气象资料同化对PM_(2.5)预报影响的模拟分析
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摘要
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/m~3,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.
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 werecarried out to compare the differences in PM_(2.5) with and without assimilating high-resolution meteorological observation data andradar data. It was shown that the WRF-Chem model can successfully simulate the spatial pattern and its evolution in the pollutionzone of Beijing-Shijiazhuang-Handan. The convergence of low-level wind was an important factor for the pollution zone. But, theexperiment 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 winddecreases, and the planetary boundary layer height(PBLH) increases resulted from the increases of the ground temperature byassimilation of meteorological data. Compared with the experiment without assimilation, the mean bias reduced by up to 7.55μg/m~3,the root-mean-square errors reduced by up to 5.42μg/m~3, the mean fractional bias reduced by over 28.8%, and the mean fractionalerror reduced by about 9.4% for the average of 0～36 h forecasts in the experiment with assimilation. The positive impact in theassimilation experiment was very significant during the 10～30 h forecasts.
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 werecarried out to compare the differences in PM_(2.5) with and without assimilating high-resolution meteorological observation data andradar data. It was shown that the WRF-Chem model can successfully simulate the spatial pattern and its evolution in the pollutionzone of Beijing-Shijiazhuang-Handan. The convergence of low-level wind was an important factor for the pollution zone. But, theexperiment 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 winddecreases, and the planetary boundary layer height(PBLH) increases resulted from the increases of the ground temperature byassimilation of meteorological data. Compared with the experiment without assimilation, the mean bias reduced by up to 7.55μg/m~3,the root-mean-square errors reduced by up to 5.42μg/m~3, the mean fractional bias reduced by over 28.8%, and the mean fractionalerror reduced by about 9.4% for the average of 0～36 h forecasts in the experiment with assimilation. The positive impact in theassimilation experiment was very significant during the 10～30 h forecasts.
引文
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[18]沈菲菲,闵锦忠,许冬梅,等.Hybrid ETKF-3DVAR方法同化多普勒雷达速度观测资料Ⅰ:模拟资料试验[J].大气科学学报,2016,39(1):81-89.Shen F F,Min J Z,Xu D M,et al.Assimilation of Doppler radar velocity observations with hybrid ETKF-3DVAR method part I:Experiments with simulated data[J].Trans.Atmos.Sci.,2016,39(1):81-89.
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[2]孙家仁,许振成,刘煜,等.气候变化对环境空气质量影响的研究进展[J].气候与环境研究,2011,16(6):805-814.Sun J R,Xu Z C,Liu Y,et al.Advances in the Effect of Climate Change on Air Quality[J].Climatic and Environmental Research,2011,16(6):805-814.
[3]庞杨,韩志伟,朱彬,等.利用WRF-Chem模拟研究京津冀地区夏季大气污染物的分布和演变[J].大气科学学报,2013,36(6):674-682.Pang Y,Han Z W,Zhu B,et al.2013.A model study on distribution and evolution of atmospheric pollutants over Beijing-Tianjin-Hebei region in summertime with WRF-Chem[J].Trans Atmos Sci,36(6):674-682.
[4]刘琳,王玲玲,白永清,等.应用WRF/Chem模拟河南冬季大气颗粒物的区域输送特征[J].环境科学学报,2017,37(5):1843-1854.Liu L,Wang L L,Bai Y Q,et al.Simulation for impacts of regional trasport on winter particulate matter levels over Henan based on WRF/Chem model[J].Acta Scientiae Circumstantiae,2017,37(5):1843-1854.
[5]蒋伊蓉,朱蓉,朱克云,等.京津冀地区重污染天气过程的污染气象条件数值模拟研究[J].环境科学学报,2015,35(9):2681-2692.Jiang Y R,Zhu R,Zhu K Y,et al.Numerical simulation on the air pollution potential in the severe air pollution episodes in BeijingTianjin-Hebei Region[J].Acta Scientiae Circumstantiace,2015,35(9):2681-2692.
[6]张艳,余琦,伏晴艳,等.长江三角洲区域输送对上海市空气质量影响的特征分析[J].中国环境科学,2010,30(7):914-923.Zhang Y,Yu Q,Fu Q Y,et al.Impact of the transport of atmospheric pollutants from the Yangtze River Delta on the air quality in Shanghai[J].China Environmental Science,2010,30(7):914-923.
[7]李锋,朱彬,安俊岭,等.2013年12月初长江三角洲及周边地区重霾污染的数值模拟[J].中国环境科学,2015,37(7):1965-1974.Li F,Zhu B,An J L,et al.Modeling study of a severe haze episode occurred over the Yangtze River Delta and its surrounding regions during early December,2013.[J].China Environmental Science,2015,35(7):1965-1974.
[8]麦健华,邓涛,黄烨琪,等.中山市一次灰霾天气过程污染物来源数值模拟分析[J].中国环境科学,2017,37(9):3258-3267.Mai J H,Deng T,Huang Y Q,et al.Source analysis of a haze event in Zhongshan by numerical simulation[J].China Environmental Science,2017,37(9):3258-3267.
[9]薛谌彬,陈娴,吴俞,等.雷达资料同化在局地强对流预报中的应用[J].大气科学,2017,41(4):673-690.Xue C B,Chen X,Wu Y,et al.Application of radar data assimilation in local severe convective weather forecast[J].Chinese Journal of Atmospheric Sciences,2017,41(4):673-690.
[10]Wan Q L,Xu J J.A numerical study of the rainstorm characteristics of the June 2005 flash flood with WRF/GSI data assimilation system over south-east China[J].Hydrological Processes,2011,25(8):1327-1341.
[11]Igri P M,Tanessong R S,Vondou D A,et al.Added-Value of 3D VARData assimilation in the simulation of heavy rainfall events over west and Central Africa[J].Pure&Applied Geophysics,2015,172(10):2751-2776.
[12]Grell G A,Peckham S E,Schmitz R,et al.Fully coupled“online”chemistry within the WRF model[J].Atmospheric Environment,2005,39(37):6957-6975.
[13]Zhang Y,Liao H,Zhu K F,et al.Role of black carbon-induced changes in snow albedo in predictions of temperature and precipitation during a snowstorm[J].Atmospheric&Oceanic Science Letters,2009,2(4):230-236.
[14]Forkel R,Balzarini A,BaróR,et al.Analysis of the WRF-Chem contributions to AQMEII phase2 with respect to aerosol radiative feedbacks on meteorology and pollutant distributions[J].Atmospheric Environment,2015,115:630-645.
[15]靳璐滨,臧增亮,潘晓滨,等.PM2.5和PM2.5~10资料同化及在南京青奥会期间的应用试验[J].中国环境科学,2016,36(2):331-341.Jin L B,Zang Z L,Pan X B,et al.Data assimilation and application experiments of PM2.5 and PM2.5~10 during Nanjing Youth Olympic Games[J].China Environmental Science,2016,36(2):331-341.
[16]Purser R J,Wu W S,Parrish D F,et al.Numerical aspects of the application of recursive filters to variational statistical analysis.Part II:Spatially Inhomogeneous and Anisotropic General Covariances[J].Monthly Weather Review,2003,131(8):211-4.
[17]邹晓蕾,秦正坤,翁富忠.晨昏轨道微波温度计资料同化对降水定量预报的影响及其对三轨卫星系统的意义[J].大气科学,2016,40(1):46-62.Zou X L,Qin Z K,Wen F Z.Impact of Dawn-Dusk Satellite AMSU-A Data on quantitative precipitation forecasts and the implications for three-orbit constellation[J].Chinese Journal of Atmospheric Sciences,2016,40(1):46-62.
[18]沈菲菲,闵锦忠,许冬梅,等.Hybrid ETKF-3DVAR方法同化多普勒雷达速度观测资料Ⅰ:模拟资料试验[J].大气科学学报,2016,39(1):81-89.Shen F F,Min J Z,Xu D M,et al.Assimilation of Doppler radar velocity observations with hybrid ETKF-3DVAR method part I:Experiments with simulated data[J].Trans.Atmos.Sci.,2016,39(1):81-89.
[19]Boylan J W,Russell A G.PM and light extinction model performance metrics,goals,and criteria for three-dimensional air quality models[J].Atmospheric Environment,2006,40(26):4946-4959.