Research on the effects of assimilation meteorological observation data on aerosol concentration
HU Yi-wen1, ZANG Zeng-liang2, MA Xiao-Yan1, LIANG Yan-fei2,4, ZHAO Ding-chi3, YOU Wei2
1. Key Laboratory of Meteorological Disaster, Ministry of Education(KLME)/Joint International Research Laboratory of Climate and Environment Change(ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD)/Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science & Technology, Nanjing 210044, China;
2. Institute of Meteorology and Oceanography, National University of Defense Technology, Nanjing 211101, China;
3. No. 75839 Unit of PLA, Guangzhou 510510, China;
4. No. 32145 Unit of PLA, Xinxiang 453000, China
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 PM2.5 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 PM2.5 concentration. There was an obvious decrease of PM2.5 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.
胡译文, 臧增亮, 马晓燕, 梁延飞, 赵定池, 尤伟. 气象资料同化对PM2.5预报影响的模拟分析[J]. 中国环境科学, 2019, 39(2): 523-532.
HU Yi-wen, ZANG Zeng-liang, MA Xiao-Yan, LIANG Yan-fei, ZHAO Ding-chi, YOU Wei. Research on the effects of assimilation meteorological observation data on aerosol concentration. CHINA ENVIRONMENTAL SCIENCECE, 2019, 39(2): 523-532.
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