大气污染资料同化与应用综述

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。

A Review of Air Quality Data Assimilation Methods and Their Application

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.