[Application research of data assimilation in air pollution numerical prediction].
Based on an air pollution modeling system coupling with the non-hydrostatic fifth generation mesoscale meteorological model (MM5) and the regional modeling system for aerosols and deposition (REMSAD), the forecast results of NOx and SO2 in August and September 2002 in Nanjing were assimilated with the optimal interpolation method and the ensemble Kalman filter. The results show that the improvement rates of deviation mean value of NOx and SO2 after assimilated with the optimal interpolation method are 34.20% and 47.53%, and the improvement rates of root mean square errors are 31.95% and 42.04% respectively. It is also demonstrated that the improvement rates of deviation mean value of NOx and SO2 after assimilated with the ensemble Kalman filter with 30 ensemble members are 26.73% and 60.75%, and the improvement rates of root mean square errors are 25.20% and 55.16% respectively. So, the optimal interpolation method and the ensemble Kalman filter both can improve the quality of the initial state from the air pollution numerical prediction model. The comparative experiments on the assimilation performance with the optimal interpolation method and the ensemble Kalman filter with 61 ensemble members were performed, and the experiments demonstrate that the assimilation performance of the ensemble Kalman filter with 61 ensemble members were improved compared with 30 ensemble members, and with the increase of the ensemble members, the improvement to the initial state of NOx and SO2 with the ensemble Kalman filter will be better than the optimal interpolation method.
Bai
Xiao-ping
XP
Institute of Nuclear and Energy Technology, Tsinghua University, Beijing 100084, China. bxp03@mails.tsinghua.edu.cn
Li
Hong
H
Fang
Dong
D
Costabile
Francesca
F
Liu
Feng-lei
FL
D000393:Air Pollutants / Q000032:analysis*; D000397:Air Pollution / Q000032:analysis*; D002681:China; D004784:Environmental Monitoring / Q000379:methods* / Q000706:statistics & numerical data; D005544:Forecasting; D008962:Models, Theoretical; D009585:Nitrogen Dioxide / Q000032:analysis; D012106:Research / Q000706:statistics & numerical data / Q000639:trends; D012107:Research Design; D013458:Sulfur Dioxide / Q000032:analysis
D000393:Air Pollutants; D013458:Sulfur Dioxide; D009585:Nitrogen Dioxide