基于京津冀高密度地面观测网络的大气污染物浓度地面观测代表性误差估计
|
摘要
地面观测提供空间点的浓度信息,三维化学模式提供网格面的浓度信息,两者在进行对比验证或同化融合时会因为空间尺度不匹配引入误差,即观测代表性误差。本研究将大气污染地面国控监测站与区县监测站结合起来,获得了京津冀地区高密度地面观测数据,利用该数据首次对京津冀地区6项常规大气污染物(PM_(2.5)、PM_(10)、SO_2、NO_2、CO和O_3)的地面观测代表性误差进行了客观估计,并与Elbern et al.(2007)方法估计的代表性误差进行了对比。结果发现:两种方法对京津冀地区NO_2地面观测代表性误差估计非常接近,但Elbern et al.(2007)方法显著低估了SO_2、CO和O_3地面观测的代表性误差。在此基础上,我们对Elbern et al.(2007)方法及其误差特征参数进行了本地化修正,并增加了PM_(2.5)和PM_(10)的代表性误差特征参数,建立了京津冀大气污染地面观测代表性误差的客观估计方法。
Ground stations provide raw monitored point data of air pollutant concentration, and three-dimensional chemical models can simulate the concentration distribution on grids. When ground stations observations are used for verification of model performance or assimilated into model simulations, representative errors may occur due to the mismatch between the spatial scales of discrete monitored point data and model simulations. This study produces a high-resolution observational dataset for Beijing–Tianjin–Hebei region by combining the information obtained from China National Monitoring Center and from local monitoring centers. The combined dataset allows the computation of representative errors of ground observations of six typical air pollutants(PM2.5, PM10, SO2, NO2, CO and O3) in Beijing–Tianjin–Hebei region. Results from the aforementioned method are compared with those obtained from the theory of Elbern et al.(2007). It is found that the results from the two methods agree well in terms of the representative errors of ground observations of NO2. However, representative errors of SO2, CO and O3 are significantly underestimated by using the Elbern's approach. Therefore, characteristic parameters associated with the four air pollutants used in the Elbern's method are modified and new characteristic parameters relevant to PM2.5 and PM10 are introduced in the present study, which makes the method to be more applicable and can yield more accurate results when processing ground observations in Beijing–Tianjin–Hebei region.
Ground stations provide raw monitored point data of air pollutant concentration, and three-dimensional chemical models can simulate the concentration distribution on grids. When ground stations observations are used for verification of model performance or assimilated into model simulations, representative errors may occur due to the mismatch between the spatial scales of discrete monitored point data and model simulations. This study produces a high-resolution observational dataset for Beijing–Tianjin–Hebei region by combining the information obtained from China National Monitoring Center and from local monitoring centers. The combined dataset allows the computation of representative errors of ground observations of six typical air pollutants(PM2.5, PM10, SO2, NO2, CO and O3) in Beijing–Tianjin–Hebei region. Results from the aforementioned method are compared with those obtained from the theory of Elbern et al.(2007). It is found that the results from the two methods agree well in terms of the representative errors of ground observations of NO2. However, representative errors of SO2, CO and O3 are significantly underestimated by using the Elbern's approach. Therefore, characteristic parameters associated with the four air pollutants used in the Elbern's method are modified and new characteristic parameters relevant to PM2.5 and PM10 are introduced in the present study, which makes the method to be more applicable and can yield more accurate results when processing ground observations in Beijing–Tianjin–Hebei region.
引文
白晓平,李红,方栋,等.2008.资料同化方法在空气污染数值预报中的应用研究[J].环境科学,29(2):283-289.Bai Xiaoping,Li Hong,Fang Dong,et al.Application research of data assimilation in air pollution numerical prediction[J].Environmental Science(in Chinese),29(2):283-289,doi:10.3321/j.issn:0250-3301.2008.02.002.
Carton J A,Giese B S.2008.A reanalysis of ocean climate using Simple Ocean Data Assimilation(SODA)[J].Mon.Wea.Rev.,136(8):2999-3017,doi:10.1175/2007MWR1978.1.
Daley R.1985.The analysis of synoptic scale divergence by a statistical interpolation procedure[J].Mon.Wea.Rev.,113(6):1066-1079,doi:10.1175/1520-0493(1985)113<1066:TAOSSD>2.0.CO;2.
Dee D P,Da Silva A M.1999.Maximum-likelihood estimation of forecast and observation error covariance parameters.Part I:Methodology[J].Mon.Wea.Rev.,127(8):1822-1834,doi:10.1175/1520-0493(1999)127<1822:MLEOFA>2.0.CO;2.
Desroziers G,Berre L,Chapnik B,et al.2005.Diagnosis of observation,background and analysis-error statistics in observation space[J].Quart.J.Roy.Meteor.Soc.,131(613):3385-3396,doi:10.1256/qj.05.108.
Elbern H,Strunk A,Schmidt H,et al.2007.Emission rate and chemical state estimation by 4-dimensional variational inversion[J].Atmospheric Chemistry and Physics,7(1):3749-3769.
Hollingsworth A,L?nnberg P.1986.The statistical structure of short-range forecast errors as determined from radiosonde data.Part I.The wind-field[J].Tellus A:Dynamic Meteorology and Oceanography,38(2):111-136,doi:10.3402/tellusa.v38i2.11707.
Hollingsworth A,Engelen R J,Textor C,et al.2008.Toward a monitoring and forecasting system for atmospheric composition:The GEMS project[J].Bull.Amer.Meteor.Soc,89(8):1147-1164,doi:10.1175/2008BAMS2355.1.
黄思,唐晓,王自发,等.2016.基于观测、模拟和同化数据的PM2.5污染回顾分析[J].气候与环境研究,21(6):700-710.Huang Si,Tang Xiao,Wang Zifa,et al.2016.Evaluating the PM2.5 pollution over Beijing-Hebei-Tianjin region based on observations,simulations,and data assimilation results[J].Climatic and Environmental Research(in Chinese),21(6):700-710,doi:10.3878/j.issn.1006-9585.2016.14284.
Jiang Z Q,Liu Z Q,Wang T J,et al.2013.Probing into the impact of3DVAR assimilation of surface PM10 observations over China using process analysis[J].J.Geophys.Res.,118(12):6738-6749,doi:10.1002/jgrd.50495.
刘海猛,方创琳,黄解军,等.2018.京津冀城市群大气污染的时空特征与影响因素解析[J].地理学报,73(1):177-191.Liu Haimeng,Fang Chuanglin,Huang Jiejun,et al.2018.The spatial-temporal characteristics and influencing factors of air pollution in Beijing-Tianjin-Hebei urban agglomeration[J].Acta Geographica Sinica(in Chinese),73(1):177-191,doi:10.11821/dlxb201801015.
Pagowski M,Grell G A,Mckeen S A,et al.2010.Three-dimensional variational data assimilation of ozone and fine particulate matter observations:Some results using the Weather Research and Forecasting-Chemistry model and Grid-Point Statistical Interpolation[J].Quart.J.Roy.Meteor.Soc.,136(653):2013-2024,doi:10.1002/qj.700.
Schwartz C S,Liu Z Q,Lin H C,et al.2012.Simultaneous threedimensional variational assimilation of surface fine particulate matter and MODIS aerosol optical depth[J].J.Geophys.Res.,117(D13):13202,doi:10.1029/2011JD017383.
Tang X,Zhu J,Wang Z F,et al.2011.Improvement of ozone forecast over Beijing based on ensemble Kalman filter with simultaneous adjustment of initial conditions and emissions[J].Atmospheric Chemistry and Physics Discussions,11(3):7811-7849,doi:10.5194/acp-11-12901-2011.
Tang X,Zhu J,Wang Z F,et al.2013.Inversion of CO emissions over Beijing and its surrounding areas with ensemble Kalman filter[J].Atmos.Environ.,81:676-686,doi:10.1016/j.atmosenv.2013.08.051.
唐孝炎,张远航,邵敏.2006.大气环境化学(第2版)[M].北京:高等教育出版社.Tang Xiaoyan,Zhang Yuanhang,Shao Min.2006.Atmospheric Environmental Chemistry(in Chinese)[M].Beijing:Higher Education Press.
Van Loon M,Builtjes P J H,Segers A J.2000.Data assimilation of ozone in the atmospheric transport chemistry model LOTOS[J].Environmental Modelling&Software,15(6-7):603-609,doi:10.1016/S1364-8152(00)00048-7.
王喜全,杨婷,王自发.2011.灰霾污染的跨控制区影响--一次京津冀与东北地区灰霾污染个案分析[J].气候与环境研究,16(6):690-696.Wang Xiquan,Yang Ting,Wang Zifa.2011.Impact of dusthaze episode from one air pollution control region to the other-One Case Study[J].Climatic and Environmental Research(in Chinese),16(6):690-696.
王跃思,姚利,刘子锐,等.2013.京津冀大气霾污染及控制策略思考[J].中国科学院院刊,(3):353-363.Wang Yuesi,Yao Li,Liu Zirui,et al.2013.Formation of haze pollution in Beijing-Tianjin-Hebei region and their control strategies[J].Bulletin of Chinese Academy of Sciences(in Chinese),(3):353-363,doi:10.3969/j.issn.1000-3045.2013.03.009.
王自发,李杰,王哲,等.2014.2013年1月我国中东部强霾污染的数值模拟和防控对策[J].中国科学:地球科学,44(1):3-14.Wang Zifa,Li Jie,Wang Zhe,et al.2014.Modeling study of regional severe hazes over mid-eastern China in January 2013 and its implications on pollution prevention and control[J].Science China Earth Sciences,57(1):3-13,doi:10.1007/s11430-013-4793-0.
Yumimoto K,Uno I,Sugimoto N,et al.2008.Adjoint inversion modeling of Asian dust emission using lidar observations[J].Atmospheric Chemistry and Physics,8(11):2869-2884,doi:10.5194/acp-8-2869-2008.
张小曳,孙俊英,王亚强,等.2013.我国雾-霾成因及其治理的思考[J].科学通报,58(13):1178-1187.Zhang Xiaoye,Sun Junying,Wang Yaqiang,et al.2013.Factors contributing to haze and fog in China[J].Chinese Science Bulletin(in Chinese),58(13):1178-1187,doi:10.1360/972013-150.
Zheng B,Zhang Q,Zhang Y,et al.2015.Heterogeneous chemistry:Amechanism missing in current models to explain secondary inorganic aerosol formation during the January 2013 haze episode in North China[J].Atmospheric Chemistry and Physics,14(11):16731-16776,doi:10.5194/acpd-14-16731-2014.
Carton J A,Giese B S.2008.A reanalysis of ocean climate using Simple Ocean Data Assimilation(SODA)[J].Mon.Wea.Rev.,136(8):2999-3017,doi:10.1175/2007MWR1978.1.
Daley R.1985.The analysis of synoptic scale divergence by a statistical interpolation procedure[J].Mon.Wea.Rev.,113(6):1066-1079,doi:10.1175/1520-0493(1985)113<1066:TAOSSD>2.0.CO;2.
Dee D P,Da Silva A M.1999.Maximum-likelihood estimation of forecast and observation error covariance parameters.Part I:Methodology[J].Mon.Wea.Rev.,127(8):1822-1834,doi:10.1175/1520-0493(1999)127<1822:MLEOFA>2.0.CO;2.
Desroziers G,Berre L,Chapnik B,et al.2005.Diagnosis of observation,background and analysis-error statistics in observation space[J].Quart.J.Roy.Meteor.Soc.,131(613):3385-3396,doi:10.1256/qj.05.108.
Elbern H,Strunk A,Schmidt H,et al.2007.Emission rate and chemical state estimation by 4-dimensional variational inversion[J].Atmospheric Chemistry and Physics,7(1):3749-3769.
Hollingsworth A,L?nnberg P.1986.The statistical structure of short-range forecast errors as determined from radiosonde data.Part I.The wind-field[J].Tellus A:Dynamic Meteorology and Oceanography,38(2):111-136,doi:10.3402/tellusa.v38i2.11707.
Hollingsworth A,Engelen R J,Textor C,et al.2008.Toward a monitoring and forecasting system for atmospheric composition:The GEMS project[J].Bull.Amer.Meteor.Soc,89(8):1147-1164,doi:10.1175/2008BAMS2355.1.
黄思,唐晓,王自发,等.2016.基于观测、模拟和同化数据的PM2.5污染回顾分析[J].气候与环境研究,21(6):700-710.Huang Si,Tang Xiao,Wang Zifa,et al.2016.Evaluating the PM2.5 pollution over Beijing-Hebei-Tianjin region based on observations,simulations,and data assimilation results[J].Climatic and Environmental Research(in Chinese),21(6):700-710,doi:10.3878/j.issn.1006-9585.2016.14284.
Jiang Z Q,Liu Z Q,Wang T J,et al.2013.Probing into the impact of3DVAR assimilation of surface PM10 observations over China using process analysis[J].J.Geophys.Res.,118(12):6738-6749,doi:10.1002/jgrd.50495.
刘海猛,方创琳,黄解军,等.2018.京津冀城市群大气污染的时空特征与影响因素解析[J].地理学报,73(1):177-191.Liu Haimeng,Fang Chuanglin,Huang Jiejun,et al.2018.The spatial-temporal characteristics and influencing factors of air pollution in Beijing-Tianjin-Hebei urban agglomeration[J].Acta Geographica Sinica(in Chinese),73(1):177-191,doi:10.11821/dlxb201801015.
Pagowski M,Grell G A,Mckeen S A,et al.2010.Three-dimensional variational data assimilation of ozone and fine particulate matter observations:Some results using the Weather Research and Forecasting-Chemistry model and Grid-Point Statistical Interpolation[J].Quart.J.Roy.Meteor.Soc.,136(653):2013-2024,doi:10.1002/qj.700.
Schwartz C S,Liu Z Q,Lin H C,et al.2012.Simultaneous threedimensional variational assimilation of surface fine particulate matter and MODIS aerosol optical depth[J].J.Geophys.Res.,117(D13):13202,doi:10.1029/2011JD017383.
Tang X,Zhu J,Wang Z F,et al.2011.Improvement of ozone forecast over Beijing based on ensemble Kalman filter with simultaneous adjustment of initial conditions and emissions[J].Atmospheric Chemistry and Physics Discussions,11(3):7811-7849,doi:10.5194/acp-11-12901-2011.
Tang X,Zhu J,Wang Z F,et al.2013.Inversion of CO emissions over Beijing and its surrounding areas with ensemble Kalman filter[J].Atmos.Environ.,81:676-686,doi:10.1016/j.atmosenv.2013.08.051.
唐孝炎,张远航,邵敏.2006.大气环境化学(第2版)[M].北京:高等教育出版社.Tang Xiaoyan,Zhang Yuanhang,Shao Min.2006.Atmospheric Environmental Chemistry(in Chinese)[M].Beijing:Higher Education Press.
Van Loon M,Builtjes P J H,Segers A J.2000.Data assimilation of ozone in the atmospheric transport chemistry model LOTOS[J].Environmental Modelling&Software,15(6-7):603-609,doi:10.1016/S1364-8152(00)00048-7.
王喜全,杨婷,王自发.2011.灰霾污染的跨控制区影响--一次京津冀与东北地区灰霾污染个案分析[J].气候与环境研究,16(6):690-696.Wang Xiquan,Yang Ting,Wang Zifa.2011.Impact of dusthaze episode from one air pollution control region to the other-One Case Study[J].Climatic and Environmental Research(in Chinese),16(6):690-696.
王跃思,姚利,刘子锐,等.2013.京津冀大气霾污染及控制策略思考[J].中国科学院院刊,(3):353-363.Wang Yuesi,Yao Li,Liu Zirui,et al.2013.Formation of haze pollution in Beijing-Tianjin-Hebei region and their control strategies[J].Bulletin of Chinese Academy of Sciences(in Chinese),(3):353-363,doi:10.3969/j.issn.1000-3045.2013.03.009.
王自发,李杰,王哲,等.2014.2013年1月我国中东部强霾污染的数值模拟和防控对策[J].中国科学:地球科学,44(1):3-14.Wang Zifa,Li Jie,Wang Zhe,et al.2014.Modeling study of regional severe hazes over mid-eastern China in January 2013 and its implications on pollution prevention and control[J].Science China Earth Sciences,57(1):3-13,doi:10.1007/s11430-013-4793-0.
Yumimoto K,Uno I,Sugimoto N,et al.2008.Adjoint inversion modeling of Asian dust emission using lidar observations[J].Atmospheric Chemistry and Physics,8(11):2869-2884,doi:10.5194/acp-8-2869-2008.
张小曳,孙俊英,王亚强,等.2013.我国雾-霾成因及其治理的思考[J].科学通报,58(13):1178-1187.Zhang Xiaoye,Sun Junying,Wang Yaqiang,et al.2013.Factors contributing to haze and fog in China[J].Chinese Science Bulletin(in Chinese),58(13):1178-1187,doi:10.1360/972013-150.
Zheng B,Zhang Q,Zhang Y,et al.2015.Heterogeneous chemistry:Amechanism missing in current models to explain secondary inorganic aerosol formation during the January 2013 haze episode in North China[J].Atmospheric Chemistry and Physics,14(11):16731-16776,doi:10.5194/acpd-14-16731-2014.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |