without the assimilation overestimated the convergence and thus leaded to an overestimate of the PM_2.5 concentration. There was an obvious decrease of PM_2.5 concentration in the assimilation experiment since the convergence of low-level wind decreases, and the planetary boundary layer height (PBLH) increases resulted from the increases of the ground temperature by assimilation of meteorological data. Compared with the experiment without assimilation, the mean bias reduced by up to 7.55μg/m³, the root-mean-square errors reduced by up to 5.42μg/m³, the mean fractional bias reduced by over 28.8%, and the mean fractional error reduced by about 9.4% for the average of 0~36h forecasts in the experiment with assimilation. The positive impact in the assimilation experiment was very significant during the 10~30h forecasts.