doi:  10.3878/j.issn.1006-9895.1802.17260
大气复合污染资料同化与应用综述

Review of the air quality data assimilation method and its application
摘要点击 1490  全文点击 308  投稿时间:2017-10-28  修订日期:2018-01-15
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基金:  国家自然科学基金
中文关键词:  资料同化  大气复合污染 模式不确定性  浓度场同化 源反演
英文关键词:  data  assimilation, air  pollution, model  uncertainty, concentration  field assimilation, emission  inversion
           
作者中文名作者英文名单位
朱江ZHU JIANGICCES
唐晓TANG XIAO
王自发LAPC
吴林LAPC
引用:朱江,唐晓,王自发,吴林.2018.大气复合污染资料同化与应用综述[J].大气科学
Citation:ZHU JIANG,TANG XIAO.2018.Review of the air quality data assimilation method and its application[J].Chinese Journal of Atmospheric Sciences (in Chinese)
中文摘要:
      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,探讨大气复合污染资料同化面临的主要挑战和发展趋势。
Abstract:
      China is facing serious air pollution problem especially for high concentrations of ozone and fine particles. A key step to effectively manage the air pollution problem is the modeling and forecasting of the air pollution. However, the uncertainty of air pollution forecast is still large and the uncertainty sources are very complex. The chemical nonlinearity in the model makes it more difficult to identify the key uncertainty sources and to carry out targeted constraints and correction. Data assimilation method can combine the information of modeling and multi-source observations to improve the accuracy of air pollution simulation and forecast. In this paper, we will briefly introduce the model uncertainty, assimilation algorithm, optimizing initial concentrations and emissions of the air quality modeling in the field of air pollution data assimilation, and highlight the challenges and development trends of atmospheric pollution data assimilation.
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