| 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 |
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| 引用本文： | 李飞,唐晓,王自发,朱莉莉,王晓彦,吴煌坚,卢苗苗,李健军,朱江.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[J].大气科学,2019,43(2):277-284. |
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| 作者姓名： | 李飞 唐晓 王自发 朱莉莉 王晓彦 吴煌坚 卢苗苗 李健军 朱江 |
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| 作者单位： | 中国科学院大气物理研究所大气边界层物理与大气化学国家重点实验室,北京100029;中国科学院大学,北京100049;中国科学院大气物理研究所大气边界层物理与大气化学国家重点实验室,北京,100029;中国环境监测总站,北京,100012;中国科学院大气物理研究所国际气候与环境科学中心,北京100029;中国科学院大学,北京100049 |
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| 基金项目： | 国家自然科学基金项目41575128、91644216，中国科学院信息化专项课题XXH13506-302 |
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| 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（PM2.5、PM10、SO2、NO2、CO和O3）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NO2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SO2、CO和O3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PM2.5和PM10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。
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| 关 键 词： | 地面观测 代表性误差 资料同化 大气污染物 |
| 收稿时间： | 2017/11/6 0:00:00 |
Estimation of Representative Errors of Surface Observations of Air Pollutant Concentrations Based on High-Density Observation Network over Beijing-Tianjin-Hebei Region |
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| LI Fei,TANG Xiao,WANG Zif,ZHU Lili,WANG Xiaoyan,WU Huangjian,LU Miaomiao,LI Jianjun and ZHU Jiang.Estimation of Representative Errors of Surface Observations of Air Pollutant Concentrations Based on High-Density Observation Network over Beijing-Tianjin-Hebei Region[J].Chinese Journal of Atmospheric Sciences,2019,43(2):277-284. |
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| Authors: | LI Fei TANG Xiao WANG Zif ZHU Lili WANG Xiaoyan WU Huangjian LU Miaomiao LI Jianjun and ZHU Jiang |
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| Institution: | 1.State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 1000292.International Center for Climate and Environment Sciences, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 1000293.China National Environmental Monitoring Center, Beijing 1000124.University of Chinese Academy of Science, Beijing 100049 |
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| Abstract: | 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. |
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| Keywords: | Surface observation Representative error Data assimilation Air pollutants |
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