中文摘要：

基于集合卡尔曼滤波方法和嵌套网格空气质量模式系统建立了一个区域空气质量资料同化系统（RAQDAS）,并利用该系统开展了京津冀地区2008年北京奥运会期间的地面臭氧观测资料同化试验,分析了同化系统订正臭氧初始场对24 h臭氧预报的影响.试验结果表明采用50个集合样本的集合卡尔曼滤波同化不仅改进了观测站点的臭氧预报,也提高了观测以外区域的臭氧预报技巧,使得臭氧预报的均方根误差平均下降了15%,并且当集合样本数减小到20时也可达到相近的预报改进效果.为了解决滤波发散问题,分别采用了放大集合离散度和扰动模式误差源两种方法.其中放大集合离散度能避免滤波发散,但没有提高臭氧预报技巧,反而导致预报误差的增加;扰动模式误差源不仅解决了滤波发散问题,也使同化导致的臭氧预报均方根误差下降比例从15%进一步提高到20%.

英文摘要：

A regional air quality data assimilation system （RAQDAS） was established based on ensemble Kalman fiher and Nested Air Quality Prediction Model System. This system was employed to assimilate surface ozone observation of Beijing-Tianjin-Hebei areas daring the 2008 Beijing Olympics period and to optimize ozone initial conditions. The effects of data assimilation on 24 h ozone forecast were investigated. The results show that the assimilation with 50 ensemble members can improve the ozone forecast not only over observational areas, but also over non-observed areas. On average, the data assimilation can decrease the root mean square error （RMSE） of 24 h ozone forecast by 15%. Furthermore, the ensemble size can be reduced to 20 with similar improvement on forecast capability. In order to solve the problem of filter divergence, inflating ensemble spread and perturbing model error sources were employed. Inflating ensemble spread can solve the problem of filter divergence, but it can hardly improve ozone forecast and lead to an increase of ozone forecast error; perturbing model error sources can avoid filter divergence and also bring improvement of 24 b ozone forecast with the RMSE decreased by 20%.