大气污染资料同化与应用综述

朱江; 唐晓; 王自发; 吴林

doi:10.3878/j.issn.1006-9895.1802.17260

大气污染资料同化与应用综述

doi: 10.3878/j.issn.1006-9895.1802.17260

朱江^{1, 3,},
唐晓²,
王自发^{2, 3},
吴林²

1.
中国科学院大气物理研究所国际气候与环境科学中心(ICCES), 北京 100029
2.
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(LAPC), 北京 100029
3.
中国科学院大学, 北京 100049

基金项目:

国家自然科学基金项目 91644216

国家自然科学基金项目 41575128

详细信息

作者简介:
朱江, 男, 1963年出生, 研究员, 主要从事资料同化理论与应用研究。E-mail:jzhu@mail.iap.ac.cn

中图分类号: P402
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- 文章访问数: 2280
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- 被引次数: 0
出版历程
- 收稿日期: 2017-10-28
- 网络出版日期: 2018-03-12
- 刊出日期: 2018-05-15

A Review of Air Quality Data Assimilation Methods and Their Application

Jiang ZHU^{1, 3
,},
Xiao TANG²,
Zifa WANG^{2, 3},
Lin WU²

1.
International Center for Climate and Environment Sciences(ICCES), Institute of Atmospheric Physics, Beijing 100029
2.
State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry(LAPC), Institute of Atmospheric Physics, Beijing 100029
3.
University of Chinese Academy of Sciences, Beijing 100049

Funds:

National Natural Science Foundation 91644216

National Natural Science Foundation 41575128

摘要

摘要: 我国正面临以高浓度臭氧和细颗粒物为典型特征的大气复合污染问题，对其进行模拟和预报是有效应对大气污染的关键。大气复合污染预报的不确定性来源复杂，同时存在化学非线性的影响，各种模式输入不确定性对模拟预报影响的时空差异较大，从而导致很多不确定性约束方法难以确定关键的不确定性因子而进行有针对性的约束和订正。利用资料同化方法融合模式、多源观测等信息，减小模式输入数据的不确定性成为提升大气污染模拟预报精度的关键。本文将简要介绍大气污染资料同化相关的模式不确定性、同化算法以及污染物浓度场同化、源反演研究上的进展，探讨大气污染资料同化面临的主要挑战和发展趋势。
- 资料同化 /
- 大气复合污染 /
- 模式不确定性 /
- 浓度场同化 /
- 源反演
Abstract: China is facing serious air pollution problems especially that caused by high concentrations of ozone and fine particles. A key step to effectively control air pollution is the modeling and forecasting of air pollution. However, large uncertainties with complicated sources still exist in air pollution forecasting. The nonlinearity in chemical processes makes it difficult to identify those key uncertainty sources and carry out targeted constraints and corrections in the modeling study. Data assimilation method can combine modeling information with multi-source observations to improve the accuracy of air pollution simulation and forecast. In this paper, we briefly introduce model uncertainties, assimilation algorithms, and optimization of initial concentrations and emissions for air quality model in the field of air pollution data assimilation. Challenges and development trends in the study of atmospheric pollution data assimilation are also highlighted.
- Data assimilation /
- Air pollution /
- Model uncertainty /
- Concentration field assimilation /
- Emission inversion

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表 1 国际一些研究对大气化学模式输入变量不确定性的估计

Table 1. Estimate the uncertainty of input variables in atmospheric chemistry model by some international studies

模式输入变量	不确定性估算的标准差
模式输入变量	Moore and Londergan（2001）	Hanna et al.（1998）	Beekmann and Derognat（2003）
人为排放源（NO_x）	25%~50%	30%~40%	40%
人为排放源（VOC）	25%~50%	50%~80%	40%
生物排放源（NO_x）	25%~0	200%	－
生物排放源（VOC）	25%~0	200%	50%
风向	11°	30°	－
风速	25%~0	30%	1.5 m/s
垂直混合高度	25%~0	50%	50%
温度	3 K	3 K	1.5 K
相对湿度	－	30%	20%
边界层高度	－	－	20%
沉降速度	25%~0	30%	25%
侧边界条件（O₃）	5%~10%	30%	－
侧边界条件（NO_x、VOC）	5%~10%	80%	－
上边界条件（O₃）	5%~10%	70%	－
上边界条件（NO_x、VOC）	5%~10%	200%	－
初始浓度（O₃）	15%~20%	30%	－
初始浓度（NO_x、VOC）	15%~20%	50%	－
化学反应系数	25%	30%	10%~30%

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表 2 近6年大气污染源反演的一些典型应用

Table 2. Typical applications of air pollution source inversion in recent six years

文献出处	物种	观测数据	反演区域	空间分辨率	反演算法
Bergamaschi et al.（2014）	CH₄、N₂O	地面站点	欧洲	约1°×1°	四维变分
Koohkan et al.（2013）	VOCs	地面站点	西欧	0.5°×0.5°	四维变分
Tang et al.（2013）	CO	地面站点	北京及周边	约0.11°×0.08°	集合卡尔曼滤波
Ghude et al.（2013）	NO_x	卫星观测	印度半岛	2.8°×2.8°	质量平衡方法
Miyazaki et al.（2012）	NO₂、O₃、CO、HNO₃	卫星观测	全球	2.8°×2.8°	集合卡尔曼滤波
Huneeus et al.（2012）	沙尘、海盐、BC（黑炭）、OC（有机碳）、SO₂	卫星观测	全球	全球分为9至11个区域	四维变分
Hooghiemstra et al.（2012）	CO	卫星观测	全球	6°×4°	四维变分
Sugimoto et al.（2010）	沙尘	激光雷达	蒙古及周边	约0.5°×0.4°	四维变分

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